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somerset county cricket club in 2010 | https://en.wikipedia.org/wiki/Somerset_County_Cricket_Club_in_2010 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28846752-9.html.csv | unique | mark turner is the only player who played 6 matches in the 2010 season of the somerset county cricket club . | {'scope': 'all', 'row': '7', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '6', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'matches', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose matches record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; matches ; 6 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; matches ; 6 } }', 'tointer': 'select the rows whose matches record is equal to 6 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'matches', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose matches record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; matches ; 6 }'}, 'player'], 'result': 'mark turner', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; matches ; 6 } ; player }'}, 'mark turner'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; matches ; 6 } ; player } ; mark turner }', 'tointer': 'the player record of this unqiue row is mark turner .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; matches ; 6 } } ; eq { hop { filter_eq { all_rows ; matches ; 6 } ; player } ; mark turner } } = true', 'tointer': 'select the rows whose matches record is equal to 6 . there is only one such row in the table . the player record of this unqiue row is mark turner .'} | and { only { filter_eq { all_rows ; matches ; 6 } } ; eq { hop { filter_eq { all_rows ; matches ; 6 } ; player } ; mark turner } } = true | select the rows whose matches record is equal to 6 . there is only one such row in the table . the player record of this unqiue row is mark turner . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'matches_7': 7, '6_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'mark turner_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'matches_7': 'matches', '6_8': '6', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'mark turner_10': 'mark turner'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'matches_7': [0], '6_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'mark turner_10': [3]} | ['player', 'matches', 'overs', 'wickets', 'average', 'economy', 'bbi', '4wi'] | [['murali kartik', '10', '69.3', '20', '16.05', '4.61', '4 / 30', '1'], ['alfonso thomas', '14', '81.1', '27', '15.92', '5.29', '4 / 34', '2'], ['max waller', '8', '39.0', '4', '51.75', '5.30', '2 / 24', '0'], ['ben phillips', '13', '83.5', '19', '24.52', '5.55', '4 / 31', '1'], ['peter trego', '14', '75.3', '13', '33.00', '5.68', '2 / 29', '0'], ['zander de bruyn', '12', '47.2', '15', '19.40', '6.14', '3 / 27', '0'], ['mark turner', '6', '31.5', '9', '26.00', '7.35', '4 / 36', '1']] |
felice bonetto | https://en.wikipedia.org/wiki/Felice_Bonetto | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1228353-1.html.csv | unique | the second scuderia milano entry is the only one where felice bonetto used a milano chassis . | {'scope': 'all', 'row': '2', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'milano speluzzi', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'milano speluzzi'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to milano speluzzi .', 'tostr': 'filter_eq { all_rows ; chassis ; milano speluzzi }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; chassis ; milano speluzzi } }', 'tointer': 'select the rows whose chassis record fuzzily matches to milano speluzzi . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'milano speluzzi'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to milano speluzzi .', 'tostr': 'filter_eq { all_rows ; chassis ; milano speluzzi }'}, 'entrant'], 'result': 'scuderia milano', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; chassis ; milano speluzzi } ; entrant }'}, 'scuderia milano'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; chassis ; milano speluzzi } ; entrant } ; scuderia milano }', 'tointer': 'the entrant record of this unqiue row is scuderia milano .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; chassis ; milano speluzzi } } ; eq { hop { filter_eq { all_rows ; chassis ; milano speluzzi } ; entrant } ; scuderia milano } } = true', 'tointer': 'select the rows whose chassis record fuzzily matches to milano speluzzi . there is only one such row in the table . the entrant record of this unqiue row is scuderia milano .'} | and { only { filter_eq { all_rows ; chassis ; milano speluzzi } } ; eq { hop { filter_eq { all_rows ; chassis ; milano speluzzi } ; entrant } ; scuderia milano } } = true | select the rows whose chassis record fuzzily matches to milano speluzzi . there is only one such row in the table . the entrant record of this unqiue row is scuderia milano . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'chassis_7': 7, 'Milano Speluzzi_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'entrant_9': 9, 'scuderia milano_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'chassis_7': 'chassis', 'Milano Speluzzi_8': 'milano speluzzi', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'entrant_9': 'entrant', 'scuderia milano_10': 'scuderia milano'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'chassis_7': [0], 'Milano Speluzzi_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'entrant_9': [2], 'scuderia milano_10': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1950', 'scuderia milano', 'maserati 4clt / 50', 'maserati straight - 4', '2'], ['1950', 'scuderia milano', 'milano speluzzi', 'maserati straight - 4', '2'], ['1951', 'alfa romeo spa', 'alfa romeo 159a', 'alfa romeo straight - 8', '7'], ['1951', 'alfa romeo spa', 'alfa romeo 159 m', 'alfa romeo straight - 8', '7'], ['1952', 'officine alfieri maserati', 'maserati a6 gcm', 'maserati straight - 6', '2'], ['1953', 'officine alfieri maserati', 'maserati a6 gcm', 'maserati straight - 6', '6.5']] |
2008 - 09 fc barcelona season | https://en.wikipedia.org/wiki/2008%E2%80%9309_FC_Barcelona_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17686681-3.html.csv | count | in the 2008 - 09 fc barcelona season , among the players that were transfered , 2 of them were moving to milan . | {'scope': 'subset', 'criterion': 'equal', 'value': 'milan', 'result': '2', 'col': '3', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'transfer'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'transfer'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; type ; transfer }', 'tointer': 'select the rows whose type record fuzzily matches to transfer .'}, 'moving to', 'milan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose type record fuzzily matches to transfer . among these rows , select the rows whose moving to record fuzzily matches to milan .', 'tostr': 'filter_eq { filter_eq { all_rows ; type ; transfer } ; moving to ; milan }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; type ; transfer } ; moving to ; milan } }', 'tointer': 'select the rows whose type record fuzzily matches to transfer . among these rows , select the rows whose moving to record fuzzily matches to milan . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; type ; transfer } ; moving to ; milan } } ; 2 } = true', 'tointer': 'select the rows whose type record fuzzily matches to transfer . among these rows , select the rows whose moving to record fuzzily matches to milan . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; type ; transfer } ; moving to ; milan } } ; 2 } = true | select the rows whose type record fuzzily matches to transfer . among these rows , select the rows whose moving to record fuzzily matches to milan . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'type_6': 6, 'transfer_7': 7, 'moving to_8': 8, 'milan_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'type_6': 'type', 'transfer_7': 'transfer', 'moving to_8': 'moving to', 'milan_9': 'milan', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'type_6': [0], 'transfer_7': [0], 'moving to_8': [1], 'milan_9': [1], '2_10': [3]} | ['nat', 'name', 'moving to', 'type', 'transfer window', 'transfer fee', 'source'] | [['bra', 'edmílson', 'villarreal', 'contract termination', 'summer', 'free', 'barcelonacat'], ['ita', 'zambrotta', 'milan', 'transfer', 'summer', '9 m + 2 m in variables', 'barcelonacat'], ['mex', 'dos santos', 'tottenham hotspur', 'transfer', 'summer', '6 m + 5 m in variables', 'barcelonacat'], ['fra', 'thuram', 'retired', 'contract termination', 'summer', 'free', 'barcelonacat'], ['esp', 'ezquerro', 'osasuna', 'contract termination', 'summer', 'free', 'barcelonacat'], ['por', 'deco', 'chelsea', 'transfer', 'summer', '10 m', 'barcelonacat'], ['bra', 'ronaldinho', 'milan', 'transfer', 'summer', '21 m + 4 m in variables', 'barcelonacat'], ['bra', 'henrique', 'bayer leverkusen', 'loan', 'summer', 'n / a', 'barcelonacat'], ['esp', 'oleguer', 'ajax', 'transfer', 'summer', '3 m + 2 , 25 m in variables', 'barcelonacat'], ['esp', 'crosas', 'celtic', 'transfer', 'summer', '0.5 m + 0.8 m in variables', 'barcelonacat']] |
1953 washington redskins season | https://en.wikipedia.org/wiki/1953_Washington_Redskins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15123292-1.html.csv | unique | the october 2 , 1953 game against the philadelphia eagles was the only one to end in a tie in the 1953 washington redskins season . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': '2,3', 'criterion': 'fuzzily_match', 'value': 't', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 't'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to t .', 'tostr': 'filter_eq { all_rows ; result ; t }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; t } }', 'tointer': 'select the rows whose result record fuzzily matches to t . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 't'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to t .', 'tostr': 'filter_eq { all_rows ; result ; t }'}, 'date'], 'result': 'october 2 , 1953', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; t } ; date }'}, 'october 2 , 1953'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; t } ; date } ; october 2 , 1953 }', 'tointer': 'the date record of this unqiue row is october 2 , 1953 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 't'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to t .', 'tostr': 'filter_eq { all_rows ; result ; t }'}, 'opponent'], 'result': 'philadelphia eagles', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; result ; t } ; opponent }'}, 'philadelphia eagles'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; t } ; opponent } ; philadelphia eagles }', 'tointer': 'the opponent record of this unqiue row is philadelphia eagles .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; result ; t } ; date } ; october 2 , 1953 } ; eq { hop { filter_eq { all_rows ; result ; t } ; opponent } ; philadelphia eagles } }', 'tointer': 'the date record of this unqiue row is october 2 , 1953 . the opponent record of this unqiue row is philadelphia eagles .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; result ; t } } ; and { eq { hop { filter_eq { all_rows ; result ; t } ; date } ; october 2 , 1953 } ; eq { hop { filter_eq { all_rows ; result ; t } ; opponent } ; philadelphia eagles } } } = true', 'tointer': 'select the rows whose result record fuzzily matches to t . there is only one such row in the table . the date record of this unqiue row is october 2 , 1953 . the opponent record of this unqiue row is philadelphia eagles .'} | and { only { filter_eq { all_rows ; result ; t } } ; and { eq { hop { filter_eq { all_rows ; result ; t } ; date } ; october 2 , 1953 } ; eq { hop { filter_eq { all_rows ; result ; t } ; opponent } ; philadelphia eagles } } } = true | select the rows whose result record fuzzily matches to t . there is only one such row in the table . the date record of this unqiue row is october 2 , 1953 . the opponent record of this unqiue row is philadelphia eagles . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'result_10': 10, 't_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'date_12': 12, 'october 2 , 1953_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'opponent_14': 14, 'philadelphia eagles_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'result_10': 'result', 't_11': 't', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_12': 'date', 'october 2 , 1953_13': 'october 2 , 1953', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'opponent_14': 'opponent', 'philadelphia eagles_15': 'philadelphia eagles'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'result_10': [0], 't_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'date_12': [2], 'october 2 , 1953_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'opponent_14': [4], 'philadelphia eagles_15': [5]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 27 , 1953', 'chicago cardinals', 'w 24 - 13', '16055'], ['2', 'october 2 , 1953', 'philadelphia eagles', 't 21 - 21', '19099'], ['3', 'october 11 , 1953', 'new york giants', 'w 13 - 9', '26241'], ['4', 'october 18 , 1953', 'cleveland browns', 'l 30 - 14', '33963'], ['5', 'october 25 , 1953', 'baltimore colts', 'l 27 - 17', '34031'], ['6', 'november 1 , 1953', 'cleveland browns', 'l 27 - 3', '47845'], ['7', 'november 8 , 1953', 'chicago cardinals', 'w 28 - 17', '19654'], ['8', 'november 15 , 1953', 'chicago bears', 'l 27 - 24', '21392'], ['9', 'november 22 , 1953', 'new york giants', 'w 24 - 21', '16887'], ['10', 'november 29 , 1953', 'pittsburgh steelers', 'w 17 - 9', '17026'], ['11', 'december 6 , 1953', 'philadelphia eagles', 'w 10 - 0', '21579'], ['12', 'december 13 , 1953', 'pittsburgh steelers', 'l 14 - 13', '22057']] |
mauricio cienfuegos | https://en.wikipedia.org/wiki/Mauricio_Cienfuegos | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1114137-1.html.csv | count | mauricio cienfuegos scored a total of two times in the 1995 uncaf nations cup . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '1995', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '1995'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 1995 .', 'tostr': 'filter_eq { all_rows ; date ; 1995 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date ; 1995 } }', 'tointer': 'select the rows whose date record fuzzily matches to 1995 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date ; 1995 } } ; 2 } = true', 'tointer': 'select the rows whose date record fuzzily matches to 1995 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; date ; 1995 } } ; 2 } = true | select the rows whose date record fuzzily matches to 1995 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, '1995_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', '1995_6': '1995', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], '1995_6': [0], '2_7': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['23 july 1992', 'estadio cuscatlán , san salvador , el salvador', '2 - 0', '5 - 1', '1994 fifa world cup qualification'], ['1 november 1992', 'estadio cuscatlán , san salvador , el salvador', '3 - 0', '4 - 1', '1994 fifa world cup qualification'], ['23 march 1993', 'estadio cuscatlán , san salvador , el salvador', '2 - 2', '2 - 2', 'friendly match'], ['29 november 1995', 'estadio oscar quiteno , santa ana , el salvador', '1 - 0', '3 - 0', '1995 uncaf nations cup'], ['3 december 1995', 'estadio flor blanca , san salvador , el salvador', '2 - 1', '2 - 1', '1995 uncaf nations cup'], ['8 september 1996', 'estadio cuscatlán , san salvador , el salvador', '5 - 0', '5 - 0', '1998 fifa world cup qualification'], ['14 september 1997', 'estadio cuscatlán , san salvador , el salvador', '3 - 1', '4 - 1', '1998 fifa world cup qualification'], ['16 july 2000', 'estadio cuscatlán , san salvador , el salvador', '2 - 5', '2 - 5', '2002 fifa world cup qualification']] |
supernatural ( season 6 ) | https://en.wikipedia.org/wiki/Supernatural_%28season_6%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27892955-1.html.csv | unique | in season 6 of supernatural , the only episode directed by guy bee was the one titled family matters . | {'scope': 'all', 'row': '6', 'col': '4', 'col_other': '3', 'criterion': 'fuzzily_match', 'value': 'guy bee', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'guy bee'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to guy bee .', 'tostr': 'filter_eq { all_rows ; directed by ; guy bee }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; directed by ; guy bee } }', 'tointer': 'select the rows whose directed by record fuzzily matches to guy bee . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'guy bee'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to guy bee .', 'tostr': 'filter_eq { all_rows ; directed by ; guy bee }'}, 'title'], 'result': 'family matters', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; directed by ; guy bee } ; title }'}, 'family matters'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; directed by ; guy bee } ; title } ; family matters }', 'tointer': 'the title record of this unqiue row is family matters .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; directed by ; guy bee } } ; eq { hop { filter_eq { all_rows ; directed by ; guy bee } ; title } ; family matters } } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to guy bee . there is only one such row in the table . the title record of this unqiue row is family matters .'} | and { only { filter_eq { all_rows ; directed by ; guy bee } } ; eq { hop { filter_eq { all_rows ; directed by ; guy bee } ; title } ; family matters } } = true | select the rows whose directed by record fuzzily matches to guy bee . there is only one such row in the table . the title record of this unqiue row is family matters . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'directed by_7': 7, 'Guy bee_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'family matters_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'directed by_7': 'directed by', 'Guy bee_8': 'guy bee', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'family matters_10': 'family matters'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'directed by_7': [0], 'Guy bee_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'family matters_10': [3]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( million )'] | [['105', '1', 'exile on main st', 'phil sgriccia', 'sera gamble', 'september 24 , 2010', '3x6052', '2.90'], ['106', '2', 'two and a half men', 'john showalter', 'adam glass', 'october 1 , 2010', '3x6053', '2.33'], ['107', '3', 'the third man', 'robert singer', 'ben edlund', 'october 8 , 2010', '3x6054', '2.16'], ['108', '4', "weekend at bobby 's", 'jensen ackles', 'andrew dabb & daniel loflin', 'october 15 , 2010', '3x6051', '2.84'], ['109', '5', 'live free or twihard', 'rod hardy', 'brett matthews', 'october 22 , 2010', '3x6056', '2.47'], ['111', '7', 'family matters', 'guy bee', 'andrew dabb & daniel loflin', 'november 5 , 2010', '3x6057', '2.46'], ['112', '8', 'all dogs go to heaven', 'phil sgriccia', 'adam glass', 'november 12 , 2010', '3x6058', '2.09'], ['113', '9', 'clap your hands if you believe', 'john showalter', 'ben edlund', 'november 19 , 2010', '3x6059', '1.94'], ['114', '10', 'caged heat', 'robert singer', 'brett matthews & jenny klein', 'december 3 , 2010', '3x6060', '2.15'], ['115', '11', 'appointment in samarra', 'mike rohl', 'sera gamble & robert singer', 'december 10 , 2010', '3x6061', '2.27'], ['116', '12', 'like a virgin', 'phil sgriccia', 'adam glass', 'february 4 , 2011', '3x6062', '2.25'], ['117', '13', 'unforgiven', 'david barrett', 'andrew dabb & daniel loflin', 'february 11 , 2011', '3x6063', '1.97'], ['118', '14', 'mannequin 3 : the reckoning', 'jeannot szwarc', 'eric charmelo & nicole snyder', 'february 18 , 2011', '3x6064', '2.25'], ['119', '15', 'the french mistake', 'charles beeson', 'ben edlund', 'february 25 , 2011', '3x6065', '2.18'], ['120', '16', 'and then there were none', 'mike rohl', 'brett matthews', 'march 4 , 2011', '3x6066', '2.14'], ['121', '17', 'my heart will go on', 'phil sgriccia', 'eric charmelo & nicole snyder', 'april 15 , 2011', '3x6068', '2.26'], ['123', '19', 'mommy dearest', 'john showalter', 'adam glass', 'april 29 , 2011', '3x6069', '2.01'], ['124', '20', 'the man who would be king', 'ben edlund', 'ben edlund', 'may 6 , 2011', '3x6070', '2.11'], ['125', '21', 'let it bleed', 'john showalter', 'sera gamble', 'may 20 , 2011', '3x6071', '2.02']] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1805191-2.html.csv | ordinal | the person elected to the united states house of representatives in the fourth earliest year was robert aderholt . | {'row': '3', 'col': '4', 'order': '4', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'first elected', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first elected ; 4 }'}, 'incumbent'], 'result': 'robert aderholt', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 4 } ; incumbent }'}, 'robert aderholt'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 4 } ; incumbent } ; robert aderholt } = true', 'tointer': 'select the row whose first elected record of all rows is 4th minimum . the incumbent record of this row is robert aderholt .'} | eq { hop { nth_argmin { all_rows ; first elected ; 4 } ; incumbent } ; robert aderholt } = true | select the row whose first elected record of all rows is 4th minimum . the incumbent record of this row is robert aderholt . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '4_6': 6, 'incumbent_7': 7, 'robert aderholt_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '4_6': '4', 'incumbent_7': 'incumbent', 'robert aderholt_8': 'robert aderholt'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '4_6': [0], 'incumbent_7': [1], 'robert aderholt_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'results', 'candidates'] | [['alabama 1', 'jo bonner', 'republican', '2002', 're - elected', 'jo bonner ( r ) 68.1 % vivian beckerle ( d ) 31.8 %'], ['alabama 2', 'terry everett', 'republican', '1992', 're - elected', 'terry everett ( r ) 69.5 % chuck james ( d ) 30.4 %'], ['alabama 4', 'robert aderholt', 'republican', '1996', 're - elected', 'robert aderholt ( r ) 70.2 % barbara bobo ( d ) 29.7 %'], ['alabama 5', 'robert cramer', 'democratic', '1990', 're - elected', 'robert cramer ( d ) unopposed'], ['alabama 6', 'spencer bachus', 'republican', '1992', 're - elected', 'spencer bachus ( r ) unopposed']] |
list of maserati vehicles | https://en.wikipedia.org/wiki/List_of_Maserati_vehicles | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1810336-2.html.csv | ordinal | in the list of maserati vehicles used for racing , maserati model type 26 was the very first model used for racing . | {'row': '1', 'col': '2', 'order': '1', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 1 }'}, 'model'], 'result': 'type 26', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 1 } ; model }'}, 'type 26'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 1 } ; model } ; type 26 } = true', 'tointer': 'select the row whose year record of all rows is 1st minimum . the model record of this row is type 26 .'} | eq { hop { nth_argmin { all_rows ; year ; 1 } ; model } ; type 26 } = true | select the row whose year record of all rows is 1st minimum . the model record of this row is type 26 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '1_6': 6, 'model_7': 7, 'type 26_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', '1_6': '1', 'model_7': 'model', 'type 26_8': 'type 26'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '1_6': [0], 'model_7': [1], 'type 26_8': [2]} | ['model', 'year', 'type', 'engine', 'displacement cc'] | [['type 26', '1926', 'grand prix', 'i8', '1500'], ['type 26b', '1928', 'grand prix', 'i8', '2000'], ['type v4 sedici cilindri', '1929', 'grand prix', 'v16', '4000'], ['8c', '1929', 'grand prix', 'i8', '1100 1500 2500'], ['6 cm', '1936', 'voiturette', 'i6', '1100 1500 2500'], ['4cl', '1939', 'voiturette', 'i4', '1491'], ['4clt', '1948', 'formula one', 'i4', '1491'], ['250f', '1953', 'formula one', 'i6', '2493'], ['350s', '1957', 'sports car', 'i6', '3500'], ['450s', '1957', 'sports car', 'v8', '4500'], ['type 61 birdcage', '1961', 'sports car', 'i4', '3000'], ['tipo 151', '19621963', 'sports car', 'v8', '4941'], ['tipo 154', '1965', 'sports car', 'v8', '5046.8']] |
1968 vfl season | https://en.wikipedia.org/wiki/1968_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10808933-2.html.csv | aggregation | in the 1968 vfl season , the average score for home teams was 13.98 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '13.98', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'home team score'], 'result': '13.98', 'ind': 0, 'tostr': 'avg { all_rows ; home team score }'}, '13.98'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; home team score } ; 13.98 } = true', 'tointer': 'the average of the home team score record of all rows is 13.98 .'} | round_eq { avg { all_rows ; home team score } ; 13.98 } = true | the average of the home team score record of all rows is 13.98 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'home team score_4': 4, '13.98_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'home team score_4': 'home team score', '13.98_5': '13.98'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'home team score_4': [0], '13.98_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '17.24 ( 126 )', 'south melbourne', '19.12 ( 126 )', 'glenferrie oval', '13536', '20 april 1968'], ['st kilda', '16.22 ( 118 )', 'melbourne', '9.8 ( 62 )', 'moorabbin oval', '21758', '20 april 1968'], ['geelong', '9.17 ( 71 )', 'footscray', '6.11 ( 47 )', 'kardinia park', '14589', '20 april 1968'], ['north melbourne', '9.9 ( 63 )', 'essendon', '10.22 ( 82 )', 'arden street oval', '14810', '20 april 1968'], ['fitzroy', '14.16 ( 100 )', 'collingwood', '10.11 ( 71 )', 'princes park', '17149', '20 april 1968'], ['richmond', '17.16 ( 118 )', 'carlton', '10.12 ( 72 )', 'mcg', '51889', '20 april 1968']] |
1990 england rugby union tour of argentina | https://en.wikipedia.org/wiki/1990_England_rugby_union_tour_of_Argentina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17058667-1.html.csv | count | a total of five matches were designated the tour match status . | {'scope': 'all', 'criterion': 'equal', 'value': 'tour match', 'result': '5', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 'tour match'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to tour match .', 'tostr': 'filter_eq { all_rows ; status ; tour match }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; status ; tour match } }', 'tointer': 'select the rows whose status record fuzzily matches to tour match . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; status ; tour match } } ; 5 } = true', 'tointer': 'select the rows whose status record fuzzily matches to tour match . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; status ; tour match } } ; 5 } = true | select the rows whose status record fuzzily matches to tour match . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'status_5': 5, 'tour match_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'status_5': 'status', 'tour match_6': 'tour match', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'status_5': [0], 'tour match_6': [0], '5_7': [2]} | ['opposing team', 'against', 'date', 'venue', 'status'] | [['banco nación', '29', '14 july 1990', 'buenos aires', 'tour match'], ['tucumán selection', '14', '18 july 1990', 'tucumán', 'tour match'], ['buenos aires selection', '26', '21 july 1990', 'buenos aires', 'tour match'], ['cuyo selection', '22', '24 july 1990', 'mendoza', 'tour match'], ['argentina', '12', '28 july 1990', 'vélez sársfield , buenos aires', 'first test'], ['córdoba', '12', '31 july 1990', 'córdoba', 'tour match'], ['argentina', '15', '4 august 1990', 'vélez sársfield , buenos aires', 'second test']] |
world team chess championship | https://en.wikipedia.org/wiki/World_Team_Chess_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15669776-3.html.csv | unique | only russia achieved 5 top three placements among all the countries . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '2', 'criterion': 'equal', 'value': '5', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'total', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total record is equal to 5 .', 'tostr': 'filter_eq { all_rows ; total ; 5 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; total ; 5 } }', 'tointer': 'select the rows whose total record is equal to 5 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'total', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total record is equal to 5 .', 'tostr': 'filter_eq { all_rows ; total ; 5 }'}, 'country'], 'result': 'russia', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; total ; 5 } ; country }'}, 'russia'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; total ; 5 } ; country } ; russia }', 'tointer': 'the country record of this unqiue row is russia .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; total ; 5 } } ; eq { hop { filter_eq { all_rows ; total ; 5 } ; country } ; russia } } = true', 'tointer': 'select the rows whose total record is equal to 5 . there is only one such row in the table . the country record of this unqiue row is russia .'} | and { only { filter_eq { all_rows ; total ; 5 } } ; eq { hop { filter_eq { all_rows ; total ; 5 } ; country } ; russia } } = true | select the rows whose total record is equal to 5 . there is only one such row in the table . the country record of this unqiue row is russia . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'total_7': 7, '5_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'country_9': 9, 'russia_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'total_7': 'total', '5_8': '5', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'country_9': 'country', 'russia_10': 'russia'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'total_7': [0], '5_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'country_9': [2], 'russia_10': [3]} | ['rank', 'country', '1st place', '2nd place', '3rd place', 'total'] | [['1', 'russia', '3', '1', '1', '5'], ['2', 'soviet union', '2', '0', '0', '2'], ['3', 'united states', '1', '2', '0', '3'], ['4', 'ukraine', '1', '1', '1', '3'], ['5', 'armenia', '1', '0', '3', '4'], ['6', 'china', '0', '2', '0', '2'], ['7', 'hungary', '0', '1', '0', '1'], ['7', 'yugoslavia', '0', '1', '0', '1'], ['9', 'england', '0', '0', '2', '2'], ['10', 'india', '0', '0', '1', '1']] |
list of the green green grass episodes | https://en.wikipedia.org/wiki/List_of_The_Green_Green_Grass_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17641206-2.html.csv | count | 4 episodes of the green green grass had more than 6 million viewers . | {'scope': 'all', 'criterion': 'greater_than', 'value': '6.00 million', 'result': '4', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'viewership', '6.00 million'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose viewership record is greater than 6.00 million .', 'tostr': 'filter_greater { all_rows ; viewership ; 6.00 million }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; viewership ; 6.00 million } }', 'tointer': 'select the rows whose viewership record is greater than 6.00 million . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; viewership ; 6.00 million } } ; 4 } = true', 'tointer': 'select the rows whose viewership record is greater than 6.00 million . the number of such rows is 4 .'} | eq { count { filter_greater { all_rows ; viewership ; 6.00 million } } ; 4 } = true | select the rows whose viewership record is greater than 6.00 million . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'viewership_5': 5, '6.00 million_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'viewership_5': 'viewership', '6.00 million_6': '6.00 million', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'viewership_5': [0], '6.00 million_6': [0], '4_7': [2]} | ['episode', 'title', 'directed by', 'written by', 'original airdate', 'duration', 'viewership'] | [['1', 'keep on running', 'tony dow', 'john sullivan', '9 september 2005', '30 minutes', '8.88 million'], ['2', 'a rocky start', 'tony dow', 'john sullivan', '16 september 2005', '30 minutes', '6.34 million'], ['3', 'the country wife', 'tony dow', 'john sullivan', '23 september 2005', '30 minutes', '5.86 million'], ['4', 'hay fever', 'tony dow', 'john sullivan', '30 september 2005', '30 minutes', '6.33 million'], ['5', 'pillow talk', 'tony dow', 'john sullivan', '7 october 2005', '30 minutes', '6.63 million']] |
list of north queensland cowboys records | https://en.wikipedia.org/wiki/List_of_North_Queensland_Cowboys_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10848585-12.html.csv | count | north queensland cowboys played against the south sydney rabbitohs 2 times . | {'scope': 'all', 'criterion': 'equal', 'value': 'south sydney rabbitohs', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'south sydney rabbitohs'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to south sydney rabbitohs .', 'tostr': 'filter_eq { all_rows ; opponent ; south sydney rabbitohs }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; south sydney rabbitohs } }', 'tointer': 'select the rows whose opponent record fuzzily matches to south sydney rabbitohs . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; south sydney rabbitohs } } ; 2 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to south sydney rabbitohs . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; opponent ; south sydney rabbitohs } } ; 2 } = true | select the rows whose opponent record fuzzily matches to south sydney rabbitohs . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'south sydney rabbitohs_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'south sydney rabbitohs_6': 'south sydney rabbitohs', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'south sydney rabbitohs_6': [0], '2_7': [2]} | ['opponent', 'result', 'score', 'date', 'venue'] | [['penrith panthers', 'loss', '24 - 28', '7 june 2003', 'dairy farmers stadium'], ['south sydney rabbitohs', 'draw', '20 - 20', '15 may 2004', 'bluetongue stadium'], ['new zealand warriors', 'win', '28 - 26', '20 june 2004', 'ericsson stadium'], ['newcastle knights', 'win', '28 - 24', '25 july 2004', 'energyaustralia stadium'], ['canberra raiders', 'loss', '14 - 15', '27 may 2006', 'dairy farmers stadium'], ['penrith panthers', 'win', '30 - 26', '13 august 2007', 'cua stadium'], ['penrith panthers', 'loss', '18 - 19', '31 may 2008', 'dairy farmers stadium'], ['cronulla sharks', 'loss', '19 - 20', '26 june 2010', 'dairy farmers stadium'], ['newcastle knights', 'win', '28 - 24', '24 july 2010', 'dairy farmers stadium'], ['south sydney rabbitohs', 'loss', '24 - 26', '19 august 2011', 'anz stadium']] |
