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wru division five south west | https://en.wikipedia.org/wiki/WRU_Division_Five_South_West | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17675675-2.html.csv | aggregation | all the clubs in the wru division five south west lost an average of 9.7 games . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '9.7', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'lost'], 'result': '9.7', 'ind': 0, 'tostr': 'avg { all_rows ; lost }'}, '9.7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; lost } ; 9.7 } = true', 'tointer': 'the average of the lost record of all rows is 9.7 .'} | round_eq { avg { all_rows ; lost } ; 9.7 } = true | the average of the lost record of all rows is 9.7 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'lost_4': 4, '9.7_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'lost_4': 'lost', '9.7_5': '9.7'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'lost_4': [0], '9.7_5': [1]} | ['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus'] | [['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus'], ['betws rfc', '20', '0', '2', '727', '243', '111', '29', '14'], ['ystradgynlais rfc', '20', '0', '2', '667', '200', '107', '24', '15'], ['alltwen rfc', '20', '1', '4', '434', '237', '55', '21', '7'], ['new dock stars rfc', '20', '1', '8', '367', '318', '51', '38', '5'], ['pontardawe rfc', '20', '0', '10', '441', '381', '64', '51', '9'], ['trebanos rfc', '20', '1', '9', '441', '404', '51', '58', '5'], ['glais rfc', '20', '0', '11', '293', '325', '36', '42', '4'], ['gowerton rfc', '20', '0', '13', '313', '468', '38', '69', '2'], ['cwmtwrch rfc', '20', '2', '13', '261', '406', '28', '58', '0'], ['swansea uplands rfc', '20', '1', '17', '197', '574', '28', '89', '1'], ['bynea rfc', '20', '0', '18', '139', '724', '21', '111', '1']] |
steam locomotives of ireland | https://en.wikipedia.org/wiki/Steam_locomotives_of_Ireland | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1290024-13.html.csv | aggregation | there were a total of 22 steam locomotives of ireland made between 1882-1912 . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '22', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'quantity made'], 'result': '22', 'ind': 0, 'tostr': 'sum { all_rows ; quantity made }'}, '22'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; quantity made } ; 22 } = true', 'tointer': 'the sum of the quantity made record of all rows is 22 .'} | round_eq { sum { all_rows ; quantity made } ; 22 } = true | the sum of the quantity made record of all rows is 22 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'quantity made_4': 4, '22_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'quantity made_4': 'quantity made', '22_5': '22'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'quantity made_4': [0], '22_5': [1]} | ['type', 'fleet numbers', 'quantity made', 'manufacturer', 'date made', 'date withdrawn'] | [['0 - 6 - 2wt', '1', '1', 'black , hawthorn & co', '1882', '1911'], ['0 - 6 - 2t', '2 - 3', '2', 'black , hawthorn & co', '1883', '1912 - 1913'], ['0 - 6 - 0t', '4', '1', 'black , hawthorn & co', '1885', '1940'], ['2 - 4 - 0t', '5 - 6', '2', 'robert stephenson & co', '1874', '1899'], ['4 - 6 - 2t', '5 - 8', '4', 'hudswell clarke', '1899 - 1902', '1940 - 1954'], ['4 - 6 - 0t', '1 - 4', '4', 'andrew barclay sons & co', '1902', '1940 - 1954'], ['4 - 6 - 2t', '9 - 10', '2', 'kerr stuart', '1904', '1928 - 1954'], ['4 - 8 - 0', '11 - 12', '2', 'hudswell clarke', '1905', '1933 - 1954'], ['4 - 6 - 2t', '13 - 14', '2', 'hawthorn leslie', '1910', '1940 - 1943'], ['4 - 8 - 4t', '5 - 6', '2', 'hudswell clarke', '1912', '1954']] |
1971 washington redskins season | https://en.wikipedia.org/wiki/1971_Washington_Redskins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15093626-1.html.csv | count | in the 1971 washington redskins season , for the picks after round two , 2 of the players were defensive backs . | {'scope': 'subset', 'criterion': 'equal', 'value': 'defensive back', 'result': '2', 'col': '4', 'subset': {'col': '1', 'criterion': 'greater_than', 'value': '2'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'round', '2'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; round ; 2 }', 'tointer': 'select the rows whose round record is greater than 2 .'}, 'position', 'defensive back'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose round record is greater than 2 . among these rows , select the rows whose position record fuzzily matches to defensive back .', 'tostr': 'filter_eq { filter_greater { all_rows ; round ; 2 } ; position ; defensive back }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_greater { all_rows ; round ; 2 } ; position ; defensive back } }', 'tointer': 'select the rows whose round record is greater than 2 . among these rows , select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_greater { all_rows ; round ; 2 } ; position ; defensive back } } ; 2 } = true', 'tointer': 'select the rows whose round record is greater than 2 . among these rows , select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 .'} | eq { count { filter_eq { filter_greater { all_rows ; round ; 2 } ; position ; defensive back } } ; 2 } = true | select the rows whose round record is greater than 2 . among these rows , select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'round_6': 6, '2_7': 7, 'position_8': 8, 'defensive back_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'round_6': 'round', '2_7': '2', 'position_8': 'position', 'defensive back_9': 'defensive back', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'round_6': [0], '2_7': [0], 'position_8': [1], 'defensive back_9': [1], '2_10': [3]} | ['round', 'pick', 'player', 'position', 'school / club team'] | [['2', '38', 'cotton speyrer', 'wide receiver', 'texas'], ['6', '141', 'conway hayman', 'guard', 'delaware'], ['7', '166', 'willie germany', 'defensive back', 'morgan state'], ['9', '219', 'mike fanucci', 'defensive end', 'arizona state'], ['10', '244', 'jesse taylor', 'running back', 'cincinnati'], ['11', '272', 'george starke', 'tackle', 'columbia'], ['12', '297', 'jeff severson', 'defensive back', 'cal - long beach'], ['13', '322', 'dan ryczek', 'center', 'virginia'], ['14', '349', 'bill bynum', 'quarterback', 'west new mexico'], ['15', '375', 'anthony christnovich', 'guard', 'la crosse ( wis )'], ['16', '400', 'glenn tucker', 'linebacker', 'north texas']] |
1965 baltimore colts season | https://en.wikipedia.org/wiki/1965_Baltimore_Colts_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14977592-1.html.csv | comparative | the game played by the 1965 baltimore colts at tiger stadium had a larger attendance than the game at wrigley field . | {'row_1': '11', 'row_2': '8', 'col': '7', 'col_other': '6', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game site', 'tiger stadium'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose game site record fuzzily matches to tiger stadium .', 'tostr': 'filter_eq { all_rows ; game site ; tiger stadium }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; game site ; tiger stadium } ; attendance }', 'tointer': 'select the rows whose game site record fuzzily matches to tiger stadium . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game site', 'wrigley field'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose game site record fuzzily matches to wrigley field .', 'tostr': 'filter_eq { all_rows ; game site ; wrigley field }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; game site ; wrigley field } ; attendance }', 'tointer': 'select the rows whose game site record fuzzily matches to wrigley field . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; game site ; tiger stadium } ; attendance } ; hop { filter_eq { all_rows ; game site ; wrigley field } ; attendance } } = true', 'tointer': 'select the rows whose game site record fuzzily matches to tiger stadium . take the attendance record of this row . select the rows whose game site record fuzzily matches to wrigley field . take the attendance record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; game site ; tiger stadium } ; attendance } ; hop { filter_eq { all_rows ; game site ; wrigley field } ; attendance } } = true | select the rows whose game site record fuzzily matches to tiger stadium . take the attendance record of this row . select the rows whose game site record fuzzily matches to wrigley field . take the attendance 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, 'game site_7': 7, 'tiger stadium_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'game site_11': 11, 'wrigley field_12': 12, 'attendance_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', 'game site_7': 'game site', 'tiger stadium_8': 'tiger stadium', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'game site_11': 'game site', 'wrigley field_12': 'wrigley field', 'attendance_13': 'attendance'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'game site_7': [0], 'tiger stadium_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'game site_11': [1], 'wrigley field_12': [1], 'attendance_13': [3]} | ['week', 'date', 'opponent', 'result', 'record', 'game site', 'attendance'] | [['1', 'september 19 , 1965', 'minnesota vikings', 'w 35 - 16', '1 - 0', 'memorial stadium', '56562'], ['2', 'september 26 , 1965', 'green bay packers', 'l 17 - 20', '1 - 1', 'milwaukee county stadium', '48130'], ['3', 'october 3 , 1965', 'san francisco 49ers', 'w 27 - 24', '2 - 1', 'memorial stadium', '58609'], ['4', 'october 10 , 1965', 'detroit lions', 'w 31 - 7', '3 - 1', 'memorial stadium', '60238'], ['5', 'october 17 , 1965', 'washington redskins', 'w 38 - 7', '4 - 1', 'rfk stadium', '50405'], ['6', 'october 24 , 1965', 'los angeles rams', 'w 35 - 20', '5 - 1', 'memorial stadium', '45827'], ['7', 'october 31 , 1966', 'san francisco 49ers', 'w 34 - 28', '6 - 1', 'kezar stadium', '45827'], ['8', 'november 7 , 1965', 'chicago bears', 'w 26 - 21', '7 - 1', 'wrigley field', '45656'], ['9', 'november 14 , 1965', 'minnesota vikings', 'w 41 - 21', '8 - 1', 'metropolitan stadium', '47426'], ['10', 'november 21 , 1965', 'philadelphia eagles', 'w 34 - 24', '9 - 1', 'memorial stadium', '60238'], ['11', 'november 25 , 1965', 'detroit lions', 't 24 - 24', '9 - 1 - 1', 'tiger stadium', '55036'], ['12', 'december 5 , 1966', 'chicago bears', 'l 0 - 13', '9 - 2 - 1', 'memorial stadium', '60238'], ['13', 'december 12 , 1965', 'green bay packers', 'l 27 - 42', '9 - 3 - 1', 'memorial stadium', '60238'], ['14', 'december 18 , 1965', 'los angeles rams', 'w 20 - 17', '10 - 3 - 1', 'la memorial coliseum', '46636']] |
tom kristensen | https://en.wikipedia.org/wiki/Tom_Kristensen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1802063-1.html.csv | unique | the only year that tom kristensen finished second in a race was in 2012 . | {'scope': 'all', 'row': '16', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': '2nd', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pos', '2nd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pos record fuzzily matches to 2nd .', 'tostr': 'filter_eq { all_rows ; pos ; 2nd }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; pos ; 2nd } }', 'tointer': 'select the rows whose pos record fuzzily matches to 2nd . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pos', '2nd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pos record fuzzily matches to 2nd .', 'tostr': 'filter_eq { all_rows ; pos ; 2nd }'}, 'year'], 'result': '2012', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; pos ; 2nd } ; year }'}, '2012'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; pos ; 2nd } ; year } ; 2012 }', 'tointer': 'the year record of this unqiue row is 2012 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; pos ; 2nd } } ; eq { hop { filter_eq { all_rows ; pos ; 2nd } ; year } ; 2012 } } = true', 'tointer': 'select the rows whose pos record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 2012 .'} | and { only { filter_eq { all_rows ; pos ; 2nd } } ; eq { hop { filter_eq { all_rows ; pos ; 2nd } ; year } ; 2012 } } = true | select the rows whose pos record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 2012 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'pos_7': 7, '2nd_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '2012_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'pos_7': 'pos', '2nd_8': '2nd', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '2012_10': '2012'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'pos_7': [0], '2nd_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '2012_10': [3]} | ['year', 'team', 'co - drivers', 'class', 'laps', 'pos'] | [['1997', 'joest racing', 'michele alboreto stefan johansson', 'lmp', '361', '1st'], ['1998', 'team bmw motorsport', 'hans joachim stuck steve soper', 'lmp1', '60', 'dnf'], ['1999', 'team bmw motorsport', 'jj lehto jörg müller', 'lmp', '304', 'dnf'], ['2000', 'audi sport team joest', 'frank biela emanuele pirro', 'lmp900', '368', '1st'], ['2001', 'audi sport team joest', 'frank biela emanuele pirro', 'lmp900', '321', '1st'], ['2002', 'audi sport team joest', 'frank biela emanuele pirro', 'lmp900', '375', '1st'], ['2003', 'team bentley', 'rinaldo capello guy smith', 'lmgtp', '377', '1st'], ['2004', 'audi sport japan team goh', 'seiji ara rinaldo capello', 'lmp1', '379', '1st'], ['2005', 'adt champion racing', 'jj lehto marco werner', 'lmp1', '370', '1st'], ['2006', 'audi sport team joest', 'rinaldo capello allan mcnish', 'lmp1', '367', '3rd'], ['2007', 'audi sport north america', 'rinaldo capello allan mcnish', 'lmp1', '262', 'dnf'], ['2008', 'audi sport north america', 'rinaldo capello allan mcnish', 'lmp1', '381', '1st'], ['2009', 'audi sport team joest', 'rinaldo capello allan mcnish', 'lmp1', '376', '3rd'], ['2010', 'audi sport team joest', 'rinaldo capello allan mcnish', 'lmp1', '394', '3rd'], ['2011', 'audi sport north america', 'rinaldo capello allan mcnish', 'lmp1', '14', 'dnf'], ['2012', 'audi sport team joest', 'allan mcnish rinaldo capello', 'lmp1', '377', '2nd'], ['2013', 'audi sport team joest', 'allan mcnish loïc duval', 'lmp1', '348', '1st']] |
international rankings of iran | https://en.wikipedia.org/wiki/International_rankings_of_Iran | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15733308-7.html.csv | superlative | the environmental sustainability index is ranked the highest in the international rankings of iran . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', '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', 'rank'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; rank }'}, 'name'], 'result': 'environmental sustainability index', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; rank } ; name }'}, 'environmental sustainability index'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; rank } ; name } ; environmental sustainability index } = true', 'tointer': 'select the row whose rank record of all rows is maximum . the name record of this row is environmental sustainability index .'} | eq { hop { argmax { all_rows ; rank } ; name } ; environmental sustainability index } = true | select the row whose rank record of all rows is maximum . the name record of this row is environmental sustainability index . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'rank_5': 5, 'name_6': 6, 'environmental sustainability index_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'rank_5': 'rank', 'name_6': 'name', 'environmental sustainability index_7': 'environmental sustainability index'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'rank_5': [0], 'name_6': [1], 'environmental sustainability index_7': [2]} | ['name', 'rank', 'out of', 'source', 'year'] | [['environmental sustainability index', '132', '146', 'yale university', '2005'], ['greenhouse emissions per capita', '74', 'world', 'world resources institute', '2000'], ['number of species under threat of extinction', '37', '158', 'united nations', '1999'], ['happy planet index', '81', '178', 'new economics foundation', '2009'], ['environmental performance index', '78', '153', 'yale university / columbia university', '2010'], ['total renewable water resources', '58', '151', 'cia world factbook', '2008'], ['water availability per capita', '116', '141', 'united nations', '2001'], ['biodiversity richness', '13', '53', 'world conservation monitoring centre', '1994'], ['carbon efficiency', '28', '141', 'carbon dioxide information analysis center', '2005'], ['coral reefs area', '19', '28', 'united nations', '2005'], ['endangered species protection', '71', '141', 'cites', '2000'], ['land use statistics by country', '16', '176', 'cia world factbook', '2005'], ['carbon dioxide emissions per capita', '70', '210', 'united nations', '2003'], ['total carbon dioxide emissions', '11', '210', 'united nations', '2006'], ['total forest area', '47', '220', 'united nations', '2007'], ['fresh water withdrawal', '11', '168', 'cia world factbook', '2000'], ['industrial water pollution', '14', '129', 'world bank', '2003']] |
jeev milkha singh | https://en.wikipedia.org/wiki/Jeev_Milkha_Singh | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1610384-4.html.csv | unique | jeev milkha singh managed to make 3 cuts only at the us open . | {'scope': 'all', 'row': '2', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': '3', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'cuts made', '3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose cuts made record is equal to 3 .', 'tostr': 'filter_eq { all_rows ; cuts made ; 3 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; cuts made ; 3 } }', 'tointer': 'select the rows whose cuts made record is equal to 3 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'cuts made', '3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose cuts made record is equal to 3 .', 'tostr': 'filter_eq { all_rows ; cuts made ; 3 }'}, 'tournament'], 'result': 'us open', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; cuts made ; 3 } ; tournament }'}, 'us open'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; cuts made ; 3 } ; tournament } ; us open }', 'tointer': 'the tournament record of this unqiue row is us open .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; cuts made ; 3 } } ; eq { hop { filter_eq { all_rows ; cuts made ; 3 } ; tournament } ; us open } } = true', 'tointer': 'select the rows whose cuts made record is equal to 3 . there is only one such row in the table . the tournament record of this unqiue row is us open .'} | and { only { filter_eq { all_rows ; cuts made ; 3 } } ; eq { hop { filter_eq { all_rows ; cuts made ; 3 } ; tournament } ; us open } } = true | select the rows whose cuts made record is equal to 3 . there is only one such row in the table . the tournament record of this unqiue row is us open . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'cuts made_7': 7, '3_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'us open_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'cuts made_7': 'cuts made', '3_8': '3', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'us open_10': 'us open'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'cuts made_7': [0], '3_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'us open_10': [3]} | ['tournament', 'wins', 'top - 10', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '0', '0', '1', '3', '2'], ['us open', '0', '0', '0', '4', '3'], ['the open championship', '0', '0', '0', '2', '1'], ['pga championship', '0', '1', '1', '4', '2'], ['totals', '0', '1', '2', '13', '8']] |
1976 pittsburgh steelers season | https://en.wikipedia.org/wiki/1976_Pittsburgh_Steelers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14414265-1.html.csv | ordinal | during the 1976 season , the pittsburgh steelers ' 6th opponent for a sunday game was the new york giants . | {'scope': 'subset', 'row': '7', 'col': '1', 'order': '6', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'sunday'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'sunday'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; sunday }', 'tointer': 'select the rows whose date record fuzzily matches to sunday .'}, 'week', '6'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; date ; sunday } ; week ; 6 }'}, 'opponent'], 'result': 'new york giants', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; date ; sunday } ; week ; 6 } ; opponent }'}, 'new york giants'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; date ; sunday } ; week ; 6 } ; opponent } ; new york giants } = true', 'tointer': 'select the rows whose date record fuzzily matches to sunday . select the row whose week record of these rows is 6th minimum . the opponent record of this row is new york giants .'} | eq { hop { nth_argmin { filter_eq { all_rows ; date ; sunday } ; week ; 6 } ; opponent } ; new york giants } = true | select the rows whose date record fuzzily matches to sunday . select the row whose week record of these rows is 6th minimum . the opponent record of this row is new york giants . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'date_6': 6, 'sunday_7': 7, 'week_8': 8, '6_9': 9, 'opponent_10': 10, 'new york giants_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'date_6': 'date', 'sunday_7': 'sunday', 'week_8': 'week', '6_9': '6', 'opponent_10': 'opponent', 'new york giants_11': 'new york giants'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'date_6': [0], 'sunday_7': [0], 'week_8': [1], '6_9': [1], 'opponent_10': [2], 'new york giants_11': [3]} | ['week', 'date', 'opponent', 'time ( et )', 'result'] | [['1', 'sunday september 12', 'oakland raiders', '4:00 pm', 'l 31 - 28'], ['2', 'sunday september 19', 'cleveland browns', '1:00 pm', 'w 31 - 14'], ['3', 'sunday september 26', 'new england patriots', '1:00 pm', 'l 30 - 27'], ['4', 'monday october 4', 'minnesota vikings', '9:00 pm', 'l 17 - 6'], ['5', 'sunday october 10', 'cleveland browns', '1:00 pm', 'l 18 - 16'], ['6', 'sunday october 17', 'cincinnati bengals', '1:00 pm', 'w 23 - 6'], ['7', 'sunday october 24', 'new york giants', '1:00 pm', 'w 27 - 0'], ['8', 'sunday october 31', 'san diego chargers', '1:00 pm', 'w 23 - 0'], ['9', 'sunday november 7', 'kansas city chiefs', '1:00 pm', 'w 45 - 0'], ['10', 'sunday november 14', 'miami dolphins', '1:00 pm', 'w 14 - 3'], ['11', 'sunday november 21', 'houston oilers', '1:00 pm', 'w 32 - 16'], ['12', 'sunday november 28', 'cincinnati bengals', '1:00 pm', 'w 7 - 3'], ['13', 'sunday december 5', 'tampa bay buccaneers', '1:00 pm', 'w 42 - 0'], ['14', 'saturday december 11', 'houston oilers', '1:00 pm', 'w 21 - 0']] |
2009 - 10 toronto raptors season | https://en.wikipedia.org/wiki/2009%E2%80%9310_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22893781-7.html.csv | aggregation | during the 2009-10 toronto raptors season , in the games where chris bosh had at least a share of the high points , his average number of points was 29 . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '29', 'subset': {'col': '5', 'criterion': 'fuzzily_match', 'value': 'chris bosh'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'chris bosh'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high points ; chris bosh }', 'tointer': 'select the rows whose high points record fuzzily matches to chris bosh .'}, 'high points'], 'result': '29', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; high points ; chris bosh } ; high points }'}, '29'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; high points ; chris bosh } ; high points } ; 29 } = true', 'tointer': 'select the rows whose high points record fuzzily matches to chris bosh . the average of the high points record of these rows is 29 .'} | round_eq { avg { filter_eq { all_rows ; high points ; chris bosh } ; high points } ; 29 } = true | select the rows whose high points record fuzzily matches to chris bosh . the average of the high points record of these rows is 29 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high points_5': 5, 'chris bosh_6': 6, 'high points_7': 7, '29_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high points_5': 'high points', 'chris bosh_6': 'chris bosh', 'high points_7': 'high points', '29_8': '29'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high points_5': [0], 'chris bosh_6': [0], 'high points_7': [1], '29_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['49', 'february 2', 'indiana', 'l 115 - 130 ( ot )', 'chris bosh ( 35 )', 'chris bosh ( 15 )', 'josé calderón ( 8 )', 'conseco fieldhouse 11191', '26 - 23'], ['50', 'february 3', 'new jersey', 'w 108 - 99 ( ot )', 'andrea bargnani , chris bosh ( 20 )', 'sonny weems ( 11 )', 'jarrett jack ( 9 )', 'air canada centre 15222', '27 - 23'], ['51', 'february 7', 'sacramento', 'w 115 - 104 ( ot )', 'chris bosh ( 35 )', 'chris bosh ( 11 )', 'jarrett jack ( 9 )', 'air canada centre 18007', '28 - 23'], ['52', 'february 10', 'philadelphia', 'w 104 - 93 ( ot )', 'chris bosh ( 23 )', 'chris bosh ( 12 )', 'jarrett jack ( 8 )', 'air canada centre 16651', '29 - 23'], ['53', 'february 17', 'memphis', 'l 102 - 109 ( ot )', 'chris bosh ( 32 )', 'andrea bargnani , chris bosh ( 10 )', 'josé calderón ( 9 )', 'air canada centre 16829', '29 - 24'], ['54', 'february 19', 'new jersey', 'w 106 - 89 ( ot )', 'jarrett jack ( 18 )', 'radoslav nesterović ( 7 )', 'jarrett jack ( 10 )', 'izod center 11994', '30 - 24'], ['55', 'february 20', 'washington', 'w 109 - 104 ( ot )', 'jarrett jack ( 23 )', 'reggie evans ( 7 )', 'jarrett jack ( 8 )', 'air canada centre 19149', '31 - 24'], ['56', 'february 24', 'portland', 'l 87 - 101 ( ot )', 'hedo türkoğlu ( 24 )', 'reggie evans , amir johnson ( 8 )', 'jarrett jack ( 8 )', 'air canada centre 16161', '31 - 25'], ['57', 'february 26', 'cleveland', 'l 118 - 126 ( ot )', 'andrea bargnani , jarrett jack ( 24 )', 'demar derozan , reggie evans ( 5 )', 'josé calderón ( 8 )', 'air canada centre 20107', '31 - 26']] |
2008 australian sports sedan series | https://en.wikipedia.org/wiki/2008_Australian_Sports_Sedan_Series | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18124534-2.html.csv | unique | mallala was the only race in the 2008 australian sports sedan series where luke youlden won . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'luke youlden', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'luke youlden'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to luke youlden .', 'tostr': 'filter_eq { all_rows ; winner ; luke youlden }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winner ; luke youlden } }', 'tointer': 'select the rows whose winner record fuzzily matches to luke youlden . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'luke youlden'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to luke youlden .', 'tostr': 'filter_eq { all_rows ; winner ; luke youlden }'}, 'race title'], 'result': 'mallala', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winner ; luke youlden } ; race title }'}, 'mallala'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; winner ; luke youlden } ; race title } ; mallala }', 'tointer': 'the race title record of this unqiue row is mallala .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; winner ; luke youlden } } ; eq { hop { filter_eq { all_rows ; winner ; luke youlden } ; race title } ; mallala } } = true', 'tointer': 'select the rows whose winner record fuzzily matches to luke youlden . there is only one such row in the table . the race title record of this unqiue row is mallala .'} | and { only { filter_eq { all_rows ; winner ; luke youlden } } ; eq { hop { filter_eq { all_rows ; winner ; luke youlden } ; race title } ; mallala } } = true | select the rows whose winner record fuzzily matches to luke youlden . there is only one such row in the table . the race title record of this unqiue row is mallala . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winner_7': 7, 'luke youlden_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'race title_9': 9, 'mallala_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winner_7': 'winner', 'luke youlden_8': 'luke youlden', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'race title_9': 'race title', 'mallala_10': 'mallala'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'winner_7': [0], 'luke youlden_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'race title_9': [2], 'mallala_10': [3]} | ['race title', 'circuit', 'city / state', 'date', 'winner'] | [['mallala', 'mallala motor sport park', 'adelaide , south australia', '1718 may', 'luke youlden'], ['phillip island', 'phillip island grand prix circuit', 'phillip island , victoria', '14 - 15 jun', 'darren hossack'], ['eastern creek', 'eastern creek raceway', 'sydney , new south wales', '12 - 13 jul', 'darren hossack'], ['oran park', 'oran park raceway', 'sydney , new south wales', '30 - 31 aug', 'tony ricciardello'], ['sandown', 'sandown raceway', 'melbourne , victoria', '29 - 30 nov', 'tony ricciardello']] |
locomotives of the great western railway | https://en.wikipedia.org/wiki/Locomotives_of_the_Great_Western_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1169521-12.html.csv | superlative | beyer peacock & co has the highest quantity among the other manufacturers of locomotives of the great western railway . | {'scope': 'all', 'col_superlative': '3', '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', 'quantity'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; quantity }'}, 'manufacturer'], 'result': 'beyer , peacock & co', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; quantity } ; manufacturer }'}, 'beyer , peacock & co'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; quantity } ; manufacturer } ; beyer , peacock & co } = true', 'tointer': 'select the row whose quantity record of all rows is maximum . the manufacturer record of this row is beyer , peacock & co .'} | eq { hop { argmax { all_rows ; quantity } ; manufacturer } ; beyer , peacock & co } = true | select the row whose quantity record of all rows is maximum . the manufacturer record of this row is beyer , peacock & co . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'quantity_5': 5, 'manufacturer_6': 6, 'beyer , peacock & co_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'quantity_5': 'quantity', 'manufacturer_6': 'manufacturer', 'beyer , peacock & co_7': 'beyer , peacock & co'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'quantity_5': [0], 'manufacturer_6': [1], 'beyer , peacock & co_7': [2]} | ['manufacturer', 'type', 'quantity', 'm & swj nos', 'gwr nos'] | [['beyer , peacock & co', '0 - 4 - 4t', '1', '15', '23'], ['beyer , peacock & co', '2 - 6 - 0', '1', '16', '24'], ['sharp , stewart & co', '4 - 4 - 4t', '2', '17 - 18', '25 , 27'], ['dübs & co', '0 - 6 - 0t', '2', '13 - 14', '825 , 843'], ['beyer , peacock & co', '0 - 6 - 0', '10', '19 - 28', '1003 - 1011 , 1013'], ['north british locomotive co', '4 - 4 - 0', '9', '1 - 8 , 31', '1119 - 1126 , 1128'], ['dübs & co', '4 - 4 - 0', '1', '9', '1127'], ['dübs & co', '2 - 4 - 0', '3', '10 - 12', '1334 - 1336']] |
