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dalymount park

https://en.wikipedia.org/wiki/Dalymount_Park

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1711684-2.html.csv

majority

most of the dalymont cup finals that were played at dalymount park took place in the month of march .

{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': '03', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'date', '03'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to 03 .', 'tostr': 'most_eq { all_rows ; date ; 03 } = true'}

most_eq { all_rows ; date ; 03 } = true

for the date records of all rows , most of them fuzzily match to 03 .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '03_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '03_4': '03'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '03_4': [0]}

['date', 'competition', 'winners', 'score', 'runners - up']

[['13 / 3 / 1903', 'irish cup', 'distillery', '1 - 1', 'bohemians'], ['28 / 04 / 1906', 'irish cup', 'shelbourne fc', '2 - 0', 'belfast celtic'], ['20 / 04 / 1907', 'irish cup', 'cliftonville fc', '1 - 0', 'shelbourne fc'], ['21 / 03 / 1908', 'irish cup', 'bohemians', '1 - 1', 'shelbourne fc'], ['28 / 03 / 1908', 'irish cup ( replay )', 'bohemians', '3 - 1', 'shelbourne fc'], ['10 / 04 / 1909', 'irish cup', 'cliftonville fc', '2 - 1', 'bohemians'], ['25 / 03 / 1911', 'irish cup', 'shelbourne fc', '0 - 0', 'bohemians'], ['15 / 04 / 1911', 'irish cup ( replay )', 'shelbourne fc', '2 - 1', 'bohemians'], ['17 / 03 / 1922', 'irish free state cup final', "st james 's gate fc", '1 - 1', 'shamrock rovers fc'], ['8 / 04 / 1922', 'irish free state cup final replay', "st james 's gate", '1 - 0', 'shamrock rovers'], ['17 / 03 / 1923', 'irish free state cup final', 'alton united fc', '1 - 0', 'shelbourne fc'], ['17 / 03 / 1924', 'irish free state cup final', 'athlone town afc', '1 - 0', 'fordsons fc'], ['17 / 03 / 1925', 'irish free state cup final', 'shamrock rovers fc', '1 - 0', 'shelbourne fc'], ['22 / 05 / 1968', 'blaxnit cup final ( 2nd leg )', 'shamrock rovers fc', '1 - 2', 'crusaders fc'], ['00 / 00 / 1969', 'blaxnit cup final ( 2nd leg )', 'shamrock rovers fc', '2 - 2', 'coleraine fc'], ['22 / 05 / 1970', 'blaxnit cup final ( 2nd leg )', 'sligo rovers fc', '1 - 4', 'crusaders fc'], ['01 / 05 / 1996', 'fai cup ( replay )', 'shelbourne fc', '2 - 1', "st patrick 's athletic fc"], ['04 / 05 / 1997', 'fai cup', 'shelbourne fc', '2 - 0', 'derry city fc'], ['10 / 05 / 1998', 'fai cup', 'cork city fc', '0 - 0', 'shelbourne fc'], ['16 / 05 / 1998', 'fai cup ( replay )', 'cork city fc', '1 - 0', 'shelbourne fc']]

cfl all - star game

https://en.wikipedia.org/wiki/CFL_All-Star_Game

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16090565-1.html.csv

aggregation

for cfl all-star games in the 1950s , the average crowd size was 10158.5 .

{'scope': 'subset', 'col': '8', 'type': 'average', 'result': '10158.5', 'subset': {'col': '1', 'criterion': 'less_than_eq', 'value': '1958'}}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'season', '1958'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; season ; 1958 }', 'tointer': 'select the rows whose season record is less than or equal to 1958 .'}, 'attendance'], 'result': '10158.5', 'ind': 1, 'tostr': 'avg { filter_less_eq { all_rows ; season ; 1958 } ; attendance }'}, '10158.5'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_less_eq { all_rows ; season ; 1958 } ; attendance } ; 10158.5 } = true', 'tointer': 'select the rows whose season record is less than or equal to 1958 . the average of the attendance record of these rows is 10158.5 .'}

round_eq { avg { filter_less_eq { all_rows ; season ; 1958 } ; attendance } ; 10158.5 } = true

select the rows whose season record is less than or equal to 1958 . the average of the attendance record of these rows is 10158.5 .

3

3

{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_less_eq_0': 0, 'all_rows_4': 4, 'season_5': 5, '1958_6': 6, 'attendance_7': 7, '10158.5_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_4': 'all_rows', 'season_5': 'season', '1958_6': '1958', 'attendance_7': 'attendance', '10158.5_8': '10158.5'}

{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_less_eq_0': [1], 'all_rows_4': [0], 'season_5': [0], '1958_6': [0], 'attendance_7': [1], '10158.5_8': [2]}

['season', 'date', 'venue', 'city', 'visitor', 'score', 'home', 'attendance']

[['1955', 'december 3 , 1955', 'varsity stadium', 'toronto , on', 'wifu all - stars', '6 - 6 ( tie )', 'irfu all - stars', '15088'], ['1956', 'december 8 , 1956', 'empire stadium', 'vancouver , bc', 'irfu all - stars', '0 - 35', 'wifu all - stars', '13546'], ['1957', 'december 7 , 1957', 'molson stadium', 'montreal , qc', 'wifu all - stars', '2 - 20', 'irfu all - stars', '5000'], ['1958', 'december 6 , 1958', 'civic stadium', 'hamilton , on', 'wifu all - stars', '9 - 3', 'irfu all - stars', '7000'], ['1970', 'july 2 , 1970', 'lansdowne park', 'ottawa , on', 'cfl all - stars', '35 - 14', 'ottawa rough riders', '23094'], ['1971', 'june 29 , 1971', 'autostade', 'montreal , qc', 'cfl all - stars', '30 - 13', 'montreal alouettes', '9000'], ['1972', 'june 28 , 1972', 'mcmahon stadium', 'calgary , ab', 'cfl all - stars', '22 - 23', 'calgary stampeders', '23616'], ['1973', 'june 27 , 1973', 'ivor wynne stadium', 'hamilton , on', 'cfl all - stars', '22 - 11', 'hamilton tiger - cats', '24765'], ['1974', 'june 26 , 1974', 'lansdowne park', 'ottawa , on', 'cfl all - stars', '22 - 25', 'ottawa rough riders', '15102'], ['1976', 'may 29 , 1976', 'clarke stadium', 'edmonton , ab', 'east all - stars', '16 - 27', 'west all - stars', '21762'], ['1977', 'june 4 , 1977', 'exhibition stadium', 'toronto , on', 'west all - stars', '19 - 20', 'east all - stars', '7500'], ['1978', 'june 3 , 1978', 'mcmahon stadium', 'calgary , ab', 'east all - stars', '12 - 24', 'west all - stars', '21000'], ['1983', 'december 3 , 1983', 'bc place stadium', 'vancouver , bc', 'east all - stars', '15 - 25', 'west all - stars', '14000'], ['1988', 'june 23 , 1988', 'commonwealth stadium', 'edmonton , ab', 'cfl all - stars', '15 - 4', 'edmonton eskimos', '27573']]

1985 new orleans saints season

https://en.wikipedia.org/wiki/1985_New_Orleans_Saints_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16712803-2.html.csv

aggregation

the 1985 new orleans saint season had a total of 741290 attendance .

{'scope': 'all', 'col': '5', 'type': 'sum', 'result': '741290', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '741290', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '741290'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 741290 } = true', 'tointer': 'the sum of the attendance record of all rows is 741290 .'}

round_eq { sum { all_rows ; attendance } ; 741290 } = true

the sum of the attendance record of all rows is 741290 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '741290_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '741290_5': '741290'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '741290_5': [1]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 8 , 1985', 'kansas city chiefs', 'l 47 - 27', '57760'], ['2', 'september 15 , 1985', 'denver broncos', 'l 34 - 23', '74488'], ['3', 'september 22 , 1985', 'tampa bay buccaneers', 'w 20 - 13', '45320'], ['4', 'september 29 , 1985', 'san francisco 49ers', 'w 20 - 17', '58053'], ['5', 'october 6 , 1985', 'philadelphia eagles', 'w 23 - 21', '56364'], ['6', 'october 13 , 1985', 'los angeles raiders', 'l 23 - 13', '48152'], ['7', 'october 20 , 1985', 'atlanta falcons', 'l 31 - 24', '44784'], ['8', 'october 27 , 1985', 'new york giants', 'l 21 - 13', '54082'], ['9', 'november 3 , 1985', 'los angeles rams', 'l 28 - 10', '49030'], ['10', 'november 10 , 1985', 'seattle seahawks', 'l 27 - 3', '47365'], ['11', 'november 17 , 1985', 'green bay packers', 'l 38 - 14', '52104'], ['12', 'november 24 , 1985', 'minnesota vikings', 'w 30 - 23', '54117'], ['13', 'december 1 , 1985', 'los angeles rams', 'w 29 - 3', '44122'], ['14', 'december 8 , 1985', 'st louis cardinals', 'l 28 - 16', '29527'], ['15', 'december 15 , 1985', 'san francisco 49ers', 'l 31 - 19', '46065'], ['16', 'december 22 , 1985', 'atlanta falcons', 'l 16 - 10', '37717']]

lard

https://en.wikipedia.org/wiki/Lard

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18621997-1.html.csv

aggregation

of the fats listed , the average amount of saturated fat per 100 grams total fat is close to 23 grams .

{'scope': 'all', 'col': '3', 'type': 'average', 'result': '22', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'saturated fat'], 'result': '22', 'ind': 0, 'tostr': 'avg { all_rows ; saturated fat }'}, '22'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; saturated fat } ; 22 } = true', 'tointer': 'the average of the saturated fat record of all rows is 22 .'}

round_eq { avg { all_rows ; saturated fat } ; 22 } = true

the average of the saturated fat record of all rows is 22 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'saturated fat_4': 4, '22_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'saturated fat_4': 'saturated fat', '22_5': '22'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'saturated fat_4': [0], '22_5': [1]}

['', 'total fat', 'saturated fat', 'monounsaturated fat', 'polyunsaturated fat', 'smoke point']

[['sunflower oil', '100 g', '11 g', '20 g ( 84 g in high oleic variety )', '69 g ( 4 g in high oleic variety )', 'degree'], ['soybean oil', '100 g', '16 g', '23 g', '58 g', 'degree'], ['canola oil', '100 g', '7 g', '63 g', '28 g', 'degree'], ['olive oil', '100 g', '14 g', '73 g', '11 g', 'degree'], ['corn oil', '100 g', '15 g', '30 g', '55 g', 'degree'], ['peanut oil', '100 g', '17 g', '46 g', '32 g', 'degree'], ['rice bran oil', '100 g', '25 g', '38 g', '37 g', 'degree'], ['vegetable shortening ( hydrogenated )', '71 g', '23 g ( 34 % )', '8 g ( 11 % )', '37 g ( 52 % )', 'degree'], ['lard', '100 g', '39 g', '45 g', '11 g', 'degree'], ['suet', '94 g', '52 g ( 55 % )', '32 g ( 34 % )', '3 g ( 3 % )', '200degree ( 400degree )']]

1983 - 84 north west counties football league

https://en.wikipedia.org/wiki/1983%E2%80%9384_North_West_Counties_Football_League

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17718005-3.html.csv

ordinal

the blackpool mechanics team recorded the 2nd highest number of ' goals for ' in the 1983 - 84 north west counties football league .

{'row': '7', 'col': '6', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'goals for', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; goals for ; 2 }'}, 'team'], 'result': 'blackpool mechanics', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; goals for ; 2 } ; team }'}, 'blackpool mechanics'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; goals for ; 2 } ; team } ; blackpool mechanics } = true', 'tointer': 'select the row whose goals for record of all rows is 2nd maximum . the team record of this row is blackpool mechanics .'}

eq { hop { nth_argmax { all_rows ; goals for ; 2 } ; team } ; blackpool mechanics } = true

select the row whose goals for record of all rows is 2nd maximum . the team record of this row is blackpool mechanics .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'goals for_5': 5, '2_6': 6, 'team_7': 7, 'blackpool mechanics_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', 'goals for_5': 'goals for', '2_6': '2', 'team_7': 'team', 'blackpool mechanics_8': 'blackpool mechanics'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'goals for_5': [0], '2_6': [0], 'team_7': [1], 'blackpool mechanics_8': [2]}

['position', 'team', 'played', 'drawn', 'lost', 'goals for', 'goals against', 'goal difference', 'points 1']

[['1', 'clitheroe', '34', '7', '5', '79', '29', '+ 50', '51'], ['2', 'padiham', '34', '8', '7', '58', '34', '+ 24', '46'], ['3', 'ashton town', '34', '7', '8', '54', '42', '+ 12', '45'], ['4', 'oldham dew', '34', '9', '8', '63', '37', '+ 26', '43'], ['5', 'daisy hill', '34', '3', '12', '54', '40', '+ 14', '41'], ['6', 'maghull', '34', '8', '10', '60', '50', '+ 10', '40'], ['7', 'blackpool mechanics', '34', '5', '12', '70', '49', '+ 21', '39'], ['8', 'atherton collieries', '34', '9', '11', '54', '50', '+ 4', '37'], ['9', 'vulcan newton', '34', '8', '11', '64', '54', '+ 10', '36 2'], ['10', 'prestwich heys', '34', '5', '14', '61', '59', '+ 2', '33 2'], ['11', 'whitworth valley', '34', '8', '15', '45', '53', '8', '30'], ['12', 'bolton st', '34', '10', '14', '49', '64', '15', '30'], ['13', 'bacup borough', '34', '9', '14', '65', '60', '+ 5', '27 3'], ['14', 'nelson', '34', '10', '16', '49', '55', '6', '26'], ['15', 'cheadle town', '34', '8', '17', '39', '67', '28', '26'], ['16', 'urmston town', '34', '9', '18', '35', '67', '32', '23'], ['17', 'newton', '34', '4', '22', '33', '63', '30', '20'], ['18', 'ashton athletic', '34', '3', '27', '30', '89', '59', '11']]

the rob brydon show

https://en.wikipedia.org/wiki/The_Rob_Brydon_Show

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29135051-3.html.csv

ordinal

the rob brydon show recorded the highest ratings on the 14th of august 2012 .

{'row': '1', 'col': '5', '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', 'ratings', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; ratings ; 1 }'}, 'broadcast date'], 'result': '14 august 2012', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; ratings ; 1 } ; broadcast date }'}, '14 august 2012'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; ratings ; 1 } ; broadcast date } ; 14 august 2012 } = true', 'tointer': 'select the row whose ratings record of all rows is 1st maximum . the broadcast date record of this row is 14 august 2012 .'}

eq { hop { nth_argmax { all_rows ; ratings ; 1 } ; broadcast date } ; 14 august 2012 } = true

select the row whose ratings record of all rows is 1st maximum . the broadcast date record of this row is 14 august 2012 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'ratings_5': 5, '1_6': 6, 'broadcast date_7': 7, '14 august 2012_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', 'ratings_5': 'ratings', '1_6': '1', 'broadcast date_7': 'broadcast date', '14 august 2012_8': '14 august 2012'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'ratings_5': [0], '1_6': [0], 'broadcast date_7': [1], '14 august 2012_8': [2]}

['episode', 'broadcast date', 'guest ( s )', 'singer ( s )', 'ratings']

[['1', '14 august 2012', 'michael mcintyre and alex james', 'amy macdonald', '1.44 m'], ['2', '21 august 2012', 'barbara windsor and heston blumenthal', 'the overtones', 'under 1.39 m'], ['3', '28 august 2012', 'sarah millican and grayson perry', 'newton faulkner', 'under 1.39 m'], ['4', '4 september 2012', 'jason manford and neil morrissey', 'ronan keating', 'under 1.25 m'], ['5', '11 september 2012', 'emilia fox and steve backshall', 'tom jones', 'under 1.37 m']]

bms scuderia italia

https://en.wikipedia.org/wiki/BMS_Scuderia_Italia

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226647-2.html.csv

majority

most of the racers used a dallara branded chassis .

{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'dallara', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'chassis', 'dallara'], 'result': True, 'ind': 0, 'tointer': 'for the chassis records of all rows , most of them fuzzily match to dallara .', 'tostr': 'most_eq { all_rows ; chassis ; dallara } = true'}

most_eq { all_rows ; chassis ; dallara } = true

for the chassis records of all rows , most of them fuzzily match to dallara .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'chassis_3': 3, 'dallara_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'chassis_3': 'chassis', 'dallara_4': 'dallara'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'chassis_3': [0], 'dallara_4': [0]}

['year', 'chassis', 'engine ( s )', 'tyres', 'points']

[['1988', 'dallara 3087 dallara 188', 'ford dfz 3.5 v8', 'g', '0'], ['1989', 'dallara 189', 'ford dfr 3.5 v8', 'p', '8'], ['1990', 'dallara 190', 'ford dfr 3.5 v8', 'p', '0'], ['1991', 'dallara 191', 'judd gv 3.5 v10', 'p', '5'], ['1992', 'dallara 192', 'ferrari 037 3.5 v12', 'g', '2'], ['1993', 'lola t93 / 30', 'ferrari 040 3.5 v12', 'g', '0']]

2008 - 09 scottish first division

https://en.wikipedia.org/wiki/2008%E2%80%9309_Scottish_First_Division

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14003085-5.html.csv

count

there are 7 stadiums with a capacity of over 10,000 .

{'scope': 'all', 'criterion': 'greater_than', 'value': '10000', 'result': '7', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'capacity', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose capacity record is greater than 10000 .', 'tostr': 'filter_greater { all_rows ; capacity ; 10000 }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; capacity ; 10000 } }', 'tointer': 'select the rows whose capacity record is greater than 10000 . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; capacity ; 10000 } } ; 7 } = true', 'tointer': 'select the rows whose capacity record is greater than 10000 . the number of such rows is 7 .'}

eq { count { filter_greater { all_rows ; capacity ; 10000 } } ; 7 } = true

select the rows whose capacity record is greater than 10000 . the number of such rows is 7 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'capacity_5': 5, '10000_6': 6, '7_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'capacity_5': 'capacity', '10000_6': '10000', '7_7': '7'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'capacity_5': [0], '10000_6': [0], '7_7': [2]}

['team', 'stadium', 'capacity', 'highest', 'lowest', 'average']

[['dundee', 'dens park', '11856', '6537', '2831', '3995'], ['st johnstone', 'mcdiarmid park', '10673', '7238', '2259', '3502'], ['dunfermline athletic', 'east end park', '11998', '4998', '1371', '3255'], ['partick thistle', 'firhill stadium', '10887', '3378', '2296', '2956'], ['queen of the south', 'palmerston park', '6412', '3339', '2029', '2720'], ['greenock morton', 'cappielow', '11612', '3323', '1685', '2279'], ['ross county', 'victoria park', '6310', '3444', '1625', '2279'], ['livingston', 'almondvale stadium', '10016', '2169', '1068', '1728'], ['airdrie united', 'new broomfield', '10171', '2165', '633', '1356'], ['clyde', 'broadwood stadium', '8006', '2114', '776', '1236']]

1945 vfl season

https://en.wikipedia.org/wiki/1945_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809271-12.html.csv

ordinal

punt road oval recorded the 2nd highest crowd participation during the 1945 vfl season .

{'row': '6', 'col': '6', 'order': '2', 'col_other': '5', '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', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'venue'], 'result': 'punt road oval', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; venue }'}, 'punt road oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; punt road oval } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is punt road oval .'}

eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; punt road oval } = true

select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is punt road oval .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '2_6': 6, 'venue_7': 7, 'punt road oval_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', 'crowd_5': 'crowd', '2_6': '2', 'venue_7': 'venue', 'punt road oval_8': 'punt road oval'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'venue_7': [1], 'punt road oval_8': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['footscray', '15.12 ( 102 )', 'richmond', '11.8 ( 74 )', 'western oval', '17000', '7 july 1945'], ['fitzroy', '17.27 ( 129 )', 'geelong', '4.9 ( 33 )', 'brunswick street oval', '7000', '7 july 1945'], ['south melbourne', '14.22 ( 106 )', 'st kilda', '6.14 ( 50 )', 'junction oval', '9000', '7 july 1945'], ['hawthorn', '15.17 ( 107 )', 'essendon', '7.12 ( 54 )', 'glenferrie oval', '5500', '7 july 1945'], ['north melbourne', '11.11 ( 77 )', 'collingwood', '13.5 ( 83 )', 'arden street oval', '14000', '7 july 1945'], ['melbourne', '12.8 ( 80 )', 'carlton', '12.9 ( 81 )', 'punt road oval', '16000', '7 july 1945']]

most daring

https://en.wikipedia.org/wiki/Most_Daring

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18821196-1.html.csv

unique

australia is the only country where most daring airs on the network fox8 .

{'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'fox8', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tv network ( s )', 'fox8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tv network ( s ) record fuzzily matches to fox8 .', 'tostr': 'filter_eq { all_rows ; tv network ( s ) ; fox8 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; tv network ( s ) ; fox8 } }', 'tointer': 'select the rows whose tv network ( s ) record fuzzily matches to fox8 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tv network ( s )', 'fox8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tv network ( s ) record fuzzily matches to fox8 .', 'tostr': 'filter_eq { all_rows ; tv network ( s ) ; fox8 }'}, 'country'], 'result': 'australia', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tv network ( s ) ; fox8 } ; country }'}, 'australia'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; tv network ( s ) ; fox8 } ; country } ; australia }', 'tointer': 'the country record of this unqiue row is australia .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; tv network ( s ) ; fox8 } } ; eq { hop { filter_eq { all_rows ; tv network ( s ) ; fox8 } ; country } ; australia } } = true', 'tointer': 'select the rows whose tv network ( s ) record fuzzily matches to fox8 . there is only one such row in the table . the country record of this unqiue row is australia .'}

and { only { filter_eq { all_rows ; tv network ( s ) ; fox8 } } ; eq { hop { filter_eq { all_rows ; tv network ( s ) ; fox8 } ; country } ; australia } } = true

select the rows whose tv network ( s ) record fuzzily matches to fox8 . there is only one such row in the table . the country record of this unqiue row is australia .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'tv network (s)_7': 7, 'fox8_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'country_9': 9, 'australia_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'tv network (s)_7': 'tv network ( s )', 'fox8_8': 'fox8', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'country_9': 'country', 'australia_10': 'australia'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'tv network (s)_7': [0], 'fox8_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'country_9': [2], 'australia_10': [3]}

['country', 'tv network ( s )', 'series premiere', 'weekly schedule', 'status']

[['australia', 'fox8', 'unknown', 'weekdays 2:30 pm', 'currently airing'], ['belgium', '2be', 'unknown', 'mondays 8:00 pm', 'currently airing'], ['brazil', 'trutv', 'unknown', 'saturdays 11:00 pm', 'currently airing'], ['estonia', 'kanal 12', 'unknown', 'weekends', 'currently airing'], ['greece', 'skai tv', 'unknown', 'weekends 3:00 pm', 'currently airing'], ['india', 'axn india', 'season 5 & 6', 'monday to thursday 11:00 pm', 'currently airing'], ['italy', 'sky italia', 'unknown', 'unknown', 'currently airing'], ['norway', 'viasat 4', 'unknown', 'fridays 8:35 pm', 'currently airing'], ['pakistan', 'axn', 'unknown', 'unknown', 'currently airing'], ['poland', 'polsat play', 'season 3 & 4', 'every day 7:00 pm', 'currently airing'], ['united arab emirates', 'mbc action', 'unknown', 'thursday 4:00 pm', 'currently airing']]

united states district court for the western district of washington

https://en.wikipedia.org/wiki/United_States_District_Court_for_the_Western_District_of_Washington

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1137899-2.html.csv

unique

clinton woodbury howard is the only western washington judge whose reason for termination was not being confirmed .

{'scope': 'all', 'row': '4', 'col': '8', 'col_other': '1', 'criterion': 'equal', 'value': 'not confirmed', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for termination', 'not confirmed'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for termination record fuzzily matches to not confirmed .', 'tostr': 'filter_eq { all_rows ; reason for termination ; not confirmed }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; reason for termination ; not confirmed } }', 'tointer': 'select the rows whose reason for termination record fuzzily matches to not confirmed . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'reason for termination', 'not confirmed'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reason for termination record fuzzily matches to not confirmed .', 'tostr': 'filter_eq { all_rows ; reason for termination ; not confirmed }'}, 'judge'], 'result': 'clinton woodbury howard', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; reason for termination ; not confirmed } ; judge }'}, 'clinton woodbury howard'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; reason for termination ; not confirmed } ; judge } ; clinton woodbury howard }', 'tointer': 'the judge record of this unqiue row is clinton woodbury howard .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; reason for termination ; not confirmed } } ; eq { hop { filter_eq { all_rows ; reason for termination ; not confirmed } ; judge } ; clinton woodbury howard } } = true', 'tointer': 'select the rows whose reason for termination record fuzzily matches to not confirmed . there is only one such row in the table . the judge record of this unqiue row is clinton woodbury howard .'}

and { only { filter_eq { all_rows ; reason for termination ; not confirmed } } ; eq { hop { filter_eq { all_rows ; reason for termination ; not confirmed } ; judge } ; clinton woodbury howard } } = true

select the rows whose reason for termination record fuzzily matches to not confirmed . there is only one such row in the table . the judge record of this unqiue row is clinton woodbury howard .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'reason for termination_7': 7, 'not confirmed_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'judge_9': 9, 'clinton woodbury howard_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'reason for termination_7': 'reason for termination', 'not confirmed_8': 'not confirmed', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'judge_9': 'judge', 'clinton woodbury howard_10': 'clinton woodbury howard'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'reason for termination_7': [0], 'not confirmed_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'judge_9': [2], 'clinton woodbury howard_10': [3]}

['judge', 'state', 'born / died', 'active service', 'chief judge', 'senior status', 'appointed by', 'reason for termination']

[['cornelius holgate hanford', 'wa', '1849 - 1926', '1890 - 1912', '-', '-', 'harrison', 'resignation'], ['george donworth', 'wa', '1861 - 1947', '1909 - 1912', '-', '-', 'taft', 'resignation'], ['edward e cushman', 'wa', '1865 - 1944', '1912 - 1939', '-', '1939 - 1944', 'taft', 'death'], ['clinton woodbury howard', 'wa', '1864 - 1937', '1912 - 1913', '-', '-', 'taft', 'not confirmed'], ['jeremiah neterer', 'wa', '1862 - 1943', '1913 - 1933', '-', '1933 - 1943', 'wilson', 'death'], ['john clyde bowen', 'wa', '1888 - 1978', '1934 - 1961', '-', '1961 - 1978', 'f roosevelt', 'death'], ['lloyd llewellyn black', 'wa', '1889 - 1950', '1939 - 1950', '-', '-', 'f roosevelt', 'death'], ['charles henry leavy', 'wa', '1884 - 1952', '1942 - 1952', '-', '1952 - 1952', 'f roosevelt', 'death'], ['william james lindberg', 'wa', '1904 - 1981', '1951 - 1971', '-', '1971 - 1981', 'truman', 'death'], ['george hugo boldt', 'wa', '1903 - 1984', '1953 - 1971', '-', '1971 - 1984', 'eisenhower', 'death'], ['william trulock beeks', 'wa', '1906 - 1988', '1961 - 1973', '-', '1973 - 1988', 'kennedy', 'death'], ['william nelson goodwin', 'wa', '1909 - 1975', '1966 - 1975', '-', '-', 'johnson', 'death'], ['morell edward sharp', 'wa', '1920 - 1980', '1971 - 1980', '-', '-', 'nixon', 'death'], ['donald s voorhees', 'wa', '1916 - 1989', '1974 - 1986', '-', '1986 - 1989', 'nixon', 'death'], ['jack edward tanner', 'wa', '1919 - 2006', '1978 - 1991', '-', '1991 - 2006', 'carter', 'death'], ['william lee dwyer', 'wa', '1929 - 2002', '1987 - 1998', '-', '1998 - 2002', 'reagan', 'death'], ['franklin d burgess', 'wa', '1935 - 2010', '1994 - 2005', '-', '2005 - 2010', 'clinton', 'death']]

1956 - 57 new york rangers season

https://en.wikipedia.org/wiki/1956%E2%80%9357_New_York_Rangers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17323267-7.html.csv

superlative

game 67 against the toronto maple leafs had the most total goals scored .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '7', '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', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; score }'}, 'opponent'], 'result': 'toronto maple leafs', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; score } ; opponent }'}, 'toronto maple leafs'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; score } ; opponent } ; toronto maple leafs } = true', 'tointer': 'select the row whose score record of all rows is maximum . the opponent record of this row is toronto maple leafs .'}

eq { hop { argmax { all_rows ; score } ; opponent } ; toronto maple leafs } = true

select the row whose score record of all rows is maximum . the opponent record of this row is toronto maple leafs .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'score_5': 5, 'opponent_6': 6, 'toronto maple leafs_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'score_5': 'score', 'opponent_6': 'opponent', 'toronto maple leafs_7': 'toronto maple leafs'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'score_5': [0], 'opponent_6': [1], 'toronto maple leafs_7': [2]}

['game', 'march', 'opponent', 'score', 'record']

[['61', '2', 'boston bruins', '3 - 2', '23 - 27 - 11'], ['62', '3', 'detroit red wings', '1 - 1', '23 - 27 - 12'], ['63', '7', 'chicago black hawks', '2 - 2', '23 - 27 - 13'], ['64', '9', 'toronto maple leafs', '2 - 1', '24 - 27 - 13'], ['65', '10', 'detroit red wings', '4 - 1', '25 - 27 - 13'], ['66', '13', 'boston bruins', '2 - 1', '25 - 28 - 13'], ['67', '16', 'toronto maple leafs', '14 - 1', '25 - 29 - 13'], ['68', '17', 'toronto maple leafs', '5 - 3', '25 - 30 - 13'], ['69', '23', 'boston bruins', '4 - 2', '26 - 30 - 13'], ['70', '24', 'chicago black hawks', '4 - 4', '26 - 30 - 14']]

madawaska county , new brunswick

https://en.wikipedia.org/wiki/Madawaska_County%2C_New_Brunswick

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-171250-2.html.csv

comparative

the parish of madawaska ( madawaska county , new brunswick ) has a larger land area than the parish of baker brook .