1983 - 84 north west counties football league | https://en.wikipedia.org/wiki/1983%E2%80%9384_North_West_Counties_Football_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17718005-2.html.csv | majority | the majority of teams in the 1983 - 84 north west counties football league won more than 10 games . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'won', '10'], 'result': True, 'ind': 0, 'tointer': 'for the won records of all rows , most of them are greater than 10 .', 'tostr': 'most_greater { all_rows ; won ; 10 } = true'} | most_greater { all_rows ; won ; 10 } = true | for the won records of all rows , most of them are greater than 10 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'won_3': 3, '10_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'won_3': 'won', '10_4': '10'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'won_3': [0], '10_4': [0]} | ['position', 'team', 'played', 'won', 'drawn', 'lost', 'goals for', 'goals against', 'goal difference', 'points 1'] | [['1', 'fleetwood town', '34', '24', '8', '2', '73', '24', '+ 49', '56'], ['2', 'eastwood hanley', '34', '21', '6', '7', '69', '35', '+ 34', '48'], ['3', 'irlam town', '34', '19', '8', '7', '67', '41', '+ 26', '46'], ['4', 'warrington town', '34', '18', '7', '9', '65', '45', '+ 20', '43'], ['5', 'droylsden', '34', '19', '5', '10', '59', '42', '+ 17', '43'], ['6', 'colne dynamoes', '34', '16', '9', '9', '55', '37', '+ 18', '41'], ['7', 'ellesmere port & neston', '34', '12', '10', '12', '49', '38', '+ 11', '34'], ['8', 'chadderton', '34', '14', '6', '14', '56', '46', '+ 10', '34'], ['9', 'atherton laburnum rovers', '34', '11', '11', '12', '37', '41', '4', '33'], ['10', 'wren rovers', '34', '11', '10', '13', '45', '47', '2', '33'], ['11', 'skelmersdale united', '34', '13', '6', '15', '60', '63', '3', '32'], ['12', 'ford motors', '34', '9', '9', '16', '38', '53', '15', '27'], ['13', 'prescot bi', '34', '9', '9', '16', '50', '66', '16', '27'], ['14', 'lytham', '34', '13', '3', '18', '56', '81', '25', '27 2'], ['15', 'rossendale united', '34', '10', '6', '18', '53', '84', '31', '26'], ['16', 'great harwood town', '34', '5', '12', '17', '36', '60', '24', '22'], ['17', 'salford', '34', '5', '11', '18', '24', '60', '36', '21']] |
1972 vfl season | https://en.wikipedia.org/wiki/1972_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826385-8.html.csv | count | three of the games played in the 1972 vfl season drew a crowd of under 20000 . | {'scope': 'all', 'criterion': 'less_than', 'value': '20000', 'result': '3', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'crowd', '20000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crowd record is less than 20000 .', 'tostr': 'filter_less { all_rows ; crowd ; 20000 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; crowd ; 20000 } }', 'tointer': 'select the rows whose crowd record is less than 20000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; crowd ; 20000 } } ; 3 } = true', 'tointer': 'select the rows whose crowd record is less than 20000 . the number of such rows is 3 .'} | eq { count { filter_less { all_rows ; crowd ; 20000 } } ; 3 } = true | select the rows whose crowd record is less than 20000 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '20000_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '20000_6': '20000', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '20000_6': [0], '3_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['collingwood', '7.14 ( 56 )', 'footscray', '11.14 ( 80 )', 'victoria park', '25986', '20 may 1972'], ['melbourne', '20.14 ( 134 )', 'geelong', '14.17 ( 101 )', 'mcg', '19023', '20 may 1972'], ['south melbourne', '9.7 ( 61 )', 'fitzroy', '18.11 ( 119 )', 'lake oval', '12421', '20 may 1972'], ['north melbourne', '8.13 ( 61 )', 'essendon', '14.12 ( 96 )', 'arden street oval', '14091', '20 may 1972'], ['st kilda', '10.12 ( 72 )', 'carlton', '14.15 ( 99 )', 'moorabbin oval', '31547', '20 may 1972'], ['richmond', '11.25 ( 91 )', 'hawthorn', '13.6 ( 84 )', 'vfl park', '25845', '20 may 1972']] |
united states house of representatives elections , 1926 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1926 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342379-45.html.csv | comparative | of the incumbents in the 1926 election for united states house of representatives , clifton a woodrum was first elected 2 years before joseph whitehead . | {'row_1': '6', 'row_2': '5', 'col': '4', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '2 years', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'clifton a woodrum'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to clifton a woodrum .', 'tostr': 'filter_eq { all_rows ; incumbent ; clifton a woodrum }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; clifton a woodrum } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to clifton a woodrum . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'joseph whitehead'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to joseph whitehead .', 'tostr': 'filter_eq { all_rows ; incumbent ; joseph whitehead }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; joseph whitehead } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to joseph whitehead . take the first elected record of this row .'}], 'result': '-2 years', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; incumbent ; clifton a woodrum } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; joseph whitehead } ; first elected } }'}, '-2 years'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; incumbent ; clifton a woodrum } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; joseph whitehead } ; first elected } } ; -2 years } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to clifton a woodrum . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to joseph whitehead . take the first elected record of this row . the second record is 2 years larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; incumbent ; clifton a woodrum } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; joseph whitehead } ; first elected } } ; -2 years } = true | select the rows whose incumbent record fuzzily matches to clifton a woodrum . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to joseph whitehead . take the first elected record of this row . the second record is 2 years larger than the first record . | 6 | 6 | {'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'incumbent_8': 8, 'clifton a woodrum_9': 9, 'first elected_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'incumbent_12': 12, 'joseph whitehead_13': 13, 'first elected_14': 14, '-2 years_15': 15} | {'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'incumbent_8': 'incumbent', 'clifton a woodrum_9': 'clifton a woodrum', 'first elected_10': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'incumbent_12': 'incumbent', 'joseph whitehead_13': 'joseph whitehead', 'first elected_14': 'first elected', '-2 years_15': '-2 years'} | {'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'incumbent_8': [0], 'clifton a woodrum_9': [0], 'first elected_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'incumbent_12': [1], 'joseph whitehead_13': [1], 'first elected_14': [3], '-2 years_15': [5]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['virginia 1', 's otis bland', 'democratic', '1918', 're - elected', 's otis bland ( d ) unopposed'], ['virginia 2', 'joseph t deal', 'democratic', '1920', 're - elected', 'joseph t deal ( d ) 65.4 % l s parsons ( r ) 34.6 %'], ['virginia 3', 'andrew jackson montague', 'democratic', '1912', 're - elected', 'andrew jackson montague ( d ) unopposed'], ['virginia 4', 'patrick h drewry', 'democratic', '1920', 're - elected', 'patrick h drewry ( d ) unopposed'], ['virginia 5', 'joseph whitehead', 'democratic', '1924', 're - elected', 'joseph whitehead ( d ) unopposed'], ['virginia 6', 'clifton a woodrum', 'democratic', '1922', 're - elected', 'clifton a woodrum ( d ) unopposed'], ['virginia 8', 'r walton moore', 'democratic', '1919', 're - elected', 'r walton moore ( d ) 95.5 % j w leedy ( r ) 4.5 %'], ['virginia 9', 'george c peery', 'democratic', '1922', 're - elected', 'george c peery ( d ) 53.4 % s r hurley ( r ) 46.6 %']] |
list of ultras of africa | https://en.wikipedia.org/wiki/List_of_Ultras_of_Africa | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18947170-11.html.csv | unique | the piton de niegues peak is the only one with an elevation of over 3000 meters . | {'scope': 'all', 'row': '1', 'col': '4', 'col_other': '1', 'criterion': 'greater_than', 'value': '3000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'prominence ( m )', '3000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose prominence ( m ) record is greater than 3000 .', 'tostr': 'filter_greater { all_rows ; prominence ( m ) ; 3000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; prominence ( m ) ; 3000 } }', 'tointer': 'select the rows whose prominence ( m ) record is greater than 3000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'prominence ( m )', '3000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose prominence ( m ) record is greater than 3000 .', 'tostr': 'filter_greater { all_rows ; prominence ( m ) ; 3000 }'}, 'peak'], 'result': 'piton des neiges', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; prominence ( m ) ; 3000 } ; peak }'}, 'piton des neiges'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; prominence ( m ) ; 3000 } ; peak } ; piton des neiges }', 'tointer': 'the peak record of this unqiue row is piton des neiges .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; prominence ( m ) ; 3000 } } ; eq { hop { filter_greater { all_rows ; prominence ( m ) ; 3000 } ; peak } ; piton des neiges } } = true', 'tointer': 'select the rows whose prominence ( m ) record is greater than 3000 . there is only one such row in the table . the peak record of this unqiue row is piton des neiges .'} | and { only { filter_greater { all_rows ; prominence ( m ) ; 3000 } } ; eq { hop { filter_greater { all_rows ; prominence ( m ) ; 3000 } ; peak } ; piton des neiges } } = true | select the rows whose prominence ( m ) record is greater than 3000 . there is only one such row in the table . the peak record of this unqiue row is piton des neiges . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'prominence (m)_7': 7, '3000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'peak_9': 9, 'piton des neiges_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'prominence (m)_7': 'prominence ( m )', '3000_8': '3000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'peak_9': 'peak', 'piton des neiges_10': 'piton des neiges'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'prominence (m)_7': [0], '3000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'peak_9': [2], 'piton des neiges_10': [3]} | ['peak', 'country', 'elevation ( m )', 'prominence ( m )', 'col ( m )'] | [['piton des neiges', 'france ( rãunion )', '3069', '3069', '0'], ['maromokotro', 'madagascar', '2876', '2876', '0'], ['mount karthala', 'comoros ( grande comore )', '2361', '2361', '0'], ['pic boby', 'madagascar', '2658', '1875', '783'], ['tsiafajavona', 'madagascar', '2643', '1663', '980'], ['ntingui', 'comoros ( anjouan )', '1595', '1595', '0']] |
united states house of representatives elections , 1986 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1986 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341586-39.html.csv | count | in the 1986 election for united states house of representatives , four of the candidates were from the democratic party . | {'scope': 'all', 'criterion': 'equal', 'value': 'democratic', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to democratic .', 'tostr': 'filter_eq { all_rows ; party ; democratic }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; party ; democratic } }', 'tointer': 'select the rows whose party record fuzzily matches to democratic . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; party ; democratic } } ; 4 } = true', 'tointer': 'select the rows whose party record fuzzily matches to democratic . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; party ; democratic } } ; 4 } = true | select the rows whose party record fuzzily matches to democratic . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'party_5': 5, 'democratic_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'party_5': 'party', 'democratic_6': 'democratic', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'party_5': [0], 'democratic_6': [0], '4_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['pennsylvania 6', 'gus yatron', 'democratic', '1968', 're - elected', 'gus yatron ( d ) 69.1 % norm bertasavage ( r ) 30.9 %'], ['pennsylvania 7', 'robert w edgar', 'democratic', '1974', 'retired to run for u s senate republican gain', 'curt weldon ( r ) 61.3 % bill spingler ( d ) 38.7 %'], ['pennsylvania 9', 'bud shuster', 'republican', '1972', 're - elected', 'bud shuster ( r ) unopposed'], ['pennsylvania 12', 'john murtha', 'democratic', '1974', 're - elected', 'john murtha ( d ) 67.4 % kathy holtzman ( r ) 32.6 %'], ['pennsylvania 15', 'donald l ritter', 'republican', '1978', 're - elected', 'donald l ritter ( r ) 56.8 % joe simonetta ( d ) 43.2 %'], ['pennsylvania 17', 'george gekas', 'republican', '1982', 're - elected', 'george gekas ( r ) 73.6 % michael s ogden ( d ) 26.4 %'], ['pennsylvania 18', 'doug walgren', 'democratic', '1976', 're - elected', 'doug walgren ( d ) 63.0 % ernie buckman ( r ) 37.0 %'], ['pennsylvania 21', 'tom ridge', 'republican', '1982', 're - elected', 'tom ridge ( r ) 80.9 % joylyn blackwell ( d ) 19.1 %']] |
united states house of representatives elections , 1926 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1926 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342379-41.html.csv | count | in 1926 , two of the people elected to the house of representatives were republican . | {'scope': 'all', 'criterion': 'equal', 'value': 'republican', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to republican .', 'tostr': 'filter_eq { all_rows ; party ; republican }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; party ; republican } }', 'tointer': 'select the rows whose party record fuzzily matches to republican . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; party ; republican } } ; 2 } = true', 'tointer': 'select the rows whose party record fuzzily matches to republican . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; party ; republican } } ; 2 } = true | select the rows whose party record fuzzily matches to republican . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'party_5': 5, 'republican_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'party_5': 'party', 'republican_6': 'republican', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'party_5': [0], 'republican_6': [0], '2_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['tennessee 1', 'b carroll reece', 'republican', '1920', 're - elected', 'b carroll reece ( r ) 88.0 % w i giles ( d ) 12.0 %'], ['tennessee 2', 'j will taylor', 'republican', '1918', 're - elected', 'j will taylor ( r ) 99.8 % r l swann ( d ) 0.2 %'], ['tennessee 4', 'cordell hull', 'democratic', '1922', 're - elected', 'cordell hull ( d ) 71.4 % w thompson ( r ) 28.6 %'], ['tennessee 5', 'ewin l davis', 'democratic', '1918', 're - elected', 'ewin l davis ( d ) unopposed'], ['tennessee 6', 'joseph w byrns , sr', 'democratic', '1908', 're - elected', 'joseph w byrns , sr ( d ) unopposed'], ['tennessee 7', 'edward everett eslick', 'democratic', '1924', 're - elected', 'edward everett eslick ( d ) unopposed'], ['tennessee 8', 'gordon browning', 'democratic', '1922', 're - elected', 'gordon browning ( d ) unopposed'], ['tennessee 9', 'finis j garrett', 'democratic', '1904', 're - elected', 'finis j garrett ( d ) unopposed']] |
2011 capital one world women 's curling championship | https://en.wikipedia.org/wiki/2011_Capital_One_World_Women%27s_Curling_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26745426-2.html.csv | superlative | anna kubešková had the lowest shot percentage of the athletes listed . | {'scope': 'all', 'col_superlative': '11', 'row_superlative': '11', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'shot %'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; shot % }'}, 'skip'], 'result': 'anna kubešková', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; shot % } ; skip }'}, 'anna kubešková'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; shot % } ; skip } ; anna kubešková } = true', 'tointer': 'select the row whose shot % record of all rows is minimum . the skip record of this row is anna kubešková .'} | eq { hop { argmin { all_rows ; shot % } ; skip } ; anna kubešková } = true | select the row whose shot % record of all rows is minimum . the skip record of this row is anna kubešková . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'shot %_5': 5, 'skip_6': 6, 'anna kubešková_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'shot %_5': 'shot %', 'skip_6': 'skip', 'anna kubešková_7': 'anna kubešková'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'shot %_5': [0], 'skip_6': [1], 'anna kubešková_7': [2]} | ['country', 'skip', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends', 'shot %'] | [['sweden', 'anette norberg', '9', '2', '67', '53', '40', '41', '12', '8', '73 %'], ['china', 'wang bingyu', '8', '3', '64', '43', '44', '30', '14', '16', '82 %'], ['denmark', 'lene nielsen', '7', '4', '77', '55', '47', '33', '15', '14', '78 %'], ['canada', 'amber holland', '7', '4', '68', '55', '42', '40', '12', '7', '82 %'], ['switzerland', 'mirjam ott', '7', '4', '68', '58', '46', '37', '15', '15', '82 %'], ['russia', 'anna sidorova', '6', '5', '70', '65', '40', '45', '8', '8', '72 %'], ['united states', 'patti lank', '6', '5', '64', '63', '48', '36', '10', '17', '72 %'], ['germany', 'andrea schöpp', '5', '6', '61', '67', '40', '49', '12', '13', '78 %'], ['scotland', 'anna sloan', '4', '7', '49', '69', '33', '43', '15', '6', '76 %'], ['norway', 'linn githmark', '3', '8', '54', '71', '42', '48', '15', '7', '77 %'], ['czech republic', 'anna kubešková', '2', '9', '40', '73', '35', '43', '11', '7', '71 %']] |
col de menté | https://en.wikipedia.org/wiki/Col_de_Ment%C3%A9 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12179265-1.html.csv | unique | the only american leader at the summit was tom danielson . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': '( usa )', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'leader at the summit', '( usa )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose leader at the summit record fuzzily matches to ( usa ) .', 'tostr': 'filter_eq { all_rows ; leader at the summit ; ( usa ) }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; leader at the summit ; ( usa ) } } = true', 'tointer': 'select the rows whose leader at the summit record fuzzily matches to ( usa ) . there is only one such row in the table .'} | only { filter_eq { all_rows ; leader at the summit ; ( usa ) } } = true | select the rows whose leader at the summit record fuzzily matches to ( usa ) . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'leader at the summit_4': 4, '( usa )_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'leader at the summit_4': 'leader at the summit', '( usa )_5': '( usa )'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'leader at the summit_4': [0], '( usa )_5': [0]} | ['year', 'stage', 'category', 'start', 'finish', 'leader at the summit'] | [['2013', '9', '1', 'saint - girons', 'bagnères - de - bigorre', 'tom danielson ( usa )'], ['2012', '17', '1', 'bagnères - de - luchon', 'peyragudes', 'thomas voeckler ( fra )'], ['2007', '15', '1', 'foix', 'loudenvielle', 'juan manuel gárate ( esp )'], ['2005', '15', '1', 'lézat - sur - lèze', "pla d'adet", 'erik dekker ( ned )'], ['2003', '14', '1', 'saint - girons', 'loudenvielle', 'richard virenque ( fra )'], ['2001', '13', '1', 'foix', "pla d'adet", 'laurent jalabert ( fra )'], ['1999', '15', '1', 'saint - gaudens', 'piau - engaly', 'alberto elli ( ita )'], ['1998', '11', '1', 'bagnères - de - luchon', 'plateau de beille', 'alberto elli ( ita )'], ['1995', '15', '1', 'saint - girons', 'cauterets', 'richard virenque ( fra )'], ['1988', '15', '1', 'saint - girons', 'luz ardiden', 'robert millar ( gbr )'], ['1979', '1', '2', 'fleurance', 'bagnères - de - luchon', 'bernard hinault ( fra )'], ['1976', '14', '2', 'saint - gaudens', 'saint - lary - soulan', 'lucien van impe ( bel )'], ['1973', '13', '2', 'bourg - madame', 'bagnères - de - luchon', 'josé - manuel fuente ( esp )'], ['1971', '14', '2', 'revel', 'bagnères - de - luchon', 'josé - manuel fuente ( esp )'], ['1970', '18', '2', 'saint - gaudens', 'la mongie', 'guerrino tosello ( ita )'], ['1969', '16', '2', 'castelnaudary', 'bagnères - de - luchon', 'raymond delisle ( fra )'], ['1967', '16', '1', 'toulouse', 'bagnères - de - luchon', 'fernando manzanèque ( esp )'], ['1966', '11', '2', 'pau', 'bagnères - de - luchon', 'joaquim galera ( esp )']] |
wru division five south east | https://en.wikipedia.org/wiki/WRU_Division_Five_South_East | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17625749-1.html.csv | aggregation | the average number of points for rugby clubs in the wru division five south east is 53 . | {'scope': 'all', 'col': '12', 'type': 'average', 'result': '53', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '53', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '53'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 53 } = true', 'tointer': 'the average of the points record of all rows is 53 .'} | round_eq { avg { all_rows ; points } ; 53 } = true | the average of the points record of all rows is 53 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '53_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '53_5': '53'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '53_5': [1]} | ['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'] | [['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['barry rfc', '22', '21', '0', '1', '811', '157', '109', '16', '16', '1', '101'], ['senghenydd rfc', '22', '20', '1', '1', '1013', '148', '150', '19', '17', '1', '100'], ['blackwood stars rfc', '22', '16', '3', '3', '622', '337', '94', '41', '14', '0', '84'], ['penygraig rfc', '22', '16', '0', '6', '595', '296', '88', '37', '13', '0', '77'], ['deri rfc', '22', '10', '1', '11', '548', '583', '80', '72', '9', '0', '51'], ['cefn coed rfc', '22', '9', '1', '12', '338', '445', '45', '55', '3', '4', '45'], ['old penarthians rfc', '22', '7', '2', '13', '329', '523', '35', '74', '3', '3', '38'], ['cilfynydd rfc', '22', '8', '0', '14', '268', '590', '32', '78', '1', '2', '35'], ['st albans rfc', '22', '6', '0', '16', '258', '739', '31', '107', '1', '6', '31'], ['cowbridge rfc', '22', '6', '1', '15', '309', '636', '36', '96', '3', '1', '30'], ['canton rfc', '22', '4', '1', '17', '305', '581', '38', '84', '2', '2', '22'], ['dinas powys rfc', '22', '3', '2', '17', '224', '585', '25', '84', '0', '5', '21']] |
1957 ohio state buckeyes football team | https://en.wikipedia.org/wiki/1957_Ohio_State_Buckeyes_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17998617-1.html.csv | aggregation | the total attendance of all the 1957 ohio state football games was 771872 . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '771872', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '771872', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '771872'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 771872 } = true', 'tointer': 'the sum of the attendance record of all rows is 771872 .'} | round_eq { sum { all_rows ; attendance } ; 771872 } = true | the sum of the attendance record of all rows is 771872 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '771872_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '771872_5': '771872'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '771872_5': [1]} | ['date', 'opponent', 'site', 'result', 'attendance'] | [['september 28', 'tcu', 'ohio stadium columbus , oh', 'l14 - 18', '81784'], ['october 5', 'washington', 'husky stadium seattle , wa', 'w35 - 7', '37500'], ['october 12', 'illinois', 'ohio stadium columbus , oh', 'w21 - 7', '82239'], ['october 19', 'indiana', 'ohio stadium columbus , oh', 'w56 - 0', '78348'], ['october 26', 'wisconsin', 'camp randall stadium madison , wi', 'w16 - 13', '51051'], ['november 2', 'northwestern', 'ohio stadium columbus , oh', 'w47 - 6', '79635'], ['november 9', 'purdue', 'ohio stadium columbus , oh', 'w20 - 7', '79177'], ['november 16', '5 iowa', 'ohio stadium columbus , oh', 'w17 - 13', '82935'], ['november 23', '19 michigan', 'michigan stadium ann arbor , mi', 'w31 - 14', '101001'], ['january 1', 'oregon', 'rose bowl pasadena , ca ( rose bowl )', 'w10 - 7', '98202']] |
atlanta falcons draft history | https://en.wikipedia.org/wiki/Atlanta_Falcons_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15198842-45.html.csv | unique | dominique franks was the only player whose college was oklahoma . | {'scope': 'all', 'row': '5', 'col': '6', 'col_other': '4', 'criterion': 'equal', 'value': 'oklahoma', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'oklahoma'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to oklahoma .', 'tostr': 'filter_eq { all_rows ; college ; oklahoma }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; oklahoma } }', 'tointer': 'select the rows whose college record fuzzily matches to oklahoma . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'oklahoma'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to oklahoma .', 'tostr': 'filter_eq { all_rows ; college ; oklahoma }'}, 'name'], 'result': 'dominique franks', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; oklahoma } ; name }'}, 'dominique franks'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; oklahoma } ; name } ; dominique franks }', 'tointer': 'the name record of this unqiue row is dominique franks .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; oklahoma } } ; eq { hop { filter_eq { all_rows ; college ; oklahoma } ; name } ; dominique franks } } = true', 'tointer': 'select the rows whose college record fuzzily matches to oklahoma . there is only one such row in the table . the name record of this unqiue row is dominique franks .'} | and { only { filter_eq { all_rows ; college ; oklahoma } } ; eq { hop { filter_eq { all_rows ; college ; oklahoma } ; name } ; dominique franks } } = true | select the rows whose college record fuzzily matches to oklahoma . there is only one such row in the table . the name record of this unqiue row is dominique franks . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'oklahoma_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'dominique franks_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'oklahoma_8': 'oklahoma', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'dominique franks_10': 'dominique franks'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'oklahoma_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'dominique franks_10': [3]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '19', '19', 'sean weatherspoon', 'linebacker', 'missouri'], ['3', '19', '83', 'corey peters', 'defensive tackle', 'kentucky'], ['3', '34', '98', 'mike johnson', 'guard', 'alabama'], ['4', '19', '117', 'joe hawley', 'guard', 'unlv'], ['5', '4', '135', 'dominique franks', 'cornerback', 'oklahoma'], ['5', '34', '165', 'kerry meier', 'wide receiver', 'kansas'], ['6', '2', '171', 'shann schillinger', 'safety', 'montana']] |
2009 big 12 conference football season | https://en.wikipedia.org/wiki/2009_Big_12_Conference_football_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23718905-6.html.csv | aggregation | the total payout for the 2009 big 12 conference football season was 16,030,000 . | {'scope': 'all', 'col': '8', 'type': 'sum', 'result': '16,030,000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'payout ( us )'], 'result': '16,030,000', 'ind': 0, 'tostr': 'sum { all_rows ; payout ( us ) }'}, '16,030,000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; payout ( us ) } ; 16,030,000 } = true', 'tointer': 'the sum of the payout ( us ) record of all rows is 16,030,000 .'} | round_eq { sum { all_rows ; payout ( us ) } ; 16,030,000 } = true | the sum of the payout ( us ) record of all rows is 16,030,000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'payout (us)_4': 4, '16,030,000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'payout (us)_4': 'payout ( us )', '16,030,000_5': '16,030,000'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'payout (us)_4': [0], '16,030,000_5': [1]} | ['bowl game', 'date', 'stadium', 'city', 'television', 'matchup / results', 'attendance', 'payout ( us )'] | [['advocare v100 independence bowl', 'december 28 , 2009', 'independence stadium', 'shreveport , louisiana', 'espn2', 'georgia 44 , texas a & m 20', '49653', '1100000'], ['pacific life holiday bowl', 'december 30 , 2009', 'qualcomm stadium', 'san diego , california', 'espn', 'nebraska 33 , arizona 0', '64607', '2130000'], ['brut sun bowl', 'december 31 , 2009', 'sun bowl stadium', 'el paso , texas', 'cbs', 'oklahoma 31 , stanford 27', '53713', '1900000'], ['texas bowl', 'december 31 , 2009', 'reliant stadium', 'houston , texas', 'espn', 'navy 35 , missouri 13', '69441', '750000'], ['insight bowl', 'december 31 , 2009', 'sun devil stadium', 'tempe , arizona', 'nfl network', 'iowa state 14 , minnesota 13', '45090', '1200000'], ['at & t cotton bowl classic', 'january 2 , 2009', 'cowboys stadium', 'arlington , texas', 'fox', 'ole miss 21 , oklahoma state 7', '77928', '6750000'], ['valero energy alamo bowl', 'january 2 , 2010', 'alamodome', 'san antonio , texas', 'espn', 'texas tech 41 , michigan state 31', '64757', '2200000']] |
whrz - lp | https://en.wikipedia.org/wiki/WHRZ-LP | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11966193-1.html.csv | unique | whrz-lp in anderson , south carolina is the only location where the station 's frequency is higher than 100 on the fm dial . | {'scope': 'all', 'row': '3', 'col': '2', 'col_other': '3', 'criterion': 'greater_than', 'value': '100', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'frequency mhz', '100'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose frequency mhz record is greater than 100 .', 'tostr': 'filter_greater { all_rows ; frequency mhz ; 100 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; frequency mhz ; 100 } }', 'tointer': 'select the rows whose frequency mhz record is greater than 100 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'frequency mhz', '100'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose frequency mhz record is greater than 100 .', 'tostr': 'filter_greater { all_rows ; frequency mhz ; 100 }'}, 'city of license'], 'result': 'anderson , south carolina', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; frequency mhz ; 100 } ; city of license }'}, 'anderson , south carolina'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; frequency mhz ; 100 } ; city of license } ; anderson , south carolina }', 'tointer': 'the city of license record of this unqiue row is anderson , south carolina .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; frequency mhz ; 100 } } ; eq { hop { filter_greater { all_rows ; frequency mhz ; 100 } ; city of license } ; anderson , south carolina } } = true', 'tointer': 'select the rows whose frequency mhz record is greater than 100 . there is only one such row in the table . the city of license record of this unqiue row is anderson , south carolina .'} | and { only { filter_greater { all_rows ; frequency mhz ; 100 } } ; eq { hop { filter_greater { all_rows ; frequency mhz ; 100 } ; city of license } ; anderson , south carolina } } = true | select the rows whose frequency mhz record is greater than 100 . there is only one such row in the table . the city of license record of this unqiue row is anderson , south carolina . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'frequency mhz_7': 7, '100_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'city of license_9': 9, 'anderson , south carolina_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'frequency mhz_7': 'frequency mhz', '100_8': '100', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'city of license_9': 'city of license', 'anderson , south carolina_10': 'anderson , south carolina'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'frequency mhz_7': [0], '100_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'city of license_9': [2], 'anderson , south carolina_10': [3]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info'] | [['w238aw', '95.5', 'west view , south carolina', '55', 'd', 'fcc'], ['w242bx', '96.3', 'greenville , south carolina', '100', 'd', 'fcc'], ['w289ao', '105.9', 'anderson , south carolina', '27', 'd', 'fcc'], ['w216bj', '91.1', 'wando , south carolina', '10', 'd', 'fcc'], ['w220cn', '91.9', 'charleston , south carolina', '10', 'd', 'fcc']] |