list of 10 metre air pistol records | https://en.wikipedia.org/wiki/List_of_10_metre_air_pistol_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18986934-1.html.csv | count | in the list of 10 metre air pistol records , 3 of those in czechoslovakia their date was earlier than 1980 . | {'scope': 'subset', 'criterion': 'less_than', 'value': '1980', 'result': '3', 'col': '3', 'subset': {'col': '5', 'criterion': 'fuzzily_match', 'value': 'czechoslovakia'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'place', 'czechoslovakia'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; place ; czechoslovakia }', 'tointer': 'select the rows whose place record fuzzily matches to czechoslovakia .'}, 'date', '1980'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose place record fuzzily matches to czechoslovakia . among these rows , select the rows whose date record is less than 1980 .', 'tostr': 'filter_less { filter_eq { all_rows ; place ; czechoslovakia } ; date ; 1980 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_less { filter_eq { all_rows ; place ; czechoslovakia } ; date ; 1980 } }', 'tointer': 'select the rows whose place record fuzzily matches to czechoslovakia . among these rows , select the rows whose date record is less than 1980 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_eq { all_rows ; place ; czechoslovakia } ; date ; 1980 } } ; 3 } = true', 'tointer': 'select the rows whose place record fuzzily matches to czechoslovakia . among these rows , select the rows whose date record is less than 1980 . the number of such rows is 3 .'} | eq { count { filter_less { filter_eq { all_rows ; place ; czechoslovakia } ; date ; 1980 } } ; 3 } = true | select the rows whose place record fuzzily matches to czechoslovakia . among these rows , select the rows whose date record is less than 1980 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'place_6': 6, 'czechoslovakia_7': 7, 'date_8': 8, '1980_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'place_6': 'place', 'czechoslovakia_7': 'czechoslovakia', 'date_8': 'date', '1980_9': '1980', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'place_6': [0], 'czechoslovakia_7': [0], 'date_8': [1], '1980_9': [1], '3_10': [3]} | ['score', 'shooter', 'date', 'comp', 'place'] | [['385', 'h mertel ( frg )', '1969', 'ech', 'pilsen , czechoslovakia'], ['385', 'rasskazov ( urs )', '1969', 'ech', 'pilsen , czechoslovakia'], ['387', 'v stolypin ( urs )', '1971', 'ech', 'meziboří , czechoslovakia'], ['392', 'grigori kosych ( urs )', '1973', 'ech', 'linz , austria'], ['393', 'harald vollmar ( gdr )', '1976', 'ech', 'paris , france'], ['394', 'uwe potteck ( gdr )', '1979', 'ech', 'graz , austria'], ['60 shots from 1981', '60 shots from 1981', '60 shots from 1981', '60 shots from 1981', '60 shots from 1981'], ['582', 'vladas turla ( urs )', '1981', 'ech', 'athens , greece'], ['582', 'i mandov ( bul )', '1981', 'ech', 'athens , greece'], ['587', 'vladas turla ( urs )', '1982', 'ech', 'the hague , netherlands'], ['590', 'vladas turla ( urs )', '1982', 'wch', 'caracas , venezuela'], ['590', 'igor basinski ( urs )', '1987', 'ech', 'bratislava , czechoslovakia'], ['590', 'igor basinski ( urs )', '1988', 'ech', 'stavanger , norway'], ['590', 'erich buljung ( usa )', '1988', 'og', 'seoul , south korea'], ['new targets from 1989', 'new targets from 1989', 'new targets from 1989', 'new targets from 1989', 'new targets from 1989'], ['583', 'sorin babii ( rou )', '1989', 'ech', 'copenhagen , denmark'], ['590', 'sergei pyzhianov ( urs )', '1989', 'wch', 'sarajevo , yugoslavia'], ['593', 'sergei pyzhianov ( urs )', '13 oct 1989', 'wcf', 'munich , west germany'], ['594', 'jin jong - oh ( kor )', '12 apr 2009', 'wc', 'changwon , south korea']] |
1978 pittsburgh steelers season | https://en.wikipedia.org/wiki/1978_Pittsburgh_Steelers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14423274-1.html.csv | unique | nat terry was the only player from florida state college picked in the 1978 pittsburgh steelers season . | {'scope': 'all', 'row': '12', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'florida state', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'florida state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to florida state .', 'tostr': 'filter_eq { all_rows ; college ; florida state }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; florida state } }', 'tointer': 'select the rows whose college record fuzzily matches to florida state . 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', 'florida state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to florida state .', 'tostr': 'filter_eq { all_rows ; college ; florida state }'}, 'player'], 'result': 'terry , nat nat terry', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; florida state } ; player }'}, 'terry , nat nat terry'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; terry , nat nat terry }', 'tointer': 'the player record of this unqiue row is terry , nat nat terry .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; terry , nat nat terry } } = true', 'tointer': 'select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the player record of this unqiue row is terry , nat nat terry .'} | and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; terry , nat nat terry } } = true | select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the player record of this unqiue row is terry , nat nat terry . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'florida state_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'terry , nat nat terry_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', 'florida state_8': 'florida state', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'terry , nat nat terry_10': 'terry , nat nat terry'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'florida state_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'terry , nat nat terry_10': [3]} | ['round', 'pick', 'player', 'position', 'college', 'tenure w / steelers'] | [['1', '22', 'johnson , ron ron johnson', 'defensive back', 'eastern michigan', '1978 - 1984'], ['2', '49', 'fry , willie willie fry', 'defensive end', 'notre dame', '-'], ['3', '76', 'colquitt , craig craig colquitt', 'punter', 'tennessee', '1978 - 1984'], ['4', '101', 'anderson , larry larry anderson', 'defensive back', 'louisiana tech', '1978 - 1981'], ['6', '160', 'reutershan , randy randy reutershan', 'wide receiver', 'pitt', '1978'], ['7', '187', 'dufresne , mark mark dufresne', 'tight end', 'nebraska', '-'], ['8', '208', 'moser , rick rick moser', 'running back', 'rhode island', '1978 - 1979 , 1981 , 1982'], ['8', '214', 'keys , andre andre keys', 'wide receiver', 'cal poly', '-'], ['9', '241', 'reynolds , lance lance reynolds', 'offensive tackle', 'byu', '-'], ['10', '268', 'becker , doug doug becker', 'linebacker', 'notre dame', '-'], ['10', '276', 'jurich , tom tom jurich', 'placekicker', 'northern arizona', '-'], ['11', '279', 'terry , nat nat terry', 'defensive back', 'florida state', '1978'], ['11', '300', 'brzoza , tom tom brzoza', 'center', 'pitt', '-'], ['12', '327', 'carr , brad brad carr', 'linebacker', 'maryland', '-']] |
gp2 series | https://en.wikipedia.org/wiki/GP2_Series | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1430822-5.html.csv | ordinal | fabio leimer ( racing engineering ) is the latest champion of the gp2 series . | {'row': '9', 'col': '1', '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', 'season', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; season ; 1 }'}, 'champion'], 'result': 'fabio leimer ( racing engineering )', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; season ; 1 } ; champion }'}, 'fabio leimer ( racing engineering )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; season ; 1 } ; champion } ; fabio leimer ( racing engineering ) } = true', 'tointer': 'select the row whose season record of all rows is 1st maximum . the champion record of this row is fabio leimer ( racing engineering ) .'} | eq { hop { nth_argmax { all_rows ; season ; 1 } ; champion } ; fabio leimer ( racing engineering ) } = true | select the row whose season record of all rows is 1st maximum . the champion record of this row is fabio leimer ( racing engineering ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'season_5': 5, '1_6': 6, 'champion_7': 7, 'fabio leimer ( racing engineering )_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', 'season_5': 'season', '1_6': '1', 'champion_7': 'champion', 'fabio leimer ( racing engineering )_8': 'fabio leimer ( racing engineering )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'season_5': [0], '1_6': [0], 'champion_7': [1], 'fabio leimer ( racing engineering )_8': [2]} | ['season', 'champion', 'second', 'third', 'team champion'] | [['2005', 'nico rosberg ( art grand prix )', 'heikki kovalainen ( arden international )', 'scott speed ( isport international )', 'art grand prix'], ['2006', 'lewis hamilton ( art grand prix )', 'nelson piquet , jr ( piquet sports )', 'alexandre prémat ( art grand prix )', 'art grand prix'], ['2007', 'timo glock ( isport international )', 'lucas di grassi ( art grand prix )', 'giorgio pantano ( campos grand prix )', 'isport international'], ['2008', 'giorgio pantano ( racing engineering )', 'bruno senna ( isport international )', 'lucas di grassi ( barwa int campos team )', 'barwa international campos team'], ['2009', 'nico hülkenberg ( art grand prix )', 'vitaly petrov ( barwa addax team )', 'lucas di grassi ( fat burner racing engineering )', 'art grand prix'], ['2010', 'pastor maldonado ( rapax )', 'sergio pérez ( barwa addax team )', 'jules bianchi ( art grand prix )', 'rapax'], ['2011', 'romain grosjean ( dams )', 'luca filippi ( super nova / scuderia coloni )', 'jules bianchi ( lotus art )', 'barwa addax team'], ['2012', 'davide valsecchi ( dams )', 'luiz razia ( arden international )', 'esteban gutiérrez ( lotus gp )', 'dams'], ['2013', 'fabio leimer ( racing engineering )', 'sam bird ( russian time )', 'james calado ( art grand prix )', 'russian time']] |
carrefour | https://en.wikipedia.org/wiki/Carrefour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-167638-3.html.csv | aggregation | carrefour has a total of 5096 stores classified as hard discounters in europe . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '5096', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'hard discounters'], 'result': '5096', 'ind': 0, 'tostr': 'sum { all_rows ; hard discounters }'}, '5096'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; hard discounters } ; 5096 } = true', 'tointer': 'the sum of the hard discounters record of all rows is 5096 .'} | round_eq { sum { all_rows ; hard discounters } ; 5096 } = true | the sum of the hard discounters record of all rows is 5096 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'hard discounters_4': 4, '5096_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'hard discounters_4': 'hard discounters', '5096_5': '5096'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'hard discounters_4': [0], '5096_5': [1]} | ['country', 'first store', 'hypermarkets', 'supermarkets', 'hard discounters'] | [['albania', '2011', '1', '-', '-'], ['belgium', '2000', '45', '370', '-'], ['bulgaria', '2009', '5', '3', '-'], ['cyprus', '2006', '7', '8', '-'], ['france', '1960', '221', '1021', '897'], ['georgia', '2012', '1', '1', '-'], ['greece', '1991', '28', '210', '397'], ['italy', '1993', '45', '485', '-'], ['macedonia', '2012', '1', '-', '-'], ['monaco', '-', '-', '1', '-'], ['poland', '1997', '84', '277', '-'], ['portugal', '1991', '-', '-', '365'], ['romania', '2001', '25', '50', '-'], ['spain', '1973', '172', '115', '2912'], ['slovakia', '1998', '4', '0', '0'], ['slovenia', '1998', '15', '12', '6'], ['turkey', '1993', '73', '99', '519'], ['united kingdom', '1972', '-', '-', '-']] |
pedro rodríguez ( racing driver ) | https://en.wikipedia.org/wiki/Pedro_Rodr%C3%ADguez_%28racing_driver%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1156744-1.html.csv | unique | 1964 was the only year that pedro rodriguez drove with the ferrari 156 aero chassis . | {'scope': 'all', 'row': '2', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'ferrari 156 aero', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'ferrari 156 aero'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to ferrari 156 aero .', 'tostr': 'filter_eq { all_rows ; chassis ; ferrari 156 aero }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; chassis ; ferrari 156 aero } }', 'tointer': 'select the rows whose chassis record fuzzily matches to ferrari 156 aero . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'chassis', 'ferrari 156 aero'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose chassis record fuzzily matches to ferrari 156 aero .', 'tostr': 'filter_eq { all_rows ; chassis ; ferrari 156 aero }'}, 'year'], 'result': '1964', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year }'}, '1964'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year } ; 1964 }', 'tointer': 'the year record of this unqiue row is 1964 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; chassis ; ferrari 156 aero } } ; eq { hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year } ; 1964 } } = true', 'tointer': 'select the rows whose chassis record fuzzily matches to ferrari 156 aero . there is only one such row in the table . the year record of this unqiue row is 1964 .'} | and { only { filter_eq { all_rows ; chassis ; ferrari 156 aero } } ; eq { hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year } ; 1964 } } = true | select the rows whose chassis record fuzzily matches to ferrari 156 aero . there is only one such row in the table . the year record of this unqiue row is 1964 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'chassis_7': 7, 'ferrari 156 aero_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1964_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', 'ferrari 156 aero_8': 'ferrari 156 aero', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1964_10': '1964'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'chassis_7': [0], 'ferrari 156 aero_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1964_10': [3]} | ['year', 'entrant', 'chassis', 'engine', 'pts'] | [['1963', 'team lotus', 'lotus 25', 'climax v8', '0'], ['1964', 'north american racing team', 'ferrari 156 aero', 'ferrari v6', '1'], ['1965', 'north american racing team', 'ferrari 1512', 'ferrari v12', '2'], ['1966', 'team lotus', 'lotus 33', 'climax v8', '0'], ['1966', 'team lotus', 'lotus f2 44', 'cosworth straight - 4', '0'], ['1966', 'team lotus', 'lotus 33', 'brm v8', '0'], ['1967', 'cooper car company', 'cooper t81', 'maserati v12', '15'], ['1968', 'owen racing organisation', 'brm p126', 'brm v12', '18'], ['1968', 'owen racing organisation', 'brm p133', 'brm v12', '18'], ['1968', 'owen racing organisation', 'brm p138', 'brm v12', '18'], ['1969', 'reg parnell racing', 'brm p126', 'brm v12', '3'], ['1969', 'scuderia ferrari', 'ferrari 312', 'ferrari v12', '3'], ['1969', 'north american racing team', 'ferrari 312', 'ferrari v12', '3'], ['1970', 'yardley team brm', 'brm p153', 'brm v12', '23'], ['1971', 'yardley team brm', 'brm p160', 'brm v12', '9']] |
list of government bonds | https://en.wikipedia.org/wiki/List_of_government_bonds | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2764267-2.html.csv | comparative | italian euros have a higher negotiable debt at mid value than german euros do . | {'row_1': '3', 'row_2': '5', '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', 'country', 'italy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to italy .', 'tostr': 'filter_eq { all_rows ; country ; italy }'}, 'negotiable debt at mid - 2005 ( us dollar bn equivalent )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; italy } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) }', 'tointer': 'select the rows whose country record fuzzily matches to italy . take the negotiable debt at mid - 2005 ( us dollar bn equivalent ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'germany'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to germany .', 'tostr': 'filter_eq { all_rows ; country ; germany }'}, 'negotiable debt at mid - 2005 ( us dollar bn equivalent )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; country ; germany } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) }', 'tointer': 'select the rows whose country record fuzzily matches to germany . take the negotiable debt at mid - 2005 ( us dollar bn equivalent ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; country ; italy } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) } ; hop { filter_eq { all_rows ; country ; germany } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) } } = true', 'tointer': 'select the rows whose country record fuzzily matches to italy . take the negotiable debt at mid - 2005 ( us dollar bn equivalent ) record of this row . select the rows whose country record fuzzily matches to germany . take the negotiable debt at mid - 2005 ( us dollar bn equivalent ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; country ; italy } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) } ; hop { filter_eq { all_rows ; country ; germany } ; negotiable debt at mid - 2005 ( us dollar bn equivalent ) } } = true | select the rows whose country record fuzzily matches to italy . take the negotiable debt at mid - 2005 ( us dollar bn equivalent ) record of this row . select the rows whose country record fuzzily matches to germany . take the negotiable debt at mid - 2005 ( us dollar bn equivalent ) 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, 'country_7': 7, 'italy_8': 8, 'negotiable debt at mid - 2005 ( us dollar bn equivalent)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'country_11': 11, 'germany_12': 12, 'negotiable debt at mid - 2005 ( us dollar bn equivalent)_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', 'country_7': 'country', 'italy_8': 'italy', 'negotiable debt at mid - 2005 ( us dollar bn equivalent)_9': 'negotiable debt at mid - 2005 ( us dollar bn equivalent )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'country_11': 'country', 'germany_12': 'germany', 'negotiable debt at mid - 2005 ( us dollar bn equivalent)_13': 'negotiable debt at mid - 2005 ( us dollar bn equivalent )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'country_7': [0], 'italy_8': [0], 'negotiable debt at mid - 2005 ( us dollar bn equivalent)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'country_11': [1], 'germany_12': [1], 'negotiable debt at mid - 2005 ( us dollar bn equivalent)_13': [3]} | ['currency', 'country', 'generic name or nickname', 'rating ( s & p / moodys )', 'negotiable debt at mid - 2005 ( us dollar bn equivalent )', 'government financial liabilities as % of gdp ( end 2003 )', 'issuer', 'internet site'] | [['yen', 'japan', 's jgb', 'aa - / a2', '6666', '157.5 %', 'ministry of finance ( mof )', 'site'], ['us dollar', 'united states', 'us treasuries', 'aa + / aaa', '4000', '62.5 %', 'bureau of the public debt', 'site'], ['euro', 'italy', 's btp', 'bbb + / baa2', '1530', '120.9 %', 'dipartimento del tesoro', 'site'], ['euro', 'france', 's oat', 'aa + / aaa', '1300', '71.2 %', 'agence france trãsor', 'site'], ['euro', 'germany', 'bunds', 'aaa / aaa', '1020', '65.1 %', 'finanzagentur gmbh', 'site']] |
list of rizzoli & isles episodes | https://en.wikipedia.org/wiki/List_of_Rizzoli_%26_Isles_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27969432-4.html.csv | superlative | the episode of rizzoli & isles that had the highest number of us viewers was the one titled crazy for you . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'us viewers ( in millions )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; us viewers ( in millions ) }'}, 'title'], 'result': 'crazy for you', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; us viewers ( in millions ) } ; title }'}, 'crazy for you'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; crazy for you } = true', 'tointer': 'select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is crazy for you .'} | eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; crazy for you } = true | select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is crazy for you . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'us viewers (in millions)_5': 5, 'title_6': 6, 'crazy for you_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'us viewers (in millions)_5': 'us viewers ( in millions )', 'title_6': 'title', 'crazy for you_7': 'crazy for you'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'us viewers (in millions)_5': [0], 'title_6': [1], 'crazy for you_7': [2]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production', 'us viewers ( in millions )'] | [['26', '1', "what does n't kill you", 'michael katleman', 'janet tamaro', 'june 5 , 2012', '2 m5901', '5.62'], ['27', '2', 'dirty little secret', 'aaron lipstadt', 'steve lichtman & kiersten van home', 'june 12 , 2012', '2 m5902', '5.13'], ['28', '3', 'this is how a heart breaks', 'steve robin', 'david gould & sal calleros', 'june 19 , 2012', '2 m5903', '5.36'], ['29', '4', 'welcome to the dollhouse', 'mark haber', 'russell j grant & janet tamaro', 'june 26 , 2012', '2 m5904', '5.43'], ['30', '5', 'throwing down the gauntlet', 'jamie babbit', 'antoinette stella & janet tamaro', 'july 3 , 2012', '2 m5905', '5.32'], ['32', '7', 'crazy for you', 'frederick e o toye', 'antoinette stella & lindsay sturman', 'july 17 , 2012', '2 m5907', '5.84'], ['33', '8', 'cuts like a knife', 'randy zisk', 'david gould & sal calleros', 'july 24 , 2012', '2 m5908', '5.59'], ['34', '9', 'home town glory', 'milan cheylov', 'janet tamaro', 'july 31 , 2012', '2 m5909', '4.44'], ['36', '11', 'class action satisfaction', 'norman buckley', 'antoinette stella & lindsay sturman', 'november 27 , 2012', '2 m5911', '3.44'], ['37', '12', 'love the way you lie', 'mark harber', 'steve lichtman & david gould', 'december 4 , 2012', '2 m5912', '4.75']] |
reasons to be pretty | https://en.wikipedia.org/wiki/Reasons_to_be_pretty | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18963715-1.html.csv | unique | reasons to be pretty only won one award despite being nominated several times . | {'scope': 'all', 'row': '7', 'col': '5', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'won', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'won'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to won .', 'tostr': 'filter_eq { all_rows ; result ; won }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; won } } = true', 'tointer': 'select the rows whose result record fuzzily matches to won . there is only one such row in the table .'} | only { filter_eq { all_rows ; result ; won } } = true | select the rows whose result record fuzzily matches to won . 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, 'result_4': 4, 'won_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'result_4': 'result', 'won_5': 'won'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'result_4': [0], 'won_5': [0]} | ['year', 'award ceremony', 'category', 'nominee', 'result'] | [['2009', 'tony award', 'best play', 'neil labute', 'nominated'], ['2009', 'tony award', 'best performance by a leading actor in a play', 'thomas sadoski', 'nominated'], ['2009', 'tony award', 'best performance by a featured actress in a play', 'marin ireland', 'nominated'], ['2009', 'drama desk award', 'outstanding play', 'outstanding play', 'nominated'], ['2009', 'drama desk award', 'outstanding actor in a play', 'thomas sadoski', 'nominated'], ['2009', 'drama desk award', 'outstanding director of a play', 'terry kinney', 'nominated'], ['2009', 'theatre world award', 'theatre world award', 'marin ireland', 'won']] |
4th and long | https://en.wikipedia.org/wiki/4th_and_Long | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22603701-1.html.csv | superlative | the oldest person participating in 4th and long is donte gamble . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '7', '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', 'age'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; age }'}, 'name'], 'result': 'donte gamble', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; age } ; name }'}, 'donte gamble'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; age } ; name } ; donte gamble } = true', 'tointer': 'select the row whose age record of all rows is maximum . the name record of this row is donte gamble .'} | eq { hop { argmax { all_rows ; age } ; name } ; donte gamble } = true | select the row whose age record of all rows is maximum . the name record of this row is donte gamble . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'age_5': 5, 'name_6': 6, 'donte gamble_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'age_5': 'age', 'name_6': 'name', 'donte gamble_7': 'donte gamble'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'age_5': [0], 'name_6': [1], 'donte gamble_7': [2]} | ['position', 'name', 'jersey number', 'age', 'height', 'weight', 'college', 'result'] | [['wr', 'jesse holley', '83', '25', "6 ' 3", '216', 'north carolina', 'winner in episode 10'], ['wr', 'andrew hawkins', '82', '22', "5 ' 7", '175', 'toledo', 'runners up in episode 10'], ['db', 'ahmaad smith', '25', '25', "6 ' 0", '196', 'tennessee state', 'runners up in episode 10'], ['db', 'eddie moten', '24', '27', "5 ' 10", '185', 'texas a & mkingsville', 'runners up in episode 10'], ['db', 'moses washington', '26', '28', "6 ' 0", '164', 'oklahoma', 'cut in episode 9'], ['wr', 'montrell jones', '84', '27', "6 ' 2", '205', 'tennessee / louisville', 'cut in episode 8'], ['db', 'donte gamble', '22', '30', "5 ' 8", '165', 'san diego state', 'cut in episode 7'], ['wr', 'steve gonzalez', '81', '24', "6 ' 2", '205', 'menlo college', 'cut in episode 5'], ['wr', 'luke swan', '86', '24', "6 ' 0", '193', 'wisconsin', 'cut in episode 4'], ['db', 'erick jackson', '23', '24', "6 ' 1", '195', 'texas', 'cut in episode 3'], ['wr', 'preston mcgann', '85', '25', "6 ' 3", '203', 'seminole community college', 'cut in episode 2']] |
jim clark | https://en.wikipedia.org/wiki/Jim_Clark | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-181892-4.html.csv | count | between 1963 and 1967 , jim clark completed three years of completing all 200 laps in the formula one . | {'scope': 'all', 'criterion': 'equal', 'value': '200', 'result': '3', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'laps completed', '200'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps completed record is equal to 200 .', 'tostr': 'filter_eq { all_rows ; laps completed ; 200 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; laps completed ; 200 } }', 'tointer': 'select the rows whose laps completed record is equal to 200 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; laps completed ; 200 } } ; 3 } = true', 'tointer': 'select the rows whose laps completed record is equal to 200 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; laps completed ; 200 } } ; 3 } = true | select the rows whose laps completed record is equal to 200 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'laps completed_5': 5, '200_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'laps completed_5': 'laps completed', '200_6': '200', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'laps completed_5': [0], '200_6': [0], '3_7': [2]} | ['year', 'car number', 'start', 'qual speed', 'speed rank', 'finish', 'laps completed', 'laps led', 'race status', 'chassis'] | [['1963', '92', '5', '149.750', '7', '2', '200', '28', 'running', 'lotus - ford 29 / 3'], ['1964', '6', '1', '158.828', '1', '24', '47', '14', 'suspension', 'lotus - ford 34 / 3'], ['1965', '82', '2', '160.729', '2', '1', '200', '190', 'running', 'lotus - ford 38 / 1'], ['1966', '19', '2', '164.114', '2', '2', '200', '66', 'running', 'lotus - ford 38 / 4'], ['1967', '31', '16', '163.213', '23', '31', '35', '0', 'piston', 'lotus - ford 38 / 7']] |
canadian open ( badminton ) | https://en.wikipedia.org/wiki/Canadian_Open_%28badminton%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12552861-1.html.csv | count | tan joe hok played in the men 's singles twice . | {'scope': 'all', 'criterion': 'equal', 'value': 'tan joe hok', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', "men 's singles", 'tan joe hok'], 'result': None, 'ind': 0, 'tointer': "select the rows whose men 's singles record fuzzily matches to tan joe hok .", 'tostr': "filter_eq { all_rows ; men 's singles ; tan joe hok }"}], 'result': '2', 'ind': 1, 'tostr': "count { filter_eq { all_rows ; men 's singles ; tan joe hok } }", 'tointer': "select the rows whose men 's singles record fuzzily matches to tan joe hok . the number of such rows is 2 ."}, '2'], 'result': True, 'ind': 2, 'tostr': "eq { count { filter_eq { all_rows ; men 's singles ; tan joe hok } } ; 2 } = true", 'tointer': "select the rows whose men 's singles record fuzzily matches to tan joe hok . the number of such rows is 2 ."} | eq { count { filter_eq { all_rows ; men 's singles ; tan joe hok } } ; 2 } = true | select the rows whose men 's singles record fuzzily matches to tan joe hok . 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, "men 's singles_5": 5, 'tan joe hok_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', "men 's singles_5": "men 's singles", 'tan joe hok_6': 'tan joe hok', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], "men 's singles_5": [0], 'tan joe hok_6': [0], '2_7': [2]} | ['year', "men 's singles", "women 's singles", "men 's doubles", "women 's doubles", 'mixed doubles'] | [['1957', 'dave f mctaggart', 'judy devlin', 'don k smythe h budd porter', 'sue devlin judy devlin', 'robert b williams ethel marshall'], ['1958', 'dave f mctaggart', 'jean miller', 'don k smythe h budd porter', 'marjorie shedd joan hennessy', 'william purcell marjorie shedd'], ['1959', 'tan joe hok', 'judy devlin', 'lim say hup teh kew san', 'sue devlin judy devlin', 'don p davis judy devlin'], ['1960', 'tan joe hok', 'marjorie shedd', 'lim say hup teh kew san', 'lois alston beulah armendariz', 'finn kobberø jean miller'], ['1961', 'erland kops', 'marjorie shedd', 'finn kobberø jörgen hammergaard hansen', 'marjorie shedd dorothy tinline', 'finn kobberø jean miller']] |
steamboats of coos bay | https://en.wikipedia.org/wiki/Steamboats_of_Coos_Bay | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15289945-1.html.csv | majority | of the seven steamboats from coos bay , most of them were built in marshfield . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'marshfield', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'where built', 'marshfield'], 'result': True, 'ind': 0, 'tointer': 'for the where built records of all rows , most of them fuzzily match to marshfield .', 'tostr': 'most_eq { all_rows ; where built ; marshfield } = true'} | most_eq { all_rows ; where built ; marshfield } = true | for the where built records of all rows , most of them fuzzily match to marshfield . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'where built_3': 3, 'marshfield_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'where built_3': 'where built', 'marshfield_4': 'marshfield'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'where built_3': [0], 'marshfield_4': [0]} | ['name', 'type', 'year built', 'where built', 'length'] | [['messenger', 'sternwheeler', '1872', 'empire city', "91 '"], ['juno', 'propeller', '1906', 'marshfield', "60.8 '"], ['millicoma', 'sternwheeler', '1909', 'marshfield', "55 '"], ['pedler', 'sternwheeler', '1908', 'marshfield', "124 '"], ['fay no 4', 'sternwheeler ( gasoline )', '1912', 'north bend', "136 '"], ['life - line', 'propeller ( gasoline )', '1912', 'marshfield', "36 '"], ['rainbow', 'sternwheeler', '1912', 'marshfield', "64 '"]] |
dwkt | https://en.wikipedia.org/wiki/DWKT | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23915973-1.html.csv | superlative | 106.7 energy fm has the highest power kw of all the radio stations . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', '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', 'power kw'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; power kw }'}, 'branding'], 'result': '106.7 energy fm', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; power kw } ; branding }'}, '106.7 energy fm'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; power kw } ; branding } ; 106.7 energy fm } = true', 'tointer': 'select the row whose power kw record of all rows is maximum . the branding record of this row is 106.7 energy fm .'} | eq { hop { argmax { all_rows ; power kw } ; branding } ; 106.7 energy fm } = true | select the row whose power kw record of all rows is maximum . the branding record of this row is 106.7 energy fm . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'power kw_5': 5, 'branding_6': 6, '106.7 energy fm_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'power kw_5': 'power kw', 'branding_6': 'branding', '106.7 energy fm_7': '106.7 energy fm'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'power kw_5': [0], 'branding_6': [1], '106.7 energy fm_7': [2]} | ['branding', 'callsign', 'frequency', 'power kw', 'coverage'] | [['106.7 energy fm', 'dwet - fm', '106.7 mhz', '25 kw', 'mega manila'], ['106.3 energy fm naga', 'dwbq - fm', '106.3 mhz', '10 kw', 'naga bicol region'], ['94.7 energy fm cebu', 'dykt - fm', '94.7 mhz', '10 kw', 'cebu visayas region'], ['93.7 energy fm dumaguete', 'dymd - fm', '93.7 mhz', '10 kw', 'dumaguete central visayas region'], ['103.7 energy fm dipolog', 'dxru - fm', '103.7 mhz', '5 kw', 'dipolog western mindanao region'], ['88.3 energy fm davao', 'dxdr - fm', '88.3 mhz', '10 kw', 'davao mindanao region']] |