{'row_1': '13', 'row_2': '12', 'col': '3', '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', 'official name', 'madawaska'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose official name record fuzzily matches to madawaska .', 'tostr': 'filter_eq { all_rows ; official name ; madawaska }'}, 'area km 2'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; official name ; madawaska } ; area km 2 }', 'tointer': 'select the rows whose official name record fuzzily matches to madawaska . take the area km 2 record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'official name', 'baker brook'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose official name record fuzzily matches to baker brook .', 'tostr': 'filter_eq { all_rows ; official name ; baker brook }'}, 'area km 2'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; official name ; baker brook } ; area km 2 }', 'tointer': 'select the rows whose official name record fuzzily matches to baker brook . take the area km 2 record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; official name ; madawaska } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; baker brook } ; area km 2 } } = true', 'tointer': 'select the rows whose official name record fuzzily matches to madawaska . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to baker brook . take the area km 2 record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; official name ; madawaska } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; baker brook } ; area km 2 } } = true

select the rows whose official name record fuzzily matches to madawaska . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to baker brook . take the area km 2 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, 'official name_7': 7, 'madawaska_8': 8, 'area km 2_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'official name_11': 11, 'baker brook_12': 12, 'area km 2_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', 'official name_7': 'official name', 'madawaska_8': 'madawaska', 'area km 2_9': 'area km 2', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'official name_11': 'official name', 'baker brook_12': 'baker brook', 'area km 2_13': 'area km 2'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'official name_7': [0], 'madawaska_8': [0], 'area km 2_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'official name_11': [1], 'baker brook_12': [1], 'area km 2_13': [3]}

['official name', 'status', 'area km 2', 'population', 'census ranking']

[['saint - joseph', 'parish', '321.87', '1696', '1472 of 5008'], ['saint - jacques', 'parish', '298.82', '1607', '1531 of 5008'], ['sainte - anne', 'parish', '369.25', '1081', '1942 of 5008'], ['saint - léonard', 'parish', '343.95', '1039', '2011 of 5008'], ['saint - basile', 'parish', '129.73', '799', '2364 of 5008'], ['rivière - verte', 'parish', '715.58', '791', '2384 of 5008'], ['saint - françois', 'parish', '344.70', '754', '2458 of 5008'], ['lac - baker', 'parish', '57.38', '566', '2847 of 5008'], ['saint - hilaire', 'parish', '41.55', '531', '2928 of 5008'], ['notre - dame - de - lourdes', 'parish', '188.63', '284', '3729 of 5008'], ['clair', 'parish', '44.29', '282', '3737 of 5008'], ['baker brook', 'parish', '125.69', '177', '4103 of 5008'], ['madawaska', 'parish', '173.32', '10', '4889 of 5008']]

lella lombardi

https://en.wikipedia.org/wiki/Lella_Lombardi

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235922-1.html.csv

count

lelle lombardi entered a total of 3 events in 1975 .

{'scope': 'all', 'criterion': 'equal', 'value': '1975', 'result': '3', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '1975'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 1975 .', 'tostr': 'filter_eq { all_rows ; year ; 1975 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; year ; 1975 } }', 'tointer': 'select the rows whose year record is equal to 1975 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; year ; 1975 } } ; 3 } = true', 'tointer': 'select the rows whose year record is equal to 1975 . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; year ; 1975 } } ; 3 } = true

select the rows whose year record is equal to 1975 . 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, 'year_5': 5, '1975_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '1975_6': '1975', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '1975_6': [0], '3_7': [2]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1974', 'allied polymer group', 'brabham bt42', 'cosworth v8', '0'], ['1975', 'march engineering', 'march 741', 'cosworth v8', '0.5'], ['1975', 'lavazza march', 'march 751', 'cosworth v8', '0.5'], ['1975', 'frank williams racing cars', 'williams fw04', 'cosworth v8', '0.5'], ['1976', 'lavazza march', 'march 761', 'cosworth v8', '0'], ['1976', 'ram racing with lavazza', 'brabham bt44b', 'cosworth v8', '0']]

iowa corn cy - hawk series

https://en.wikipedia.org/wiki/Iowa_Corn_Cy-Hawk_Series

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14175075-5.html.csv

count

iowa state won the events of the iowa corn cy - hawk series 8 times .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'iowa state', 'result': '8', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning team', 'iowa state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning team record fuzzily matches to iowa state .', 'tostr': 'filter_eq { all_rows ; winning team ; iowa state }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; winning team ; iowa state } }', 'tointer': 'select the rows whose winning team record fuzzily matches to iowa state . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; winning team ; iowa state } } ; 8 } = true', 'tointer': 'select the rows whose winning team record fuzzily matches to iowa state . the number of such rows is 8 .'}

eq { count { filter_eq { all_rows ; winning team ; iowa state } } ; 8 } = true

select the rows whose winning team record fuzzily matches to iowa state . the number of such rows is 8 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'winning team_5': 5, 'iowa state_6': 6, '8_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'winning team_5': 'winning team', 'iowa state_6': 'iowa state', '8_7': '8'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'winning team_5': [0], 'iowa state_6': [0], '8_7': [2]}

['date', 'site', 'sport', 'winning team', 'series']

[['september 4 , 2007', 'cedar rapids', 'm golf', 'iowa state', 'iowa state 2 - 0'], ['september 8 , 2007', 'des moines', 'volleyball', 'iowa state', 'iowa state 4 - 0'], ['september 9 , 2007', 'iowa city', 'w soccer', 'tie', 'iowa state 5 - 1'], ['september 15 , 2007', 'ames', 'football', 'iowa state', 'iowa state 8 - 1'], ['november 10 , 2007', 'peoria', 'm cross country', 'iowa state', 'iowa state 10 - 1'], ['november 10 , 2007', 'peoria', 'w cross country', 'iowa', 'iowa state 10 - 3'], ['december 5 , 2007', 'ames', 'w basketball', 'iowa state', 'iowa state 12 - 3'], ['december 7 , 2007', 'ames', 'w swimming', 'iowa state', 'iowa state 14 - 3'], ['december 8 , 2007', 'ames', 'm basketball', 'iowa state', 'iowa state 16 - 3'], ['december 9 , 2007', 'ames', 'wrestling', 'iowa', 'iowa state 16 - 5'], ['february 22 , 2008', 'ames', 'w gymnastics', 'iowa state', 'iowa state 18 - 5'], ['march 7 , 2008', 'iowa city', 'w gymnastics', 'iowa', 'iowa state 18 - 7'], ['april 1 , 2008', 'ames', 'softball', 'iowa', 'iowa state 18 - 9']]

ray crawford

https://en.wikipedia.org/wiki/Ray_Crawford

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1252146-3.html.csv

majority

a majority of chassis for ray crawford were kurtis kraft 500bs .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'kurtis kraft 500b', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'chassis', 'kurtis kraft 500b'], 'result': True, 'ind': 0, 'tointer': 'for the chassis records of all rows , most of them fuzzily match to kurtis kraft 500b .', 'tostr': 'most_eq { all_rows ; chassis ; kurtis kraft 500b } = true'}

most_eq { all_rows ; chassis ; kurtis kraft 500b } = true

for the chassis records of all rows , most of them fuzzily match to kurtis kraft 500b .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'chassis_3': 3, 'kurtis kraft 500b_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'chassis_3': 'chassis', 'kurtis kraft 500b_4': 'kurtis kraft 500b'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'chassis_3': [0], 'kurtis kraft 500b_4': [0]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1955', 'ray crawford', 'kurtis kraft 500b', 'offenhauser l4', '0'], ['1956', 'ray crawford', 'kurtis kraft 500b', 'offenhauser l4', '0'], ['1957', "meguiar 's mirror / crawford", 'kurtis kraft 500 g', 'offenhauser l4', '0'], ['1958', "meguiar 's mirror / crawford", 'kurtis kraft 500 g', 'offenhauser l4', '0'], ['1959', "meguiar 's mirror / crawford", 'elder', 'offenhauser l4', '0']]

caroline vis

https://en.wikipedia.org/wiki/Caroline_Vis

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15335011-3.html.csv

unique

the tournament on october 25 , 1999 , was the only tournament for caroline vis , that was in austria .

{'scope': 'all', 'row': '7', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'austria', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'austria'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to austria .', 'tostr': 'filter_eq { all_rows ; tournament ; austria }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; tournament ; austria } }', 'tointer': 'select the rows whose tournament record fuzzily matches to austria . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'austria'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to austria .', 'tostr': 'filter_eq { all_rows ; tournament ; austria }'}, 'date'], 'result': '25 october 1999', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; austria } ; date }'}, '25 october 1999'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; tournament ; austria } ; date } ; 25 october 1999 }', 'tointer': 'the date record of this unqiue row is 25 october 1999 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; tournament ; austria } } ; eq { hop { filter_eq { all_rows ; tournament ; austria } ; date } ; 25 october 1999 } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to austria . there is only one such row in the table . the date record of this unqiue row is 25 october 1999 .'}

and { only { filter_eq { all_rows ; tournament ; austria } } ; eq { hop { filter_eq { all_rows ; tournament ; austria } ; date } ; 25 october 1999 } } = true

select the rows whose tournament record fuzzily matches to austria . there is only one such row in the table . the date record of this unqiue row is 25 october 1999 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'tournament_7': 7, 'austria_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '25 october 1999_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'tournament_7': 'tournament', 'austria_8': 'austria', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '25 october 1999_10': '25 october 1999'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'tournament_7': [0], 'austria_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '25 october 1999_10': [3]}

['date', 'tournament', 'surface', 'partnering', 'opponent in the final', 'score']

[['4 may 1992', 'waregem , belgium', 'clay', 'manon bollegraf', 'elena bryukhovets petra langrová', '6 - 4 , 6 - 3'], ['18 october 1993', 'budapest , hungary', 'carpet ( i )', 'inés gorrochategui', 'sandra cecchini patricia tarabini', '6 - 1 , 6 - 3'], ['4 august 1997', 'los angeles , usa', 'hard', 'yayuk basuki', 'larisa neiland helena suková', '7 - 6 ( 9 - 7 ) , 6 - 3'], ['11 august 1997', 'toronto , canada', 'hard', 'yayuk basuki', 'nicole arendt manon bollegraf', '3 - 6 , 7 - 5 , 6 - 4'], ['22 february 1999', 'paris , france', 'carpet ( i )', 'irina spîrlea', 'elena likhovtseva ai sugiyama', '7 - 5 , 3 - 6 , 6 - 3'], ['20 september 1999', 'luxembourg , luxembourg', 'carpet ( i )', 'irina spîrlea', 'tina križan katarina srebotnik', '6 - 1 , 6 - 2'], ['25 october 1999', 'linz , austria', 'carpet ( i )', 'irina spîrlea', 'tina križan larisa neiland', '6 - 4 , 6 - 3'], ['13 november 2000', 'pattaya , thailand', 'hard', 'yayuk basuki', 'tina križan katarina srebotnik', '6 - 3 , 6 - 3'], ['12 february 2001', 'dubai , uae', 'hard', 'yayuk basuki', 'åsa svensson karina habšudová', '6 - 0 , 4 - 6 , 6 - 2']]

list of australian football league team songs

https://en.wikipedia.org/wiki/List_of_Australian_Football_League_team_songs

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28243323-1.html.csv

count

ken walther is the writer/composer of two team songs of the australian football league .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'ken walther', 'result': '2', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'writer / composer', 'ken walther'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose writer / composer record fuzzily matches to ken walther .', 'tostr': 'filter_eq { all_rows ; writer / composer ; ken walther }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; writer / composer ; ken walther } }', 'tointer': 'select the rows whose writer / composer record fuzzily matches to ken walther . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; writer / composer ; ken walther } } ; 2 } = true', 'tointer': 'select the rows whose writer / composer record fuzzily matches to ken walther . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; writer / composer ; ken walther } } ; 2 } = true

select the rows whose writer / composer record fuzzily matches to ken walther . 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, 'writer / composer_5': 5, 'ken walther_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', 'writer / composer_5': 'writer / composer', 'ken walther_6': 'ken walther', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'writer / composer_5': [0], 'ken walther_6': [0], '2_7': [2]}

['club name', 'name of team song', 'basis for team song', 'first used as team song', 'writer / composer']

[['adelaide', 'the pride of south australia', "us marines ' hymn", '1992', 'bill sanders'], ['brisbane lions', 'the pride of brisbane town', 'la marseillaise', '1955', 'fitzroy players'], ['carlton', 'we are the navy blues', 'lily of laguna', 'c 1930', 'carlton players'], ['collingwood', 'good old collingwood forever', 'goodbye , dolly gray', '1906', 'tom nelson'], ['essendon', 'see the bombers fly up', '( keep your ) sunny side up', '1960s', 'unknown'], ['fremantle', 'freo way to go', 'original', '1995', 'ken walther'], ['geelong', 'we are geelong', 'the toreador song', '1963', 'john k watts'], ['gold coast', 'we are the suns of the gold coast sky', 'original', '2010', 'rosco elliott'], ['greater western sydney', "there 's a big big sound", 'original', '2012', 'harry angus'], ['hawthorn', 'the mighty fighting hawks', 'the yankee doodle boy', 'c 1956', 'chick lander'], ['melbourne', "it 's a grand old flag", "you 're a grand old flag", 'c 1912', 'unknown ( second verse by keith bluey truscott )'], ['north melbourne', 'join in the chorus', 'wee deoch an doris', '1920s', 'unknown'], ['port adelaide', 'power to win', 'original', '1997', 'quentin eyers and les kaczmarek'], ['richmond', "we 're from tiger land", 'row , row , row', '1962', 'jack malcolmson'], ['st kilda', 'when the saints go marching in', 'when the saints go marching in', 'c 1965', 'unknown'], ['sydney', 'the red and the white', 'notre dame victory march', '1950s', 'larry spokes'], ['west coast', "we 're flying high", 'original', '1987', 'kevin peek and ken walther']]

maxus ( rocket )

https://en.wikipedia.org/wiki/Maxus_%28rocket%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16003024-1.html.csv

majority

all maxus mission rockets relied on a castor 4b motor .

{'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'castor 4b', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'motor', 'castor 4b'], 'result': True, 'ind': 0, 'tointer': 'for the motor records of all rows , all of them fuzzily match to castor 4b .', 'tostr': 'all_eq { all_rows ; motor ; castor 4b } = true'}

all_eq { all_rows ; motor ; castor 4b } = true

for the motor records of all rows , all of them fuzzily match to castor 4b .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'motor_3': 3, 'castor 4b_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'motor_3': 'motor', 'castor 4b_4': 'castor 4b'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'motor_3': [0], 'castor 4b_4': [0]}

['mission', 'date', 'launch site', 'motor', 'apogee']

[['maxus 1', '1991 may 8', 'esrange', 'castor 4b', '154 km'], ['maxus 1b', '1992 nov 8', 'esrange', 'castor 4b', '717 km'], ['maxus 2', '1995 nov 29', 'esrange', 'castor 4b', '706 km'], ['maxus 3', '1998 nov 24', 'esrange', 'castor 4b', '713 km'], ['maxus 4', '2001 apr 29', 'esrange', 'castor 4b', '704 km'], ['maxus 5', '2003 apr 1', 'esrange', 'castor 4b', '703 km'], ['maxus 6', '2004 nov 22', 'esrange', 'castor 4b', '707 km'], ['maxus 7', '2006 may 2', 'esrange', 'castor 4b', '705 km'], ['maxus 8', '2010 march 26', 'esrange', 'castor 4b', '703 km']]

1957 argentine grand prix

https://en.wikipedia.org/wiki/1957_Argentine_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1122148-1.html.csv

unique

the only maserati driver at the 1957 argentine grand prix to complete less than 92 laps was luigi piotti .

{'scope': 'subset', 'row': '10', 'col': '3', 'col_other': '1', 'criterion': 'less_than', 'value': '92', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'maserati'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'maserati'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; constructor ; maserati }', 'tointer': 'select the rows whose constructor record fuzzily matches to maserati .'}, 'laps', '92'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose constructor record fuzzily matches to maserati . among these rows , select the rows whose laps record is less than 92 .', 'tostr': 'filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } }', 'tointer': 'select the rows whose constructor record fuzzily matches to maserati . among these rows , select the rows whose laps record is less than 92 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'maserati'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; constructor ; maserati }', 'tointer': 'select the rows whose constructor record fuzzily matches to maserati .'}, 'laps', '92'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose constructor record fuzzily matches to maserati . among these rows , select the rows whose laps record is less than 92 .', 'tostr': 'filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 }'}, 'driver'], 'result': 'luigi piotti', 'ind': 3, 'tostr': 'hop { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } ; driver }'}, 'luigi piotti'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } ; driver } ; luigi piotti }', 'tointer': 'the driver record of this unqiue row is luigi piotti .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } } ; eq { hop { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } ; driver } ; luigi piotti } } = true', 'tointer': 'select the rows whose constructor record fuzzily matches to maserati . among these rows , select the rows whose laps record is less than 92 . there is only one such row in the table . the driver record of this unqiue row is luigi piotti .'}

and { only { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } } ; eq { hop { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } ; driver } ; luigi piotti } } = true

select the rows whose constructor record fuzzily matches to maserati . among these rows , select the rows whose laps record is less than 92 . there is only one such row in the table . the driver record of this unqiue row is luigi piotti .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'constructor_8': 8, 'maserati_9': 9, 'laps_10': 10, '92_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'driver_12': 12, 'luigi piotti_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'constructor_8': 'constructor', 'maserati_9': 'maserati', 'laps_10': 'laps', '92_11': '92', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'driver_12': 'driver', 'luigi piotti_13': 'luigi piotti'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_less_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'constructor_8': [0], 'maserati_9': [0], 'laps_10': [1], '92_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'driver_12': [3], 'luigi piotti_13': [4]}

['driver', 'constructor', 'laps', 'time / retired', 'grid']

[['juan manuel fangio', 'maserati', '100', '3:00:55.9', '2'], ['jean behra', 'maserati', '100', '+ 18.3 secs', '3'], ['carlos menditeguy', 'maserati', '99', '+ 1 lap', '8'], ['harry schell', 'maserati', '98', '+ 2 laps', '9'], ['alfonso de portago josé froilán gonzález', 'ferrari', '98', '+ 2 laps', '10'], ['cesare perdisa peter collins wolfgang von trips', 'ferrari', '98', '+ 2 laps', '11'], ['jo bonnier', 'maserati', '95', '+ 5 laps', '13'], ['stirling moss', 'maserati', '93', '+ 7 laps', '1'], ['alessandro de tomaso', 'ferrari', '91', '+ 9 laps', '12'], ['luigi piotti', 'maserati', '90', '+ 10 laps', '14'], ['eugenio castellotti', 'ferrari', '75', 'wheel', '4'], ['mike hawthorn', 'ferrari', '35', 'clutch', '7'], ['luigi musso', 'ferrari', '31', 'clutch', '6'], ['peter collins', 'ferrari', '26', 'clutch', '5']]

1995 - 96 toronto raptors season

https://en.wikipedia.org/wiki/1995%E2%80%9396_Toronto_Raptors_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13464416-5.html.csv

superlative

the toronto raptors highest scoring game was 110 points on december 17 .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '10', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '4', 'subset': None}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'score'], 'result': 'w 110 - 93 ( ot )', 'ind': 0, 'tostr': 'max { all_rows ; score }', 'tointer': 'the maximum score record of all rows is w 110 - 93 ( ot ) .'}, 'w 110 - 93 ( ot )'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; score } ; w 110 - 93 ( ot ) }', 'tointer': 'the maximum score record of all rows is w 110 - 93 ( ot ) .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'score'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; score }'}, 'score'], 'result': 'w 110 - 93 ( ot )', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; score } ; score }'}, 'w 110 - 93 ( ot )'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; score } ; score } ; w 110 - 93 ( ot ) }', 'tointer': 'the score record of the row with superlative score record is w 110 - 93 ( ot ) .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; score } ; w 110 - 93 ( ot ) } ; eq { hop { argmax { all_rows ; score } ; score } ; w 110 - 93 ( ot ) } } = true', 'tointer': 'the maximum score record of all rows is w 110 - 93 ( ot ) . the score record of the row with superlative score record is w 110 - 93 ( ot ) .'}

and { eq { max { all_rows ; score } ; w 110 - 93 ( ot ) } ; eq { hop { argmax { all_rows ; score } ; score } ; w 110 - 93 ( ot ) } } = true

the maximum score record of all rows is w 110 - 93 ( ot ) . the score record of the row with superlative score record is w 110 - 93 ( ot ) .

6

6

{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'score_8': 8, 'w 110 - 93 (ot)_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'score_11': 11, 'score_12': 12, 'w 110 - 93 (ot)_13': 13}

{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'score_8': 'score', 'w 110 - 93 (ot)_9': 'w 110 - 93 ( ot )', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'score_11': 'score', 'score_12': 'score', 'w 110 - 93 (ot)_13': 'w 110 - 93 ( ot )'}

{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'score_8': [0], 'w 110 - 93 (ot)_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'score_11': [2], 'score_12': [3], 'w 110 - 93 (ot)_13': [4]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['16', 'december 1', 'philadelphia', 'w 105 - 102 ( ot )', 'willie anderson ( 23 )', 'ed pinckney ( 16 )', 'damon stoudamire ( 10 )', 'skydome 19789', '6 - 10'], ['17', 'december 3', 'miami', 'l 94 - 112 ( ot )', 'oliver miller ( 29 )', 'ed pinckney ( 12 )', 'damon stoudamire ( 15 )', 'skydome 21238', '6 - 11'], ['18', 'december 5', 'seattle', 'l 89 - 119 ( ot )', 'tracy murray ( 23 )', 'oliver miller , alvin robertson , žan tabak ( 5 )', 'alvin robertson , damon stoudamire ( 5 )', 'keyarena 17072', '6 - 12'], ['19', 'december 7', 'portland', 'l 88 - 96 ( ot )', 'tracy murray ( 28 )', 'ed pinckney ( 15 )', 'damon stoudamire ( 10 )', 'rose garden 20039', '6 - 13'], ['20', 'december 8', 'la lakers', 'l 103 - 120 ( ot )', 'damon stoudamire ( 20 )', 'ed pinckney ( 8 )', 'damon stoudamire ( 10 )', 'great western forum 12982', '6 - 14'], ['21', 'december 10', 'vancouver', 'w 93 - 81 ( ot )', 'damon stoudamire ( 24 )', 'ed pinckney ( 16 )', 'damon stoudamire ( 8 )', 'general motors place 17438', '7 - 14'], ['22', 'december 12', 'boston', 'l 96 - 116 ( ot )', 'damon stoudamire ( 18 )', 'ed pinckney ( 8 )', 'damon stoudamire ( 9 )', 'skydome 21875', '7 - 15'], ['23', 'december 14', 'indiana', 'l 100 - 102 ( ot )', 'oliver miller ( 22 )', 'oliver miller ( 12 )', 'damon stoudamire ( 13 )', 'skydome 19763', '7 - 16'], ['24', 'december 15', 'boston', 'l 103 - 122 ( ot )', 'žan tabak ( 18 )', 'žan tabak ( 8 )', 'alvin robertson , damon stoudamire ( 7 )', 'fleetcenter 17580', '7 - 17'], ['25', 'december 17', 'orlando', 'w 110 - 93 ( ot )', 'damon stoudamire ( 21 )', 'ed pinckney ( 11 )', 'damon stoudamire ( 10 )', 'skydome 25820', '8 - 17'], ['26', 'december 19', 'detroit', 'l 82 - 94 ( ot )', 'damon stoudamire ( 19 )', 'oliver miller ( 11 )', 'damon stoudamire ( 8 )', 'skydome 21128', '8 - 18'], ['27', 'december 22', 'chicago', 'l 104 - 113 ( ot )', 'žan tabak ( 24 )', 'damon stoudamire , žan tabak ( 8 )', 'damon stoudamire ( 13 )', 'united center 22987', '8 - 19'], ['28', 'december 23', 'new york', 'l 91 - 103 ( ot )', 'damon stoudamire ( 25 )', 'ed pinckney ( 10 )', 'damon stoudamire ( 8 )', 'madison square garden 19763', '8 - 20'], ['29', 'december 26', 'milwaukee', 'w 93 - 87 ( ot )', 'damon stoudamire ( 21 )', 'ed pinckney ( 9 )', 'damon stoudamire ( 11 )', 'copps coliseum 17242', '9 - 20']]

1931 vfl season

https://en.wikipedia.org/wiki/1931_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10789881-5.html.csv

comparative

of the games played on may 30th , the geelong versus fitzroy game in the 1931 vfl season had a smaller crowd size than the essendon versus richmond game .

{'row_1': '6', 'row_2': '2', 'col': '6', 'col_other': '1,3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'geelong'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to geelong .', 'tostr': 'filter_eq { all_rows ; home team ; geelong }'}, 'crowd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; geelong } ; crowd }', 'tointer': 'select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'essendon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to essendon .', 'tostr': 'filter_eq { all_rows ; home team ; essendon }'}, 'crowd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; essendon } ; crowd }', 'tointer': 'select the rows whose home team record fuzzily matches to essendon . take the crowd record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; home team ; geelong } ; crowd } ; hop { filter_eq { all_rows ; home team ; essendon } ; crowd } }', 'tointer': 'select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . select the rows whose home team record fuzzily matches to essendon . take the crowd record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'geelong'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to geelong .', 'tostr': 'filter_eq { all_rows ; home team ; geelong }'}, 'away team'], 'result': 'fitzroy', 'ind': 5, 'tostr': 'hop { filter_eq { all_rows ; home team ; geelong } ; away team }'}, 'fitzroy'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; fitzroy }', 'tointer': 'the away team record of the first row is fitzroy .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'essendon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to essendon .', 'tostr': 'filter_eq { all_rows ; home team ; essendon }'}, 'away team'], 'result': 'richmond', 'ind': 7, 'tostr': 'hop { filter_eq { all_rows ; home team ; essendon } ; away team }'}, 'richmond'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { filter_eq { all_rows ; home team ; essendon } ; away team } ; richmond }', 'tointer': 'the away team record of the second row is richmond .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; home team ; essendon } ; away team } ; richmond } }', 'tointer': 'the away team record of the first row is fitzroy . the away team record of the second row is richmond .'}], 'result': True, 'ind': 10, 'tostr': 'and { less { hop { filter_eq { all_rows ; home team ; geelong } ; crowd } ; hop { filter_eq { all_rows ; home team ; essendon } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; home team ; essendon } ; away team } ; richmond } } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . select the rows whose home team record fuzzily matches to essendon . take the crowd record of this row . the first record is less than the second record . the away team record of the first row is fitzroy . the away team record of the second row is richmond .'}

and { less { hop { filter_eq { all_rows ; home team ; geelong } ; crowd } ; hop { filter_eq { all_rows ; home team ; essendon } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; home team ; essendon } ; away team } ; richmond } } } = true

select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . select the rows whose home team record fuzzily matches to essendon . take the crowd record of this row . the first record is less than the second record . the away team record of the first row is fitzroy . the away team record of the second row is richmond .

13

11

{'and_10': 10, 'result_11': 11, 'less_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_12': 12, 'home team_13': 13, 'geelong_14': 14, 'crowd_15': 15, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_16': 16, 'home team_17': 17, 'essendon_18': 18, 'crowd_19': 19, 'and_9': 9, 'str_eq_6': 6, 'str_hop_5': 5, 'away team_20': 20, 'fitzroy_21': 21, 'str_eq_8': 8, 'str_hop_7': 7, 'away team_22': 22, 'richmond_23': 23}

{'and_10': 'and', 'result_11': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_12': 'all_rows', 'home team_13': 'home team', 'geelong_14': 'geelong', 'crowd_15': 'crowd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_16': 'all_rows', 'home team_17': 'home team', 'essendon_18': 'essendon', 'crowd_19': 'crowd', 'and_9': 'and', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'away team_20': 'away team', 'fitzroy_21': 'fitzroy', 'str_eq_8': 'str_eq', 'str_hop_7': 'str_hop', 'away team_22': 'away team', 'richmond_23': 'richmond'}

{'and_10': [11], 'result_11': [], 'less_4': [10], 'num_hop_2': [4], 'filter_str_eq_0': [2, 5], 'all_rows_12': [0], 'home team_13': [0], 'geelong_14': [0], 'crowd_15': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3, 7], 'all_rows_16': [1], 'home team_17': [1], 'essendon_18': [1], 'crowd_19': [3], 'and_9': [10], 'str_eq_6': [9], 'str_hop_5': [6], 'away team_20': [5], 'fitzroy_21': [6], 'str_eq_8': [9], 'str_hop_7': [8], 'away team_22': [7], 'richmond_23': [8]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['hawthorn', '11.10 ( 76 )', 'south melbourne', '11.13 ( 79 )', 'glenferrie oval', '11000', '30 may 1931'], ['essendon', '10.12 ( 72 )', 'richmond', '11.20 ( 86 )', 'windy hill', '15000', '30 may 1931'], ['carlton', '16.11 ( 107 )', 'collingwood', '13.13 ( 91 )', 'princes park', '35000', '30 may 1931'], ['st kilda', '13.19 ( 97 )', 'north melbourne', '7.10 ( 52 )', 'junction oval', '10000', '30 may 1931'], ['melbourne', '13.9 ( 87 )', 'footscray', '10.11 ( 71 )', 'mcg', '20244', '30 may 1931'], ['geelong', '20.22 ( 142 )', 'fitzroy', '12.9 ( 81 )', 'corio oval', '9000', '30 may 1931']]

1996 - 97 atlanta hawks season

https://en.wikipedia.org/wiki/1996%E2%80%9397_Atlanta_Hawks_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18493040-9.html.csv

comparative

in april of the 1996-97 season , the atlanta hawks allowed fewer points against the minnesota timberwolves than against the indiana pacers .

{'row_1': '7', 'row_2': '6', 'col': '4', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'minnesota timberwolves'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to minnesota timberwolves .', 'tostr': 'filter_eq { all_rows ; opponent ; minnesota timberwolves }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; minnesota timberwolves } ; score }', 'tointer': 'select the rows whose opponent record fuzzily matches to minnesota timberwolves . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'indiana pacers'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to indiana pacers .', 'tostr': 'filter_eq { all_rows ; opponent ; indiana pacers }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; indiana pacers } ; score }', 'tointer': 'select the rows whose opponent record fuzzily matches to indiana pacers . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; minnesota timberwolves } ; score } ; hop { filter_eq { all_rows ; opponent ; indiana pacers } ; score } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to minnesota timberwolves . take the score record of this row . select the rows whose opponent record fuzzily matches to indiana pacers . take the score record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; opponent ; minnesota timberwolves } ; score } ; hop { filter_eq { all_rows ; opponent ; indiana pacers } ; score } } = true

select the rows whose opponent record fuzzily matches to minnesota timberwolves . take the score record of this row . select the rows whose opponent record fuzzily matches to indiana pacers . take the score record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'minnesota timberwolves_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'indiana pacers_12': 12, 'score_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'minnesota timberwolves_8': 'minnesota timberwolves', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'indiana pacers_12': 'indiana pacers', 'score_13': 'score'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'minnesota timberwolves_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'indiana pacers_12': [1], 'score_13': [3]}

['game', 'date', 'opponent', 'score', 'location attendance', 'record']

[['73', 'april 2', 'charlotte hornets', 'l 84 - 95', 'charlotte coliseum 24042', '50 - 23'], ['game', 'date', 'opponent', 'score', 'location attendance', 'record'], ['74', 'april 4', 'detroit pistons', 'w 103 - 89', 'omni coliseum 16378', '51 - 23'], ['75', 'april 5', 'new york knicks', 'l 97 - 102', 'omni coliseum 16378', '51 - 24'], ['76', 'april 9', 'philadelphia 76ers', 'w 116 - 101', 'corestates center 16549', '52 - 24'], ['77', 'april 11', 'indiana pacers', 'w 104 - 92', 'market square arena 16403', '53 - 24'], ['78', 'april 12', 'minnesota timberwolves', 'w 80 - 66', 'target center 18874', '54 - 24'], ['79', 'april 15', 'new jersey nets', 'w 109 - 101', 'omni coliseum 14458', '55 - 24'], ['80', 'april 16', 'new york knicks', 'l 92 - 96', 'madison square garden 19763', '55 - 25'], ['81', 'april 19', 'philadelphia 76ers', 'w 136 - 104', 'omni coliseum 16457', '56 - 25'], ['82', 'april 20', 'new jersey nets', 'l 92 - 108', 'continental airlines arena 18702', '56 - 26']]

casey martin

https://en.wikipedia.org/wiki/Casey_Martin

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1697190-2.html.csv

majority

most years casey martin played 3 or fewer tournaments .