1981 buffalo bills season | https://en.wikipedia.org/wiki/1981_Buffalo_Bills_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17386087-1.html.csv | count | a total of two running back position players were drafted in the 1981 buffalo bills season . | {'scope': 'all', 'criterion': 'equal', 'value': 'running back', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'running back'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to running back .', 'tostr': 'filter_eq { all_rows ; position ; running back }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; running back } }', 'tointer': 'select the rows whose position record fuzzily matches to running back . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; running back } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to running back . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; position ; running back } } ; 2 } = true | select the rows whose position record fuzzily matches to running back . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'running back_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'running back_6': 'running back', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'running back_6': [0], '2_7': [2]} | ['round', 'pick', 'player', 'position', 'college'] | [['1', '28', 'booker moore', 'running back', 'penn state'], ['2', '49', 'chris williams', 'defensive back', 'lsu'], ['2', '49', 'byron franklin', 'wide receiver', 'auburn'], ['3', '76', 'mike mosley', 'wide receiver', 'texas a & m'], ['3', '84', 'robert geathers', 'defensive tackle', 'south carolina state'], ['5', '135', 'calvin clark', 'defensive end', 'purdue'], ['6', '161', 'robert holt', 'wide receiver', 'baylor'], ['7', '188', 'steve doolittle', 'linebacker', 'colorado'], ['9', '241', 'robb riddick', 'running back', 'millersville ( pa )'], ['10', '272', 'justin cross', 'offensive tackle', 'western colorado'], ['11', '299', 'buster barnett', 'tight end', 'jackson state'], ['12', '326', 'keith clark', 'linebacker', 'memphis state']] |
1962 vfl season | https://en.wikipedia.org/wiki/1962_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10776868-9.html.csv | superlative | in the 1962 vfl season , the game with the largest crowd was melbourne against st. kilda . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,3', 'subset': None} | {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'home team'], 'result': 'melbourne', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; home team }'}, 'melbourne'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; home team } ; melbourne }', 'tointer': 'select the row whose crowd record of all rows is maximum . the home team record of this row is melbourne .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'away team'], 'result': 'st kilda', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; crowd } ; away team }'}, 'st kilda'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; away team } ; st kilda }', 'tointer': 'the away team record of this row is st kilda .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { hop { argmax { all_rows ; crowd } ; home team } ; melbourne } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; st kilda } } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the home team record of this row is melbourne . the away team record of this row is st kilda .'} | and { eq { hop { argmax { all_rows ; crowd } ; home team } ; melbourne } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; st kilda } } = true | select the row whose crowd record of all rows is maximum . the home team record of this row is melbourne . the away team record of this row is st kilda . | 7 | 6 | {'and_5': 5, 'result_6': 6, 'str_eq_2': 2, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_7': 7, 'crowd_8': 8, 'home team_9': 9, 'melbourne_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'away team_11': 11, 'st kilda_12': 12} | {'and_5': 'and', 'result_6': 'true', 'str_eq_2': 'str_eq', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_7': 'all_rows', 'crowd_8': 'crowd', 'home team_9': 'home team', 'melbourne_10': 'melbourne', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'away team_11': 'away team', 'st kilda_12': 'st kilda'} | {'and_5': [6], 'result_6': [], 'str_eq_2': [5], 'str_hop_1': [2], 'argmax_0': [1, 3], 'all_rows_7': [0], 'crowd_8': [0], 'home team_9': [1], 'melbourne_10': [2], 'str_eq_4': [5], 'str_hop_3': [4], 'away team_11': [3], 'st kilda_12': [4]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '11.18 ( 84 )', 'st kilda', '11.6 ( 72 )', 'mcg', '48952', '23 june 1962'], ['essendon', '15.17 ( 107 )', 'geelong', '10.7 ( 67 )', 'windy hill', '35000', '23 june 1962'], ['collingwood', '10.14 ( 74 )', 'fitzroy', '9.11 ( 65 )', 'victoria park', '26488', '23 june 1962'], ['carlton', '12.9 ( 81 )', 'footscray', '9.10 ( 64 )', 'princes park', '32400', '23 june 1962'], ['south melbourne', '10.13 ( 73 )', 'richmond', '11.13 ( 79 )', 'lake oval', '17000', '23 june 1962'], ['north melbourne', '10.8 ( 68 )', 'hawthorn', '10.7 ( 67 )', 'arden street oval', '8470', '23 june 1962']] |
united states house of representatives elections , 1886 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1886 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1431467-4.html.csv | ordinal | d wyatt aiken is the incumbent of the 1886 house of representatives elections with the earliest first elected year . | {'row': '3', 'col': '4', 'order': '1', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'first elected', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first elected ; 1 }'}, 'incumbent'], 'result': 'd wyatt aiken', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent }'}, 'd wyatt aiken'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; d wyatt aiken } = true', 'tointer': 'select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is d wyatt aiken .'} | eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; d wyatt aiken } = true | select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is d wyatt aiken . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '1_6': 6, 'incumbent_7': 7, 'd wyatt aiken_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '1_6': '1', 'incumbent_7': 'incumbent', 'd wyatt aiken_8': 'd wyatt aiken'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '1_6': [0], 'incumbent_7': [1], 'd wyatt aiken_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result'] | [['south carolina 1', 'samuel dibble', 'democratic', '1882', 're - elected'], ['south carolina 2', 'george d tillman', 'democratic', '1878', 're - elected'], ['south carolina 3', 'd wyatt aiken', 'democratic', '1876', 'retired democratic hold'], ['south carolina 4', 'william h perry', 'democratic', '1884', 're - elected'], ['south carolina 5', 'john j hemphill', 'democratic', '1882', 're - elected'], ['south carolina 6', 'george w dargan', 'democratic', '1882', 're - elected'], ['south carolina 7', 'robert smalls', 'republican', '1884 ( special )', 'lost re - election democratic gain']] |
2008 chicago sky season | https://en.wikipedia.org/wiki/2008_Chicago_Sky_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17118657-10.html.csv | count | chicago played against new york 2 times in september . | {'scope': 'all', 'criterion': 'equal', 'value': 'new york', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'new york'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to new york .', 'tostr': 'filter_eq { all_rows ; opponent ; new york }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; new york } }', 'tointer': 'select the rows whose opponent record fuzzily matches to new york . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; new york } } ; 2 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to new york . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; opponent ; new york } } ; 2 } = true | select the rows whose opponent record fuzzily matches to new york . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'new york_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'new york_6': 'new york', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'new york_6': [0], '2_7': [2]} | ['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record'] | [['29', 'september 4', 'seattle', '62 - 70', 'perkins ( 22 )', 'dupree ( 6 )', 'canty ( 6 )', 'uic pavilion 3829', '11 - 18'], ['30', 'september 5', 'connecticut', '75 - 80', 'perkins ( 18 )', 'fowles ( 6 )', 'canty ( 7 )', 'mohegan sun arena 8088', '11 - 19'], ['31', 'september 7', 'new york', '61 - 69', 'perkins ( 18 )', 'fowles ( 12 )', 'canty , sharp ( 2 )', 'madison square garden 7903', '11 - 20'], ['32', 'september 9', 'washington', '78 - 59', 'perkins ( 17 )', 'dupree ( 10 )', 'dupree ( 6 )', 'uic pavilion 3087', '12 - 20'], ['33', 'september 12', 'new york', '62 - 69', 'dupree ( 18 )', 'dupree , fowles ( 6 )', 'canty , wyckoff ( 4 )', 'uic pavilion 5681', '12 - 21'], ['34', 'september 14', 'houston', '76 - 79', 'dupree ( 20 )', 'price ( 7 )', 'canty ( 6 )', 'uic pavilion 4917', '12 - 22']] |
romney , hythe and dymchurch railway | https://en.wikipedia.org/wiki/Romney%2C_Hythe_and_Dymchurch_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-142178-2.html.csv | superlative | in romney , hythe and dymchurch railway the most recent year built for builder rh & dr was c1949 . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '6', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '3', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'rh & dr'}} | {'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'builder', 'rh & dr'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; builder ; rh & dr }', 'tointer': 'select the rows whose builder record fuzzily matches to rh & dr .'}, 'year built'], 'result': 'c1949', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; builder ; rh & dr } ; year built }', 'tointer': 'select the rows whose builder record fuzzily matches to rh & dr . the maximum year built record of these rows is c1949 .'}, 'c1949'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; builder ; rh & dr } ; year built } ; c1949 } = true', 'tointer': 'select the rows whose builder record fuzzily matches to rh & dr . the maximum year built record of these rows is c1949 .'} | eq { max { filter_eq { all_rows ; builder ; rh & dr } ; year built } ; c1949 } = true | select the rows whose builder record fuzzily matches to rh & dr . the maximum year built record of these rows is c1949 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'max_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'builder_5': 5, 'rh&dr_6': 6, 'year built_7': 7, 'c1949_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'max_1': 'max', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'builder_5': 'builder', 'rh&dr_6': 'rh & dr', 'year built_7': 'year built', 'c1949_8': 'c1949'} | {'eq_2': [3], 'result_3': [], 'max_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'builder_5': [0], 'rh&dr_6': [0], 'year built_7': [1], 'c1949_8': [2]} | ["' name ' or designation", 'wheel arrangement', 'builder', 'year built', 'year withdrawn'] | [['theakston fordson', "bo ' 2 '", 'theakston', '1928', 'c1935'], ['super - scooter ( jap scooter )', 'ultra - light 4 - wheel scooter', 'rh & dr', 'c1929', 'c1945'], ['war department locomotive', '4 - wheel scooter', 'war department', '1929', '1949'], ['rolls royce locomotive', "bo ' 2 '", 'rh & dr', 'c1932', '1961'], ['firefly', '0 - 6 - 0', 'hcs bullock ( re - built rh & dr )', '1936 ( re - built 1945 )', '1947'], ['motor cycle scooter', 'ultra - light 4 - wheel scooter', 'rh & dr', 'c1949', 'c1952'], ["' royal anchor '", 'b - b', 'charles lane of liphook', '1956', '1956']] |
lexus ls ( xf40 ) | https://en.wikipedia.org/wiki/Lexus_LS_%28XF40%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21530474-1.html.csv | count | the lexus ls had an 8 speed transmission 6 times . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '8 - speed', 'result': '6', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'transmission', '8 - speed'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose transmission record fuzzily matches to 8 - speed .', 'tostr': 'filter_eq { all_rows ; transmission ; 8 - speed }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; transmission ; 8 - speed } }', 'tointer': 'select the rows whose transmission record fuzzily matches to 8 - speed . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; transmission ; 8 - speed } } ; 6 } = true', 'tointer': 'select the rows whose transmission record fuzzily matches to 8 - speed . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; transmission ; 8 - speed } } ; 6 } = true | select the rows whose transmission record fuzzily matches to 8 - speed . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'transmission_5': 5, '8 - speed_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'transmission_5': 'transmission', '8 - speed_6': '8 - speed', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'transmission_5': [0], '8 - speed_6': [0], '6_7': [2]} | ['chassis code', 'model no', 'production years', 'drivetrain', 'transmission', 'engine type', 'engine code', 'region ( s )'] | [['usf40 ( japanese )', 'ls 460', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america , asia , europe , oceania'], ['usf40 ( japanese )', 'ls 460', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fe', 'middle east'], ['usf41', 'ls 460 l', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america , asia , europe'], ['usf41', 'ls 460 l', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fe', 'middle east'], ['usf45', 'ls 460 awd', '2007 -', 'awd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america'], ['usf46', 'ls 460 l awd', '2007 -', 'awd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america'], ['uvf45 ( japanese )', 'ls 600h', '2007 -', 'awd', 'l110f cvt', '5.0 l hybrid v8', '2ur - fse', 'asia , europe']] |
1975 masters tournament | https://en.wikipedia.org/wiki/1975_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16456989-2.html.csv | aggregation | in the 1975 masters tournament , the average number of strokes to par is -2.19 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '-2.19', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'to par'], 'result': '-2.19', 'ind': 0, 'tostr': 'avg { all_rows ; to par }'}, '-2.19'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; to par } ; -2.19 } = true', 'tointer': 'the average of the to par record of all rows is -2.19 .'} | round_eq { avg { all_rows ; to par } ; -2.19 } = true | the average of the to par record of all rows is -2.19 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'to par_4': 4, '-2.19_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'to par_4': 'to par', '-2.19_5': '-2.19'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'to par_4': [0], '-2.19_5': [1]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'bobby nichols', 'united states', '67', '- 5'], ['t2', 'allen miller', 'united states', '68', '- 4'], ['t2', 'jack nicklaus', 'united states', '68', '- 4'], ['t4', 'arnold palmer', 'united states', '69', '- 3'], ['t4', 'j c snead', 'united states', '69', '- 3'], ['t4', 'tom weiskopf', 'united states', '69', '- 3'], ['t7', 'billy casper', 'united states', '70', '- 2'], ['t7', 'bob murphy', 'united states', '70', '- 2'], ['t7', 'tom watson', 'united states', '70', '- 2'], ['t10', 'tommy aaron', 'united states', '71', '- 1'], ['t10', 'jerry heard', 'united states', '71', '- 1'], ['t10', 'mac mclendon', 'united states', '71', '- 1'], ['t10', 'jerry pate ( a )', 'united states', '71', '- 1'], ['t10', 'sam snead', 'united states', '71', '- 1'], ['t10', 'lee trevino', 'united states', '71', '- 1'], ['t10', 'larry ziegler', 'united states', '71', '- 1']] |
usa south athletic conference | https://en.wikipedia.org/wiki/USA_South_Athletic_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-255188-3.html.csv | count | there are three public schools in the usa south athletic conference . | {'scope': 'all', 'criterion': 'equal', 'value': 'public', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'public'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to public .', 'tostr': 'filter_eq { all_rows ; type ; public }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; type ; public } }', 'tointer': 'select the rows whose type record fuzzily matches to public . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; type ; public } } ; 3 } = true', 'tointer': 'select the rows whose type record fuzzily matches to public . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; type ; public } } ; 3 } = true | select the rows whose type record fuzzily matches to public . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'type_5': 5, 'public_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'type_5': 'type', 'public_6': 'public', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'type_5': [0], 'public_6': [0], '3_7': [2]} | ['institution', 'location', 'nickname', 'founded', 'type', 'enrollment', 'joined', 'left', 'current conference'] | [['chowan university', 'murfreesboro , north carolina', 'hawks', '1848', 'private', '1260', '2000', '2004', 'ciaa ( ncaa division ii )'], ['christopher newport university', 'newport news , virginia', 'captains', '1960', 'public', '4793', '1972', '2013', 'capital'], ['college of charleston', 'charleston , south carolina', 'cougars', '1770', 'private', '11320', '1963', '1970', 'caa ( ncaa division i )'], ['lynchburg college', 'lynchburg , virginia', 'fighting hornets', '1903', 'private', '2500', '1963', '1976', 'odac'], ['shenandoah university', 'winchester , virginia', 'hornets', '1875', 'private', '1767', '1992', '2012', 'odac'], ['st andrews presbyterian university', 'laurinburg , north carolina', 'knights', '1958', 'private', '600', '1963', '1988', 'aac ( naia )'], ['university of north carolina at charlotte', 'charlotte , north carolina', '49ers', '1961', 'public', '25227', '1963', '1970', 'c - usa ( ncaa division i )'], ['university of north carolina at greensboro', 'greensboro , north carolina', 'spartans', '1891', 'public', '18502', '1968', '1988', 'socon ( ncaa division i )']] |
list of montreal canadiens draft picks | https://en.wikipedia.org/wiki/List_of_Montreal_Canadiens_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18259953-8.html.csv | majority | most players of montreal canadiens draft picks were for the defence position . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'defence', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'position', 'defence'], 'result': True, 'ind': 0, 'tointer': 'for the position records of all rows , most of them fuzzily match to defence .', 'tostr': 'most_eq { all_rows ; position ; defence } = true'} | most_eq { all_rows ; position ; defence } = true | for the position records of all rows , most of them fuzzily match to defence . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'position_3': 3, 'defence_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'position_3': 'position', 'defence_4': 'defence'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'position_3': [0], 'defence_4': [0]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['1', 'nathan beaulieu', 'defence', 'canada', 'saint john sea dogs ( qmjhl )'], ['4', 'josiah didier', 'defence', 'canada', 'cedar rapids roughriders ( ushl )'], ['4', 'olivier archambault', 'left wing', 'canada', "val d'or foreurs ( qmjhl )"], ['4', 'magnus nygren', 'defence', 'sweden', 'fã ¤ rjestads bk ( elitserien )'], ['5', 'darren dietz', 'defence', 'canada', 'saskatoon blades ( whl )'], ['6', '-', 'forward', 'czech republic', 'hc sparta praha ( czech extraliga )'], ['7', 'colin sullivan', 'defence', 'united states', 'avon old farms hs ( ushs )']] |
liselotte neumann | https://en.wikipedia.org/wiki/Liselotte_Neumann | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1710991-1.html.csv | count | liselotte neumann won by a margin of victory of 1 stroke two times . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '1 stroke', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'margin of victory', '1 stroke'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose margin of victory record fuzzily matches to 1 stroke .', 'tostr': 'filter_eq { all_rows ; margin of victory ; 1 stroke }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; margin of victory ; 1 stroke } }', 'tointer': 'select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; margin of victory ; 1 stroke } } ; 2 } = true', 'tointer': 'select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; margin of victory ; 1 stroke } } ; 2 } = true | select the rows whose margin of victory record fuzzily matches to 1 stroke . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'margin of victory_5': 5, '1 stroke_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'margin of victory_5': 'margin of victory', '1 stroke_6': '1 stroke', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'margin of victory_5': [0], '1 stroke_6': [0], '2_7': [2]} | ['date', 'tournament', 'winning score', 'margin of victory', 'runner ( s ) - up'] | [['7 sep 1988', "us women 's open", '- 7 ( 67 + 72 + 69 + 69 = 277 )', '3 strokes', 'patty sheehan'], ['10 nov 1991', 'mazda japan classic', '- 5 ( 70 + 72 + 69 = 211 )', '2 strokes', 'caroline keggi , dottie pepper'], ['12 jun 1994', 'minnesota lpga classic', '- 11 ( 68 + 71 + 66 = 205 )', '2 strokes', 'hiromi kobayashi'], ['12 aug 1994', "weetabix women 's british open", '- 14 ( 71 + 67 + 70 + 72 = 280 )', '3 strokes', 'dottie pepper , annika sörenstam'], ['2 oct 1994', 'ghp heartland classic', '- 10 ( 70 + 71 + 67 + 70 = 278 )', '3 strokes', 'elaine crosby , pearl sinn'], ['14 jan 1996', 'chrysler - plymouth tournament of champions', '- 13 ( 67 + 66 + 72 + 70 = 275 )', '11 strokes', 'karrie webb'], ['17 mar 1996', "ping / welch 's championship ( tucson )", '- 12 ( 68 + 71 + 69 + 68 = 276 )', '1 stroke', 'cathy johnston - forbes'], ['6 jun 1996', 'edina realty lpga classic', '- 9 ( 67 + 73 + 67 = 207 )', 'playoff', 'brandie burton , carin koch , suzanne strudwick'], ['21 sep 1997', "welch 's championship", '- 12 ( 67 + 70 + 69 + 70 = 276 )', '3 strokes', 'nancy harvey'], ['9 nov 1997', 'toray japan queens cup', '- 11 ( 68 + 70 + 67 = 205 )', '1 sttroke', 'lorie kane'], ['22 mar 1998', 'standard register ping', '- 13 ( 69 + 67 + 69 + 74 = 279 )', 'playoff', 'rosie jones'], ['26 apr 1998', 'chick - fil - a charity championship', '- 14 ( 67 + 65 + 70 = 202 )', '2 strokes', 'lori kane , dottie pepper'], ['10 oct 2004', 'asahi ryokuken international championship', '- 15 ( 68 + 68 + 69 + 68 = 273 )', '3 strokes', 'grace park']] |
fiba eurobasket 2007 squads | https://en.wikipedia.org/wiki/FIBA_EuroBasket_2007_squads | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12962773-2.html.csv | comparative | israeli eurobasket 2007 team member ido kozikaro was shorter than lior eliyahu . | {'row_1': '10', 'row_2': '4', 'col': '2', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'ido kozikaro'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to ido kozikaro .', 'tostr': 'filter_eq { all_rows ; player ; ido kozikaro }'}, 'height'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; ido kozikaro } ; height }', 'tointer': 'select the rows whose player record fuzzily matches to ido kozikaro . take the height record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'lior eliyahu'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to lior eliyahu .', 'tostr': 'filter_eq { all_rows ; player ; lior eliyahu }'}, 'height'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; lior eliyahu } ; height }', 'tointer': 'select the rows whose player record fuzzily matches to lior eliyahu . take the height record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; ido kozikaro } ; height } ; hop { filter_eq { all_rows ; player ; lior eliyahu } ; height } } = true', 'tointer': 'select the rows whose player record fuzzily matches to ido kozikaro . take the height record of this row . select the rows whose player record fuzzily matches to lior eliyahu . take the height record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; ido kozikaro } ; height } ; hop { filter_eq { all_rows ; player ; lior eliyahu } ; height } } = true | select the rows whose player record fuzzily matches to ido kozikaro . take the height record of this row . select the rows whose player record fuzzily matches to lior eliyahu . take the height record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'ido kozikaro_8': 8, 'height_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'lior eliyahu_12': 12, 'height_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'ido kozikaro_8': 'ido kozikaro', 'height_9': 'height', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'lior eliyahu_12': 'lior eliyahu', 'height_13': 'height'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'ido kozikaro_8': [0], 'height_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'lior eliyahu_12': [1], 'height_13': [3]} | ['player', 'height', 'position', 'year born', 'current club'] | [['dror hagag', '1.78', 'guard', '1978', 'hapoel jerusalem'], ['moran rot', '1.78', 'guard', '1982', 'hapoel holon'], ['yotam halperin', '1.96', 'guard', '1984', 'maccabi tel aviv'], ['lior eliyahu', '2.05', 'forward', '1985', 'maccabi tel aviv'], ['erez markovich', '2.08', 'center', '1978', 'hapoel jerusalem'], ['jeron roberts', '1.94', 'guard', '1976', 'demon astronauts amsterdam'], ['guy pnini', '2.01', 'forward', '1983', 'hapoel jerusalem'], ['meir tapiro', '1.94', 'guard', '1975', 'bnei hasharon'], ['matan naor', '1.94', 'forward', '1980', 'ironi nahariya'], ['ido kozikaro', '2.02', 'forward', '1978', 'ironi nahariya'], ['yaniv green', '2.06', 'forward', '1980', 'csk vvs samara'], ['amit tamir', '2.08', 'center', '1979', 'cherkassy monkeys']] |
2008 - 09 san antonio spurs season | https://en.wikipedia.org/wiki/2008%E2%80%9309_San_Antonio_Spurs_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17288845-11.html.csv | majority | during this period of the 2008-09 san antonio spurs season , tim duncan led the san antonio spurs in rebounds in the majority of games . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'tim duncan', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high rebounds', 'tim duncan'], 'result': True, 'ind': 0, 'tointer': 'for the high rebounds records of all rows , most of them fuzzily match to tim duncan .', 'tostr': 'most_eq { all_rows ; high rebounds ; tim duncan } = true'} | most_eq { all_rows ; high rebounds ; tim duncan } = true | for the high rebounds records of all rows , most of them fuzzily match to tim duncan . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high rebounds_3': 3, 'tim duncan_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high rebounds_3': 'high rebounds', 'tim duncan_4': 'tim duncan'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high rebounds_3': [0], 'tim duncan_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'april 18', 'dallas', 'l 97 - 105 ( ot )', 'tim duncan ( 27 )', 'tim duncan ( 9 )', 'tony parker ( 8 )', 'at & t center 18797', '0 - 1'], ['2', 'april 20', 'dallas', 'w 105 - 84 ( ot )', 'tony parker ( 38 )', 'tim duncan ( 11 )', 'tony parker ( 8 )', 'at & t center 18797', '1 - 1'], ['3', 'april 23', 'dallas', 'l 67 - 88 ( ot )', 'tony parker ( 12 )', 'kurt thomas ( 10 )', 'tony parker ( 3 )', 'american airlines center 20491', '1 - 2'], ['4', 'april 25', 'dallas', 'l 90 - 99 ( ot )', 'tony parker ( 43 )', 'tim duncan ( 10 )', 'tim duncan ( 7 )', 'american airlines center 20829', '1 - 3'], ['5', 'april 28', 'dallas', 'l 93 - 106 ( ot )', 'tim duncan ( 31 )', 'tim duncan ( 12 )', 'tony parker ( 6 )', 'at & t center 20829', '1 - 4']] |
maltese first division | https://en.wikipedia.org/wiki/Maltese_First_Division | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10645859-2.html.csv | count | lija came out second in the maltese first division twice between 2000 and 2012 . | {'scope': 'all', 'criterion': 'equal', 'value': 'lija', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2nd position', 'lija'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2nd position record fuzzily matches to lija .', 'tostr': 'filter_eq { all_rows ; 2nd position ; lija }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; 2nd position ; lija } }', 'tointer': 'select the rows whose 2nd position record fuzzily matches to lija . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; 2nd position ; lija } } ; 2 } = true', 'tointer': 'select the rows whose 2nd position record fuzzily matches to lija . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; 2nd position ; lija } } ; 2 } = true | select the rows whose 2nd position record fuzzily matches to lija . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, '2nd position_5': 5, 'lija_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', '2nd position_5': '2nd position', 'lija_6': 'lija', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], '2nd position_5': [0], 'lija_6': [0], '2_7': [2]} | ['year', '1st position', '2nd position', '9th position', '10th position'] | [['2000 / 01', 'marsa', 'lija', 'tarxien', 'zurrieq'], ['2001 / 02', 'marsaxlokk', 'mosta', 'qormi', 'luxol'], ['2002 / 03', 'msida', 'balzan', 'gozo', 'xghajra'], ['2003 / 04', 'stpatrick', 'lija', 'tarxien', 'rabat ajax'], ['2004 / 05', 'hamrun', 'mosta', 'balzan', 'gozo'], ['2005 / 06', "stgeorge 's", 'marsa', 'lija', 'luxol'], ['2006 / 07', 'hamrun', 'mqabba', 'san gwann', 'naxxar'], ['2007 / 08', 'tarxien', 'qormi', 'mellieha', 'marsa'], ['2008 / 09', 'dingli swallows', 'vittoriosa stars', 'rabat ajax', 'senglea'], ['2009 / 10', 'marsaxlokk', 'vittoriosa stars', 'stpatrick', 'san gwann'], ['2010 / 11', 'balzan youths', 'mqabba', 'pietã hotspurs', 'msida saint - joseph'], ['2011 / 12', 'melita', 'rabat ajax', 'st patrick fc', "st george 's fc"]] |
narratives of empire | https://en.wikipedia.org/wiki/Narratives_of_Empire | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11251694-1.html.csv | comparative | of the narratives of empire , the title empire came one title before hollywood . | {'row_1': '4', 'row_2': '5', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'empire'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to empire .', 'tostr': 'filter_eq { all_rows ; title ; empire }'}, 'order'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; empire } ; order }', 'tointer': 'select the rows whose title record fuzzily matches to empire . take the order record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'hollywood'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to hollywood .', 'tostr': 'filter_eq { all_rows ; title ; hollywood }'}, 'order'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; hollywood } ; order }', 'tointer': 'select the rows whose title record fuzzily matches to hollywood . take the order record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; title ; empire } ; order } ; hop { filter_eq { all_rows ; title ; hollywood } ; order } }', 'tointer': 'select the rows whose title record fuzzily matches to empire . take the order record of this row . select the rows whose title record fuzzily matches to hollywood . take the order record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'empire'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to empire .', 'tostr': 'filter_eq { all_rows ; title ; empire }'}, 'order'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; empire } ; order }', 'tointer': 'select the rows whose title record fuzzily matches to empire . take the order record of this row .'}, '4'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; title ; empire } ; order } ; 4 }', 'tointer': 'the order record of the first row is 4 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'hollywood'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to hollywood .', 'tostr': 'filter_eq { all_rows ; title ; hollywood }'}, 'order'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; hollywood } ; order }', 'tointer': 'select the rows whose title record fuzzily matches to hollywood . take the order record of this row .'}, '5'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; title ; hollywood } ; order } ; 5 }', 'tointer': 'the order record of the second row is 5 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; title ; empire } ; order } ; 4 } ; eq { hop { filter_eq { all_rows ; title ; hollywood } ; order } ; 5 } }', 'tointer': 'the order record of the first row is 4 . the order record of the second row is 5 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; title ; empire } ; order } ; hop { filter_eq { all_rows ; title ; hollywood } ; order } } ; and { eq { hop { filter_eq { all_rows ; title ; empire } ; order } ; 4 } ; eq { hop { filter_eq { all_rows ; title ; hollywood } ; order } ; 5 } } } = true', 'tointer': 'select the rows whose title record fuzzily matches to empire . take the order record of this row . select the rows whose title record fuzzily matches to hollywood . take the order record of this row . the first record is less than the second record . the order record of the first row is 4 . the order record of the second row is 5 .'} | and { less { hop { filter_eq { all_rows ; title ; empire } ; order } ; hop { filter_eq { all_rows ; title ; hollywood } ; order } } ; and { eq { hop { filter_eq { all_rows ; title ; empire } ; order } ; 4 } ; eq { hop { filter_eq { all_rows ; title ; hollywood } ; order } ; 5 } } } = true | select the rows whose title record fuzzily matches to empire . take the order record of this row . select the rows whose title record fuzzily matches to hollywood . take the order record of this row . the first record is less than the second record . the order record of the first row is 4 . the order record of the second row is 5 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'less_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'title_11': 11, 'empire_12': 12, 'order_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'title_15': 15, 'hollywood_16': 16, 'order_17': 17, 'and_7': 7, 'eq_5': 5, '4_18': 18, 'eq_6': 6, '5_19': 19} | {'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'empire_12': 'empire', 'order_13': 'order', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'title_15': 'title', 'hollywood_16': 'hollywood', 'order_17': 'order', 'and_7': 'and', 'eq_5': 'eq', '4_18': '4', 'eq_6': 'eq', '5_19': '5'} | {'and_8': [9], 'result_9': [], 'less_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'title_11': [0], 'empire_12': [0], 'order_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'title_15': [1], 'hollywood_16': [1], 'order_17': [3], 'and_7': [8], 'eq_5': [7], '4_18': [5], 'eq_6': [7], '5_19': [6]} | ['order', 'title', 'story timeline', 'published', 'in order of publication'] | [['1', 'burr', '1775 - 1808 , 1833 - 1836 , 1840', '1973', 'second'], ['2', 'lincoln', '1861 - 1865', '1984', 'fourth'], ['3', '1876', '1875 - 1877', '1976', 'third'], ['4', 'empire', '1898 - 1907', '1987', 'fifth'], ['5', 'hollywood', '1917 - 1923', '1990', 'sixth'], ['6', 'washington , dc', '1937 - 1952', '1967', 'first'], ['7', 'the golden age', '1939 - 1954 , 2000', '2000', 'seventh']] |