katarina srebotnik | https://en.wikipedia.org/wiki/Katarina_Srebotnik | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1729366-2.html.csv | majority | katarina srebotnik played the majority of her championship tennis rounds on clay courts . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to clay .', 'tostr': 'most_eq { all_rows ; surface ; clay } = true'} | most_eq { all_rows ; surface ; clay } = true | for the surface records of all rows , most of them fuzzily match to clay . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]} | ['outcome', 'year', 'championship', 'surface', 'partner', 'opponents in the final', 'score in the final'] | [['winner', '1999', 'french open', 'clay', 'piet norval', 'larisa neiland rick leach', '6 - 3 , 3 - 6 , 6 - 3'], ['runner - up', '2002', 'us open', 'hard', 'bob bryan', 'lisa raymond mike bryan', '6 - 7 , 6 - 7'], ['winner', '2003', 'us open', 'hard', 'bob bryan', 'lina krasnoroutskaya daniel nestor', '5 - 7 , 7 - 5 , 7 - 6 ( 7 - 5 )'], ['runner - up', '2005', 'us open', 'hard', 'nenad zimonjić', 'daniela hantuchová mahesh bhupathi', '4 - 6 , 2 - 6'], ['winner', '2006', 'french open ( 2 )', 'clay', 'nenad zimonjić', 'elena likhovtseva daniel nestor', '6 - 3 , 6 - 4'], ['runner - up', '2007', 'french open', 'clay', 'nenad zimonjić', 'nathalie dechy andy ram', '5 - 7 , 3 - 6'], ['runner - up', '2008', 'french open', 'clay', 'nenad zimonjić', 'victoria azarenka bob bryan', '2 - 6 , 6 - 7 ( 4 - 7 )'], ['runner - up', '2008', 'wimbledon', 'grass', 'mike bryan', 'samantha stosur bob bryan', '5 - 7 , 4 - 6'], ['winner', '2010', 'french open ( 3 )', 'clay', 'nenad zimonjić', 'yaroslava shvedova julian knowle', '4 - 6 , 7 - 6 ( 7 - 5 ) ,'], ['winner', '2011', 'australian open', 'hard', 'daniel nestor', 'yung - jan chan paul hanley', '6 - 3 , 3 - 6 ,'], ['runner - up', '2011', 'french open', 'clay', 'nenad zimonjić', 'casey dellacqua scott lipsky', '6 - 7 ( 6 - 8 ) , 6 - 4 ,']] |
international wrestling association | https://en.wikipedia.org/wiki/International_Wrestling_Association | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1272033-1.html.csv | ordinal | atomo & sonico are the fourth most recent champions of the international wrestling association . | {'row': '5', 'col': '4', 'order': '4', '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', 'date won', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; date won ; 4 }'}, 'champion ( s )'], 'result': 'atomo & sonico', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; date won ; 4 } ; champion ( s ) }'}, 'atomo & sonico'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; date won ; 4 } ; champion ( s ) } ; atomo & sonico } = true', 'tointer': 'select the row whose date won record of all rows is 4th maximum . the champion ( s ) record of this row is atomo & sonico .'} | eq { hop { nth_argmax { all_rows ; date won ; 4 } ; champion ( s ) } ; atomo & sonico } = true | select the row whose date won record of all rows is 4th maximum . the champion ( s ) record of this row is atomo & sonico . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'date won_5': 5, '4_6': 6, 'champion (s)_7': 7, 'atomo & sonico_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', 'date won_5': 'date won', '4_6': '4', 'champion (s)_7': 'champion ( s )', 'atomo & sonico_8': 'atomo & sonico'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'date won_5': [0], '4_6': [0], 'champion (s)_7': [1], 'atomo & sonico_8': [2]} | ['championship', 'champion ( s )', 'previous champion ( s )', 'date won', 'location'] | [['iwa undisputed world heavyweight championship', 'bonecrusher', 'jay - cobs', 'january 29 , 2012', 'bayamón , puerto rico'], ['iwa intercontinental heavyweight championship', 'chris angel', 'diabólico', 'december 5 , 2010', 'bayamón , puerto rico'], ['iwa caribbean heavyweight championship', 'xix xavant', 'vacant', 'october 16 , 2010', 'aguas buenas , puerto rico'], ['iwa puerto rico heavyweight championship', 'noel rodríguez', 'vacant', 'december 5 , 2010', 'bayamón , puerto rico'], ['iwa world tag team championship', 'atomo & sonico', 'rick stanley & dennis rivera', 'november 20 , 2010', 'bayamón , puerto rico'], ["iwa world women 's championship", 'vacant', 'genesis', 'july 7 , 2010', 'bayamón , puerto rico'], ['iwa xtreme combat division championship', 'havok', 'lash', 'october 16 , 2010', 'bayamón , puerto rico']] |
karen kavaleryan | https://en.wikipedia.org/wiki/Karen_Kavaleryan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17632536-1.html.csv | ordinal | karen kavaleryan 's song peace will come received the 2nd lowest number of points . | {'row': '6', 'col': '5', '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', 'points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; points ; 2 }'}, 'song'], 'result': 'peace will come', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; points ; 2 } ; song }'}, 'peace will come'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; points ; 2 } ; song } ; peace will come } = true', 'tointer': 'select the row whose points record of all rows is 2nd minimum . the song record of this row is peace will come .'} | eq { hop { nth_argmin { all_rows ; points ; 2 } ; song } ; peace will come } = true | select the row whose points record of all rows is 2nd minimum . the song record of this row is peace will come . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'points_5': 5, '2_6': 6, 'song_7': 7, 'peace will come_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', 'points_5': 'points', '2_6': '2', 'song_7': 'song', 'peace will come_8': 'peace will come'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'points_5': [0], '2_6': [0], 'song_7': [1], 'peace will come_8': [2]} | ['year', 'song', 'artist', 'place', 'points', 'composer'] | [['2002', 'northern girl 1', 'prime minister', '10', '55', 'kim breitburg'], ['2006', 'never let you go 2', 'dima bilan', '2 ( sf : 3rd )', '248 ( sf : 217 )', 'alexander lunyov'], ['2007', 'work your magic', 'dmitry koldun', '6 ( sf : 4th )', '145 ( sf : 176 )', 'philipp kirkorov'], ['2007', 'anytime you need 3', 'hayko', '8 ( sf : - )', '138 ( sf : - )', 'hayko'], ['2008', 'shady lady', 'ani lorak', '2 ( sf : 1 )', '230 ( sf : 152 )', 'philipp kirkorov'], ['2008', 'peace will come', 'diana gurtskaya', '11 ( sf : 5 )', '83 ( sf : 107 )', 'kim breitburg'], ['2010', 'apricot stone', 'eva rivas', '7 ( sf :6 )', '141 ( sf :83 )', 'armen martirosyan'], ['2013', 'gravity', 'zlata ognevich', '3 ( sf :3 )', '214 ( sf :140 )', 'm nekrosov']] |
1961 houston oilers season | https://en.wikipedia.org/wiki/1961_Houston_Oilers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15991313-3.html.csv | comparative | more people attended the first game of the oilers 1961 season than the last game . | {'row_1': '1', 'row_2': '14', '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', 'week', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose week record fuzzily matches to 1 .', 'tostr': 'filter_eq { all_rows ; week ; 1 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; week ; 1 } ; attendance }', 'tointer': 'select the rows whose week record fuzzily matches to 1 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', '15'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose week record fuzzily matches to 15 .', 'tostr': 'filter_eq { all_rows ; week ; 15 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; week ; 15 } ; attendance }', 'tointer': 'select the rows whose week record fuzzily matches to 15 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; week ; 1 } ; attendance } ; hop { filter_eq { all_rows ; week ; 15 } ; attendance } } = true', 'tointer': 'select the rows whose week record fuzzily matches to 1 . take the attendance record of this row . select the rows whose week record fuzzily matches to 15 . take the attendance record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; week ; 1 } ; attendance } ; hop { filter_eq { all_rows ; week ; 15 } ; attendance } } = true | select the rows whose week record fuzzily matches to 1 . take the attendance record of this row . select the rows whose week record fuzzily matches to 15 . take the attendance 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, 'week_7': 7, '1_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'week_11': 11, '15_12': 12, 'attendance_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', 'week_7': 'week', '1_8': '1', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'week_11': 'week', '15_12': '15', 'attendance_13': 'attendance'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'week_7': [0], '1_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'week_11': [1], '15_12': [1], 'attendance_13': [3]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 9 , 1961', 'oakland raiders', 'w 55 - 0', '16231'], ['3', 'september 24 , 1961', 'san diego chargers', 'l 34 - 24', '29210'], ['4', 'october 1 , 1961', 'dallas texans', 'l 26 - 21', '28000'], ['5', 'october 8 , 1961', 'buffalo bills', 'l 22 - 12', '22761'], ['6', 'october 13 , 1961', 'boston patriots', 't 31 - 31', '15070'], ['7', 'october 22 , 1961', 'dallas texans', 'w 38 - 7', '21237'], ['8', 'october 29 , 1961', 'buffalo bills', 'w 28 - 16', '23228'], ['9', 'november 5 , 1961', 'denver broncos', 'w 55 - 14', '11564'], ['10', 'november 12 , 1961', 'boston patriots', 'w 27 - 15', '35649'], ['11', 'november 19 , 1961', 'new york titans', 'w 49 - 13', '33428'], ['12', 'november 26 , 1961', 'denver broncos', 'w 45 - 14', '27864'], ['13', 'december 3 , 1961', 'san diego chargers', 'w 33 - 13', '37845'], ['14', 'december 10 , 1961', 'new york titans', 'w 48 - 21', '9462'], ['15', 'december 17 , 1961', 'oakland raiders', 'w 47 - 16', '4821']] |
li haiqiang | https://en.wikipedia.org/wiki/Li_Haiqiang | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11408785-2.html.csv | count | li haiqiang had two appearances in international friendly competition matches . | {'scope': 'all', 'criterion': 'equal', 'value': 'friendly', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', 'friendly'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to friendly .', 'tostr': 'filter_eq { all_rows ; competition ; friendly }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; competition ; friendly } }', 'tointer': 'select the rows whose competition record fuzzily matches to friendly . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; competition ; friendly } } ; 2 } = true', 'tointer': 'select the rows whose competition record fuzzily matches to friendly . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; competition ; friendly } } ; 2 } = true | select the rows whose competition record fuzzily matches to friendly . 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, 'competition_5': 5, 'friendly_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', 'competition_5': 'competition', 'friendly_6': 'friendly', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'competition_5': [0], 'friendly_6': [0], '2_7': [2]} | ['date', 'venue', 'home / away', 'result', 'scored', 'competition'] | [['19 november 2008', 'ust stadium , macau', 'a', '9 - 1', '0', 'friendly'], ['25 august 2009', 'world games stadium , kaohsiung , taiwan', 'n', '0 - 0', '0', '2010 east asian football championship semi - final'], ['27 august 2009', 'world games stadium , kaohsiung , taiwan', 'n', '12 - 0', '0', '2010 east asian football championship semi - final'], ['18 november 2009', 'hong kong stadium , hong kong', 'h', '0 - 4', '0', '2011 afc asian cup qualification'], ['7 february 2010', 'olympic stadium , tokyo , japan', 'n', '0 - 5', '0', '2010 east asian football championship'], ['4 october 2010', 'balewadi stadium , pune , india', 'a', '1 - 0', '1', 'friendly']] |
2003 games of the small states of europe | https://en.wikipedia.org/wiki/2003_Games_of_the_Small_States_of_Europe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11316160-1.html.csv | count | two different countries had 15 bronze medals in the 2003 games of the small states of europe . | {'scope': 'all', 'criterion': 'equal', 'value': '15', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'bronze', '15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bronze record is equal to 15 .', 'tostr': 'filter_eq { all_rows ; bronze ; 15 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; bronze ; 15 } }', 'tointer': 'select the rows whose bronze record is equal to 15 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; bronze ; 15 } } ; 2 } = true', 'tointer': 'select the rows whose bronze record is equal to 15 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; bronze ; 15 } } ; 2 } = true | select the rows whose bronze record is equal to 15 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'bronze_5': 5, '15_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'bronze_5': 'bronze', '15_6': '15', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'bronze_5': [0], '15_6': [0], '2_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'cyprus', '34', '20', '27', '81'], ['2', 'luxembourg', '21', '17', '15', '53'], ['3', 'iceland', '20', '24', '23', '67'], ['4', 'malta', '11', '18', '15', '44'], ['5', 'monaco', '7', '7', '10', '24'], ['6', 'san marino', '6', '10', '9', '25'], ['7', 'andorra', '4', '6', '8', '18'], ['8', 'liechtenstein', '2', '1', '2', '5']] |
sofia mattsson | https://en.wikipedia.org/wiki/Sofia_Mattsson | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13047884-1.html.csv | unique | the only year that sofia mattson placed higher than 10th was in 2008 . | {'scope': 'all', 'row': '4', 'col': '4', 'col_other': '1', 'criterion': 'greater_than', 'value': '10', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'position', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record is greater than 10 .', 'tostr': 'filter_greater { all_rows ; position ; 10 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; position ; 10 } }', 'tointer': 'select the rows whose position record is greater than 10 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'position', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record is greater than 10 .', 'tostr': 'filter_greater { all_rows ; position ; 10 }'}, 'year'], 'result': '2008', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; position ; 10 } ; year }'}, '2008'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; position ; 10 } ; year } ; 2008 }', 'tointer': 'the year record of this unqiue row is 2008 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; position ; 10 } } ; eq { hop { filter_greater { all_rows ; position ; 10 } ; year } ; 2008 } } = true', 'tointer': 'select the rows whose position record is greater than 10 . there is only one such row in the table . the year record of this unqiue row is 2008 .'} | and { only { filter_greater { all_rows ; position ; 10 } } ; eq { hop { filter_greater { all_rows ; position ; 10 } ; year } ; 2008 } } = true | select the rows whose position record is greater than 10 . there is only one such row in the table . the year record of this unqiue row is 2008 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'position_7': 7, '10_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '2008_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'position_7': 'position', '10_8': '10', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '2008_10': '2008'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], '10_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '2008_10': [3]} | ['year', 'competition', 'venue', 'position', 'event'] | [['2007', 'european championships', 'sofia , bulgaria', '3rd', '48 kg'], ['2007', 'world championships', 'baku , azerbaijan', '8th', '48 kg'], ['2008', 'european championships', 'tampere , finland', '2nd', '51 kg'], ['2008', 'olympic games', 'beijing , republic of china', '12th', '48 kg'], ['2009', 'european championships', 'vilnius , lithuania', '10th', '51 kg'], ['2009', 'world championships', 'herning , denmark', '1st', '51 kg'], ['2010', 'european championships', 'baku , azerbaijan', '1st', '51 kg'], ['2010', 'world championships', 'moscow , russia', '3rd', '51 kg'], ['2011', 'world championships', 'istanbul , turkey', '2nd', '59 kg'], ['2012', 'european championships', 'belgrade , serbia', '2nd', '55 kg'], ['2012', 'olympic games', 'london , great britain', '7th', '55 kg'], ['2013', 'european championships', 'tbilisi , georgia', '1st', '55 kg']] |
1996 ipc ice sledge hockey world championships | https://en.wikipedia.org/wiki/1996_IPC_Ice_Sledge_Hockey_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16745556-1.html.csv | ordinal | in the 1996 ipc ice sledge hockey world championships held in nynäshamn , sweden , estonia ranked fourth with a total of two wins and three losses and four points . | {'scope': 'all', 'row': '4', 'col': '1', 'order': '4', 'col_other': '2,4,6,7', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'rank', '4'], 'result': '4', 'ind': 0, 'tostr': 'nth_min { all_rows ; rank ; 4 }', 'tointer': 'the 4th minimum rank record of all rows is 4 .'}, '4'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; rank ; 4 } ; 4 }', 'tointer': 'the 4th minimum rank record of all rows is 4 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '4'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 4 }'}, 'team'], 'result': 'estonia', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 4 } ; team }'}, 'estonia'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 4 } ; team } ; estonia }', 'tointer': 'the team record of the row with 4th minimum rank record is estonia .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '4'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 4 }'}, 'wins'], 'result': '2', 'ind': 5, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 4 } ; wins }'}, '2'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 4 } ; wins } ; 2 }', 'tointer': 'the wins record of the row with 4th minimum rank record is 2 .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '4'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 4 }'}, 'losses'], 'result': '3', 'ind': 7, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 4 } ; losses }'}, '3'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 }', 'tointer': 'the losses record of the row with 4th minimum rank record is 3 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '4'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 4 }'}, 'points'], 'result': '4', 'ind': 9, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 4 } ; points }'}, '4'], 'result': True, 'ind': 10, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 }', 'tointer': 'the points record of the row with 4th minimum rank record is 4 .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 } ; eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 } }', 'tointer': 'the losses record of the row with 4th minimum rank record is 3 . the points record of the row with 4th minimum rank record is 4 .'}], 'result': True, 'ind': 12, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; wins } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 } ; eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 } } }', 'tointer': 'the wins record of the row with 4th minimum rank record is 2 . the losses record of the row with 4th minimum rank record is 3 . the points record of the row with 4th minimum rank record is 4 .'}], 'result': True, 'ind': 13, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; team } ; estonia } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; wins } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 } ; eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 } } } }', 'tointer': 'the team record of the row with 4th minimum rank record is estonia . the wins record of the row with 4th minimum rank record is 2 . the losses record of the row with 4th minimum rank record is 3 . the points record of the row with 4th minimum rank record is 4 .'}], 'result': True, 'ind': 14, 'tostr': 'and { eq { nth_min { all_rows ; rank ; 4 } ; 4 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; team } ; estonia } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; wins } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 } ; eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 } } } } } = true', 'tointer': 'the 4th minimum rank record of all rows is 4 . the team record of the row with 4th minimum rank record is estonia . the wins record of the row with 4th minimum rank record is 2 . the losses record of the row with 4th minimum rank record is 3 . the points record of the row with 4th minimum rank record is 4 .'} | and { eq { nth_min { all_rows ; rank ; 4 } ; 4 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; team } ; estonia } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; wins } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 } ; eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 } } } } } = true | the 4th minimum rank record of all rows is 4 . the team record of the row with 4th minimum rank record is estonia . the wins record of the row with 4th minimum rank record is 2 . the losses record of the row with 4th minimum rank record is 3 . the points record of the row with 4th minimum rank record is 4 . | 18 | 15 | {'and_14': 14, 'result_15': 15, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_16': 16, 'rank_17': 17, '4_18': 18, '4_19': 19, 'and_13': 13, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_20': 20, 'rank_21': 21, '4_22': 22, 'team_23': 23, 'estonia_24': 24, 'and_12': 12, 'eq_6': 6, 'num_hop_5': 5, 'wins_25': 25, '2_26': 26, 'and_11': 11, 'eq_8': 8, 'num_hop_7': 7, 'losses_27': 27, '3_28': 28, 'eq_10': 10, 'num_hop_9': 9, 'points_29': 29, '4_30': 30} | {'and_14': 'and', 'result_15': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_16': 'all_rows', 'rank_17': 'rank', '4_18': '4', '4_19': '4', 'and_13': 'and', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_20': 'all_rows', 'rank_21': 'rank', '4_22': '4', 'team_23': 'team', 'estonia_24': 'estonia', 'and_12': 'and', 'eq_6': 'eq', 'num_hop_5': 'num_hop', 'wins_25': 'wins', '2_26': '2', 'and_11': 'and', 'eq_8': 'eq', 'num_hop_7': 'num_hop', 'losses_27': 'losses', '3_28': '3', 'eq_10': 'eq', 'num_hop_9': 'num_hop', 'points_29': 'points', '4_30': '4'} | {'and_14': [15], 'result_15': [], 'eq_1': [14], 'nth_min_0': [1], 'all_rows_16': [0], 'rank_17': [0], '4_18': [0], '4_19': [1], 'and_13': [14], 'str_eq_4': [13], 'str_hop_3': [4], 'nth_argmin_2': [3, 5, 7, 9], 'all_rows_20': [2], 'rank_21': [2], '4_22': [2], 'team_23': [3], 'estonia_24': [4], 'and_12': [13], 'eq_6': [12], 'num_hop_5': [6], 'wins_25': [5], '2_26': [6], 'and_11': [12], 'eq_8': [11], 'num_hop_7': [8], 'losses_27': [7], '3_28': [8], 'eq_10': [11], 'num_hop_9': [10], 'points_29': [9], '4_30': [10]} | ['rank', 'team', 'played', 'wins', 'ties', 'losses', 'points'] | [['1', 'sweden', '5', '4', '1', '0', '9'], ['2', 'canada', '5', '3', '2', '0', '8'], ['3', 'norway', '5', '3', '1', '1', '7'], ['4', 'estonia', '5', '2', '0', '3', '4'], ['5', 'united states', '5', '1', '0', '4', '2'], ['6', 'japan', '5', '0', '0', '5', '0']] |
list of geological features on ganymede | https://en.wikipedia.org/wiki/List_of_geological_features_on_Ganymede | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16768245-5.html.csv | majority | most of the geological features on ganymede were named prior to 1998 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1998', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'year named', '1998'], 'result': True, 'ind': 0, 'tointer': 'for the year named records of all rows , most of them are less than 1998 .', 'tostr': 'most_less { all_rows ; year named ; 1998 } = true'} | most_less { all_rows ; year named ; 1998 } = true | for the year named records of all rows , most of them are less than 1998 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year named_3': 3, '1998_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year named_3': 'year named', '1998_4': '1998'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'year named_3': [0], '1998_4': [0]} | ['name', 'latitude', 'longitude', 'diameter', 'year named', 'namesake'] | [['akitu sulcus', '38.9 n', '194.3 w', '365.0', '1997', "where marduk 's statue was carried each year"], ['apsu sulci', '39.4 s', '234.7 w', '1950.0', '1979', 'sumero - akkadian , primordial ocean'], ['arbela sulcus', '21.1 s', '349.8 w', '1940.0', '1985', 'assyrian town where ishtar was worshipped'], ['bubastis sulci', '72.3 s', '282.9 w', '2651.0', '1988', 'town in egypt where bast was worshipped'], ['dukug sulcus', '83.5 n', '3.8 w', '385.0', '1985', 'sumerian holy cosmic chamber of the gods'], ['erech sulcus', '7.3 s', '179.2 w', '953.0', '1985', 'akkadian town that was built by marduk'], ['harpagia sulcus', '11.7 s', '318.7 w', '1792.0', '1985', 'greek , where ganymede was abducted an eagle'], ['hursag sulcus', '9.7 s', '233.1 w', '750.0', '1985', 'sumerian mountain where winds dwell'], ['lagash sulcus', '10.9 s', '163.2 w', '1575.0', '1985', 'early babylonian town'], ['larsa sulcus', '3.8 n', '248.7 w', '1000.0', '2000', 'sumerian town'], ['mysia sulci', '7.0 s', '7.9 w', '5066.0', '1979', 'greek , where ganymede was abducted by an eagle'], ['nineveh sulcus', '23.5 n', '53.1 w', '1700.0', '1997', 'city where ishtar was worshipped'], ['nippur sulcus', '36.9 n', '185.0 w', '1425.0', '1985', 'sumerian city'], ['philae sulcus', '65.5 n', '169.0 w', '900.0', '1997', 'temple that was the chief sanctuary of isis'], ['sippar sulcus', '15.4 s', '189.3 w', '1508.0', '1985', 'ancient babylonian town'], ['umma sulcus', '4.1 n', '250.0 w', '1270.0', '2000', 'sumerian town'], ['ur sulcus', '49.8 n', '177.5 w', '1145.0', '1985', 'ancient sumerian seat of moon worship']] |
southwestern conference ( illinois ) | https://en.wikipedia.org/wiki/Southwestern_Conference_%28Illinois%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27653955-1.html.csv | superlative | belleville east high school has the highest enrollment number in the southwestern conference in illinois . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '2', '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', 'enrollment'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; enrollment }'}, 'school'], 'result': 'belleville east high school', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; enrollment } ; school }'}, 'belleville east high school'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; enrollment } ; school } ; belleville east high school } = true', 'tointer': 'select the row whose enrollment record of all rows is maximum . the school record of this row is belleville east high school .'} | eq { hop { argmax { all_rows ; enrollment } ; school } ; belleville east high school } = true | select the row whose enrollment record of all rows is maximum . the school record of this row is belleville east high school . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, 'school_6': 6, 'belleville east high school_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', 'school_6': 'school', 'belleville east high school_7': 'belleville east high school'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], 'school_6': [1], 'belleville east high school_7': [2]} | ['school', 'location', 'mascot', 'colors', 'enrollment', 'ihsa classes 2 / 3 / 4', 'ihsa music class', 'ihsa football class', 'ihsa cheerleading class'] | [['alton high school', 'alton , il', 'redbirds', 'red , gray', '2135', 'aa / 3a / 4a', 'aa', '7a', 'large squad'], ['belleville east high school', 'belleville , il', 'lancers', 'columbia blue , navy blue', '2600', 'aa / 3a / 4a', 'aa', '8a', 'large squad'], ['belleville west high school', 'belleville , il', 'maroons', 'maroon , white', '2434', 'aa / 3a / 4a', 'aa', '7a', 'large squad'], ['collinsville high school', 'collinsville , il', 'kahoks', 'purple , white', '2020', 'aa / 3a / 4a', 'aa', '7a', 'large squad'], ['east st louis senior high school', 'east st louis , il', 'flyers / flyerettes', 'orange , blue', '2146', 'aa / 3a / 4a', 'aa', '7a', 'large squad'], ['edwardsville high school', 'edwardsville , il', 'tigers', 'orange , black', '2514', 'aa / 3a / 4a', 'aa', '8a', 'large squad'], ['granite city high school', 'granite city , il', 'warriors', 'red , black , white', '2129', 'aa / 3a / 4a', 'aa', '7a', 'large squad']] |
armageddon ( 2003 ) | https://en.wikipedia.org/wiki/Armageddon_%282003%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18717672-3.html.csv | comparative | rosey and the hurricane were eliminated from armageddon 2003 earlier than jindrak and cade . | {'row_1': '2', 'row_2': '4', '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', 'tag team', 'rosey and the hurricane'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tag team record fuzzily matches to rosey and the hurricane .', 'tostr': 'filter_eq { all_rows ; tag team ; rosey and the hurricane }'}, 'eliminated'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tag team ; rosey and the hurricane } ; eliminated }', 'tointer': 'select the rows whose tag team record fuzzily matches to rosey and the hurricane . take the eliminated record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tag team', 'jindrak and cade'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tag team record fuzzily matches to jindrak and cade .', 'tostr': 'filter_eq { all_rows ; tag team ; jindrak and cade }'}, 'eliminated'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tag team ; jindrak and cade } ; eliminated }', 'tointer': 'select the rows whose tag team record fuzzily matches to jindrak and cade . take the eliminated record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; tag team ; rosey and the hurricane } ; eliminated } ; hop { filter_eq { all_rows ; tag team ; jindrak and cade } ; eliminated } } = true', 'tointer': 'select the rows whose tag team record fuzzily matches to rosey and the hurricane . take the eliminated record of this row . select the rows whose tag team record fuzzily matches to jindrak and cade . take the eliminated record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; tag team ; rosey and the hurricane } ; eliminated } ; hop { filter_eq { all_rows ; tag team ; jindrak and cade } ; eliminated } } = true | select the rows whose tag team record fuzzily matches to rosey and the hurricane . take the eliminated record of this row . select the rows whose tag team record fuzzily matches to jindrak and cade . take the eliminated 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, 'tag team_7': 7, 'rosey and the hurricane_8': 8, 'eliminated_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tag team_11': 11, 'jindrak and cade_12': 12, 'eliminated_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', 'tag team_7': 'tag team', 'rosey and the hurricane_8': 'rosey and the hurricane', 'eliminated_9': 'eliminated', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tag team_11': 'tag team', 'jindrak and cade_12': 'jindrak and cade', 'eliminated_13': 'eliminated'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tag team_7': [0], 'rosey and the hurricane_8': [0], 'eliminated_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tag team_11': [1], 'jindrak and cade_12': [1], 'eliminated_13': [3]} | ['eliminated', 'tag team', 'entered', 'eliminated by', 'time'] | [['1', 'la résistance ( robért conway and rené duprée )', '2', 'rosey and the hurricane', '03:16'], ['2', 'rosey and the hurricane', '1', 'mark jindrak and garrison cade', '03:34'], ['3', 'val venis and lance storm', '4', 'jindrak and cade', '07:17'], ['4', 'jindrak and cade', '3', 'the dudley boyz ( bubba ray and d - von )', '11:29'], ['5', 'test and scott steiner', '6', 'dudley boyz', '16:38'], ['6', 'dudley boyz', '5', 'evolution ( ric flair and batista )', '20:48'], ['n / a', 'evolution', '7', 'winners', 'winners']] |