{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'less_than_eq', 'value': '3', 'subset': None}

{'func': 'most_less_eq', 'args': ['all_rows', 'tournaments played', '3'], 'result': True, 'ind': 0, 'tointer': 'for the tournaments played records of all rows , most of them are less than or equal to 3 .', 'tostr': 'most_less_eq { all_rows ; tournaments played ; 3 } = true'}

most_less_eq { all_rows ; tournaments played ; 3 } = true

for the tournaments played records of all rows , most of them are less than or equal to 3 .

1

1

{'most_less_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'tournaments played_3': 3, '3_4': 4}

{'most_less_eq_0': 'most_less_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'tournaments played_3': 'tournaments played', '3_4': '3'}

{'most_less_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'tournaments played_3': [0], '3_4': [0]}

['year', 'tournaments played', 'cuts made', 'wins', 'best finish', 'earnings', 'money list rank']

[['1998', '3', '2', '0', 't - 23', '37221', '221'], ['2000', '29', '14', '0', 't - 17', '143248', '179'], ['2001', '2', '0', '0', 'cut', '0', 'n / a'], ['2002', '3', '0', '0', 'cut', '0', 'n / a'], ['2003', '1', '0', '0', 'cut', '0', 'n / a'], ['2004', '2', '2', '0', 't - 69', '15858', 'n / a'], ['2005', '1', '1', '0', 't - 65', '10547', 'n / a'], ['2012', '2', '0', '0', 'cut', '0', 'n / a']]

transatlantic lines

https://en.wikipedia.org/wiki/TransAtlantic_Lines

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13580133-1.html.csv

comparative

transatlantic lines tugboats can tow less gross tonnage than a deck cargo barge .

{'row_1': '5', 'row_2': '4', 'col': '5', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'tugboat'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type record fuzzily matches to tugboat .', 'tostr': 'filter_eq { all_rows ; type ; tugboat }'}, 'gross tonnage'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; type ; tugboat } ; gross tonnage }', 'tointer': 'select the rows whose type record fuzzily matches to tugboat . take the gross tonnage record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type', 'deck cargo barge'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose type record fuzzily matches to deck cargo barge .', 'tostr': 'filter_eq { all_rows ; type ; deck cargo barge }'}, 'gross tonnage'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; type ; deck cargo barge } ; gross tonnage }', 'tointer': 'select the rows whose type record fuzzily matches to deck cargo barge . take the gross tonnage record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; type ; tugboat } ; gross tonnage } ; hop { filter_eq { all_rows ; type ; deck cargo barge } ; gross tonnage } } = true', 'tointer': 'select the rows whose type record fuzzily matches to tugboat . take the gross tonnage record of this row . select the rows whose type record fuzzily matches to deck cargo barge . take the gross tonnage record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; type ; tugboat } ; gross tonnage } ; hop { filter_eq { all_rows ; type ; deck cargo barge } ; gross tonnage } } = true

select the rows whose type record fuzzily matches to tugboat . take the gross tonnage record of this row . select the rows whose type record fuzzily matches to deck cargo barge . take the gross tonnage 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, 'type_7': 7, 'tugboat_8': 8, 'gross tonnage_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'type_11': 11, 'deck cargo barge_12': 12, 'gross tonnage_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', 'type_7': 'type', 'tugboat_8': 'tugboat', 'gross tonnage_9': 'gross tonnage', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'type_11': 'type', 'deck cargo barge_12': 'deck cargo barge', 'gross tonnage_13': 'gross tonnage'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'type_7': [0], 'tugboat_8': [0], 'gross tonnage_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'type_11': [1], 'deck cargo barge_12': [1], 'gross tonnage_13': [3]}

['type', 'owns', 'length', 'delivery date', 'gross tonnage']

[['general cargo ship', 'yes', '83.5152 m ( lbp )', '1 june 1980', '2266'], ['general cargo ship / container ship', 'yes', '100.59 100.59 m ( loa )', '1997 1997', '4276'], ['petroleum tanker', 'yes', '109.1 109.1 m ( loa )', '2001 2001', '3469'], ['deck cargo barge', 'yes', '76.2 76.2 m ( lbp )', '1983 1 september 1983', '2529'], ['tugboat', 'yes', '27.7764 27.7764 m ( lbp )', '1974 1 september 1974', '189']]

agatha christie 's poirot

https://en.wikipedia.org/wiki/Agatha_Christie%27s_Poirot

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-170661-1.html.csv

unique

david suchet is the only actor who participated in all 13 series .

{'scope': 'all', 'row': '1', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '1 - 13', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series', '1 - 13'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose series record fuzzily matches to 1 - 13 .', 'tostr': 'filter_eq { all_rows ; series ; 1 - 13 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; series ; 1 - 13 } }', 'tointer': 'select the rows whose series record fuzzily matches to 1 - 13 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series', '1 - 13'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose series record fuzzily matches to 1 - 13 .', 'tostr': 'filter_eq { all_rows ; series ; 1 - 13 }'}, 'actor'], 'result': 'david suchet', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; series ; 1 - 13 } ; actor }'}, 'david suchet'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; series ; 1 - 13 } ; actor } ; david suchet }', 'tointer': 'the actor record of this unqiue row is david suchet .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; series ; 1 - 13 } } ; eq { hop { filter_eq { all_rows ; series ; 1 - 13 } ; actor } ; david suchet } } = true', 'tointer': 'select the rows whose series record fuzzily matches to 1 - 13 . there is only one such row in the table . the actor record of this unqiue row is david suchet .'}

and { only { filter_eq { all_rows ; series ; 1 - 13 } } ; eq { hop { filter_eq { all_rows ; series ; 1 - 13 } ; actor } ; david suchet } } = true

select the rows whose series record fuzzily matches to 1 - 13 . there is only one such row in the table . the actor record of this unqiue row is david suchet .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'series_7': 7, '1 - 13_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'actor_9': 9, 'david suchet_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'series_7': 'series', '1 - 13_8': '1 - 13', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'actor_9': 'actor', 'david suchet_10': 'david suchet'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'series_7': [0], '1 - 13_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'actor_9': [2], 'david suchet_10': [3]}

['actor', 'character', 'title / rank', 'series', 'years']

[['david suchet', 'hercule poirot', 'various', '1 - 13', '1989 - 2013'], ['hugh fraser', 'arthur hastings', 'captain obe', '1 - 8 , 13', '1989 - 2002 , 2013'], ['philip jackson', 'james japp', 'chief inspector / assistant commissioner', '1 - 8 , 13', '1989 - 2001 , 2013'], ['pauline moran', 'felicity lemon', 'secretary', '1 - 3 , 5 - 8 , 13', '1989 - 1991 , 1993 - 2001 , 2013'], ['zoë wanamaker', 'ariadne oliver', 'writer', '10 - 13', '2006 - 2013']]

list of european ultra prominent peaks

https://en.wikipedia.org/wiki/List_of_European_ultra_prominent_peaks

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18918776-1.html.csv

aggregation

the average elevation of all european ultra prominent peaks is 2069.16 .

{'scope': 'all', 'col': '3', 'type': 'average', 'result': '2069.16', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'elevation ( m )'], 'result': '2069.16', 'ind': 0, 'tostr': 'avg { all_rows ; elevation ( m ) }'}, '2069.16'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; elevation ( m ) } ; 2069.16 } = true', 'tointer': 'the average of the elevation ( m ) record of all rows is 2069.16 .'}

round_eq { avg { all_rows ; elevation ( m ) } ; 2069.16 } = true

the average of the elevation ( m ) record of all rows is 2069.16 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'elevation (m)_4': 4, '2069.16_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'elevation (m)_4': 'elevation ( m )', '2069.16_5': '2069.16'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'elevation (m)_4': [0], '2069.16_5': [1]}

['peak', 'country', 'elevation ( m )', 'prominence ( m )', 'col ( m )']

[['galdhøpiggen', 'norway', '2469', '2372', '97'], ['kebnekaise', 'sweden', '2113', '1754', '359'], ['jiehkkevárri', 'norway', '1834', '1741', '93'], ['snøhetta', 'norway', '2286', '1675', '611'], ['store lenangstind', 'norway', '1624', '1576', '48'], ['sarektjåhkkå', 'sweden', '2089', '1519', '570']]

1959 portuguese grand prix

https://en.wikipedia.org/wiki/1959_Portuguese_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1122212-1.html.csv

unique

jo bonnier was the only driver to retire due to engine failure .

{'scope': 'all', 'row': '13', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'engine', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time / retired', 'engine'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time / retired record fuzzily matches to engine .', 'tostr': 'filter_eq { all_rows ; time / retired ; engine }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; time / retired ; engine } }', 'tointer': 'select the rows whose time / retired record fuzzily matches to engine . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time / retired', 'engine'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time / retired record fuzzily matches to engine .', 'tostr': 'filter_eq { all_rows ; time / retired ; engine }'}, 'driver'], 'result': 'jo bonnier', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; time / retired ; engine } ; driver }'}, 'jo bonnier'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; time / retired ; engine } ; driver } ; jo bonnier }', 'tointer': 'the driver record of this unqiue row is jo bonnier .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; time / retired ; engine } } ; eq { hop { filter_eq { all_rows ; time / retired ; engine } ; driver } ; jo bonnier } } = true', 'tointer': 'select the rows whose time / retired record fuzzily matches to engine . there is only one such row in the table . the driver record of this unqiue row is jo bonnier .'}

and { only { filter_eq { all_rows ; time / retired ; engine } } ; eq { hop { filter_eq { all_rows ; time / retired ; engine } ; driver } ; jo bonnier } } = true

select the rows whose time / retired record fuzzily matches to engine . there is only one such row in the table . the driver record of this unqiue row is jo bonnier .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'time / retired_7': 7, 'engine_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'driver_9': 9, 'jo bonnier_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'time / retired_7': 'time / retired', 'engine_8': 'engine', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'driver_9': 'driver', 'jo bonnier_10': 'jo bonnier'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'time / retired_7': [0], 'engine_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'driver_9': [2], 'jo bonnier_10': [3]}

['driver', 'constructor', 'laps', 'time / retired', 'grid']

[['stirling moss', 'cooper - climax', '62', '2:11:55.41', '1'], ['masten gregory', 'cooper - climax', '61', '+ 1 lap', '3'], ['dan gurney', 'ferrari', '61', '+ 1 lap', '6'], ['maurice trintignant', 'cooper - climax', '60', '+ 2 laps', '4'], ['harry schell', 'brm', '59', '+ 3 laps', '9'], ['roy salvadori', 'aston martin', '59', '+ 3 laps', '12'], ['ron flockhart', 'brm', '59', '+ 3 laps', '11'], ['carroll shelby', 'aston martin', '58', '+ 4 laps', '13'], ['tony brooks', 'ferrari', '57', '+ 5 laps', '10'], ['mário de araújo cabral', 'cooper - maserati', '56', '+ 6 laps', '14'], ['bruce mclaren', 'cooper - climax', '38', 'transmission', '8'], ['jack brabham', 'cooper - climax', '23', 'transmission', '2'], ['jo bonnier', 'brm', '10', 'engine', '5'], ['phil hill', 'ferrari', '5', 'accident', '7'], ['graham hill', 'lotus - climax', '5', 'accident', '15'], ['innes ireland', 'lotus - climax', '3', 'gearbox', '16']]

1972 england rugby union tour of south africa

https://en.wikipedia.org/wiki/1972_England_rugby_union_tour_of_South_Africa

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17020783-1.html.csv

unique

the event on may 17th , 1972 , was the only time in the 1972 england rugby union tour of south africa , that the opposing team was natal .

{'scope': 'all', 'row': '1', 'col': '1', 'col_other': '3', 'criterion': 'equal', 'value': 'natal', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing team', 'natal'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opposing team record fuzzily matches to natal .', 'tostr': 'filter_eq { all_rows ; opposing team ; natal }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opposing team ; natal } }', 'tointer': 'select the rows whose opposing team record fuzzily matches to natal . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing team', 'natal'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opposing team record fuzzily matches to natal .', 'tostr': 'filter_eq { all_rows ; opposing team ; natal }'}, 'date'], 'result': 'may 17 , 1972', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opposing team ; natal } ; date }'}, 'may 17 , 1972'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opposing team ; natal } ; date } ; may 17 , 1972 }', 'tointer': 'the date record of this unqiue row is may 17 , 1972 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; opposing team ; natal } } ; eq { hop { filter_eq { all_rows ; opposing team ; natal } ; date } ; may 17 , 1972 } } = true', 'tointer': 'select the rows whose opposing team record fuzzily matches to natal . there is only one such row in the table . the date record of this unqiue row is may 17 , 1972 .'}

and { only { filter_eq { all_rows ; opposing team ; natal } } ; eq { hop { filter_eq { all_rows ; opposing team ; natal } ; date } ; may 17 , 1972 } } = true

select the rows whose opposing team record fuzzily matches to natal . there is only one such row in the table . the date record of this unqiue row is may 17 , 1972 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opposing team_7': 7, 'natal_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'may 17 , 1972_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opposing team_7': 'opposing team', 'natal_8': 'natal', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'may 17 , 1972_10': 'may 17 , 1972'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'opposing team_7': [0], 'natal_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'may 17 , 1972_10': [3]}

['opposing team', 'against', 'date', 'venue', 'status']

[['natal', '0', 'may 17 , 1972', 'durban', 'tour match'], ['western province', '6', 'may 20 , 1972', 'cape town', 'tour match'], ['sa rugby fed xv', '6', 'may 22 , 1972', 'cape town', 'tour match'], ['sa leopards', '3', 'may 24 , 1972', 'port elizabeth', 'tour match'], ['northern transvaal', '13', 'may 27 , 1972', 'pretoria', 'tour match'], ['giqualand west', '21', 'may 30 , 1972', 'kimberley', 'tour match'], ['south africa', '9', 'june 3 , 1972', 'ellis park , johannesburg', 'test match']]

list of teachers ( uk tv series ) episodes

https://en.wikipedia.org/wiki/List_of_Teachers_%28UK_TV_series%29_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18335117-4.html.csv

comparative

brian kelly directed an episode of teachers before andrew lincoln directed an episode .

{'row_1': '1', 'row_2': '12', 'col': '3', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'brian kelly'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose director record fuzzily matches to brian kelly .', 'tostr': 'filter_eq { all_rows ; director ; brian kelly }'}, 'title'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; director ; brian kelly } ; title }', 'tointer': 'select the rows whose director record fuzzily matches to brian kelly . take the title record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'andrew lincoln'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose director record fuzzily matches to andrew lincoln .', 'tostr': 'filter_eq { all_rows ; director ; andrew lincoln }'}, 'title'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; director ; andrew lincoln } ; title }', 'tointer': 'select the rows whose director record fuzzily matches to andrew lincoln . take the title record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; director ; brian kelly } ; title } ; hop { filter_eq { all_rows ; director ; andrew lincoln } ; title } } = true', 'tointer': 'select the rows whose director record fuzzily matches to brian kelly . take the title record of this row . select the rows whose director record fuzzily matches to andrew lincoln . take the title record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; director ; brian kelly } ; title } ; hop { filter_eq { all_rows ; director ; andrew lincoln } ; title } } = true

select the rows whose director record fuzzily matches to brian kelly . take the title record of this row . select the rows whose director record fuzzily matches to andrew lincoln . take the title record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'director_7': 7, 'brian kelly_8': 8, 'title_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'director_11': 11, 'andrew lincoln_12': 12, 'title_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'director_7': 'director', 'brian kelly_8': 'brian kelly', 'title_9': 'title', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'director_11': 'director', 'andrew lincoln_12': 'andrew lincoln', 'title_13': 'title'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'director_7': [0], 'brian kelly_8': [0], 'title_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'director_11': [1], 'andrew lincoln_12': [1], 'title_13': [3]}

['no overall', 'no in series', 'title', 'director', 'writer', 'original air date', 'production code']

[['19', '1', 'episode 1', 'brian kelly', 'ed roe', '6 august 2003', '301'], ['20', '2', 'episode 2', 'brian kelly', 'richard stoneman', '13 august 2003', '302'], ['21', '3', 'episode 3', 'brian kelly', 'andrew rattenburry', '20 august 2003', '303'], ['22', '4', 'episode 4', 'otto bathurst', 'richard stoneman', '27 august 2003', '304'], ['23', '5', 'episode 5', 'otto bathurst', 'charlie martin', '3 september 2003', '305'], ['24', '6', 'episode 6', 'otto bathurst', 'richard stoneman', '10 september 2003', '306'], ['25', '7', 'episode 7', 'jonathan fox bassett', 'ed roe', '17 september 2003', '307'], ['26', '8', 'episode 8', 'jonathan fox bassett', 'tony basgallop', '23 september 2003', '308'], ['27', '9', 'episode 9', 'jonathan fox bassett', 'ed roe', '30 september 2003', '309'], ['28', '10', 'episode 10', 'susanna white', 'andrew rattenbury', '7 october 2003', '310'], ['29', '11', 'episode 11', 'susanna white', 'jack lothian', '13 october 2003', '311'], ['30', '12', 'episode 12', 'andrew lincoln', 'richard stoneman', '20 october 2003', '312']]

1991 u.s. open ( golf )

https://en.wikipedia.org/wiki/1991_U.S._Open_%28golf%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162268-2.html.csv

comparative

in the 1991 u.s. open , scott simpson was 3 strokes more below par than larry nelson .

{'row_1': '1', 'row_2': '2', 'col': '5', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '3', 'bigger': 'row2'}}

{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'scott simpson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to scott simpson .', 'tostr': 'filter_eq { all_rows ; player ; scott simpson }'}, 'to par'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; scott simpson } ; to par }', 'tointer': 'select the rows whose player record fuzzily matches to scott simpson . take the to par record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'larry nelson'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to larry nelson .', 'tostr': 'filter_eq { all_rows ; player ; larry nelson }'}, 'to par'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; larry nelson } ; to par }', 'tointer': 'select the rows whose player record fuzzily matches to larry nelson . take the to par record of this row .'}], 'result': '-3', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; scott simpson } ; to par } ; hop { filter_eq { all_rows ; player ; larry nelson } ; to par } }'}, '-3'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; scott simpson } ; to par } ; hop { filter_eq { all_rows ; player ; larry nelson } ; to par } } ; -3 } = true', 'tointer': 'select the rows whose player record fuzzily matches to scott simpson . take the to par record of this row . select the rows whose player record fuzzily matches to larry nelson . take the to par record of this row . the second record is 3 larger than the first record .'}

eq { diff { hop { filter_eq { all_rows ; player ; scott simpson } ; to par } ; hop { filter_eq { all_rows ; player ; larry nelson } ; to par } } ; -3 } = true

select the rows whose player record fuzzily matches to scott simpson . take the to par record of this row . select the rows whose player record fuzzily matches to larry nelson . take the to par record of this row . the second record is 3 larger than the first record .

6

6

{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'player_8': 8, 'scott simpson_9': 9, 'to par_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'player_12': 12, 'larry nelson_13': 13, 'to par_14': 14, '-3_15': 15}

{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'player_8': 'player', 'scott simpson_9': 'scott simpson', 'to par_10': 'to par', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'larry nelson_13': 'larry nelson', 'to par_14': 'to par', '-3_15': '-3'}

{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'player_8': [0], 'scott simpson_9': [0], 'to par_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'player_12': [1], 'larry nelson_13': [1], 'to par_14': [3], '-3_15': [5]}

['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish']

[['scott simpson', 'united states', '1987', '282', '- 6', '2'], ['larry nelson', 'united states', '1983', '285', '- 3', 't3'], ['fuzzy zoeller', 'united states', '1984', '286', '- 2', '5'], ['raymond floyd', 'united states', '1986', '289', '+ 1', 't8'], ['hale irwin', 'united states', '1974 , 1979 , 1990', '290', '+ 2', 't11'], ['tom watson', 'united states', '1982', '291', '+ 3', 't16'], ['andy north', 'united states', '1978 , 1985', '295', '+ 7', 't37'], ['jack nicklaus', 'united states', '1962 , 1967 , 1972 , 1980', '297', '+ 9', 't46'], ['david graham', 'australia', '1981', '302', '+ 14', '60']]

miguel amaral

https://en.wikipedia.org/wiki/Miguel_Amaral

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1616765-1.html.csv

count

miguel amaral 's co-drivers were oliver pla and guy smith for two of the 24 hours of le mans races he was in .

{'scope': 'all', 'criterion': 'equal', 'value': 'olivier pla guy smith', 'result': '2', 'col': '2', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'co - drivers', 'olivier pla guy smith'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose co - drivers record fuzzily matches to olivier pla guy smith .', 'tostr': 'filter_eq { all_rows ; co - drivers ; olivier pla guy smith }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; co - drivers ; olivier pla guy smith } }', 'tointer': 'select the rows whose co - drivers record fuzzily matches to olivier pla guy smith . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; co - drivers ; olivier pla guy smith } } ; 2 } = true', 'tointer': 'select the rows whose co - drivers record fuzzily matches to olivier pla guy smith . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; co - drivers ; olivier pla guy smith } } ; 2 } = true

select the rows whose co - drivers record fuzzily matches to olivier pla guy smith . 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, 'co - drivers_5': 5, 'olivier pla guy smith_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', 'co - drivers_5': 'co - drivers', 'olivier pla guy smith_6': 'olivier pla guy smith', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'co - drivers_5': [0], 'olivier pla guy smith_6': [0], '2_7': [2]}

['year', 'co - drivers', 'class', 'laps', 'pos', 'class pos']

[['2006', 'warren hughes miguel ángel de castro', 'lmp2', '196', 'dnf', 'dnf'], ['2007', 'warren hughes miguel ángel de castro', 'lmp2', '137', 'dnf', 'dnf'], ['2008', 'olivier pla guy smith', 'lmp2', '325', '20th', '4th'], ['2009', 'olivier pla guy smith', 'lmp2', '46', 'dnf', 'dnf'], ['2010', 'olivier pla warren hughes', 'lmp2', '318', '20th', '7th'], ['2011', 'olivier pla warren hughes', 'lmp1', '48', 'dnf', 'dnf']]

harry hinton

https://en.wikipedia.org/wiki/Harry_Hinton

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16877441-3.html.csv

majority

most of the time , the team for harry hinton was norton .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'norton', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'team', 'norton'], 'result': True, 'ind': 0, 'tointer': 'for the team records of all rows , most of them fuzzily match to norton .', 'tostr': 'most_eq { all_rows ; team ; norton } = true'}

most_eq { all_rows ; team ; norton } = true

for the team records of all rows , most of them fuzzily match to norton .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'team_3': 3, 'norton_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'team_3': 'team', 'norton_4': 'norton'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'team_3': [0], 'norton_4': [0]}

['year', 'class', 'team', 'points', 'wins']

[['1949', '350cc', 'norton', '0', '0'], ['1949', '500cc', 'norton', '0', '0'], ['1950', '350cc', 'norton', '9', '0'], ['1950', '500cc', 'norton', '5', '0'], ['1951', '350cc', 'norton', '0', '0'], ['1958', '350cc', 'velocette', '0', '0'], ['1958', '500cc', 'norton', '0', '0']]

united states house of representatives elections , 1972

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1972

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341707-20.html.csv

comparative

hale boggs was elected to the house of representatives before joe waggoner was .

{'row_1': '2', 'row_2': '3', 'col': '4', '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', 'incumbent', 'hale boggs'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to hale boggs .', 'tostr': 'filter_eq { all_rows ; incumbent ; hale boggs }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; hale boggs } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to hale boggs . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'joe waggonner'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to joe waggonner .', 'tostr': 'filter_eq { all_rows ; incumbent ; joe waggonner }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; joe waggonner } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to joe waggonner . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; hale boggs } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; joe waggonner } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to hale boggs . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to joe waggonner . take the first elected record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; incumbent ; hale boggs } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; joe waggonner } ; first elected } } = true

select the rows whose incumbent record fuzzily matches to hale boggs . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to joe waggonner . take the first elected 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, 'incumbent_7': 7, 'hale boggs_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'joe waggonner_12': 12, 'first elected_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', 'incumbent_7': 'incumbent', 'hale boggs_8': 'hale boggs', 'first elected_9': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'joe waggonner_12': 'joe waggonner', 'first elected_13': 'first elected'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'hale boggs_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'joe waggonner_12': [1], 'first elected_13': [3]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['louisiana 1', 'f edward hebert', 'democratic', '1940', 're - elected', 'f edward hebert ( d ) unopposed'], ['louisiana 2', 'hale boggs', 'democratic', '1946', 're - elected', 'hale boggs ( d ) unopposed'], ['louisiana 4', 'joe waggonner', 'democratic', '1961', 're - elected', 'joe waggonner ( d ) unopposed'], ['louisiana 5', 'otto passman', 'democratic', '1946', 're - elected', 'otto passman ( d ) unopposed'], ['louisiana 6', 'john rarick', 'democratic', '1966', 're - elected', 'john rarick ( d ) unopposed']]

wwfm

https://en.wikipedia.org/wiki/WWFM

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12472016-2.html.csv

comparative

call sign w245ac has a higher fequency than call sign k216fw .

{'row_1': '4', 'row_2': '1', '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', 'call sign', 'w245ac'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose call sign record fuzzily matches to w245ac .', 'tostr': 'filter_eq { all_rows ; call sign ; w245ac }'}, 'frequency mhz'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; call sign ; w245ac } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to w245ac . take the frequency mhz record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'k216fw'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose call sign record fuzzily matches to k216fw .', 'tostr': 'filter_eq { all_rows ; call sign ; k216fw }'}, 'frequency mhz'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; call sign ; k216fw } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to k216fw . take the frequency mhz record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; call sign ; w245ac } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k216fw } ; frequency mhz } } = true', 'tointer': 'select the rows whose call sign record fuzzily matches to w245ac . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k216fw . take the frequency mhz record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; call sign ; w245ac } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k216fw } ; frequency mhz } } = true

select the rows whose call sign record fuzzily matches to w245ac . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k216fw . take the frequency mhz 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, 'call sign_7': 7, 'w245ac_8': 8, 'frequency mhz_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'call sign_11': 11, 'k216fw_12': 12, 'frequency mhz_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', 'call sign_7': 'call sign', 'w245ac_8': 'w245ac', 'frequency mhz_9': 'frequency mhz', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'call sign_11': 'call sign', 'k216fw_12': 'k216fw', 'frequency mhz_13': 'frequency mhz'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'call sign_7': [0], 'w245ac_8': [0], 'frequency mhz_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'call sign_11': [1], 'k216fw_12': [1], 'frequency mhz_13': [3]}

['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info']

[['k216fw', '91.1 fm', 'steamboat springs , colorado', '10', 'd', 'fcc'], ['w224au', '92.7 fm', 'allentown , pennsylvania', '8', 'd', 'fcc'], ['w226aa', '93.1 fm', 'easton , pennsylvania', '150', 'd', 'fcc'], ['w245ac', '96.9 fm', 'harmony township , new jersey', '10', 'd', 'fcc'], ['w300ac', '107.9 fm', 'chatsworth , new jersey', '35', 'd', 'fcc'], ['w230aa', '93.9 fm', 'atlantic city , new jersey', '27', 'd', 'fcc'], ['w284bw', '104.7 fm', 'franklin township , somerset county , new jersey', '13', 'd', 'fcc']]

carrefour

https://en.wikipedia.org/wiki/Carrefour

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-167638-3.html.csv

ordinal

spain was the third european country into which carrefour expanded .

{'row': '14', 'col': '2', 'order': '3', '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', 'first store', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first store ; 3 }'}, 'country'], 'result': 'spain', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first store ; 3 } ; country }'}, 'spain'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first store ; 3 } ; country } ; spain } = true', 'tointer': 'select the row whose first store record of all rows is 3rd minimum . the country record of this row is spain .'}

eq { hop { nth_argmin { all_rows ; first store ; 3 } ; country } ; spain } = true

select the row whose first store record of all rows is 3rd minimum . the country record of this row is spain .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first store_5': 5, '3_6': 6, 'country_7': 7, 'spain_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 store_5': 'first store', '3_6': '3', 'country_7': 'country', 'spain_8': 'spain'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first store_5': [0], '3_6': [0], 'country_7': [1], 'spain_8': [2]}

['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', '-', '-', '-']]

curling at the 2006 winter olympics

https://en.wikipedia.org/wiki/Curling_at_the_2006_Winter_Olympics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1543845-63.html.csv

majority

most teams had a shot percentage in the 70 % range .

{'scope': 'all', 'col': '11', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '80 %', 'subset': None}

{'func': 'most_less', 'args': ['all_rows', 'shot pct', '80 %'], 'result': True, 'ind': 0, 'tointer': 'for the shot pct records of all rows , most of them are less than 80 % .', 'tostr': 'most_less { all_rows ; shot pct ; 80 % } = true'}

most_less { all_rows ; shot pct ; 80 % } = true

for the shot pct records of all rows , most of them are less than 80 % .

1

1

{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'shot pct_3': 3, '80%_4': 4}

{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'shot pct_3': 'shot pct', '80%_4': '80 %'}

{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'shot pct_3': [0], '80%_4': [0]}

['locale', 'skip', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends', 'shot pct']

[['finland', 'markku uusipaavalniemi', '7', '2', '53', '40', '32', '31', '23', '9', '78 %'], ['canada', 'brad gushue', '6', '3', '66', '46', '47', '31', '9', '23', '80 %'], ['united states', 'pete fenson', '6', '3', '66', '47', '36', '33', '16', '13', '80 %'], ['great britain', 'david murdoch', '6', '3', '59', '49', '36', '31', '17', '12', '81 %'], ['norway', 'pål trulsen', '5', '4', '57', '47', '33', '32', '17', '9', '78 %'], ['switzerland', 'ralph stöckli', '5', '4', '56', '45', '31', '34', '18', '10', '76 %'], ['italy', 'joël retornaz', '4', '5', '47', '66', '37', '38', '10', '7', '70 %'], ['germany', 'andy kapp', '3', '6', '53', '55', '34', '34', '17', '12', '77 %'], ['sweden', 'peja lindholm', '3', '6', '45', '68', '31', '40', '12', '4', '78 %']]

largest gold companies

https://en.wikipedia.org/wiki/Largest_gold_companies

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24307126-3.html.csv

aggregation

the average assets in 2013 of the largest gold companies is 18.9 billion .

