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fm - and tv - mast kosztowy | https://en.wikipedia.org/wiki/FM-_and_TV-mast_Kosztowy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1249698-1.html.csv | ordinal | the program with the 3rd highest frequency has an erp kw value of 3 . | {'row': '8', 'col': '2', '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', 'frequency mhz', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; frequency mhz ; 3 }'}, 'erp kw'], 'result': '3', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; frequency mhz ; 3 } ; erp kw }'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; frequency mhz ; 3 } ; erp kw } ; 3 } = true', 'tointer': 'select the row whose frequency mhz record of all rows is 3rd maximum . the erp kw record of this row is 3 .'} | eq { hop { nth_argmax { all_rows ; frequency mhz ; 3 } ; erp kw } ; 3 } = true | select the row whose frequency mhz record of all rows is 3rd maximum . the erp kw record of this row is 3 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'frequency mhz_5': 5, '3_6': 6, 'erp kw_7': 7, '3_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', 'frequency mhz_5': 'frequency mhz', '3_6': '3', 'erp kw_7': 'erp kw', '3_8': '3'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'frequency mhz_5': [0], '3_6': [0], 'erp kw_7': [1], '3_8': [2]} | ['program', 'frequency mhz', 'erp kw', 'polarisation', 'antenna diagram around ( nd ) / directional ( d )'] | [['rmf fm', '93 , 00', '60', 'horizontal', 'nd'], ['94 , 5 roxy fm', '94 , 50', '0 , 50', 'horizontal', 'd'], ['eska rock', '95 , 50', '1', 'horizontal', 'd'], ['polskie radio program i', '97 , 90', '60', 'horizontal', 'nd'], ['radio rezonans', '99 , 10', '0 , 30', 'horizontal', 'd'], ['polskie radio program iii', '99 , 70', '60', 'horizontal', 'nd'], ['polskie radio katowice', '102 , 20', '60', 'horizontal', 'nd'], ['radio maryja', '103 , 70', '3', 'horizontal', 'd'], ['polskie radio program ii', '105 , 60', '60', 'horizontal', 'nd'], ['radio em', '107 , 60', '60', 'horizontal', 'nd']] |
1959 team speedway polish championship | https://en.wikipedia.org/wiki/1959_Team_Speedway_Polish_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17543955-3.html.csv | unique | in the 1959 team speedway polish championship , for the teams that had under 20 points , the only one with 2 draws was wanda nowa huta . | {'scope': 'subset', 'row': '4', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '2', 'subset': {'col': '3', 'criterion': 'less_than', 'value': '20'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'points', '20'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; points ; 20 }', 'tointer': 'select the rows whose points record is less than 20 .'}, 'draw', '2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 .', 'tostr': 'filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } }', 'tointer': 'select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'points', '20'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; points ; 20 }', 'tointer': 'select the rows whose points record is less than 20 .'}, 'draw', '2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 .', 'tostr': 'filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 }'}, 'team'], 'result': 'wanda nowa huta', 'ind': 3, 'tostr': 'hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team }'}, 'wanda nowa huta'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team } ; wanda nowa huta }', 'tointer': 'the team record of this unqiue row is wanda nowa huta .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } } ; eq { hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team } ; wanda nowa huta } } = true', 'tointer': 'select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 . there is only one such row in the table . the team record of this unqiue row is wanda nowa huta .'} | and { only { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } } ; eq { hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team } ; wanda nowa huta } } = true | select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 . there is only one such row in the table . the team record of this unqiue row is wanda nowa huta . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_eq_1': 1, 'filter_less_0': 0, 'all_rows_7': 7, 'points_8': 8, '20_9': 9, 'draw_10': 10, '2_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'team_12': 12, 'wanda nowa huta_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_eq_1': 'filter_eq', 'filter_less_0': 'filter_less', 'all_rows_7': 'all_rows', 'points_8': 'points', '20_9': '20', 'draw_10': 'draw', '2_11': '2', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'team_12': 'team', 'wanda nowa huta_13': 'wanda nowa huta'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_eq_1': [2, 3], 'filter_less_0': [1], 'all_rows_7': [0], 'points_8': [0], '20_9': [0], 'draw_10': [1], '2_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'team_12': [3], 'wanda nowa huta_13': [4]} | ['team', 'match', 'points', 'draw', 'lost'] | [['stal rzeszów', '14', '27', '1', '0'], ['unia tarnów', '14', '18', '0', '5'], ['stal gorzów wlkp', '14', '16', '0', '6'], ['wanda nowa huta', '14', '14', '2', '6'], ['tramwajarz łódź', '14', '14', '0', '7'], ['skra warszawa', '14', '12', '0', '8'], ['ostrovia ostrów wlkp', '14', '11', '1', '8'], ['stal świętochłowice', '14', '0', '0', '14']] |