1973 england rugby union tour of fiji and new zealand | https://en.wikipedia.org/wiki/1973_England_rugby_union_tour_of_Fiji_and_New_Zealand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17020789-1.html.csv | majority | the majority of matches played in the 1973 england rugby union tour of fiji and new zealand were tour matches . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'tour match', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'status', 'tour match'], 'result': True, 'ind': 0, 'tointer': 'for the status records of all rows , most of them fuzzily match to tour match .', 'tostr': 'most_eq { all_rows ; status ; tour match } = true'} | most_eq { all_rows ; status ; tour match } = true | for the status records of all rows , most of them fuzzily match to tour match . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'status_3': 3, 'tour match_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'status_3': 'status', 'tour match_4': 'tour match'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'status_3': [0], 'tour match_4': [0]} | ['opposing team', 'against', 'date', 'venue', 'status'] | [['fiji', '12', '28 / 08 / 1973', 'buckhurst park , suva', 'tour match'], ['taranaki', '6', '01 / 09 / 1973', 'rugby park , new plymouth', 'tour match'], ['wellington', '25', '05 / 09 / 1973', 'athletic park , wellington', 'tour match'], ['canterbury', '19', '08 / 09 / 1973', 'lancaster park , christchurch', 'tour match'], ['new zealand', '10', '15 / 09 / 1973', 'eden park , auckland', 'test match']] |
football records in spain | https://en.wikipedia.org/wiki/Football_records_in_Spain | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17937080-2.html.csv | unique | the only team that scored over 120 goals in a season was real madrid . | {'scope': 'all', 'row': '1', 'col': '4', 'col_other': '2', 'criterion': 'greater_than', 'value': '120', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'goals', '120'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals record is greater than 120 .', 'tostr': 'filter_greater { all_rows ; goals ; 120 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; goals ; 120 } }', 'tointer': 'select the rows whose goals record is greater than 120 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'goals', '120'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals record is greater than 120 .', 'tostr': 'filter_greater { all_rows ; goals ; 120 }'}, 'club'], 'result': 'real madrid', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; goals ; 120 } ; club }'}, 'real madrid'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; goals ; 120 } ; club } ; real madrid }', 'tointer': 'the club record of this unqiue row is real madrid .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; goals ; 120 } } ; eq { hop { filter_greater { all_rows ; goals ; 120 } ; club } ; real madrid } } = true', 'tointer': 'select the rows whose goals record is greater than 120 . there is only one such row in the table . the club record of this unqiue row is real madrid .'} | and { only { filter_greater { all_rows ; goals ; 120 } } ; eq { hop { filter_greater { all_rows ; goals ; 120 } ; club } ; real madrid } } = true | select the rows whose goals record is greater than 120 . there is only one such row in the table . the club record of this unqiue row is real madrid . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'goals_7': 7, '120_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'real madrid_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'goals_7': 'goals', '120_8': '120', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'real madrid_10': 'real madrid'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'goals_7': [0], '120_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'real madrid_10': [3]} | ['rank', 'club', 'season', 'goals', 'apps'] | [['1', 'real madrid', '2011 / 12', '121', '38'], ['2', 'barcelona', '2012 / 13', '115', '38'], ['3', 'barcelona', '2011 / 12', '114', '38'], ['4', 'real madrid', '1989 / 90', '107', '38'], ['5', 'barcelona', '2008 / 09', '105', '38'], ['6', 'real madrid', '2012 / 13', '103', '38'], ['7', 'real madrid', '2009 / 10', '102', '38'], ['7', 'real madrid', '2010 / 11', '102', '38'], ['7', 'barcelona', '1996 / 97', '102', '42']] |
rotavirus | https://en.wikipedia.org/wiki/Rotavirus | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-140968-1.html.csv | count | two of the rna segments of the rotovirus are located in the vertices of the core . | {'scope': 'all', 'criterion': 'equal', 'value': 'the vertices of the core', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'the vertices of the core'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to the vertices of the core .', 'tostr': 'filter_eq { all_rows ; location ; the vertices of the core }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; the vertices of the core } }', 'tointer': 'select the rows whose location record fuzzily matches to the vertices of the core . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; the vertices of the core } } ; 2 } = true', 'tointer': 'select the rows whose location record fuzzily matches to the vertices of the core . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; location ; the vertices of the core } } ; 2 } = true | select the rows whose location record fuzzily matches to the vertices of the core . 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, 'location_5': 5, 'the vertices of the core_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', 'location_5': 'location', 'the vertices of the core_6': 'the vertices of the core', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'the vertices of the core_6': [0], '2_7': [2]} | ['rna segment ( gene )', 'size ( s base pair )', 'protein', 'molecular weight kda', 'location', 'copies per particle'] | [['1', '3302', 'vp1', '125', 'the vertices of the core', '< 25'], ['2', '2690', 'vp2', '102', 'forms inner shell of the core', '120'], ['3', '2591', 'vp3', '88', 'the vertices of the core', '< 25'], ['4', '2362', 'vp4', '87', 'surface spike', '120'], ['5', '1611', 'nsp1', '59', 'nonstructural', '0'], ['6', '1356', 'vp6', '45', 'inner capsid', '780'], ['7', '1104', 'nsp3', '37', 'nonstructural', '0'], ['8', '1059', 'nsp2', '35', 'nonstructural', '0'], ['9', '1062', 'vp7 1 vp7 2', '38 and 34', 'surface', '780'], ['10', '751', 'nsp4', '20', 'nonstructural', '0'], ['11', '667', 'nsp5 nsp6', '22', 'nonstructural', '0']] |
el salvador national under - 23 football team | https://en.wikipedia.org/wiki/El_Salvador_national_under-23_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13887424-4.html.csv | majority | most of the games played by the el salvador national under - 23 football team , were played in nashville , united states . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'nashville , united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'location :', 'nashville , united states'], 'result': True, 'ind': 0, 'tointer': 'for the location : records of all rows , most of them fuzzily match to nashville , united states .', 'tostr': 'most_eq { all_rows ; location : ; nashville , united states } = true'} | most_eq { all_rows ; location : ; nashville , united states } = true | for the location : records of all rows , most of them fuzzily match to nashville , united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'location :_3': 3, 'nashville , united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'location :_3': 'location :', 'nashville , united states_4': 'nashville , united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'location :_3': [0], 'nashville , united states_4': [0]} | ['date :', 'location :', 'opponent :', 'score', 'competition :'] | [['february 22 , 2012', 'san salvador , el salvador', 'puerto rico', '2 - 1', 'f'], ['march 1 , 2012', 'santa tecla , el salvador', 'santa tecla', '0 - 0', 'uf'], ['march 11 , 2012', 'germantown , united states', 'maryland terrapins', '3 - 1', 'f'], ['march 17 , 2012', 'houston , united states', 'honduras', '0 - 2', 'f'], ['march 22 , 2012', 'nashville , united states', 'canada', '0 - 0', 'oq - gs'], ['march 24 , 2012', 'nashville , united states', 'cuba', '4 - 0', 'oq - gs'], ['march 26 , 2012', 'nashville , united states', 'united states', '3 - 3', 'oq - gs'], ['march 31 , 2012', 'kansas city , united states', 'honduras', '2 - 3 ( aet )', 'oq - sf']] |
soo line locomotives | https://en.wikipedia.org/wiki/Soo_Line_locomotives | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17248696-8.html.csv | superlative | the earliest year alco - schenectady manufactured soo line locomotives was 1904 . | {'scope': 'subset', 'col_superlative': '5', 'row_superlative': '3', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '4', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'alco - schenectady'}} | {'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manufacturer', 'alco - schenectady'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; manufacturer ; alco - schenectady }', 'tointer': 'select the rows whose manufacturer record fuzzily matches to alco - schenectady .'}, 'year made'], 'result': '1904 - 1907', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; manufacturer ; alco - schenectady } ; year made }', 'tointer': 'select the rows whose manufacturer record fuzzily matches to alco - schenectady . the minimum year made record of these rows is 1904 - 1907 .'}, '1904 - 1907'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; manufacturer ; alco - schenectady } ; year made } ; 1904 - 1907 } = true', 'tointer': 'select the rows whose manufacturer record fuzzily matches to alco - schenectady . the minimum year made record of these rows is 1904 - 1907 .'} | eq { min { filter_eq { all_rows ; manufacturer ; alco - schenectady } ; year made } ; 1904 - 1907 } = true | select the rows whose manufacturer record fuzzily matches to alco - schenectady . the minimum year made record of these rows is 1904 - 1907 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'manufacturer_5': 5, 'alco - schenectady_6': 6, 'year made_7': 7, '1904 - 1907_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'manufacturer_5': 'manufacturer', 'alco - schenectady_6': 'alco - schenectady', 'year made_7': 'year made', '1904 - 1907_8': '1904 - 1907'} | {'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'manufacturer_5': [0], 'alco - schenectady_6': [0], 'year made_7': [1], '1904 - 1907_8': [2]} | ['class', 'wheel arrangement', 'fleet number ( s )', 'manufacturer', 'year made', 'quantity made', 'quantity preserved'] | [['4 - 6 - 2 - oooooo - pacific', '4 - 6 - 2 - oooooo - pacific', '4 - 6 - 2 - oooooo - pacific', '4 - 6 - 2 - oooooo - pacific', '4 - 6 - 2 - oooooo - pacific', '4 - 6 - 2 - oooooo - pacific', '4 - 6 - 2 - oooooo - pacific'], ['h', '4 - 6 - 2', '700', 'baldwin', '1904', '1', '0'], ['h - 1', '4 - 6 - 2', '701 - 722', 'alco - schenectady', '1904 - 1907', '22', '0'], ['h - 2', '4 - 6 - 2', '723 - 726', 'alco - schenectady', '1910', '4', '0'], ['h - 3', '4 - 6 - 2', '727 - 737', 'alco - schenectady', '1911 - 1913', '11', '3'], ['h - 20', '4 - 6 - 2', '2700 - 2703', 'alco - schenectady', '1909', '4', '0'], ['h - 21', '4 - 6 - 2', '2704 - 2713', 'alco - schenectady', '1911 - 1913', '10', '1'], ['h - 22', '4 - 6 - 2', '2714 - 2717', 'alco - schenectady', '1914', '4', '1'], ['h - 23', '4 - 6 - 2', '2718 - 2719', 'alco - schenectady', '1923', '6', '2']] |
87th united states congress | https://en.wikipedia.org/wiki/87th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1802522-4.html.csv | count | in the 87th united states congress , seven congressman died in 1961 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '( d )', 'result': '7', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'vacator', '( d )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose vacator record fuzzily matches to ( d ) .', 'tostr': 'filter_eq { all_rows ; vacator ; ( d ) }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; vacator ; ( d ) } }', 'tointer': 'select the rows whose vacator record fuzzily matches to ( d ) . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; vacator ; ( d ) } } ; 7 } = true', 'tointer': 'select the rows whose vacator record fuzzily matches to ( d ) . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; vacator ; ( d ) } } ; 7 } = true | select the rows whose vacator record fuzzily matches to ( d ) . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'vacator_5': 5, '(d)_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'vacator_5': 'vacator', '(d)_6': '( d )', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'vacator_5': [0], '(d)_6': [0], '7_7': [2]} | ['district', 'vacator', 'reason for change', 'successor', 'date successor seated'] | [['arkansas 6th', 'william f norrell ( d )', 'died february 15 , 1961', 'catherine dorris norrell ( d )', 'april 18 , 1961'], ['pennsylvania 16th', 'walter m mumma ( r )', 'died february 25 , 1961', 'john c kunkel ( r )', 'may 16 , 1961'], ['tennessee 1st', 'b carroll reece ( r )', 'died march 19 , 1961', 'louise goff reece ( r )', 'may 16 , 1961'], ['louisiana 4th', 'overton brooks ( d )', 'died september 16 , 1961', 'joe waggonner ( d )', 'october 19 , 1961'], ['michigan 14th', 'louis c rabaut ( d )', 'died november 12 , 1961', 'harold m ryan ( d )', 'february 13 , 1962'], ['texas 4th', 'sam rayburn ( d )', 'died november 16 , 1961', 'ray roberts ( d )', 'january 30 , 1962'], ['texas 13th', 'frank n ikard ( d )', 'resigned december 15 , 1961', 'graham b purcell , jr ( d )', 'january 27 , 1962'], ['south carolina 2nd', 'john j riley ( d )', 'died january 1 , 1962', 'corinne boyd riley ( d )', 'april 10 , 1962'], ['california 1st', 'clement w miller ( d )', 'died october 7 , 1962', 'vacant', 'not filled this term']] |
zdeněk zikán | https://en.wikipedia.org/wiki/Zden%C4%9Bk_Zik%C3%A1n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15813318-1.html.csv | aggregation | zdeněk zikán 's average score in these games was about 3.25 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '3.25', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '3.25', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '3.25'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 3.25 } = true', 'tointer': 'the average of the score record of all rows is 3.25 .'} | round_eq { avg { all_rows ; score } ; 3.25 } = true | the average of the score record of all rows is 3.25 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '3.25_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '3.25_5': '3.25'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '3.25_5': [1]} | ['date', 'venue', 'score', 'result', 'competition'] | [['2 april 1958', 'strahov stadium , prague , czechoslovakia', '3 - 2', 'win', 'friendly'], ['11 june 1958', 'olympiastadion , helsingborg , sweden', '2 - 2', 'draw', '1958 world cup'], ['15 june 1958', 'olympiastadion , helsingborg , sweden', '6 - 1', 'win', '1958 world cup'], ['17 june 1958', 'malmö stadion , malmö , sweden', '2 - 1', 'lost', '1958 world cup']] |
1972 isle of man tt | https://en.wikipedia.org/wiki/1972_Isle_of_Man_TT | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15753390-1.html.csv | majority | most of the participants in the 1972 isle of man tt were from the united kingdom . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united kingdom', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united kingdom'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united kingdom .', 'tostr': 'most_eq { all_rows ; country ; united kingdom } = true'} | most_eq { all_rows ; country ; united kingdom } = true | for the country records of all rows , most of them fuzzily match to united kingdom . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united kingdom_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united kingdom_4': 'united kingdom'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united kingdom_4': [0]} | ['place', 'rider', 'country', 'machine', 'speed', 'time', 'points'] | [['1', 'giacomo agostini', 'italy', 'mv agusta', '102.03 mph', '1:50.56.8', '15'], ['2', 'tony rutter', 'united kingdom', 'yamaha', '98.13 mph', '1:55.21.4', '12'], ['3', 'mick grant', 'united kingdom', 'yamaha', '97.57 mph', '1:56.01.0', '10'], ['4', 'jack findlay', 'australia', 'yamaha', '97.41 mph', '1:53.13.0', '8'], ['5', 'derek chatterton', 'united kingdom', 'yamaha', '95.65 mph', '1:58.21.4', '6'], ['6', 'selwyn griffiths', 'united kingdom', 'yamaha', '94.16 mph', '2:00.13.8', '5'], ['7', 'mick chatterton', 'united kingdom', 'yamaha', '92.98 mph', '2:01.45.2', '4'], ['8', 'lászló szabó', 'hungary', 'yamaha', '90.52 mph', '2:05.03.80', '3'], ['9', 'bill rae', 'united kingdom', 'yamaha', '90.51 mph', '2:05.04.80', '2'], ['10', 'blee', 'united kingdom', 'yamaha', '89.85 mph', '2:05.59.6', '1']] |
canadian interuniversity sport men 's soccer | https://en.wikipedia.org/wiki/Canadian_Interuniversity_Sport_men%27s_soccer | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27369069-4.html.csv | ordinal | for canadian interuniversity sport men 's soccer , the 2nd highest stadium capacity is at université laval . | {'row': '2', 'col': '7', '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', 'stadium capacity', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; stadium capacity ; 2 }'}, 'university'], 'result': 'université laval', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; stadium capacity ; 2 } ; university }'}, 'université laval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; stadium capacity ; 2 } ; university } ; université laval } = true', 'tointer': 'select the row whose stadium capacity record of all rows is 2nd maximum . the university record of this row is université laval .'} | eq { hop { nth_argmax { all_rows ; stadium capacity ; 2 } ; university } ; université laval } = true | select the row whose stadium capacity record of all rows is 2nd maximum . the university record of this row is université laval . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'stadium capacity_5': 5, '2_6': 6, 'university_7': 7, 'université laval_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', 'stadium capacity_5': 'stadium capacity', '2_6': '2', 'university_7': 'university', 'université laval_8': 'université laval'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'stadium capacity_5': [0], '2_6': [0], 'university_7': [1], 'université laval_8': [2]} | ['university', 'varsity name', 'city', 'province', 'founded', 'soccer stadium', 'stadium capacity'] | [['concordia university', 'stingers', 'montreal', 'qc', '1896', 'concordia stadium', '4000'], ['université laval', 'rouge et or', 'quebec city', 'qc', '1663', 'peps stadium', '12257'], ['mcgill university', 'redmen', 'montreal', 'qc', '1821', 'percival molson memorial stadium', '25012'], ['université de montréal', 'carabins', 'montreal', 'qc', '1821', 'cepsum stadium', '5100'], ['université de sherbrooke', 'vert et or', 'sherbrooke', 'qc', '1843', "stade de l'université de sherbrooke", '3359'], ['université du québec à montréal', 'citadins', 'montreal', 'qc', '1969', 'terrain 2 of complexe sportif claude - robillard', '1000']] |
atlantic city , new jersey | https://en.wikipedia.org/wiki/Atlantic_City%2C_New_Jersey | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-106211-1.html.csv | count | four casinos are located in the uptown section of atlantic city . | {'scope': 'all', 'criterion': 'equal', 'value': 'uptown', 'result': '4', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'section of atlantic city', 'uptown'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose section of atlantic city record fuzzily matches to uptown .', 'tostr': 'filter_eq { all_rows ; section of atlantic city ; uptown }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; section of atlantic city ; uptown } }', 'tointer': 'select the rows whose section of atlantic city record fuzzily matches to uptown . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; section of atlantic city ; uptown } } ; 4 } = true', 'tointer': 'select the rows whose section of atlantic city record fuzzily matches to uptown . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; section of atlantic city ; uptown } } ; 4 } = true | select the rows whose section of atlantic city record fuzzily matches to uptown . 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, 'section of atlantic city_5': 5, 'uptown_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', 'section of atlantic city_5': 'section of atlantic city', 'uptown_6': 'uptown', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'section of atlantic city_5': [0], 'uptown_6': [0], '4_7': [2]} | ['casino', 'opening date', 'theme', 'hotel rooms', 'section of atlantic city'] | [['atlantic club', 'december 12 , 1980', 'beach resort', '809', 'downbeach'], ["bally 's ᴮ", 'december 29 , 1979', 'modern', '1749', 'midtown'], ['borgata', 'july 2 , 2003', 'tuscany', '2767', 'marina'], ['caesars', 'june 26 , 1979', 'roman empire', '1141', 'midtown'], ['golden nugget', 'june 19 , 1985', 'gold rush era', '727', 'marina'], ["harrah 's", 'november 27 , 1980', 'marina waterfront', '2590', 'marina'], ['resorts', 'may 28 , 1978', 'roaring twenties', '942', 'uptown'], ['revel', 'april 2 , 2012', 'oceanfront', '1399', 'uptown'], ['showboat', 'april 2 , 1987', 'mardi gras', '1329', 'uptown'], ['tropicana', 'november 26 , 1981', 'old havana', '2078', 'downbeach'], ['trump plaza ᴬ', 'may 26 , 1984', 'luxury resort', '906', 'midtown'], ['taj mahal', 'april 2 , 1990', 'taj mahal', '2010', 'uptown']] |
2008 issf world cup final ( shotgun ) | https://en.wikipedia.org/wiki/2008_ISSF_World_Cup_Final_%28shotgun%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18351792-6.html.csv | count | in the 2008 issf world cup final , 2 of the shotters were from cze . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'cze', 'result': '2', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'shooter', 'cze'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose shooter record fuzzily matches to cze .', 'tostr': 'filter_eq { all_rows ; shooter ; cze }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; shooter ; cze } }', 'tointer': 'select the rows whose shooter record fuzzily matches to cze . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; shooter ; cze } } ; 2 } = true', 'tointer': 'select the rows whose shooter record fuzzily matches to cze . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; shooter ; cze } } ; 2 } = true | select the rows whose shooter record fuzzily matches to cze . 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, 'shooter_5': 5, 'cze_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', 'shooter_5': 'shooter', 'cze_6': 'cze', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'shooter_5': [0], 'cze_6': [0], '2_7': [2]} | ['shooter', 'event', 'rank points', 'score points', 'total'] | [['georgios achilleos ( cyp )', 'wcf 2007', 'defending champion', 'defending champion', 'defending champion'], ['vincent hancock ( usa )', 'og beijing', 'olympic gold medalist', 'olympic gold medalist', 'olympic gold medalist'], ['tore brovold ( nor )', 'og beijing', 'olympic silver medalist', 'olympic silver medalist', 'olympic silver medalist'], ['anthony terras ( fra )', 'og beijing', 'olympic bronze medalist', 'olympic bronze medalist', 'olympic bronze medalist'], ['ariel mauricio flores ( mex )', 'wc kerrville', '15', '12', '27'], ['qu ridong ( chn )', 'wc beijing', '15', '10', '25'], ['andrea benelli ( ita )', 'wc belgrade', '10', '13', '23'], ['konstantin tsuranov ( rus )', 'wc beijing', '10', '10', '20'], ['jan sychra ( cze )', 'wc belgrade', '5', '13', '18'], ['valerio luchini ( ita )', 'wc kerrville', '8', '10', '18'], ['leos hlavacek ( cze )', 'wc suhl', '5', '11', '16'], ['abdullah alrashidi ( kuw )', 'wc belgrade', '3', '12', '15']] |
the paul mccartney world tour | https://en.wikipedia.org/wiki/The_Paul_McCartney_World_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14936656-2.html.csv | majority | the majority of linda mccartney 's performances were on the keyboards on the paul mccartney world tour . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'keyboards', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'linda mccartney', 'keyboards'], 'result': True, 'ind': 0, 'tointer': 'for the linda mccartney records of all rows , most of them fuzzily match to keyboards .', 'tostr': 'most_eq { all_rows ; linda mccartney ; keyboards } = true'} | most_eq { all_rows ; linda mccartney ; keyboards } = true | for the linda mccartney records of all rows , most of them fuzzily match to keyboards . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'linda mccartney_3': 3, 'keyboards_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'linda mccartney_3': 'linda mccartney', 'keyboards_4': 'keyboards'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'linda mccartney_3': [0], 'keyboards_4': [0]} | ['paul mccartney', 'stuart', 'mcintosh', 'whitten', 'linda mccartney'] | [['bass', 'electric guitar', 'electric guitar', 'drums', 'tambourine'], ['bass', 'electric guitar', 'electric guitar', 'drums', 'keyboards'], ['bass', 'acoustic guitar', 'electric guitar', 'drums', 'keyboards'], ['piano', 'bass', 'electric guitar', 'drums', 'keyboards or drum'], ['piano', 'bass', 'electric guitar', 'drums', 'keyboards'], ['electric guitar', 'bass', 'electric guitar', 'drums', 'keyboards'], ['acoustic guitar', 'bass', 'electric guitar', 'drums', 'keyboards'], ['acoustic guitar', 'none', 'none', 'none', 'none'], ['piano', 'bass', 'electric guitar', 'drums', 'tambourine / keyboards'], ['none', 'bass', 'electric guitar', 'drums', 'keyboards'], ['keyboards / electric guitar', 'bass / electric guitar', 'electric guitar', 'drums', 'keyboards']] |
jennifer jones ( curler ) | https://en.wikipedia.org/wiki/Jennifer_Jones_%28curler%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1534617-2.html.csv | comparative | in 2012 - 2013 , jennifer jones had the same entry results for the masters that she had for autumn gold . | {'row_1': '3', 'row_2': '1', 'col': '8', 'col_other': '1', 'relation': 'equal', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'masters'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose event record fuzzily matches to masters .', 'tostr': 'filter_eq { all_rows ; event ; masters }'}, '2012 - 13'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; event ; masters } ; 2012 - 13 }', 'tointer': 'select the rows whose event record fuzzily matches to masters . take the 2012 - 13 record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'autumn gold'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose event record fuzzily matches to autumn gold .', 'tostr': 'filter_eq { all_rows ; event ; autumn gold }'}, '2012 - 13'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; event ; autumn gold } ; 2012 - 13 }', 'tointer': 'select the rows whose event record fuzzily matches to autumn gold . take the 2012 - 13 record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; event ; masters } ; 2012 - 13 } ; hop { filter_eq { all_rows ; event ; autumn gold } ; 2012 - 13 } } = true', 'tointer': 'select the rows whose event record fuzzily matches to masters . take the 2012 - 13 record of this row . select the rows whose event record fuzzily matches to autumn gold . take the 2012 - 13 record of this row . the first record fuzzily matches to the second record .'} | eq { hop { filter_eq { all_rows ; event ; masters } ; 2012 - 13 } ; hop { filter_eq { all_rows ; event ; autumn gold } ; 2012 - 13 } } = true | select the rows whose event record fuzzily matches to masters . take the 2012 - 13 record of this row . select the rows whose event record fuzzily matches to autumn gold . take the 2012 - 13 record of this row . the first record fuzzily matches to the second record . | 5 | 5 | {'str_eq_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'event_7': 7, 'masters_8': 8, '2012 - 13_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'event_11': 11, 'autumn gold_12': 12, '2012 - 13_13': 13} | {'str_eq_4': 'str_eq', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'event_7': 'event', 'masters_8': 'masters', '2012 - 13_9': '2012 - 13', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'event_11': 'event', 'autumn gold_12': 'autumn gold', '2012 - 13_13': '2012 - 13'} | {'str_eq_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'event_7': [0], 'masters_8': [0], '2012 - 13_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'event_11': [1], 'autumn gold_12': [1], '2012 - 13_13': [3]} | ['event', '2006 - 07', '2007 - 08', '2008 - 09', '2009 - 10', '2010 - 11', '2011 - 12', '2012 - 13'] | [['autumn gold', 'q', 'c', 'q', 'c', 'sf', 'q', 'dnp'], ['manitoba liquor & lotteries', 'f', 'f', 'qf', 'f', 'qf', 'qf', 'dnp'], ['masters', 'n / a', 'n / a', 'n / a', 'n / a', 'n / a', 'n / a', 'dnp'], ['colonial square', 'n / a', 'n / a', 'n / a', 'n / a', 'n / a', 'n / a', 'dnp'], ["players '", 'c', 'q', 'c', 'qf', 'c', 'sf', 'sf']] |
burma | https://en.wikipedia.org/wiki/Burma | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19457-1.html.csv | superlative | yangon region is the region of burma that has the highest amount of wards . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '12', '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', 'wards'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; wards }'}, 'state / region'], 'result': 'yangon region', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; wards } ; state / region }'}, 'yangon region'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; wards } ; state / region } ; yangon region } = true', 'tointer': 'select the row whose wards record of all rows is maximum . the state / region record of this row is yangon region .'} | eq { hop { argmax { all_rows ; wards } ; state / region } ; yangon region } = true | select the row whose wards record of all rows is maximum . the state / region record of this row is yangon region . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'wards_5': 5, 'state / region_6': 6, 'yangon region_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'wards_5': 'wards', 'state / region_6': 'state / region', 'yangon region_7': 'yangon region'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'wards_5': [0], 'state / region_6': [1], 'yangon region_7': [2]} | ['no', 'state / region', 'districts', 'town ships', 'cities / towns', 'wards', 'village groups', 'villages'] | [['1', 'kachin state', '3', '18', '20', '116', '606', '2630'], ['2', 'kayah state', '2', '7', '7', '29', '79', '624'], ['3', 'kayin state', '3', '7', '10', '46', '376', '2092'], ['4', 'chin state', '2', '9', '9', '29', '475', '1355'], ['5', 'sagaing region', '8', '37', '37', '171', '1769', '6095'], ['6', 'tanintharyi region', '3', '10', '10', '63', '265', '1255'], ['7', 'bago region', '4', '28', '33', '246', '1424', '6498'], ['8', 'magway region', '5', '25', '26', '160', '1543', '4774'], ['9', 'mandalay region', '7', '31', '29', '259', '1611', '5472'], ['10', 'mon state', '2', '10', '11', '69', '381', '1199'], ['11', 'rakhine state', '4', '17', '17', '120', '1041', '3871'], ['12', 'yangon region', '4', '45', '20', '685', '634', '2119'], ['13', 'shan state', '11', '54', '54', '336', '1626', '15513'], ['14', 'ayeyarwady region', '6', '26', '29', '219', '1912', '11651']] |