{'scope': 'all', 'col': '8', 'type': 'average', 'result': '18.9', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'assets 2013 ( bil )'], 'result': '18.9', 'ind': 0, 'tostr': 'avg { all_rows ; assets 2013 ( bil ) }'}, '18.9'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; assets 2013 ( bil ) } ; 18.9 } = true', 'tointer': 'the average of the assets 2013 ( bil ) record of all rows is 18.9 .'}

round_eq { avg { all_rows ; assets 2013 ( bil ) } ; 18.9 } = true

the average of the assets 2013 ( bil ) record of all rows is 18.9 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'assets 2013 (bil)_4': 4, '18.9_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'assets 2013 (bil)_4': 'assets 2013 ( bil )', '18.9_5': '18.9'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'assets 2013 (bil)_4': [0], '18.9_5': [1]}

['april 2013 cum rank', 'name', 'rank 2012', 'rank 2013', 'base', '2013 rev ( bil usd )', '2013 profit ( mil usd )', 'assets 2013 ( bil )', 'market cap march 15 ( mil )']

[['1', 'freeport - mcmoran', '235', '273', 'united states', '18.0', '3000', '35.4', '23100'], ['2', 'newmont mining', '639', '448', 'united states', '9.9', '1800', '29.6', '19700'], ['3', 'goldcorp', '507', '559', 'canada', '5.4', '1700', '31.2', '26400'], ['4', 'barrick gold', '225', '659', 'canada', '14.5', '( 700 )', '47.3', '28700'], ['5', 'newcrest mining', '735', '744', 'australia', '4.5', '1100', '20.8', '17500'], ['6', 'anglogold ashanti', '794', '936', 'south africa', '6.1', '800', '12.6', '9500'], ['7', 'yamana gold', '1219', '1279', 'canada', '2.3', '400', '11.8', '11600'], ['8', 'polyus gold', '1810', '1293', 'russia', '2.8', '900', '5.6', '9800'], ['9', 'gold fields', '968', '1435', 'south africa', '3.4', '700', '11.2', '5900'], ['10', 'kinross gold', '1384', '1551', 'canada', '4.3', '( 2500 )', '14.9', '9100'], ['11', 'buenaventura', '1276', '1601', 'peru', '1.5', '700', '4.5', '6300'], ['12', 'shandong gold - mining', '1980', '1613', 'china', '6.3', '300', '2.0', '7600']]

list of mountains in norway by prominence

https://en.wikipedia.org/wiki/List_of_mountains_in_Norway_by_prominence

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12280396-1.html.csv

ordinal

the mountain in norway with the 2nd highest elevation is snøhetta .

{'row': '3', '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', 'elevation ( m )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; elevation ( m ) ; 2 }'}, 'peak'], 'result': 'snøhetta', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak }'}, 'snøhetta'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak } ; snøhetta } = true', 'tointer': 'select the row whose elevation ( m ) record of all rows is 2nd maximum . the peak record of this row is snøhetta .'}

eq { hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak } ; snøhetta } = true

select the row whose elevation ( m ) record of all rows is 2nd maximum . the peak record of this row is snøhetta .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'elevation (m)_5': 5, '2_6': 6, 'peak_7': 7, 'snøhetta_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', 'elevation (m)_5': 'elevation ( m )', '2_6': '2', 'peak_7': 'peak', 'snøhetta_8': 'snøhetta'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'elevation (m)_5': [0], '2_6': [0], 'peak_7': [1], 'snøhetta_8': [2]}

['peak', 'elevation ( m )', 'prominence ( m )', 'isolation ( km )', 'municipality', 'county']

[['galdhøpiggen', '2469', '2372', '1570', 'lom', 'oppland'], ['jiehkkevárri', '1833', '1741', '140', 'lyngen , tromsø', 'troms'], ['snøhetta', '2286', '1675', '83', 'dovre', 'oppland'], ['store lenangstind', '1625', '1576', '47', 'lyngen', 'troms'], ['gjegnen / blånibba', '1670', '1460', '47', 'bremanger', 'sogn og fjordane'], ['hamperokken', '1404', '1396', '18', 'tromsø', 'troms'], ['skårasalen', '1542', '1385', '7', 'ørsta', 'møre og romsdal'], ['oksskolten', '1916', '1384', '185', 'hemnes', 'nordland'], ['botnafjellet', '1572', '1339', '15', 'gloppen', 'sogn og fjordane'], ['kvitegga', '1717', '1324', '23', 'stranda , ørsta', 'møre og romsdal'], ['fresvikbreen', '1660', '1310', '17', 'vik', 'sogn og fjordane'], ['smørskredtindane', '1630', '1306', '12', 'stranda , ørsta', 'møre og romsdal'], ['njunis', '1717', '1305', '53', 'målselv', 'troms'], ['store trolla', '1850', '1292', '11', 'sunndal', 'møre og romsdal'], ['langlitinden', '1276', '1276', '26', 'ibestad', 'troms'], ['indre russetind', '1527', '1268', '9', 'balsfjord', 'troms'], ['møysalen', '1262', '1262', '60', 'hinnøya', 'nordland'], ['stortind', '1320', '1242', '14', 'tromsø', 'troms'], ['folgefonna', '1660', '1233', '29', 'kvinnherad , odda', 'hordaland'], ['daurmål', '1446', '1230', '4', 'gloppen , jølster', 'sogn og fjordane']]

list of awards and nominations received by er

https://en.wikipedia.org/wiki/List_of_awards_and_nominations_received_by_ER

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18540176-11.html.csv

ordinal

the first year that er was nominated for an award in the 60 minute category was in 1996 .

{'row': '1', 'col': '1', 'order': '1', 'col_other': '1,2,5', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'year', '1'], 'result': '1996', 'ind': 0, 'tostr': 'nth_min { all_rows ; year ; 1 }', 'tointer': 'the 1st minimum year record of all rows is 1996 .'}, '1996'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; year ; 1 } ; 1996 }', 'tointer': 'the 1st minimum year record of all rows is 1996 .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; year ; 1 }'}, 'year'], 'result': '1996', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; year ; 1 } ; year }'}, '1996'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 1 } ; year } ; 1996 }', 'tointer': 'the year record of the row with 1st minimum year record is 1996 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; year ; 1 }'}, 'category'], 'result': '60 minute category', 'ind': 5, 'tostr': 'hop { nth_argmin { all_rows ; year ; 1 } ; category }'}, '60 minute category'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 1 } ; category } ; 60 minute category }', 'tointer': 'the category record of the row with 1st minimum year record is 60 minute category .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; year ; 1 }'}, 'result'], 'result': 'nominated', 'ind': 7, 'tostr': 'hop { nth_argmin { all_rows ; year ; 1 } ; result }'}, 'nominated'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 1 } ; result } ; nominated }', 'tointer': 'the result record of the row with 1st minimum year record is nominated .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; category } ; 60 minute category } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; result } ; nominated } }', 'tointer': 'the category record of the row with 1st minimum year record is 60 minute category . the result record of the row with 1st minimum year record is nominated .'}], 'result': True, 'ind': 10, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; year } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; category } ; 60 minute category } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; result } ; nominated } } }', 'tointer': 'the year record of the row with 1st minimum year record is 1996 . the category record of the row with 1st minimum year record is 60 minute category . the result record of the row with 1st minimum year record is nominated .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { nth_min { all_rows ; year ; 1 } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; year } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; category } ; 60 minute category } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; result } ; nominated } } } } = true', 'tointer': 'the 1st minimum year record of all rows is 1996 . the year record of the row with 1st minimum year record is 1996 . the category record of the row with 1st minimum year record is 60 minute category . the result record of the row with 1st minimum year record is nominated .'}

and { eq { nth_min { all_rows ; year ; 1 } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; year } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; category } ; 60 minute category } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; result } ; nominated } } } } = true

the 1st minimum year record of all rows is 1996 . the year record of the row with 1st minimum year record is 1996 . the category record of the row with 1st minimum year record is 60 minute category . the result record of the row with 1st minimum year record is nominated .

14

12

{'and_11': 11, 'result_12': 12, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_13': 13, 'year_14': 14, '1_15': 15, '1996_16': 16, 'and_10': 10, 'eq_4': 4, 'num_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_17': 17, 'year_18': 18, '1_19': 19, 'year_20': 20, '1996_21': 21, 'and_9': 9, 'str_eq_6': 6, 'str_hop_5': 5, 'category_22': 22, '60 minute category_23': 23, 'str_eq_8': 8, 'str_hop_7': 7, 'result_24': 24, 'nominated_25': 25}

{'and_11': 'and', 'result_12': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_13': 'all_rows', 'year_14': 'year', '1_15': '1', '1996_16': '1996', 'and_10': 'and', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_17': 'all_rows', 'year_18': 'year', '1_19': '1', 'year_20': 'year', '1996_21': '1996', 'and_9': 'and', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'category_22': 'category', '60 minute category_23': '60 minute category', 'str_eq_8': 'str_eq', 'str_hop_7': 'str_hop', 'result_24': 'result', 'nominated_25': 'nominated'}

{'and_11': [12], 'result_12': [], 'eq_1': [11], 'nth_min_0': [1], 'all_rows_13': [0], 'year_14': [0], '1_15': [0], '1996_16': [1], 'and_10': [11], 'eq_4': [10], 'num_hop_3': [4], 'nth_argmin_2': [3, 5, 7], 'all_rows_17': [2], 'year_18': [2], '1_19': [2], 'year_20': [3], '1996_21': [4], 'and_9': [10], 'str_eq_6': [9], 'str_hop_5': [6], 'category_22': [5], '60 minute category_23': [6], 'str_eq_8': [9], 'str_hop_7': [8], 'result_24': [7], 'nominated_25': [8]}

['year', 'category', 'nominee ( s )', 'episode', 'result']

[['1996', '60 minute category', 'john wells', 'the healers', 'nominated'], ['1998', '60 minute category', 'carol flint', 'family practice', 'nominated'], ['2001', '60 minute category', 'john wells', 'a walk in the woods', 'nominated'], ['2003', '60 minute category', 'john wells', 'on the beach', 'nominated'], ['2004', '60 minute category', 'john wells', 'makemba', 'nominated'], ['2005', '60 minute category', 'dee johnson', 'alone in a crowd', 'nominated'], ['2006', '60 minute category', 'janine sherman', 'darfur', 'nominated'], ['2007', '60 minute category', 'r scott gemmill , david zabel', 'there are no angels here', 'won']]

list of teachers ( uk tv series ) episodes

https://en.wikipedia.org/wiki/List_of_Teachers_%28UK_TV_series%29_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18335117-5.html.csv

comparative

for the uk tv series teachers , episode 4 aired seven days before episode 5 .

{'row_1': '4', 'row_2': '5', 'col': '6', 'col_other': '3', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '7', 'bigger': 'row2'}}

{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'episode 4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to episode 4 .', 'tostr': 'filter_eq { all_rows ; title ; episode 4 }'}, 'original air date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; episode 4 } ; original air date }', 'tointer': 'select the rows whose title record fuzzily matches to episode 4 . take the original air date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'episode 5'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to episode 5 .', 'tostr': 'filter_eq { all_rows ; title ; episode 5 }'}, 'original air date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; episode 5 } ; original air date }', 'tointer': 'select the rows whose title record fuzzily matches to episode 5 . take the original air date record of this row .'}], 'result': '-7', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; title ; episode 4 } ; original air date } ; hop { filter_eq { all_rows ; title ; episode 5 } ; original air date } }'}, '-7'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; title ; episode 4 } ; original air date } ; hop { filter_eq { all_rows ; title ; episode 5 } ; original air date } } ; -7 } = true', 'tointer': 'select the rows whose title record fuzzily matches to episode 4 . take the original air date record of this row . select the rows whose title record fuzzily matches to episode 5 . take the original air date record of this row . the second record is 7 larger than the first record .'}

eq { diff { hop { filter_eq { all_rows ; title ; episode 4 } ; original air date } ; hop { filter_eq { all_rows ; title ; episode 5 } ; original air date } } ; -7 } = true

select the rows whose title record fuzzily matches to episode 4 . take the original air date record of this row . select the rows whose title record fuzzily matches to episode 5 . take the original air date record of this row . the second record is 7 larger than the first record .

6

6

{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'title_8': 8, 'episode 4_9': 9, 'original air date_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'title_12': 12, 'episode 5_13': 13, 'original air date_14': 14, '-7_15': 15}

{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'title_8': 'title', 'episode 4_9': 'episode 4', 'original air date_10': 'original air date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'title_12': 'title', 'episode 5_13': 'episode 5', 'original air date_14': 'original air date', '-7_15': '-7'}

{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'title_8': [0], 'episode 4_9': [0], 'original air date_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'title_12': [1], 'episode 5_13': [1], 'original air date_14': [3], '-7_15': [5]}

['no overall', 'no in series', 'title', 'director', 'writer', 'original air date', 'production code']

[['32', '1', 'episode 1', 'barnaby southcomb', 'richard stoneman', '26 october 2004', '401'], ['33', '2', 'episode 2', 'barnaby southcomb', 'ed roe', '3 november 2004', '402'], ['34', '3', 'episode 3', 'barnaby southcomb', 'charlie martin', '10 november 2004', '403'], ['35', '4', 'episode 4', 'sean grundy', 'linton chiswick', '17 november 2004', '404'], ['36', '5', 'episode 5', 'sean grundy', 'jack lothian', '24 november 2004', '405'], ['37', '6', 'episode 6', 'sean grundy', 'tony basgallop', '1 december 2004', '406'], ['38', '7', 'episode 7', 'iain b macdonald', 'charlie martin', '8 december 2004', '407'], ['39', '8', 'episode 8', 'iain b macdonald', 'richard stoneman', '15 december 2004', '408']]

northern pacific railway locomotives

https://en.wikipedia.org/wiki/Northern_Pacific_Railway_locomotives

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18620528-14.html.csv

comparative

the northern pacific railway locomotive 's class a - 4 and class a-3 had the same amount of quantity made .

{'row_1': '6', 'row_2': '5', 'col': '7', 'col_other': '1', 'relation': 'equal', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'a - 4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to a - 4 .', 'tostr': 'filter_eq { all_rows ; class ; a - 4 }'}, 'quantity made'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; class ; a - 4 } ; quantity made }', 'tointer': 'select the rows whose class record fuzzily matches to a - 4 . take the quantity made record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'a - 3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose class record fuzzily matches to a - 3 .', 'tostr': 'filter_eq { all_rows ; class ; a - 3 }'}, 'quantity made'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; class ; a - 3 } ; quantity made }', 'tointer': 'select the rows whose class record fuzzily matches to a - 3 . take the quantity made record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; class ; a - 4 } ; quantity made } ; hop { filter_eq { all_rows ; class ; a - 3 } ; quantity made } } = true', 'tointer': 'select the rows whose class record fuzzily matches to a - 4 . take the quantity made record of this row . select the rows whose class record fuzzily matches to a - 3 . take the quantity made record of this row . the first record is equal to the second record .'}

eq { hop { filter_eq { all_rows ; class ; a - 4 } ; quantity made } ; hop { filter_eq { all_rows ; class ; a - 3 } ; quantity made } } = true

select the rows whose class record fuzzily matches to a - 4 . take the quantity made record of this row . select the rows whose class record fuzzily matches to a - 3 . take the quantity made record of this row . the first record is equal to the second record .

5

5

{'eq_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'class_7': 7, 'a - 4_8': 8, 'quantity made_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'class_11': 11, 'a - 3_12': 12, 'quantity made_13': 13}

{'eq_4': 'eq', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'class_7': 'class', 'a - 4_8': 'a - 4', 'quantity made_9': 'quantity made', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'class_11': 'class', 'a - 3_12': 'a - 3', 'quantity made_13': 'quantity made'}

{'eq_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'class_7': [0], 'a - 4_8': [0], 'quantity made_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'class_11': [1], 'a - 3_12': [1], 'quantity made_13': [3]}

['class', 'wheel arrangement', 'fleet number ( s )', 'manufacturer', 'serial numbers', 'year made', 'quantity made', 'quantity preserved', 'year ( s ) retired']

[['4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern', '4 - 8 - 4 - oooooooo - northern'], ['a', '4 - 8 - 4', '2600 - 2611', 'alco', '67010 - 67021', '1926', '12', '0', '1949 - 1959'], ['a - 1', '4 - 8 - 4', '2626', 'alco', '68056', '1930', '1', '0', '1955'], ['a - 2', '4 - 8 - 4', '2650 - 2659', 'baldwin', '61771 - 61780', '1934 - 1935', '10', '0', '1953 - 1958'], ['a - 3', '4 - 8 - 4', '2660 - 2667', 'baldwin', '62163 - 62170', '1938', '8', '0', '1954 - 1958'], ['a - 4', '4 - 8 - 4', '2670 - 2677', 'baldwin', '64155 - 64162', '1941', '8', '0', '1954 - 1958'], ['a - 5', '4 - 8 - 4', '2680 - 2689', 'baldwin', '64667 - 64676', '1943', '10', '0', '1957 - 1959']]

2009 - 10 fis ski jumping world cup

https://en.wikipedia.org/wiki/2009%E2%80%9310_FIS_Ski_Jumping_World_Cup

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24489942-10.html.csv

unique

andreas kofler is the only jumper to score less than 260 points .

{'scope': 'all', 'row': '5', 'col': '6', 'col_other': '2', 'criterion': 'less_than', 'value': '260', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'points', '260'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is less than 260 .', 'tostr': 'filter_less { all_rows ; points ; 260 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; points ; 260 } }', 'tointer': 'select the rows whose points record is less than 260 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'points', '260'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is less than 260 .', 'tostr': 'filter_less { all_rows ; points ; 260 }'}, 'name'], 'result': 'andreas kofler', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; points ; 260 } ; name }'}, 'andreas kofler'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; points ; 260 } ; name } ; andreas kofler }', 'tointer': 'the name record of this unqiue row is andreas kofler .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; points ; 260 } } ; eq { hop { filter_less { all_rows ; points ; 260 } ; name } ; andreas kofler } } = true', 'tointer': 'select the rows whose points record is less than 260 . there is only one such row in the table . the name record of this unqiue row is andreas kofler .'}

and { only { filter_less { all_rows ; points ; 260 } } ; eq { hop { filter_less { all_rows ; points ; 260 } ; name } ; andreas kofler } } = true

select the rows whose points record is less than 260 . there is only one such row in the table . the name record of this unqiue row is andreas kofler .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'points_7': 7, '260_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'andreas kofler_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'points_7': 'points', '260_8': '260', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'andreas kofler_10': 'andreas kofler'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'points_7': [0], '260_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'andreas kofler_10': [3]}

['rank', 'name', 'nationality', '1st ( m )', '2nd ( m )', 'points', 'overall fht points', 'overall wc points ( rank )']

[['1', 'thomas morgenstern', 'austria', '133.0', '136.0', '264.7', '987.1 ( 6 )', '440 ( 4 )'], ['2', 'janne ahonen', 'finland', '134.0', '133.5', '264.0', '1013.9 ( 2 )', '350 ( 7 )'], ['3', 'simon ammann', 'switzerland', '136.0', '131.5', '261.5', '1008.3 ( 5 )', '669 ( 1 )'], ['4', 'wolfgang loitzl', 'austria', '130.5', '135.0', '260.9', '1011.6 ( 3 )', '411 ( 5 )'], ['5', 'andreas kofler', 'austria', '129.0', '133.5', '255.0', '1027.2 ( 1 )', '521 ( 3 )']]

1938 vfl season

https://en.wikipedia.org/wiki/1938_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10806592-9.html.csv

count

there was a total of 6 venues during the 1938 vfl season .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'venue'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record is arbitrary .', 'tostr': 'filter_all { all_rows ; venue }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; venue } }', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; venue } } ; 6 } = true', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}

eq { count { filter_all { all_rows ; venue } } ; 6 } = true

select the rows whose venue record is arbitrary . the number of such rows is 6 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'venue_5': 5, '6_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'venue_5': 'venue', '6_6': '6'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'venue_5': [0], '6_6': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['geelong', '11.23 ( 89 )', 'hawthorn', '6.13 ( 49 )', 'corio oval', '7000', '18 june 1938'], ['fitzroy', '16.12 ( 108 )', 'south melbourne', '8.8 ( 56 )', 'brunswick street oval', '12000', '18 june 1938'], ['st kilda', '14.12 ( 96 )', 'melbourne', '16.16 ( 112 )', 'junction oval', '14000', '18 june 1938'], ['richmond', '15.14 ( 104 )', 'essendon', '15.9 ( 99 )', 'punt road oval', '20000', '18 june 1938'], ['footscray', '13.9 ( 87 )', 'collingwood', '10.5 ( 65 )', 'western oval', '18000', '18 june 1938'], ['north melbourne', '11.5 ( 71 )', 'carlton', '16.25 ( 121 )', 'arden street oval', '13000', '18 june 1938']]

2005 jeux de la francophonie

https://en.wikipedia.org/wiki/2005_Jeux_de_la_Francophonie

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12402019-5.html.csv

majority

the majority of nations won 0 gold medals at the 2005 jeux de la francophonie .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None}

{'func': 'most_eq', 'args': ['all_rows', 'gold', '0'], 'result': True, 'ind': 0, 'tointer': 'for the gold records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; gold ; 0 } = true'}

most_eq { all_rows ; gold ; 0 } = true

for the gold records of all rows , most of them are equal to 0 .

1

1

{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'gold_3': 3, '0_4': 4}

{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'gold_3': 'gold', '0_4': '0'}

{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'gold_3': [0], '0_4': [0]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'lebanon', '2', '1', '0', '3'], ['2', 'french community of belgium', '1', '0', '1', '2'], ['3', 'benin', '1', '0', '0', '1'], ['3', 'canada', '1', '0', '0', '1'], ['3', 'lithuania', '1', '0', '0', '1'], ['3', 'madagascar', '1', '0', '0', '1'], ['7', 'france', '0', '1', '1', '2'], ['7', 'niger', '0', '1', '1', '2'], ['9', 'new brunswick', '0', '1', '0', '1'], ['9', 'quebec', '0', '1', '0', '1'], ['9', 'cape verde', '0', '1', '0', '1'], ['9', 'morocco', '0', '1', '0', '1'], ['13', 'burkina faso', '0', '0', '1', '1'], ['13', 'republic of the congo', '0', '0', '1', '1'], ['13', 'ivory coast', '0', '0', '1', '1'], ['13', 'macedonia', '0', '0', '1', '1']]

list of ottawa senators draft picks

https://en.wikipedia.org/wiki/List_of_Ottawa_Senators_draft_picks

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11803648-7.html.csv

count

five of the players that the ottawa senators drafted were from canada .

{'scope': 'all', 'criterion': 'equal', 'value': 'canada', 'result': '5', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'canada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to canada .', 'tostr': 'filter_eq { all_rows ; nationality ; canada }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; canada } }', 'tointer': 'select the rows whose nationality record fuzzily matches to canada . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; canada } } ; 5 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to canada . the number of such rows is 5 .'}

eq { count { filter_eq { all_rows ; nationality ; canada } } ; 5 } = true

select the rows whose nationality record fuzzily matches to canada . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nationality_5': 5, 'canada_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nationality_5': 'nationality', 'canada_6': 'canada', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'canada_6': [0], '5_7': [2]}

['round', 'overall', 'player', 'nationality', 'club team']

[['1', '15', 'mathieu chouinard', 'canada', 'shawinigan cataractes ( qmjhl )'], ['2', '44', 'mike fisher', 'canada', 'sudbury wolves ( ohl )'], ['2', '58', 'chris bala', 'united states', 'harvard university ( ncaa )'], ['3', '74', 'julien vauclair', 'switzerland', 'lugano ( switzerland )'], ['4', '101', 'petr schastlivy', 'russia', 'yaroslavl torpedo ( russia )'], ['5', '130', 'gavin mcleod', 'canada', 'kelowna rockets ( whl )'], ['6', '161', 'chris neil', 'canada', 'north bay centennials ( ohl )'], ['7', '188', 'michel periard', 'canada', 'shawinigan cataractes ( qmjhl )'], ['8', '223', 'sergei verenkin', 'russia', 'yaroslavl torpedo ( russia )'], ['9', '246', 'rastislav pavlikovsky', 'slovakia', 'utah grizzlies ( ihl )']]

1947 vfl season

https://en.wikipedia.org/wiki/1947_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809444-2.html.csv

count

there are 6 games that took place on april 36 , 1947 in the vfl .

{'scope': 'all', 'criterion': 'equal', 'value': '26 april 1947', 'result': '6', 'col': '7', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '26 april 1947'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 26 april 1947 .', 'tostr': 'filter_eq { all_rows ; date ; 26 april 1947 }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date ; 26 april 1947 } }', 'tointer': 'select the rows whose date record fuzzily matches to 26 april 1947 . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date ; 26 april 1947 } } ; 6 } = true', 'tointer': 'select the rows whose date record fuzzily matches to 26 april 1947 . the number of such rows is 6 .'}

eq { count { filter_eq { all_rows ; date ; 26 april 1947 } } ; 6 } = true

select the rows whose date record fuzzily matches to 26 april 1947 . the number of such rows is 6 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, '26 april 1947_6': 6, '6_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', '26 april 1947_6': '26 april 1947', '6_7': '6'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], '26 april 1947_6': [0], '6_7': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['north melbourne', '8.11 ( 59 )', 'st kilda', '8.13 ( 61 )', 'arden street oval', '8000', '26 april 1947'], ['fitzroy', '13.22 ( 100 )', 'richmond', '11.11 ( 77 )', 'brunswick street oval', '22000', '26 april 1947'], ['melbourne', '14.25 ( 109 )', 'geelong', '11.7 ( 73 )', 'mcg', '12000', '26 april 1947'], ['footscray', '15.13 ( 103 )', 'essendon', '13.11 ( 89 )', 'western oval', '22000', '26 april 1947'], ['hawthorn', '13.9 ( 87 )', 'collingwood', '19.20 ( 134 )', 'glenferrie oval', '15000', '26 april 1947'], ['south melbourne', '12.12 ( 84 )', 'carlton', '9.16 ( 70 )', 'lake oval', '30000', '26 april 1947']]

tiburones rojos de veracruz

https://en.wikipedia.org/wiki/Tiburones_Rojos_de_Veracruz

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1193316-2.html.csv

unique

2001-02 was the only season in which the tiburones rojos were champions of the playoffs 1 .

{'scope': 'all', 'row': '1', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'champions', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'playoffs 1', 'champions'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose playoffs 1 record fuzzily matches to champions .', 'tostr': 'filter_eq { all_rows ; playoffs 1 ; champions }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; playoffs 1 ; champions } }', 'tointer': 'select the rows whose playoffs 1 record fuzzily matches to champions . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'playoffs 1', 'champions'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose playoffs 1 record fuzzily matches to champions .', 'tostr': 'filter_eq { all_rows ; playoffs 1 ; champions }'}, 'season'], 'result': '2001 - 02', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; playoffs 1 ; champions } ; season }'}, '2001 - 02'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; playoffs 1 ; champions } ; season } ; 2001 - 02 }', 'tointer': 'the season record of this unqiue row is 2001 - 02 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; playoffs 1 ; champions } } ; eq { hop { filter_eq { all_rows ; playoffs 1 ; champions } ; season } ; 2001 - 02 } } = true', 'tointer': 'select the rows whose playoffs 1 record fuzzily matches to champions . there is only one such row in the table . the season record of this unqiue row is 2001 - 02 .'}

and { only { filter_eq { all_rows ; playoffs 1 ; champions } } ; eq { hop { filter_eq { all_rows ; playoffs 1 ; champions } ; season } ; 2001 - 02 } } = true

select the rows whose playoffs 1 record fuzzily matches to champions . there is only one such row in the table . the season record of this unqiue row is 2001 - 02 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'playoffs 1_7': 7, 'champions_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'season_9': 9, '2001 - 02_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'playoffs 1_7': 'playoffs 1', 'champions_8': 'champions', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'season_9': 'season', '2001 - 02_10': '2001 - 02'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'playoffs 1_7': [0], 'champions_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'season_9': [2], '2001 - 02_10': [3]}

['season', 'pyramid level', 'regular season 1', 'playoffs 1', 'regular season 2', 'playoffs 2', 'copa mãxico', 'concacaf']

[['2001 - 02', '2 and 1', '4th', 'champions', '11th', 'did not qualify', 'no longer played', 'did not qualify'], ['2002 - 03', '1', '18th', 'did not qualify', '7th', 'quarterfinals', 'no longer played', 'did not qualify'], ['2003 - 04', '1', '12th', 'did not qualify', '20th', 'did not qualify', 'no longer played', 'did not qualify'], ['2004 - 05', '1', '1st', 'quarterfinals', '17th', 'did not qualify', 'no longer played', 'did not qualify'], ['2005 - 06', '1', '18th', 'did not qualify', '16th', 'did not qualify', 'no longer played', 'did not qualify'], ['2006 - 07', '1', '9th', 'repechaje', '18th', 'did not qualify', 'no longer played', 'did not qualify'], ['2007 - 08', '1', '13th', 'did not qualify', '16th', 'did not qualify', 'no longer played', 'did not qualify'], ['2008 - 09', '2', '11th', 'did not qualify', '3rd', 'semifinal', 'no longer played', 'did not qualify'], ['2009 - 10', '2', '4th', 'semifinal', '15th', 'did not qualify', 'no longer played', 'did not qualify'], ['2010 - 11', '2', '5th', 'second place', '5th', 'disqualified', 'no longer played', 'did not qualify'], ['2011 - 12', '2', '8th', 'did not qualify', '13th', 'did not qualify', 'no longer played', 'did not qualify'], ['2012 - 13', '2', '12th', 'did not qualify', '4th', 'quarterfinals', '4th ( dnq )', 'did not qualify']]

cjbc ( am )

https://en.wikipedia.org/wiki/CJBC_%28AM%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1873304-1.html.csv

count

four of the cjbc radio channels are of the b class .