2008 - 09 football league two | https://en.wikipedia.org/wiki/2008%E2%80%9309_Football_League_Two | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18795125-6.html.csv | count | three of the managers left because their contracts were terminated . | {'scope': 'all', 'criterion': 'equal', 'value': 'contract terminated', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manner of departure', 'contract terminated'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manner of departure record fuzzily matches to contract terminated .', 'tostr': 'filter_eq { all_rows ; manner of departure ; contract terminated }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; manner of departure ; contract terminated } }', 'tointer': 'select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 3 } = true', 'tointer': 'select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 3 } = true | select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'manner of departure_5': 5, 'contract terminated_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'manner of departure_5': 'manner of departure', 'contract terminated_6': 'contract terminated', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'manner of departure_5': [0], 'contract terminated_6': [0], '3_7': [2]} | ['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment', 'position in table'] | [['bournemouth', 'kevin bond', 'contract terminated', '1 september 2008', 'jimmy quinn', '2 september 2008', '23rd'], ['grimsby town', 'alan buckley', 'contract terminated', '15 september 2008', 'mike newell', '6 october 2008', '20th'], ['port vale', 'lee sinnott', 'mutual consent', '22 september 2008', 'dean glover', '6 october 2008', '16th'], ['chester city', 'simon davies', 'contract terminated', '11 november 2008', 'mark wright', '14 november 2008', '19th'], ['barnet', 'paul fairclough', 'resigned', '28 december 2008', 'ian hendon', '21 april 2009', '16th']] |
loonie | https://en.wikipedia.org/wiki/Loonie | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18400-2.html.csv | unique | the special loonie from 2007 themed the trumpeter swan was the only one with an issued price of 45.95 . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '1,2', 'criterion': 'equal', 'value': '45.95', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'issue price', '45.95'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose issue price record is equal to 45.95 .', 'tostr': 'filter_eq { all_rows ; issue price ; 45.95 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; issue price ; 45.95 } }', 'tointer': 'select the rows whose issue price record is equal to 45.95 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'issue price', '45.95'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose issue price record is equal to 45.95 .', 'tostr': 'filter_eq { all_rows ; issue price ; 45.95 }'}, 'year'], 'result': '2007', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; issue price ; 45.95 } ; year }'}, '2007'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; year } ; 2007 }', 'tointer': 'the year record of this unqiue row is 2007 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'issue price', '45.95'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose issue price record is equal to 45.95 .', 'tostr': 'filter_eq { all_rows ; issue price ; 45.95 }'}, 'theme'], 'result': 'trumpeter swan', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; issue price ; 45.95 } ; theme }'}, 'trumpeter swan'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; theme } ; trumpeter swan }', 'tointer': 'the theme record of this unqiue row is trumpeter swan .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; year } ; 2007 } ; eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; theme } ; trumpeter swan } }', 'tointer': 'the year record of this unqiue row is 2007 . the theme record of this unqiue row is trumpeter swan .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; issue price ; 45.95 } } ; and { eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; year } ; 2007 } ; eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; theme } ; trumpeter swan } } } = true', 'tointer': 'select the rows whose issue price record is equal to 45.95 . there is only one such row in the table . the year record of this unqiue row is 2007 . the theme record of this unqiue row is trumpeter swan .'} | and { only { filter_eq { all_rows ; issue price ; 45.95 } } ; and { eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; year } ; 2007 } ; eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; theme } ; trumpeter swan } } } = true | select the rows whose issue price record is equal to 45.95 . there is only one such row in the table . the year record of this unqiue row is 2007 . the theme record of this unqiue row is trumpeter swan . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_9': 9, 'issue price_10': 10, '45.95_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'year_12': 12, '2007_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'theme_14': 14, 'trumpeter swan_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_9': 'all_rows', 'issue price_10': 'issue price', '45.95_11': '45.95', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_12': 'year', '2007_13': '2007', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'theme_14': 'theme', 'trumpeter swan_15': 'trumpeter swan'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_eq_0': [1, 2, 4], 'all_rows_9': [0], 'issue price_10': [0], '45.95_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'year_12': [2], '2007_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'theme_14': [4], 'trumpeter swan_15': [5]} | ['year', 'theme', 'artist', 'mintage', 'issue price'] | [['2002', '15th anniversary loonie', 'dora de pãdery - hunt', '67672', '39.95'], ['2004', 'jack miner bird sanctuary', 'susan taylor', '46493', '39.95'], ['2005', 'tufted puffin', 'n / a', '39818', '39.95'], ['2006', 'snowy owl', 'glen loates', '39935', '44.95'], ['2007', 'trumpeter swan', 'kerri burnett', '40000', '45.95'], ['2008', 'common eider', 'mark hobson', '40000', '47.95'], ['2009', 'great blue heron', 'chris jordison', '40000', '47.95'], ['2010', 'northern harrier', 'arnold nogy', '35000', '49.95'], ['2011', 'great gray owl', 'arnold nogy', '35000', '49.95'], ['2012', '25th anniversary loonie', 'arnold nogy', '35000', '49.95']] |
olivier occéan | https://en.wikipedia.org/wiki/Olivier_Occ%C3%A9an | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1109407-1.html.csv | comparative | olivier occéan scored more goals in the match played on october 7 , 2011 than he did in the match played on june 8 , 2012 . | {'row_1': '4', 'row_2': '6', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october 7 , 2011'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to october 7 , 2011 .', 'tostr': 'filter_eq { all_rows ; date ; october 7 , 2011 }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; october 7 , 2011 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to october 7 , 2011 . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'june 8 , 2012'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to june 8 , 2012 .', 'tostr': 'filter_eq { all_rows ; date ; june 8 , 2012 }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; june 8 , 2012 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to june 8 , 2012 . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; october 7 , 2011 } ; score } ; hop { filter_eq { all_rows ; date ; june 8 , 2012 } ; score } } = true', 'tointer': 'select the rows whose date record fuzzily matches to october 7 , 2011 . take the score record of this row . select the rows whose date record fuzzily matches to june 8 , 2012 . take the score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; date ; october 7 , 2011 } ; score } ; hop { filter_eq { all_rows ; date ; june 8 , 2012 } ; score } } = true | select the rows whose date record fuzzily matches to october 7 , 2011 . take the score record of this row . select the rows whose date record fuzzily matches to june 8 , 2012 . take the score record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'october 7 , 2011_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'june 8 , 2012_12': 12, 'score_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'october 7 , 2011_8': 'october 7 , 2011', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'june 8 , 2012_12': 'june 8 , 2012', 'score_13': 'score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'october 7 , 2011_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'june 8 , 2012_12': [1], 'score_13': [3]} | ['date', 'venue', 'score', 'result', 'competition'] | [['february 9 , 2005', 'windsor park , belfast , northern ireland', '1 - 0', '1 - 0', 'friendly'], ['august 22 , 2007', 'laugardalsvöllur , reykjavík , iceland', '1 - 1', '1 - 1', 'friendly'], ['october 7 , 2011', 'beausejour stadium , gros islet , saint lucia', '3 - 0', '7 - 0', '2014 fifa world cup qualification'], ['october 7 , 2011', 'beausejour stadium , gros islet , saint lucia', '5 - 0', '7 - 0', '2014 fifa world cup qualification'], ['november 15 , 2011', 'bmo field , toronto , canada', '1 - 0', '4 - 0', '2014 fifa world cup qualification'], ['june 8 , 2012', 'estadio pedro marrero , havana , cuba', '1 - 0', '1 - 0', '2014 fifa world cup qualification']] |
b " rowing at the 2008 summer olympics - women 's lightweight double sculls " | https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_lightweight_double_sculls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662704-5.html.csv | count | two duos finished with a time under 7:00 in the 2008 summer olympics - women 's lightweight double sculls . | {'scope': 'all', 'criterion': 'less_than', 'value': '7:00', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'time', '7:00'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record is less than 7:00 .', 'tostr': 'filter_less { all_rows ; time ; 7:00 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; time ; 7:00 } }', 'tointer': 'select the rows whose time record is less than 7:00 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; time ; 7:00 } } ; 2 } = true', 'tointer': 'select the rows whose time record is less than 7:00 . the number of such rows is 2 .'} | eq { count { filter_less { all_rows ; time ; 7:00 } } ; 2 } = true | select the rows whose time record is less than 7:00 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'time_5': 5, '7:00_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'time_5': 'time', '7:00_6': '7:00', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'time_5': [0], '7:00_6': [0], '2_7': [2]} | ['rank', 'rowers', 'country', 'time', 'notes'] | [['1', 'xu dongxiang , chen haixia', 'china', '6:57.58', 'sa / b'], ['2', 'katrin olsen , juliane rasmussen', 'denmark', '6:58.63', 'sa / b'], ['3', 'misaki kumakura , akiko iwamoto', 'japan', '7:05.67', 'r'], ['4', 'yaima velazquez , ismaray marrero', 'cuba', '7:13.35', 'r'], ['5', 'ko young - eun , ji yoo - jin', 'south korea', '7:39.70', 'r']] |
1981 cincinnati bengals season | https://en.wikipedia.org/wiki/1981_Cincinnati_Bengals_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16764846-2.html.csv | aggregation | the average attendence in all cincinnati bengals games in the season of 1981 was around 50540 people . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '50540', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '50540', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '50540'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 50540 } = true', 'tointer': 'the average of the attendance record of all rows is 50540 .'} | round_eq { avg { all_rows ; attendance } ; 50540 } = true | the average of the attendance record of all rows is 50540 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '50540_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '50540_5': '50540'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '50540_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 6 , 1981', 'seattle seahawks', 'w 27 - 21', '41177'], ['2', 'september 13 , 1981', 'new york jets', 'w 31 - 30', '49454'], ['3', 'september 20 , 1981', 'cleveland browns', 'l 20 - 17', '52170'], ['4', 'september 27 , 1981', 'buffalo bills', 'w 27 - 24', '46418'], ['5', 'october 4 , 1981', 'houston oilers', 'l 17 - 10', '44350'], ['6', 'october 11 , 1981', 'baltimore colts', 'w 41 - 19', '33060'], ['7', 'october 18 , 1981', 'pittsburgh steelers', 'w 34 - 7', '57090'], ['8', 'october 25 , 1981', 'new orleans saints', 'l 17 - 7', '46336'], ['9', 'november 1 , 1981', 'houston oilers', 'w 34 - 21', '54736'], ['10', 'november 8 , 1981', 'san diego chargers', 'w 40 - 17', '51259'], ['11', 'november 15 , 1981', 'los angeles rams', 'w 24 - 10', '56836'], ['12', 'november 22 , 1981', 'denver broncos', 'w 38 - 21', '57207'], ['13', 'november 29 , 1981', 'cleveland browns', 'w 41 - 21', '75186'], ['14', 'december 6 , 1981', 'san francisco 49ers', 'l 21 - 3', '56796'], ['15', 'december 13 , 1981', 'pittsburgh steelers', 'w 17 - 10', '50623'], ['16', 'december 20 , 1981', 'atlanta falcons', 'w 30 - 28', '35972']] |
list of state leaders in 820s bc | https://en.wikipedia.org/wiki/List_of_state_leaders_in_820s_BC | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17338083-13.html.csv | unique | the leader of the qin state was the only state leader with the title of ruler . | {'scope': 'all', 'row': '14', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'ruler', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'ruler'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to ruler .', 'tostr': 'filter_eq { all_rows ; title ; ruler }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; title ; ruler } }', 'tointer': 'select the rows whose title record fuzzily matches to ruler . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'ruler'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to ruler .', 'tostr': 'filter_eq { all_rows ; title ; ruler }'}, 'state'], 'result': 'qin', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; ruler } ; state }'}, 'qin'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; title ; ruler } ; state } ; qin }', 'tointer': 'the state record of this unqiue row is qin .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; title ; ruler } } ; eq { hop { filter_eq { all_rows ; title ; ruler } ; state } ; qin } } = true', 'tointer': 'select the rows whose title record fuzzily matches to ruler . there is only one such row in the table . the state record of this unqiue row is qin .'} | and { only { filter_eq { all_rows ; title ; ruler } } ; eq { hop { filter_eq { all_rows ; title ; ruler } ; state } ; qin } } = true | select the rows whose title record fuzzily matches to ruler . there is only one such row in the table . the state record of this unqiue row is qin . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'title_7': 7, 'ruler_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'state_9': 9, 'qin_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'title_7': 'title', 'ruler_8': 'ruler', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'state_9': 'state', 'qin_10': 'qin'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'title_7': [0], 'ruler_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'state_9': [2], 'qin_10': [3]} | ['state', 'type', 'name', 'title', 'royal house', 'from'] | [['cai', 'sovereign', 'yi', 'marquis', 'ji', '837 bc'], ['cao', 'sovereign', 'you', 'count', '-', '835 bc'], ['cao', 'sovereign', 'dai', 'count', '-', '826 bc'], ['chen', 'sovereign', 'li', 'duke', '-', '831 bc'], ['chu', 'sovereign', 'xiong yan the younger', 'viscount', 'mi', '837 bc'], ['chu', 'sovereign', 'xiong shuang', 'viscount', 'mi', '827 bc'], ['chu', 'sovereign', 'xiong xun', 'viscount', 'mi', '821 bc'], ['jin', 'sovereign', 'xi', 'marquis', 'ji', '840 bc'], ['jin', 'sovereign', 'xian', 'marquis', 'ji', '822 bc'], ['lu', 'sovereign', 'shen', 'duke', 'ji', '854 bc'], ['lu', 'sovereign', 'wu', 'duke', 'ji', '825 bc'], ['qi', 'sovereign', 'wu', 'duke', 'jiang', '850 bc'], ['qi', 'sovereign', 'li', 'duke', 'jiang', '824 bc'], ['qin', 'sovereign', 'qin zhong', 'ruler', 'ying', '845 bc'], ['qin', 'sovereign', 'zhuang', 'duke', 'ying', '822 bc'], ['song', 'sovereign', 'hui', 'duke', '-', '830 bc'], ['wey', 'sovereign', 'li', 'marquis', '-', '855 bc'], ['yan', 'sovereign', 'hui', 'marquis', '-', '864 bc'], ['yan', 'sovereign', 'li', 'marquis', '-', '826 bc']] |
toronto , grey and bruce railway | https://en.wikipedia.org/wiki/Toronto%2C_Grey_and_Bruce_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15339223-1.html.csv | unique | caledon was the only one of the 0 - 6 - 6 - 0 fairlie type . | {'scope': 'all', 'row': '7', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': '0 - 6 - 6 - 0 fairlie type', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', '0 - 6 - 6 - 0 fairlie type'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to 0 - 6 - 6 - 0 fairlie type .', 'tostr': 'filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } }', 'tointer': 'select the rows whose type record fuzzily matches to 0 - 6 - 6 - 0 fairlie type . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', '0 - 6 - 6 - 0 fairlie type'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to 0 - 6 - 6 - 0 fairlie type .', 'tostr': 'filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type }'}, 'name'], 'result': 'caledon', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } ; name }'}, 'caledon'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } ; name } ; caledon }', 'tointer': 'the name record of this unqiue row is caledon .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } } ; eq { hop { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } ; name } ; caledon } } = true', 'tointer': 'select the rows whose type record fuzzily matches to 0 - 6 - 6 - 0 fairlie type . there is only one such row in the table . the name record of this unqiue row is caledon .'} | and { only { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } } ; eq { hop { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } ; name } ; caledon } } = true | select the rows whose type record fuzzily matches to 0 - 6 - 6 - 0 fairlie type . there is only one such row in the table . the name record of this unqiue row is caledon . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'type_7': 7, '0 - 6 - 6 - 0 fairlie type_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'caledon_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'type_7': 'type', '0 - 6 - 6 - 0 fairlie type_8': '0 - 6 - 6 - 0 fairlie type', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'caledon_10': 'caledon'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'type_7': [0], '0 - 6 - 6 - 0 fairlie type_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'caledon_10': [3]} | ['number', 'name', 'builder', 'type', 'date', 'works number'] | [['1', 'gordon', 'avonside engine company', '4 - 6 - 0', 'aug 1870', '799'], ['2', 'ar mcmaster', 'avonside engine company', '4 - 4 - 0', 'aug 1870', '800'], ['3', 'kincardine', 'avonside engine company', '4 - 4 - 0', 'september 1870', '809'], ['4', 'r walker & sons', 'avonside engine company', '4 - 4 - 0', 'may 1871', '838'], ['5', 'albion', 'avonside engine company', '4 - 4 - 0', 'july 1871', '839'], ['6', 'rice lewis & son', 'avonside engine company', '4 - 4 - 0', 'mid 1871', '840'], ['7', 'caledon', 'avonside engine company', '0 - 6 - 6 - 0 fairlie type', 'late 1872', '862 & 863'], ['8', 'mono', 'avonside engine company', '4 - 6 - 0', 'late 1871', '866'], ['9', 'toronto', 'baldwin locomotive works', '2 - 6 - 0', 'september 1871', '2534'], ['10', 'amaranth', 'baldwin locomotive works', '2 - 6 - 0', 'september 1871', '2538'], ['11', 'holland', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 935 - 939'], ['12', 'sydenham', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 935 - 939'], ['13', 'artemisia', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 935 - 939'], ['14', 'owen sound', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 931932933 , or 934'], ['15', 'mount forest', 'baldwin locomotive works', '2 - 8 - 0', 'february 1874', '3524'], ['16', 'orangeville', 'baldwin locomotive works', '2 - 8 - 0', 'february 1874', '3525'], ['17', 'sarawak', 'baldwin locomotive works', '2 - 8 - 0', 'april 1874', '3551'], ['18', 'melancthon', 'baldwin locomotive works', '2 - 8 - 0', 'april 1874', '3552'], ['19', 'howick', 'baldwin locomotive works', '2 - 8 - 0', 'september 1874', '3636'], ['20', 'culross', 'baldwin locomotive works', '2 - 8 - 0', 'september 1874', '3640']] |
miss namibia 2009 | https://en.wikipedia.org/wiki/Miss_Namibia_2009 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23576576-2.html.csv | count | two of the contestants in the miss namibia 2009 pageant have a hometown of swakopmund . | {'scope': 'all', 'criterion': 'equal', 'value': 'swakopmund', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'hometown', 'swakopmund'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose hometown record fuzzily matches to swakopmund .', 'tostr': 'filter_eq { all_rows ; hometown ; swakopmund }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; hometown ; swakopmund } }', 'tointer': 'select the rows whose hometown record fuzzily matches to swakopmund . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; hometown ; swakopmund } } ; 2 } = true', 'tointer': 'select the rows whose hometown record fuzzily matches to swakopmund . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; hometown ; swakopmund } } ; 2 } = true | select the rows whose hometown record fuzzily matches to swakopmund . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'hometown_5': 5, 'swakopmund_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'hometown_5': 'hometown', 'swakopmund_6': 'swakopmund', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'hometown_5': [0], 'swakopmund_6': [0], '2_7': [2]} | ['represented', 'contestant', 'age', 'height ( in )', 'height ( cm )', 'hometown'] | [['caprivi', 'happie ntelamo', '21', "6 ' 1", '185', 'katima mulilo'], ['erongo', 'theodora amutjira', '18', "5 ' 8", '176', 'walvis bay'], ['karas', 'mari venter', '23', "5 ' 10", '179', 'swakopmund'], ['kavango', 'albertina shigwedha', '26', "5 ' 9", '177', 'rundu'], ['khomas', 'tanya schemmer', '19', "6 ' 0", '183', 'windhoek'], ['ohangwena', 'jayne david', '24', "5 ' 5", '166', 'eenhana'], ['omusati', 'susan van zyl', '20', "5 ' 11", '182', 'oshakati'], ['oshikoto', 'selma usiku', '22', "6 ' 0", '184', 'omuthiya'], ['swakopmund', 'daniella filipovic', '25', "5 ' 7", '172', 'swakopmund']] |
list of doctor who audio plays by big finish | https://en.wikipedia.org/wiki/List_of_Doctor_Who_audio_plays_by_Big_Finish | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1620397-2.html.csv | superlative | according to the list of doctor who audio plays by big finish , first series featuring tractators was released in february 2010 . | {'scope': 'subset', 'col_superlative': '5', 'row_superlative': '4', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '4', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'tractators'}} | {'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'featuring', 'tractators'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; featuring ; tractators }', 'tointer': 'select the rows whose featuring record fuzzily matches to tractators .'}, 'released'], 'result': 'february 2010', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; featuring ; tractators } ; released }', 'tointer': 'select the rows whose featuring record fuzzily matches to tractators . the minimum released record of these rows is february 2010 .'}, 'february 2010'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; featuring ; tractators } ; released } ; february 2010 } = true', 'tointer': 'select the rows whose featuring record fuzzily matches to tractators . the minimum released record of these rows is february 2010 .'} | eq { min { filter_eq { all_rows ; featuring ; tractators } ; released } ; february 2010 } = true | select the rows whose featuring record fuzzily matches to tractators . the minimum released record of these rows is february 2010 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'featuring_5': 5, 'tractators_6': 6, 'released_7': 7, 'february 2010_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'featuring_5': 'featuring', 'tractators_6': 'tractators', 'released_7': 'released', 'february 2010_8': 'february 2010'} | {'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'featuring_5': [0], 'tractators_6': [0], 'released_7': [1], 'february 2010_8': [2]} | ['series sorted', 'title', 'doctor', 'featuring', 'released'] | [['6y / aa', 'the nightmare fair', '6th', 'peri , celestial toymaker', 'november 2009'], ['6y / ab', 'mission to magnus', '6th', 'peri , s ice warrior , sil', 'december 2009'], ['6y / ac', 'leviathan', '6th', 'peri', 'january 2010'], ['6y / ad', 'the hollows of time', '6th', 'peri , tractators', 'february 2010'], ['6y / ae', 'paradise 5', '6th', 'peri', 'march 2010'], ['6y / af', 'point of entry', '6th', 'peri', 'april 2010'], ['6y / ag', 'the song of megaptera', '6th', 'peri', 'may 2010'], ['6y / ah', 'the macros', '6th', 'peri', 'june 2010']] |
the curse of steptoe | https://en.wikipedia.org/wiki/The_Curse_of_Steptoe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15982651-1.html.csv | unique | the curse of steptoe was originally released march 19 , 2008 . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'original release', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'notes', 'original release'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose notes record fuzzily matches to original release .', 'tostr': 'filter_eq { all_rows ; notes ; original release }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; notes ; original release } }', 'tointer': 'select the rows whose notes record fuzzily matches to original release . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'notes', 'original release'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose notes record fuzzily matches to original release .', 'tostr': 'filter_eq { all_rows ; notes ; original release }'}, 'date'], 'result': '19 march 2008', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; notes ; original release } ; date }'}, '19 march 2008'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; notes ; original release } ; date } ; 19 march 2008 }', 'tointer': 'the date record of this unqiue row is 19 march 2008 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; notes ; original release } } ; eq { hop { filter_eq { all_rows ; notes ; original release } ; date } ; 19 march 2008 } } = true', 'tointer': 'select the rows whose notes record fuzzily matches to original release . there is only one such row in the table . the date record of this unqiue row is 19 march 2008 .'} | and { only { filter_eq { all_rows ; notes ; original release } } ; eq { hop { filter_eq { all_rows ; notes ; original release } ; date } ; 19 march 2008 } } = true | select the rows whose notes record fuzzily matches to original release . there is only one such row in the table . the date record of this unqiue row is 19 march 2008 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'notes_7': 7, 'original release_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '19 march 2008_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'notes_7': 'notes', 'original release_8': 'original release', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '19 march 2008_10': '19 march 2008'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'notes_7': [0], 'original release_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '19 march 2008_10': [3]} | ['date', 'time', 'channel', 'running time', 'notes'] | [['19 march 2008', '21:00', 'bbc four', '66 min 44 sec', 'original release'], ['20 march 2008', '00:05', 'bbc four', '66 min 44 sec', 'repeat'], ['21 march 2008', '22:00', 'bbc four', '66 min 44 sec', 'repeat'], ['23 march 2008', '22:45', 'bbc four', '66 min 44 sec', 'repeat'], ['28 december 2008', '22:30', 'bbc four', '66 min 21 sec', 'revised repeat'], ['29 december 2008', '03:40', 'bbc four', '66 min 21 sec', 'revised repeat'], ['2 december 2009', '22:00', 'bbc hd', '65 min 35 sec', 're - revised repeat']] |
1940 vfl season | https://en.wikipedia.org/wiki/1940_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807253-11.html.csv | comparative | the fitzroy vs. footscray game had a bigger crowd than the geelong vs. st. kilda game . | {'row_1': '2', 'row_2': '1', 'col': '6', 'col_other': '1,3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'fitzroy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to fitzroy .', 'tostr': 'filter_eq { all_rows ; home team ; fitzroy }'}, 'crowd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd }', 'tointer': 'select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'geelong'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to geelong .', 'tostr': 'filter_eq { all_rows ; home team ; geelong }'}, 'crowd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; geelong } ; crowd }', 'tointer': 'select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } }', 'tointer': 'select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'fitzroy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to fitzroy .', 'tostr': 'filter_eq { all_rows ; home team ; fitzroy }'}, 'away team'], 'result': 'footscray', 'ind': 5, 'tostr': 'hop { filter_eq { all_rows ; home team ; fitzroy } ; away team }'}, 'footscray'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; home team ; fitzroy } ; away team } ; footscray }', 'tointer': 'the away team record of the first row is footscray .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'geelong'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to geelong .', 'tostr': 'filter_eq { all_rows ; home team ; geelong }'}, 'away team'], 'result': 'st kilda', 'ind': 7, 'tostr': 'hop { filter_eq { all_rows ; home team ; geelong } ; away team }'}, 'st kilda'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; st kilda }', 'tointer': 'the away team record of the second row is st kilda .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { filter_eq { all_rows ; home team ; fitzroy } ; away team } ; footscray } ; eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; st kilda } }', 'tointer': 'the away team record of the first row is footscray . the away team record of the second row is st kilda .'}], 'result': True, 'ind': 10, 'tostr': 'and { greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; fitzroy } ; away team } ; footscray } ; eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; st kilda } } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . the first record is greater than the second record . the away team record of the first row is footscray . the away team record of the second row is st kilda .'} | and { greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; fitzroy } ; away team } ; footscray } ; eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; st kilda } } } = true | select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . the first record is greater than the second record . the away team record of the first row is footscray . the away team record of the second row is st kilda . | 13 | 11 | {'and_10': 10, 'result_11': 11, 'greater_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_12': 12, 'home team_13': 13, 'fitzroy_14': 14, 'crowd_15': 15, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_16': 16, 'home team_17': 17, 'geelong_18': 18, 'crowd_19': 19, 'and_9': 9, 'str_eq_6': 6, 'str_hop_5': 5, 'away team_20': 20, 'footscray_21': 21, 'str_eq_8': 8, 'str_hop_7': 7, 'away team_22': 22, 'st kilda_23': 23} | {'and_10': 'and', 'result_11': 'true', 'greater_4': 'greater', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_12': 'all_rows', 'home team_13': 'home team', 'fitzroy_14': 'fitzroy', 'crowd_15': 'crowd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_16': 'all_rows', 'home team_17': 'home team', 'geelong_18': 'geelong', 'crowd_19': 'crowd', 'and_9': 'and', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'away team_20': 'away team', 'footscray_21': 'footscray', 'str_eq_8': 'str_eq', 'str_hop_7': 'str_hop', 'away team_22': 'away team', 'st kilda_23': 'st kilda'} | {'and_10': [11], 'result_11': [], 'greater_4': [10], 'num_hop_2': [4], 'filter_str_eq_0': [2, 5], 'all_rows_12': [0], 'home team_13': [0], 'fitzroy_14': [0], 'crowd_15': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3, 7], 'all_rows_16': [1], 'home team_17': [1], 'geelong_18': [1], 'crowd_19': [3], 'and_9': [10], 'str_eq_6': [9], 'str_hop_5': [6], 'away team_20': [5], 'footscray_21': [6], 'str_eq_8': [9], 'str_hop_7': [8], 'away team_22': [7], 'st kilda_23': [8]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '12.22 ( 94 )', 'st kilda', '11.10 ( 76 )', 'corio oval', '6500', '6 july 1940'], ['fitzroy', '17.8 ( 110 )', 'footscray', '14.13 ( 97 )', 'brunswick street oval', '18000', '6 july 1940'], ['essendon', '19.14 ( 128 )', 'north melbourne', '16.9 ( 105 )', 'windy hill', '11000', '6 july 1940'], ['south melbourne', '10.18 ( 78 )', 'melbourne', '19.16 ( 130 )', 'lake oval', '10000', '6 july 1940'], ['hawthorn', '10.17 ( 77 )', 'collingwood', '14.17 ( 101 )', 'glenferrie oval', '10000', '6 july 1940'], ['richmond', '9.11 ( 65 )', 'carlton', '9.22 ( 76 )', 'punt road oval', '18000', '6 july 1940']] |