1980 world judo championships | https://en.wikipedia.org/wiki/1980_World_Judo_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15826103-2.html.csv | superlative | austria had the most gold in the world judo championships of 1980 . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', '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', 'gold'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; gold }'}, 'nation'], 'result': 'austria', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; gold } ; nation }'}, 'austria'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; gold } ; nation } ; austria } = true', 'tointer': 'select the row whose gold record of all rows is maximum . the nation record of this row is austria .'} | eq { hop { argmax { all_rows ; gold } ; nation } ; austria } = true | select the row whose gold record of all rows is maximum . the nation record of this row is austria . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'gold_5': 5, 'nation_6': 6, 'austria_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'gold_5': 'gold', 'nation_6': 'nation', 'austria_7': 'austria'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'gold_5': [0], 'nation_6': [1], 'austria_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'austria', '3', '0', '0', '3'], ['2', 'france', '1', '3', '4', '8'], ['3', 'italy', '1', '2', '0', '3'], ['4', 'great britain', '1', '1', '3', '5'], ['5', 'belgium', '1', '0', '2', '3'], ['6', 'netherlands', '1', '0', '1', '2'], ['7', 'germany', '0', '1', '3', '4'], ['8', 'japan', '0', '1', '0', '1'], ['9', 'united states', '0', '0', '3', '3']] |
los angeles lakers all - time roster | https://en.wikipedia.org/wiki/Los_Angeles_Lakers_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10560886-17.html.csv | ordinal | jim pollard has the earliest ' from ' year in the los angeles lakers all - time roster . | {'row': '11', 'col': '4', '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', 'from', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; from ; 1 }'}, 'player'], 'result': 'jim pollard', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; from ; 1 } ; player }'}, 'jim pollard'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; from ; 1 } ; player } ; jim pollard } = true', 'tointer': 'select the row whose from record of all rows is 1st minimum . the player record of this row is jim pollard .'} | eq { hop { nth_argmin { all_rows ; from ; 1 } ; player } ; jim pollard } = true | select the row whose from record of all rows is 1st minimum . the player record of this row is jim pollard . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'from_5': 5, '1_6': 6, 'player_7': 7, 'jim pollard_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', 'from_5': 'from', '1_6': '1', 'player_7': 'player', 'jim pollard_8': 'jim pollard'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'from_5': [0], '1_6': [0], 'player_7': [1], 'jim pollard_8': [2]} | ['player', 'nationality', 'position', 'from', 'school / country'] | [['jannero pargo', 'united states', 'guard', '2002', 'arkansas'], ['parker , smush smush parker', 'united states', 'guard', '2005', 'fordham'], ['myles patrick', 'united states', 'forward', '1980', 'auburn'], ['ruben patterson', 'united states', 'guard / forward', '1998', 'cincinnati'], ['jim paxson', 'united states', 'guard / forward', '1956', 'dayton'], ['gary payton', 'united states', 'guard', '2003', 'oregon state'], ['peeler , anthony anthony peeler', 'united states', 'guard', '1992', 'missouri'], ['mike penberthy', 'united states', 'guard', '2000', "the master 's college"], ['sam perkins', 'united states', 'forward / center', '1990', 'north carolina'], ['john pilch', 'united states', 'forward', '1951', 'wyoming'], ['jim pollard', 'united states', 'forward / center', '1949', 'stanford'], ['powell , josh josh powell', 'united states', 'forward', '2008', 'north carolina state'], ['jim price', 'united states', 'guard', '1972 , 1978', 'louisville'], ['laron profit', 'united states', 'guard / forward', '2005', 'maryland']] |
1976 vfl season | https://en.wikipedia.org/wiki/1976_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10885968-7.html.csv | aggregation | there are on average around 25000 in crowd attendance in the 1976 vfl season . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '25000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '25000', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '25000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 25000 } = true', 'tointer': 'the average of the crowd record of all rows is 25000 .'} | round_eq { avg { all_rows ; crowd } ; 25000 } = true | the average of the crowd record of all rows is 25000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '25000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '25000_5': '25000'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '25000_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['fitzroy', '11.22 ( 88 )', 'south melbourne', '12.15 ( 87 )', 'junction oval', '11267', '15 may 1976'], ['carlton', '21.14 ( 140 )', 'richmond', '9.15 ( 69 )', 'princes park', '30095', '15 may 1976'], ['melbourne', '14.13 ( 97 )', 'hawthorn', '21.19 ( 145 )', 'mcg', '25876', '15 may 1976'], ['geelong', '13.17 ( 95 )', 'footscray', '14.9 ( 93 )', 'kardinia park', '30395', '15 may 1976'], ['st kilda', '9.15 ( 69 )', 'essendon', '14.13 ( 97 )', 'moorabbin oval', '19864', '15 may 1976'], ['collingwood', '11.9 ( 75 )', 'north melbourne', '5.16 ( 46 )', 'vfl park', '34051', '15 may 1976']] |
1962 vfl season | https://en.wikipedia.org/wiki/1962_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10776868-8.html.csv | superlative | the game played at the junction oval venue drew the highest attendance . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'venue'], 'result': 'junction oval', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; venue }'}, 'junction oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; venue } ; junction oval } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the venue record of this row is junction oval .'} | eq { hop { argmax { all_rows ; crowd } ; venue } ; junction oval } = true | select the row whose crowd record of all rows is maximum . the venue record of this row is junction oval . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'venue_6': 6, 'junction oval_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'venue_6': 'venue', 'junction oval_7': 'junction oval'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'venue_6': [1], 'junction oval_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '9.10 ( 64 )', 'south melbourne', '6.8 ( 44 )', 'kardinia park', '17873', '9 june 1962'], ['fitzroy', '12.4 ( 76 )', 'melbourne', '9.20 ( 74 )', 'brunswick street oval', '15499', '9 june 1962'], ['richmond', '9.10 ( 64 )', 'footscray', '11.11 ( 77 )', 'punt road oval', '26656', '9 june 1962'], ['hawthorn', '9.15 ( 69 )', 'essendon', '7.10 ( 52 )', 'glenferrie oval', '21000', '9 june 1962'], ['north melbourne', '7.10 ( 52 )', 'collingwood', '15.19 ( 109 )', 'arden street oval', '18503', '9 june 1962'], ['st kilda', '5.13 ( 43 )', 'carlton', '6.10 ( 46 )', 'junction oval', '38300', '9 june 1962']] |
ariana afghan airlines | https://en.wikipedia.org/wiki/Ariana_Afghan_Airlines | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-158904-1.html.csv | majority | most of the incidents involving ariana afghan airlines aircrafts were located in kabul . | {'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'kabul', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'location', 'kabul'], 'result': True, 'ind': 0, 'tointer': 'for the location records of all rows , most of them fuzzily match to kabul .', 'tostr': 'most_eq { all_rows ; location ; kabul } = true'} | most_eq { all_rows ; location ; kabul } = true | for the location records of all rows , most of them fuzzily match to kabul . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'location_3': 3, 'kabul_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'location_3': 'location', 'kabul_4': 'kabul'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'location_3': [0], 'kabul_4': [0]} | ['location', 'aircraft', 'tail number', 'aircraft damage', 'fatalities'] | [['greece', 'douglas c - 47a', 'ya - aad', 'w / o', 'unknown'], ['off beirut', 'dc - 4', 'ya - bag', 'w / o', '24 / 27'], ['london', 'boeing 727 - 100c', 'ya - far', 'w / o', '50'], ['kabul', 'douglas c - 47dl', 'ya - bad', 'w / o', 'unknown'], ['pakistan', 'an - 26', 'unknown', 'w / o', '25 / 25'], ['zabol', 'an - 26', 'ya - bak', 'w / o', '6 / 39'], ['kabul', 'tu - 154 m', 'ya - tap', 'w / o', '0 / 0'], ['kabul', 'an - 26', 'ya - ban', 'w / o', 'unknown'], ['jalalabad', 'an - 26b', 'ya - bao', 'w / o', '3 / 46'], ['jalalabad', 'yak - 40', 'ya - kae', 'w / o', '1'], ['charasyab', 'boeing 727 - 200', 'ya - faz', 'w / o', '45 / 45'], ['kabul', 'an - 12b', 'ya - daa', 'w / o', '0 / 0'], ['kabul', 'an - 12bk', 'ya - dab', 'w / o', '0 / 0'], ['kabul', 'an - 24', 'unknown', 'w / o', '0 / 0'], ['kabul', 'an - 24b', 'ya - dah', 'w / o', '0 / 0'], ['kabul', 'an - 24rv', 'ya - daj', 'w / o', '0 / 0'], ['kabul', 'boeing 727 - 100c', 'ya - fau', 'w / o', '0 / 0'], ['kabul', 'boeing 727 - 100c', 'ya - faw', 'w / o', '0 / 0'], ['istanbul', 'a300b4 - 200', 'ya - bad', 'w / o', '0']] |
1964 summer paralympics | https://en.wikipedia.org/wiki/1964_Summer_Paralympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-175110-1.html.csv | unique | the united states was the only team to win more than 100 medals at the 1964 summer paralympics . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '2', 'criterion': 'greater_than', 'value': '100', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'total', '100'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total record is greater than 100 .', 'tostr': 'filter_greater { all_rows ; total ; 100 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; total ; 100 } }', 'tointer': 'select the rows whose total 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', 'total', '100'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total record is greater than 100 .', 'tostr': 'filter_greater { all_rows ; total ; 100 }'}, 'nation'], 'result': 'united states', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; total ; 100 } ; nation }'}, 'united states'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; total ; 100 } ; nation } ; united states }', 'tointer': 'the nation record of this unqiue row is united states .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; total ; 100 } } ; eq { hop { filter_greater { all_rows ; total ; 100 } ; nation } ; united states } } = true', 'tointer': 'select the rows whose total record is greater than 100 . there is only one such row in the table . the nation record of this unqiue row is united states .'} | and { only { filter_greater { all_rows ; total ; 100 } } ; eq { hop { filter_greater { all_rows ; total ; 100 } ; nation } ; united states } } = true | select the rows whose total record is greater than 100 . there is only one such row in the table . the nation record of this unqiue row is united states . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'total_7': 7, '100_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'nation_9': 9, 'united states_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'total_7': 'total', '100_8': '100', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'nation_9': 'nation', 'united states_10': 'united states'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'total_7': [0], '100_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'nation_9': [2], 'united states_10': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'united states', '50', '41', '32', '123'], ['2', 'great britain', '18', '23', '20', '61'], ['3', 'italy', '14', '15', '16', '45'], ['4', 'australia', '12', '11', '7', '30'], ['5', 'rhodesia', '10', '5', '2', '17'], ['6', 'south africa', '8', '8', '3', '19'], ['7', 'israel', '7', '3', '11', '21'], ['8', 'argentina', '6', '15', '16', '37'], ['9', 'west germany', '5', '2', '5', '12'], ['10', 'netherlands', '4', '6', '4', '14']] |
2007 asp world tour | https://en.wikipedia.org/wiki/2007_ASP_World_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16135219-1.html.csv | majority | in the 2007 asp world tour , among the billabong pro events that year , the majority were won by players on team usa . | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'usa', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'billabong pro'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'billabong pro'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; event ; billabong pro }', 'tointer': 'select the rows whose event record fuzzily matches to billabong pro .'}, 'winner', 'usa'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose event record fuzzily matches to billabong pro . for the winner records of these rows , most of them fuzzily match to usa .', 'tostr': 'most_eq { filter_eq { all_rows ; event ; billabong pro } ; winner ; usa } = true'} | most_eq { filter_eq { all_rows ; event ; billabong pro } ; winner ; usa } = true | select the rows whose event record fuzzily matches to billabong pro . for the winner records of these rows , most of them fuzzily match to usa . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'event_4': 4, 'billabong pro_5': 5, 'winner_6': 6, 'usa_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'event_4': 'event', 'billabong pro_5': 'billabong pro', 'winner_6': 'winner', 'usa_7': 'usa'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'event_4': [0], 'billabong pro_5': [0], 'winner_6': [1], 'usa_7': [1]} | ['date', 'location', 'country', 'event', 'winner', 'runner - up'] | [['february 27 - march 11', 'gold coast', 'australia', 'quiksilver pro', 'mick fanning ( aus )', 'bede durbidge ( aus )'], ['april 3 - april 13', 'bells beach', 'australia', 'rip curl pro', 'taj burrow ( aus )', 'andy irons ( haw )'], ['may 4 - may 14', 'teahupoo , tahiti', 'french polynesia', 'billabong pro', 'damien hobgood ( usa )', 'mick fanning ( aus )'], ['june 20 - july 1', 'arica', 'chile', 'rip curl pro search', 'andy irons ( haw )', 'damien hobgood ( usa )'], ['june 11 - july 22', 'jeffreys bay', 'south africa', 'billabong pro', 'taj burrow ( aus )', 'kelly slater ( usa )'], ['september 9 - september 15', 'trestles', 'united states', 'boost mobile pro', 'kelly slater ( usa )', 'pancho sullivan ( haw )'], ['september 20 - september 30', 'hossegor', 'france', 'quiksilver pro', 'mick fanning ( aus )', 'greg emslie ( rsa )'], ['october 1 - october 14', 'mundaka', 'spain', 'billabong pro', 'bobby martinez ( usa )', 'taj burrow ( aus )'], ['october 30 - november 7', 'santa catarina', 'brazil', 'hang loose pro', 'mick fanning ( aus )', 'kai otton ( aus )'], ['december 8 - december 20', 'pipeline , hawaii', 'united states', 'billabong pipeline masters', 'bede durbidge ( aus )', 'dean morrison ( aus )']] |
2009 - 10 washington capitals season | https://en.wikipedia.org/wiki/2009%E2%80%9310_Washington_Capitals_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23308178-4.html.csv | aggregation | the average attendance for the first 12 games of the washington capitals 2009 – 2010 season is 16,962 people . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '16962', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '16962', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '16962'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 16962 } = true', 'tointer': 'the average of the attendance record of all rows is 16962 .'} | round_eq { avg { all_rows ; attendance } ; 16962 } = true | the average of the attendance record of all rows is 16962 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '16962_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '16962_5': '16962'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '16962_5': [1]} | ['game', 'date', 'opponent', 'score', 'location', 'attendance', 'record', 'points'] | [['1', 'october 1', 'boston bruins', '4 - 1', 'td garden', '17565', '1 - 0 - 0', '2'], ['2', 'october 3', 'toronto maple leafs', '6 - 4', 'verizon center', '18277', '2 - 0 - 0', '4'], ['3', 'october 6', 'philadelphia flyers', '6 - 5 ot', 'wachovia center', '19567', '2 - 0 - 1', '5'], ['4', 'october 8', 'new york rangers', '4 - 3', 'verizon center', '18277', '2 - 1 - 1', '5'], ['5', 'october 10', 'detroit red wings', '3 - 2', 'joe louis arena', '19122', '2 - 2 - 1', '5'], ['6', 'october 12', 'new jersey devils', '3 - 2 so', 'verizon center', '18277', '2 - 2 - 2', '6'], ['7', 'october 15', 'san jose sharks', '4 - 1', 'verizon center', '18277', '3 - 2 - 2', '8'], ['8', 'october 17', 'nashville predators', '3 - 2 so', 'verizon center', '18277', '4 - 2 - 2', '10'], ['9', 'october 22', 'atlanta thrashers', '5 - 4', 'philips arena', '13192', '5 - 2 - 2', '12'], ['10', 'october 24', 'new york islanders', '3 - 2 ot', 'nassau veterans memorial coliseum', '11541', '6 - 2 - 2', '14'], ['11', 'october 27', 'philadelphia flyers', '4 - 2', 'verizon center', '18277', '7 - 2 - 2', '16'], ['12', 'october 29', 'atlanta thrashers', '4 - 3', 'philips arena', '12893', '8 - 2 - 2', '18']] |
united states house of representatives elections , 1954 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1954 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342013-42.html.csv | ordinal | of the candidates who ran in the 1954 u.s. house of representatives elections in texas , sam rayburn had first been elected in the earliest year . | {'row': '4', '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': 'sam rayburn', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent }'}, 'sam rayburn'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; sam rayburn } = true', 'tointer': 'select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is sam rayburn .'} | eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; sam rayburn } = true | select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is sam rayburn . | 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, 'sam rayburn_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', 'sam rayburn_8': 'sam rayburn'} | {'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], 'sam rayburn_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['texas 1', 'wright patman', 'democratic', '1928', 're - elected', 'wright patman ( d ) unopposed'], ['texas 2', 'jack brooks', 'democratic', '1952', 're - elected', 'jack brooks ( d ) unopposed'], ['texas 3', 'brady p gentry', 'democratic', '1952', 're - elected', 'brady p gentry ( d ) unopposed'], ['texas 4', 'sam rayburn', 'democratic', '1912', 're - elected', 'sam rayburn ( d ) unopposed'], ['texas 5', 'joseph franklin wilson', 'democratic', '1946', 'retired republican gain', 'bruce r alger ( r ) 52.9 % wallace savage ( d ) 47.1 %'], ['texas 6', 'olin e teague', 'democratic', '1946', 're - elected', 'olin e teague ( d ) unopposed'], ['texas 7', 'john dowdy', 'democratic', '1952', 're - elected', 'john dowdy ( d ) unopposed'], ['texas 9', 'clark w thompson', 'democratic', '1947', 're - elected', 'clark w thompson ( d ) unopposed'], ['texas 10', 'homer thornberry', 'democratic', '1948', 're - elected', 'homer thornberry ( d ) unopposed'], ['texas 11', 'william r poage', 'democratic', '1936', 're - elected', 'william r poage ( d ) unopposed'], ['texas 12', 'wingate h lucas', 'democratic', '1946', 'lost renomination democratic hold', 'jim wright ( d ) unopposed'], ['texas 13', 'frank n ikard', 'democratic', '1951', 're - elected', 'frank n ikard ( d ) unopposed'], ['texas 14', 'john e lyle , jr', 'democratic', '1944', 'retired democratic hold', 'john j bell ( d ) 93.8 % d c dewitt ( r ) 6.2 %'], ['texas 15', 'lloyd bentsen', 'democratic', '1948', 'retired democratic hold', 'joe m kilgore ( d ) unopposed'], ['texas 16', 'kenneth m regan', 'democratic', '1947', 'lost renomination democratic hold', 'j t rutherford ( d ) unopposed'], ['texas 17', 'omar burleson', 'democratic', '1946', 're - elected', 'omar burleson ( d ) unopposed'], ['texas 19', 'george h mahon', 'democratic', '1934', 're - elected', 'george h mahon ( d ) unopposed'], ['texas 20', 'paul j kilday', 'democratic', '1938', 're - elected', 'paul j kilday ( d ) unopposed'], ['texas 21', 'o c fisher', 'democratic', '1942', 're - elected', 'o c fisher ( d ) unopposed']] |
katja seizinger | https://en.wikipedia.org/wiki/Katja_Seizinger | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1489417-1.html.csv | superlative | katja seizinger had her highest score in the super g event in the year 1990 . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'super g'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; super g }'}, 'season'], 'result': '1990', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; super g } ; season }'}, '1990'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; super g } ; season } ; 1990 } = true', 'tointer': 'select the row whose super g record of all rows is maximum . the season record of this row is 1990 .'} | eq { hop { argmax { all_rows ; super g } ; season } ; 1990 } = true | select the row whose super g record of all rows is maximum . the season record of this row is 1990 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'super g_5': 5, 'season_6': 6, '1990_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'super g_5': 'super g', 'season_6': 'season', '1990_7': '1990'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'super g_5': [0], 'season_6': [1], '1990_7': [2]} | ['season', 'overall', 'slalom', 'giant slalom', 'super g', 'downhill', 'combined'] | [['1990', '44', '-', '39', '12', '-', '21'], ['1991', '15', '-', '29', '3', '13', '12'], ['1992', '3', '-', '10', '4', '1', '-'], ['1993', '2', '58', '7', '1', '1', '7'], ['1994', '3', '49', '6', '1', '1', '19'], ['1995', '2', '19', '9', '1', '3', '4'], ['1996', '1', '39', '2', '1', '2', '-'], ['1997', '2', '19', '2', '2', '5', '-'], ['1998', '1', '12', '6', '1', '1', '2']] |
10k run | https://en.wikipedia.org/wiki/10K_run | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17370134-3.html.csv | count | of the athletes from kenya in the 10k run , two of them had a time faster than 30:34 . | {'scope': 'subset', 'criterion': 'less_than', 'value': '30:34', 'result': '2', 'col': '2', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'kenya'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'kenya'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nation ; kenya }', 'tointer': 'select the rows whose nation record fuzzily matches to kenya .'}, 'time', '30:34'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 .', 'tostr': 'filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 } }', 'tointer': 'select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 } } ; 2 } = true', 'tointer': 'select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 . the number of such rows is 2 .'} | eq { count { filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 } } ; 2 } = true | select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'nation_6': 6, 'kenya_7': 7, 'time_8': 8, '30:34_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'nation_6': 'nation', 'kenya_7': 'kenya', 'time_8': 'time', '30:34_9': '30:34', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'nation_6': [0], 'kenya_7': [0], 'time_8': [1], '30:34_9': [1], '2_10': [3]} | ['rank', 'time', 'athlete', 'nation', 'date', 'race'] | [['1', '30:21', 'paula radcliffe', 'united kingdom', '23 february 2003', "world 's best 10k"], ['2', '30:27', 'isabella ochichi', 'kenya', '26 march 2005', 'crescent city classic'], ['3', '30:29', 'asmae leghzaoui', 'morocco', '8 june 2002', 'new york mini 10k'], ['4', '30:32', 'lornah kiplagat', 'kenya', '4 july 2002', 'peachtree road race'], ['5', '30:38', 'joyce chepkirui', 'kenya', '4 september 2011', 'tilburg 10k'], ['6', '30:39', 'liz mccolgan', 'united kingdom', '11 march 1989', 'red lobster classic'], ['7 =', '30:45', 'lineth chepkurui', 'kenya', '3 april 2010', 'crescent city classic'], ['7 =', '30:45 +', 'mary jepkosgei keitany', 'kenya', '18 february 2011', 'ras al khaimah half marathon'], ['9', '30:47', 'vivian jepkemoi cheruiyot', 'kenya', '26 february 2012', "world 's best 10k"], ['10', '30:48', 'linet chepkwemoi masai', 'kenya', '12 june 2010', 'new york mini 10k']] |
list of earthquakes in iran | https://en.wikipedia.org/wiki/List_of_earthquakes_in_Iran | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10677198-1.html.csv | ordinal | the 2003 bam earthquake was the second largest in magnitude that happened in iran between 2002-2013 . | {'row': '12', 'col': '4', 'order': '2', 'col_other': '6', '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', 'magnitude', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; magnitude ; 2 }'}, 'name'], 'result': '2003 bam earthquake', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; magnitude ; 2 } ; name }'}, '2003 bam earthquake'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; magnitude ; 2 } ; name } ; 2003 bam earthquake } = true', 'tointer': 'select the row whose magnitude record of all rows is 2nd maximum . the name record of this row is 2003 bam earthquake .'} | eq { hop { nth_argmax { all_rows ; magnitude ; 2 } ; name } ; 2003 bam earthquake } = true | select the row whose magnitude record of all rows is 2nd maximum . the name record of this row is 2003 bam earthquake . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'magnitude_5': 5, '2_6': 6, 'name_7': 7, '2003 bam earthquake_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', 'magnitude_5': 'magnitude', '2_6': '2', 'name_7': 'name', '2003 bam earthquake_8': '2003 bam earthquake'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'magnitude_5': [0], '2_6': [0], 'name_7': [1], '2003 bam earthquake_8': [2]} | ['date', 'time', 'epicenter', 'magnitude', 'fatalities', 'name'] | [['apr 16 , 2013', '10:44:13', 'saravan , iran', '7.8', '1 ( non - residential area , due to landslide )', '2013 sistan and baluchestan earthquake'], ['apr 9 , 2013', '16:22:50', 'bushehr', '6.3', '30 ( early estimate )', '2013 bushehr earthquake'], ['aug 11 , 2012', '12:23:18', 'tabriz', '6.4 and 6.3', '306', '2012 tabriz earthquakes'], ['jun 15 , 2011', '01:05:30', 'kahnooj', '5.3', '2', '2011 kahnooj earthquake'], ['dec 20 , 2010', '22:12:01', 'hosseinabad', '6.5', '11', '2010 hosseinabad earthquake'], ['aug 27 , 2010', '23:56:34', 'damghan', '5.9', '19', '2010 damghan earthquake'], ['sep 10 , 2008', '11:00:34', 'qeshm', '6.1', '7', '2008 bandar abbas earthquake'], ['march 31 , 2006', '01:17:01', 'borujerd', '6.1', '70', '2006 borujerd earthquake'], ['november 27 , 2005', '10:22:19', 'qeshm', '6.0', '13', '2005 qeshm earthquake'], ['february 22 , 2005', '02:25:22', 'zarand', '6.4', 'least 602', '2005 zarand earthquake'], ['may 28 , 2004', '12:38:46', 'm훮zandar훮n', '6.3', 'least 35', '2004 m훮zandar훮n earthquake'], ['december 26 , 2003', '01:56:52', 'bam', '6.6', 'least 30000', '2003 bam earthquake'], ['june 22 , 2002', '02:58:21', 'qazvin', '6.5', '262', "2002 bou'in - zahra earthquake"]] |
athletics at the 1990 central american and caribbean games | https://en.wikipedia.org/wiki/Athletics_at_the_1990_Central_American_and_Caribbean_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10260670-3.html.csv | superlative | cuba won more silver medals than any other country at the 1990 central american and caribbean games . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', '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', 'silver'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; silver }'}, 'nation'], 'result': 'cuba', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; silver } ; nation }'}, 'cuba'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; silver } ; nation } ; cuba } = true', 'tointer': 'select the row whose silver record of all rows is maximum . the nation record of this row is cuba .'} | eq { hop { argmax { all_rows ; silver } ; nation } ; cuba } = true | select the row whose silver record of all rows is maximum . the nation record of this row is cuba . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'silver_5': 5, 'nation_6': 6, 'cuba_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'silver_5': 'silver', 'nation_6': 'nation', 'cuba_7': 'cuba'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'silver_5': [0], 'nation_6': [1], 'cuba_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'cuba', '27', '17', '7', '51'], ['2', 'mexico', '10', '13', '5', '28'], ['3', 'colombia', '2', '4', '9', '15'], ['4', 'puerto rico', '2', '3', '3', '8'], ['5', 'suriname', '1', '1', '0', '2'], ['6', 'jamaica', '1', '0', '2', '3'], ['7', 'antigua and barbuda', '0', '2', '1', '3'], ['8', 'venezuela', '0', '1', '7', '8'], ['9', 'trinidad and tobago', '0', '1', '3', '4'], ['10', 'barbados', '0', '1', '0', '1'], ['11', 'costa rica', '0', '0', '2', '2'], ['11', 'bermuda', '0', '0', '2', '2'], ['13', 'bahamas', '0', '0', '1', '1'], ['13', 'guyana', '0', '0', '1', '1']] |
locomotives of the glasgow and south western railway | https://en.wikipedia.org/wiki/Locomotives_of_the_Glasgow_and_South_Western_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15412381-3.html.csv | ordinal | in locomotives of the glasgow and south western railway , class 153 is the 3rd highest in no built among those built by g & swr kilmarnock . | {'scope': 'subset', 'row': '3', 'col': '4', 'order': '3', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'g & swr kilmarnock'}} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'builder', 'g & swr kilmarnock'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; builder ; g & swr kilmarnock }', 'tointer': 'select the rows whose builder record fuzzily matches to g & swr kilmarnock .'}, 'no built', '3'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; no built ; 3 }'}, 'class'], 'result': '153', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; no built ; 3 } ; class }'}, '153'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; no built ; 3 } ; class } ; 153 } = true', 'tointer': 'select the rows whose builder record fuzzily matches to g & swr kilmarnock . select the row whose no built record of these rows is 3rd maximum . the class record of this row is 153 .'} | eq { hop { nth_argmax { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; no built ; 3 } ; class } ; 153 } = true | select the rows whose builder record fuzzily matches to g & swr kilmarnock . select the row whose no built record of these rows is 3rd maximum . the class record of this row is 153 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'num_hop_2': 2, 'nth_argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'builder_6': 6, 'g&swr kilmarnock_7': 7, 'no built_8': 8, '3_9': 9, 'class_10': 10, '153_11': 11} | {'eq_3': 'eq', 'result_4': 'true', 'num_hop_2': 'num_hop', 'nth_argmax_1': 'nth_argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'builder_6': 'builder', 'g&swr kilmarnock_7': 'g & swr kilmarnock', 'no built_8': 'no built', '3_9': '3', 'class_10': 'class', '153_11': '153'} | {'eq_3': [4], 'result_4': [], 'num_hop_2': [3], 'nth_argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'builder_6': [0], 'g&swr kilmarnock_7': [0], 'no built_8': [1], '3_9': [1], 'class_10': [2], '153_11': [3]} | ['class', 'date', 'builder', 'no built', '1919 nos', 'lms class', 'lms nos'] | [['157', '1879 - 81', 'g & swr kilmarnock', '12', '720 - 5', '1p', '14001 - 2'], ['119', '1882 - 5', 'g & swr kilmarnock', '24', '467 - 8 , 700 - 719', '1p', '14116 - 37'], ['153', '1886 - 9', 'g & swr kilmarnock', '20', '448 - 466', '1p', '14138 - 56'], ['1', '1879 - 81', 'g & swr kilmarnock', '4', '728 - 31', '1p', '15241 - 4'], ['291', '1883', 'abarclay', '1', '734', 'u', '16042'], ['218', '1881', 'andrews , barr & co', '2', '658 - 9', 'u', '16040 - 1'], ['224', '1881 - 92', 'g & swr kilmarnock', '44', '135 - 9 , 560 - 616 with gaps', '1f', '17112 - 64'], ['224', '1883', 'neilson', '10', '135 - 9 , 560 - 616 with gaps', '1f', '17112 - 64'], ['224', '1889', 'dã ¼ bs', '10', '135 - 9 , 560 - 616 with gaps', '1f', '17112 - 64']] |