{'scope': 'all', 'criterion': 'equal', 'value': 'b', 'result': '4', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'b'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to b .', 'tostr': 'filter_eq { all_rows ; class ; b }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; class ; b } }', 'tointer': 'select the rows whose class record fuzzily matches to b . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; class ; b } } ; 4 } = true', 'tointer': 'select the rows whose class record fuzzily matches to b . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; class ; b } } ; 4 } = true

select the rows whose class record fuzzily matches to b . 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, 'class_5': 5, 'b_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', 'class_5': 'class', 'b_6': 'b', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'class_5': [0], 'b_6': [0], '4_7': [2]}

['city of license', 'identifier', 'frequency', 'power', 'class', 'recnet']

[['belleville', 'cjbc - 1 - fm', '94.3 fm', '34950 s watt', 'b', 'query'], ['kingston', 'cjbc - 2 - fm', '99.5 fm', '1560 watts', 'a', 'query'], ['london', 'cjbc - 4 - fm', '99.3 fm', '22500 watts', 'b', 'query'], ['penetanguishene', 'cjbc - 3 - fm', '96.5 fm', '15300 watts', 'b', 'query'], ['peterborough', 'cjbc - 5 - fm', '106.3 fm', '13000 watts', 'b', 'query']]

the church of jesus christ of latter - day saints in arkansas

https://en.wikipedia.org/wiki/The_Church_of_Jesus_Christ_of_Latter-day_Saints_in_Arkansas

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15978776-2.html.csv

ordinal

springdale arkansas stake is the stake of the church of jesus christ of latter - day saints in arkansas that has the third highest wards/branches .

{'row': '5', 'col': '3', 'order': '3', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'wards / branches in arkansas', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; wards / branches in arkansas ; 3 }'}, 'stake'], 'result': 'springdale arkansas stake', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; wards / branches in arkansas ; 3 } ; stake }'}, 'springdale arkansas stake'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; wards / branches in arkansas ; 3 } ; stake } ; springdale arkansas stake } = true', 'tointer': 'select the row whose wards / branches in arkansas record of all rows is 3rd maximum . the stake record of this row is springdale arkansas stake .'}

eq { hop { nth_argmax { all_rows ; wards / branches in arkansas ; 3 } ; stake } ; springdale arkansas stake } = true

select the row whose wards / branches in arkansas record of all rows is 3rd maximum . the stake record of this row is springdale arkansas stake .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'wards / branches in arkansas_5': 5, '3_6': 6, 'stake_7': 7, 'springdale arkansas stake_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', 'wards / branches in arkansas_5': 'wards / branches in arkansas', '3_6': '3', 'stake_7': 'stake', 'springdale arkansas stake_8': 'springdale arkansas stake'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'wards / branches in arkansas_5': [0], '3_6': [0], 'stake_7': [1], 'springdale arkansas stake_8': [2]}

['stake', 'organized', 'wards / branches in arkansas', 'stake president', 'occupation']

[['fort smith arkansas', 'april 30 , 1978', '5', 'glenn richard titsworth', 'realtor for american equity realty'], ['little rock arkansas', 'june 1 , 1969', '11', 'michael v beheshti', 'interventional radiologist at uams'], ['north little rock arkansas', 'june 19 , 1983', '16', 'bruce kevin berkheimer', 'podiatrist'], ['rogers arkansas stake', 'august 11 , 1991', '7', 'david owen stout', 'senior buyer for wal - mart'], ['springdale arkansas stake', 'june 4 , 2006', '10', 'thomas hal bradford', 'physician']]

2008 bdo world darts championship

https://en.wikipedia.org/wiki/2008_BDO_World_Darts_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13535824-2.html.csv

majority

most of the players in the 2008 bdo world darts championship had 2 sets lost .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '2', 'subset': None}

{'func': 'most_eq', 'args': ['all_rows', 'sets lost', '2'], 'result': True, 'ind': 0, 'tointer': 'for the sets lost records of all rows , most of them are equal to 2 .', 'tostr': 'most_eq { all_rows ; sets lost ; 2 } = true'}

most_eq { all_rows ; sets lost ; 2 } = true

for the sets lost records of all rows , most of them are equal to 2 .

1

1

{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'sets lost_3': 3, '2_4': 4}

{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'sets lost_3': 'sets lost', '2_4': '2'}

{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'sets lost_3': [0], '2_4': [0]}

['player', 'played', 'sets won', 'sets lost', 'legs won', 'legs lost', '100 +', '140 +', '180s', 'high checkout', '3 - dart average']

[['anastasia dobromyslova', '3', '6', '0', '18', '4', '32', '16', '1', '94', '79.07'], ['dee bateman', '1', '0', '2', '2', '6', '9', '2', '0', '40', '69.72'], ['francis hoenselaar', '1', '0', '2', '1', '6', '6', '2', '1', '40', '53.19'], ['stephanie smee', '2', '2', '2', '6', '7', '21', '4', '0', '101', '65.36'], ['karin krappen', '2', '2', '3', '8', '11', '22', '7', '2', '116', '67.01'], ['rilana erades', '1', '1', '2', '5', '6', '7', '4', '1', '40', '64.80'], ['trina gulliver', '3', '4', '3', '16', '11', '27', '15', '4', '103', '75.02']]

2007 colorado crush season

https://en.wikipedia.org/wiki/2007_Colorado_Crush_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11785718-5.html.csv

aggregation

in the 2007 colorado crush season the total yardage achieved by players with less than 50 receptions was 37 yards .

{'scope': 'subset', 'col': '3', 'type': 'sum', 'result': '37', 'subset': {'col': '2', 'criterion': 'less_than', 'value': '50'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'rec', '50'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; rec ; 50 }', 'tointer': 'select the rows whose rec record is less than 50 .'}, 'yards'], 'result': '37', 'ind': 1, 'tostr': 'sum { filter_less { all_rows ; rec ; 50 } ; yards }'}, '37'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_less { all_rows ; rec ; 50 } ; yards } ; 37 } = true', 'tointer': 'select the rows whose rec record is less than 50 . the sum of the yards record of these rows is 37 .'}

round_eq { sum { filter_less { all_rows ; rec ; 50 } ; yards } ; 37 } = true

select the rows whose rec record is less than 50 . the sum of the yards record of these rows is 37 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'rec_5': 5, '50_6': 6, 'yards_7': 7, '37_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'rec_5': 'rec', '50_6': '50', 'yards_7': 'yards', '37_8': '37'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'rec_5': [0], '50_6': [0], 'yards_7': [1], '37_8': [2]}

['player', 'rec', 'yards', 'avg', "td 's", 'long']

[['damian harrell', '132', '1537', '11.7', '47', '45'], ['brad pyatt', '95', '1169', '12.3', '19', '46'], ['willie quinnie', '72', '844', '11.7', '14', '42'], ['robert redd', '62', '547', '8.8', '5', '34'], ['alonzo nix', '50', '509', '10.2', '3', '34'], ['robert thomas', '2', '18', '9', '0', '19'], ['john peaua', '2', '11', '5.5', '1', '9'], ['anthony dunn', '1', '7', '7', '0', '7'], ['chris watton', '1', '1', '1', '0', '1'], ['brandon kirsch', '1', '0', '0', '0', '0']]

1968 vfl season

https://en.wikipedia.org/wiki/1968_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10808933-1.html.csv

aggregation

the average crowd size during the 1968 vfl season was 25,185 .

{'scope': 'all', 'col': '6', 'type': 'average', 'result': '25185', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '25185', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '25185'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 25185 } = true', 'tointer': 'the average of the crowd record of all rows is 25185 .'}

round_eq { avg { all_rows ; crowd } ; 25185 } = true

the average of the crowd record of all rows is 25185 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '25185_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '25185_5': '25185'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '25185_5': [1]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['footscray', '9.18 ( 72 )', 'north melbourne', '13.10 ( 88 )', 'western oval', '18269', '13 april 1968'], ['essendon', '26.16 ( 172 )', 'hawthorn', '15.9 ( 99 )', 'windy hill', '17000', '13 april 1968'], ['collingwood', '10.12 ( 72 )', 'richmond', '11.22 ( 88 )', 'victoria park', '37018', '13 april 1968'], ['carlton', '13.22 ( 100 )', 'geelong', '7.12 ( 54 )', 'princes park', '30158', '13 april 1968'], ['south melbourne', '18.17 ( 125 )', 'st kilda', '13.22 ( 100 )', 'lake oval', '23675', '13 april 1968'], ['melbourne', '14.10 ( 94 )', 'fitzroy', '12.18 ( 90 )', 'mcg', '24991', '13 april 1968']]

2007 saskatchewan roughriders season

https://en.wikipedia.org/wiki/2007_Saskatchewan_Roughriders_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16945617-4.html.csv

unique

week 9 was the only week of the 2007 saskatchewan roughriders season in which a game was not played .

{'scope': 'all', 'row': '9', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '-', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'date', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record is equal to - .', 'tostr': 'filter_eq { all_rows ; date ; - }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; date ; - } }', 'tointer': 'select the rows whose date record is equal to - . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'date', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record is equal to - .', 'tostr': 'filter_eq { all_rows ; date ; - }'}, 'week'], 'result': '9', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; - } ; week }'}, '9'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 9 }', 'tointer': 'the week record of this unqiue row is 9 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 9 } } = true', 'tointer': 'select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 9 .'}

and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 9 } } = true

select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 9 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, '-_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '9_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', '-_8': '-', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '9_10': '9'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'date_7': [0], '-_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '9_10': [3]}

['week', 'date', 'opponent', 'score', 'result', 'attendance', 'record']

[['1', 'fri , june 29', 'montreal alouettes', '16 - 7', 'win', '20202', '1 - 0'], ['2', 'sun , july 8', 'calgary stampeders', '49 - 8', 'win', '25862', '2 - 0'], ['3', 'fri , july 13', 'bc lions', '42 - 12', 'loss', '26981', '2 - 1'], ['4', 'fri , july 20', 'edmonton eskimos', '21 - 20', 'loss', '46704', '2 - 2'], ['5', 'sat , july 28', 'edmonton eskimos', '54 - 14', 'win', '26840', '3 - 2'], ['6', 'thurs , aug 2', 'bc lions', '21 - 9', 'win', '31858', '4 - 2'], ['7', 'fri , aug 10', 'toronto argonauts', '24 - 13', 'win', '34234', '5 - 2'], ['8', 'sat , aug 18', 'edmonton eskimos', '39 - 32', 'win', '28800', '6 - 2'], ['9', '-', '-', '-', '-', '-', ''], ['10', 'sun , sept 2', 'winnipeg blue bombers', '31 - 26', 'win', '28800', '7 - 2'], ['11', 'sun , sept 9', 'winnipeg blue bombers', '34 - 15', 'loss', '29783', '7 - 3'], ['12', 'sat , sept 15', 'calgary stampeders', '44 - 22', 'loss', '35650', '7 - 4'], ['13', 'sat , sept 22', 'bc lions', '37 - 34', 'loss', '28800', '7 - 5'], ['14', 'sat , sept 29', 'montreal alouettes', '33 - 22', 'win', '28800', '8 - 5'], ['15', 'mon , oct 8', 'calgary stampeders', '33 - 21', 'win', '33075', '9 - 5'], ['16', 'sun , oct 14', 'hamilton tiger cats', '40 - 23', 'win', '22167', '10 - 5'], ['17', 'sun , oct 21', 'hamilton tiger cats', '38 - 11', 'win', '28800', '11 - 5'], ['18', 'fri , oct 26', 'edmonton eskimos', '36 - 29 ( ot )', 'win', '40127', '12 - 5'], ['19', 'sat , nov 3', 'toronto argonauts', '41 - 13', 'loss', '28800', '12 - 6']]

media in bismarck - mandan

https://en.wikipedia.org/wiki/Media_in_Bismarck-Mandan

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14623167-1.html.csv

superlative

kndx has the highest virtual channel number for tv stations in bismarck .

{'scope': 'all', 'col_superlative': '1', 'row_superlative': '5', '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', 'virtual'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; virtual }'}, 'call sign'], 'result': 'kndx', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; virtual } ; call sign }'}, 'kndx'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; virtual } ; call sign } ; kndx } = true', 'tointer': 'select the row whose virtual record of all rows is maximum . the call sign record of this row is kndx .'}

eq { hop { argmax { all_rows ; virtual } ; call sign } ; kndx } = true

select the row whose virtual record of all rows is maximum . the call sign record of this row is kndx .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'virtual_5': 5, 'call sign_6': 6, 'kndx_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'virtual_5': 'virtual', 'call sign_6': 'call sign', 'kndx_7': 'kndx'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'virtual_5': [0], 'call sign_6': [1], 'kndx_7': [2]}

['virtual', 'physical', 'call sign', 'branding', 'network', 'owner']

[['3', '22', 'kbme - tv', 'prairie public', 'pbs', 'prairie public broadcasting'], ['5', '31', 'kfyr - tv', 'kfyr - tv nbc north dakota', 'nbc', 'hoak media corporation'], ['12', '12', 'kxmb - tv', 'kxmb cbs 12 kx television', 'cbs', 'reiten broadcasting'], ['17', '17', 'kbmy', 'kbmy 17', 'abc', 'forum communications'], ['26', '26', 'kndx', 'fox 26', 'fox', 'prime cities broadcasting']]

2010 - 11 los angeles clippers season

https://en.wikipedia.org/wiki/2010%E2%80%9311_Los_Angeles_Clippers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27756572-10.html.csv

aggregation

in the 2010 - 11 los angeles clippers season , when blake griffin had at least a share of the high assists , his average number of assists was 9 .

{'scope': 'subset', 'col': '7', 'type': 'average', 'result': '9', 'subset': {'col': '7', 'criterion': 'fuzzily_match', 'value': 'blake griffin'}}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high assists', 'blake griffin'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high assists ; blake griffin }', 'tointer': 'select the rows whose high assists record fuzzily matches to blake griffin .'}, 'high assists'], 'result': '9', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; high assists ; blake griffin } ; high assists }'}, '9'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; high assists ; blake griffin } ; high assists } ; 9 } = true', 'tointer': 'select the rows whose high assists record fuzzily matches to blake griffin . the average of the high assists record of these rows is 9 .'}

round_eq { avg { filter_eq { all_rows ; high assists ; blake griffin } ; high assists } ; 9 } = true

select the rows whose high assists record fuzzily matches to blake griffin . the average of the high assists record of these rows is 9 .

3

3

{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high assists_5': 5, 'blake griffin_6': 6, 'high assists_7': 7, '9_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high assists_5': 'high assists', 'blake griffin_6': 'blake griffin', 'high assists_7': 'high assists', '9_8': '9'}

{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high assists_5': [0], 'blake griffin_6': [0], 'high assists_7': [1], '9_8': [2]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['62', 'march 2', 'houston', 'w 106 - 103 ( ot )', 'eric gordon ( 24 )', 'deandre jordan ( 16 )', 'mo williams ( 11 )', 'staples center 19060', '22 - 40'], ['63', 'march 5', 'denver', 'w 100 - 94 ( ot )', 'eric bledsoe ( 20 )', 'blake griffin ( 12 )', 'blake griffin ( 9 )', 'staples center 19060', '23 - 40'], ['64', 'march 7', 'charlotte', 'w 92 - 87 ( ot )', 'blake griffin , mo williams ( 17 )', 'blake griffin ( 15 )', 'mo williams ( 7 )', 'time warner cable arena 16438', '24 - 40'], ['65', 'march 9', 'boston', 'w 108 - 103 ( ot )', 'mo williams ( 28 )', 'deandre jordan ( 9 )', 'randy foye ( 12 )', 'td garden 18624', '25 - 40'], ['66', 'march 11', 'new jersey', 'l 98 - 102 ( ot )', 'blake griffin , chris kaman ( 23 )', 'chris kaman ( 10 )', 'randy foye ( 7 )', 'prudential center 18711', '25 - 41'], ['67', 'march 12', 'washington', 'w 122 - 101 ( ot )', 'blake griffin ( 26 )', 'deandre jordan ( 17 )', 'eric bledsoe , mo williams ( 6 )', 'verizon center 20278', '26 - 41'], ['68', 'march 14', 'memphis', 'l 82 - 105 ( ot )', 'eric bledsoe ( 19 )', 'blake griffin ( 9 )', 'eric bledsoe ( 4 )', 'fedexforum 15989', '26 - 42'], ['69', 'march 16', 'philadelphia', 'l 94 - 104 ( ot )', 'randy foye ( 20 )', 'deandre jordan ( 15 )', 'mo williams ( 8 )', 'staples center 19060', '26 - 43'], ['70', 'march 19', 'cleveland', 'w 100 - 92 ( ot )', 'blake griffin ( 30 )', 'blake griffin ( 8 )', 'blake griffin ( 8 )', 'staples center 19060', '27 - 43'], ['71', 'march 20', 'phoenix', 'l 99 - 108 ( ot )', 'chris kaman ( 21 )', 'chris kaman ( 11 )', 'mo williams ( 7 )', 'staples center 19060', '27 - 44'], ['72', 'march 23', 'washington', 'w 127 - 119 ( 2ot )', 'blake griffin ( 33 )', 'blake griffin ( 17 )', 'blake griffin , mo williams ( 10 )', 'staples center 19060', '28 - 44'], ['73', 'march 25', 'la lakers', 'l 104 - 112 ( ot )', 'mo williams ( 30 )', 'deandre jordan ( 7 )', 'mo williams ( 6 )', 'staples center 18997', '28 - 45'], ['74', 'march 26', 'toronto', 'w 94 - 90 ( ot )', 'blake griffin ( 22 )', 'blake griffin ( 16 )', 'mo williams ( 6 )', 'staples center 19060', '29 - 45']]

list of cities , towns and villages in vojvodina

https://en.wikipedia.org/wiki/List_of_cities%2C_towns_and_villages_in_Vojvodina

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2562572-56.html.csv

majority

most of the ethnic groups in the settlements in vojvodina are serbs .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'serbs', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'largest ethnic group ( year )', 'serbs'], 'result': True, 'ind': 0, 'tointer': 'for the largest ethnic group ( year ) records of all rows , most of them fuzzily match to serbs .', 'tostr': 'most_eq { all_rows ; largest ethnic group ( year ) ; serbs } = true'}

most_eq { all_rows ; largest ethnic group ( year ) ; serbs } = true

for the largest ethnic group ( year ) records of all rows , most of them fuzzily match to serbs .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'largest ethnic group (year)_3': 3, 'serbs_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'largest ethnic group (year)_3': 'largest ethnic group ( year )', 'serbs_4': 'serbs'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'largest ethnic group (year)_3': [0], 'serbs_4': [0]}

['settlement', 'cyrillic name other names', 'type / location', 'settlement destiny', 'largest ethnic group ( year )']

[['aleksandrovo', 'александрово', 'former village in bačka', 'today neighborhood of subotica', 'serbs ( 1910 )'], ['bikač', 'бикач', 'former village in banat', 'today part of bašaid', 'serbs ( 1971 )'], ['mužlja', 'мужља ( hungarian : muzslya )', 'former village in banat', 'today neighborhood of zrenjanin', 'hungarians ( 1971 )'], ['novi vladimirovac', 'нови владимировац', 'former village in banat', 'today part of vladimirovac', 'serbs ( 1971 )'], ['tankosićevo', 'танкосићево ( slovak : tankosiťevo )', 'former village in bačka', 'today part of kisač', 'slovaks ( 1971 )']]

neuza silva

https://en.wikipedia.org/wiki/Neuza_Silva

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16893837-6.html.csv

count

four of neuza silva 's doubles finals were played on clay court surfaces .

{'scope': 'all', 'criterion': 'equal', 'value': 'clay', 'result': '4', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; clay } }', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; clay } } ; 4 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; surface ; clay } } ; 4 } = true

select the rows whose surface record fuzzily matches to clay . 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, 'surface_5': 5, 'clay_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', 'surface_5': 'surface', 'clay_6': 'clay', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'clay_6': [0], '4_7': [2]}

['date', 'tournament', 'surface', 'partner', 'opponent in the final', 'score']

[['23 june 2002', 'montemor - o - novo', 'hard', 'carlota santos', 'alberta brianti & frederica piedade', '6 - 4 , 6 - 2'], ['5 august 2002', 'pontevedra', 'hard', 'frederica piedade', 'alberta brianti & ipek şenoğlu', '6 - 2 , 4 - 6 , 6 - 2'], ['29 september 2002', 'lleida', 'clay', 'frederica piedade', 'caroline ann basu & aliénor tricerri', '6 - 7 ( 5 - 7 ) , 6 - 2 , 6 - 4'], ['25 may 2003', 'almeria', 'hard', 'ipek şenoğlu', 'romy farah & astrid waernes', '7 - 5 , 5 - 7 , 6 - 3'], ['3 august 2003', 'pontevedra', 'hard', 'gabriela velasco andreu', 'veronika litvinskaya & elena poliakova', '6 - 0 , 6 - 1'], ['16 may 2004', 'monzón', 'hard', 'larissa carvalho', 'joana cortez & marina tavares', '6 - 2 , 6 - 4'], ['29 august 2005', 'amarante', 'hard', 'joana cortez', 'flavia mignola & gabriela velasco andreu', '6 - 2 , 6 - 3'], ['11 october 2005', 'bolton', 'hard', 'daniela kix', 'veronika chvojková & claire peterzan', '6 - 0 , 6 - 2'], ['11 october 2006', 'mallorca', 'clay', 'nuria sanchez garcia', 'anja prislan & laura siegemund', '6 - 3 , 6 - 1'], ['30 january 2007', 'hull', 'hard', 'claire de gubernatis', 'danielle brown & elizabeth thomas', '6 - 7 ( 2 - 7 ) , 7 - 5 , 6 - 4'], ['25 february 2007', 'portimão', 'hard', 'nicole thijssen', 'jessica lehnhoff & robin stephenson', '6 - 4 , 6 - 2'], ['26 march 2007', 'athens', 'hard', 'nicole thijssen', 'anna koumantou & pemra özgen', '6 - 2 , 6 - 4'], ['26 may 2007', 'fuerteventura', 'carpet', 'nicole thijssen', 'mariana duque marino & roxane vaisemberg', '6 - 1 , 6 - 2'], ['9 july 2007', 'mont - de - marsan', 'clay', 'nina bratchikova', 'joana cortez & teliana pereira', '6 - 3 , 7 - 6 ( 7 - 3 )'], ['10 august 2007', 'coimbra', 'hard', 'kira nagy', 'magdalena kiszczyńska & yanina wickmayer', '6 - 3 , 3 - 6 , 7 - 5'], ['5 july 2008', 'mont - de - marsan', 'clay', 'ipek şenoğlu', 'melanie klaffner & frederica piedade', '6 - 4 , 6 - 2'], ['27 july 2008', 'la coruña', 'hard', 'nicole thijssen', 'karen emilia castiblanco duarte & paula zabala', '6 - 2 , 6 - 2'], ['3 august 2008', 'vigo', 'hard', 'nicole thijssen', 'nina bratchikova & frederica piedade', '6 - 2 , 6 - 4']]

united states house of representatives elections , 1966

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1966

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341843-15.html.csv

unique

e ross adair was the only republican incumbent in the 1966 elections .

{'scope': 'all', 'row': '3', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'republican', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to republican .', 'tostr': 'filter_eq { all_rows ; party ; republican }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; party ; republican } }', 'tointer': 'select the rows whose party record fuzzily matches to republican . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to republican .', 'tostr': 'filter_eq { all_rows ; party ; republican }'}, 'incumbent'], 'result': 'e ross adair', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; party ; republican } ; incumbent }'}, 'e ross adair'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; party ; republican } ; incumbent } ; e ross adair }', 'tointer': 'the incumbent record of this unqiue row is e ross adair .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; party ; republican } } ; eq { hop { filter_eq { all_rows ; party ; republican } ; incumbent } ; e ross adair } } = true', 'tointer': 'select the rows whose party record fuzzily matches to republican . there is only one such row in the table . the incumbent record of this unqiue row is e ross adair .'}

and { only { filter_eq { all_rows ; party ; republican } } ; eq { hop { filter_eq { all_rows ; party ; republican } ; incumbent } ; e ross adair } } = true

select the rows whose party record fuzzily matches to republican . there is only one such row in the table . the incumbent record of this unqiue row is e ross adair .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'party_7': 7, 'republican_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'e ross adair_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'party_7': 'party', 'republican_8': 'republican', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'e ross adair_10': 'e ross adair'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'party_7': [0], 'republican_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'e ross adair_10': [3]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['indiana 1', 'ray j madden', 'democratic', '1942', 're - elected', 'ray j madden ( d ) 58.3 % albert harrigan ( r ) 41.7 %'], ['indiana 3', 'john brademas', 'democratic', '1958', 're - elected', 'john brademas ( d ) 55.8 % robert a ehlers ( r ) 44.2 %'], ['indiana 4', 'e ross adair', 'republican', '1950', 're - elected', 'e ross adair ( r ) 63.5 % j byron hayes ( d ) 36.5 %'], ['indiana 5', 'j edward roush', 'democratic', '1958', 're - elected', 'j edward roush ( d ) 51.1 % kenneth bowman ( r ) 48.9 %'], ['indiana 7', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat republican gain', 'john t myers ( r ) 54.3 % elden c tipton ( d ) 45.7 %']]

kathleen horvath

https://en.wikipedia.org/wiki/Kathleen_Horvath

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17727652-3.html.csv

count

of the tournaments that kathleen horvath participated in , there were 5 that were on a clay surface .

{'scope': 'all', 'criterion': 'equal', 'value': 'clay', 'result': '5', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; clay } }', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; clay } } ; 5 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .'}

eq { count { filter_eq { all_rows ; surface ; clay } } ; 5 } = true

select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'surface_5': 5, 'clay_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'surface_5': 'surface', 'clay_6': 'clay', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'clay_6': [0], '5_7': [2]}

['outcome', 'date', 'tournament', 'surface', 'opponent', 'score']

[['winner', 'january 19 , 1981', 'montreal', 'carpet ( i )', 'candy reynolds', '6 - 4 , 7 - 6'], ['winner', 'february 28 , 1983', 'nashville', 'carpet ( i )', 'marcela skuherská', '6 - 4 , 6 - 3'], ['runner - up', 'may 16 , 1983', 'berlin', 'clay', 'chris evert - lloyd', '4 - 6 , 6 - 7 ( 1 )'], ['winner', 'november 7 , 1983', 'honolulu', 'carpet ( i )', 'carling bassett', '4 - 6 , 6 - 2 , 7 - 6 ( 1 )'], ['runner - up', 'january 23 , 1984', 'marco island', 'clay', 'bonnie gadusek', '6 - 3 , 0 - 6 , 4 - 6'], ['runner - up', 'may 14 , 1984', 'berlin', 'clay', 'claudia kohde - kilsch', '6 - 7 ( 8 ) , 1 - 6'], ['winner', 'march 4 , 1985', 'indianapolis', 'carpet ( i )', 'elise burgin', '6 - 2 , 6 - 4'], ['winner', 'march 25 , 1985', 'palm beach gardens', 'clay', 'petra delhees - jauch', '3 - 6 , 6 - 3 , 6 - 3'], ['winner', 'july 6 , 1987', 'knokke', 'clay', 'bettina bunge', '6 - 1 , 7 - 6 ( 5 )']]

badminton at the pan american games

https://en.wikipedia.org/wiki/Badminton_at_the_Pan_American_Games

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10371133-1.html.csv

count

4 nations won 0 gold medals in badminton at the pan american games .

{'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '4', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; gold ; 0 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; gold ; 0 } }', 'tointer': 'select the rows whose gold record is equal to 0 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; gold ; 0 } } ; 4 } = true', 'tointer': 'select the rows whose gold record is equal to 0 . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; gold ; 0 } } ; 4 } = true

select the rows whose gold record is equal to 0 . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'gold_5': 5, '0_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'gold_5': 'gold', '0_6': '0', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'gold_5': [0], '0_6': [0], '4_7': [2]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'canada ( can )', '16', '16', '11', '43'], ['2', 'united states ( usa )', '7', '6', '12', '25'], ['3', 'guatemala ( gua )', '1', '2', '3', '6'], ['4', 'jamaica ( jam )', '1', '0', '5', '6'], ['5', 'cuba ( cub )', '0', '1', '0', '1'], ['6', 'peru ( per )', '0', '0', '14', '14'], ['7', 'mexico ( mex )', '0', '0', '3', '3'], ['8', 'brazil ( bra )', '0', '0', '2', '2'], ['total', 'total', '25', '25', '50', '100']]

united states house of representatives elections , 1984

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1984

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341598-14.html.csv

unique

the only republican candidate who lost in the 1984 united states house of representatives elections was dan crane .

{'scope': 'subset', 'row': '10', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'lost', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'republican'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; republican }', 'tointer': 'select the rows whose party record fuzzily matches to republican .'}, 'result', 'lost'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to lost .', 'tostr': 'filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } }', 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to lost . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; republican }', 'tointer': 'select the rows whose party record fuzzily matches to republican .'}, 'result', 'lost'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to lost .', 'tostr': 'filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost }'}, 'incumbent'], 'result': 'dan crane', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } ; incumbent }'}, 'dan crane'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } ; incumbent } ; dan crane }', 'tointer': 'the incumbent record of this unqiue row is dan crane .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } ; incumbent } ; dan crane } } = true', 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the incumbent record of this unqiue row is dan crane .'}

and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; lost } ; incumbent } ; dan crane } } = true

select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the incumbent record of this unqiue row is dan crane .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'party_8': 8, 'republican_9': 9, 'result_10': 10, 'lost_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'incumbent_12': 12, 'dan crane_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'party_8': 'party', 'republican_9': 'republican', 'result_10': 'result', 'lost_11': 'lost', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'incumbent_12': 'incumbent', 'dan crane_13': 'dan crane'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'party_8': [0], 'republican_9': [0], 'result_10': [1], 'lost_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'incumbent_12': [3], 'dan crane_13': [4]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['illinois 2', 'gus savage', 'democratic', '1980', 're - elected', 'gus savage ( d ) 83.0 % dale f harman ( r ) 17.0 %'], ['illinois 3', 'marty russo', 'democratic', '1974', 're - elected', 'marty russo ( d ) 64.4 % richard d murphy ( r ) 35.6 %'], ['illinois 6', 'henry hyde', 'republican', '1974', 're - elected', 'henry hyde ( r ) 75.1 % robert h renshaw ( d ) 24.9 %'], ['illinois 7', 'cardiss collins', 'democratic', '1973', 're - elected', 'cardiss collins ( d ) 78.4 % james l bevel ( r ) 21.6 %'], ['illinois 9', 'sidney r yates', 'democratic', '1964', 're - elected', 'sidney r yates ( d ) 67.5 % herbert sohn ( r ) 32.5 %'], ['illinois 10', 'john e porter', 'republican', '1980', 're - elected', 'john e porter ( r ) 72.6 % ruth c braver ( d ) 27.4 %'], ['illinois 12', 'phil crane', 'republican', '1969', 're - elected', 'phil crane ( r ) 77.8 % edward j laflamme ( d ) 22.2 %'], ['illinois 14', 'tom corcoran', 'republican', '1976', 'retired to run for u s senate republican hold', 'john e grotberg ( r ) 62.2 % dan mcgrath ( d ) 37.8 %'], ['illinois 17', 'lane evans', 'democratic', '1982', 're - elected', 'lane evans ( d ) 56.7 % kenneth g mcmillan ( r ) 43.3 %'], ['illinois 19', 'dan crane', 'republican', '1978', 'lost re - election democratic gain', 'terry l bruce ( d ) 52.3 % dan crane ( r ) 47.7 %'], ['illinois 20', 'dick durbin', 'democratic', '1982', 're - elected', 'dick durbin ( d ) 61.3 % richard g austin ( r ) 38.7 %'], ['illinois 21', 'melvin price', 'democratic', '1944', 're - elected', 'melvin price ( d ) 60.2 % robert h gaffner ( r ) 39.8 %']]

mona - jeanette berntsen

https://en.wikipedia.org/wiki/Mona-Jeanette_Berntsen

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18615911-1.html.csv

comparative

mona - jeanette berntsen did a lyrical jazz dance in an earlier week than a jive dance .