comedy circus | https://en.wikipedia.org/wiki/Comedy_Circus | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12316202-5.html.csv | count | there were two first runner up singers in comedy circus . | {'scope': 'all', 'criterion': 'equal', 'value': '1st runner - up', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'place ( result )', '1st runner - up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose place ( result ) record fuzzily matches to 1st runner - up .', 'tostr': 'filter_eq { all_rows ; place ( result ) ; 1st runner - up }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; place ( result ) ; 1st runner - up } }', 'tointer': 'select the rows whose place ( result ) record fuzzily matches to 1st runner - up . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; place ( result ) ; 1st runner - up } } ; 2 } = true', 'tointer': 'select the rows whose place ( result ) record fuzzily matches to 1st runner - up . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; place ( result ) ; 1st runner - up } } ; 2 } = true | select the rows whose place ( result ) record fuzzily matches to 1st runner - up . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'place (result)_5': 5, '1st runner - up_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'place (result)_5': 'place ( result )', '1st runner - up_6': '1st runner - up', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'place (result)_5': [0], '1st runner - up_6': [0], '2_7': [2]} | ['pair no', 'comedian :1', 'comedian : ii', 'tansen ( singers )', 'place ( result )'] | [['1', 'kapil sharma', 'ali asgar', 'neha kakkar', 'winner'], ['2', 'vip', 'swapnil joshi', 'sugandha mishra', '1st runner - up'], ['3', 'sudesh lehri', 'krushna abhishek', 'hard kaur', '1st runner - up'], ['4', 'bharti singh', 'aksshat saluja', 'abhijeet sawant', '2nd runner - up'], ['5', 'rajeev thakur', 'shweta tiwari', 'krishna beura', '2nd runner - up'], ['6', 'sumit arora', 'jimmy moses', 'bhoomi trivedi', 'eliminated'], ['7', 'jaswant singh', 'anjum farooki', 'abhas joshi', 'eliminated'], ['8', 'raja sagoo', 'preeti amin', 'raja hasan', 'eliminated']] |
list of cold feet episodes | https://en.wikipedia.org/wiki/List_of_Cold_Feet_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12919003-2.html.csv | comparative | during the first five episodes of cold feet , episode 4 had more viewers than episode 2 . | {'row_1': '4', 'row_2': '2', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode', 'episode 4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode record fuzzily matches to episode 4 .', 'tostr': 'filter_eq { all_rows ; episode ; episode 4 }'}, 'viewers ( millions )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; episode ; episode 4 } ; viewers ( millions ) }', 'tointer': 'select the rows whose episode record fuzzily matches to episode 4 . take the viewers ( millions ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode', 'episode 2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose episode record fuzzily matches to episode 2 .', 'tostr': 'filter_eq { all_rows ; episode ; episode 2 }'}, 'viewers ( millions )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; episode ; episode 2 } ; viewers ( millions ) }', 'tointer': 'select the rows whose episode record fuzzily matches to episode 2 . take the viewers ( millions ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; episode ; episode 4 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; episode ; episode 2 } ; viewers ( millions ) } } = true', 'tointer': 'select the rows whose episode record fuzzily matches to episode 4 . take the viewers ( millions ) record of this row . select the rows whose episode record fuzzily matches to episode 2 . take the viewers ( millions ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; episode ; episode 4 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; episode ; episode 2 } ; viewers ( millions ) } } = true | select the rows whose episode record fuzzily matches to episode 4 . take the viewers ( millions ) record of this row . select the rows whose episode record fuzzily matches to episode 2 . take the viewers ( millions ) record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'episode_7': 7, 'episode 4_8': 8, 'viewers (millions)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'episode_11': 11, 'episode 2_12': 12, 'viewers (millions)_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'episode_7': 'episode', 'episode 4_8': 'episode 4', 'viewers (millions)_9': 'viewers ( millions )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'episode_11': 'episode', 'episode 2_12': 'episode 2', 'viewers (millions)_13': 'viewers ( millions )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'episode_7': [0], 'episode 4_8': [0], 'viewers (millions)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'episode_11': [1], 'episode 2_12': [1], 'viewers (millions)_13': [3]} | ['no', 'episode', 'writer', 'director', 'viewers ( millions )', 'original airdate'] | [['1', 'episode 1', 'mike bullen', 'declan lowney', '7.47', '15 november 1998'], ['2', 'episode 2', 'mike bullen', 'declan lowney', '7.33', '22 november 1998'], ['3', 'episode 3', 'mike bullen', 'mark mylod', '7.46', '29 november 1998'], ['4', 'episode 4', 'mike bullen', 'mark mylod', '7.44', '6 december 1998'], ['5', 'episode 5', 'mike bullen', 'nigel cole', '7.91', '13 december 1998']] |
forbes global 2000 | https://en.wikipedia.org/wiki/Forbes_Global_2000 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1682026-10.html.csv | ordinal | general electric has the highest market value ( billion ) in the forbes global 2000 rankings . | {'row': '2', 'col': '8', 'order': '1', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'market value ( billion )', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; market value ( billion ) ; 1 }'}, 'company'], 'result': 'general electric', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; market value ( billion ) ; 1 } ; company }'}, 'general electric'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; market value ( billion ) ; 1 } ; company } ; general electric } = true', 'tointer': 'select the row whose market value ( billion ) record of all rows is 1st maximum . the company record of this row is general electric .'} | eq { hop { nth_argmax { all_rows ; market value ( billion ) ; 1 } ; company } ; general electric } = true | select the row whose market value ( billion ) record of all rows is 1st maximum . the company record of this row is general electric . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'market value (billion )_5': 5, '1_6': 6, 'company_7': 7, 'general electric_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'market value (billion )_5': 'market value ( billion )', '1_6': '1', 'company_7': 'company', 'general electric_8': 'general electric'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'market value (billion )_5': [0], '1_6': [0], 'company_7': [1], 'general electric_8': [2]} | ['rank', 'company', 'headquarters', 'industry', 'sales ( billion )', 'profits ( billion )', 'assets ( billion )', 'market value ( billion )'] | [['1', 'citigroup', 'usa', 'banking', '94.71', '17.85', '1264.03', '255.30'], ['2', 'general electric', 'usa', 'conglomerates', '134.19', '15.59', '626.93', '328.54'], ['3', 'american international group', 'usa', 'insurance', '76.66', '6.46', '647.66', '194.87'], ['4', 'exxonmobil', 'usa', 'oil & gas', '222.88', '20.96', '166.99', '277.02'], ['5', 'bp', 'uk', 'oil & gas', '232.57', '10.27', '177.57', '173.54'], ['6', 'bank of america', 'usa', 'banking', '49.01', '10.81', '736.45', '117.55'], ['7', 'hsbc', 'uk', 'banking', '44.33', '6.66', '757.60', '177.96'], ['8', 'toyota', 'japan', 'automotive', '135.82', '7.99', '171.71', '115.40'], ['9', 'fannie mae', 'usa', 'diversified financials', '53.13', '6.48', '1019.17', '76.84'], ['10', 'walmart', 'usa', 'ing retail', '256.33', '9.05', '104.91', '243.74']] |
1985 tampa bay buccaneers season | https://en.wikipedia.org/wiki/1985_Tampa_Bay_Buccaneers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11449311-2.html.csv | aggregation | the average attendance for tampa bay buccaneers games in 1985 was 45585 . | {'scope': 'all', 'col': '7', 'type': 'average', 'result': '45585', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '45585', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '45585'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 45585 } = true', 'tointer': 'the average of the attendance record of all rows is 45585 .'} | round_eq { avg { all_rows ; attendance } ; 45585 } = true | the average of the attendance record of all rows is 45585 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '45585_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '45585_5': '45585'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '45585_5': [1]} | ['week', 'date', 'opponent', 'result', 'kickoff', 'game site', 'attendance', 'record'] | [['week', 'date', 'opponent', 'result', 'kickoff', 'game site', 'attendance', 'record'], ['1', 'september 8 , 1985', 'chicago bears', 'l 38 - 28', '1:00', 'soldier field', '57828', '0 - 1'], ['2', 'september 15 , 1985', 'minnesota vikings', 'l 31 - 16', '4:00', 'tampa stadium', '46188', '0 - 2'], ['3', 'september 22 , 1985', 'new orleans saints', 'l 20 - 13', '1:00', 'louisiana superdome', '45320', '0 - 3'], ['4', 'september 29 , 1985', 'detroit lions', 'l 30 - 9', '1:00', 'pontiac silverdome', '45023', '0 - 4'], ['5', 'october 6 , 1985', 'chicago bears', 'l 27 - 19', '1:00', 'tampa stadium', '51795', '0 - 5'], ['6', 'october 13 , 1985', 'los angeles rams', 'l 31 - 27', '1:00', 'tampa stadium', '39607', '0 - 6'], ['7', 'october 20 , 1985', 'miami dolphins', 'l 41 - 38', '4:00', 'orange bowl', '62335', '0 - 7'], ['8', 'october 27 , 1985', 'new england patriots', 'l 32 - 14', '1:00', 'tampa stadium', '34661', '0 - 8'], ['9', 'november 3 , 1985', 'new york giants', 'l 22 - 20', '1:00', 'giants stadium', '72031', '0 - 9'], ['10', 'november 10 , 1985', 'st louis cardinals', 'w 16 - 0', '1:00', 'tampa stadium', '34736', '1 - 9'], ['11', 'november 17 , 1985', 'new york jets', 'l 62 - 28', '1:00', 'the meadowlands', '65344', '1 - 10'], ['12', 'november 24 , 1985', 'detroit lions', 'w 19 - 16 ot', '1:00', 'tampa stadium', '43471', '2 - 10'], ['13', 'december 1 , 1985', 'green bay packers', 'l 21 - 0', '1:00', 'lambeau field', '19856', '2 - 11'], ['14', 'december 8 , 1985', 'minnesota vikings', 'l 26 - 7', '4:00', 'hubert h humphrey metrodome', '51593', '2 - 12'], ['15', 'december 15 , 1985', 'indianapolis colts', 'l 31 - 23', '1:00', 'tampa stadium', '25577', '2 - 13'], ['16', 'december 22 , 1985', 'green bay packers', 'l 20 - 17', '1:00', 'tampa stadium', '33992', '2 - 14']] |
1957 world wrestling championships | https://en.wikipedia.org/wiki/1957_World_Wrestling_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16852841-1.html.csv | comparative | turkey won more gold medals than the soviet union . | {'row_1': '1', 'row_2': '2', 'col': '3', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'turkey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to turkey .', 'tostr': 'filter_eq { all_rows ; nation ; turkey }'}, 'gold'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; turkey } ; gold }', 'tointer': 'select the rows whose nation record fuzzily matches to turkey . take the gold record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'soviet union'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to soviet union .', 'tostr': 'filter_eq { all_rows ; nation ; soviet union }'}, 'gold'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; soviet union } ; gold }', 'tointer': 'select the rows whose nation record fuzzily matches to soviet union . take the gold record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; turkey } ; gold } ; hop { filter_eq { all_rows ; nation ; soviet union } ; gold } }', 'tointer': 'select the rows whose nation record fuzzily matches to turkey . take the gold record of this row . select the rows whose nation record fuzzily matches to soviet union . take the gold record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'turkey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to turkey .', 'tostr': 'filter_eq { all_rows ; nation ; turkey }'}, 'gold'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; turkey } ; gold }', 'tointer': 'select the rows whose nation record fuzzily matches to turkey . take the gold record of this row .'}, '4'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; nation ; turkey } ; gold } ; 4 }', 'tointer': 'the gold record of the first row is 4 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'soviet union'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to soviet union .', 'tostr': 'filter_eq { all_rows ; nation ; soviet union }'}, 'gold'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; soviet union } ; gold }', 'tointer': 'select the rows whose nation record fuzzily matches to soviet union . take the gold record of this row .'}, '2'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; nation ; soviet union } ; gold } ; 2 }', 'tointer': 'the gold record of the second row is 2 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; nation ; turkey } ; gold } ; 4 } ; eq { hop { filter_eq { all_rows ; nation ; soviet union } ; gold } ; 2 } }', 'tointer': 'the gold record of the first row is 4 . the gold record of the second row is 2 .'}], 'result': True, 'ind': 8, 'tostr': 'and { greater { hop { filter_eq { all_rows ; nation ; turkey } ; gold } ; hop { filter_eq { all_rows ; nation ; soviet union } ; gold } } ; and { eq { hop { filter_eq { all_rows ; nation ; turkey } ; gold } ; 4 } ; eq { hop { filter_eq { all_rows ; nation ; soviet union } ; gold } ; 2 } } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to turkey . take the gold record of this row . select the rows whose nation record fuzzily matches to soviet union . take the gold record of this row . the first record is greater than the second record . the gold record of the first row is 4 . the gold record of the second row is 2 .'} | and { greater { hop { filter_eq { all_rows ; nation ; turkey } ; gold } ; hop { filter_eq { all_rows ; nation ; soviet union } ; gold } } ; and { eq { hop { filter_eq { all_rows ; nation ; turkey } ; gold } ; 4 } ; eq { hop { filter_eq { all_rows ; nation ; soviet union } ; gold } ; 2 } } } = true | select the rows whose nation record fuzzily matches to turkey . take the gold record of this row . select the rows whose nation record fuzzily matches to soviet union . take the gold record of this row . the first record is greater than the second record . the gold record of the first row is 4 . the gold record of the second row is 2 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'greater_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'nation_11': 11, 'turkey_12': 12, 'gold_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'nation_15': 15, 'soviet union_16': 16, 'gold_17': 17, 'and_7': 7, 'eq_5': 5, '4_18': 18, 'eq_6': 6, '2_19': 19} | {'and_8': 'and', 'result_9': 'true', 'greater_4': 'greater', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'turkey_12': 'turkey', 'gold_13': 'gold', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'nation_15': 'nation', 'soviet union_16': 'soviet union', 'gold_17': 'gold', 'and_7': 'and', 'eq_5': 'eq', '4_18': '4', 'eq_6': 'eq', '2_19': '2'} | {'and_8': [9], 'result_9': [], 'greater_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'nation_11': [0], 'turkey_12': [0], 'gold_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'nation_15': [1], 'soviet union_16': [1], 'gold_17': [3], 'and_7': [8], 'eq_5': [7], '4_18': [5], 'eq_6': [7], '2_19': [6]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'turkey', '4', '2', '2', '8'], ['2', 'soviet union', '2', '3', '1', '6'], ['3', 'iran', '1', '1', '0', '2'], ['4', 'bulgaria', '1', '0', '2', '3'], ['5', 'finland', '0', '1', '0', '1'], ['5', 'west germany', '0', '1', '0', '1'], ['7', 'japan', '0', '0', '2', '2'], ['8', 'italy', '0', '0', '1', '1'], ['total', 'total', '8', '8', '8', '24']] |
list of montreal canadiens draft picks | https://en.wikipedia.org/wiki/List_of_Montreal_Canadiens_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18259953-8.html.csv | count | the montreal canadiens draft picks lasted for 7 rounds . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '7', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'round'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round record is arbitrary .', 'tostr': 'filter_all { all_rows ; round }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; round } }', 'tointer': 'select the rows whose round record is arbitrary . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; round } } ; 7 } = true', 'tointer': 'select the rows whose round record is arbitrary . the number of such rows is 7 .'} | eq { count { filter_all { all_rows ; round } } ; 7 } = true | select the rows whose round record is arbitrary . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'round_5': 5, '7_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'round_5': 'round', '7_6': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'round_5': [0], '7_6': [2]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['1', 'nathan beaulieu', 'defence', 'canada', 'saint john sea dogs ( qmjhl )'], ['4', 'josiah didier', 'defence', 'canada', 'cedar rapids roughriders ( ushl )'], ['4', 'olivier archambault', 'left wing', 'canada', "val d'or foreurs ( qmjhl )"], ['4', 'magnus nygren', 'defence', 'sweden', 'fã ¤ rjestads bk ( elitserien )'], ['5', 'darren dietz', 'defence', 'canada', 'saskatoon blades ( whl )'], ['6', '-', 'forward', 'czech republic', 'hc sparta praha ( czech extraliga )'], ['7', 'colin sullivan', 'defence', 'united states', 'avon old farms hs ( ushs )']] |
canterbury golf club | https://en.wikipedia.org/wiki/Canterbury_Golf_Club | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15280042-2.html.csv | unique | for the canterbury golf club , when the tournament was the senior tournament players championship , the only time the winner was miller barber was in 1983 . | {'scope': 'subset', 'row': '8', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'miller barber', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'senior tournament players championship'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'senior tournament players championship'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; tournament ; senior tournament players championship }', 'tointer': 'select the rows whose tournament record fuzzily matches to senior tournament players championship .'}, 'winner', 'miller barber'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to senior tournament players championship . among these rows , select the rows whose winner record fuzzily matches to miller barber .', 'tostr': 'filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber } }', 'tointer': 'select the rows whose tournament record fuzzily matches to senior tournament players championship . among these rows , select the rows whose winner record fuzzily matches to miller barber . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'senior tournament players championship'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; tournament ; senior tournament players championship }', 'tointer': 'select the rows whose tournament record fuzzily matches to senior tournament players championship .'}, 'winner', 'miller barber'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to senior tournament players championship . among these rows , select the rows whose winner record fuzzily matches to miller barber .', 'tostr': 'filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber }'}, 'year'], 'result': '1983', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber } ; year }'}, '1983'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber } ; year } ; 1983 }', 'tointer': 'the year record of this unqiue row is 1983 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber } } ; eq { hop { filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber } ; year } ; 1983 } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to senior tournament players championship . among these rows , select the rows whose winner record fuzzily matches to miller barber . there is only one such row in the table . the year record of this unqiue row is 1983 .'} | and { only { filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber } } ; eq { hop { filter_eq { filter_eq { all_rows ; tournament ; senior tournament players championship } ; winner ; miller barber } ; year } ; 1983 } } = true | select the rows whose tournament record fuzzily matches to senior tournament players championship . among these rows , select the rows whose winner record fuzzily matches to miller barber . there is only one such row in the table . the year record of this unqiue row is 1983 . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'tournament_8': 8, 'senior tournament players championship_9': 9, 'winner_10': 10, 'miller barber_11': 11, 'eq_4': 4, 'num_hop_3': 3, 'year_12': 12, '1983_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'tournament_8': 'tournament', 'senior tournament players championship_9': 'senior tournament players championship', 'winner_10': 'winner', 'miller barber_11': 'miller barber', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'year_12': 'year', '1983_13': '1983'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'tournament_8': [0], 'senior tournament players championship_9': [0], 'winner_10': [1], 'miller barber_11': [1], 'eq_4': [5], 'num_hop_3': [4], 'year_12': [3], '1983_13': [4]} | ['year', 'tournament', 'winner', 'country', 'score', 'to par', 'margin of victory', "winner 's share"] | [['1932', 'western open ( a )', 'walter hagen', 'united states', '288', 'even', '1 stroke', 'u'], ['1937', 'western open', 'ralph guldahl', 'united states', '287', '- 1', 'playoff ( b )', 'u'], ['1940', 'us open', 'lawson little', 'united states', '287', '- 1', 'playoff ( c )', '1000'], ['1946', 'us open', 'lloyd mangrum', 'united states', '284', '- 4', 'playoff ( d )', '1833'], ['1964', 'us amateur', 'william c campbell', 'united states', '1 up', 'n / a', 'n / a', 'n / a'], ['1973', 'pga championship', 'jack nicklaus', 'united states', '277', '- 7', '4 strokes', '45000'], ['1979', 'us amateur', "mark o'meara", 'united states', '8 & 7', 'n / a', 'n / a', 'n / a'], ['1983', 'senior tournament players championship', 'miller barber', 'united states', '278', '- 10', '1 stroke', '40000'], ['1984', 'senior tournament players championship', 'arnold palmer', 'united states', '276', '- 12', '3 strokes', '36000'], ['1985', 'senior tournament players championship', 'arnold palmer', 'united states', '274', '- 14', '11 strokes', '36000'], ['1986', 'senior tournament players championship', 'chi - chi rodríguez', 'united states', '206', '- 10', '2 strokes', '45000'], ['1996', 'us senior open', 'dave stockton', 'united states', '277', '- 11', '2 strokes', '215500'], ['2009', 'senior pga championship', 'michael allen', 'united states', '274', '- 6', '2 strokes', '360000']] |
1962 baltimore colts season | https://en.wikipedia.org/wiki/1962_Baltimore_Colts_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14984078-1.html.csv | comparative | less people attended the baltimore colts game on september 23 , 1962 than on october 14 , 1962 . | {'row_1': '2', 'row_2': '5', 'col': '7', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'september 23 , 1962'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to september 23 , 1962 .', 'tostr': 'filter_eq { all_rows ; date ; september 23 , 1962 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; september 23 , 1962 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to september 23 , 1962 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october 14 , 1962'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october 14 , 1962 .', 'tostr': 'filter_eq { all_rows ; date ; october 14 , 1962 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; october 14 , 1962 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to october 14 , 1962 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; date ; september 23 , 1962 } ; attendance } ; hop { filter_eq { all_rows ; date ; october 14 , 1962 } ; attendance } } = true', 'tointer': 'select the rows whose date record fuzzily matches to september 23 , 1962 . take the attendance record of this row . select the rows whose date record fuzzily matches to october 14 , 1962 . take the attendance record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; date ; september 23 , 1962 } ; attendance } ; hop { filter_eq { all_rows ; date ; october 14 , 1962 } ; attendance } } = true | select the rows whose date record fuzzily matches to september 23 , 1962 . take the attendance record of this row . select the rows whose date record fuzzily matches to october 14 , 1962 . take the attendance record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'september 23 , 1962_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'october 14 , 1962_12': 12, 'attendance_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'september 23 , 1962_8': 'september 23 , 1962', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'october 14 , 1962_12': 'october 14 , 1962', 'attendance_13': 'attendance'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'september 23 , 1962_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'october 14 , 1962_12': [1], 'attendance_13': [3]} | ['week', 'date', 'opponent', 'result', 'record', 'game site', 'attendance'] | [['1', 'september 16 , 1962', 'los angeles rams', 'w 30 - 27', '1 - 0', 'memorial stadium', '54796'], ['2', 'september 23 , 1962', 'minnesota vikings', 'w 34 - 7', '2 - 0', 'metropolitan stadium', '30787'], ['3', 'september 30 , 1962', 'detroit lions', 'l 20 - 29', '2 - 1', 'memorial stadium', '57966'], ['4', 'october 7 , 1962', 'san francisco 49ers', 'l 13 - 21', '2 - 2', 'memorial stadium', '54158'], ['5', 'october 14 , 1962', 'cleveland browns', 'w 36 - 14', '3 - 2', 'cleveland municipal stadium', '80132'], ['6', 'october 21 , 1962', 'chicago bears', 'l 15 - 35', '3 - 3', 'wrigley field', '49066'], ['7', 'october 28 , 1962', 'green bay packers', 'l 6 - 17', '3 - 4', 'memorial stadium', '57966'], ['8', 'november 4 , 1962', 'san francisco 49ers', 'w 22 - 3', '4 - 4', 'kezar stadium', '44875'], ['9', 'november 11 , 1962', 'los angeles rams', 'w 14 - 2', '5 - 4', 'los angeles memorial coliseum', '39502'], ['10', 'november 18 , 1962', 'green bay packers', 'l 13 - 17', '5 - 5', 'lambeau field', '38669'], ['11', 'november 25 , 1962', 'chicago bears', 'l 0 - 57', '5 - 6', 'memorial stadium', '56164'], ['12', 'december 2 , 1962', 'detroit lions', 'l 14 - 21', '5 - 7', 'tiger stadium', '53012'], ['13', 'december 8 , 1962', 'washington redskins', 'w 34 - 21', '6 - 7', 'memorial stadium', '56964'], ['14', 'december 16 , 1962', 'minnesota vikings', 'w 42 - 17', '7 - 7', 'memorial stadium', '53645']] |
list of ottawa senators draft picks | https://en.wikipedia.org/wiki/List_of_Ottawa_Senators_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11803648-20.html.csv | count | three of the players held the position left wing . | {'scope': 'all', 'criterion': 'equal', 'value': 'left wing', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'left wing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to left wing .', 'tostr': 'filter_eq { all_rows ; position ; left wing }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; left wing } }', 'tointer': 'select the rows whose position record fuzzily matches to left wing . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; left wing } } ; 3 } = true', 'tointer': 'select the rows whose position record fuzzily matches to left wing . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; position ; left wing } } ; 3 } = true | select the rows whose position record fuzzily matches to left wing . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'left wing_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'left wing_6': 'left wing', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'left wing_6': [0], '3_7': [2]} | ['round', 'overall', 'player', 'position', 'nationality', 'club team'] | [['1', '6', 'mika zibanejad', 'centre', 'sweden', 'djurgårdens if hockey ( sel )'], ['1', '21 ( from nashville )', 'stefan noesen', 'right wing', 'united states', 'plymouth whalers ( ohl )'], ['1', '24 ( from detroit )', 'matthew puempel', 'left wing', 'canada', 'peterborough petes ( ohl )'], ['2', '61 ( from boston )', 'shane prince', 'left wing', 'united states', "ottawa 67 's ( ohl )"], ['4', '96', 'jean - gabriel pageau', 'centre', 'canada', 'gatineau olympiques ( qmjhl )'], ['5', '126', 'fredrik claesson', 'defense', 'sweden', 'djurgårdens if hockey ( sel )'], ['6', '156', 'darren kramer', 'centre', 'canada', 'spokane chiefs ( whl )'], ['6', '171 ( from phoenix )', 'max mccormick', 'left wing', 'united states', 'sioux city musketeers ( ushl )'], ['7', '186', 'jordan fransoo', 'defense', 'canada', 'brandon wheat kings ( whl )']] |
peoria , illinois | https://en.wikipedia.org/wiki/Peoria%2C_Illinois | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-111774-2.html.csv | ordinal | of the clubs in peoria , illinois , the one that had the 2nd most recent date of establishment was in the ahl league . | {'row': '3', 'col': '5', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'established', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; established ; 2 }'}, 'league'], 'result': 'ahl', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; established ; 2 } ; league }'}, 'ahl'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; established ; 2 } ; league } ; ahl } = true', 'tointer': 'select the row whose established record of all rows is 2nd maximum . the league record of this row is ahl .'} | eq { hop { nth_argmax { all_rows ; established ; 2 } ; league } ; ahl } = true | select the row whose established record of all rows is 2nd maximum . the league record of this row is ahl . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'established_5': 5, '2_6': 6, 'league_7': 7, 'ahl_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'established_5': 'established', '2_6': '2', 'league_7': 'league', 'ahl_8': 'ahl'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'established_5': [0], '2_6': [0], 'league_7': [1], 'ahl_8': [2]} | ['club', 'league', 'sport', 'venue', 'established', 'disbanded', 'championships'] | [['peoria chiefs', 'midwest league class - a', 'baseball', 'dozer park', '1983', 'n / a', '1 league title'], ['peoria rivermen', 'sphl', 'hockey', 'carver arena', '2013', 'n / a', '0'], ['peoria rivermen', 'ahl', 'ice hockey', 'carver arena', '2005', '2013', '0'], ['peoria pirates', 'af2', 'arena football', 'carver arena', '1999', '2009', '2 s arenacup'], ['peoria redwings', 'aagpbl', 'baseball', 'peoria stadium', '1946', '1951', '0']] |
cas haley | https://en.wikipedia.org/wiki/Cas_Haley | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12543751-1.html.csv | majority | cas haley was advanced at the end of the majority of these weeks . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'advanced', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'advanced'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to advanced .', 'tostr': 'most_eq { all_rows ; result ; advanced } = true'} | most_eq { all_rows ; result ; advanced } = true | for the result records of all rows , most of them fuzzily match to advanced . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'advanced_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'advanced_4': 'advanced'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'advanced_4': [0]} | ['week', 'theme', 'song choice', 'original artist', 'result'] | [['audition', 'chicago', 'walking on the moon', 'the police', 'advanced'], ['vegas verdicts', 'n / a', 'living for the city', 'stevie wonder', 'advanced'], ['top 20', 'group 2', 'higher and higher', 'jackie wilson', 'advanced'], ['top 10', 'n / a', 'bring it on home to me', 'sam cooke', 'advanced'], ['top 8', 'heroes', 'easy', 'lionel richie', 'advanced'], ['top 4', "judges ' choice contestant 's choice", "ca n't help falling in love sir duke", 'elvis presley stevie wonder', 'n / a'], ['finale', 'duets', 'red red wine', 'neil diamond', 'runner up']] |