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 | unique | 23 march 1993 was the only date that mauricio cienfuegos scored in a friendly match competition . | {'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'friendly match', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', 'friendly match'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to friendly match .', 'tostr': 'filter_eq { all_rows ; competition ; friendly match }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; competition ; friendly match } }', 'tointer': 'select the rows whose competition record fuzzily matches to friendly match . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', 'friendly match'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to friendly match .', 'tostr': 'filter_eq { all_rows ; competition ; friendly match }'}, 'date'], 'result': '23 march 1993', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; competition ; friendly match } ; date }'}, '23 march 1993'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; competition ; friendly match } ; date } ; 23 march 1993 }', 'tointer': 'the date record of this unqiue row is 23 march 1993 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; competition ; friendly match } } ; eq { hop { filter_eq { all_rows ; competition ; friendly match } ; date } ; 23 march 1993 } } = true', 'tointer': 'select the rows whose competition record fuzzily matches to friendly match . there is only one such row in the table . the date record of this unqiue row is 23 march 1993 .'} | and { only { filter_eq { all_rows ; competition ; friendly match } } ; eq { hop { filter_eq { all_rows ; competition ; friendly match } ; date } ; 23 march 1993 } } = true | select the rows whose competition record fuzzily matches to friendly match . there is only one such row in the table . the date record of this unqiue row is 23 march 1993 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'competition_7': 7, 'friendly match_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '23 march 1993_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'competition_7': 'competition', 'friendly match_8': 'friendly match', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '23 march 1993_10': '23 march 1993'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'competition_7': [0], 'friendly match_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '23 march 1993_10': [3]} | ['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']] |
1981 san francisco 49ers season | https://en.wikipedia.org/wiki/1981_San_Francisco_49ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15353865-2.html.csv | majority | the san francisco 49ers won all their matches in the month of october during the 1981 season games . | {'scope': 'subset', 'col': '4', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'october'}} | {'func': 'all_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; october }', 'tointer': 'select the rows whose date record fuzzily matches to october .'}, 'result', 'w'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w .', 'tostr': 'all_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true'} | all_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true | select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w . | 2 | 2 | {'all_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'october_5': 5, 'result_6': 6, 'w_7': 7} | {'all_str_eq_1': 'all_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'october_5': 'october', 'result_6': 'result', 'w_7': 'w'} | {'all_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'october_5': [0], 'result_6': [1], 'w_7': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 6 , 1981', 'detroit lions', 'l 17 - 24', '63710'], ['2', 'september 13 , 1981', 'chicago bears', 'w 28 - 17', '49520'], ['3', 'september 20 , 1981', 'atlanta falcons', 'l 17 - 34', '56653'], ['4', 'september 27 , 1981', 'new orleans saints', 'w 21 - 14', '44433'], ['5', 'october 4 , 1981', 'washington redskins', 'w 30 - 17', '51843'], ['6', 'october 11 , 1981', 'dallas cowboys', 'w 45 - 14', '57574'], ['7', 'october 18 , 1981', 'green bay packers', 'w 13 - 3', '50171'], ['8', 'october 25 , 1981', 'los angeles rams', 'w 20 - 17', '59190'], ['9', 'november 1 , 1981', 'pittsburgh steelers', 'w 17 - 14', '52878'], ['10', 'november 8 , 1981', 'atlanta falcons', 'w 17 - 14', '59127'], ['11', 'november 15 , 1981', 'cleveland browns', 'l 12 - 15', '52455'], ['12', 'november 22 , 1981', 'los angeles rams', 'w 33 - 31', '63456'], ['13', 'november 29 , 1981', 'new york giants', 'w 17 - 10', '57186'], ['14', 'december 6 , 1981', 'cincinnati bengals', 'w 21 - 3', '56796'], ['15', 'december 13 , 1981', 'houston oilers', 'w 28 - 6', '55707'], ['16', 'december 20 , 1981', 'new orleans saints', 'w 21 - 17', '43639']] |
adelaide united fc | https://en.wikipedia.org/wiki/Adelaide_United_FC | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1257184-2.html.csv | aggregation | the five players for adelaide united fc had an average of around 10-11 caps . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '10.4', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'caps'], 'result': '10.4', 'ind': 0, 'tostr': 'avg { all_rows ; caps }'}, '10.4'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; caps } ; 10.4 } = true', 'tointer': 'the average of the caps record of all rows is 10.4 .'} | round_eq { avg { all_rows ; caps } ; 10.4 } = true | the average of the caps record of all rows is 10.4 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'caps_4': 4, '10.4_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'caps_4': 'caps', '10.4_5': '10.4'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'caps_4': [0], '10.4_5': [1]} | ['player', 'country', 'caps', 'goals', 'years active', 'years at club'] | [['eugene galeković', 'australia', '8', '( 0 )', '2009 -', '2007 -'], ['jonathan mckain', 'australia', '16', '( 0 )', '2004 -', '2011 -'], ['dario vidošić', 'australia', '18', '( 1 )', '2009 -', '2011 - 2013'], ['bruce djite', 'australia', '9', '( 0 )', '2008 -', '2006 - 2008 , 2011 -'], ['fabian barbiero', 'australia', '1', '( 0 )', '2009', '2007 - 2013']] |
fivb volleyball world grand champions cup | https://en.wikipedia.org/wiki/FIVB_Volleyball_World_Grand_Champions_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13807771-4.html.csv | aggregation | the total number of medals won by teams ranked 4th to 7th in the fivb volleyball world grand champions cup is 5 . | {'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '5', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': '4'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'rank', '4'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; rank ; 4 }', 'tointer': 'select the rows whose rank record is greater than or equal to 4 .'}, 'total'], 'result': '5', 'ind': 1, 'tostr': 'sum { filter_greater_eq { all_rows ; rank ; 4 } ; total }'}, '5'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_greater_eq { all_rows ; rank ; 4 } ; total } ; 5 } = true', 'tointer': 'select the rows whose rank record is greater than or equal to 4 . the sum of the total record of these rows is 5 .'} | round_eq { sum { filter_greater_eq { all_rows ; rank ; 4 } ; total } ; 5 } = true | select the rows whose rank record is greater than or equal to 4 . the sum of the total record of these rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'rank_5': 5, '4_6': 6, 'total_7': 7, '5_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'rank_5': 'rank', '4_6': '4', 'total_7': 'total', '5_8': '5'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'rank_5': [0], '4_6': [0], 'total_7': [1], '5_8': [2]} | ['rank', 'gold', 'silver', 'bronze', 'total'] | [['1', '1', '1', '1', '3'], ['4', '1', '1', '0', '2'], ['5', '1', '0', '0', '1'], ['6', '0', '1', '0', '1'], ['7', '0', '0', '1', '1'], ['total', '5', '5', '5', '15']] |
ádammo | https://en.wikipedia.org/wiki/%C3%81dammo | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27501971-2.html.csv | unique | 2011 was the only year that adammo was nominated at the mtv europe music awards . | {'scope': 'all', 'row': '13', 'col': '3', 'col_other': '1,6', 'criterion': 'equal', 'value': 'mtv europe music awards', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'premio', 'mtv europe music awards'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose premio record fuzzily matches to mtv europe music awards .', 'tostr': 'filter_eq { all_rows ; premio ; mtv europe music awards }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; premio ; mtv europe music awards } }', 'tointer': 'select the rows whose premio record fuzzily matches to mtv europe music awards . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'premio', 'mtv europe music awards'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose premio record fuzzily matches to mtv europe music awards .', 'tostr': 'filter_eq { all_rows ; premio ; mtv europe music awards }'}, 'año'], 'result': '2011', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; año }'}, '2011'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; año } ; 2011 }', 'tointer': 'the año record of this unqiue row is 2011 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'premio', 'mtv europe music awards'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose premio record fuzzily matches to mtv europe music awards .', 'tostr': 'filter_eq { all_rows ; premio ; mtv europe music awards }'}, 'resultado'], 'result': 'nominate', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; resultado }'}, 'nominate'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; resultado } ; nominate }', 'tointer': 'the resultado record of this unqiue row is nominate .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; año } ; 2011 } ; eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; resultado } ; nominate } }', 'tointer': 'the año record of this unqiue row is 2011 . the resultado record of this unqiue row is nominate .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; premio ; mtv europe music awards } } ; and { eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; año } ; 2011 } ; eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; resultado } ; nominate } } } = true', 'tointer': 'select the rows whose premio record fuzzily matches to mtv europe music awards . there is only one such row in the table . the año record of this unqiue row is 2011 . the resultado record of this unqiue row is nominate .'} | and { only { filter_eq { all_rows ; premio ; mtv europe music awards } } ; and { eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; año } ; 2011 } ; eq { hop { filter_eq { all_rows ; premio ; mtv europe music awards } ; resultado } ; nominate } } } = true | select the rows whose premio record fuzzily matches to mtv europe music awards . there is only one such row in the table . the año record of this unqiue row is 2011 . the resultado record of this unqiue row is nominate . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'premio_10': 10, 'mtv europe music awards_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'año_12': 12, '2011_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'resultado_14': 14, 'nominate_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'premio_10': 'premio', 'mtv europe music awards_11': 'mtv europe music awards', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'año_12': 'año', '2011_13': '2011', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'resultado_14': 'resultado', 'nominate_15': 'nominate'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'premio_10': [0], 'mtv europe music awards_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'año_12': [2], '2011_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'resultado_14': [4], 'nominate_15': [5]} | ['año', 'trabajo nominado', 'premio', 'categoría', 'country', 'resultado'] | [['2009', 'adammo', 'mtv latin america', 'revelation artist', 'colombia', 'nominate'], ['2009', 'adammo', 'mtv latin america', 'best new artist : center', 'colombia', 'winner'], ['2009', 'adammo', 'mtv latin america', 'prize zone', 'colombia', 'nominate'], ['2010', 'adammo', 'premios apdayc', 'rock group of the year', 'perú', 'winner'], ['2010', 'adammo', 'premios apdayc', 'artist of the year', 'perú', 'nominate'], ['2010', 'adammo', 'premios orgullosamente latino', 'grupo latin of the year', 'mexico', 'nominate'], ['2010', 'algún día', 'latin grammy awards', 'short video of the year', 'eeuu', 'nominate'], ['2010', 'adammo', 'premios clarín', 'best music video of the year', 'argentina', 'nominate'], ['2010', 'adammo', 'premios clarín', 'best international breakthrough', 'argentina', 'nominate'], ['2010', 'adammo', 'premios clarín', 'best international album', 'argentina', 'nominate'], ['2010', 'algún día', 'radio can', 'best video', 'colombia', 'nominate'], ['2011', 'adammo', 'premios apdayc', 'rock group of the year', 'perú', 'winner'], ['2011', 'adammo', 'mtv europe music awards', 'world wide act latin american', 'europa', 'nominate'], ['2011', 'adammo', 'zona joven', 'best pop rock peruano', 'perú', 'winner'], ['2012', 'siento que caigo', 'radio can', 'song of the year', 'perú', 'nominate']] |
1966 los angeles rams season | https://en.wikipedia.org/wiki/1966_Los_Angeles_Rams_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11171288-1.html.csv | ordinal | in the 1966 los angeles rams season , their second game against chicago bears drew 47475 people . | {'scope': 'subset', 'row': '7', 'col': '2', 'order': '2', 'col_other': '5', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'chicago bears'}} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'chicago bears'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; chicago bears }', 'tointer': 'select the rows whose opponent record fuzzily matches to chicago bears .'}, 'date', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 }'}, 'attendance'], 'result': '47475', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 } ; attendance }'}, '47475'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 } ; attendance } ; 47475 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to chicago bears . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 47475 .'} | eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 } ; attendance } ; 47475 } = true | select the rows whose opponent record fuzzily matches to chicago bears . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 47475 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'num_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'opponent_6': 6, 'chicago bears_7': 7, 'date_8': 8, '2_9': 9, 'attendance_10': 10, '47475_11': 11} | {'eq_3': 'eq', 'result_4': 'true', 'num_hop_2': 'num_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'opponent_6': 'opponent', 'chicago bears_7': 'chicago bears', 'date_8': 'date', '2_9': '2', 'attendance_10': 'attendance', '47475_11': '47475'} | {'eq_3': [4], 'result_4': [], 'num_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'opponent_6': [0], 'chicago bears_7': [0], 'date_8': [1], '2_9': [1], 'attendance_10': [2], '47475_11': [3]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 11 , 1966', 'atlanta falcons', 'w 19 - 14', '54418'], ['2', 'september 16 , 1966', 'chicago bears', 'w 31 - 17', '58916'], ['3', 'september 25 , 1966', 'green bay packers', 'l 24 - 13', '50861'], ['4', 'september 30 , 1966', 'san francisco 49ers', 'w 34 - 3', '45642'], ['5', 'october 9 , 1966', 'detroit lions', 'w 14 - 7', '52793'], ['6', 'october 16 , 1966', 'minnesota vikings', 'l 35 - 7', '47426'], ['7', 'october 23 , 1966', 'chicago bears', 'l 17 - 10', '47475'], ['8', 'october 30 , 1966', 'baltimore colts', 'l 17 - 3', '57898'], ['9', 'november 6 , 1966', 'san francisco 49ers', 'l 21 - 13', '35372'], ['10', 'november 13 , 1966', 'new york giants', 'w 55 - 14', '34746'], ['11', 'november 20 , 1966', 'minnesota vikings', 'w 21 - 6', '38775'], ['12', 'november 27 , 1966', 'baltimore colts', 'w 23 - 7', '60238'], ['13', 'december 4 , 1966', 'detroit lions', 'w 23 - 3', '40039'], ['15', 'december 18 , 1966', 'green bay packers', 'l 27 - 23', '72416']] |
1991 - 92 seattle supersonics season | https://en.wikipedia.org/wiki/1991%E2%80%9392_Seattle_SuperSonics_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27902171-8.html.csv | majority | r pierce had the most games where he was the highest scorer . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'r pierce', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high points', 'r pierce'], 'result': True, 'ind': 0, 'tointer': 'for the high points records of all rows , most of them fuzzily match to r pierce .', 'tostr': 'most_eq { all_rows ; high points ; r pierce } = true'} | most_eq { all_rows ; high points ; r pierce } = true | for the high points records of all rows , most of them fuzzily match to r pierce . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high points_3': 3, 'r pierce_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high points_3': 'high points', 'r pierce_4': 'r pierce'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high points_3': [0], 'r pierce_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['58', 'march 1', 'cleveland cavaliers', 'w 113 - 107', 'e johnson , r pierce ( 22 )', 'b benjamin , m cage ( 14 )', 'r pierce ( 6 )', 'seattle center coliseum 13647', '32 - 26'], ['59', 'march 3', 'denver nuggets', 'w 111 - 92', 's kemp ( 21 )', 's kemp ( 13 )', 'g payton ( 9 )', 'seattle center coliseum 9865', '33 - 26'], ['60', 'march 5', 'phoenix suns', 'l 105 - 118', 'r pierce ( 23 )', 's kemp ( 19 )', 'g payton ( 12 )', 'arizona veterans memorial coliseum 14496', '33 - 27'], ['61', 'march 7', 'new jersey nets', 'w 109 - 98', 'r pierce ( 27 )', 'm cage ( 13 )', 'n mcmillan ( 7 )', 'seattle center coliseum 13419', '34 - 27'], ['62', 'march 8', 'portland trail blazers', 'l 97 - 109', 'r pierce ( 28 )', 'r pierce ( 10 )', 'g payton ( 7 )', 'memorial coliseum 12888', '34 - 28'], ['63', 'march 10', 'detroit pistons', 'l 92 - 98', 'g payton ( 19 )', 's kemp ( 9 )', 'n mcmillan ( 5 )', 'seattle center coliseum 13098', '34 - 29'], ['64', 'march 11', 'los angeles clippers', 'w 104 - 96', 'r pierce ( 19 )', 'b benjamin , m cage ( 6 )', 'g payton ( 9 )', 'los angeles memorial sports arena 10912', '35 - 29'], ['65', 'march 15', 'dallas mavericks', 'w 109 - 100', 'r pierce ( 23 )', 's kemp ( 15 )', 'g payton ( 8 )', 'seattle center coliseum 12163', '36 - 29'], ['66', 'march 17', 'golden state warriors', 'l 107 - 119', 'r pierce ( 24 )', 's kemp ( 15 )', 'r pierce ( 5 )', 'seattle center coliseum 13163', '36 - 30'], ['67', 'march 19', 'houston rockets', 'w 112 - 91', 'r pierce ( 22 )', 'm cage , s kemp ( 14 )', 'g payton ( 11 )', 'the summit 15122', '37 - 30'], ['68', 'march 21', 'san antonio spurs', 'l 96 - 101', 'e johnson ( 23 )', 's kemp ( 13 )', 'd barros , m cage , n mcmillan ( 4 )', 'hemisfair arena 16057', '37 - 31'], ['69', 'march 22', 'dallas mavericks', 'w 113 - 105', 'e johnson ( 31 )', 's kemp ( 17 )', 'n mcmillan ( 8 )', 'reunion arena 14345', '38 - 31'], ['70', 'march 24', 'houston rockets', 'w 128 - 106', 'd mckey ( 23 )', 'm cage , s kemp ( 11 )', 'n mcmillan , g payton ( 7 )', 'seattle center coliseum 11377', '39 - 31'], ['71', 'march 27', 'milwaukee bucks', 'w 96 - 95', 'e johnson ( 21 )', 'n mcmillan ( 7 )', 'n mcmillan ( 6 )', 'seattle center coliseum 11450', '40 - 31'], ['72', 'march 28', 'new york knicks', 'l 87 - 92', 's kemp ( 27 )', 's kemp ( 12 )', 'n mcmillan ( 6 )', 'seattle center coliseum 14812', '40 - 32']] |
westmorland county , new brunswick | https://en.wikipedia.org/wiki/Westmorland_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-176529-1.html.csv | superlative | moncton city has the highest population in westmorland county , new brunswick . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', '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', 'population'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population }'}, 'official name'], 'result': 'moncton', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population } ; official name }'}, 'moncton'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population } ; official name } ; moncton } = true', 'tointer': 'select the row whose population record of all rows is maximum . the official name record of this row is moncton .'} | eq { hop { argmax { all_rows ; population } ; official name } ; moncton } = true | select the row whose population record of all rows is maximum . the official name record of this row is moncton . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, 'official name_6': 6, 'moncton_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population_5': 'population', 'official name_6': 'official name', 'moncton_7': 'moncton'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], 'official name_6': [1], 'moncton_7': [2]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['moncton', 'city', '141.17', '69074', '79 of 5008'], ['dieppe', 'city', '51.17', '23310', '174 of 5008'], ['beaubassin east', 'rural community', '291.04', '6200', '600 of 5008'], ['shediac', 'town', '11.97', '6053', '610 of 5008'], ['sackville', 'town', '74.32', '5558', '655 of 5008'], ['memramcook', 'village', '185.71', '4831', '727 of 5008'], ['cap - pelã', 'village', '23.78', '2256', '1229 of 5008'], ['salisbury', 'village', '13.68', '2208', '1243 of 5008'], ['petitcodiac', 'village', '17.22', '1429', '1658 of 5008'], ['dorchester', 'village', '5.74', '1167', '1878 of 5008'], ['port elgin', 'village', '2.61', '418', '3238 of 5008']] |
utah jazz all - time roster | https://en.wikipedia.org/wiki/Utah_Jazz_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11545282-17.html.csv | unique | aleksandar radojeviä ‡ is the only player who 's nationality is not the united states . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'not_equal', 'value': 'united states', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record does not match to united states .', 'tostr': 'filter_not_eq { all_rows ; nationality ; united states }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; nationality ; united states } }', 'tointer': 'select the rows whose nationality record does not match to united states . 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', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record does not match to united states .', 'tostr': 'filter_not_eq { all_rows ; nationality ; united states }'}, 'player'], 'result': 'aleksandar radojeviä ‡', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; nationality ; united states } ; player }'}, 'aleksandar radojeviä ‡'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; nationality ; united states } ; player } ; aleksandar radojeviä ‡ }', 'tointer': 'the player record of this unqiue row is aleksandar radojeviä ‡ .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_not_eq { all_rows ; nationality ; united states } ; player } ; aleksandar radojeviä ‡ } } = true', 'tointer': 'select the rows whose nationality record does not match to united states . there is only one such row in the table . the player record of this unqiue row is aleksandar radojeviä ‡ .'} | and { only { filter_not_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_not_eq { all_rows ; nationality ; united states } ; player } ; aleksandar radojeviä ‡ } } = true | select the rows whose nationality record does not match to united states . there is only one such row in the table . the player record of this unqiue row is aleksandar radojeviä ‡ . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'united states_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'aleksandar radojeviä‡_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', 'nationality_7': 'nationality', 'united states_8': 'united states', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'aleksandar radojeviä‡_10': 'aleksandar radojeviä ‡'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_not_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'united states_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'aleksandar radojeviä‡_10': [3]} | ['player', 'nationality', 'position', 'years for jazz', 'school / club team'] | [['aleksandar radojeviä ‡', 'serbia', 'center', '2004 - 05', 'barton college'], ['rick roberson', 'united states', 'forward', '1974 - 75', 'cincinnati'], ['fred roberts', 'united states', 'forward', '1984 - 86', 'byu'], ['truck robinson', 'united states', 'power forward', '1977 - 79', 'tennessee state'], ['bill robinzine', 'united states', 'power forward', '1981 - 82', 'depaul'], ['scott roth', 'united states', 'forward', '1987 - 89', 'wisconsin'], ['delaney rudd', 'united states', 'guard', '1989 - 92', 'wake forest'], ['michael ruffin', 'united states', 'forward - center', '2003 - 04', 'tulsa'], ['bryon russell', 'united states', 'small forward', '1993 - 02', 'long beach state']] |
2008 in canadian music | https://en.wikipedia.org/wiki/2008_in_Canadian_music | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18382316-1.html.csv | unique | the album ' my love : essential collection ' was the only canadian album to be certified 2x platinum . | {'scope': 'all', 'row': '3', 'col': '6', 'col_other': '3', 'criterion': 'equal', 'value': '2x platinum', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'certification', '2x platinum'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose certification record fuzzily matches to 2x platinum .', 'tostr': 'filter_eq { all_rows ; certification ; 2x platinum }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; certification ; 2x platinum } }', 'tointer': 'select the rows whose certification record fuzzily matches to 2x platinum . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'certification', '2x platinum'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose certification record fuzzily matches to 2x platinum .', 'tostr': 'filter_eq { all_rows ; certification ; 2x platinum }'}, 'album'], 'result': 'my love : essential collection', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; certification ; 2x platinum } ; album }'}, 'my love : essential collection'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; certification ; 2x platinum } ; album } ; my love : essential collection }', 'tointer': 'the album record of this unqiue row is my love : essential collection .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; certification ; 2x platinum } } ; eq { hop { filter_eq { all_rows ; certification ; 2x platinum } ; album } ; my love : essential collection } } = true', 'tointer': 'select the rows whose certification record fuzzily matches to 2x platinum . there is only one such row in the table . the album record of this unqiue row is my love : essential collection .'} | and { only { filter_eq { all_rows ; certification ; 2x platinum } } ; eq { hop { filter_eq { all_rows ; certification ; 2x platinum } ; album } ; my love : essential collection } } = true | select the rows whose certification record fuzzily matches to 2x platinum . there is only one such row in the table . the album record of this unqiue row is my love : essential collection . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'certification_7': 7, '2x platinum_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'album_9': 9, 'my love : essential collection_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'certification_7': 'certification', '2x platinum_8': '2x platinum', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'album_9': 'album', 'my love : essential collection_10': 'my love : essential collection'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'certification_7': [0], '2x platinum_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'album_9': [2], 'my love : essential collection_10': [3]} | ['rank', 'artist', 'album', 'peak position', 'sales', 'certification'] | [['1', 'nickelback', 'dark horse', '1', '480000', '6x platinum'], ['2', 'simple plan', 'simple plan', '2', '200000', 'platinum'], ['3', 'celine dion', 'my love : essential collection', '2', '160000', '2x platinum'], ['4', 'the canadian tenors', 'the canadian tenors', '22', '80000', 'platinum'], ['5', 'city and colour', 'bring me your love', '3', '80000', 'platinum'], ['6', 'cur de pirate', 'cur de pirate', 'n / a', '80000', 'platinum'], ['7', 'sarah mclachlan', 'closer : the best of sarah mclachlan', '3', '80000', 'platinum'], ['8', 'theory of a deadman', 'scars & souvenirs', '2', '80000', 'platinum'], ['9', 'sam roberts', 'love at the end of the world', '1', '50000', 'gold'], ['10', 'tara oram', 'chasing the sun', '8', '50000', 'gold']] |
1989 indianapolis colts season | https://en.wikipedia.org/wiki/1989_Indianapolis_Colts_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14875671-1.html.csv | ordinal | the 3rd highest attendance in the 1989 colts season took place on october 1 , 1989 . | {'row': '4', 'col': '7', 'order': '3', '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', 'attendance', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 3 }'}, 'date'], 'result': 'october 1 , 1989', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 3 } ; date }'}, 'october 1 , 1989'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; october 1 , 1989 } = true', 'tointer': 'select the row whose attendance record of all rows is 3rd maximum . the date record of this row is october 1 , 1989 .'} | eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; october 1 , 1989 } = true | select the row whose attendance record of all rows is 3rd maximum . the date record of this row is october 1 , 1989 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '3_6': 6, 'date_7': 7, 'october 1 , 1989_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', 'attendance_5': 'attendance', '3_6': '3', 'date_7': 'date', 'october 1 , 1989_8': 'october 1 , 1989'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '3_6': [0], 'date_7': [1], 'october 1 , 1989_8': [2]} | ['week', 'date', 'opponent', 'result', 'record', 'game site', 'attendance'] | [['1', 'september 10 , 1989', 'san francisco 49ers', 'l 24 - 30', '0 - 1', 'hoosier dome', '60111'], ['2', 'september 17 , 1989', 'los angeles rams', 'l 17 - 31', '0 - 2', 'anaheim stadium', '63995'], ['3', 'september 24 , 1989', 'atlanta falcons', 'w 13 - 9', '1 - 2', 'hoosier dome', '57816'], ['4', 'october 1 , 1989', 'new york jets', 'w 17 - 10', '2 - 2', 'the meadowlands', '65542'], ['5', 'october 8 , 1989', 'buffalo bills', 'w 37 - 14', '3 - 2', 'hoosier dome', '58890'], ['6', 'october 15 , 1989', 'denver broncos', 'l 3 - 14', '3 - 3', 'mile high stadium', '74680'], ['7', 'october 22 , 1989', 'cincinnati bengals', 'w 23 - 12', '4 - 3', 'riverfront stadium', '57642'], ['8', 'october 29 , 1989', 'new england patriots', 'l 20 - 23', '4 - 4', 'hoosier dome', '59356'], ['9', 'november 5 , 1989', 'miami dolphins', 'l 13 - 19', '4 - 5', 'joe robbie stadium', '52680'], ['10', 'november 12 , 1989', 'buffalo bills', 'l 7 - 30', '4 - 6', 'rich stadium', '79256'], ['11', 'november 19 , 1989', 'new york jets', 'w 27 - 10', '5 - 6', 'hoosier dome', '58236'], ['12', 'november 26 , 1989', 'san diego chargers', 'w 10 - 6', '6 - 6', 'hoosier dome', '58822'], ['13', 'december 3 , 1989', 'new england patriots', 'l 16 - 22', '6 - 7', 'sullivan stadium', '32234'], ['14', 'december 10 , 1989', 'cleveland browns', 'w 23 - 17', '7 - 7', 'hoosier dome', '58550'], ['15', 'december 17 , 1989', 'miami dolphins', 'w 42 - 13', '8 - 7', 'hoosier dome', '55665'], ['16', 'december 24 , 1989', 'new orleans saints', 'l 6 - 41', '8 - 8', 'louisiana superdome', '49009']] |