{'row_1': '2', 'row_2': '5', 'col': '1', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'dance', 'lyrical jazz'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose dance record fuzzily matches to lyrical jazz .', 'tostr': 'filter_eq { all_rows ; dance ; lyrical jazz }'}, 'week'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; dance ; lyrical jazz } ; week }', 'tointer': 'select the rows whose dance record fuzzily matches to lyrical jazz . take the week record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'dance', 'jive'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose dance record fuzzily matches to jive .', 'tostr': 'filter_eq { all_rows ; dance ; jive }'}, 'week'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; dance ; jive } ; week }', 'tointer': 'select the rows whose dance record fuzzily matches to jive . take the week record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; dance ; lyrical jazz } ; week } ; hop { filter_eq { all_rows ; dance ; jive } ; week } } = true', 'tointer': 'select the rows whose dance record fuzzily matches to lyrical jazz . take the week record of this row . select the rows whose dance record fuzzily matches to jive . take the week record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; dance ; lyrical jazz } ; week } ; hop { filter_eq { all_rows ; dance ; jive } ; week } } = true

select the rows whose dance record fuzzily matches to lyrical jazz . take the week record of this row . select the rows whose dance record fuzzily matches to jive . take the week 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, 'dance_7': 7, 'lyrical jazz_8': 8, 'week_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'dance_11': 11, 'jive_12': 12, 'week_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', 'dance_7': 'dance', 'lyrical jazz_8': 'lyrical jazz', 'week_9': 'week', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'dance_11': 'dance', 'jive_12': 'jive', 'week_13': 'week'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'dance_7': [0], 'lyrical jazz_8': [0], 'week_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'dance_11': [1], 'jive_12': [1], 'week_13': [3]}

['week', 'partner', 'dance', 'music', 'result']

[['1', 'endre jansen', 'afro', "wan na be startin ' somethin' - michael jackson", 'safe'], ['2', 'endre jansen', 'lyrical jazz', "hangin ' by a thread - jann arden", 'safe'], ['3', 'endre jansen', 'locking', 'rock steady - aretha franklin', 'bottom 3'], ['3', 'results show solo', 'results show solo', 'ring the alarm - beyoncé knowles', 'bottom 3'], ['4', 'endre jansen', 'jive', 'bye bye-david cerra', 'safe'], ['5', 'ole petter knarvik', 'hip - hop', 'ice box - omarion', 'injured']]

1981 vfl season

https://en.wikipedia.org/wiki/1981_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823950-20.html.csv

count

there were 6 game venues used during the 1981 vfl season .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'venue'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record is arbitrary .', 'tostr': 'filter_all { all_rows ; venue }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; venue } }', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; venue } } ; 6 } = true', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}

eq { count { filter_all { all_rows ; venue } } ; 6 } = true

select the rows whose venue record is arbitrary . the number of such rows is 6 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'venue_5': 5, '6_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'venue_5': 'venue', '6_6': '6'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'venue_5': [0], '6_6': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['carlton', '15.8 ( 98 )', 'essendon', '14.15 ( 99 )', 'princes park', '36736', '15 august 1981'], ['north melbourne', '21.19 ( 145 )', 'melbourne', '13.9 ( 87 )', 'arden street oval', '7749', '15 august 1981'], ['south melbourne', '12.14 ( 86 )', 'geelong', '21.13 ( 139 )', 'lake oval', '11489', '15 august 1981'], ['footscray', '12.14 ( 86 )', 'fitzroy', '22.15 ( 147 )', 'western oval', '11770', '15 august 1981'], ['richmond', '11.20 ( 86 )', 'collingwood', '14.7 ( 91 )', 'mcg', '69217', '15 august 1981'], ['hawthorn', '10.17 ( 77 )', 'st kilda', '9.14 ( 68 )', 'vfl park', '20863', '15 august 1981']]

mariano scartezzini

https://en.wikipedia.org/wiki/Mariano_Scartezzini

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15168755-1.html.csv

aggregation

in the 1980s , the average world ranking for mariano scartezzini was 9.5 .

{'scope': 'subset', 'col': '3', 'type': 'average', 'result': '9.5', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': '1980'}}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'year', '1980'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; year ; 1980 }', 'tointer': 'select the rows whose year record is greater than or equal to 1980 .'}, 'world ranking'], 'result': '9.5', 'ind': 1, 'tostr': 'avg { filter_greater_eq { all_rows ; year ; 1980 } ; world ranking }'}, '9.5'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_greater_eq { all_rows ; year ; 1980 } ; world ranking } ; 9.5 } = true', 'tointer': 'select the rows whose year record is greater than or equal to 1980 . the average of the world ranking record of these rows is 9.5 .'}

round_eq { avg { filter_greater_eq { all_rows ; year ; 1980 } ; world ranking } ; 9.5 } = true

select the rows whose year record is greater than or equal to 1980 . the average of the world ranking record of these rows is 9.5 .

3

3

{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '1980_6': 6, 'world ranking_7': 7, '9.5_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '1980_6': '1980', 'world ranking_7': 'world ranking', '9.5_8': '9.5'}

{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '1980_6': [0], 'world ranking_7': [1], '9.5_8': [2]}

['year', 'performance', 'world ranking', 'venue', 'date']

[['1979', '8.22.72', '3', 'turin', 'jun 9'], ['1980', '8.12.5', '4', 'rome', 'aug 5'], ['1981', '8.13.32', '1', 'zagreb', 'aug 16'], ['1982', '8.22.34', '14', 'berlin', 'aug 20'], ['1983', '8.21.17', '19', 'helsinki', 'aug 12']]

nauru

https://en.wikipedia.org/wiki/Nauru

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21302-1.html.csv

comparative

anabar district is larger by area than the aiwa district .

{'row_1': '2', 'row_2': '1', 'col': '4', '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', 'district', 'anabar'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose district record fuzzily matches to anabar .', 'tostr': 'filter_eq { all_rows ; district ; anabar }'}, 'area ( ha )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; district ; anabar } ; area ( ha ) }', 'tointer': 'select the rows whose district record fuzzily matches to anabar . take the area ( ha ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'district', 'aiwo'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose district record fuzzily matches to aiwo .', 'tostr': 'filter_eq { all_rows ; district ; aiwo }'}, 'area ( ha )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; district ; aiwo } ; area ( ha ) }', 'tointer': 'select the rows whose district record fuzzily matches to aiwo . take the area ( ha ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; district ; anabar } ; area ( ha ) } ; hop { filter_eq { all_rows ; district ; aiwo } ; area ( ha ) } } = true', 'tointer': 'select the rows whose district record fuzzily matches to anabar . take the area ( ha ) record of this row . select the rows whose district record fuzzily matches to aiwo . take the area ( ha ) record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; district ; anabar } ; area ( ha ) } ; hop { filter_eq { all_rows ; district ; aiwo } ; area ( ha ) } } = true

select the rows whose district record fuzzily matches to anabar . take the area ( ha ) record of this row . select the rows whose district record fuzzily matches to aiwo . take the area ( ha ) 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, 'district_7': 7, 'anabar_8': 8, 'area ( ha )_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'district_11': 11, 'aiwo_12': 12, 'area ( ha )_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', 'district_7': 'district', 'anabar_8': 'anabar', 'area ( ha )_9': 'area ( ha )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'district_11': 'district', 'aiwo_12': 'aiwo', 'area ( ha )_13': 'area ( ha )'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'district_7': [0], 'anabar_8': [0], 'area ( ha )_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'district_11': [1], 'aiwo_12': [1], 'area ( ha )_13': [3]}

['nr', 'district', 'former name', 'area ( ha )', 'population ( 2005 )', 'no of villages', 'density persons / ha']

[['1', 'aiwo', 'aiue', '100', '1092', '8', '10.9'], ['2', 'anabar', 'anebwor', '143', '502', '15', '3.5'], ['3', 'anetan', 'añetañ', '100', '516', '12', '5.2'], ['4', 'anibare', 'anybody', '314', '160', '17', '0.5'], ['5', 'baiti', 'beidi', '123', '572', '15', '4.7'], ['6', 'boe', 'boi', '66', '795', '4', '12.0'], ['7', 'buada', 'arenibok', '266', '716', '14', '2.7'], ['8', 'denigomodu', 'denikomotu', '118', '2827', '17', '24.0'], ['9', 'ewa', 'eoa', '117', '318', '12', '2.7'], ['10', 'ijuw', 'ijub', '112', '303', '13', '2.7'], ['11', 'meneng', 'meneñ', '288', '1830', '18', '6.4'], ['12', 'nibok', 'ennibeck', '136', '432', '11', '3.2'], ['13', 'uaboe', 'ueboi', '97', '335', '6', '3.5'], ['14', 'yaren', 'moqua', '150', '820', '7', '5.5']]

1992 - 93 argentine primera divisi \ xc3 \ xb3n

https://en.wikipedia.org/wiki/1992%E2%80%9393_Argentine_Primera_Divisi%C3%B3n

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17968282-1.html.csv

count

two teams in the 1992 - 93 argentine primera división had exactly 37 points .

{'scope': 'all', 'criterion': 'equal', 'value': '37', 'result': '2', 'col': '6', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', '1992 - 93', '37'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1992 - 93 record is equal to 37 .', 'tostr': 'filter_eq { all_rows ; 1992 - 93 ; 37 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; 1992 - 93 ; 37 } }', 'tointer': 'select the rows whose 1992 - 93 record is equal to 37 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; 1992 - 93 ; 37 } } ; 2 } = true', 'tointer': 'select the rows whose 1992 - 93 record is equal to 37 . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; 1992 - 93 ; 37 } } ; 2 } = true

select the rows whose 1992 - 93 record is equal to 37 . 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, '1992 - 93_5': 5, '37_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', '1992 - 93_5': '1992 - 93', '37_6': '37', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], '1992 - 93_5': [0], '37_6': [0], '2_7': [2]}

['team', 'average', 'points', 'played', '1991 - 92', '1992 - 93', '1993 - 94']

[['boca juniors', '1.307', '149', '114', '51', '50', '48'], ['river plate', '1.281', '146', '114', '45', '55', '46'], ['vélez sársfield', '1.237', '141', '114', '45', '48', '48'], ['san lorenzo', '1.088', '124', '114', '45', '45', '45'], ['huracán', '1.061', '121', '114', '40', '38', '43'], ['independiente', '1.026', '117', '114', '40', '36', '41'], ["newell 's old boys", '1.026', '117', '114', '48', '44', '25'], ['racing club', '1.009', '115', '114', '40', '39', '36'], ['deportivo español', '1.000', '114', '114', '28', '45', '41'], ['ferro carril oeste', '0.991', '113', '114', '38', '37', '38'], ['rosario central', '0.982', '112', '114', '39', '34', '39'], ['lanús', '0.974', '37', '38', 'n / a', 'n / a', '37'], ['belgrano de córdoba', '0.961', '73', '76', 'n / a', '35', '38'], ['deportivo mandiyú', '0.947', '108', '114', '38', '33', '37'], ['gimnasia de la plata', '0.947', '108', '114', '33', '41', '34'], ['estudiantes de la plata', '0.930', '106', '114', '39', '29', '38'], ['platense', '0.921', '105', '114', '35', '42', '28'], ['argentinos juniors', '0.912', '104', '114', '36', '35', '33'], ['talleres de córdoba', '0.851', '97', '114', '29', '37', '31'], ['san martín de tucumán', '0.789', '30', '38', 'n / a', 'n / a', '30']]

1991 do n't drink drive sandown 500

https://en.wikipedia.org/wiki/1991_Don%27t_Drink_Drive_Sandown_500

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18169240-1.html.csv

unique

in the 1991 do n't drink drive sandown 500 , alan jones and peter fitzgerald were the only b class team to finish .

{'scope': 'all', 'row': '2', 'col': '2', 'col_other': '4', 'criterion': 'equal', 'value': 'b', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'b'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to b .', 'tostr': 'filter_eq { all_rows ; class ; b }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; class ; b } }', 'tointer': 'select the rows whose class record fuzzily matches to b . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'b'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to b .', 'tostr': 'filter_eq { all_rows ; class ; b }'}, 'drivers'], 'result': 'alan jones peter fitzgerald', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; class ; b } ; drivers }'}, 'alan jones peter fitzgerald'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; class ; b } ; drivers } ; alan jones peter fitzgerald }', 'tointer': 'the drivers record of this unqiue row is alan jones peter fitzgerald .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; class ; b } } ; eq { hop { filter_eq { all_rows ; class ; b } ; drivers } ; alan jones peter fitzgerald } } = true', 'tointer': 'select the rows whose class record fuzzily matches to b . there is only one such row in the table . the drivers record of this unqiue row is alan jones peter fitzgerald .'}

and { only { filter_eq { all_rows ; class ; b } } ; eq { hop { filter_eq { all_rows ; class ; b } ; drivers } ; alan jones peter fitzgerald } } = true

select the rows whose class record fuzzily matches to b . there is only one such row in the table . the drivers record of this unqiue row is alan jones peter fitzgerald .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'class_7': 7, 'b_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'drivers_9': 9, 'alan jones peter fitzgerald_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'class_7': 'class', 'b_8': 'b', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'drivers_9': 'drivers', 'alan jones peter fitzgerald_10': 'alan jones peter fitzgerald'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'class_7': [0], 'b_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'drivers_9': [2], 'alan jones peter fitzgerald_10': [3]}

['pos', 'class', 'entrant', 'drivers', 'laps']

[['1', 'a', 'gio racing', 'mark gibbs rohan onslow', '161'], ['2', 'b', 'benson & hedges racing', 'alan jones peter fitzgerald', '155'], ['3', 'a', 'playscape racing australia', 'kevin waldock brett peters', '152'], ['4', 'a', 'gemspares', 'daryl hendrick john white', '144'], ['5', 'c', 'toyota team australia', 'ron searle don griffiths', '141'], ['6', 'c', 'speedtech motorsport', 'geoff full paul morris', '122'], ['dnf', 'a', 'peter jackson racing', 'glenn seton gregg hansford', '146'], ['dnf', 'a', 'mobil 1 racing team', 'peter brock andrew miedecke tomas mezera', '133'], ['dnf', 'a', 'bob jones', 'bob jones ed lamont', '122'], ['dnf', 'a', 'holden racing team', 'neil crompton brad jones', '103'], ['dnf', 'a', 'mobil 1 racing team', 'larry perkins peter brock', '103'], ['dnf', 'c', 'bob holden motors', 'bob holden dennis rogers', '103'], ['dnf', 'a', 'holden racing team', 'win percy allan grice', '101'], ['nc', 'c', 'bob holden motors', 'mike conway calvin gardiner', '97'], ['dnf', 'a', 'peter hudson', 'peter hudson ian carrig ian clark', '56']]

list of festivals at donington park

https://en.wikipedia.org/wiki/List_of_Festivals_at_Donington_Park

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10311801-2.html.csv

majority

most of the festivals that took place at donlington park were mosters of rock concerts .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'monsters of rock', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'event', 'monsters of rock'], 'result': True, 'ind': 0, 'tointer': 'for the event records of all rows , most of them fuzzily match to monsters of rock .', 'tostr': 'most_eq { all_rows ; event ; monsters of rock } = true'}

most_eq { all_rows ; event ; monsters of rock } = true

for the event records of all rows , most of them fuzzily match to monsters of rock .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'event_3': 3, 'monsters of rock_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'event_3': 'event', 'monsters of rock_4': 'monsters of rock'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'event_3': [0], 'monsters of rock_4': [0]}

['year', 'date', 'event', 'days', 'stages', 'acts']

[['1990', '18 august', 'monsters of rock', '1 day', '1 stage', '5 bands'], ['1991', '17 august', 'monsters of rock', '1 day', '1 stage', '5 bands'], ['1992', '2526 july', 'one step beyond', '24 hours', '1 stage', "60 + dj 's"], ['1992', '22 august', 'monsters of rock', '1 day', '1 stage', '6 bands'], ['1994', '4 june', 'monsters of rock', '1 day', '2 stages', '12 bands'], ['1995', '26 august', 'metallica : escape from the studio', '1 day', '1 stage', '9 bands'], ['1996', '17 august', 'monsters of rock', '1 day', '2 stages', '13 bands']]

united states house of representatives elections , 1952

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1952

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342149-38.html.csv

comparative

ivor d fenton was first elected to the house of representatives sooner than hardie scott .

{'row_1': '3', 'row_2': '1', 'col': '4', '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', 'incumbent', 'ivor d fenton'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to ivor d fenton .', 'tostr': 'filter_eq { all_rows ; incumbent ; ivor d fenton }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; ivor d fenton } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to ivor d fenton . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'hardie scott'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to hardie scott .', 'tostr': 'filter_eq { all_rows ; incumbent ; hardie scott }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; hardie scott } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to hardie scott . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; ivor d fenton } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; hardie scott } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to ivor d fenton . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to hardie scott . take the first elected record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; incumbent ; ivor d fenton } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; hardie scott } ; first elected } } = true

select the rows whose incumbent record fuzzily matches to ivor d fenton . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to hardie scott . take the first elected 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, 'incumbent_7': 7, 'ivor d fenton_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'hardie scott_12': 12, 'first elected_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', 'incumbent_7': 'incumbent', 'ivor d fenton_8': 'ivor d fenton', 'first elected_9': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'hardie scott_12': 'hardie scott', 'first elected_13': 'first elected'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'ivor d fenton_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'hardie scott_12': [1], 'first elected_13': [3]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['pennsylvania 3', 'hardie scott', 'republican', '1946', 'retired democratic gain', 'james a byrne ( d ) 58.4 % morton witkin ( r ) 41.6 %'], ['pennsylvania 9', 'paul b dague', 'republican', '1946', 're - elected', 'paul b dague ( r ) 66.2 % philip e ragan ( d ) 33.8 %'], ['pennsylvania 12', 'ivor d fenton', 'republican', '1938', 're - elected', 'ivor d fenton ( r ) 60.7 % peter krehel ( d ) 39.3 %'], ['pennsylvania 23', 'leon h gavin redistricted from 19th', 'republican', '1942', 're - elected', 'leon h gavin ( r ) 67.8 % fred c barr ( d ) 32.2 %'], ['pennsylvania 25', 'louis e graham', 'republican', '1938', 're - elected', 'louis e graham ( r ) 50.4 % frank m clark ( d ) 49.6 %']]

united states house of representatives elections , 2006

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-43.html.csv

unique

harold ford jr is the only tennessee representative to retire to run for senate .

{'scope': 'all', 'row': '9', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'retired to run for us senate democratic hold', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'results', 'retired to run for us senate democratic hold'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose results record fuzzily matches to retired to run for us senate democratic hold .', 'tostr': 'filter_eq { all_rows ; results ; retired to run for us senate democratic hold }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } }', 'tointer': 'select the rows whose results record fuzzily matches to retired to run for us senate democratic hold . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'results', 'retired to run for us senate democratic hold'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose results record fuzzily matches to retired to run for us senate democratic hold .', 'tostr': 'filter_eq { all_rows ; results ; retired to run for us senate democratic hold }'}, 'incumbent'], 'result': 'harold ford jr', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } ; incumbent }'}, 'harold ford jr'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } ; incumbent } ; harold ford jr }', 'tointer': 'the incumbent record of this unqiue row is harold ford jr .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } } ; eq { hop { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } ; incumbent } ; harold ford jr } } = true', 'tointer': 'select the rows whose results record fuzzily matches to retired to run for us senate democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is harold ford jr .'}

and { only { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } } ; eq { hop { filter_eq { all_rows ; results ; retired to run for us senate democratic hold } ; incumbent } ; harold ford jr } } = true

select the rows whose results record fuzzily matches to retired to run for us senate democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is harold ford jr .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'results_7': 7, 'retired to run for us senate democratic hold_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'harold ford jr_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'results_7': 'results', 'retired to run for us senate democratic hold_8': 'retired to run for us senate democratic hold', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'harold ford jr_10': 'harold ford jr'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'results_7': [0], 'retired to run for us senate democratic hold_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'harold ford jr_10': [3]}

['district', 'incumbent', 'party', 'first elected', 'results']

[['tennessee 1', 'william l jenkins', 'republican', '1996', 'retired republican hold'], ['tennessee 2', 'jimmy duncan jr', 'republican', '1998', 're - elected'], ['tennessee 3', 'zach wamp', 'republican', '1994', 're - elected'], ['tennessee 4', 'lincoln davis', 'democratic', '2002', 're - elected'], ['tennessee 5', 'jim cooper', 'democratic', '2002', 're - elected'], ['tennessee 6', 'bart gordon', 'democratic', '1984', 're - elected'], ['tennessee 7', 'marsha blackburn', 'republican', '2002', 're - elected'], ['tennessee 8', 'john tanner', 'democratic', '1988', 're - elected'], ['tennessee 9', 'harold ford jr', 'democratic', '1996', 'retired to run for us senate democratic hold']]

1982 pga tour

https://en.wikipedia.org/wiki/1982_PGA_Tour

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14640525-4.html.csv

majority

all players of the 1982 pga tour were from the united states .

{'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; country ; united states } = true'}

all_eq { all_rows ; country ; united states } = true

for the country records of all rows , all of them fuzzily match to united states .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]}

['rank', 'player', 'country', 'earnings', 'wins']

[['1', 'jack nicklaus', 'united states', '3992070', '71'], ['2', 'tom watson', 'united states', '2866383', '32'], ['3', 'lee trevino', 'united states', '2643085', '28'], ['4', 'raymond floyd', 'united states', '2178796', '18'], ['5', 'tom weiskopf', 'united states', '2158631', '16']]

equestrian at the asian games

https://en.wikipedia.org/wiki/Equestrian_at_the_Asian_Games

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14781412-8.html.csv

count

the equestrian asian games have taken place in 7 different locations .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '7', 'col': '2', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'location'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record is arbitrary .', 'tostr': 'filter_all { all_rows ; location }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; location } }', 'tointer': 'select the rows whose location record is arbitrary . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; location } } ; 7 } = true', 'tointer': 'select the rows whose location record is arbitrary . the number of such rows is 7 .'}

eq { count { filter_all { all_rows ; location } } ; 7 } = true

select the rows whose location record is arbitrary . the number of such rows is 7 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'location_5': 5, '7_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'location_5': 'location', '7_6': '7'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'location_5': [0], '7_6': [2]}

['year', 'location', 'gold', 'silver', 'bronze']

[['1982', 'new delhi', 'nadia al - moutawaa', 'jamila al - moutawaa', 'bariaa salem al - sabbah'], ['1986', 'seoul', 'takashi tomura', 'shuichi toki', 'ryuzo okuno'], ['1994', 'hiroshima', 'konoshin kuwahara', 'ryuzo okuno', 'natya chantrasmi'], ['1998', 'bangkok', 'jin kanno', 'sohn bong - gak', 'quzier ambak fathil'], ['2002', 'busan', 'mikaela marã\xada jaworski', 'lee jin - kyung', 'tadayoshi hayashi'], ['2006', 'doha', 'ali yousuf al - rumaihi', 'jasmine chen - shao man', 'joo jung - hyun'], ['2010', 'guangzhou', 'ramzy al duhami', 'latifa al maktom', 'khaled al - eid']]

aro 10

https://en.wikipedia.org/wiki/ARO_10

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1371853-2.html.csv

unique

only the daewoo engine has a capacity equal to 1598 cc .

{'scope': 'all', 'row': '4', 'col': '2', 'col_other': '3', 'criterion': 'equal', 'value': '1598 cc', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'capacity', '1598 cc'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose capacity record fuzzily matches to 1598 cc .', 'tostr': 'filter_eq { all_rows ; capacity ; 1598 cc }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; capacity ; 1598 cc } }', 'tointer': 'select the rows whose capacity record fuzzily matches to 1598 cc . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'capacity', '1598 cc'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose capacity record fuzzily matches to 1598 cc .', 'tostr': 'filter_eq { all_rows ; capacity ; 1598 cc }'}, 'power'], 'result': 'daewoo', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; capacity ; 1598 cc } ; power }'}, 'daewoo'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; capacity ; 1598 cc } ; power } ; daewoo }', 'tointer': 'the power record of this unqiue row is daewoo .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; capacity ; 1598 cc } } ; eq { hop { filter_eq { all_rows ; capacity ; 1598 cc } ; power } ; daewoo } } = true', 'tointer': 'select the rows whose capacity record fuzzily matches to 1598 cc . there is only one such row in the table . the power record of this unqiue row is daewoo .'}

and { only { filter_eq { all_rows ; capacity ; 1598 cc } } ; eq { hop { filter_eq { all_rows ; capacity ; 1598 cc } ; power } ; daewoo } } = true

select the rows whose capacity record fuzzily matches to 1598 cc . there is only one such row in the table . the power record of this unqiue row is daewoo .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'capacity_7': 7, '1598 cc_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'power_9': 9, 'daewoo_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'capacity_7': 'capacity', '1598 cc_8': '1598 cc', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'power_9': 'power', 'daewoo_10': 'daewoo'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'capacity_7': [0], '1598 cc_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'power_9': [2], 'daewoo_10': [3]}

['name', 'capacity', 'power', 'type', 'torque']

[['1.2 petrol', '1239 cc', 'renault', '5300 rpm', 'at 2800 rpm'], ['1.4 petrol', '1397 cc', 'dacia', '5500 rpm', 'at 3300 rpm'], ['1.6 petrol', '1557 cc', 'dacia', '5000 rpm', 'at 2500 rpm'], ['1.6 petrol', '1598 cc', 'daewoo', '5800 rpm', 'at 3400 rpm'], ['1.9 diesel', '1870 cc', 'renault', '4500 rpm', 'at 2250 rpm'], ['1.9 diesel', '1870 cc', 'renault', '4250 rpm', 'at 2250 rpm']]

golden gate transit

https://en.wikipedia.org/wiki/Golden_Gate_Transit

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1524075-2.html.csv

unique

the only type of bus with diesel-electric hybrid fuel propulsion is the new flyer de35lf .

{'scope': 'all', 'row': '2', 'col': '7', 'col_other': '3', 'criterion': 'equal', 'value': 'diesel - electric hybrid', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'fuel propulsion', 'diesel - electric hybrid'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose fuel propulsion record fuzzily matches to diesel - electric hybrid .', 'tostr': 'filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } }', 'tointer': 'select the rows whose fuel propulsion record fuzzily matches to diesel - electric hybrid . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'fuel propulsion', 'diesel - electric hybrid'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose fuel propulsion record fuzzily matches to diesel - electric hybrid .', 'tostr': 'filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid }'}, 'make and model'], 'result': 'new flyer de35lf', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } ; make and model }'}, 'new flyer de35lf'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } ; make and model } ; new flyer de35lf }', 'tointer': 'the make and model record of this unqiue row is new flyer de35lf .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } } ; eq { hop { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } ; make and model } ; new flyer de35lf } } = true', 'tointer': 'select the rows whose fuel propulsion record fuzzily matches to diesel - electric hybrid . there is only one such row in the table . the make and model record of this unqiue row is new flyer de35lf .'}

and { only { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } } ; eq { hop { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } ; make and model } ; new flyer de35lf } } = true

select the rows whose fuel propulsion record fuzzily matches to diesel - electric hybrid . there is only one such row in the table . the make and model record of this unqiue row is new flyer de35lf .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'fuel propulsion_7': 7, 'diesel - electric hybrid_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'make and model_9': 9, 'new flyer de35lf_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'fuel propulsion_7': 'fuel propulsion', 'diesel - electric hybrid_8': 'diesel - electric hybrid', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'make and model_9': 'make and model', 'new flyer de35lf_10': 'new flyer de35lf'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'fuel propulsion_7': [0], 'diesel - electric hybrid_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'make and model_9': [2], 'new flyer de35lf_10': [3]}

['length ( feet )', 'year', 'make and model', 'floor type', 'number of seats', 'bicycle capacity', 'fuel propulsion', 'quantity']

[['30', '2001', 'novabus rts', 'high', '27', '2', 'diesel', '4'], ['35', '2010', 'new flyer de35lf', 'low', '29', '3', 'diesel - electric hybrid', '7'], ['40', '2000', 'novabus rts', 'high', '39', '3', 'diesel', '14'], ['40', '2003', 'orion bus industries v', 'high', '41', '3', 'diesel', '80'], ['45', '1999', 'mci 102dl3', 'high', '57', '2', 'diesel', '14'], ['45', '2003', 'mci d4500', 'high', '57', '2', 'diesel', '6'], ['45', '2010 , 2012', 'mci d4500ct', 'high', '57', '2', 'diesel', '55'], ['60 ( articulated )', '2007', 'new flyer d60lf', 'low', '58', '3', 'diesel', '10']]

bemidji , minnesota

https://en.wikipedia.org/wiki/Bemidji%2C_Minnesota

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-119428-2.html.csv

unique

kawe is the only radio station in bemidji , minnesota to be affiliated with pbs .