1976 - 77 new york rangers season | https://en.wikipedia.org/wiki/1976%E2%80%9377_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17562992-4.html.csv | count | in the 1976 - 77 new york rangers season they played 15 games . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '15', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'game'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose game record is arbitrary .', 'tostr': 'filter_all { all_rows ; game }'}], 'result': '15', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; game } }', 'tointer': 'select the rows whose game record is arbitrary . the number of such rows is 15 .'}, '15'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; game } } ; 15 } = true', 'tointer': 'select the rows whose game record is arbitrary . the number of such rows is 15 .'} | eq { count { filter_all { all_rows ; game } } ; 15 } = true | select the rows whose game record is arbitrary . the number of such rows is 15 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'game_5': 5, '15_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'game_5': 'game', '15_6': '15'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'game_5': [0], '15_6': [2]} | ['game', 'december', 'opponent', 'score', 'record'] | [['26', '1', 'washington capitals', '4 - 1', '11 - 11 - 4'], ['27', '4', 'minnesota north stars', '11 - 4', '12 - 11 - 4'], ['28', '5', 'toronto maple leafs', '5 - 5', '12 - 11 - 5'], ['29', '8', 'st louis blues', '4 - 4', '12 - 11 - 6'], ['30', '11', 'toronto maple leafs', '4 - 1', '12 - 12 - 6'], ['31', '12', 'montreal canadiens', '5 - 2', '13 - 12 - 6'], ['32', '14', 'new york islanders', '4 - 4', '13 - 12 - 7'], ['33', '16', 'buffalo sabres', '7 - 2', '13 - 13 - 7'], ['34', '18', 'chicago black hawks', '3 - 3', '13 - 13 - 8'], ['35', '19', 'cleveland barons', '3 - 2', '14 - 13 - 8'], ['36', '22', 'philadelphia flyers', '3 - 3', '14 - 13 - 9'], ['37', '23', 'boston bruins', '3 - 3', '14 - 13 - 10'], ['38', '26', 'new york islanders', '2 - 1', '14 - 14 - 10'], ['39', '28', 'washington capitals', '5 - 2', '15 - 14 - 10'], ['40', '31', 'atlanta flames', '4 - 2', '15 - 15 - 10']] |
dom events | https://en.wikipedia.org/wiki/DOM_events | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1507852-1.html.csv | unique | domnoderemovedfromdocument is the only type that does not have a bubble . | {'scope': 'all', 'row': '9', 'col': '5', 'col_other': '2', 'criterion': 'not_equal', 'value': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'bubbles', 'yes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bubbles record does not match to yes .', 'tostr': 'filter_not_eq { all_rows ; bubbles ; yes }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; bubbles ; yes } }', 'tointer': 'select the rows whose bubbles record does not match to yes . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'bubbles', 'yes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bubbles record does not match to yes .', 'tostr': 'filter_not_eq { all_rows ; bubbles ; yes }'}, 'type'], 'result': 'domnoderemovedfromdocument', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; bubbles ; yes } ; type }'}, 'domnoderemovedfromdocument'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; bubbles ; yes } ; type } ; domnoderemovedfromdocument }', 'tointer': 'the type record of this unqiue row is domnoderemovedfromdocument .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; bubbles ; yes } } ; eq { hop { filter_not_eq { all_rows ; bubbles ; yes } ; type } ; domnoderemovedfromdocument } } = true', 'tointer': 'select the rows whose bubbles record does not match to yes . there is only one such row in the table . the type record of this unqiue row is domnoderemovedfromdocument .'} | and { only { filter_not_eq { all_rows ; bubbles ; yes } } ; eq { hop { filter_not_eq { all_rows ; bubbles ; yes } ; type } ; domnoderemovedfromdocument } } = true | select the rows whose bubbles record does not match to yes . there is only one such row in the table . the type record of this unqiue row is domnoderemovedfromdocument . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_6': 6, 'bubbles_7': 7, 'yes_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'type_9': 9, 'domnoderemovedfromdocument_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_6': 'all_rows', 'bubbles_7': 'bubbles', 'yes_8': 'yes', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'type_9': 'type', 'domnoderemovedfromdocument_10': 'domnoderemovedfromdocument'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_not_eq_0': [1, 2], 'all_rows_6': [0], 'bubbles_7': [0], 'yes_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'type_9': [2], 'domnoderemovedfromdocument_10': [3]} | ['category', 'type', 'attribute', 'description', 'bubbles', 'cancelable'] | [['mouse', 'dragstart', 'ondragstart', 'fired on an element when a drag is started', 'yes', 'yes'], ['keyboard', 'keyup', 'onkeyup', 'fires when a key on the keyboard is released', 'yes', 'yes'], ['html frame / object', 'resize', 'onresize', 'fires when a document view is resized', 'yes', 'no'], ['html frame / object', 'scroll', 'onscroll', 'fires when a document view is scrolled', 'yes', 'no'], ['html form', 'submit', 'onsubmit', 'fires when a form is submitted', 'yes', 'yes'], ['html form', 'reset', 'onreset', 'fires when a form is reset', 'yes', 'no'], ['mutation', 'domsubtreemodified', '( none )', 'fires when the subtree is modified', 'yes', 'no'], ['mutation', 'domnoderemoved', '( none )', 'fires when a node has been removed from a dom - tree', 'yes', 'no'], ['mutation', 'domnoderemovedfromdocument', '( none )', 'fires when a node is being removed from a document', 'no', 'no'], ['mutation', 'domattrmodified', '( none )', 'fires when an attribute has been modified', 'yes', 'no']] |
1970 new york giants season | https://en.wikipedia.org/wiki/1970_New_York_Giants_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15451468-2.html.csv | superlative | during the 1970 new york giants season , the new york giants experienced their highest attendance on october 4th in their game against the new orleans saints . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'date'], 'result': '1970 - 10 - 04', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, '1970 - 10 - 04'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; 1970 - 10 - 04 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is 1970 - 10 - 04 .'} | eq { hop { argmax { all_rows ; attendance } ; date } ; 1970 - 10 - 04 } = true | select the row whose attendance record of all rows is maximum . the date record of this row is 1970 - 10 - 04 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, '1970 - 10 - 04_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', '1970 - 10 - 04_7': '1970 - 10 - 04'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], '1970 - 10 - 04_7': [2]} | ['week', 'date', 'opponent', 'result', 'game site', 'attendance'] | [['1', '1970 - 09 - 19', 'chicago bears', 'l 24 - 16', 'yankee stadium', '62936'], ['2', '1970 - 09 - 27', 'dallas cowboys', 'l 28 - 10', 'cotton bowl', '57236'], ['3', '1970 - 10 - 04', 'new orleans saints', 'l 14 - 10', 'tulane stadium', '69126'], ['4', '1970 - 10 - 11', 'philadelphia eagles', 'w 30 - 23', 'yankee stadium', '62820'], ['5', '1970 - 10 - 18', 'boston patriots', 'w 16 - 0', 'harvard stadium', '39091'], ['6', '1970 - 10 - 25', 'st louis cardinals', 'w 35 - 17', 'yankee stadium', '62984'], ['7', '1970 - 11 - 01', 'new york jets', 'w 22 - 10', 'shea stadium', '63903'], ['8', '1970 - 11 - 08', 'dallas cowboys', 'w 23 - 20', 'yankee stadium', '62938'], ['9', '1970 - 11 - 15', 'washington redskins', 'w 35 - 33', 'yankee stadium', '62915'], ['10', '1970 - 11 - 23', 'philadelphia eagles', 'l 23 - 20', 'franklin field', '59117'], ['11', '1970 - 11 - 29', 'washington redskins', 'w 27 - 24', 'robert f kennedy memorial stadium', '50415'], ['12', '1970 - 12 - 06', 'buffalo bills', 'w 20 - 6', 'yankee stadium', '62870'], ['13', '1970 - 12 - 13', 'st louis cardinals', 'w 34 - 17', 'busch memorial stadium', '50845'], ['14', '1970 - 12 - 20', 'los angeles rams', 'l 31 - 3', 'yankee stadium', '62870']] |
1989 - 90 illinois fighting illini men 's basketball team | https://en.wikipedia.org/wiki/1989%E2%80%9390_Illinois_Fighting_Illini_men%27s_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22824324-2.html.csv | ordinal | andy kaufmann recorded the 2nd highest number of points in the 1989 - 90 illinois fighting illini men 's basketball team . | {'row': '2', 'col': '10', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; points ; 2 }'}, 'player'], 'result': 'andy kaufmann', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; points ; 2 } ; player }'}, 'andy kaufmann'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; points ; 2 } ; player } ; andy kaufmann } = true', 'tointer': 'select the row whose points record of all rows is 2nd maximum . the player record of this row is andy kaufmann .'} | eq { hop { nth_argmax { all_rows ; points ; 2 } ; player } ; andy kaufmann } = true | select the row whose points record of all rows is 2nd maximum . the player record of this row is andy kaufmann . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, '2_6': 6, 'player_7': 7, 'andy kaufmann_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', '2_6': '2', 'player_7': 'player', 'andy kaufmann_8': 'andy kaufmann'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], '2_6': [0], 'player_7': [1], 'andy kaufmann_8': [2]} | ['player', 'games played', 'field goals', 'three pointers', 'free throws', 'rebounds', 'assists', 'blocks', 'steals', 'points'] | [['kendall gill', '29', '211', '23', '136', '143', '96', '16', '63', '581'], ['andy kaufmann', '29', '91', '22', '81', '93', '54', '5', '27', '285'], ['steve bardo', '29', '99', '28', '55', '178', '137', '14', '37', '281'], ['rodney jones', '29', '88', '0', '40', '126', '9', '18', '17', '216'], ['ervin small', '29', '75', '1', '49', '151', '12', '5', '23', '200']] |
eastern states collegiate hockey league | https://en.wikipedia.org/wiki/Eastern_States_Collegiate_Hockey_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16382861-1.html.csv | majority | in the eastern states collegiate hockey league , most of the schools are public . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'public', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'affiliation', 'public'], 'result': True, 'ind': 0, 'tointer': 'for the affiliation records of all rows , most of them fuzzily match to public .', 'tostr': 'most_eq { all_rows ; affiliation ; public } = true'} | most_eq { all_rows ; affiliation ; public } = true | for the affiliation records of all rows , most of them fuzzily match to public . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'affiliation_3': 3, 'public_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'affiliation_3': 'affiliation', 'public_4': 'public'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'affiliation_3': [0], 'public_4': [0]} | ['school', 'location', 'founded', 'affiliation', 'enrollment', 'nickname', 'primary conference'] | [['university of delaware', 'newark , de', '1743', 'public', '19067', "fightin ' blue hens", 'colonial athletic association ( d - i )'], ['lebanon valley college', 'annville , pa', '1866', 'private / methodist', '2100', 'flying dutchmen', 'mac commonwealth conference ( d - iii )'], ['university of rhode island', 'kingston , ri', '1892', 'public', '19095', 'rams', 'atlantic 10 conference ( d - i )'], ['rutgers university', 'new brunswick , nj', '1766', 'public', '56868', 'scarlet knights', 'american athletic conference ( d - i )'], ['stony brook university', 'stony brook , ny', '1957', 'public', '23997', 'seawolves', 'america east conference ( d - i )'], ['west chester university', 'west chester , pa', '1871', 'public', '12800', 'golden rams', 'psac ( d - ii )']] |
felice herrig | https://en.wikipedia.org/wiki/Felice_Herrig | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16182887-2.html.csv | comparative | in the bellator 14 event , felice herrig made it 1 round further than in the unconquered 1 : november reign event . | {'row_1': '10', 'row_2': '11', 'col': '6', 'col_other': '5', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'bellator 14'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose event record fuzzily matches to bellator 14 .', 'tostr': 'filter_eq { all_rows ; event ; bellator 14 }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; event ; bellator 14 } ; round }', 'tointer': 'select the rows whose event record fuzzily matches to bellator 14 . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'unconquered 1 : november reign'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose event record fuzzily matches to unconquered 1 : november reign .', 'tostr': 'filter_eq { all_rows ; event ; unconquered 1 : november reign }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; event ; unconquered 1 : november reign } ; round }', 'tointer': 'select the rows whose event record fuzzily matches to unconquered 1 : november reign . take the round record of this row .'}], 'result': '1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; event ; bellator 14 } ; round } ; hop { filter_eq { all_rows ; event ; unconquered 1 : november reign } ; round } }'}, '1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; event ; bellator 14 } ; round } ; hop { filter_eq { all_rows ; event ; unconquered 1 : november reign } ; round } } ; 1 } = true', 'tointer': 'select the rows whose event record fuzzily matches to bellator 14 . take the round record of this row . select the rows whose event record fuzzily matches to unconquered 1 : november reign . take the round record of this row . the first record is 1 larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; event ; bellator 14 } ; round } ; hop { filter_eq { all_rows ; event ; unconquered 1 : november reign } ; round } } ; 1 } = true | select the rows whose event record fuzzily matches to bellator 14 . take the round record of this row . select the rows whose event record fuzzily matches to unconquered 1 : november reign . take the round record of this row . the first record is 1 larger than the second record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'event_8': 8, 'bellator 14_9': 9, 'round_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'event_12': 12, 'unconquered 1: november reign_13': 13, 'round_14': 14, '1_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'event_8': 'event', 'bellator 14_9': 'bellator 14', 'round_10': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'event_12': 'event', 'unconquered 1: november reign_13': 'unconquered 1 : november reign', 'round_14': 'round', '1_15': '1'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'event_8': [0], 'bellator 14_9': [0], 'round_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'event_12': [1], 'unconquered 1: november reign_13': [1], 'round_14': [3], '1_15': [5]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '9 - 4', 'heather clark', 'decision ( split )', 'bellator 94', '3', '5:00', 'tampa , florida , united states'], ['win', '8 - 4', 'patricia vidonic', 'decision ( unanimous )', 'bellator 84', '3', '5:00', 'hammond , indiana , united states'], ['win', '7 - 4', 'simona soukupova', 'decision ( unanimous )', 'xfc 19 : charlotte showdown', '3', '5:00', 'charlotte , north carolina , united states'], ['win', '6 - 4', 'patricia vidonic', 'decision ( unanimous )', 'xfc 17 : apocalypse', '3', '5:00', 'jackson , tennessee , united states'], ['loss', '5 - 4', 'carla esparza', 'decision ( unanimous )', 'xfc 15 : tribute', '3', '5:00', 'tampa , florida , united states'], ['win', '5 - 3', 'nicdali rivera - calanoc', 'decision ( unanimous )', 'xtreme fighting organization 39', '3', '5:00', 'hoffman estates , illinois , united states'], ['win', '4 - 3', 'andrea miller', 'tko ( punches )', 'chicago cagefighting championship 3', '1', '3:30', 'villa park , illinois , united states'], ['loss', '3 - 3', 'barb honchak', 'decision ( unanimous )', 'hoosier fight club 6 : new years nemesis', '3', '5:00', 'valparaiso , indiana , united states'], ['win', '3 - 2', 'amanda lavoy', 'submission ( armbar )', 'xtreme fighting organization 37', '1', '3:35', 'chicago , illinois , united states'], ['win', '2 - 2', 'jessica rakoczy', 'decision ( split )', 'bellator 14', '3', '5:00', 'chicago , illinois , united states'], ['win', '1 - 2', 'michele gutierrez', 'submission ( armbar )', 'unconquered 1 : november reign', '2', '2:03', 'coral gables , florida , united states'], ['loss', '0 - 2', 'valerie coolbaugh', 'decision ( split )', 'xtreme fighting organization 29', '3', '5:00', 'lakemoor , illinois , united states'], ['loss', '0 - 1', 'iman achhal', 'decision ( split )', 'uwc : man o war', '3', '5:00', 'fairfax , virginia , united states']] |
list of entertainment events in greater moncton | https://en.wikipedia.org/wiki/List_of_entertainment_events_in_Greater_Moncton | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11758927-2.html.csv | unique | the dieppe kite international was the only event to take place at dover park . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'dover park', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'main venue', 'dover park'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose main venue record fuzzily matches to dover park .', 'tostr': 'filter_eq { all_rows ; main venue ; dover park }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; main venue ; dover park } }', 'tointer': 'select the rows whose main venue record fuzzily matches to dover park . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'main venue', 'dover park'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose main venue record fuzzily matches to dover park .', 'tostr': 'filter_eq { all_rows ; main venue ; dover park }'}, 'event name'], 'result': 'dieppe kite international', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; main venue ; dover park } ; event name }'}, 'dieppe kite international'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; main venue ; dover park } ; event name } ; dieppe kite international }', 'tointer': 'the event name record of this unqiue row is dieppe kite international .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; main venue ; dover park } } ; eq { hop { filter_eq { all_rows ; main venue ; dover park } ; event name } ; dieppe kite international } } = true', 'tointer': 'select the rows whose main venue record fuzzily matches to dover park . there is only one such row in the table . the event name record of this unqiue row is dieppe kite international .'} | and { only { filter_eq { all_rows ; main venue ; dover park } } ; eq { hop { filter_eq { all_rows ; main venue ; dover park } ; event name } ; dieppe kite international } } = true | select the rows whose main venue record fuzzily matches to dover park . there is only one such row in the table . the event name record of this unqiue row is dieppe kite international . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'main venue_7': 7, 'dover park_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'event name_9': 9, 'dieppe kite international_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'main venue_7': 'main venue', 'dover park_8': 'dover park', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'event name_9': 'event name', 'dieppe kite international_10': 'dieppe kite international'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'main venue_7': [0], 'dover park_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'event name_9': [2], 'dieppe kite international_10': [3]} | ['event name', 'established', 'category', 'sub category', 'main venue'] | [['dieppe kite international', '2001', 'sporting', 'kite flying', 'dover park'], ['the frye festival', '2000', 'arts', 'literary', 'university of moncton'], ['hubcap comedy festival', '2000', 'arts', 'comedy', 'various'], ['touchdown atlantic', '2010', 'sporting', 'football', 'moncton stadium'], ['atlantic nationals automotive extravaganza', '2000', 'transportation', 'automotive', 'moncton coliseum'], ['world wine & food expo', '1990', 'arts', 'food & drink', 'moncton coliseum'], ['shediac lobster festival', '1950', 'arts', 'food & drink', 'shediac festival grounds'], ['mosaã ¯ q multicultural festival', '2004', 'festival', 'multicultural', 'moncton city hall plaza']] |
1985 masters tournament | https://en.wikipedia.org/wiki/1985_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16488699-1.html.csv | aggregation | in the 1985 masters tournament , the average number of strokes to par is -2.54 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '-2.54', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'to par'], 'result': '-2.54', 'ind': 0, 'tostr': 'avg { all_rows ; to par }'}, '-2.54'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; to par } ; -2.54 } = true', 'tointer': 'the average of the to par record of all rows is -2.54 .'} | round_eq { avg { all_rows ; to par } ; -2.54 } = true | the average of the to par record of all rows is -2.54 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'to par_4': 4, '-2.54_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'to par_4': 'to par', '-2.54_5': '-2.54'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'to par_4': [0], '-2.54_5': [1]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'bernhard langer', 'west germany', '72 + 74 + 68 + 68 = 282', '- 6', '126000'], ['t2', 'seve ballesteros', 'spain', '72 + 71 + 71 + 70 = 284', '- 4', '52267'], ['t2', 'raymond floyd', 'united states', '70 + 73 + 69 + 72 = 284', '- 4', '52267'], ['t2', 'curtis strange', 'united states', '80 + 65 + 68 + 71 = 284', '- 4', '52267'], ['5', 'jay haas', 'united states', '73 + 73 + 72 + 67 = 285', '- 3', '28000'], ['t6', 'gary hallberg', 'united states', '68 + 73 + 75 + 70 = 286', '- 2', '22663'], ['t6', 'bruce lietzke', 'united states', '72 + 71 + 73 + 70 = 286', '- 2', '22663'], ['t6', 'jack nicklaus', 'united states', '71 + 74 + 72 + 69 = 286', '- 2', '22663'], ['t6', 'craig stadler', 'united states', '73 + 67 + 76 + 70 = 286', '- 2', '22663'], ['t10', 'fred couples', 'united states', '75 + 73 + 69 + 70 = 287', '- 1', '16800'], ['t10', 'david graham', 'australia', '74 + 71 + 71 + 71 = 287', '- 1', '16800'], ['t10', 'lee trevino', 'united states', '70 + 73 + 72 + 72 = 287', '- 1', '16800'], ['t10', 'tom watson', 'united states', '69 + 71 + 75 + 72 = 287', '- 1', '16800']] |
napa auto parts 200 | https://en.wikipedia.org/wiki/NAPA_Auto_Parts_200 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10716893-3.html.csv | comparative | jack arute hosted the napa auto parts 200 race earlier than allen bestwick did . | {'row_1': '5', 'row_2': '3', 'col': '1', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'host', 'jack arute'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose host record fuzzily matches to jack arute .', 'tostr': 'filter_eq { all_rows ; host ; jack arute }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; host ; jack arute } ; year }', 'tointer': 'select the rows whose host record fuzzily matches to jack arute . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'host', 'allen bestwick'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose host record fuzzily matches to allen bestwick .', 'tostr': 'filter_eq { all_rows ; host ; allen bestwick }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; host ; allen bestwick } ; year }', 'tointer': 'select the rows whose host record fuzzily matches to allen bestwick . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; host ; jack arute } ; year } ; hop { filter_eq { all_rows ; host ; allen bestwick } ; year } } = true', 'tointer': 'select the rows whose host record fuzzily matches to jack arute . take the year record of this row . select the rows whose host record fuzzily matches to allen bestwick . take the year record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; host ; jack arute } ; year } ; hop { filter_eq { all_rows ; host ; allen bestwick } ; year } } = true | select the rows whose host record fuzzily matches to jack arute . take the year record of this row . select the rows whose host record fuzzily matches to allen bestwick . take the year record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'host_7': 7, 'jack arute_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'host_11': 11, 'allen bestwick_12': 12, 'year_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'host_7': 'host', 'jack arute_8': 'jack arute', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'host_11': 'host', 'allen bestwick_12': 'allen bestwick', 'year_13': 'year'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'host_7': [0], 'jack arute_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'host_11': [1], 'allen bestwick_12': [1], 'year_13': [3]} | ['year', 'network', 'host', 'pre - race analyst', 'lap - by - lap', 'color commentator ( s )', 'pit reporters'] | [['2012', 'espn', 'shannon spake', 'n / a', 'marty reid', 'ricky craven', 'rick debruhl jim noble shannon spake'], ['2011', 'espn', 'marty reid', 'n / a', 'marty reid', 'rusty wallace ricky craven', 'rick debruhl jim noble shannon spake'], ['2010', 'espn2', 'allen bestwick', 'n / a', 'allen bestwick', 'andy petree rusty wallace', 'mike massaro vince welch shannon spake'], ['2009', 'espn2', 'shannon spake', 'n / a', 'marty reid', 'andy petree rusty wallace', 'dave burns jamie little shannon spake'], ['2008', 'espn2', 'jack arute', 'n / a', 'marty reid', 'randy lajoie rusty wallace', 'jack arute vince welch mike massaro']] |
1986 pga championship | https://en.wikipedia.org/wiki/1986_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18150398-2.html.csv | superlative | jack nicklaus was the player with the highest total that made the cut in the 1986 pga championships . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total }'}, 'player'], 'result': 'jack nicklaus', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total } ; player }'}, 'jack nicklaus'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total } ; player } ; jack nicklaus } = true', 'tointer': 'select the row whose total record of all rows is maximum . the player record of this row is jack nicklaus .'} | eq { hop { argmax { all_rows ; total } ; player } ; jack nicklaus } = true | select the row whose total record of all rows is maximum . the player record of this row is jack nicklaus . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, 'player_6': 6, 'jack nicklaus_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total_5': 'total', 'player_6': 'player', 'jack nicklaus_7': 'jack nicklaus'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], 'player_6': [1], 'jack nicklaus_7': [2]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['david graham', 'australia', '1979', '282', '2', 't7'], ['lee trevino', 'united states', '1974 , 1984', '284', 'e', 't11'], ['lanny wadkins', 'united states', '1977', '284', 'e', 't11'], ['jack nicklaus', 'united states', '1963 , 1971 , 1973 1975 , 1980', '296', '+ 1', 't16'], ['hal sutton', 'united states', '1983', '286', '+ 2', 't21'], ['hubert green', 'united states', '1985', '290', '+ 6', 't41'], ['dave stockton', 'united states', '1970 , 1976', '292', '+ 8', 't53']] |
true blood ( season 3 ) | https://en.wikipedia.org/wiki/True_Blood_%28season_3%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26493520-1.html.csv | count | there are 11 episodes in the 3rd season of the true blood series . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '11', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'no in series'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose no in series record is arbitrary .', 'tostr': 'filter_all { all_rows ; no in series }'}], 'result': '11', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; no in series } }', 'tointer': 'select the rows whose no in series record is arbitrary . the number of such rows is 11 .'}, '11'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; no in series } } ; 11 } = true', 'tointer': 'select the rows whose no in series record is arbitrary . the number of such rows is 11 .'} | eq { count { filter_all { all_rows ; no in series } } ; 11 } = true | select the rows whose no in series record is arbitrary . the number of such rows is 11 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'no in series_5': 5, '11_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'no in series_5': 'no in series', '11_6': '11'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'no in series_5': [0], '11_6': [2]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'us viewers ( million )'] | [['25', '1', 'bad blood', 'daniel minahan', 'brian buckner', 'june 13 , 2010', '5.09'], ['26', '2', 'beautifully broken', 'scott winant', 'raelle tucker', 'june 20 , 2010', '4.26'], ['27', '3', 'it hurts me too', 'michael lehmann', 'alexander woo', 'june 27 , 2010', '4.46'], ['28', '4', '9 crimes', 'david petrarca', 'kate barnow & elisabeth r finch', 'july 11 , 2010', '4.68'], ['29', '5', 'trouble', 'scott winant', 'nancy oliver', 'july 18 , 2010', '4.86'], ['30', '6', 'i got a right to sing the blues', 'michael lehmann', 'alan ball', 'july 25 , 2010', '4.74'], ['31', '7', 'hitting the ground', 'john dahl', 'brian buckner', 'august 1 , 2010', '5.24'], ['32', '8', 'night on the sun', 'lesli linka glatter', 'raelle tucker', 'august 8 , 2010', '5.09'], ['33', '9', 'everything is broken', 'scott winant', 'alexander woo', 'august 15 , 2010', '5.00'], ['34', '10', 'i smell a rat', 'michael lehmann', 'kate barnow & elisabeth r finch', 'august 22 , 2010', '5.39'], ['35', '11', 'fresh blood', 'daniel minahan', 'nancy oliver', 'august 29 , 2010', '5.44']] |
list of intel atom microprocessors | https://en.wikipedia.org/wiki/List_of_Intel_Atom_microprocessors | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16729930-18.html.csv | superlative | the highest release price of intel atom microprocessors is 97 usd . | {'scope': 'all', 'col_superlative': '13', 'row_superlative': '5', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'release price ( usd )'], 'result': '97', 'ind': 0, 'tostr': 'max { all_rows ; release price ( usd ) }', 'tointer': 'the maximum release price ( usd ) record of all rows is 97 .'}, '97'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; release price ( usd ) } ; 97 } = true', 'tointer': 'the maximum release price ( usd ) record of all rows is 97 .'} | eq { max { all_rows ; release price ( usd ) } ; 97 } = true | the maximum release price ( usd ) record of all rows is 97 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'release price ( usd )_4': 4, '97_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'release price ( usd )_4': 'release price ( usd )', '97_5': '97'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'release price ( usd )_4': [0], '97_5': [1]} | ['model number', 'sspec number', 'frequency', 'gpu frequency', 'l2 cache', 'i / o bus', 'memory', 'voltage', 'tdp', 'socket', 'release date', 'part number ( s )', 'release price ( usd )'] | [['atom e625c', 'slh9z ( b0 )', '600 mhz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', '2.7 w', 'fc - bga 1466', 'november 22 , 2010', 'cy80632007227ab', '61'], ['atome625ct', 'slh9k ( b0 )', '600 mhz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', '2.7 w', 'fc - bga 1466', 'november 22 , 2010', 'cy80632007227aa', '65'], ['atom e645c', 'slh9y ( b0 )', '1 ghz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', '3.6 w', 'fc - bga 1466', 'november 22 , 2010', 'cy80632007221ab', '72'], ['atome645ct', 'slh9j ( b0 )', '1 ghz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', '3.6 w', 'fc - bga 1466', 'november 22 , 2010', 'cy80632007221aa', '79'], ['atom e665c', 'slh9x ( b0 )', '1.3 ghz', '400 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', '3.6 w', 'fc - bga 1466', 'november 22 , 2010', 'cy80632007224ab', '97']] |
2004 centrix financial grand prix of denver | https://en.wikipedia.org/wiki/2004_Centrix_Financial_Grand_Prix_of_Denver | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16789804-1.html.csv | majority | most drivers of the 2004 centrix financial grand prix of denver had a qual 2 time . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'not_equal', 'value': '-', 'subset': None} | {'func': 'most_not_eq', 'args': ['all_rows', 'qual 2', '-'], 'result': True, 'ind': 0, 'tointer': 'for the qual 2 records of all rows , most of them are not equal to - .', 'tostr': 'most_not_eq { all_rows ; qual 2 ; - } = true'} | most_not_eq { all_rows ; qual 2 ; - } = true | for the qual 2 records of all rows , most of them are not equal to - . | 1 | 1 | {'most_not_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'qual 2_3': 3, '-_4': 4} | {'most_not_eq_0': 'most_not_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'qual 2_3': 'qual 2', '-_4': '-'} | {'most_not_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'qual 2_3': [0], '-_4': [0]} | ['name', 'team', 'qual 1', 'qual 2', 'best'] | [['sébastien bourdais', 'newman / haas racing', '1:00.413', '59.942', '59.942'], ['bruno junqueira', 'newman / haas racing', '1:01.203', '1:00.525', '1:00.525'], ['paul tracy', 'forsythe racing', '1:00.885', '1:00.588', '1:00.588'], ['patrick carpentier', 'forsythe racing', '1:01.416', '1:00.595', '1:00.595'], ['mario domínguez', 'herdez competition', '1:00.721', '-', '1:00.721'], ['oriol servià', 'dale coyne racing', '1:02.046', '1:00.813', '1:00.813'], ['a j allmendinger', 'rusport', '-', '1:00.907', '1:00.907'], ['ryan hunter - reay', 'herdez competition', '1:01.545', '1:01.072', '1:01.072'], ['mario haberfeld', 'walker racing', '1:01.198', '1:01.285', '1:01.198'], ['justin wilson', 'mi - jack conquest racing', '1:01.782', '1:01.265', '1:01.265'], ['alex tagliani', 'rocketsports racing', '1:01.757', '1:01.266', '1:01.266'], ['jimmy vasser', 'pkv racing', '1:01.334', '1:02.090', '1:01.334'], ['michel jourdain , jr', 'rusport', '1:01.447', '1:01.345', '1:01.345'], ['nelson philippe', 'mi - jack conquest racing', '1:02.354', '1:01.522', '1:01.522'], ['rodolfo lavín', 'forsythe racing', '1:02.130', '1:01.794', '1:01.794'], ['guy smith', 'rocketsports racing', '1:02.113', '1:02.137', '1:02.113'], ['gastón mazzacane', 'dale coyne racing', '1:02.412', '-', '1:02.412'], ['roberto gonzález', 'pkv racing', '1:02.604', '1:02.507', '1:02.507']] |