2008 dallas cowboys season | https://en.wikipedia.org/wiki/2008_Dallas_Cowboys_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15582870-1.html.csv | superlative | in the 2008 dallas cowboys season , felix jones was the heaviest player picked in round 1 . | {'scope': 'subset', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,3', 'subset': {'col': '1', 'criterion': 'equal', 'value': '1'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'round', '1'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; round ; 1 }', 'tointer': 'select the rows whose round record is equal to 1 .'}, 'weight'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; round ; 1 } ; weight }'}, 'player name'], 'result': 'felix jones', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; round ; 1 } ; weight } ; player name }'}, 'felix jones'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; round ; 1 } ; weight } ; player name } ; felix jones } = true', 'tointer': 'select the rows whose round record is equal to 1 . select the row whose weight record of these rows is maximum . the player name record of this row is felix jones .'} | eq { hop { argmax { filter_eq { all_rows ; round ; 1 } ; weight } ; player name } ; felix jones } = true | select the rows whose round record is equal to 1 . select the row whose weight record of these rows is maximum . the player name record of this row is felix jones . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'round_6': 6, '1_7': 7, 'weight_8': 8, 'player name_9': 9, 'felix jones_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'round_6': 'round', '1_7': '1', 'weight_8': 'weight', 'player name_9': 'player name', 'felix jones_10': 'felix jones'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'round_6': [0], '1_7': [0], 'weight_8': [1], 'player name_9': [2], 'felix jones_10': [3]} | ['round', 'choice', 'player name', 'position', 'height', 'weight', 'college'] | [['1', '22', 'felix jones', 'running back', "6 ' 0", '207', 'arkansas'], ['1', '25', 'mike jenkins', 'cornerback', "6 ' 0", '197', 'south florida'], ['2', '61', 'martellus bennett', 'tight end', "6 ' 6", '259', 'texas a & m'], ['4', '122', 'tashard choice', 'running back', "6 ' 1", '205', 'georgia tech'], ['5', '143', 'orlando scandrick', 'cornerback', "5 ' 11", '198', 'boise state']] |
1962 - 63 segunda división | https://en.wikipedia.org/wiki/1962%E2%80%9363_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17724929-2.html.csv | count | in 1962 - 63 segunda división , there were eight clubs with negative goal difference . | {'scope': 'all', 'criterion': 'less_than', 'value': '0', 'result': '8', 'col': '10', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'goal difference', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goal difference record is less than 0 .', 'tostr': 'filter_less { all_rows ; goal difference ; 0 }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; goal difference ; 0 } }', 'tointer': 'select the rows whose goal difference record is less than 0 . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 8 } = true', 'tointer': 'select the rows whose goal difference record is less than 0 . the number of such rows is 8 .'} | eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 8 } = true | select the rows whose goal difference record is less than 0 . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'goal difference_5': 5, '0_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'goal difference_5': 'goal difference', '0_6': '0', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'goal difference_5': [0], '0_6': [0], '8_7': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'pontevedra cf', '30', '41', '16', '9', '5', '44', '31', '+ 13'], ['2', 'rcd español', '30', '39', '17', '5', '8', '40', '24', '+ 16'], ['3', 'real santander', '30', '37', '15', '7', '8', '53', '39', '+ 14'], ['4', 'real sociedad', '30', '35', '14', '7', '9', '77', '44', '+ 33'], ['5', 'real gijón', '30', '34', '16', '2', '12', '50', '46', '+ 4'], ['6', 'rc celta de vigo', '30', '32', '13', '6', '11', '47', '31', '+ 16'], ['7', 'cd orense', '30', '31', '14', '3', '13', '43', '37', '+ 6'], ['8', 'deportivo alavés', '30', '30', '12', '6', '12', '43', '46', '- 3'], ['9', 'sd indauchu', '30', '30', '11', '8', '11', '46', '42', '+ 4'], ['10', 'burgos cf', '30', '29', '12', '5', '13', '39', '47', '- 8'], ['11', 'ud salamanca', '30', '27', '10', '7', '13', '40', '46', '- 6'], ['12', 'cd constancia', '30', '26', '11', '4', '15', '42', '51', '- 9'], ['13', 'up langreo', '30', '25', '8', '9', '13', '33', '42', '- 9'], ['14', 'atlético baleares', '30', '23', '9', '5', '16', '37', '51', '- 14'], ['15', 'cd basconia', '30', '21', '9', '3', '18', '31', '65', '- 34'], ['16', 'cd sabadell cf', '30', '20', '8', '4', '18', '43', '66', '- 23']] |