list of tallest buildings in the european union | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_the_European_Union | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10728418-4.html.csv | count | only 3 of the tallest buildings in the eu are below 200 metres tall . | {'scope': 'all', 'criterion': 'less_than', 'value': '200', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'metres', '200'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose metres record is less than 200 .', 'tostr': 'filter_less { all_rows ; metres ; 200 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; metres ; 200 } }', 'tointer': 'select the rows whose metres record is less than 200 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; metres ; 200 } } ; 3 } = true', 'tointer': 'select the rows whose metres record is less than 200 . the number of such rows is 3 .'} | eq { count { filter_less { all_rows ; metres ; 200 } } ; 3 } = true | select the rows whose metres record is less than 200 . 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, 'metres_5': 5, '200_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'metres_5': 'metres', '200_6': '200', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'metres_5': [0], '200_6': [0], '3_7': [2]} | ['name', 'city', 'years as tallest', 'metres', 'feet', 'floors'] | [['the shard', 'london', '2011 - present', '306', '1004', '87'], ['commerzbank tower', 'frankfurt', '1997 - 2011', '259', '850', '56'], ['messeturm', 'frankfurt', '1990 - 1997', '257', '843', '55'], ['tour montparnasse', 'paris', '1972 - 1990', '210', '689', '59'], ['tour du midi / zuidertoren', 'brussels', '1966 - 1972', '150', '492', '38'], ['pirelli tower', 'milan', '1958 - 1966', '127', '417', '32'], ['torre breda', 'milan', '1957 - 1958', '117', '384', '30']] |
2000 open championship | https://en.wikipedia.org/wiki/2000_Open_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18027810-3.html.csv | aggregation | between all of the players a total of 1,353 was scored at the 2000 open championship . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '1,353', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'total'], 'result': '1,353', 'ind': 0, 'tostr': 'sum { all_rows ; total }'}, '1,353'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; total } ; 1,353 } = true', 'tointer': 'the sum of the total record of all rows is 1,353 .'} | round_eq { sum { all_rows ; total } ; 1,353 } = true | the sum of the total record of all rows is 1,353 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'total_4': 4, '1,353_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'total_4': 'total', '1,353_5': '1,353'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'total_4': [0], '1,353_5': [1]} | ['player', 'country', 'year ( s ) won', 'total', 'to par'] | [['nick price', 'zimbabwe', '1994', '146', '+ 2'], ['seve ballesteros', 'spain', '1979 , 1984 , 1988', '147', '+ 3'], ['bob charles', 'new zealand', '1963', '147', '+ 3'], ['john daly', 'united states', '1995', '148', '+ 4'], ['sandy lyle', 'scotland', '1985', '149', '+ 7'], ['jack nicklaus', 'united states', '1966 , 1970 , 1978', '150', '+ 6'], ['paul lawrie', 'scotland', '1999', '153', '+ 9'], ['gary player', 'south africa', '1959 , 1968 , 1974', '156', '+ 12'], ['lee trevino', 'united states', '1971 , 1972', '157', '+ 13']] |
international softball congress | https://en.wikipedia.org/wiki/International_Softball_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18618672-2.html.csv | unique | 1952 was the only year that plainview , tx hosted the international softball congress . | {'scope': 'all', 'row': '2', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'plainview , tx', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'host location', 'plainview , tx'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose host location record fuzzily matches to plainview , tx .', 'tostr': 'filter_eq { all_rows ; host location ; plainview , tx }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; host location ; plainview , tx } }', 'tointer': 'select the rows whose host location record fuzzily matches to plainview , tx . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'host location', 'plainview , tx'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose host location record fuzzily matches to plainview , tx .', 'tostr': 'filter_eq { all_rows ; host location ; plainview , tx }'}, 'year'], 'result': '1952', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; host location ; plainview , tx } ; year }'}, '1952'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; host location ; plainview , tx } ; year } ; 1952 }', 'tointer': 'the year record of this unqiue row is 1952 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; host location ; plainview , tx } } ; eq { hop { filter_eq { all_rows ; host location ; plainview , tx } ; year } ; 1952 } } = true', 'tointer': 'select the rows whose host location record fuzzily matches to plainview , tx . there is only one such row in the table . the year record of this unqiue row is 1952 .'} | and { only { filter_eq { all_rows ; host location ; plainview , tx } } ; eq { hop { filter_eq { all_rows ; host location ; plainview , tx } ; year } ; 1952 } } = true | select the rows whose host location record fuzzily matches to plainview , tx . there is only one such row in the table . the year record of this unqiue row is 1952 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'host location_7': 7, 'plainview , tx_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1952_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'host location_7': 'host location', 'plainview , tx_8': 'plainview , tx', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1952_10': '1952'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'host location_7': [0], 'plainview , tx_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1952_10': [3]} | ['year', '1st place team', '2nd place team', '3rd place team', '4th place team', 'host location'] | [['1951', 'hoak packers , fresno , ca', 'nitehawks , long beach , ca', 'robitaille motors , montreal , qc', 'wells motors , greeley , co', 'greeley , co'], ['1952', 'hoak packers , fresno , ca', 'nitehawks , long beach , ca', 'pointers , barbers point , hi', 'wyoming angus , johnstown , co', 'plainview , tx'], ['1953', 'nitehawks , long beach , ca', 'merchants , tampico , il', 'lions , lorenzo , tx', 'hoak packers , fresno , ca', 'selma , ca'], ['1954', 'hoak packers , fresno , ca', 'condors , dinuba , ca', 'nitehawks , long beach , ca', 'lions , lorenzo , tx', 'selma , ca'], ['1955', 'nitehawks , long beach , ca', 'condors , dinuba , ca', 'elites , new bedford , il', 'local 1014 chiefs , gary , in', 'new bedford , il'], ['1956', 'nitehawks , long beach , ca', 'siebren hybrids , geneseo , il', 'elites , new bedford , il', 'national cash register , dayton , oh', 'new bedford , il']] |
list of rugby league stadiums by capacity | https://en.wikipedia.org/wiki/List_of_rugby_league_stadiums_by_capacity | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18735456-2.html.csv | ordinal | when it comes to rugby league stadiums , the one with the second largest capacity is the sydney sports ground . | {'row': '2', 'col': '2', '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', 'capacity', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; capacity ; 2 }'}, 'stadium'], 'result': 'sydney sports ground', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium }'}, 'sydney sports ground'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; sydney sports ground } = true', 'tointer': 'select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is sydney sports ground .'} | eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; sydney sports ground } = true | select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is sydney sports ground . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'capacity_5': 5, '2_6': 6, 'stadium_7': 7, 'sydney sports ground_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', 'capacity_5': 'capacity', '2_6': '2', 'stadium_7': 'stadium', 'sydney sports ground_8': 'sydney sports ground'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'capacity_5': [0], '2_6': [0], 'stadium_7': [1], 'sydney sports ground_8': [2]} | ['stadium', 'capacity', 'city', 'country', 'home team / s', 'closed ( as a rl stadium )'] | [['anz stadium', '59000', 'brisbane', 'australia', 'brisbane broncos', '2003'], ['sydney sports ground', '35000', 'sydney', 'australia', 'eastern suburbs', '1986'], ['redfern oval', '23000', 'sydney', 'australia', 'south sydney', '1987'], ['stade sébastien charléty', '20000', 'paris', 'france', 'paris saint - germain', '1997'], ['olympic park stadium', '18500', 'melbourne', 'australia', 'melbourne storm', '2009'], ['knowsley road', '17500', 'st helens', 'england', 'st helens rlfc', '2010'], ['the willows', '11363', 'salford', 'england', 'salford city reds', '2011'], ['the boulevard', '10500', 'kingston upon hull', 'england', 'hull', '2000'], ['barnet copthall', '5000', 'london', 'england', 'london crusaders', '1994']] |
winston parks | https://en.wikipedia.org/wiki/Winston_Parks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1272045-1.html.csv | unique | the only goal for winston parks that came in the month of march , was his third goal . | {'scope': 'all', 'row': '3', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'march', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'march'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to march .', 'tostr': 'filter_eq { all_rows ; date ; march }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; date ; march } }', 'tointer': 'select the rows whose date record fuzzily matches to march . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'march'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to march .', 'tostr': 'filter_eq { all_rows ; date ; march }'}, 'goal'], 'result': '3', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; march } ; goal }'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; march } ; goal } ; 3 }', 'tointer': 'the goal record of this unqiue row is 3 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; date ; march } } ; eq { hop { filter_eq { all_rows ; date ; march } ; goal } ; 3 } } = true', 'tointer': 'select the rows whose date record fuzzily matches to march . there is only one such row in the table . the goal record of this unqiue row is 3 .'} | and { only { filter_eq { all_rows ; date ; march } } ; eq { hop { filter_eq { all_rows ; date ; march } ; goal } ; 3 } } = true | select the rows whose date record fuzzily matches to march . there is only one such row in the table . the goal record of this unqiue row is 3 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'march_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'goal_9': 9, '3_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'march_8': 'march', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'goal_9': 'goal', '3_10': '3'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'date_7': [0], 'march_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'goal_9': [2], '3_10': [3]} | ['goal', 'date', 'score', 'result', 'competition'] | [['1', '17 april 2002', '1 - 1', '1 - 1', 'friendly'], ['2', '9 june 2002', '1 - 1', '1 - 1', '2002 fifa world cup'], ['3', '29 march 2003', '2 - 1', '2 - 1', 'friendly'], ['4', '4 june 2004', '2 - 0', '5 - 1', 'friendly'], ['5', '4 june 2004', '4 - 0', '5 - 1', 'friendly'], ['6', '1 june 2010', '0 - 1', '0 - 1', 'friendly']] |
list of ngc objects ( 5001 - 6000 ) | https://en.wikipedia.org/wiki/List_of_NGC_objects_%285001%E2%80%936000%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11051845-9.html.csv | count | three of the objects are of the type " lenticular galaxy " . | {'scope': 'all', 'criterion': 'equal', 'value': 'lenticular galaxy', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'lenticular galaxy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose object type record fuzzily matches to lenticular galaxy .', 'tostr': 'filter_eq { all_rows ; object type ; lenticular galaxy }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; object type ; lenticular galaxy } }', 'tointer': 'select the rows whose object type record fuzzily matches to lenticular galaxy . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; object type ; lenticular galaxy } } ; 3 } = true', 'tointer': 'select the rows whose object type record fuzzily matches to lenticular galaxy . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; object type ; lenticular galaxy } } ; 3 } = true | select the rows whose object type record fuzzily matches to lenticular galaxy . 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, 'object type_5': 5, 'lenticular galaxy_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', 'object type_5': 'object type', 'lenticular galaxy_6': 'lenticular galaxy', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'object type_5': [0], 'lenticular galaxy_6': [0], '3_7': [2]} | ['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )'] | [['5822', 'open cluster', 'lupus', '15h04 m', 'degree24 ′'], ['5823', 'open cluster', 'circinus', '15h05 m44 .8 s', 'degree37 ′ 30 ″'], ['5824', 'globular cluster', 'lupus', '15h03 m58 .5 s', 'degree04 ′ 04 ″'], ['5825', 'elliptical galaxy', 'boötes', '14h54 m31 .5 s', 'degree38 ′ 31 ″'], ['5838', 'lenticular galaxy', 'virgo', '15h05 m26 .3 s', 'degree05 ′ 57 ″'], ['5846', 'elliptical galaxy', 'virgo', '15h06 m29 .4 s', 'degree36 ′ 19 ″'], ['5850', 'spiral galaxy', 'virgo', '15h07 m07 .8 s', 'degree32 ′ 39 ″'], ['5866', 'lenticular galaxy', 'draco', '15h06 m29 .5 s', 'degree45 ′ 47 ″'], ['5877', 'triple star', 'lupus', '15h12 m53 .1 s', 'degree55 ′ 38 ″'], ['5879', 'galaxy', 'draco', '15h09 m46 .8 s', 'degree00 ′ 01 ″'], ['5882', 'planetary nebula', 'libra', '15h16 m49 .9 s', 'degree38 ′ 58 ″'], ['5885', 'barred spiral galaxy', 'libra', '15h15 m04 .1 s', 'degree05 ′ 10.0 ″'], ['5886', 'elliptical galaxy', 'boötes', '15h12 m45 .4 s', 'degree12 ′ 02 ″'], ['5888', 'barred spiral galaxy', 'boötes', '15h13 m07 .4 s', 'degree15 ′ 52 ″'], ['5890', 'lenticular galaxy', 'libra', '15h17 m51 .1 s', 'degree35 ′ 19 ″']] |
1929 vfl season | https://en.wikipedia.org/wiki/1929_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767118-2.html.csv | majority | most of the games had a crowd of less than 30000 people attending . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '30000', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'crowd', '30000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are less than 30000 .', 'tostr': 'most_less { all_rows ; crowd ; 30000 } = true'} | most_less { all_rows ; crowd ; 30000 } = true | for the crowd records of all rows , most of them are less than 30000 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '30000_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '30000_4': '30000'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '30000_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '12.15 ( 87 )', 'melbourne', '3.10 ( 28 )', 'corio oval', '11000', '4 may 1929'], ['fitzroy', '14.23 ( 107 )', 'north melbourne', '8.7 ( 55 )', 'brunswick street oval', '13000', '4 may 1929'], ['essendon', '17.10 ( 112 )', 'footscray', '16.8 ( 104 )', 'windy hill', '20000', '4 may 1929'], ['south melbourne', '12.17 ( 89 )', 'st kilda', '9.9 ( 63 )', 'lake oval', '21270', '4 may 1929'], ['hawthorn', '11.7 ( 73 )', 'collingwood', '18.18 ( 126 )', 'glenferrie oval', '12000', '4 may 1929'], ['richmond', '16.23 ( 119 )', 'carlton', '16.13 ( 109 )', 'punt road oval', '36000', '4 may 1929']] |
indiana high school athletics conferences : allen county - metropolitan | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Allen_County_%E2%80%93_Metropolitan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13986492-6.html.csv | ordinal | among the schools in the allen county - metropolitan division ( indiana high school athletics conference ) , the school with the second highest enrollment is portage . | {'row': '7', 'col': '4', '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', 'enrollment', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; enrollment ; 2 }'}, 'school'], 'result': 'portage', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; enrollment ; 2 } ; school }'}, 'portage'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; portage } = true', 'tointer': 'select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is portage .'} | eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; portage } = true | select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is portage . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, '2_6': 6, 'school_7': 7, 'portage_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', 'enrollment_5': 'enrollment', '2_6': '2', 'school_7': 'school', 'portage_8': 'portage'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], '2_6': [0], 'school_7': [1], 'portage_8': [2]} | ['school', 'mascot', 'location', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['chesterton', 'trojans', 'chesterton', '1986', 'aaaa', 'aaaaa', '64 porter'], ['crown point', 'bulldogs', 'crown point', '2532', 'aaaa', 'aaaaa', '45 lake'], ['laporte', 'slicers', 'laporte', '1839', 'aaaa', 'aaaaa', '46 laporte'], ['lake central', 'indians', 'saint john', '3225', 'aaaa', 'aaaaa', '45 lake'], ['merrillville', 'pirates', 'merrillville', '2396', 'aaaa', 'aaaaa', '45 lake'], ['michigan city', 'wolves', 'michigan city', '1909', 'aaaa', 'aaaaa', '46 laporte'], ['portage', 'indians', 'portage', '2668', 'aaaa', 'aaaaa', '64 porter'], ['valparaiso', 'vikings', 'valparaiso', '2114', 'aaaa', 'aaaaa', '64 porter']] |
thor - christian ebbesvik | https://en.wikipedia.org/wiki/Thor-Christian_Ebbesvik | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20398823-1.html.csv | count | there were four occasions when thor-christian ebbesvik 's team was team jlr . | {'scope': 'all', 'criterion': 'equal', 'value': 'team jlr', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'team jlr'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to team jlr .', 'tostr': 'filter_eq { all_rows ; team ; team jlr }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; team ; team jlr } }', 'tointer': 'select the rows whose team record fuzzily matches to team jlr . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; team ; team jlr } } ; 4 } = true', 'tointer': 'select the rows whose team record fuzzily matches to team jlr . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; team ; team jlr } } ; 4 } = true | select the rows whose team record fuzzily matches to team jlr . 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, 'team_5': 5, 'team jlr_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', 'team_5': 'team', 'team jlr_6': 'team jlr', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'team_5': [0], 'team jlr_6': [0], '4_7': [2]} | ['season', 'series', 'team', 'races', 'wins', 'poles', 'f / laps', 'podiums', 'points', 'position'] | [['2005', 'british formula ford championship', 'team jlr', '20', '0', '0', '0', '0', '321', '6th'], ['2005', 'formula ford festival', 'team jlr', '1', '0', '0', '0', '0', 'n / a', 'nc'], ['2006', 'british formula ford championship', 'team jlr', '20', '1', '0', '2', '4', '357', '4th'], ['2006', 'formula ford festival - duratec class', 'team jlr', '1', '0', '0', '0', '0', 'n / a', 'nc'], ['2007', 'spanish formula three championship', 'team west - tec', '14', '0', '0', '0', '0', '7', '16th'], ['2008', 'spanish formula three championship', 'team west - tec', '17', '2', '1', '0', '2', '49', '10th'], ['2009', 'european f3 open championship', 'team west - tec', '16', '1', '1', '1', '3', '64', '5th'], ['2009', 'formula le mans cup', 'hope polevision racing', '2', '0', '0', '0', '0', '16', '20th'], ['2010', 'le mans series - lmp2', 'team bruichladdich', '5', '0', '0', '0', '1', '46', '5th']] |
1950 masters tournament | https://en.wikipedia.org/wiki/1950_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13059194-1.html.csv | majority | in the 1950 masters tournament , most of the winners received at least 400 . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '400', 'subset': None} | {'func': 'most_greater_eq', 'args': ['all_rows', 'money', '400'], 'result': True, 'ind': 0, 'tointer': 'for the money records of all rows , most of them are greater than or equal to 400 .', 'tostr': 'most_greater_eq { all_rows ; money ; 400 } = true'} | most_greater_eq { all_rows ; money ; 400 } = true | for the money records of all rows , most of them are greater than or equal to 400 . | 1 | 1 | {'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'money_3': 3, '400_4': 4} | {'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'money_3': 'money', '400_4': '400'} | {'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'money_3': [0], '400_4': [0]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'jimmy demaret', 'united states', '70 + 72 + 72 + 69 = 283', '- 5', '2400'], ['2', 'jim ferrier', 'australia', '70 + 67 + 73 + 75 = 285', '- 3', '1500'], ['3', 'sam snead', 'united states', '71 + 74 + 70 + 72 = 287', '- 1', '1020'], ['t4', 'ben hogan', 'united states', '73 + 68 + 71 + 76 = 288', 'e', '725'], ['t4', 'byron nelson', 'united states', '75 + 70 + 69 + 74 = 288', 'e', '725'], ['6', 'lloyd mangrum', 'united states', '76 + 74 + 73 + 68 = 291', '+ 3', '480'], ['t7', 'clayton heafner', 'united states', '74 + 77 + 69 + 72 = 292', '+ 4', '405'], ['t7', 'cary middlecoff', 'united states', '75 + 76 + 68 + 73 = 292', '+ 4', '405'], ['9', 'lawson little', 'united states', '70 + 73 + 75 + 75 = 293', '+ 5', '360'], ['t10', 'fred haas', 'united states', '74 + 76 + 73 + 71 = 294', '+ 6', '333'], ['t10', 'gene sarazen', 'united states', '80 + 70 + 72 + 72 = 294', '+ 6', '333']] |
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 | aggregation | the average attendance of the 1964 world series was 48,000 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '45,972', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '45,972', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '45,972'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 45,972 } = true', 'tointer': 'the average of the attendance record of all rows is 45,972 .'} | round_eq { avg { all_rows ; attendance } ; 45,972 } = true | the average of the attendance record of all rows is 45,972 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '45,972_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '45,972_5': '45,972'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '45,972_5': [1]} | ['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']] |
indiana high school athletics conferences : allen county - metropolitan | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Allen_County_%E2%80%93_Metropolitan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13986492-11.html.csv | comparative | more students attend avon community than attend brownsburg in indiana . | {'row_1': '1', 'row_2': '2', 'col': '4', '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', 'school', 'avon community'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school record fuzzily matches to avon community .', 'tostr': 'filter_eq { all_rows ; school ; avon community }'}, 'enrollment'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school ; avon community } ; enrollment }', 'tointer': 'select the rows whose school record fuzzily matches to avon community . take the enrollment record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school', 'brownsburg'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school record fuzzily matches to brownsburg .', 'tostr': 'filter_eq { all_rows ; school ; brownsburg }'}, 'enrollment'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; school ; brownsburg } ; enrollment }', 'tointer': 'select the rows whose school record fuzzily matches to brownsburg . take the enrollment record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; school ; avon community } ; enrollment } ; hop { filter_eq { all_rows ; school ; brownsburg } ; enrollment } } = true', 'tointer': 'select the rows whose school record fuzzily matches to avon community . take the enrollment record of this row . select the rows whose school record fuzzily matches to brownsburg . take the enrollment record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; school ; avon community } ; enrollment } ; hop { filter_eq { all_rows ; school ; brownsburg } ; enrollment } } = true | select the rows whose school record fuzzily matches to avon community . take the enrollment record of this row . select the rows whose school record fuzzily matches to brownsburg . take the enrollment 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, 'school_7': 7, 'avon community_8': 8, 'enrollment_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'school_11': 11, 'brownsburg_12': 12, 'enrollment_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', 'school_7': 'school', 'avon community_8': 'avon community', 'enrollment_9': 'enrollment', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'school_11': 'school', 'brownsburg_12': 'brownsburg', 'enrollment_13': 'enrollment'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'school_7': [0], 'avon community_8': [0], 'enrollment_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'school_11': [1], 'brownsburg_12': [1], 'enrollment_13': [3]} | ['school', 'mascot', 'location', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['avon community', 'orioles', 'avon', '2512', 'aaaa', 'aaaaa', '32 hendricks'], ['brownsburg', 'bulldogs', 'brownsburg', '2222', 'aaaa', 'aaaaa', '32 hendricks'], ['fishers', 'tigers', 'fishers', '2236', 'aaaa', 'aaaaa', '29 hamilton'], ['hamilton southeastern', 'royals', 'fishers', '2700', 'aaaa', 'aaaaa', '29 hamilton'], ['west lafayette wh harrison', 'raiders', 'west lafayette', '1663', 'aaaa', 'aaaaa', '79 tippecanoe'], ['lafayette t jefferson', 'bronchos', 'lafayette', '2191', 'aaaa', 'aaaaa', '79 tippecanoe'], ['west lafaette mccutcheon', 'mavericks', 'west lafayette', '1834', 'aaaa', 'aaaaa', '79 tippecanoe'], ['noblesville', 'millers', 'noblesville', '2502', 'aaaa', 'aaaaa', '29 hamilton'], ['westfield', 'shamrocks', 'westfield', '2048', 'aaaa', 'aaaaa', '29 hamilton'], ['zionsville community', 'eagles', 'zionsville', '1749', 'aaaa', 'aaaaa', '06 boone']] |
1953 - 54 segunda división | https://en.wikipedia.org/wiki/1953%E2%80%9354_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17416195-2.html.csv | comparative | the team in position 7 recorded a higher number of draws than the team in position 6 . | {'row_1': '7', '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', 'position', '7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 7 .', 'tostr': 'filter_eq { all_rows ; position ; 7 }'}, 'draws'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; 7 } ; draws }', 'tointer': 'select the rows whose position record fuzzily matches to 7 . take the draws record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '6'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose position record fuzzily matches to 6 .', 'tostr': 'filter_eq { all_rows ; position ; 6 }'}, 'draws'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; position ; 6 } ; draws }', 'tointer': 'select the rows whose position record fuzzily matches to 6 . take the draws record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; position ; 7 } ; draws } ; hop { filter_eq { all_rows ; position ; 6 } ; draws } } = true', 'tointer': 'select the rows whose position record fuzzily matches to 7 . take the draws record of this row . select the rows whose position record fuzzily matches to 6 . take the draws record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; position ; 7 } ; draws } ; hop { filter_eq { all_rows ; position ; 6 } ; draws } } = true | select the rows whose position record fuzzily matches to 7 . take the draws record of this row . select the rows whose position record fuzzily matches to 6 . take the draws 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, 'position_7': 7, '7_8': 8, 'draws_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'position_11': 11, '6_12': 12, 'draws_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', 'position_7': 'position', '7_8': '7', 'draws_9': 'draws', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'position_11': 'position', '6_12': '6', 'draws_13': 'draws'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'position_7': [0], '7_8': [0], 'draws_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'position_11': [1], '6_12': [1], 'draws_13': [3]} | ['position', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', '30', '41', '17', '7', '6', '65', '42', '+ 23'], ['2', '30', '38', '17', '4', '9', '56', '36', '+ 20'], ['3', '30', '38', '16', '6', '8', '62', '44', '+ 18'], ['4', '30', '34', '15', '4', '11', '63', '46', '+ 17'], ['5', '30', '33', '12', '9', '9', '62', '48', '+ 14'], ['6', '30', '32', '14', '4', '12', '52', '53', '- 1'], ['7', '30', '29', '10', '9', '11', '56', '54', '+ 2'], ['8', '30', '29', '12', '5', '13', '44', '58', '- 14'], ['9', '30', '29', '11', '7', '12', '76', '59', '+ 17'], ['10', '30', '28', '12', '4', '14', '65', '55', '+ 10'], ['11', '30', '28', '9', '10', '11', '45', '61', '- 16'], ['12', '30', '28', '11', '6', '13', '38', '46', '- 8'], ['13', '30', '25', '10', '5', '15', '49', '63', '- 14'], ['14', '30', '25', '11', '3', '16', '35', '64', '- 29'], ['15', '30', '22', '8', '6', '16', '44', '56', '- 12'], ['16', '30', '21', '8', '5', '17', '42', '69', '- 27']] |
katarina srebotnik | https://en.wikipedia.org/wiki/Katarina_Srebotnik | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1729366-2.html.csv | unique | katarina srebotnik played her first championship tennis at the french open . | {'scope': 'all', 'row': '1', 'col': '2', 'col_other': '3', 'criterion': 'equal', 'value': '1999', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '1999'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 1999 .', 'tostr': 'filter_eq { all_rows ; year ; 1999 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; year ; 1999 } }', 'tointer': 'select the rows whose year record is equal to 1999 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '1999'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 1999 .', 'tostr': 'filter_eq { all_rows ; year ; 1999 }'}, 'championship'], 'result': 'french open', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1999 } ; championship }'}, 'french open'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; year ; 1999 } ; championship } ; french open }', 'tointer': 'the championship record of this unqiue row is french open .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; year ; 1999 } } ; eq { hop { filter_eq { all_rows ; year ; 1999 } ; championship } ; french open } } = true', 'tointer': 'select the rows whose year record is equal to 1999 . there is only one such row in the table . the championship record of this unqiue row is french open .'} | and { only { filter_eq { all_rows ; year ; 1999 } } ; eq { hop { filter_eq { all_rows ; year ; 1999 } ; championship } ; french open } } = true | select the rows whose year record is equal to 1999 . there is only one such row in the table . the championship record of this unqiue row is french open . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1999_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'championship_9': 9, 'french open_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1999_8': '1999', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'championship_9': 'championship', 'french open_10': 'french open'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'year_7': [0], '1999_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'championship_9': [2], 'french open_10': [3]} | ['outcome', 'year', 'championship', 'surface', 'partner', 'opponents in the final', 'score in the final'] | [['winner', '1999', 'french open', 'clay', 'piet norval', 'larisa neiland rick leach', '6 - 3 , 3 - 6 , 6 - 3'], ['runner - up', '2002', 'us open', 'hard', 'bob bryan', 'lisa raymond mike bryan', '6 - 7 , 6 - 7'], ['winner', '2003', 'us open', 'hard', 'bob bryan', 'lina krasnoroutskaya daniel nestor', '5 - 7 , 7 - 5 , 7 - 6 ( 7 - 5 )'], ['runner - up', '2005', 'us open', 'hard', 'nenad zimonjić', 'daniela hantuchová mahesh bhupathi', '4 - 6 , 2 - 6'], ['winner', '2006', 'french open ( 2 )', 'clay', 'nenad zimonjić', 'elena likhovtseva daniel nestor', '6 - 3 , 6 - 4'], ['runner - up', '2007', 'french open', 'clay', 'nenad zimonjić', 'nathalie dechy andy ram', '5 - 7 , 3 - 6'], ['runner - up', '2008', 'french open', 'clay', 'nenad zimonjić', 'victoria azarenka bob bryan', '2 - 6 , 6 - 7 ( 4 - 7 )'], ['runner - up', '2008', 'wimbledon', 'grass', 'mike bryan', 'samantha stosur bob bryan', '5 - 7 , 4 - 6'], ['winner', '2010', 'french open ( 3 )', 'clay', 'nenad zimonjić', 'yaroslava shvedova julian knowle', '4 - 6 , 7 - 6 ( 7 - 5 ) ,'], ['winner', '2011', 'australian open', 'hard', 'daniel nestor', 'yung - jan chan paul hanley', '6 - 3 , 3 - 6 ,'], ['runner - up', '2011', 'french open', 'clay', 'nenad zimonjić', 'casey dellacqua scott lipsky', '6 - 7 ( 6 - 8 ) , 6 - 4 ,']] |