1977 formula one season | https://en.wikipedia.org/wiki/1977_Formula_One_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140083-2.html.csv | majority | most of the races in the 1977 formula one season took place before september . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1 september', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'date', '1 september'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them are less than 1 september .', 'tostr': 'most_less { all_rows ; date ; 1 september } = true'} | most_less { all_rows ; date ; 1 september } = true | for the date records of all rows , most of them are less than 1 september . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '1 september_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '1 september_4': '1 september'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '1 september_4': [0]} | ['race', 'date', 'location', 'pole position', 'fastest lap', 'race winner', 'constructor', 'report'] | [['argentine grand prix', '9 january', 'buenos aires', 'james hunt', 'james hunt', 'jody scheckter', 'wolf - ford', 'report'], ['brazilian grand prix', '23 january', 'interlagos', 'james hunt', 'james hunt', 'carlos reutemann', 'ferrari', 'report'], ['south african grand prix', '5 march', 'kyalami', 'james hunt', 'john watson', 'niki lauda', 'ferrari', 'report'], ['united states grand prix west', '3 april', 'long beach', 'niki lauda', 'niki lauda', 'mario andretti', 'lotus - ford', 'report'], ['spanish grand prix', '8 may', 'jarama', 'mario andretti', 'jacques laffite', 'mario andretti', 'lotus - ford', 'report'], ['monaco grand prix', '22 may', 'monaco', 'john watson', 'jody scheckter', 'jody scheckter', 'wolf - ford', 'report'], ['belgian grand prix', '5 june', 'zolder', 'mario andretti', 'gunnar nilsson', 'gunnar nilsson', 'lotus - ford', 'report'], ['swedish grand prix', '19 june', 'anderstorp', 'mario andretti', 'mario andretti', 'jacques laffite', 'ligier - matra', 'report'], ['french grand prix', '3 july', 'dijon - prenois', 'mario andretti', 'mario andretti', 'mario andretti', 'lotus - ford', 'report'], ['british grand prix', '16 july', 'silverstone', 'james hunt', 'james hunt', 'james hunt', 'mclaren - ford', 'report'], ['german grand prix', '31 july', 'hockenheimring', 'jody scheckter', 'niki lauda', 'niki lauda', 'ferrari', 'report'], ['austrian grand prix', '14 august', 'ã - sterreichring', 'niki lauda', 'john watson', 'alan jones', 'shadow - ford', 'report'], ['dutch grand prix', '28 august', 'zandvoort', 'mario andretti', 'niki lauda', 'niki lauda', 'ferrari', 'report'], ['italian grand prix', '11 september', 'monza', 'james hunt', 'mario andretti', 'mario andretti', 'lotus - ford', 'report'], ['united states grand prix', '2 october', 'watkins glen', 'james hunt', 'ronnie peterson', 'james hunt', 'mclaren - ford', 'report'], ['canadian grand prix', '9 october', 'mosport', 'mario andretti', 'mario andretti', 'jody scheckter', 'wolf - ford', 'report'], ['japanese grand prix', '23 october', 'fuji speedway', 'mario andretti', 'jody scheckter', 'james hunt', 'mclaren - ford', 'report']] |
1993 - 94 segunda división | https://en.wikipedia.org/wiki/1993%E2%80%9394_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12092001-2.html.csv | majority | all clubs which participated in the 1993 - 94 segunda división each played a total of 38 games . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': '38', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'played', '38'], 'result': True, 'ind': 0, 'tointer': 'for the played records of all rows , all of them are equal to 38 .', 'tostr': 'all_eq { all_rows ; played ; 38 } = true'} | all_eq { all_rows ; played ; 38 } = true | for the played records of all rows , all of them are equal to 38 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'played_3': 3, '38_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'played_3': 'played', '38_4': '38'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'played_3': [0], '38_4': [0]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'rcd español', '38', '52', '20', '12', '6', '59', '25', '+ 34'], ['2', 'real betis', '38', '51', '22', '7', '9', '66', '38', '+ 28'], ['3', 'sd compostela', '38', '49', '21', '7', '10', '56', '36', '+ 20'], ['4', 'cd toledo', '38', '47', '18', '11', '9', '50', '32', '+ 18'], ['5', 'rcd mallorca', '38', '47', '20', '7', '11', '66', '39', '+ 27'], ['6', 'real madrid b', '38', '46', '19', '8', '11', '57', '41', '+ 16'], ['7', 'hércules cf', '38', '44', '16', '12', '10', '41', '35', '+ 6'], ['8', 'barcelona b', '38', '39', '11', '17', '10', '59', '51', '+ 8'], ['9', 'cp mérida', '38', '37', '12', '13', '13', '47', '41', '+ 6'], ['10', 'sd eibar', '38', '35', '10', '15', '13', '30', '40', '- 10'], ['11', 'cd badajoz', '38', '35', '12', '11', '15', '45', '46', '- 1'], ['12', 'atlético marbella', '38', '35', '10', '15', '13', '40', '41', '- 1'], ['13', 'palamós cf', '38', '34', '11', '12', '15', '40', '49', '- 9'], ['14', 'athletic de bilbao b', '38', '34', '10', '14', '14', '46', '52', '- 6'], ['15', 'cd leganés', '38', '34', '11', '12', '15', '53', '59', '- 6'], ['16', 'villarreal cf', '38', '34', '14', '6', '18', '29', '48', '- 19'], ['17', 'cd castellón', '38', '32', '9', '14', '15', '30', '48', '- 18'], ['18', 'real murcia', '38', '31', '10', '11', '17', '40', '64', '- 24'], ['19', 'real burgos 1', '38', '26', '10', '6', '22', '38', '68', '- 30'], ['20', 'cádiz cf', '38', '18', '4', '10', '24', '28', '67', '- 39']] |
2008 - 09 portland trail blazers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Portland_Trail_Blazers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17058178-12.html.csv | unique | in games that steve blake provided the high assists , the portland trailblazers only won once , on april 21 . | {'scope': 'subset', 'row': '2', 'col': '4', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': {'col': '6', 'criterion': 'fuzzily_match', 'value': 'steve blake'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high assists', 'steve blake'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high assists ; steve blake }', 'tointer': 'select the rows whose high assists record fuzzily matches to steve blake .'}, 'score', 'w'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w .', 'tostr': 'filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } }', 'tointer': 'select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high assists', 'steve blake'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high assists ; steve blake }', 'tointer': 'select the rows whose high assists record fuzzily matches to steve blake .'}, 'score', 'w'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w .', 'tostr': 'filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w }'}, 'date'], 'result': 'april 21', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date }'}, 'april 21'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date } ; april 21 }', 'tointer': 'the date record of this unqiue row is april 21 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } } ; eq { hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date } ; april 21 } } = true', 'tointer': 'select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 21 .'} | and { only { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } } ; eq { hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date } ; april 21 } } = true | select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 21 . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'high assists_8': 8, 'steve blake_9': 9, 'score_10': 10, 'w_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date_12': 12, 'april 21_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'high assists_8': 'high assists', 'steve blake_9': 'steve blake', 'score_10': 'score', 'w_11': 'w', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date_12': 'date', 'april 21_13': 'april 21'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'high assists_8': [0], 'steve blake_9': [0], 'score_10': [1], 'w_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date_12': [3], 'april 21_13': [4]} | ['game', 'date', 'team', 'score', 'high points', 'high assists', 'location attendance', 'record'] | [['1', 'april 18', 'houston', 'l 81 - 108 ( ot )', 'brandon roy ( 21 )', 'steve blake ( 6 )', 'rose garden 20329', '0 - 1'], ['2', 'april 21', 'houston', 'w 107 - 103 ( ot )', 'brandon roy ( 42 )', 'steve blake ( 5 )', 'rose garden 20408', '1 - 1'], ['3', 'april 24', 'houston', 'l 83 - 86 ( ot )', 'brandon roy ( 19 )', 'steve blake ( 10 )', 'toyota center 18371', '1 - 2'], ['4', 'april 26', 'houston', 'l 88 - 89 ( ot )', 'brandon roy ( 31 )', 'steve blake ( 8 )', 'toyota center 18271', '1 - 3'], ['5', 'april 28', 'houston', 'w 88 - 77 ( ot )', 'brandon roy , lamarcus aldridge ( 25 )', 'joel przybilla ( 4 )', 'rose garden 20462', '2 - 3'], ['6', 'april 30', 'houston', 'l 76 - 92 ( ot )', 'lamarcus aldridge ( 25 )', 'steve blake ( 5 )', 'toyota center', '2 - 4']] |
hegang | https://en.wikipedia.org/wiki/Hegang | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1834138-2.html.csv | ordinal | suibin county has the 3rd largest population among districts and counties in hegang . | {'row': '9', 'col': '6', 'order': '3', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'population', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; population ; 3 }'}, 'english name'], 'result': 'suibin county', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; population ; 3 } ; english name }'}, 'suibin county'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; population ; 3 } ; english name } ; suibin county } = true', 'tointer': 'select the row whose population record of all rows is 3rd maximum . the english name record of this row is suibin county .'} | eq { hop { nth_argmax { all_rows ; population ; 3 } ; english name } ; suibin county } = true | select the row whose population record of all rows is 3rd maximum . the english name record of this row is suibin county . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, '3_6': 6, 'english name_7': 7, 'suibin county_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'population_5': 'population', '3_6': '3', 'english name_7': 'english name', 'suibin county_8': 'suibin county'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], '3_6': [0], 'english name_7': [1], 'suibin county_8': [2]} | ['english name', 'simplified', 'traditional', 'pinyin', 'area', 'population', 'density'] | [['english name', 'simplified', 'traditional', 'pinyin', 'area', 'population', 'density'], ['xingshan district', '兴山区', '興山區', 'xīngshān qū', '27', '44803', '1659'], ['xiangyang district', '向阳区', '向陽區', 'xiàngyáng qū', '9', '110916', '12324'], ['gongnong district', '工农区', '工農區', 'gōngnóng qū', '11', '140070', '12734'], ['nanshan district', '南山区', '南山區', 'nánshān qū', '30', '119047', '3968'], ["xing ' an district", '兴安区', '興安區', "xīng ' ān qū", '27', '74396', '2755'], ['dongshan district', '东山区', '東山區', 'dōngshān qū', '4575', '175239', '38'], ['luobei county', '萝北县', '蘿北縣', 'luóběi xiàn', '6761', '220131', '33'], ['suibin county', '绥滨县', '綏濱縣', 'suíbīn xiàn', '3344', '174063', '52']] |
1964 world series | https://en.wikipedia.org/wiki/1964_World_Series | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1100124-1.html.csv | superlative | the highest attendance rate of the 1964 world series was on october 10th . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'date'], 'result': 'october 10', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, 'october 10'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; october 10 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is october 10 .'} | eq { hop { argmax { all_rows ; attendance } ; date } ; october 10 } = true | select the row whose attendance record of all rows is maximum . the date record of this row is october 10 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, 'october 10_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', 'october 10_7': 'october 10'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], 'october 10_7': [2]} | ['game', 'date', 'location', 'time', 'attendance'] | [['1', 'october 7', 'busch stadium ( i )', '2:42', '30805'], ['2', 'october 8', 'busch stadium ( i )', '2:29', '30805'], ['3', 'october 10', 'yankee stadium ( i )', '2:16', '67101'], ['4', 'october 11', 'yankee stadium ( i )', '2:18', '66312'], ['5', 'october 12', 'yankee stadium ( i )', '2:37', '65633'], ['6', 'october 14', 'busch stadium ( i )', '2:37', '30805'], ['7', 'october 15', 'busch stadium ( i )', '2:40', '30346']] |
savannah braves | https://en.wikipedia.org/wiki/Savannah_Braves | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18893381-2.html.csv | aggregation | on average , the savannah braves finished around 4th place each year . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '4th', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'finish'], 'result': '4th', 'ind': 0, 'tostr': 'avg { all_rows ; finish }'}, '4th'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; finish } ; 4th } = true', 'tointer': 'the average of the finish record of all rows is 4th .'} | round_eq { avg { all_rows ; finish } ; 4th } = true | the average of the finish record of all rows is 4th . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'finish_4': 4, '4th_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'finish_4': 'finish', '4th_5': '4th'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'finish_4': [0], '4th_5': [1]} | ['year', 'record', 'finish', 'manager', 'playoffs'] | [['1971', '57 - 84', '5th', 'eddie haas', 'not eligible'], ['1972', '80 - 59', '2nd', 'clint courtney', 'not eligible'], ['1973', '71 - 68', '3rd', 'clint courtney ( 34 - 23 ) / tommie aaron ( 37 - 45 )', 'not eligible'], ['1974', '73 - 65', '4th', 'tommie aaron', 'not eligible'], ['1975', '70 - 64', '3rd ( t )', 'tommie aaron', 'not eligible'], ['1976', '69 - 71', '5th', 'tommie aaron', 'not eligible'], ['1977', '77 - 63', '3rd', 'gene hassell', 'lost in 1st round'], ['1978', '72 - 72', '4th', 'bobby dews', 'lost league finals'], ['1979', '60 - 83', '10th', 'eddie haas', 'not eligible'], ['1980', '77 - 67', '3rd', 'eddie haas', 'lost in 1st round'], ['1981', '70 - 70', '5th', 'andy gilbert', 'lost in 1st round'], ['1982', '69 - 75', '8th', 'andy gilbert', 'not eligible'], ['1983', '81 - 64', '3rd', 'bobby dews', 'lost in 1st round']] |
1964 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1964_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17277176-1.html.csv | comparative | billy casper had won a u.s. open ( golf ) championship earlier than arnold palmer . | {'row_1': '1', 'row_2': '2', 'col': '3', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'billy casper'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to billy casper .', 'tostr': 'filter_eq { all_rows ; player ; billy casper }'}, 'year ( s ) won'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; billy casper } ; year ( s ) won }', 'tointer': 'select the rows whose player record fuzzily matches to billy casper . take the year ( s ) won record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'arnold palmer'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to arnold palmer .', 'tostr': 'filter_eq { all_rows ; player ; arnold palmer }'}, 'year ( s ) won'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; arnold palmer } ; year ( s ) won }', 'tointer': 'select the rows whose player record fuzzily matches to arnold palmer . take the year ( s ) won record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; billy casper } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; arnold palmer } ; year ( s ) won } } = true', 'tointer': 'select the rows whose player record fuzzily matches to billy casper . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to arnold palmer . take the year ( s ) won record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; billy casper } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; arnold palmer } ; year ( s ) won } } = true | select the rows whose player record fuzzily matches to billy casper . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to arnold palmer . take the year ( s ) won record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'billy casper_8': 8, 'year (s) won_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'arnold palmer_12': 12, 'year (s) won_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'billy casper_8': 'billy casper', 'year (s) won_9': 'year ( s ) won', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'arnold palmer_12': 'arnold palmer', 'year (s) won_13': 'year ( s ) won'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'billy casper_8': [0], 'year (s) won_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'arnold palmer_12': [1], 'year (s) won_13': [3]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['billy casper', 'united states', '1959', '285', '+ 5', 't4'], ['arnold palmer', 'united states', '1960', '286', '+ 6', 't5'], ['gene littler', 'united states', '1961', '291', '+ 11', 't11'], ['ed furgol', 'united states', '1954', '292', '+ 12', 't14'], ['jack nicklaus', 'united states', '1962', '295', '+ 15', 't23']] |
anwar robinson | https://en.wikipedia.org/wiki/Anwar_Robinson | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1620672-1.html.csv | count | three of anwar robinson 's songs were done with the contestant 's choice theme . | {'scope': 'all', 'criterion': 'equal', 'value': "contestant 's choice", 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'theme', "contestant 's choice"], 'result': None, 'ind': 0, 'tointer': "select the rows whose theme record fuzzily matches to contestant 's choice .", 'tostr': "filter_eq { all_rows ; theme ; contestant 's choice }"}], 'result': '3', 'ind': 1, 'tostr': "count { filter_eq { all_rows ; theme ; contestant 's choice } }", 'tointer': "select the rows whose theme record fuzzily matches to contestant 's choice . the number of such rows is 3 ."}, '3'], 'result': True, 'ind': 2, 'tostr': "eq { count { filter_eq { all_rows ; theme ; contestant 's choice } } ; 3 } = true", 'tointer': "select the rows whose theme record fuzzily matches to contestant 's choice . the number of such rows is 3 ."} | eq { count { filter_eq { all_rows ; theme ; contestant 's choice } } ; 3 } = true | select the rows whose theme record fuzzily matches to contestant 's choice . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'theme_5': 5, "contestant's choice_6": 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'theme_5': 'theme', "contestant's choice_6": "contestant 's choice", '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'theme_5': [0], "contestant's choice_6": [0], '3_7': [2]} | ['week', 'theme', 'song choice', 'original artist', 'result'] | [['top 24 ( 12 men )', "contestant 's choice", 'moon river', 'andy williams', 'safe'], ['top 20 ( 10 men )', "contestant 's choice", "what 's going on", 'marvin gaye', 'safe'], ['top 16 ( 8 men )', "contestant 's choice", 'what a wonderful world', 'louis armstrong', 'safe'], ['top 12', '1960s', 'a house is not a home', 'dionne warwick', 'safe'], ['top 11', 'billboard number ones', "ai n't nobody", 'chaka khan', 'safe'], ['top 10', '1990s', 'i believe i can fly', 'r kelly', 'bottom 2'], ['top 9', 'classic broadway', 'if ever i would leave you', 'from camelot', 'safe'], ['top 8', 'songs from birth year', "i 'll never love this way again", 'dionne warwick', 'safe'], ['top 7', '1970s dance music', 'september', 'earth , wind & fire', 'eliminated']] |
st. catharines black hawks | https://en.wikipedia.org/wiki/St._Catharines_Black_Hawks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1143966-1.html.csv | superlative | from the 1962-63 season to the 1974-75 season , the season in which the st. catharines black hawks tied the most games was 1968-1969 . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'tied'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; tied }'}, 'season'], 'result': '1968 - 69', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; tied } ; season }'}, '1968 - 69'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; tied } ; season } ; 1968 - 69 } = true', 'tointer': 'select the row whose tied record of all rows is maximum . the season record of this row is 1968 - 69 .'} | eq { hop { argmax { all_rows ; tied } ; season } ; 1968 - 69 } = true | select the row whose tied record of all rows is maximum . the season record of this row is 1968 - 69 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'tied_5': 5, 'season_6': 6, '1968 - 69_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'tied_5': 'tied', 'season_6': 'season', '1968 - 69_7': '1968 - 69'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'tied_5': [0], 'season_6': [1], '1968 - 69_7': [2]} | ['season', 'games', 'won', 'lost', 'tied', 'points', 'pct %', 'goals for', 'goals against', 'standing'] | [['1962 - 63', '50', '15', '24', '11', '41', '0.410', '172', '224', '5th oha'], ['1963 - 64', '56', '29', '20', '7', '65', '0.580', '244', '215', '3rd oha'], ['1964 - 65', '56', '19', '28', '9', '41', '0.420', '236', '253', '7th oha'], ['1965 - 66', '48', '15', '26', '7', '37', '0.385', '182', '231', '8th oha'], ['1966 - 67', '48', '19', '20', '9', '47', '0.490', '175', '155', '5th oha'], ['1967 - 68', '54', '21', '30', '3', '45', '0.417', '200', '211', '6th oha'], ['1968 - 69', '54', '31', '11', '12', '74', '0.685', '296', '206', '2nd oha'], ['1969 - 70', '54', '30', '18', '6', '66', '0.611', '268', '210', '3rd oha'], ['1970 - 71', '62', '40', '17', '5', '85', '0.685', '343', '236', '2nd oha'], ['1971 - 72', '63', '25', '31', '7', '57', '0.452', '258', '311', '7th oha'], ['1972 - 73', '63', '24', '28', '11', '59', '0.468', '280', '318', '5th oha'], ['1973 - 74', '70', '41', '23', '6', '88', '0.629', '358', '278', '2nd oha'], ['1974 - 75', '70', '30', '33', '7', '67', '0.479', '284', '300', '6th oha']] |
primera división de fútbol profesional apertura 2004 | https://en.wikipedia.org/wiki/Primera_Divisi%C3%B3n_de_F%C3%BAtbol_Profesional_Apertura_2004 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13018938-1.html.csv | ordinal | the team scoring the second least goals during the 2004 primera división de fútbol profesional apertura was once municipal . | {'row': '9', 'col': '6', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'goals scored', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; goals scored ; 2 }'}, 'team'], 'result': 'once municipal', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; goals scored ; 2 } ; team }'}, 'once municipal'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; goals scored ; 2 } ; team } ; once municipal } = true', 'tointer': 'select the row whose goals scored record of all rows is 2nd minimum . the team record of this row is once municipal .'} | eq { hop { nth_argmin { all_rows ; goals scored ; 2 } ; team } ; once municipal } = true | select the row whose goals scored record of all rows is 2nd minimum . the team record of this row is once municipal . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'goals scored_5': 5, '2_6': 6, 'team_7': 7, 'once municipal_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'goals scored_5': 'goals scored', '2_6': '2', 'team_7': 'team', 'once municipal_8': 'once municipal'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'goals scored_5': [0], '2_6': [0], 'team_7': [1], 'once municipal_8': [2]} | ['place', 'team', 'played', 'draw', 'lost', 'goals scored', 'goals conceded', 'points'] | [['1', 'cd fas', '18', '5', '4', '26', '19', '32'], ['2', 'san salvador fc', '18', '5', '4', '30', '33', '32'], ['3', 'cd atlético balboa', '18', '9', '2', '26', '13', '30'], ['4', 'cd luis ángel firpo', '18', '8', '3', '26', '13', '29'], ['5', 'ad isidro metapán', '18', '5', '5', '25', '24', '29'], ['6', 'cd águila', '18', '8', '5', '24', '20', '23'], ['7', 'alianza fc', '18', '3', '9', '19', '25', '21'], ['8', 'once lobos', '18', '7', '7', '22', '21', '19'], ['9', 'once municipal', '18', '4', '9', '15', '26', '19'], ['10', 'municipal limeño', '18', '6', '12', '12', '31', '6']] |
1990 - 91 seattle supersonics season | https://en.wikipedia.org/wiki/1990%E2%80%9391_Seattle_SuperSonics_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17382360-6.html.csv | unique | game number 35 was the only game played at the great western forum . | {'scope': 'all', 'row': '8', 'col': '8', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'great western', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'great western'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location attendance record fuzzily matches to great western .', 'tostr': 'filter_eq { all_rows ; location attendance ; great western }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; location attendance ; great western } }', 'tointer': 'select the rows whose location attendance record fuzzily matches to great western . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'great western'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location attendance record fuzzily matches to great western .', 'tostr': 'filter_eq { all_rows ; location attendance ; great western }'}, 'game'], 'result': '35', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; location attendance ; great western } ; game }'}, '35'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; location attendance ; great western } ; game } ; 35 }', 'tointer': 'the game record of this unqiue row is 35 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; location attendance ; great western } } ; eq { hop { filter_eq { all_rows ; location attendance ; great western } ; game } ; 35 } } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to great western . there is only one such row in the table . the game record of this unqiue row is 35 .'} | and { only { filter_eq { all_rows ; location attendance ; great western } } ; eq { hop { filter_eq { all_rows ; location attendance ; great western } ; game } ; 35 } } = true | select the rows whose location attendance record fuzzily matches to great western . there is only one such row in the table . the game record of this unqiue row is 35 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'location attendance_7': 7, 'great western_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'game_9': 9, '35_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'location attendance_7': 'location attendance', 'great western_8': 'great western', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'game_9': 'game', '35_10': '35'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'location attendance_7': [0], 'great western_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'game_9': [2], '35_10': [3]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['28', 'january 3', 'philadelphia 76ers', 'w 127 - 99', 'd mckey ( 24 )', 'm cage ( 12 )', 'g payton ( 11 )', 'seattle center coliseum 13048', '13 - 15'], ['29', 'january 4', 'miami heat', 'w 112 - 86', 's threatt ( 30 )', 'm cage ( 13 )', 'g payton ( 12 )', 'seattle center coliseum 12074', '14 - 15'], ['30', 'january 6', 'portland trail blazers', 'l 111 - 114', 's kemp ( 25 )', 's kemp ( 9 )', 'g payton ( 7 )', 'memorial coliseum 12884', '14 - 16'], ['31', 'january 8', 'los angeles lakers', 'w 96 - 88', 'd mckey ( 29 )', 'o polynice ( 11 )', 'n mcmillan ( 10 )', 'seattle center coliseum 14441', '15 - 16'], ['32', 'january 10', 'golden state warriors', 'l 103 - 113', 'd mckey ( 19 )', 's kemp , o polynice ( 12 )', 'n mcmillan ( 7 )', 'seattle center coliseum 10813', '15 - 17'], ['33', 'january 12', 'sacramento kings', 'l 85 - 101', 'd mckey ( 20 )', 'o polynice ( 14 )', 'g payton ( 9 )', 'arco arena 17014', '15 - 18'], ['34', 'january 15', 'denver nuggets', 'w 146 - 99', 'd barros , d ellis ( 22 )', 's kemp ( 12 )', 'n mcmillan ( 9 )', 'seattle center coliseum 9618', '16 - 18'], ['35', 'january 18', 'los angeles lakers', 'l 96 - 105', 'd mckey ( 24 )', 's kemp ( 8 )', 'g payton ( 11 )', 'great western forum 17505', '16 - 19'], ['36', 'january 19', 'washington bullets', 'w 111 - 89', 'o polynice ( 27 )', 's kemp ( 13 )', 'n mcmillan ( 8 )', 'seattle center coliseum 13369', '17 - 19'], ['37', 'january 22', 'milwaukee bucks', 'w 132 - 101', 'e johnson ( 29 )', 'm cage ( 9 )', 'g payton ( 9 )', 'seattle center coliseum 9469', '18 - 19'], ['38', 'january 25', 'phoenix suns', 'l 113 - 128', 'e johnson ( 25 )', 's kemp ( 13 )', 'n mcmillan ( 7 )', 'arizona veterans memorial coliseum 14487', '18 - 20'], ['39', 'january 26', 'atlanta hawks', 'w 103 - 102', 'd mckey ( 23 )', 'd mckey ( 8 )', 'n mcmillan , g payton ( 9 )', 'seattle center coliseum 12792', '19 - 20'], ['40', 'january 28', 'san antonio spurs', 'l 107 - 119', 'e johnson ( 21 )', 'd mckey ( 14 )', 'g payton ( 11 )', 'hemisfair arena 15908', '19 - 21'], ['41', 'january 29', 'dallas mavericks', 'l 112 - 117', 'd mckey ( 24 )', 'o polynice ( 6 )', 'n mcmillan ( 8 )', 'reunion arena 15820', '19 - 22'], ['42', 'january 31', 'houston rockets', 'w 97 - 94', 's threatt ( 18 )', 's kemp ( 17 )', 'd mckey , d mckey ( 6 )', 'the summit 14659', '20 - 22']] |
1979 vfl season | https://en.wikipedia.org/wiki/1979_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823719-5.html.csv | aggregation | the average crowd attendance for games in the 1979 vfl season was 23516 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '23516', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '23516', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '23516'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 23516 } = true', 'tointer': 'the average of the crowd record of all rows is 23516 .'} | round_eq { avg { all_rows ; crowd } ; 23516 } = true | the average of the crowd record of all rows is 23516 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '23516_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '23516_5': '23516'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '23516_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '19.24 ( 138 )', 'south melbourne', '12.16 ( 88 )', 'arden street oval', '16015', '5 may 1979'], ['essendon', '10.16 ( 76 )', 'fitzroy', '25.22 ( 172 )', 'windy hill', '19741', '5 may 1979'], ['carlton', '15.20 ( 110 )', 'melbourne', '13.18 ( 96 )', 'princes park', '24248', '5 may 1979'], ['richmond', '11.16 ( 82 )', 'hawthorn', '24.17 ( 161 )', 'mcg', '31448', '5 may 1979'], ['st kilda', '17.10 ( 112 )', 'geelong', '22.10 ( 142 )', 'moorabbin oval', '15481', '5 may 1979'], ['collingwood', '20.17 ( 137 )', 'footscray', '12.17 ( 89 )', 'vfl park', '34163', '5 may 1979']] |
weightlifting at the 1999 pan american games | https://en.wikipedia.org/wiki/Weightlifting_at_the_1999_Pan_American_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11279593-4.html.csv | comparative | walter llerena lifted more weight than guy hamilton lifted . | {'row_1': '2', 'row_2': '6', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'walter llerena ( ecu )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to walter llerena ( ecu ) .', 'tostr': 'filter_eq { all_rows ; name ; walter llerena ( ecu ) }'}, 'total ( kg )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; walter llerena ( ecu ) } ; total ( kg ) }', 'tointer': 'select the rows whose name record fuzzily matches to walter llerena ( ecu ) . take the total ( kg ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'guy hamilton ( can )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to guy hamilton ( can ) .', 'tostr': 'filter_eq { all_rows ; name ; guy hamilton ( can ) }'}, 'total ( kg )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; guy hamilton ( can ) } ; total ( kg ) }', 'tointer': 'select the rows whose name record fuzzily matches to guy hamilton ( can ) . take the total ( kg ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; walter llerena ( ecu ) } ; total ( kg ) } ; hop { filter_eq { all_rows ; name ; guy hamilton ( can ) } ; total ( kg ) } } = true', 'tointer': 'select the rows whose name record fuzzily matches to walter llerena ( ecu ) . take the total ( kg ) record of this row . select the rows whose name record fuzzily matches to guy hamilton ( can ) . take the total ( kg ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; name ; walter llerena ( ecu ) } ; total ( kg ) } ; hop { filter_eq { all_rows ; name ; guy hamilton ( can ) } ; total ( kg ) } } = true | select the rows whose name record fuzzily matches to walter llerena ( ecu ) . take the total ( kg ) record of this row . select the rows whose name record fuzzily matches to guy hamilton ( can ) . take the total ( kg ) record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'walter llerena ( ecu )_8': 8, 'total (kg)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'guy hamilton ( can )_12': 12, 'total (kg)_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'walter llerena ( ecu )_8': 'walter llerena ( ecu )', 'total (kg)_9': 'total ( kg )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'guy hamilton ( can )_12': 'guy hamilton ( can )', 'total (kg)_13': 'total ( kg )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'walter llerena ( ecu )_8': [0], 'total (kg)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'guy hamilton ( can )_12': [1], 'total (kg)_13': [3]} | ['name', 'bodyweight', 'snatch', 'clean & jerk', 'total ( kg )'] | [['idalberto aranda ( cub )', '76.55', '150.0', '205.5 wr', '355.0'], ['walter llerena ( ecu )', '76.78', '150.0', '182.5', '332.5'], ['oscar chaplin iii ( usa )', '76.95', '150.0', '182.5', '332.5'], ['carlos sauri ( pur )', '76.91', '140.0', '165.0', '305.0'], ['marcelo gandolfo ( arg )', '76.25', '130.0', '170.0', '300.0'], ['guy hamilton ( can )', '76.86', '132.5', '167.5', '300.0'], ['edward silva ( uru )', '76.22', '122.5', '145.0', '267.5'], ['luis urriche ( chi )', '76.18', '127.5', '152.5', '']] |