{'scope': 'all', 'row': '1', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'pbs', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'affiliation', 'pbs'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose affiliation record fuzzily matches to pbs .', 'tostr': 'filter_eq { all_rows ; affiliation ; pbs }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; affiliation ; pbs } }', 'tointer': 'select the rows whose affiliation record fuzzily matches to pbs . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'affiliation', 'pbs'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose affiliation record fuzzily matches to pbs .', 'tostr': 'filter_eq { all_rows ; affiliation ; pbs }'}, 'call sign'], 'result': 'kawe', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; affiliation ; pbs } ; call sign }'}, 'kawe'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; affiliation ; pbs } ; call sign } ; kawe }', 'tointer': 'the call sign record of this unqiue row is kawe .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; affiliation ; pbs } } ; eq { hop { filter_eq { all_rows ; affiliation ; pbs } ; call sign } ; kawe } } = true', 'tointer': 'select the rows whose affiliation record fuzzily matches to pbs . there is only one such row in the table . the call sign record of this unqiue row is kawe .'}

and { only { filter_eq { all_rows ; affiliation ; pbs } } ; eq { hop { filter_eq { all_rows ; affiliation ; pbs } ; call sign } ; kawe } } = true

select the rows whose affiliation record fuzzily matches to pbs . there is only one such row in the table . the call sign record of this unqiue row is kawe .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'affiliation_7': 7, 'pbs_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'call sign_9': 9, 'kawe_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'affiliation_7': 'affiliation', 'pbs_8': 'pbs', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'call sign_9': 'call sign', 'kawe_10': 'kawe'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'affiliation_7': [0], 'pbs_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'call sign_9': [2], 'kawe_10': [3]}

['channel', 'digital channel', 'call sign', 'affiliation', 'owner']

[['9', '9', 'kawe', 'pbs', 'northern mn public tv'], ['12', '12', 'kccw ( located near walker , mn )', 'cbs ( wcco - tv relay )', 'cbs corporation'], ['26', '26', 'kftc', 'mynetworktv ( wftc relay )', 'fox television stations'], ['28', 'none', 'k28dd', 'abc ( ksax translator )', 'hubbard broadcasting'], ['30', 'none', 'k30dk', 'fox ( kmsp translator )', 'fox television stations'], ['42', 'none', 'k42fh', 'tbn', 'trinity broadcasting network'], ['48', '48', 'k48ki', '3abn', 'three angels broadcasting network']]

dams

https://en.wikipedia.org/wiki/DAMS

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1029726-1.html.csv

ordinal

the 1st year that romain grosjean drove for dams was 2010 .

{'scope': 'subset', 'row': '13', 'col': '1', 'order': '1', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'romain grosjean'}}

{'func': 'eq', 'args': [{'func': 'nth_min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'drivers', 'romain grosjean'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; drivers ; romain grosjean }', 'tointer': 'select the rows whose drivers record fuzzily matches to romain grosjean .'}, 'year', '1'], 'result': '2010', 'ind': 1, 'tostr': 'nth_min { filter_eq { all_rows ; drivers ; romain grosjean } ; year ; 1 }', 'tointer': 'select the rows whose drivers record fuzzily matches to romain grosjean . the 1st minimum year record of these rows is 2010 .'}, '2010'], 'result': True, 'ind': 2, 'tostr': 'eq { nth_min { filter_eq { all_rows ; drivers ; romain grosjean } ; year ; 1 } ; 2010 } = true', 'tointer': 'select the rows whose drivers record fuzzily matches to romain grosjean . the 1st minimum year record of these rows is 2010 .'}

eq { nth_min { filter_eq { all_rows ; drivers ; romain grosjean } ; year ; 1 } ; 2010 } = true

select the rows whose drivers record fuzzily matches to romain grosjean . the 1st minimum year record of these rows is 2010 .

3

3

{'eq_2': 2, 'result_3': 3, 'nth_min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'drivers_5': 5, 'romain grosjean_6': 6, 'year_7': 7, '1_8': 8, '2010_9': 9}

{'eq_2': 'eq', 'result_3': 'true', 'nth_min_1': 'nth_min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'drivers_5': 'drivers', 'romain grosjean_6': 'romain grosjean', 'year_7': 'year', '1_8': '1', '2010_9': '2010'}

{'eq_2': [3], 'result_3': [], 'nth_min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'drivers_5': [0], 'romain grosjean_6': [0], 'year_7': [1], '1_8': [1], '2010_9': [2]}

['year', 'drivers', 'races', 'wins', 'poles', 'fl', 'points', 'dc', 'tc']

[['2005', 'josé maría lópez', '23', '1', '0', '0', '36', '9th', '7th'], ['2005', 'fairuz fauzy', '23', '0', '0', '0', '0', '24th', '7th'], ['2006', 'ferdinando monfardini', '21', '0', '0', '0', '6', '21st', '12th'], ['2006', 'franck perera', '21', '0', '0', '0', '8', '17th', '12th'], ['2007', 'kazuki nakajima', '21', '0', '1', '3', '44', '5th', '5th'], ['2007', 'nicolas lapierre', '21', '2', '1', '2', '23', '12th', '5th'], ['2008', "jérôme d'ambrosio", '20', '0', '0', '0', '21', '11th', '8th'], ['2008', 'kamui kobayashi', '20', '1', '0', '2', '10', '16th', '8th'], ['2009', "jérôme d'ambrosio", '20', '0', '0', '0', '29', '9th', '6th'], ['2009', 'kamui kobayashi', '20', '0', '0', '0', '13', '16th', '6th'], ['2010', "jérôme d'ambrosio", '18', '1', '1', '0', '21', '12th', '6th'], ['2010', 'ho - pin tung', '14', '0', '0', '0', '0', '28th', '6th'], ['2010', 'romain grosjean', '8', '0', '0', '0', '14', '14th', '6th'], ['2011', 'romain grosjean', '18', '5', '1', '6', '89', '1st', '2nd'], ['2011', 'pål varhaug', '18', '0', '0', '0', '0', '23rd', '2nd'], ['2012', 'davide valsecchi', '24', '4', '2', '5', '247', '1st', '1st'], ['2012', 'felipe nasr', '24', '0', '0', '0', '95', '10th', '1st']]

2010 fifa world cup statistics

https://en.wikipedia.org/wiki/2010_FIFA_World_Cup_statistics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27708484-3.html.csv

unique

soccer city stadium is the only one with a capacity of more than 84000 people in the 2010 world cup .

{'scope': 'all', 'row': '10', 'col': '3', 'col_other': '1', 'criterion': 'greater_than', 'value': '84000', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'capacity', '84000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose capacity record is greater than 84000 .', 'tostr': 'filter_greater { all_rows ; capacity ; 84000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; capacity ; 84000 } }', 'tointer': 'select the rows whose capacity record is greater than 84000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'capacity', '84000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose capacity record is greater than 84000 .', 'tostr': 'filter_greater { all_rows ; capacity ; 84000 }'}, 'stadium'], 'result': 'soccer city', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; capacity ; 84000 } ; stadium }'}, 'soccer city'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; capacity ; 84000 } ; stadium } ; soccer city }', 'tointer': 'the stadium record of this unqiue row is soccer city .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; capacity ; 84000 } } ; eq { hop { filter_greater { all_rows ; capacity ; 84000 } ; stadium } ; soccer city } } = true', 'tointer': 'select the rows whose capacity record is greater than 84000 . there is only one such row in the table . the stadium record of this unqiue row is soccer city .'}

and { only { filter_greater { all_rows ; capacity ; 84000 } } ; eq { hop { filter_greater { all_rows ; capacity ; 84000 } ; stadium } ; soccer city } } = true

select the rows whose capacity record is greater than 84000 . there is only one such row in the table . the stadium record of this unqiue row is soccer city .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'capacity_7': 7, '84000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'stadium_9': 9, 'soccer city_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'capacity_7': 'capacity', '84000_8': '84000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'stadium_9': 'stadium', 'soccer city_10': 'soccer city'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'capacity_7': [0], '84000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'stadium_9': [2], 'soccer city_10': [3]}

['stadium', 'city', 'capacity', 'matches played', 'overall attendance', 'average attendance per match', 'average attendance as % of capacity', 'overall goals scored', 'average goals scored per match', 'elevation']

[['cape town stadium', 'cape town', '64100', '8', '507340', '63418', '98.9', '22', '2.75', '0 ( sea level )'], ['ellis park stadium', 'johannesburg', '55686', '7', '372843', '53263', '95.7', '19', '2.71', '1753 m'], ['free state stadium', 'bloemfontein', '40911', '6', '196823', '32804', '80.2', '14', '2.33', '1400 m'], ['loftus versfield stadium', 'pretoria', '42858', '6', '234092', '39015', '91.0', '11', '1.83', '1214 m'], ['mbombela stadium', 'nelspruit', '40929', '4', '143492', '35873', '87.6', '9', '2.25', '660 m'], ['moses mabhida stadium', 'durban', '62760', '7', '434631', '62090', '98.9', '14', '2.00', '0 ( sea level )'], ['nelson mandela bay stadium', 'port elizabeth', '42486', '8', '285643', '35705', '84.0', '16', '2.00', '0 ( sea level )'], ['peter mokaba stadium', 'polokwane', '41733', '4', '139436', '34859', '83.5', '5', '1.25', '1310 m'], ['royal bafokeng stadium', 'rustenburg', '38646', '6', '193697', '32283', '83.5', '14', '2.33', '1500 m'], ['soccer city', 'johannesburg', '84490', '8', '670809', '83851', '99.2', '21', '2.63', '1753 m']]

wang shi - ting

https://en.wikipedia.org/wiki/Wang_Shi-ting

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15340120-1.html.csv

majority

the majority of tournaments that wang shi - ting played in were on a hard surface .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'hard', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to hard .', 'tostr': 'most_eq { all_rows ; surface ; hard } = true'}

most_eq { all_rows ; surface ; hard } = true

for the surface records of all rows , most of them fuzzily match to hard .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'hard_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'hard_4': 'hard'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'hard_4': [0]}

['date', 'tournament', 'surface', 'opponent in the final', 'score']

[['september 13 , 1993', 'hong kong', 'hard', 'marianne witmeyer', '6 - 4 , 3 - 6 , 7 - 5'], ['october 4 , 1993', 'taipei , taiwan', 'hard', 'linda wild', '6 - 1 , 7 - 6 ( 4 )'], ['november 14 , 1994', 'taipei , taiwan', 'hard', 'kyoko nagatsuka', '6 - 1 , 6 - 3'], ['october 2 , 1995', 'surabaya , indonesia', 'hard', 'yi jingqian', '6 - 1 , 6 - 1'], ['october 7 , 1996', 'surabaya , indonesia', 'hard', 'nana miyagi', '6 - 4 , 6 - 0'], ['october 14 , 1996', 'beijing , china', 'hard ( i )', 'chen li', '6 - 3 , 6 - 4']]

united states house of representatives elections , 1988

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1988

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341577-47.html.csv

majority

most of the representatives voted in by virginia in 1988 had originally been elected in 1982 .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '1982', 'subset': None}

{'func': 'most_eq', 'args': ['all_rows', 'first elected', '1982'], 'result': True, 'ind': 0, 'tointer': 'for the first elected records of all rows , most of them are equal to 1982 .', 'tostr': 'most_eq { all_rows ; first elected ; 1982 } = true'}

most_eq { all_rows ; first elected ; 1982 } = true

for the first elected records of all rows , most of them are equal to 1982 .

1

1

{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'first elected_3': 3, '1982_4': 4}

{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'first elected_3': 'first elected', '1982_4': '1982'}

{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'first elected_3': [0], '1982_4': [0]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['virginia 3', 'thomas j bliley , jr', 'republican', '1980', 're - elected', 'thomas j bliley , jr ( r ) unopposed'], ['virginia 4', 'norman sisisky', 'democratic', '1982', 're - elected', 'norman sisisky ( d ) unopposed'], ['virginia 6', 'jim olin', 'democratic', '1982', 're - elected', 'jim olin ( d ) 63.9 % charles e judd ( r ) 36.1 %'], ['virginia 7', 'd french slaughter , jr', 'republican', '1984', 're - elected', 'd french slaughter , jr ( r ) unopposed'], ['virginia 9', 'rick boucher', 'democratic', '1982', 're - elected', 'rick boucher ( d ) 63.4 % john c brown ( r ) 36.6 %']]

list of number - one singles of 2000 ( canada )

https://en.wikipedia.org/wiki/List_of_number-one_singles_of_2000_%28Canada%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17507197-1.html.csv

ordinal

the music song recorded the highest number of weeks on being on top .

{'row': '17', 'col': '3', 'order': '1', 'col_other': '4', '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', 'weeks on top', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; weeks on top ; 1 }'}, 'song'], 'result': 'music', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; weeks on top ; 1 } ; song }'}, 'music'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; weeks on top ; 1 } ; song } ; music } = true', 'tointer': 'select the row whose weeks on top record of all rows is 1st maximum . the song record of this row is music .'}

eq { hop { nth_argmax { all_rows ; weeks on top ; 1 } ; song } ; music } = true

select the row whose weeks on top record of all rows is 1st maximum . the song record of this row is music .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'weeks on top_5': 5, '1_6': 6, 'song_7': 7, 'music_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', 'weeks on top_5': 'weeks on top', '1_6': '1', 'song_7': 'song', 'music_8': 'music'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'weeks on top_5': [0], '1_6': [0], 'song_7': [1], 'music_8': [2]}

['volume : issue', 'issue date ( s )', 'weeks on top', 'song', 'artist']

[['70:8 - 9', '13 december - 3 january 2000 ÷', '2 ÷', 'blue', 'eiffel 65'], ['70:10 - 11', '10 january - 17 january', '2', 'i knew i loved you', 'savage garden'], ['70:12', '24 january', '1', 'what a girl wants', 'christina aguilera'], ['70:13 - 14', '31 january - 7 february', '2', 'i knew i loved you', 'savage garden'], ['70:15 - 16', '14 february - 21 february', '2', 'what a girl wants', 'christina aguilera'], ['70:17 - 18', '28 february - 6 march', '2', 'show me the meaning of being lonely', 'backstreet boys'], ['70:19', '13 march', '1', 'faded', 'souldecision'], ['70:20', '20 march', '1', 'bye bye bye', "'n sync"], ['70:21 - 23', '27 march - 10 april', '3', 'never let you go', 'third eye blind'], ['70:23', '17 april', '1', 'maria maria', 'santana featuring the product g & b'], ['70:24 - 25 , 71:1 - 3', '24 april - 22 may', '5', 'it feels so good', 'sonique'], ['71:4 - 9', '29 may - 3 july', '6', 'oops ! … i did it again', 'britney spears'], ['71:10 - 12', '10 july - 24 july', '3', "it 's gon na be me", "'n sync"], ['71:13 - 14', '31 july - 7 august', '2', 'bent', 'matchbox twenty'], ['71:15', '14 august', '1', 'bang bang boom', 'the moffatts'], ['71:16 - 18', '21 august - 4 september', '3', 'bent', 'matchbox twenty'], ['71:19 - 26', '11 september - 6 november', '9', 'music', 'madonna']]

2005 japanese television dramas

https://en.wikipedia.org/wiki/2005_Japanese_television_dramas

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18540104-2.html.csv

comparative

for the 2005 japanese television dramas , the one titled nyokei kazoku had one more episode than the one titled haruka seventeen .

{'row_1': '6', 'row_2': '9', 'col': '4', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row1'}}

{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'romaji title', 'nyokei kazoku'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose romaji title record fuzzily matches to nyokei kazoku .', 'tostr': 'filter_eq { all_rows ; romaji title ; nyokei kazoku }'}, 'episodes'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; romaji title ; nyokei kazoku } ; episodes }', 'tointer': 'select the rows whose romaji title record fuzzily matches to nyokei kazoku . take the episodes record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'romaji title', 'haruka seventeen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose romaji title record fuzzily matches to haruka seventeen .', 'tostr': 'filter_eq { all_rows ; romaji title ; haruka seventeen }'}, 'episodes'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; romaji title ; haruka seventeen } ; episodes }', 'tointer': 'select the rows whose romaji title record fuzzily matches to haruka seventeen . take the episodes record of this row .'}], 'result': '1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; romaji title ; nyokei kazoku } ; episodes } ; hop { filter_eq { all_rows ; romaji title ; haruka seventeen } ; episodes } }'}, '1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; romaji title ; nyokei kazoku } ; episodes } ; hop { filter_eq { all_rows ; romaji title ; haruka seventeen } ; episodes } } ; 1 } = true', 'tointer': 'select the rows whose romaji title record fuzzily matches to nyokei kazoku . take the episodes record of this row . select the rows whose romaji title record fuzzily matches to haruka seventeen . take the episodes record of this row . the first record is 1 larger than the second record .'}

eq { diff { hop { filter_eq { all_rows ; romaji title ; nyokei kazoku } ; episodes } ; hop { filter_eq { all_rows ; romaji title ; haruka seventeen } ; episodes } } ; 1 } = true

select the rows whose romaji title record fuzzily matches to nyokei kazoku . take the episodes record of this row . select the rows whose romaji title record fuzzily matches to haruka seventeen . take the episodes record of this row . the first record is 1 larger than the second record .

6

6

{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'romaji title_8': 8, 'nyokei kazoku_9': 9, 'episodes_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'romaji title_12': 12, 'haruka seventeen_13': 13, 'episodes_14': 14, '1_15': 15}

{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'romaji title_8': 'romaji title', 'nyokei kazoku_9': 'nyokei kazoku', 'episodes_10': 'episodes', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'romaji title_12': 'romaji title', 'haruka seventeen_13': 'haruka seventeen', 'episodes_14': 'episodes', '1_15': '1'}

{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'romaji title_8': [0], 'nyokei kazoku_9': [0], 'episodes_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'romaji title_12': [1], 'haruka seventeen_13': [1], 'episodes_14': [3], '1_15': [5]}

['japanese title', 'romaji title', 'tv station', 'episodes', 'average ratings']

[['電車男', 'densha otoko', 'fuji tv', '11', '21.0 %'], ['海猿 umizaru evolution', 'umizaru evolution', 'fuji tv', '11', '13.2 %'], ['スローダンス', 'slow dance', 'fuji tv', '11', '16.8 %'], ['がんばっていきまっしょい', 'ganbatte ikimasshoi', 'fuji tv', '10', '12.4 %'], ['幸せになりたい !', 'shiawase ni naritai !', 'tbs', '10', '11.8 %'], ['女系家族', 'nyokei kazoku', 'tbs', '11', '13.85 %'], ['いま 、 会いにゆきます', 'ima , ai ni yukimasu', 'tbs', '10', '11 %'], ['ドラゴン桜', 'dragon zakura', 'tbs', '11', '16.4 %'], ['はるか17', 'haruka seventeen', 'tv asahi', '10', '8.9 %'], ['菊次郎とさき 2', 'kikujirou to saki 2', 'ntv', '9', '14.9 %'], ['女王の教室', 'joou no kyoushitsu', 'ntv', '11', '15.7 %']]

2002 oakland raiders season

https://en.wikipedia.org/wiki/2002_Oakland_Raiders_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16353260-1.html.csv

ordinal

in the 2002 season , the oakland raiders had their 3rd highest score in their game against the arizona cardinals .

{'row': '12', 'col': '4', 'order': '3', 'col_other': '3', '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', 'result', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; result ; 3 }'}, 'opponent'], 'result': 'arizona cardinals', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; result ; 3 } ; opponent }'}, 'arizona cardinals'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; result ; 3 } ; opponent } ; arizona cardinals } = true', 'tointer': 'select the row whose result record of all rows is 3rd maximum . the opponent record of this row is arizona cardinals .'}

eq { hop { nth_argmax { all_rows ; result ; 3 } ; opponent } ; arizona cardinals } = true

select the row whose result record of all rows is 3rd maximum . the opponent record of this row is arizona cardinals .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'result_5': 5, '3_6': 6, 'opponent_7': 7, 'arizona cardinals_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', 'result_5': 'result', '3_6': '3', 'opponent_7': 'opponent', 'arizona cardinals_8': 'arizona cardinals'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'result_5': [0], '3_6': [0], 'opponent_7': [1], 'arizona cardinals_8': [2]}

['week', 'date', 'opponent', 'result', 'tv time', 'record', 'attendance']

[['1', 'september 8 , 2002', 'seattle seahawks', 'w 31 - 17', 'fox 4:15 et', '1 - 0', '53260'], ['2', 'september 15 , 2002', 'pittsburgh steelers', 'w 30 - 17', 'espn 8:30 et', '2 - 0', '62260'], ['3', '-', '-', '-', '-', '-', ''], ['4', 'september 29 , 2002', 'tennessee titans', 'w 52 - 25', 'cbs 4:15 et', '3 - 0', '58719'], ['5', 'october 6 , 2002', 'buffalo bills', 'w 49 - 31', 'cbs 1:00 et', '4 - 0', '73038'], ['6', 'october 13 , 2002', 'st louis rams', 'l 28 - 13', 'cbs 4:15 et', '4 - 1', '66070'], ['7', 'october 20 , 2002', 'san diego chargers', 'l 27 - 21 ( ot )', 'cbs 4:05 et', '4 - 2', '60974'], ['8', 'october 27 , 2002', 'kansas city chiefs', 'l 20 - 10', 'cbs 1:00 et', '4 - 3', '78685'], ['9', 'november 3 , 2002', 'san francisco 49ers', 'l 23 - 20 ( ot )', 'fox 4:15 et', '4 - 4', '62660'], ['10', 'november 11 , 2002', 'denver broncos', 'w 34 - 10', 'abc 9:00 et', '5 - 4', '76643'], ['11', 'november 17 , 2002', 'new england patriots', 'w 27 - 20', 'espn 8:30 et', '6 - 4', '62552'], ['12', 'november 24 , 2002', 'arizona cardinals', 'w 41 - 20', 'cbs 1:05 et', '7 - 4', '58814'], ['13', 'december 2 , 2002', 'new york jets', 'w 26 - 20', 'abc 9:00 et', '8 - 4', '62257'], ['14', 'december 8 , 2002', 'san diego chargers', 'w 27 - 7', 'cbs 4:15 et', '9 - 4', '67968'], ['15', 'december 15 , 2002', 'miami dolphins', 'l 23 - 17', 'cbs 1:00 et', '9 - 5', '73572'], ['16', 'december 22 , 2002', 'denver broncos', 'w 28 - 16', 'cbs 4:15 et', '10 - 5', '62592'], ['17', 'december 28 , 2002', 'kansas city chiefs', 'w 24 - 0', 'cbs 5:00 et', '11 - 5', '62078']]

forta

https://en.wikipedia.org/wiki/FORTA

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23143607-1.html.csv

ordinal

the ràdio televisió valenciana ( rtvv ) organization is the second oldest founded forta organization .

{'row': '2', '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', 'foundation', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; foundation ; 2 }'}, 'organization'], 'result': 'ràdio televisió valenciana ( rtvv )', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; foundation ; 2 } ; organization }'}, 'ràdio televisió valenciana ( rtvv )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; foundation ; 2 } ; organization } ; ràdio televisió valenciana ( rtvv ) } = true', 'tointer': 'select the row whose foundation record of all rows is 2nd minimum . the organization record of this row is ràdio televisió valenciana ( rtvv ) .'}

eq { hop { nth_argmin { all_rows ; foundation ; 2 } ; organization } ; ràdio televisió valenciana ( rtvv ) } = true

select the row whose foundation record of all rows is 2nd minimum . the organization record of this row is ràdio televisió valenciana ( rtvv ) .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'foundation_5': 5, '2_6': 6, 'organization_7': 7, 'ràdio televisió valenciana (rtvv)_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', 'foundation_5': 'foundation', '2_6': '2', 'organization_7': 'organization', 'ràdio televisió valenciana (rtvv)_8': 'ràdio televisió valenciana ( rtvv )'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'foundation_5': [0], '2_6': [0], 'organization_7': [1], 'ràdio televisió valenciana (rtvv)_8': [2]}

['autonomous community', 'organization', 'television channels', 'radio stations', 'foundation']

[['galicia', 'compañía de radio televisión de galicia ( crtvg )', 'tvg g2 tvg europa tvg américa', 'radio galega radio galega música son galicia radio', '1984'], ['valencia', 'ràdio televisió valenciana ( rtvv )', 'canal nou canal nou dos canal nou 24 tvvi', 'radio nou si radio radio nou música', '1988'], ['madrid', 'ente público radio televisión madrid ( eprtvm )', 'telemadrid laotra telemadrid sat', 'onda madrid', '1989'], ['canary islands', 'radio televisión canaria ( rtvc )', 'tv canaria tv canaria dos tv canaria sat', 'canarias radio', '1999'], ['castile - la mancha', 'radiotelevisión de castilla - la mancha ( rtvcm )', 'cmt cmt 2', 'rcm', '2000'], ['asturias', 'radiotelevisión del principado de asturias ( rtpa )', 'tpa tpa2 rtpa internacional', 'rpa', '2005']]

washington redskins draft history

https://en.wikipedia.org/wiki/Washington_Redskins_draft_history

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17100961-35.html.csv

count

in the washington redskins draft , the team chose 2 players from arizona state .

{'scope': 'all', 'criterion': 'equal', 'value': 'arizona state', 'result': '2', 'col': '6', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'arizona state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to arizona state .', 'tostr': 'filter_eq { all_rows ; college ; arizona state }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; college ; arizona state } }', 'tointer': 'select the rows whose college record fuzzily matches to arizona state . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; college ; arizona state } } ; 2 } = true', 'tointer': 'select the rows whose college record fuzzily matches to arizona state . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; college ; arizona state } } ; 2 } = true

select the rows whose college record fuzzily matches to arizona state . 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, 'college_5': 5, 'arizona state_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', 'college_5': 'college', 'arizona state_6': 'arizona state', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'college_5': [0], 'arizona state_6': [0], '2_7': [2]}

['round', 'pick', 'overall', 'name', 'position', 'college']

[['2', '7', '21', 'bob breitenstein', 'ot', 'tulsa'], ['3', '6', '34', 'kent mccloughan', 'cb', 'nebraska'], ['8', '7', '105', 'don croftcheck', 'g', 'indiana'], ['9', '6', '118', 'jerry smith', 'te', 'arizona state'], ['10', '7', '133', 'bob briggs', 'fb', 'central state'], ['11', '6', '146', 'willie adams', 'de', 'new mexico state'], ['12', '6', '160', 'john strohmeyer', 'ot', 'michigan'], ['13', '6', '174', 'biff bracy', 'hb', 'duke'], ['14', '7', '189', 'dave estrada', 'hb', 'arizona state'], ['15', '6', '202', 'ben baldwin', 'rb', 'vanderbilt'], ['16', '7', '217', 'bob reed', 'g', 'tennessee a & i'], ['17', '6', '230', 'gary hart', 'e', 'vanderbilt'], ['18', '7', '245', 'chris hanburger', 'lb', 'north carolina'], ['19', '6', '258', 'roosevelt ellerbe', 'rb', 'iowa state']]

1986 - 87 boston celtics season

https://en.wikipedia.org/wiki/1986%E2%80%9387_Boston_Celtics_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13821868-4.html.csv

count

in the 1986 - 87 boston celtics season , among the games played in boston garden , 4 of them were on wednesday .

{'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'wed', 'result': '4', 'col': '2', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'boston garden'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'boston garden'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; boston garden }', 'tointer': 'select the rows whose location record fuzzily matches to boston garden .'}, 'date', 'wed'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to boston garden . among these rows , select the rows whose date record fuzzily matches to wed .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; boston garden } ; date ; wed }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; location ; boston garden } ; date ; wed } }', 'tointer': 'select the rows whose location record fuzzily matches to boston garden . among these rows , select the rows whose date record fuzzily matches to wed . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; location ; boston garden } ; date ; wed } } ; 4 } = true', 'tointer': 'select the rows whose location record fuzzily matches to boston garden . among these rows , select the rows whose date record fuzzily matches to wed . the number of such rows is 4 .'}

eq { count { filter_eq { filter_eq { all_rows ; location ; boston garden } ; date ; wed } } ; 4 } = true

select the rows whose location record fuzzily matches to boston garden . among these rows , select the rows whose date record fuzzily matches to wed . the number of such rows is 4 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'location_6': 6, 'boston garden_7': 7, 'date_8': 8, 'wed_9': 9, '4_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'location_6': 'location', 'boston garden_7': 'boston garden', 'date_8': 'date', 'wed_9': 'wed', '4_10': '4'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'location_6': [0], 'boston garden_7': [0], 'date_8': [1], 'wed_9': [1], '4_10': [3]}

['game', 'date', 'opponent', 'score', 'location', 'record']

[['2', 'sat nov 1', 'milwaukee bucks', '105 - 111', 'milwaukee arena ( mecca )', '1 - 1'], ['3', 'wed nov 5', 'indiana pacers', '133 - 102', 'boston garden', '2 - 1'], ['4', 'fri nov 7', 'washington bullets', '88 - 86', 'capital centre', '3 - 1'], ['5', 'tue nov 11', 'new jersey nets', '110 - 114', 'brendan byrne arena', '3 - 2'], ['6', 'wed nov 12', 'milwaukee bucks', '124 - 116', 'boston garden', '4 - 2'], ['7', 'fri nov 14', 'chicago bulls', '124 - 105', 'chicago stadium', '5 - 2'], ['8', 'sat nov 15', 'detroit pistons', '118 - 111', 'pontiac silverdome', '6 - 2'], ['9', 'wed nov 19', 'atlanta hawks', '111 - 107', 'boston garden', '7 - 2'], ['10', 'fri nov 21', 'golden state warriors', '135 - 120', 'boston garden', '8 - 2'], ['11', 'sat nov 22', 'atlanta hawks', '96 - 97', 'the omni', '8 - 3'], ['12', 'tue nov 25', 'philadelphia 76ers', '100 - 102', 'the spectrum', '8 - 4'], ['13', 'wed nov 26', 'new york knicks', '101 - 90', 'boston garden', '9 - 4'], ['14', 'fri nov 28', 'san antonio spurs', '111 - 96', 'boston garden', '10 - 4']]

main line broadcasting

https://en.wikipedia.org/wiki/Main_Line_Broadcasting

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19131921-1.html.csv

ordinal

wgtz - fm is the main line broadcasting radio channel that broadcasts on the second lowest frequency .