2007 latvian higher league | https://en.wikipedia.org/wiki/2007_Latvian_Higher_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11236683-2.html.csv | unique | jfc olimpis riga was the only team with less than 5 wins in the 2007 latvian higher league . | {'scope': 'all', 'row': '8', 'col': '4', 'col_other': '2', 'criterion': 'less_than', 'value': '5', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'wins', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is less than 5 .', 'tostr': 'filter_less { all_rows ; wins ; 5 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; wins ; 5 } }', 'tointer': 'select the rows whose wins record is less than 5 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'wins', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is less than 5 .', 'tostr': 'filter_less { all_rows ; wins ; 5 }'}, 'club'], 'result': 'jfc olimps rīga', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; wins ; 5 } ; club }'}, 'jfc olimps rīga'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; wins ; 5 } ; club } ; jfc olimps rīga }', 'tointer': 'the club record of this unqiue row is jfc olimps rīga .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; wins ; 5 } } ; eq { hop { filter_less { all_rows ; wins ; 5 } ; club } ; jfc olimps rīga } } = true', 'tointer': 'select the rows whose wins record is less than 5 . there is only one such row in the table . the club record of this unqiue row is jfc olimps rīga .'} | and { only { filter_less { all_rows ; wins ; 5 } } ; eq { hop { filter_less { all_rows ; wins ; 5 } ; club } ; jfc olimps rīga } } = true | select the rows whose wins record is less than 5 . there is only one such row in the table . the club record of this unqiue row is jfc olimps rīga . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'wins_7': 7, '5_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'jfc olimps rīga_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'wins_7': 'wins', '5_8': '5', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'jfc olimps rīga_10': 'jfc olimps rīga'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'wins_7': [0], '5_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'jfc olimps rīga_10': [3]} | ['position', 'club', 'played', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'points', 'goal difference'] | [['1', 'fk ventspils', '28', '18', '6', '4', '59', '16', '60', '+ 43'], ['2', 'fhk liepājas metalurgs', '28', '18', '4', '6', '42', '21', '58', '+ 21'], ['3', 'fk rīga', '28', '17', '6', '5', '48', '28', '57', '+ 20'], ['4', 'skonto fc rīga', '28', '16', '7', '5', '54', '27', '55', '+ 27'], ['5', 'fk daugava daugavpils', '28', '9', '6', '13', '33', '38', '33', '- 5'], ['6', 'fk jūrmala', '28', '7', '5', '16', '28', '51', '26', '- 23'], ['7', 'dinaburg fc daugavpils', '28', '6', '2', '20', '23', '58', '20', '- 35'], ['8', 'jfc olimps rīga', '28', '2', '2', '24', '15', '63', '8', '- 48']] |
new zealand open ( badminton ) | https://en.wikipedia.org/wiki/New_Zealand_Open_%28badminton%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12275551-1.html.csv | count | dean galt won the men ’s singles in badminton at the new zealand open twice . | {'scope': 'all', 'criterion': 'equal', 'value': 'dean galt', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mens singles', 'dean galt'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose mens singles record fuzzily matches to dean galt .', 'tostr': 'filter_eq { all_rows ; mens singles ; dean galt }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; mens singles ; dean galt } }', 'tointer': 'select the rows whose mens singles record fuzzily matches to dean galt . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; mens singles ; dean galt } } ; 2 } = true', 'tointer': 'select the rows whose mens singles record fuzzily matches to dean galt . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; mens singles ; dean galt } } ; 2 } = true | select the rows whose mens singles record fuzzily matches to dean galt . 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, 'mens singles_5': 5, 'dean galt_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', 'mens singles_5': 'mens singles', 'dean galt_6': 'dean galt', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'mens singles_5': [0], 'dean galt_6': [0], '2_7': [2]} | ['year', 'mens singles', 'womens singles', 'mens doubles', 'womens doubles', 'mixed doubles'] | [['1990', 'nicholas hall', 'stephanie spicer', 'nicholas hall dean galt', 'rhona robertson lynne scutt', 'brent chapman tammy jenkins'], ['1991', 'wei yan', 'anna oi chan lao', 'peter blackburn darren mcdonald', 'rhonda cator anna oi chan lao', 'peter blackburn lisa campbell'], ['1992', 'dean galt', 'julie still', 'dean galt andrew compton', 'rhona robertson tammy jenkins', 'grant walker sheree jefferson'], ['1993', 'dean galt', 'rhona robertson', 'dean galt kerrin harrison', 'rhona robertson liao yue jin', 'dean galt liao yue jin'], ['1994', 'oliver pongratz', 'song yang', 'michael helber michael keck', 'lisa campbell amanda hardy', 'peter blackburn rhonda cator'], ['1995', 'tam kai chuen', 'song yang', 'he tim chan siu kwong', 'rhona robertson tammy jenkins', 'he tim chan oi ni'], ['1996', 'tam kai chuen', 'li feng', 'ma che kong chow kin man', 'rhona robertson tammy jenkins', 'tam kai chuen tung chau man'], ['1997', 'nicholas hall', 'li feng', 'ma che kong liu kwok wa', 'rhona robertson tammy jenkins', 'ma che kong tung chau man'], ['1998', 'geoffrey bellingham', 'li feng', 'daniel shirley dean galt', 'rhona robertson tammy jenkins', 'dean galt tammy jenkins'], ['2000', 'geoffrey bellingham', 'rhona robertson', 'daniel shirley john gordon', 'masami yamazaki keiko yoshitomi', 'peter blackburn rhonda cator'], ['2002', 'geoffrey bellingham', 'kim ji - hyun', 'daniel shirley john gordon', 'nicole gordon sara runesten - petersen', 'daniel shirley sara runesten - petersen'], ['2003', 'shōji satō', 'lenny permana', 'ashley brehaut travis denney', 'nicole gordon rebecca gordon', 'travis denney kate wilson - smith'], ['2004', 'andrew smith', 'huang chia chi', 'suichi nakao suichi sakamoto', 'rachel hindley rebecca gordon', 'craig cooper lianne shirley'], ['2005', 'sairul amar ayob', 'adriyanti firdasari', 'boyd cooper travis denney', 'rachel hindley rebecca bellingham', 'daniel shirley sara runesten - petersen'], ['2006', 'lee tsuen seng', 'huang chia - chi', 'eng hian rian sukmawan', 'jiang yanmei li yujia', 'hendri kurniawan saputra li yujia'], ['2007', 'andre kurniawan tedjono', 'zhou mi', 'chan chong ming hoon thien how', 'ikue tatani aya wakisaka', 'devin lahardi fitriawan lita nurlita'], ['2008', 'lee tsuen seng', 'zhou mi', 'chen hung - ling lin yu - lang', 'chien yu - chin chou chia - chi', 'chen hung - ling chou chia - chi'], ['2009', 'chan yan kit', 'sayaka sato', 'ruseph kumar sanave thomas', 'annisa wahyuni anneke feinya agustin', 'frans kurniawan pia zebadiah bernadet']] |
little east conference | https://en.wikipedia.org/wiki/Little_East_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1974545-2.html.csv | count | two of the institutions have tennis teams among the sports teams listed . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'tennis', 'result': '2', 'col': '8', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'lec sport', 'tennis'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose lec sport record fuzzily matches to tennis .', 'tostr': 'filter_eq { all_rows ; lec sport ; tennis }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; lec sport ; tennis } }', 'tointer': 'select the rows whose lec sport record fuzzily matches to tennis . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; lec sport ; tennis } } ; 2 } = true', 'tointer': 'select the rows whose lec sport record fuzzily matches to tennis . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; lec sport ; tennis } } ; 2 } = true | select the rows whose lec sport record fuzzily matches to tennis . 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, 'lec sport_5': 5, 'tennis_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', 'lec sport_5': 'lec sport', 'tennis_6': 'tennis', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'lec sport_5': [0], 'tennis_6': [0], '2_7': [2]} | ['institution', 'location', 'nickname', 'founded', 'type', 'enrollment', 'primary conference', 'lec sport'] | [['bridgewater state university', 'bridgewater , massachusetts', 'bears', '1840', 'public', '11201', 'mascac', 'field hockey tennis'], ['fitchburg state university', 'fitchburg , massachusetts', 'falcons', '1894', 'public', '5201', 'mascac', 'field hockey'], ['framingham state university', 'framingham , massachusetts', 'rams', '1839', 'public', '5903', 'mascac', 'field hockey'], ['salem state university', 'salem , massachusetts', 'vikings', '1854', 'public', '10125', 'mascac', "field hockey men 's lacrosse tennis"], ['westfield state university', 'westfield , massachusetts', 'owls', '1838', 'public', '5500', 'mascac', 'field hockey']] |
wbfj - fm | https://en.wikipedia.org/wiki/WBFJ-FM | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10711725-1.html.csv | majority | all of the wbfj-fm radio channels are in the d broadcast station class . | {'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'd', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'class', 'd'], 'result': True, 'ind': 0, 'tointer': 'for the class records of all rows , all of them fuzzily match to d .', 'tostr': 'all_eq { all_rows ; class ; d } = true'} | all_eq { all_rows ; class ; d } = true | for the class records of all rows , all of them fuzzily match to d . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'class_3': 3, 'd_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'class_3': 'class', 'd_4': 'd'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'class_3': [0], 'd_4': [0]} | ['call sign', 'frequency mhz', 'city of license', 'facility id', 'erp w', 'height m ( ft )', 'class', 'fcc info'] | [['w267ag', '101.3', 'salisbury , north carolina', '67830', '38', '-', 'd', 'fcc'], ['w267 am', '101.3', 'mocksville , north carolina', '87027', '33', '-', 'd', 'fcc'], ['w267an', '101.3', 'wilkesboro , north carolina', '87078', '10', '-', 'd', 'fcc'], ['w274al', '102.7', 'high point , north carolina', '87044', '10', '-', 'd', 'fcc'], ['w276ba', '103.1', 'fancy gap , virginia', '87029', '10', '-', 'd', 'fcc'], ['w278 am', '103.5', 'sedalia , north carolina', '87023', '10', '-', 'd', 'fcc'], ['w285dj', '104.9', 'mount airy , north carolina', '67829', '10', '-', 'd', 'fcc']] |
bh11960 | https://en.wikipedia.org/wiki/BH11960 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27155678-2.html.csv | count | two of the genus/species have a 2805nt / 934aa protein sequence length . | {'scope': 'all', 'criterion': 'equal', 'value': '2805nt / 934aa', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sequence length', '2805nt / 934aa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sequence length record fuzzily matches to 2805nt / 934aa .', 'tostr': 'filter_eq { all_rows ; sequence length ; 2805nt / 934aa }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; sequence length ; 2805nt / 934aa } }', 'tointer': 'select the rows whose sequence length record fuzzily matches to 2805nt / 934aa . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; sequence length ; 2805nt / 934aa } } ; 2 } = true', 'tointer': 'select the rows whose sequence length record fuzzily matches to 2805nt / 934aa . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; sequence length ; 2805nt / 934aa } } ; 2 } = true | select the rows whose sequence length record fuzzily matches to 2805nt / 934aa . 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, 'sequence length_5': 5, '2805nt / 934aa_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', 'sequence length_5': 'sequence length', '2805nt / 934aa_6': '2805nt / 934aa', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'sequence length_5': [0], '2805nt / 934aa_6': [0], '2_7': [2]} | ['genus / species', 'gene name', 'accession number', 'sequence length', 'sequence similarity'] | [['bartonella henselae', 'hypothetical protein', 'bx897699 .1', '2805nt / 934aa', '100'], ['bartonella quintana', 'hypothetical protein', 'bx897700 .1', '2805nt / 934aa', '91'], ['bartonella grahamii', 'transcription regulator', 'cp001562 .1', '2799nt / 932aa', '87'], ['bartonella tribocorum', 'alanyl - trna synthetase', 'am260525 .1', '2799nt / 932aa', '87'], ['methylobacterium nodulans', 'hypothetical protein', 'yp_002500318 .1', '2820nt / 939aa', '53'], ['nitrobacter hamburgensis', 'double transmembrane region like', 'yp_578448 .1', '2817nt / 938aa', '53'], ['hyphomicrobium denitrificans', 'conserved hypothetical protein', 'zp_05374729 .1', '2973nt / 990aa', '53'], ['rhodopseudomonas palustris', 'double transmembrane region like', 'yp_568432 .1', '2826nt / 941aa', '54'], ['hoeflea phototrophica', 'double transmembrane region like', 'yp_002289983 .1', '1832nt / 943aa', '55']] |
song - hee kim | https://en.wikipedia.org/wiki/Song-Hee_Kim | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24330912-1.html.csv | ordinal | song-hee kim earned the 2nd highest amount of money in her career in 2009 . | {'row': '3', 'col': '9', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'earnings', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; earnings ; 2 }'}, 'year'], 'result': '2009', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; earnings ; 2 } ; year }'}, '2009'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; earnings ; 2 } ; year } ; 2009 } = true', 'tointer': 'select the row whose earnings record of all rows is 2nd maximum . the year record of this row is 2009 .'} | eq { hop { nth_argmax { all_rows ; earnings ; 2 } ; year } ; 2009 } = true | select the row whose earnings record of all rows is 2nd maximum . the year record of this row is 2009 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'earnings_5': 5, '2_6': 6, 'year_7': 7, '2009_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'earnings_5': 'earnings', '2_6': '2', 'year_7': 'year', '2009_8': '2009'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'earnings_5': [0], '2_6': [0], 'year_7': [1], '2009_8': [2]} | ['year', 'tournaments played', 'cuts made', 'wins', '2nd', '3rd', 'top 10s', 'best finish', 'earnings', 'money list rank', 'scoring average', 'scoring rank'] | [['2007', '19', '10', '0', '0', '0', '0', 't22', '78660', '99', '73.72', '75'], ['2008', '25', '21', '0', '2', '1', '7', '2', '980883', '14', '71.23', '10'], ['2009', '25', '23', '0', '0', '2', '12', 't3', '1032031', '11', '70.52', '8'], ['2010', '22', '22', '0', '2', '3', '15', '2', '1208698', '8', '70.21', '4'], ['2011', '22', '19', '0', '1', '0', '2', '2', '350376', '33', '72.62', '47']] |
płock governorate | https://en.wikipedia.org/wiki/P%C5%82ock_Governorate | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12333984-1.html.csv | comparative | in the plock governorate , more people speak yiddish than the russian language . | {'row_1': '2', 'row_2': '4', 'col': '2', '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', 'language', 'yiddish'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose language record fuzzily matches to yiddish .', 'tostr': 'filter_eq { all_rows ; language ; yiddish }'}, 'number'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; language ; yiddish } ; number }', 'tointer': 'select the rows whose language record fuzzily matches to yiddish . take the number record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'language', 'russian'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose language record fuzzily matches to russian .', 'tostr': 'filter_eq { all_rows ; language ; russian }'}, 'number'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; language ; russian } ; number }', 'tointer': 'select the rows whose language record fuzzily matches to russian . take the number record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; language ; yiddish } ; number } ; hop { filter_eq { all_rows ; language ; russian } ; number } } = true', 'tointer': 'select the rows whose language record fuzzily matches to yiddish . take the number record of this row . select the rows whose language record fuzzily matches to russian . take the number record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; language ; yiddish } ; number } ; hop { filter_eq { all_rows ; language ; russian } ; number } } = true | select the rows whose language record fuzzily matches to yiddish . take the number record of this row . select the rows whose language record fuzzily matches to russian . take the number 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, 'language_7': 7, 'yiddish_8': 8, 'number_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'language_11': 11, 'russian_12': 12, 'number_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', 'language_7': 'language', 'yiddish_8': 'yiddish', 'number_9': 'number', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'language_11': 'language', 'russian_12': 'russian', 'number_13': 'number'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'language_7': [0], 'yiddish_8': [0], 'number_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'language_11': [1], 'russian_12': [1], 'number_13': [3]} | ['language', 'number', 'percentage ( % )', 'males', 'females'] | [['polish', '447 685', '80.86', '216 794', '230 891'], ['yiddish', '51 215', '9.25', '24 538', '26 677'], ['german', '35 931', '6.49', '17 409', '18 522'], ['russian', '15 137', '2.73', '13 551', '1 586'], ['ukrainian', '2 350', '0.42', '2 302', '48'], ['other', '1 285', '0.23', '1 041', '244'], ["persons that did n't name their native language", '27', '> 0.01', '14', '13'], ['total', '553 633', '100', '275 652', '277 981']] |
list of 10 metre air pistol records | https://en.wikipedia.org/wiki/List_of_10_metre_air_pistol_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18986934-4.html.csv | count | sergei pyzhianov set a total of two records in the 10 metre air pistol event . | {'scope': 'all', 'criterion': 'equal', 'value': 'sergei pyzhianov', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'shooter', 'sergei pyzhianov'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose shooter record fuzzily matches to sergei pyzhianov .', 'tostr': 'filter_eq { all_rows ; shooter ; sergei pyzhianov }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; shooter ; sergei pyzhianov } }', 'tointer': 'select the rows whose shooter record fuzzily matches to sergei pyzhianov . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; shooter ; sergei pyzhianov } } ; 2 } = true', 'tointer': 'select the rows whose shooter record fuzzily matches to sergei pyzhianov . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; shooter ; sergei pyzhianov } } ; 2 } = true | select the rows whose shooter record fuzzily matches to sergei pyzhianov . 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, 'shooter_5': 5, 'sergei pyzhianov_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', 'shooter_5': 'shooter', 'sergei pyzhianov_6': 'sergei pyzhianov', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'shooter_5': [0], 'sergei pyzhianov_6': [0], '2_7': [2]} | ['score', 'shooter', 'date', 'comp', 'place'] | [['688.6', 'igor basinski ( urs )', '1986', 'wch', 'suhl , east germany'], ['689.7', 'aleksandr melentiev ( urs )', '1987', 'wc', 'seoul , south korea'], ['692.3', 'igor basinski ( urs )', '1988', 'ech', 'stavanger , norway'], ['new targets from 1989', 'new targets from 1989', 'new targets from 1989', 'new targets from 1989', 'new targets from 1989'], ['686.4', 'sorin babii ( rou )', '1989', 'ech', 'copenhagen , denmark'], ['690.3', 'sergei pyzhianov ( urs )', '1989', 'wch', 'sarajevo , yugoslavia'], ['695.1', 'sergei pyzhianov ( urs )', '13 oct 1989', 'wcf', 'munich , west germany']] |
united states house of representatives elections , 1820 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1820 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668329-25.html.csv | count | in total , 4 representatives were elected in special electons . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'special', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'first elected', 'special'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first elected record fuzzily matches to special .', 'tostr': 'filter_eq { all_rows ; first elected ; special }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first elected ; special } }', 'tointer': 'select the rows whose first elected record fuzzily matches to special . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first elected ; special } } ; 4 } = true', 'tointer': 'select the rows whose first elected record fuzzily matches to special . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; first elected ; special } } ; 4 } = true | select the rows whose first elected record fuzzily matches to special . 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, 'first elected_5': 5, 'special_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', 'first elected_5': 'first elected', 'special_6': 'special', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'first elected_5': [0], 'special_6': [0], '4_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['virginia 4', 'william mccoy', 'democratic - republican', '1811', 're - elected', 'william mccoy ( dr )'], ['virginia 5', 'john floyd', 'democratic - republican', '1817', 're - elected', 'john floyd ( dr )'], ['virginia 6', 'alexander smyth', 'democratic - republican', '1817', 're - elected', 'alexander smyth ( dr )'], ['virginia 7', 'ballard smith', 'democratic - republican', '1815', 'retired democratic - republican hold', 'william smith ( dr ) 53.2 % james wilson ( dr ) 46.8 %'], ['virginia 11', 'philip p barbour', 'democratic - republican', '1814 ( special )', 're - elected', 'philip p barbour ( dr )'], ['virginia 12', 'robert s garnett', 'democratic - republican', '1817', 're - elected', 'robert s garnett ( dr ) 100 %'], ['virginia 14', 'william a burwell', 'democratic - republican', '1806 ( special )', 'retired democratic - republican hold', 'jabez leftwich ( dr ) 93.5 % james calloway ( dr ) 6.5 %'], ['virginia 17', 'william s archer', 'democratic - republican', '1820 ( special )', 're - elected', 'william s archer ( dr ) 100 %'], ['virginia 18', 'mark alexander', 'democratic - republican', '1819', 're - elected', 'mark alexander ( dr ) 100 %'], ['virginia 19', 'james jones', 'democratic - republican', '1819', 're - elected', 'james jones ( dr )'], ['virginia 20', 'john c gray', 'democratic - republican', '1820 ( special )', 'lost re - election democratic - republican hold', 'arthur smith ( dr ) 60.3 % john c gray ( dr ) 39.7 %'], ['virginia 21', 'thomas newton , jr', 'democratic - republican', '1797', 're - elected', 'thomas newton , jr ( dr ) 94.7 % others 5.3 %'], ['virginia 22', 'hugh nelson', 'democratic - republican', '1811', 're - elected', 'hugh nelson ( dr ) 100 %']] |
real salt lake | https://en.wikipedia.org/wiki/Real_Salt_Lake | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1053453-8.html.csv | count | there are 2 players on real salt lake who has scored 0 goals in their entire career with the team . | {'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'goals', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; goals ; 0 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; goals ; 0 } }', 'tointer': 'select the rows whose goals record is equal to 0 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; goals ; 0 } } ; 2 } = true', 'tointer': 'select the rows whose goals record is equal to 0 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; goals ; 0 } } ; 2 } = true | select the rows whose goals record is equal to 0 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'goals_5': 5, '0_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'goals_5': 'goals', '0_6': '0', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'goals_5': [0], '0_6': [0], '2_7': [2]} | ['rank', 'player', 'nation', 'caps', 'goals', 'years'] | [['1', 'nick rimando', 'usa', '201', '0', '2007 - present'], ['2', 'andy williams', 'jam', '189', '14', '2005 - 2011'], ['3', 'kyle beckerman', 'usa', '177', '21', '2007 - present'], ['4', 'chris wingert', 'usa', '174', '1', '2007 - present'], ['5', 'nat borchers', 'usa', '173', '9', '2008 - present'], ['6', 'javier morales', 'arg', '155', '28', '2007 - present'], ['7', 'tony beltran', 'usa', '135', '0', '2008 - present'], ['8', 'ned grabavoy', 'usa', '126', '8', '2009 - present'], ['9', 'fabián espíndola', 'arg', '125', '35', '2007 - 2012'], ['10', 'robbie findley', 'usa', '121', '35', '2007 - 2010 , 2013 - present']] |
1988 los angeles rams season | https://en.wikipedia.org/wiki/1988_Los_Angeles_Rams_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11157007-1.html.csv | comparative | in the 1988 los angeles rams season , the game where the detroit lions were the opponent took place 7 days before the raiders were the opponent . | {'row_1': '2', 'row_2': '3', 'col': '2', 'col_other': '3', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '7 days', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'detroit lions'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to detroit lions .', 'tostr': 'filter_eq { all_rows ; opponent ; detroit lions }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; detroit lions } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit lions . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'los angeles raiders'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to los angeles raiders .', 'tostr': 'filter_eq { all_rows ; opponent ; los angeles raiders }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row .'}], 'result': '-7 days', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; opponent ; detroit lions } ; date } ; hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date } }'}, '-7 days'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; opponent ; detroit lions } ; date } ; hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date } } ; -7 days } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit lions . take the date record of this row . select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row . the second record is 7 days larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; opponent ; detroit lions } ; date } ; hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date } } ; -7 days } = true | select the rows whose opponent record fuzzily matches to detroit lions . take the date record of this row . select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row . the second record is 7 days larger than the first record . | 6 | 6 | {'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'opponent_8': 8, 'detroit lions_9': 9, 'date_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'opponent_12': 12, 'los angeles raiders_13': 13, 'date_14': 14, '-7 days_15': 15} | {'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'opponent_8': 'opponent', 'detroit lions_9': 'detroit lions', 'date_10': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'opponent_12': 'opponent', 'los angeles raiders_13': 'los angeles raiders', 'date_14': 'date', '-7 days_15': '-7 days'} | {'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'opponent_8': [0], 'detroit lions_9': [0], 'date_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'opponent_12': [1], 'los angeles raiders_13': [1], 'date_14': [3], '-7 days_15': [5]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 4 , 1988', 'green bay packers', 'w 34 - 7', '53769'], ['2', 'september 11 , 1988', 'detroit lions', 'w 17 - 10', '46262'], ['3', 'september 18 , 1988', 'los angeles raiders', 'w 22 - 17', '84870'], ['4', 'september 25 , 1988', 'new york giants', 'w 45 - 31', '75617'], ['5', 'october 2 , 1988', 'phoenix cardinals', 'l 41 - 27', '49830'], ['6', 'october 9 , 1988', 'atlanta falcons', 'w 33 - 0', '30852'], ['7', 'october 16 , 1988', 'san francisco 49ers', 'l 24 - 21', '65450'], ['8', 'october 23 , 1988', 'seattle seahawks', 'w 31 - 10', '57033'], ['9', 'october 30 , 1988', 'new orleans saints', 'w 12 - 10', '68238'], ['10', 'november 6 , 1988', 'philadelphia eagles', 'l 30 - 24', '65624'], ['11', 'november 13 , 1988', 'new orleans saints', 'l 14 - 10', '63305'], ['12', 'november 20 , 1988', 'san diego chargers', 'l 38 - 24', '45462'], ['13', 'november 27 , 1988', 'denver broncos', 'l 35 - 24', '74141'], ['14', 'december 5 , 1988', 'chicago bears', 'w 23 - 3', '65579'], ['15', 'december 11 , 1988', 'atlanta falcons', 'w 22 - 7', '42828'], ['16', 'december 18 , 1988', 'san francisco 49ers', 'w 38 - 16', '62444']] |
pete sampras career statistics | https://en.wikipedia.org/wiki/Pete_Sampras_career_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22834834-2.html.csv | unique | 1993 was the only year pete samparas was a runner-up from 1991-1997 . | {'scope': 'all', 'row': '2', 'col': '1', 'col_other': '2', 'criterion': 'equal', 'value': 'runner-up', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'runner-up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to runner-up .', 'tostr': 'filter_eq { all_rows ; outcome ; runner-up }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; outcome ; runner-up } }', 'tointer': 'select the rows whose outcome record fuzzily matches to runner-up . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'runner-up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to runner-up .', 'tostr': 'filter_eq { all_rows ; outcome ; runner-up }'}, 'year'], 'result': '1993', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; outcome ; runner-up } ; year }'}, '1993'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; outcome ; runner-up } ; year } ; 1993 }', 'tointer': 'the year record of this unqiue row is 1993 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; outcome ; runner-up } } ; eq { hop { filter_eq { all_rows ; outcome ; runner-up } ; year } ; 1993 } } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to runner-up . there is only one such row in the table . the year record of this unqiue row is 1993 .'} | and { only { filter_eq { all_rows ; outcome ; runner-up } } ; eq { hop { filter_eq { all_rows ; outcome ; runner-up } ; year } ; 1993 } } = true | select the rows whose outcome record fuzzily matches to runner-up . there is only one such row in the table . the year record of this unqiue row is 1993 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'outcome_7': 7, 'runner-up_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1993_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'outcome_7': 'outcome', 'runner-up_8': 'runner-up', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1993_10': '1993'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'outcome_7': [0], 'runner-up_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1993_10': [3]} | ['outcome', 'year', 'championship', 'surface', 'opponent in the final', 'score in the final'] | [['winner', '1991', 'frankfurt', 'carpet ( i )', 'jim courier', '3 - 6 , 7 - 6 ( 7 - 5 ) , 6 - 3 , 6 - 4'], ['runner - up', '1993', 'frankfurt', 'carpet ( i )', 'michael stich', '6 - 7 ( 3 - 7 ) , 6 - 2 , 6 - 7 ( 7 - 9 ) , 2 - 6'], ['winner', '1994', 'frankfurt', 'carpet ( i )', 'boris becker', '4 - 6 , 6 - 3 , 7 - 5 , 6 - 4'], ['winner', '1996', 'hannover', 'carpet ( i )', 'boris becker', '3 - 6 , 7 - 6 ( 7 - 5 ) , 7 - 6 ( 7 - 4 ) , 6 - 7 ( 11 - 13 ) , 6 - 4'], ['winner', '1997', 'hannover', 'hard ( i )', 'yevgeny kafelnikov', '6 - 3 , 6 - 2 , 6 - 2']] |
Subsets and Splits