nassim akrour | https://en.wikipedia.org/wiki/Nassim_Akrour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1646697-2.html.csv | comparative | nassim akrour played at the african cup of nations before playing at the fifa world cup qualification . | {'row_1': '2', 'row_2': '6', 'col': '1', 'col_other': '5', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', '2002 african cup of nations'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to 2002 african cup of nations .', 'tostr': 'filter_eq { all_rows ; competition ; 2002 african cup of nations }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; competition ; 2002 african cup of nations } ; date }', 'tointer': 'select the rows whose competition record fuzzily matches to 2002 african cup of nations . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', '2006 fifa world cup qualification'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification .', 'tostr': 'filter_eq { all_rows ; competition ; 2006 fifa world cup qualification }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } ; date }', 'tointer': 'select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; competition ; 2002 african cup of nations } ; date } ; hop { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } ; date } } = true', 'tointer': 'select the rows whose competition record fuzzily matches to 2002 african cup of nations . take the date record of this row . select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; competition ; 2002 african cup of nations } ; date } ; hop { filter_eq { all_rows ; competition ; 2006 fifa world cup qualification } ; date } } = true | select the rows whose competition record fuzzily matches to 2002 african cup of nations . take the date record of this row . select the rows whose competition record fuzzily matches to 2006 fifa world cup qualification . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'competition_7': 7, '2002 african cup of nations_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'competition_11': 11, '2006 fifa world cup qualification_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'competition_7': 'competition', '2002 african cup of nations_8': '2002 african cup of nations', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'competition_11': 'competition', '2006 fifa world cup qualification_12': '2006 fifa world cup qualification', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'competition_7': [0], '2002 african cup of nations_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'competition_11': [1], '2006 fifa world cup qualification_12': [1], 'date_13': [3]} | ['date', 'venue', 'score', 'result', 'competition'] | [['january 14 , 2002', 'stade 5 juillet 1962 , algiers , algeria', '3 - 0', '4 - 0', 'friendly match'], ['january 25 , 2002', 'stade 26 mars , bamako , mali', '1 - 1', '2 - 2', '2002 african cup of nations'], ['october 11 , 2002', 'stade 19 mai 1956 , annaba , algeria', '1 - 0', '4 - 1', '2004 african cup of nations ( qualification )'], ['october 11 , 2002', 'stade 19 mai 1956 , annaba , algeria', '4 - 1', '4 - 1', '2004 african cup of nations ( qualification )'], ['march 29 , 2003', 'estádio da cidadela , luanda , angola', '1 - 1', '1 - 1', 'friendly match'], ['november 14 , 2003', 'stade 5 juillet 1962 , algiers , algeria', '6 - 0', '6 - 0', '2006 fifa world cup qualification']] |
kelly dullanty | https://en.wikipedia.org/wiki/Kelly_Dullanty | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17445415-2.html.csv | majority | kelly dullanty won most of his matches in california , united states . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'california , united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'location', 'california , united states'], 'result': True, 'ind': 0, 'tointer': 'for the location records of all rows , most of them fuzzily match to california , united states .', 'tostr': 'most_eq { all_rows ; location ; california , united states } = true'} | most_eq { all_rows ; location ; california , united states } = true | for the location records of all rows , most of them fuzzily match to california , united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'location_3': 3, 'california , united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'location_3': 'location', 'california , united states_4': 'california , united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'location_3': [0], 'california , united states_4': [0]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'location'] | [['loss', '4 - 2', 'lance wipf', 'ko ( punch )', 'purecombat - bring the pain', '1', 'california , united states'], ['loss', '4 - 1', 'matt serra', 'submission ( triangle choke )', 'ufc 36', '1', 'nevada , united states'], ['win', '4 - 0', 'nuri shakir', 'decision', 'ifc wc 13 - warriors challenge 13', '4', 'california , united states'], ['win', '3 - 0', 'rudy vallederas', 'tko', 'ifc wc 13 - warriors challenge 13', 'n / a', 'california , united states'], ['win', '2 - 0', 'duane ludwig', 'decision', 'kotc 6 - road warriors', '3', 'michigan , united states'], ['win', '1 - 0', 'shad smith', 'tko ( strikes )', 'kotc 3 - knockout nightmare', '1', 'california , united states']] |
2007 latvian first league | https://en.wikipedia.org/wiki/2007_Latvian_First_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18017970-2.html.csv | majority | the majority of clubs won more than 10 games in the 2007 latvian first league . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'wins', '10'], 'result': True, 'ind': 0, 'tointer': 'for the wins records of all rows , most of them are greater than 10 .', 'tostr': 'most_greater { all_rows ; wins ; 10 } = true'} | most_greater { all_rows ; wins ; 10 } = true | for the wins records of all rows , most of them are greater than 10 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'wins_3': 3, '10_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'wins_3': 'wins', '10_4': '10'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'wins_3': [0], '10_4': [0]} | ['position', 'club', 'played', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'points', 'goal difference'] | [['1', 'fk vindava ventspils', '30', '26', '2', '2', '116', '11', '80', '+ 105'], ['2', 'sk blāzma rēzekne', '30', '25', '4', '1', '101', '11', '79', '+ 90'], ['3', 'fk auda rīga', '30', '20', '5', '5', '104', '31', '65', '+ 73'], ['4', 'fk metta / lu rīga', '30', '18', '7', '5', '67', '23', '61', '+ 44'], ['5', 'fk jelgava', '30', '16', '6', '8', '70', '43', '54', '+ 27'], ['6', 'fk jaunība - parex rīga', '30', '16', '3', '11', '71', '51', '51', '+ 20'], ['7', 'fk kauguri - pblc jūrmala', '30', '14', '4', '12', '67', '55', '46', '+ 12'], ['8', 'rfs / flaminko rīga', '30', '14', '2', '14', '60', '62', '44', '- 2'], ['9', 'fk zibens / zemessardze ilūkste', '30', '13', '3', '14', '79', '68', '42', '+ 11'], ['10', 'valmieras fk', '30', '12', '4', '14', '63', '59', '40', '+ 4'], ['11', 'fsk daugava - 90 rīga', '30', '11', '6', '13', '51', '63', '39', '- 12'], ['12', 'fk tukums - 2000 / tss', '30', '11', '3', '16', '61', '75', '36', '- 14'], ['13', 'fk multibanka rīga', '30', '6', '1', '23', '45', '96', '19', '- 51'], ['14', 'fk tranzīts ventspils', '30', '4', '4', '22', '29', '103', '17', '- 74'], ['15', 'fk abuls smiltene', '30', '3', '2', '25', '22', '163', '11', '- 141'], ['16', 'fk ilūkste / bjss', '30', '2', '2', '26', '8', '93', '8', '- 85']] |
united states house of representatives elections , 1950 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1950 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342198-36.html.csv | unique | dixie gilmer is the only incumbent who lost re - election republican gain . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'lost re - election republican gain', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election republican gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election republican gain }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; lost re - election republican gain } }', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election republican gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election republican gain }'}, 'incumbent'], 'result': 'dixie gilmer', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; incumbent }'}, 'dixie gilmer'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; incumbent } ; dixie gilmer }', 'tointer': 'the incumbent record of this unqiue row is dixie gilmer .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; incumbent } ; dixie gilmer } } = true', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table . the incumbent record of this unqiue row is dixie gilmer .'} | and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; incumbent } ; dixie gilmer } } = true | select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table . the incumbent record of this unqiue row is dixie gilmer . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'lost re - election republican gain_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'dixie gilmer_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'lost re - election republican gain_8': 'lost re - election republican gain', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'dixie gilmer_10': 'dixie gilmer'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'lost re - election republican gain_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'dixie gilmer_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['oklahoma 1', 'dixie gilmer', 'democratic', '1948', 'lost re - election republican gain', 'george b schwabe ( r ) 52.9 % dixie gilmer ( d ) 47.1 %'], ['oklahoma 2', 'william g stigler', 'democratic', '1944', 're - elected', 'william g stigler ( d ) 66.2 % cleo crain ( r ) 33.8 %'], ['oklahoma 3', 'carl albert', 'democratic', '1946', 're - elected', 'carl albert ( d ) 82.8 % charles powell ( r ) 17.2 %'], ['oklahoma 4', 'tom steed', 'democratic', '1948', 're - elected', 'tom steed ( d ) 68.1 % glenn o young ( r ) 31.9 %'], ['oklahoma 5', 'a s mike monroney', 'democratic', '1938', 'retired to run for u s senate democratic hold', 'john jarman ( d ) 58.8 % c e barnes ( r ) 41.2 %'], ['oklahoma 6', 'toby morris', 'democratic', '1946', 're - elected', 'toby morris ( d ) 67.1 % george campbell ( r ) 32.9 %']] |
1984 - 85 fa cup | https://en.wikipedia.org/wiki/1984%E2%80%9385_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17438338-4.html.csv | unique | th game between sheffield wednesday and oldham athletic was the only one where a single team scored more than 5 points . | {'scope': 'all', 'row': '7', 'col': '3', 'col_other': '2,4', 'criterion': 'fuzzily_match', 'value': '5', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 5 .', 'tostr': 'filter_eq { all_rows ; score ; 5 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; score ; 5 } }', 'tointer': 'select the rows whose score record fuzzily matches to 5 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 5 .', 'tostr': 'filter_eq { all_rows ; score ; 5 }'}, 'home team'], 'result': 'sheffield wednesday', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; score ; 5 } ; home team }'}, 'sheffield wednesday'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 5 } ; home team } ; sheffield wednesday }', 'tointer': 'the home team record of this unqiue row is sheffield wednesday .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 5 .', 'tostr': 'filter_eq { all_rows ; score ; 5 }'}, 'away team'], 'result': 'oldham athletic', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; score ; 5 } ; away team }'}, 'oldham athletic'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 5 } ; away team } ; oldham athletic }', 'tointer': 'the away team record of this unqiue row is oldham athletic .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; score ; 5 } ; home team } ; sheffield wednesday } ; eq { hop { filter_eq { all_rows ; score ; 5 } ; away team } ; oldham athletic } }', 'tointer': 'the home team record of this unqiue row is sheffield wednesday . the away team record of this unqiue row is oldham athletic .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; score ; 5 } } ; and { eq { hop { filter_eq { all_rows ; score ; 5 } ; home team } ; sheffield wednesday } ; eq { hop { filter_eq { all_rows ; score ; 5 } ; away team } ; oldham athletic } } } = true', 'tointer': 'select the rows whose score record fuzzily matches to 5 . there is only one such row in the table . the home team record of this unqiue row is sheffield wednesday . the away team record of this unqiue row is oldham athletic .'} | and { only { filter_eq { all_rows ; score ; 5 } } ; and { eq { hop { filter_eq { all_rows ; score ; 5 } ; home team } ; sheffield wednesday } ; eq { hop { filter_eq { all_rows ; score ; 5 } ; away team } ; oldham athletic } } } = true | select the rows whose score record fuzzily matches to 5 . there is only one such row in the table . the home team record of this unqiue row is sheffield wednesday . the away team record of this unqiue row is oldham athletic . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'score_10': 10, '5_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'home team_12': 12, 'sheffield wednesday_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'away team_14': 14, 'oldham athletic_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'score_10': 'score', '5_11': '5', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'home team_12': 'home team', 'sheffield wednesday_13': 'sheffield wednesday', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'away team_14': 'away team', 'oldham athletic_15': 'oldham athletic'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'score_10': [0], '5_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'home team_12': [2], 'sheffield wednesday_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'away team_14': [4], 'oldham athletic_15': [5]} | ['tie no', 'home team', 'score', 'away team', 'date'] | [['1', 'darlington', '1 - 1', 'telford united', '29 january 1985'], ['replay', 'telford united', '3 - 0', 'darlington', '4 february 1985'], ['2', 'liverpool', '1 - 0', 'tottenham hotspur', '27 january 1985'], ['3', 'leicester city', '1 - 0', 'carlisle united', '26 january 1985'], ['4', 'nottingham forest', '0 - 0', 'wimbledon', '26 january 1985'], ['replay', 'wimbledon', '1 - 0', 'nottingham forest', '30 january 1985'], ['5', 'sheffield wednesday', '5 - 1', 'oldham athletic', '26 january 1985'], ['6', 'grimsby town', '1 - 3', 'watford', '26 january 1985'], ['7', 'luton town', '2 - 0', 'huddersfield town', '26 january 1985'], ['8', 'everton', '2 - 0', 'doncaster rovers', '26 january 1985'], ['9', 'ipswich town', '3 - 2', 'gillingham', '26 january 1985'], ['10', 'barnsley', '2 - 1', 'brighton & hove albion', '26 january 1985'], ['11', 'west ham united', '2 - 1', 'norwich city', '4 february 1985'], ['12', 'manchester united', '2 - 1', 'coventry city', '26 january 1985'], ['13', 'chelsea', '2 - 3', 'millwall', '4 february 1985'], ['14', 'york city', '1 - 0', 'arsenal', '26 january 1985'], ['15', 'oxford united', '0 - 1', 'blackburn rovers', '30 january 1985'], ['16', 'orient', '0 - 2', 'southampton', '26 january 1985']] |
2004 grand prix of road america | https://en.wikipedia.org/wiki/2004_Grand_Prix_of_Road_America | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16759619-2.html.csv | count | a total of four drivers retired on the 46th lap of the 2004 grand prix . | {'scope': 'all', 'criterion': 'equal', 'value': '46', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'laps', '46'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is equal to 46 .', 'tostr': 'filter_eq { all_rows ; laps ; 46 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; laps ; 46 } }', 'tointer': 'select the rows whose laps record is equal to 46 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; laps ; 46 } } ; 4 } = true', 'tointer': 'select the rows whose laps record is equal to 46 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; laps ; 46 } } ; 4 } = true | select the rows whose laps record is equal to 46 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'laps_5': 5, '46_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'laps_5': 'laps', '46_6': '46', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'laps_5': [0], '46_6': [0], '4_7': [2]} | ['driver', 'team', 'laps', 'time / retired', 'grid', 'points'] | [['alex tagliani', 'rocketsports racing', '48', '1:45:07.288', '13', '33'], ['rodolfo lavín', 'forsythe racing', '48', '+ 1.855 secs', '10', '28'], ['sébastien bourdais', 'newman / haas racing', '48', '+ 2.767 secs', '1', '27'], ['ryan hunter - reay', 'herdez competition', '48', '+ 3.814 secs', '2', '24'], ['mario domínguez', 'herdez competition', '48', '+ 4.398 secs', '15', '21'], ['oriol servià', 'dale coyne racing', '48', '+ 6.390 secs', '8', '19'], ['justin wilson', 'mi - jack conquest racing', '48', '+ 8.500 secs', '9', '17'], ['jimmy vasser', 'pkv racing', '48', '+ 8.546 secs', '3', '15'], ['michel jourdain , jr', 'rusport', '48', '+ 9.056 secs', '11', '13'], ['guy smith', 'rocketsports racing', '48', '+ 9.997 secs', '16', '11'], ['mario haberfeld', 'walker racing', '48', '+ 16.725 secs', '12', '10'], ['paul tracy', 'forsythe racing', '48', '+ 26.616 secs', '6', '10'], ['a j allmendinger', 'rusport', '47', '+ 1 lap', '7', '8'], ['patrick carpentier', 'forsythe racing', '46', '+ 2 laps', '5', '7'], ['bruno junqueira', 'newman / haas racing', '46', '+ 2 laps', '4', '7'], ['roberto gonzález', 'pkv racing', '46', '+ 2 laps', '14', '5'], ['alex sperafico', 'mi - jack conquest racing', '46', '+ 2 laps', '17', '4'], ['gastón mazzacane', 'dale coyne racing', '29', 'off course', '18', '3']] |
2010 - 11 prva hnl | https://en.wikipedia.org/wiki/2010%E2%80%9311_Prva_HNL | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27225944-3.html.csv | ordinal | ivan pudar was the third manager to be appointed as a replacement in the 2010 - 11 prva hnl season . | {'row': '5', 'col': '6', 'order': '3', 'col_other': '5', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date of appointment', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date of appointment ; 3 }'}, 'replaced by'], 'result': 'ivan pudar', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date of appointment ; 3 } ; replaced by }'}, 'ivan pudar'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date of appointment ; 3 } ; replaced by } ; ivan pudar } = true', 'tointer': 'select the row whose date of appointment record of all rows is 3rd minimum . the replaced by record of this row is ivan pudar .'} | eq { hop { nth_argmin { all_rows ; date of appointment ; 3 } ; replaced by } ; ivan pudar } = true | select the row whose date of appointment record of all rows is 3rd minimum . the replaced by record of this row is ivan pudar . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date of appointment_5': 5, '3_6': 6, 'replaced by_7': 7, 'ivan pudar_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date of appointment_5': 'date of appointment', '3_6': '3', 'replaced by_7': 'replaced by', 'ivan pudar_8': 'ivan pudar'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date of appointment_5': [0], '3_6': [0], 'replaced by_7': [1], 'ivan pudar_8': [2]} | ['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment', 'position in table'] | [['rnk split', 'tonći bašić', 'removed from position', '2 2010', 'ivan katalinić', '2 2010', 'pre - season'], ['slaven belupo', 'zlatko dalić', 'mutual consent', '2 2010', 'mile petković', '7 2010', 'pre - season'], ['istra 1961', 'ante miše', 'sacked', '3 2010', 'robert jarni', '4 2010', '16th'], ['lokomotiva', 'roy ferenčina', 'mutual consent', '2 2010', 'ljupko petrović', '2 2010', '12th'], ['hrvatski dragovoljac', 'damir biškup', 'removed from position', '3 2010', 'ivan pudar', '3 2010', '15th'], ['rijeka', 'nenad gračan', 'mutual consent', '6 2010', 'elvis scoria', '8 2010', '6th'], ['hrvatski dragovoljac', 'ivan pudar', 'mutual consent', '7 2010', 'davor mladina', '7 2010', '16th']] |
history of george mason basketball | https://en.wikipedia.org/wiki/History_of_George_Mason_basketball | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16835332-1.html.csv | superlative | rick barnes had the highest win percentage of coaches in george mason basketball . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '5', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'win %'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; win % }'}, 'coach'], 'result': 'rick barnes', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; win % } ; coach }'}, 'rick barnes'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; win % } ; coach } ; rick barnes } = true', 'tointer': 'select the row whose win % record of all rows is maximum . the coach record of this row is rick barnes .'} | eq { hop { argmax { all_rows ; win % } ; coach } ; rick barnes } = true | select the row whose win % record of all rows is maximum . the coach record of this row is rick barnes . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'win %_5': 5, 'coach_6': 6, 'rick barnes_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'win %_5': 'win %', 'coach_6': 'coach', 'rick barnes_7': 'rick barnes'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'win %_5': [0], 'coach_6': [1], 'rick barnes_7': [2]} | ['coach', 'years', 'win - loss', 'win %', 'conference titles'] | [['arnold siegfried', '1966 - 1967', '6 - 12', '333', '0'], ['raymond spuhler', '1967 - 1970', '11 - 60', '155', '0'], ['john linn', '1970 - 1980', '130 - 147', '469', '0'], ['joe harrington', '1980 - 1987', '112 - 85', '569', '0'], ['rick barnes', '1987 - 1988', '20 - 10', '667', '0'], ['ernie nestor', '1988 - 1993', '68 - 81', '456', '1'], ['paul westhead', '1993 - 1997', '38 - 70', '352', '0'], ['jim larranaga', '1997 - 2011', '207 - 131', '612', '3'], ['paul hewitt', '2011 - present', '0 - 0', 'n / a', 'n / a']] |
suwon samsung bluewings | https://en.wikipedia.org/wiki/Suwon_Samsung_Bluewings | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1054817-4.html.csv | comparative | rapido was the kit supplier for the samsung bluewings before adidas was . | {'row_1': '1', 'row_2': '7', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'kit supplier', 'rapido'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose kit supplier record fuzzily matches to rapido .', 'tostr': 'filter_eq { all_rows ; kit supplier ; rapido }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; kit supplier ; rapido } ; year }', 'tointer': 'select the rows whose kit supplier record fuzzily matches to rapido . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'kit supplier', 'adidas'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose kit supplier record fuzzily matches to adidas .', 'tostr': 'filter_eq { all_rows ; kit supplier ; adidas }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; kit supplier ; adidas } ; year }', 'tointer': 'select the rows whose kit supplier record fuzzily matches to adidas . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; kit supplier ; rapido } ; year } ; hop { filter_eq { all_rows ; kit supplier ; adidas } ; year } } = true', 'tointer': 'select the rows whose kit supplier record fuzzily matches to rapido . take the year record of this row . select the rows whose kit supplier record fuzzily matches to adidas . take the year record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; kit supplier ; rapido } ; year } ; hop { filter_eq { all_rows ; kit supplier ; adidas } ; year } } = true | select the rows whose kit supplier record fuzzily matches to rapido . take the year record of this row . select the rows whose kit supplier record fuzzily matches to adidas . take the year record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'kit supplier_7': 7, 'rapido_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'kit supplier_11': 11, 'adidas_12': 12, 'year_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'kit supplier_7': 'kit supplier', 'rapido_8': 'rapido', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'kit supplier_11': 'kit supplier', 'adidas_12': 'adidas', 'year_13': 'year'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'kit supplier_7': [0], 'rapido_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'kit supplier_11': [1], 'adidas_12': [1], 'year_13': [3]} | ['year', 'kit supplier', 'sponsor', 'shirt printing', 'notes'] | [['1996', 'rapido', 'samsung electronics', 'bluewings', 'team name'], ['1997', 'rapido', 'samsung electronics', '名品 + 1', 'television brand'], ['1998', 'rapido', 'samsung electronics', '名品 + 1', 'television brand'], ['1999', 'rapido', 'samsung electronics', 'anycall', 'mobile phone brand'], ['2000', 'rapido', 'samsung electronics', 'anycall', 'mobile phone brand'], ['2001', 'rapido', 'samsung electronics', 'sensq bluewin', 'laptop brand air conditioner brand'], ['2002', 'adidas', 'samsung electronics', 'hauzen', 'electronics brand'], ['2003', 'adidas', 'samsung electronics', 'hauzen', 'electronics brand'], ['2004', 'adidas', 'samsung electronics', 'pavv', 'television brand'], ['2005', 'adidas', 'samsung electronics', 'pavv', 'television brand'], ['2006', 'adidas', 'samsung electronics', 'pavv', 'television brand'], ['2007', 'adidas', 'samsung electronics', 'pavv', 'television brand'], ['2008', 'adidas', 'samsung electronics', 'pavv', 'television brand'], ['2009', 'adidas', 'samsung electronics', 'samsung pavv', 'television brand'], ['2010', 'adidas', 'samsung electronics', 'samsung pavv', 'television brand'], ['2011', 'adidas', 'samsung electronics', 'samsung smart tv', 'television brand'], ['2012', 'adidas', 'samsung electronics', 'samsung smart tv', 'television brand'], ['2013', 'adidas', 'samsung electronics', 'samsung smart tv', 'television brand']] |
curling at the 2006 winter olympics | https://en.wikipedia.org/wiki/Curling_at_the_2006_Winter_Olympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1543845-63.html.csv | count | three teams had a shot percentage over 80 % . | {'scope': 'all', 'criterion': 'greater_than_eq', 'value': '80 %', 'result': '3', 'col': '11', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'shot pct', '80 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose shot pct record is greater than or equal to 80 % .', 'tostr': 'filter_greater_eq { all_rows ; shot pct ; 80 % }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater_eq { all_rows ; shot pct ; 80 % } }', 'tointer': 'select the rows whose shot pct record is greater than or equal to 80 % . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater_eq { all_rows ; shot pct ; 80 % } } ; 3 } = true', 'tointer': 'select the rows whose shot pct record is greater than or equal to 80 % . the number of such rows is 3 .'} | eq { count { filter_greater_eq { all_rows ; shot pct ; 80 % } } ; 3 } = true | select the rows whose shot pct record is greater than or equal to 80 % . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'shot pct_5': 5, '80%_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'shot pct_5': 'shot pct', '80%_6': '80 %', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'shot pct_5': [0], '80%_6': [0], '3_7': [2]} | ['locale', 'skip', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends', 'shot pct'] | [['finland', 'markku uusipaavalniemi', '7', '2', '53', '40', '32', '31', '23', '9', '78 %'], ['canada', 'brad gushue', '6', '3', '66', '46', '47', '31', '9', '23', '80 %'], ['united states', 'pete fenson', '6', '3', '66', '47', '36', '33', '16', '13', '80 %'], ['great britain', 'david murdoch', '6', '3', '59', '49', '36', '31', '17', '12', '81 %'], ['norway', 'pål trulsen', '5', '4', '57', '47', '33', '32', '17', '9', '78 %'], ['switzerland', 'ralph stöckli', '5', '4', '56', '45', '31', '34', '18', '10', '76 %'], ['italy', 'joël retornaz', '4', '5', '47', '66', '37', '38', '10', '7', '70 %'], ['germany', 'andy kapp', '3', '6', '53', '55', '34', '34', '17', '12', '77 %'], ['sweden', 'peja lindholm', '3', '6', '45', '68', '31', '40', '12', '4', '78 %']] |
list of macedonian submissions for the academy award for best foreign language film | https://en.wikipedia.org/wiki/List_of_Macedonian_submissions_for_the_Academy_Award_for_Best_Foreign_Language_Film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14928423-1.html.csv | unique | before the rain was the only macedonian academy award submission that was nominated for best foreign language film . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '2', 'criterion': 'equal', 'value': 'nominee', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'nominee'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to nominee .', 'tostr': 'filter_eq { all_rows ; result ; nominee }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; nominee } }', 'tointer': 'select the rows whose result record fuzzily matches to nominee . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'nominee'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to nominee .', 'tostr': 'filter_eq { all_rows ; result ; nominee }'}, 'film title used in nomination'], 'result': 'before the rain', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; nominee } ; film title used in nomination }'}, 'before the rain'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; nominee } ; film title used in nomination } ; before the rain }', 'tointer': 'the film title used in nomination record of this unqiue row is before the rain .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; nominee } } ; eq { hop { filter_eq { all_rows ; result ; nominee } ; film title used in nomination } ; before the rain } } = true', 'tointer': 'select the rows whose result record fuzzily matches to nominee . there is only one such row in the table . the film title used in nomination record of this unqiue row is before the rain .'} | and { only { filter_eq { all_rows ; result ; nominee } } ; eq { hop { filter_eq { all_rows ; result ; nominee } ; film title used in nomination } ; before the rain } } = true | select the rows whose result record fuzzily matches to nominee . there is only one such row in the table . the film title used in nomination record of this unqiue row is before the rain . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'nominee_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'film title used in nomination_9': 9, 'before the rain_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'nominee_8': 'nominee', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'film title used in nomination_9': 'film title used in nomination', 'before the rain_10': 'before the rain'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'nominee_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'film title used in nomination_9': [2], 'before the rain_10': [3]} | ['year ( ceremony )', 'film title used in nomination', 'original title', 'language ( s )', 'director ( s )', 'result'] | [['1994 ( 67th )', 'before the rain', 'пред дождот', 'macedonian , albanian , english', 'milčo mančevski', 'nominee'], ['1997 ( 70th )', 'gypsy magic', 'џипси меџик', 'macedonian , romany', 'stole popov', 'not nominated'], ['2004 ( 77th )', 'the great water', 'γолемата вода', 'macedonian', 'ivo trajkov', 'not nominated'], ['2006 ( 79th )', 'kontakt', 'контакт', 'macedonian , german', 'sergej stanojkovski', 'not nominated'], ['2007 ( 80th )', 'shadows', 'сенки', 'macedonian', 'milčo mančevski', 'not nominated'], ['2009 ( 82nd )', 'wingless', 'ocas ještěrky', 'czech', 'ivo trajkov', 'not nominated'], ['2010 ( 83rd )', 'mothers', 'мајки', 'macedonian', 'milčo mančevski', 'not nominated'], ['2011 ( 84th )', "punk 's not dead", 'панкот не е мртов', 'macedonian', 'vladimir blazevski', 'not nominated']] |
list of tallest buildings in montreal | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Montreal | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1722194-5.html.csv | aggregation | the average number of floors of the tallest buildings in montreal is 35 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '35', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'floors'], 'result': '35', 'ind': 0, 'tostr': 'avg { all_rows ; floors }'}, '35'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; floors } ; 35 } = true', 'tointer': 'the average of the floors record of all rows is 35 .'} | round_eq { avg { all_rows ; floors } ; 35 } = true | the average of the floors record of all rows is 35 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'floors_4': 4, '35_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'floors_4': 'floors', '35_5': '35'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'floors_4': [0], '35_5': [1]} | ['name', 'street address', 'years as tallest', 'of years as tallest', 'height m / ft', 'floors'] | [['notre dame basilica', '110 notre - dame street west', '1829 - 1928', '99 years', '69 / 226', '7'], ['royal bank building', '360 saint jacques street west', '1928 - 1931', '3 years', '121 / 397', '22'], ['sun life building', '1155 metcalfe street', '1931 - 1962', '31 years', '122 / 400', '26'], ['tour cibc', '1155 rené lévesque boulevard west', '1962', '< 1 year', '187 / 614', '45'], ['place ville marie', '1 place ville - marie', '1962 - 1964', '2 years', '188 / 617', '47'], ['tour de la bourse', '800 victoria square', '1964 - 1992', '28 years', '194 / 637', '47'], ['1000 de la gauchetière', '1000 de la gauchetière street west', '1992 - present', '21 years ( current )', '205 / 673', '51']] |
Subsets and Splits