imperial vicar | https://en.wikipedia.org/wiki/Imperial_vicar | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11071897-1.html.csv | ordinal | the interregnum that began with the 20 october 1740 death of charles vi was the second longest imperial vicar interregnum . | {'row': '8', 'col': '3', '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', 'duration', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; duration ; 2 }'}, 'interregnum began'], 'result': '20 october 1740 death of charles vi', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; duration ; 2 } ; interregnum began }'}, '20 october 1740 death of charles vi'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; duration ; 2 } ; interregnum began } ; 20 october 1740 death of charles vi } = true', 'tointer': 'select the row whose duration record of all rows is 2nd maximum . the interregnum began record of this row is 20 october 1740 death of charles vi .'} | eq { hop { nth_argmax { all_rows ; duration ; 2 } ; interregnum began } ; 20 october 1740 death of charles vi } = true | select the row whose duration record of all rows is 2nd maximum . the interregnum began record of this row is 20 october 1740 death of charles vi . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'duration_5': 5, '2_6': 6, 'interregnum began_7': 7, '20 october 1740 death of charles vi_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', 'duration_5': 'duration', '2_6': '2', 'interregnum began_7': 'interregnum began', '20 october 1740 death of charles vi_8': '20 october 1740 death of charles vi'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'duration_5': [0], '2_6': [0], 'interregnum began_7': [1], '20 october 1740 death of charles vi_8': [2]} | ['interregnum began', 'interregnum ended', 'duration', 'count palatine of saxony', 'count palatine of the rhine'] | [['9 december 1437 death of sigismund', '18 march 1438 election of albert ii', '3 months , 9 days', 'frederick ii , elector of saxony', 'louis iv , elector palatine'], ['27 october 1439 death of albert ii', '2 february 1440 election of frederick iii', '3 months , 6 days', 'frederick ii , elector of saxony', 'louis iv , elector palatine'], ['12 january 1519 death of maximilian i', '17 june 1519 election of charles v', '5 months , 5 days', 'frederick iii , elector of saxony', 'louis v , elector palatine'], ['20 january 1612 death of rudolph ii', '13 june 1612 election of matthias', '4 months , 24 days', 'john george i , elector of saxony', 'frederick v , elector palatine'], ['20 march 1619 death of matthias', '28 august 1619 election of ferdinand ii', '5 months , 8 days', 'john george i , elector of saxony', 'frederick v , elector palatine'], ['2 april 1657 death of ferdinand iii', '18 july 1658 election of leopold i', '15 months , 16 days', 'john george ii , elector of saxony', 'ferdinand maria , elector of bavaria'], ['17 april 1711 death of joseph i', '12 october 1711 election of charles vi', '5 months , 25 days', 'frederick augustus i , elector of saxony', 'john william , elector palatine'], ['20 october 1740 death of charles vi', '14 january 1742 election of charles vii', '14 months , 25 days', 'frederick augustus ii , elector of saxony', 'charles albert , elector of bavaria'], ['20 january 1745 death of charles vii', '13 september 1745 election of francis i', '7 months , 24 days', 'frederick augustus ii , elector of saxony', 'maximilian iii , elector of bavaria'], ['20 february 1790 death of joseph ii', '30 september 1790 election of leopold ii', '7 months , 10 days', 'frederick augustus iii , elector of saxony', 'charles theodore , elector of bavaria'], ['1 march 1792 death of leopold ii', '5 july 1792 election of francis ii', '4 months , 4 days', 'frederick augustus iii , elector of saxony', 'charles theodore , elector of bavaria']] |