{'row': '11', 'col': '4', 'order': '2', 'col_other': '3', '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', 'frequency', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; frequency ; 2 }'}, 'station'], 'result': 'wgtz - fm', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; frequency ; 2 } ; station }'}, 'wgtz - fm'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; frequency ; 2 } ; station } ; wgtz - fm } = true', 'tointer': 'select the row whose frequency record of all rows is 2nd minimum . the station record of this row is wgtz - fm .'}

eq { hop { nth_argmin { all_rows ; frequency ; 2 } ; station } ; wgtz - fm } = true

select the row whose frequency record of all rows is 2nd minimum . the station record of this row is wgtz - fm .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'frequency_5': 5, '2_6': 6, 'station_7': 7, 'wgtz - fm_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', 'frequency_5': 'frequency', '2_6': '2', 'station_7': 'station', 'wgtz - fm_8': 'wgtz - fm'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'frequency_5': [0], '2_6': [0], 'station_7': [1], 'wgtz - fm_8': [2]}

['dma', 'market', 'station', 'frequency', 'branding', 'format']

[['53', 'louisville , ky', 'wgzb - fm', '96.5', 'b96 .5', 'urban'], ['53', 'louisville , ky', 'wdjx - fm', '99.7', '99.7 djx', 'contemporary hit radio'], ['53', 'louisville , ky', 'wmjm - fm', '101.3', 'magic 101.3', 'urban ac'], ['53', 'louisville , ky', 'wxma - fm', '102.3', '102.3 the max', 'hot ac'], ['53', 'louisville , ky', 'wesi', '105.1', 'easy rock 105.1', 'soft adult contemporary'], ['56', 'richmond - petersburg , va', 'wlfv - fm', '93.1', '93.1 the wolf', 'southern country'], ['56', 'richmond - petersburg , va', 'wwlb - fm', '98.9', '98.9 liberty', 'variety hits'], ['56', 'richmond - petersburg , va', 'warv - fm', '100.3', 'big oldies 107.3', 'oldies'], ['56', 'richmond - petersburg , va', 'wbbt - fm', '107.3', 'big oldies 107.3', 'oldies'], ['60', 'dayton , oh', 'wrou - fm', '92.1', '92.1 wrou', 'urban ac'], ['60', 'dayton , oh', 'wgtz - fm', '92.9', 'fly 92.9', 'variety hits'], ['60', 'dayton , oh', 'wcli - fm', '101.5', 'click 101.5', 'modern hit music'], ['60', 'dayton , oh', 'wdht - fm', '102.9', 'hot 102.9', 'rhythmic contemporary'], ['60', 'dayton , oh', 'wing - am', '1410', 'espn 1410', 'sports'], ['166', 'hagerstown , md - chambersburg , pa', 'wqcm - fm', '94.3', '94.3 wqcm', 'rock'], ['166', 'hagerstown , md - chambersburg , pa', 'wikz - fm', '95.1', 'mix 95.1', 'adult contemporary'], ['166', 'hagerstown , md - chambersburg , pa', 'wdld - fm', '96.7', 'wild 96.7', 'rhythmic contemporary hit radio'], ['166', 'hagerstown , md - chambersburg , pa', 'wcha - am', '800', 'true oldies 96.3', 'oldies'], ['166', 'hagerstown , md - chambersburg , pa', 'whag - am', '1410', 'true oldies 96.3', 'oldies']]

2004 baltimore ravens season

https://en.wikipedia.org/wiki/2004_Baltimore_Ravens_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18731638-1.html.csv

majority

the tv time for the majority of games played on or before october 24 , 2004 was cbs 1:00 pm .

{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'cbs 1:00 pm', 'subset': {'col': '2', 'criterion': 'less_than_eq', 'value': 'october 24 , 2004'}}

{'func': 'most_str_eq', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'date', 'october 24 , 2004'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; date ; october 24 , 2004 }', 'tointer': 'select the rows whose date record is less than or equal to october 24 , 2004 .'}, 'tv time', 'cbs 1:00 pm'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record is less than or equal to october 24 , 2004 . for the tv time records of these rows , most of them fuzzily match to cbs 1:00 pm .', 'tostr': 'most_eq { filter_less_eq { all_rows ; date ; october 24 , 2004 } ; tv time ; cbs 1:00 pm } = true'}

most_eq { filter_less_eq { all_rows ; date ; october 24 , 2004 } ; tv time ; cbs 1:00 pm } = true

select the rows whose date record is less than or equal to october 24 , 2004 . for the tv time records of these rows , most of them fuzzily match to cbs 1:00 pm .

2

2

{'most_str_eq_1': 1, 'result_2': 2, 'filter_less_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'october 24 , 2004_5': 5, 'tv time_6': 6, 'cbs 1:00 pm_7': 7}

{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'october 24 , 2004_5': 'october 24 , 2004', 'tv time_6': 'tv time', 'cbs 1:00 pm_7': 'cbs 1:00 pm'}

{'most_str_eq_1': [2], 'result_2': [], 'filter_less_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'october 24 , 2004_5': [0], 'tv time_6': [1], 'cbs 1:00 pm_7': [1]}

['week', 'date', 'opponent', 'result', 'record', 'tv time', 'attendance']

[['1', 'september 12 , 2004', 'cleveland browns', 'l 20 - 3', '0 - 1 - 0', 'cbs 1:00 pm', '73068'], ['2', 'september 19 , 2004', 'pittsburgh steelers', 'w 30 - 13', '1 - 1 - 0', 'cbs 1:00 pm', '69859'], ['3', 'september 26 , 2004', 'cincinnati bengals', 'w 23 - 9', '2 - 1 - 0', 'cbs 1:00 pm', '65575'], ['4', 'october 4 , 2004', 'kansas city chiefs', 'l 27 - 24', '2 - 2 - 0', 'abc 9:00 pm', '69827'], ['5', 'october 10 , 2004', 'washington redskins', 'w 17 - 10', '3 - 2 - 0', 'espn 8:30 pm', '90287'], ['6', '-', '-', '-', '-', '-', ''], ['7', 'october 24 , 2004', 'buffalo bills', 'w 20 - 6', '4 - 2 - 0', 'cbs 1:00 pm', '69809'], ['8', 'october 31 , 2004', 'philadelphia eagles', 'l 15 - 10', '4 - 3 - 0', 'cbs 1:00 pm', '67715'], ['9', 'november 7 , 2004', 'cleveland browns', 'w 27 - 13', '5 - 3 - 0', 'espn 8:30 pm', '69781'], ['10', 'november 14 , 2004', 'new york jets', 'w 20 - 17 ot', '6 - 3 - 0', 'cbs 1:00 pm', '77826'], ['11', 'november 21 , 2004', 'dallas cowboys', 'w 30 - 10', '7 - 3 - 0', 'fox 1:00 pm', '69924'], ['12', 'november 28 , 2004', 'new england patriots', 'l 24 - 3', '7 - 4 - 0', 'cbs 4:15 pm', '68756'], ['13', 'december 5 , 2004', 'cincinnati bengals', 'l 27 - 26', '7 - 5 - 0', 'cbs 1:00 pm', '69695'], ['14', 'december 12 , 2004', 'new york giants', 'w 37 - 14', '8 - 5 - 0', 'fox 1:00 pm', '69856'], ['15', 'december 19 , 2004', 'indianapolis colts', 'l 20 - 10', '8 - 6 - 0', 'espn 8:30 pm', '57240'], ['16', 'december 26 , 2004', 'pittsburgh steelers', 'l 20 - 7', '8 - 7 - 0', 'cbs 1:00 pm', '64227'], ['17', 'january 2 , 2005', 'miami dolphins', 'w 30 - 23', '9 - 7 - 0', 'cbs 1:00 pm', '69843']]

list of townships in north dakota

https://en.wikipedia.org/wiki/List_of_townships_in_North_Dakota

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18600760-24.html.csv

majority

the majority of townships in north dakota have under 40 square miles of land .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '40', 'subset': None}

{'func': 'most_less', 'args': ['all_rows', 'land ( sqmi )', '40'], 'result': True, 'ind': 0, 'tointer': 'for the land ( sqmi ) records of all rows , most of them are less than 40 .', 'tostr': 'most_less { all_rows ; land ( sqmi ) ; 40 } = true'}

most_less { all_rows ; land ( sqmi ) ; 40 } = true

for the land ( sqmi ) records of all rows , most of them are less than 40 .

1

1

{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'land ( sqmi )_3': 3, '40_4': 4}

{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'land ( sqmi )_3': 'land ( sqmi )', '40_4': '40'}

{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'land ( sqmi )_3': [0], '40_4': [0]}

['township', 'county', 'pop ( 2010 )', 'land ( sqmi )', 'water ( sqmi )', 'latitude', 'longitude', 'geo id', 'ansi code']

[['yellowstone', 'mckenzie', '417', '40.198', '2.136', '47.895843', '- 103.997037', '3805387820', '01759523'], ['york', 'benson', '27', '36.028', '0.273', '48.324845', '- 99.533482', '3800587900', '02397901'], ['yorktown', 'dickey', '50', '35.804', '0.000', '46.153339', '- 98.316833', '3802187940', '01036768'], ['young', 'dickey', '35', '34.347', '0.074', '46.230278', '- 98.834821', '3802187980', '01036780'], ['ypsilanti', 'stutsman', '128', '36.026', '0.000', '46.761455', '- 98.502295', '3809388060', '01036451']]

florent piétrus

https://en.wikipedia.org/wiki/Florent_Pi%C3%A9trus

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2761641-1.html.csv

unique

the 2011 eurobasket is the only tournment when florent piétrus had an 3.4 rebounds per game .

{'scope': 'all', 'row': '7', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '3.4', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'rebounds per game', '3.4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rebounds per game record is equal to 3.4 .', 'tostr': 'filter_eq { all_rows ; rebounds per game ; 3.4 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; rebounds per game ; 3.4 } }', 'tointer': 'select the rows whose rebounds per game record is equal to 3.4 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'rebounds per game', '3.4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rebounds per game record is equal to 3.4 .', 'tostr': 'filter_eq { all_rows ; rebounds per game ; 3.4 }'}, 'tournament'], 'result': '2011 eurobasket', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; rebounds per game ; 3.4 } ; tournament }'}, '2011 eurobasket'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; rebounds per game ; 3.4 } ; tournament } ; 2011 eurobasket }', 'tointer': 'the tournament record of this unqiue row is 2011 eurobasket .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; rebounds per game ; 3.4 } } ; eq { hop { filter_eq { all_rows ; rebounds per game ; 3.4 } ; tournament } ; 2011 eurobasket } } = true', 'tointer': 'select the rows whose rebounds per game record is equal to 3.4 . there is only one such row in the table . the tournament record of this unqiue row is 2011 eurobasket .'}

and { only { filter_eq { all_rows ; rebounds per game ; 3.4 } } ; eq { hop { filter_eq { all_rows ; rebounds per game ; 3.4 } ; tournament } ; 2011 eurobasket } } = true

select the rows whose rebounds per game record is equal to 3.4 . there is only one such row in the table . the tournament record of this unqiue row is 2011 eurobasket .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'rebounds per game_7': 7, '3.4_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, '2011 eurobasket_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'rebounds per game_7': 'rebounds per game', '3.4_8': '3.4', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', '2011 eurobasket_10': '2011 eurobasket'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'rebounds per game_7': [0], '3.4_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], '2011 eurobasket_10': [3]}

['tournament', 'games played', 'points per game', 'rebounds per game', 'assists per game']

[['2003 eurobasket', '6', '6.8', '5.3', '0.7'], ['2005 eurobasket', '7', '7.6', '7.1', '0.6'], ['2006 fiba world championship', '9', '9.7', '6.7', '0.6'], ['2007 eurobasket', '7', '8.9', '3.7', '0.6'], ['2009 eurobasket', '8', '6.5', '2.9', '1.1'], ['2010 fiba world championship', '4', '4.5', '4.8', '1.5'], ['2011 eurobasket', '11', '2.6', '3.4', '0.8'], ['2012 olympics', '6', '4.5', '2.8', '0.5']]

list of are you afraid of the dark ? episodes

https://en.wikipedia.org/wiki/List_of_Are_You_Afraid_of_the_Dark%3F_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10470082-3.html.csv

superlative

the episode of the first twelve episodes of the second season of the show are you afraid of the dark ? of the earliest air date had kristen as the storyteller .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '7', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'us air date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; us air date }'}, 'storyteller'], 'result': 'kristen', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; us air date } ; storyteller }'}, 'kristen'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; us air date } ; storyteller } ; kristen } = true', 'tointer': 'select the row whose us air date record of all rows is minimum . the storyteller record of this row is kristen .'}

eq { hop { argmin { all_rows ; us air date } ; storyteller } ; kristen } = true

select the row whose us air date record of all rows is minimum . the storyteller record of this row is kristen .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'us air date_5': 5, 'storyteller_6': 6, 'kristen_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'us air date_5': 'us air date', 'storyteller_6': 'storyteller', 'kristen_7': 'kristen'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'us air date_5': [0], 'storyteller_6': [1], 'kristen_7': [2]}

['no', '-', 'title', 'director', 'writer', 'us air date', 'storyteller', 'villains']

[['14', '1', 'the tale of the final wish', 'd j machale', 'chloe brown', 'june 19 , 1993', 'kristen', 'the sandman'], ['15', '2', 'the tale of the midnight madness', 'd j machale', 'chloe brown', 'june 26 , 1993', 'frank', 'nosferatu and dr vink'], ['16', '3', 'the tale of locker 22', 'david winning', 'chloe brown', 'july 3 , 1993', 'kristen', 'none'], ['17', '4', 'the tale of the thirteenth floor', 'michael keusch', 'anne appelton', 'july 10 , 1993', 'betty ann', 'leonid , olga , and raymond'], ['19', '6', 'the tale of the dark dragon', 'd j machale', 'allison lea bingeman', 'july 24 , 1993', "david ( for gary 's birthday , borrowing sardo )", 'the dark dragon potion'], ['20', '8', 'the tale of the whispering walls', 'd j machale', 'allison lea bingeman', 'july 31 , 1993', 'betty ann', 'master raymond'], ['21', '7', 'the tale of the frozen ghost', 'ron oliver', 'naomi janzen', 'august 14 , 1993', 'kristen', 'none'], ['22', '9', 'the tale of the full moon', 'ron oliver', 'ron oliver', 'august 21 , 1993', 'frank', 'gordon , the werewolf'], ['23', '10', 'the tale of the shiny red bicycle', 'david winning', 'cassandra schafhausen', 'august 28 , 1993', 'david', 'none'], ['24', '11', "the tale of the magician 's assistant", 'ron oliver', 'cassandra schafhausen', 'september 11 , 1993', 'gary', 'nazrak'], ['25', '12', 'the tale of the hatching', 'd j machale', 'chloe brown', 'september 25 , 1993', 'david', 'mr and mrs taylor']]

2008 - 09 guildford flames season

https://en.wikipedia.org/wiki/2008%E2%80%9309_Guildford_Flames_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17120964-7.html.csv

aggregation

for the 2008-09 guildford flames season the total attendance was 5452 .

{'scope': 'all', 'col': '5', 'type': 'sum', 'result': '5452', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '5452', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '5452'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 5452 } = true', 'tointer': 'the sum of the attendance record of all rows is 5452 .'}

round_eq { sum { all_rows ; attendance } ; 5452 } = true

the sum of the attendance record of all rows is 5452 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '5452_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '5452_5': '5452'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '5452_5': [1]}

['date', 'opponent', 'venue', 'result', 'attendance', 'competition', 'man of the match']

[['6th', 'milton keynes lightning', 'away', 'lost 2 - 5', '809', 'league', 'n / a'], ['7th', 'sheffield scimitars', 'home', 'won 4 - 3', '1568', 'league', 'lukas smital'], ['14th', 'peterborough phantoms', 'away', 'lost 2 - 4', '542', 'league', 'joe watkins'], ['26th', 'bracknell bees', 'away', 'lost 1 - 2', '910', 'league', 'david savage'], ['27th', 'peterborough phantoms', 'home', 'won 6 - 5 ( ot )', '1623', 'league', 'lukas smital']]

hampden football netball league

https://en.wikipedia.org/wiki/Hampden_Football_Netball_League

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18628904-27.html.csv

superlative

the club with the highest number of wins is warrnambool .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '12', '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', 'wins'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; wins }'}, 'club'], 'result': 'warrnambool', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; wins } ; club }'}, 'warrnambool'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; wins } ; club } ; warrnambool } = true', 'tointer': 'select the row whose wins record of all rows is maximum . the club record of this row is warrnambool .'}

eq { hop { argmax { all_rows ; wins } ; club } ; warrnambool } = true

select the row whose wins record of all rows is maximum . the club record of this row is warrnambool .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'wins_5': 5, 'club_6': 6, 'warrnambool_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'wins_5': 'wins', 'club_6': 'club', 'warrnambool_7': 'warrnambool'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'wins_5': [0], 'club_6': [1], 'warrnambool_7': [2]}

['club', 'active', 'wins', 'losses', 'draws', 'percentage wins', 'flags']

[['camperdown', '1930 - 2011', '723', '665', '15', '51.53 %', '6'], ['cobden', '1930 - 2011', '640', '733', '17', '46.04 %', '6'], ['colac', '1949 - 2000', '597', '373', '10', '60.92 %', '10'], ['coragulac', '1961 - 1979', '118', '225', '2', '33.91 %', '0'], ['koroit', '1961 - 2011', '431', '528', '8', '44.57 %', '5'], ['mortlake', '1930 - 1998', '473', '633', '18', '42.08 %', '3'], ['north warrnambool', '1997 - 2011', '52', '213', '3', '19.40 %', '0'], ['port fairy', '1949 - 2011', '410', '738', '2', '35.65 %', '1'], ['south warrnambool', '1933 - 2011', '745', '611', '17', '54.26 %', '11'], ['terang', '1930 - 2001', '642', '580', '10', '52.11 %', '8'], ['terang mortlake', '2002 - 2011', '141', '61', '1', '69.46 %', '3'], ['warrnambool', '1933 - 2011', '895', '490', '19', '63.75 %', '23'], ['western lions', '1999 - 2000', '2', '17', '0', '10.5 %', '0']]

1991 open championship

https://en.wikipedia.org/wiki/1991_Open_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18131508-5.html.csv

count

3 players which participated in the 1991 open championship were from the united states .

{'scope': 'all', 'criterion': 'equal', 'value': 'united states', 'result': '3', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; country ; united states }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; united states } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; united states } } ; 3 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; country ; united states } } ; 3 } = true

select the rows whose country record fuzzily matches to united states . 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, 'country_5': 5, 'united states_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', 'country_5': 'country', 'united states_6': 'united states', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], '3_7': [2]}

['place', 'player', 'country', 'score', 'to par']

[['t1', 'gary hallberg', 'united states', '68 + 70 = 138', '- 2'], ['t1', 'mike harwood', 'australia', '68 + 70 = 138', '- 2'], ['t1', 'andrew oldcorn', 'scotland', '71 + 67 = 138', '- 2'], ['t4', 'seve ballesteros', 'spain', '66 + 73 = 139', '- 1'], ['t4', 'steve elkington', 'australia', '71 + 68 = 139', '- 1'], ['t4', 'david gilford', 'england', '72 + 67 = 139', '- 1'], ['t4', 'wayne grady', 'australia', '69 + 70 = 139', '- 1'], ['t4', "mark o'meara", 'united states', '71 + 68 = 139', '- 1'], ['t4', 'mike reid', 'united states', '68 + 71 = 139', '- 1'], ['t10', 'richard boxall', 'england', '71 + 69 = 140', 'e'], ['t10', 'roger chapman', 'england', '74 + 66 = 140', 'e'], ['t10', 'howard clark', 'england', '71 + 69 = 140', 'e'], ['t10', 'mark james', 'england', '72 + 68 = 140', 'e'], ['t10', 'barry lane', 'england', '68 + 72 = 140', 'e'], ['t10', 'colin montgomerie', 'scotland', '71 + 69 = 140', 'e'], ['t10', 'vijay singh', 'fiji', '71 + 69 = 140', 'e']]

mean free path

https://en.wikipedia.org/wiki/Mean_free_path

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-170097-1.html.csv

ordinal

the second largest mean free path occurs in an ultra high vacuum .

{'row': '5', 'col': '5', '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', 'mean free path', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; mean free path ; 2 }'}, 'vacuum range'], 'result': 'ultra high vacuum', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; mean free path ; 2 } ; vacuum range }'}, 'ultra high vacuum'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; mean free path ; 2 } ; vacuum range } ; ultra high vacuum } = true', 'tointer': 'select the row whose mean free path record of all rows is 2nd maximum . the vacuum range record of this row is ultra high vacuum .'}

eq { hop { nth_argmax { all_rows ; mean free path ; 2 } ; vacuum range } ; ultra high vacuum } = true

select the row whose mean free path record of all rows is 2nd maximum . the vacuum range record of this row is ultra high vacuum .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'mean free path_5': 5, '2_6': 6, 'vacuum range_7': 7, 'ultra high vacuum_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', 'mean free path_5': 'mean free path', '2_6': '2', 'vacuum range_7': 'vacuum range', 'ultra high vacuum_8': 'ultra high vacuum'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'mean free path_5': [0], '2_6': [0], 'vacuum range_7': [1], 'ultra high vacuum_8': [2]}

['vacuum range', 'pressure in hpa ( mbar )', 'molecules / cm 3', 'molecules / m 3', 'mean free path']

[['ambient pressure', '1013', '2.7 10 19', '2.7 10 25', '68 nm'], ['low vacuum', '300 - 1', '10 19 - 10 16', '10 25 - 10 22', '0.1 - 100 μm'], ['medium vacuum', '1 - 10 3', '10 16 - 10 13', '10 22 - 10 19', '0.1 - 100 mm'], ['high vacuum', '10 3 - 10 7', '10 13 - 10 9', '10 19 - 10 15', '10 cm - 1 km'], ['ultra high vacuum', '10 7 - 10 12', '10 9 - 10 4', '10 15 - 10 10', '1 km - 10 5 km'], ['extremely high vacuum', '< 10 12', '< 10 4', '< 10 10', '> 10 5 km']]

bermuda national cricket team

https://en.wikipedia.org/wiki/Bermuda_national_cricket_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1829476-2.html.csv

superlative

david hemp was the bermuda cricket player that scored the highest average runs throughout his career .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '2', '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', 'average'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; average }'}, 'player'], 'result': 'david hemp', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; average } ; player }'}, 'david hemp'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; average } ; player } ; david hemp } = true', 'tointer': 'select the row whose average record of all rows is maximum . the player record of this row is david hemp .'}

eq { hop { argmax { all_rows ; average } ; player } ; david hemp } = true

select the row whose average record of all rows is maximum . the player record of this row is david hemp .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'average_5': 5, 'player_6': 6, 'david hemp_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'average_5': 'average', 'player_6': 'player', 'david hemp_7': 'david hemp'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'average_5': [0], 'player_6': [1], 'david hemp_7': [2]}

['rank', 'player', 'runs', 'average', 'career']

[['1', 'irving romaine', '783', '25.25', '2006 - 2009'], ['2', 'david hemp', '641', '33.73', '2006 - 2009'], ['3', 'lionel cann', '590', '26.81', '2006 - 2009'], ['4', 'janeiro tucker', '496', '19.84', '2006 - 2009'], ['5', 'dean minors', '478', '26.55', '2006 - 2007'], ['6', 'steven outerbridge', '336', '14.60', '2006 - 2009']]

1998 cfl draft

https://en.wikipedia.org/wiki/1998_CFL_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16441561-1.html.csv

count

there were two db 's drafted in the 1998 cfl draft .

{'scope': 'all', 'criterion': 'equal', 'value': 'db', 'result': '2', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'db'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to db .', 'tostr': 'filter_eq { all_rows ; position ; db }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; db } }', 'tointer': 'select the rows whose position record fuzzily matches to db . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; db } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to db . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; position ; db } } ; 2 } = true

select the rows whose position record fuzzily matches to db . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'db_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'db_6': 'db', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'db_6': [0], '2_7': [2]}

['pick', 'cfl team', 'player', 'position', 'college']

[['1', 'hamilton tiger - cats', 'tim fleiszer', 'dl', 'harvard'], ['2', 'toronto argonauts', 'dave miller - johnston', 'p / k', 'concordia'], ['3', 'british columbia lions', 'steve hardin', 't', 'oregon'], ['4', 'calgary stampeders', 'marc pilon', 'lb', 'syracuse'], ['5', 'edmonton eskimos', 'phillippe girard', 'db', 'mount allison'], ['6', 'montreal alouettes', 'ben cahoon', 'wr', 'brigham young'], ['7', 'saskatchewan roughriders', 'curtis galick', 'db', 'british columbia']]

1997 pga championship

https://en.wikipedia.org/wiki/1997_PGA_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18059698-6.html.csv

count

in the 1997 pga championship , among the players that scored less than 211 , two of them had -7 to par .

{'scope': 'subset', 'criterion': 'equal', 'value': '-7', 'result': '2', 'col': '5', 'subset': {'col': '4', 'criterion': 'less_than', 'value': '211'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'score', '211'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; score ; 211 }', 'tointer': 'select the rows whose score record is less than 211 .'}, 'to par', '-7'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose score record is less than 211 . among these rows , select the rows whose to par record is equal to -7 .', 'tostr': 'filter_eq { filter_less { all_rows ; score ; 211 } ; to par ; -7 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_less { all_rows ; score ; 211 } ; to par ; -7 } }', 'tointer': 'select the rows whose score record is less than 211 . among these rows , select the rows whose to par record is equal to -7 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less { all_rows ; score ; 211 } ; to par ; -7 } } ; 2 } = true', 'tointer': 'select the rows whose score record is less than 211 . among these rows , select the rows whose to par record is equal to -7 . the number of such rows is 2 .'}

eq { count { filter_eq { filter_less { all_rows ; score ; 211 } ; to par ; -7 } } ; 2 } = true

select the rows whose score record is less than 211 . among these rows , select the rows whose to par record is equal to -7 . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'score_6': 6, '211_7': 7, 'to par_8': 8, '-7_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'score_6': 'score', '211_7': '211', 'to par_8': 'to par', '-7_9': '-7', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'score_6': [0], '211_7': [0], 'to par_8': [1], '-7_9': [1], '2_10': [3]}

['place', 'player', 'country', 'score', 'to par']

[['t1', 'justin leonard', 'united states', '68 + 70 + 65 = 203', '- 7'], ['t1', 'davis love iii', 'united states', '66 + 71 + 66 = 203', '- 7'], ['t3', 'lee janzen', 'united states', '69 + 67 + 74 = 210', 'e'], ['t3', 'tom kite', 'united states', '68 + 71 + 71 = 210', 'e'], ['t5', 'fred couples', 'united states', '71 + 67 + 73 = 211', '+ 1'], ['t5', 'david duval', 'united states', '70 + 70 + 71 = 211', '+ 1'], ['t5', 'scott hoch', 'united states', '71 + 72 + 68 = 211', '+ 1'], ['t5', 'jeff maggert', 'united states', '69 + 69 + 73 = 211', '+ 1'], ['t5', 'phil mickelson', 'united states', '69 + 69 + 73 = 211', '+ 1'], ['t5', 'tiger woods', 'united states', '70 + 70 + 71 = 211', '+ 1']]

woden valley

https://en.wikipedia.org/wiki/Woden_Valley

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1174162-1.html.csv

ordinal

the place with the second lowest population in woden valley was phillip .

{'row': '11', 'col': '2', 'order': '2', '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', 'population ( in 2008 )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; population ( in 2008 ) ; 2 }'}, 'suburb'], 'result': 'phillip', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; population ( in 2008 ) ; 2 } ; suburb }'}, 'phillip'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; population ( in 2008 ) ; 2 } ; suburb } ; phillip } = true', 'tointer': 'select the row whose population ( in 2008 ) record of all rows is 2nd minimum . the suburb record of this row is phillip .'}

eq { hop { nth_argmin { all_rows ; population ( in 2008 ) ; 2 } ; suburb } ; phillip } = true

select the row whose population ( in 2008 ) record of all rows is 2nd minimum . the suburb record of this row is phillip .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'population (in 2008)_5': 5, '2_6': 6, 'suburb_7': 7, 'phillip_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', 'population (in 2008)_5': 'population ( in 2008 )', '2_6': '2', 'suburb_7': 'suburb', 'phillip_8': 'phillip'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'population (in 2008)_5': [0], '2_6': [0], 'suburb_7': [1], 'phillip_8': [2]}

['suburb', 'population ( in 2008 )', 'median age ( in 2006 )', 'mean household size ( in 2006 )', 'area ( km square )', 'density ( / km square )', 'date first settled as a suburb', 'gazetted as a division name']

[['chifley', '2325', '36 years', '2.3 persons', '1.6', '1453', '1966', '12 may 1966'], ['curtin', '5133', '41 years', '2.5 persons', '4.8', '1069', '1962', '20 september 1962'], ['farrer', '3360', '41 years', '2.7 persons', '2.1', '1600', '1967', '12 may 1966'], ['garran', '3175', '39 years', '2.5 persons', '2.7', '1175', '1966', '12 may 1966'], ['hughes', '2898', '41 years', '2.5 persons', '1.8', '1610', '1963', '20 september 1962'], ['isaacs', '2424', '45 years', '2.6 persons', '3.1', '781', '1986', '12 may 1966'], ['lyons', '2444', '38 years', '2.1 persons', '2.3', '1062', '1965', '20 september 1962'], ['mawson', '2861', '40 years', '2.2 persons', '2.1', '1362', '1967', '12 may 1966'], ["o'malley", '684', '47 years', '3.1 persons', '2.6', '263', '1973', '12 may 1966'], ['pearce', '2509', '41 years', '2.3 persons', '1.7', '1475', '1967', '12 may 1966'], ['phillip', '1910', '32 years', '1.7 persons', '2.6', '734', '1966', '12 may 1966']]

list of tournament performances by tiger woods

https://en.wikipedia.org/wiki/List_of_tournament_performances_by_Tiger_Woods

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13298049-7.html.csv

comparative

tiger woods made more money in 2005 than he did in 2006 .

{'row_1': '4', 'row_2': '5', 'col': '10', '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', 'year', '2005'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 2005 .', 'tostr': 'filter_eq { all_rows ; year ; 2005 }'}, 'money ( ￥ )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 2005 } ; money ( ￥ ) }', 'tointer': 'select the rows whose year record fuzzily matches to 2005 . take the money ( ￥ ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2006'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 2006 .', 'tostr': 'filter_eq { all_rows ; year ; 2006 }'}, 'money ( ￥ )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 2006 } ; money ( ￥ ) }', 'tointer': 'select the rows whose year record fuzzily matches to 2006 . take the money ( ￥ ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 2005 } ; money ( ￥ ) } ; hop { filter_eq { all_rows ; year ; 2006 } ; money ( ￥ ) } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 2005 . take the money ( ￥ ) record of this row . select the rows whose year record fuzzily matches to 2006 . take the money ( ￥ ) record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; year ; 2005 } ; money ( ￥ ) } ; hop { filter_eq { all_rows ; year ; 2006 } ; money ( ￥ ) } } = true

select the rows whose year record fuzzily matches to 2005 . take the money ( ￥ ) record of this row . select the rows whose year record fuzzily matches to 2006 . take the money ( ￥ ) 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, 'year_7': 7, '2005_8': 8, 'money (￥)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '2006_12': 12, 'money (￥)_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', 'year_7': 'year', '2005_8': '2005', 'money (￥)_9': 'money ( ￥ )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '2006_12': '2006', 'money (￥)_13': 'money ( ￥ )'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '2005_8': [0], 'money (￥)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '2006_12': [1], 'money (￥)_13': [3]}

['year', 'tournament', 'round 1', 'round 2', 'round 3', 'round 4', 'score', 'to par', 'place', 'money ( ￥ )']

[['1998', 'casio world open', '69', '74', '71', '70', '284', '4', 't15', '1602000'], ['2002', 'dunlop phoenix tournament', '71', '68', '69', '67', '275', '9', '8', '6100000'], ['2004', 'dunlop phoenix tournament', '65', '67', '65', '67', '264', '16', '1', '40000000'], ['2005', 'dunlop phoenix tournament', '65', '67', '68', '72', '272', '8', '1', '40000000'], ['2006', 'dunlop phoenix tournament', '67', '65', '72', '67', '271', '9', '2', '20000000']]