weightlifting at the 1999 pan american games | https://en.wikipedia.org/wiki/Weightlifting_at_the_1999_Pan_American_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11279593-15.html.csv | aggregation | in weightlifting at the 1999 pan american games , female contenders averaged a bodyweight of 100.55 kg . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '100.55', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'bodyweight'], 'result': '100.55', 'ind': 0, 'tostr': 'avg { all_rows ; bodyweight }'}, '100.55'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; bodyweight } ; 100.55 } = true', 'tointer': 'the average of the bodyweight record of all rows is 100.55 .'} | round_eq { avg { all_rows ; bodyweight } ; 100.55 } = true | the average of the bodyweight record of all rows is 100.55 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'bodyweight_4': 4, '100.55_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'bodyweight_4': 'bodyweight', '100.55_5': '100.55'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'bodyweight_4': [0], '100.55_5': [1]} | ['name', 'bodyweight', 'snatch', 'clean & jerk', 'total ( kg )'] | [['cheryl haworth ( usa )', '136.16', '117.5', '135.0', '252.5'], ['marã\xada isabel urrutia ( col )', '89.06', '107.5', '140.0', '247.5'], ['carmenza delgado ( col )', '88.61', '110.0', '135.0', '245.0'], ['nelly acosta ( pur )', '87.50', '95.0', '105.0', '200.0'], ['suzanne dandenault ( can )', '101.43', '85.0', '112.5', '197.5']] |
usa today all - usa high school baseball team | https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_baseball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11677100-1.html.csv | count | two players that received the the usa today all award played as catchers . | {'scope': 'all', 'criterion': 'equal', 'value': 'catcher', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'catcher'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to catcher .', 'tostr': 'filter_eq { all_rows ; position ; catcher }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; catcher } }', 'tointer': 'select the rows whose position record fuzzily matches to catcher . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; catcher } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to catcher . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; position ; catcher } } ; 2 } = true | select the rows whose position record fuzzily matches to catcher . 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, 'catcher_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', 'catcher_6': 'catcher', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'catcher_6': [0], '2_7': [2]} | ['year', 'player', 'position', 'high school', 'hometown', 'mlb draft'] | [['1989', 'tyler houston', 'catcher', 'valley high school', 'las vegas , nv', '1st round - 2nd pick of 1989 draft ( braves )'], ['1990', 'todd van poppel', 'pitcher', 'martin high school', 'arlington , tx', "1st round - 14th pick of 1990 draft ( a 's )"], ['1991', 'brien taylor', 'pitcher', 'east carteret high school', 'beaufort , nc', '1st round - 1st pick of 1991 draft ( yankees )'], ['1992', 'derek jeter', 'infielder', 'central high school', 'kalamazoo , mi', '1st round - 6th pick of 1992 draft ( yankees )'], ['1993', 'alex rodriguez', 'infielder', 'westminster christian school', 'miami , fl', '1st round - 1st pick of 1993 draft ( mariners )'], ['1994', 'doug million', 'pitcher', 'sarasota high school', 'sarasota , fl', '1st round - 7th pick of 1994 draft ( rockies )'], ['1995', 'ben davis', 'catcher', 'malvern prep', 'malvern , pa', '1st round - 2nd pick of 1995 draft ( padres )'], ['1996', 'matt white', 'pitcher', 'waynesboro high school', 'waynesboro , pa', '1st round - 7th pick of 1996 draft ( giants )'], ['1997', 'rick ankiel', 'pitcher', 'port st lucie high school', 'port st lucie , fl', '2nd round - 72nd pick of 1997 draft ( cardinals )']] |
westmorland county , new brunswick | https://en.wikipedia.org/wiki/Westmorland_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-176529-1.html.csv | ordinal | dieppe has the second lowest census ranking of westmorland county , new brunswick . | {'row': '2', 'col': '5', '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', 'census ranking', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; census ranking ; 2 }'}, 'official name'], 'result': 'dieppe', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; census ranking ; 2 } ; official name }'}, 'dieppe'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; census ranking ; 2 } ; official name } ; dieppe } = true', 'tointer': 'select the row whose census ranking record of all rows is 2nd minimum . the official name record of this row is dieppe .'} | eq { hop { nth_argmin { all_rows ; census ranking ; 2 } ; official name } ; dieppe } = true | select the row whose census ranking record of all rows is 2nd minimum . the official name record of this row is dieppe . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'census ranking_5': 5, '2_6': 6, 'official name_7': 7, 'dieppe_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', 'census ranking_5': 'census ranking', '2_6': '2', 'official name_7': 'official name', 'dieppe_8': 'dieppe'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'census ranking_5': [0], '2_6': [0], 'official name_7': [1], 'dieppe_8': [2]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['moncton', 'city', '141.17', '69074', '79 of 5008'], ['dieppe', 'city', '51.17', '23310', '174 of 5008'], ['beaubassin east', 'rural community', '291.04', '6200', '600 of 5008'], ['shediac', 'town', '11.97', '6053', '610 of 5008'], ['sackville', 'town', '74.32', '5558', '655 of 5008'], ['memramcook', 'village', '185.71', '4831', '727 of 5008'], ['cap - pelã', 'village', '23.78', '2256', '1229 of 5008'], ['salisbury', 'village', '13.68', '2208', '1243 of 5008'], ['petitcodiac', 'village', '17.22', '1429', '1658 of 5008'], ['dorchester', 'village', '5.74', '1167', '1878 of 5008'], ['port elgin', 'village', '2.61', '418', '3238 of 5008']] |
2009 - 10 washington capitals season | https://en.wikipedia.org/wiki/2009%E2%80%9310_Washington_Capitals_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23308178-4.html.csv | superlative | the washington capitals had the highest number of attendance in the first 12 games of the 2009 – 2010 versus the philadelphia flyers at 19,567 people . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '3', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'attendance'], 'result': '19567', 'ind': 0, 'tostr': 'max { all_rows ; attendance }', 'tointer': 'the maximum attendance record of all rows is 19567 .'}, '19567'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; attendance } ; 19567 }', 'tointer': 'the maximum attendance record of all rows is 19567 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; attendance }'}, 'opponent'], 'result': 'philadelphia flyers', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; attendance } ; opponent }'}, 'philadelphia flyers'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; opponent } ; philadelphia flyers }', 'tointer': 'the opponent record of the row with superlative attendance record is philadelphia flyers .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; attendance } ; 19567 } ; eq { hop { argmax { all_rows ; attendance } ; opponent } ; philadelphia flyers } } = true', 'tointer': 'the maximum attendance record of all rows is 19567 . the opponent record of the row with superlative attendance record is philadelphia flyers .'} | and { eq { max { all_rows ; attendance } ; 19567 } ; eq { hop { argmax { all_rows ; attendance } ; opponent } ; philadelphia flyers } } = true | the maximum attendance record of all rows is 19567 . the opponent record of the row with superlative attendance record is philadelphia flyers . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'attendance_8': 8, '19567_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'attendance_11': 11, 'opponent_12': 12, 'philadelphia flyers_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'attendance_8': 'attendance', '19567_9': '19567', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'attendance_11': 'attendance', 'opponent_12': 'opponent', 'philadelphia flyers_13': 'philadelphia flyers'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'attendance_8': [0], '19567_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'attendance_11': [2], 'opponent_12': [3], 'philadelphia flyers_13': [4]} | ['game', 'date', 'opponent', 'score', 'location', 'attendance', 'record', 'points'] | [['1', 'october 1', 'boston bruins', '4 - 1', 'td garden', '17565', '1 - 0 - 0', '2'], ['2', 'october 3', 'toronto maple leafs', '6 - 4', 'verizon center', '18277', '2 - 0 - 0', '4'], ['3', 'october 6', 'philadelphia flyers', '6 - 5 ot', 'wachovia center', '19567', '2 - 0 - 1', '5'], ['4', 'october 8', 'new york rangers', '4 - 3', 'verizon center', '18277', '2 - 1 - 1', '5'], ['5', 'october 10', 'detroit red wings', '3 - 2', 'joe louis arena', '19122', '2 - 2 - 1', '5'], ['6', 'october 12', 'new jersey devils', '3 - 2 so', 'verizon center', '18277', '2 - 2 - 2', '6'], ['7', 'october 15', 'san jose sharks', '4 - 1', 'verizon center', '18277', '3 - 2 - 2', '8'], ['8', 'october 17', 'nashville predators', '3 - 2 so', 'verizon center', '18277', '4 - 2 - 2', '10'], ['9', 'october 22', 'atlanta thrashers', '5 - 4', 'philips arena', '13192', '5 - 2 - 2', '12'], ['10', 'october 24', 'new york islanders', '3 - 2 ot', 'nassau veterans memorial coliseum', '11541', '6 - 2 - 2', '14'], ['11', 'october 27', 'philadelphia flyers', '4 - 2', 'verizon center', '18277', '7 - 2 - 2', '16'], ['12', 'october 29', 'atlanta thrashers', '4 - 3', 'philips arena', '12893', '8 - 2 - 2', '18']] |
1968 cleveland browns season | https://en.wikipedia.org/wiki/1968_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10652150-2.html.csv | superlative | the game played on week 5 of the 1968 cleveland browns season drew the highest attendance . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'week'], 'result': '5', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; week }'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; week } ; 5 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the week record of this row is 5 .'} | eq { hop { argmax { all_rows ; attendance } ; week } ; 5 } = true | select the row whose attendance record of all rows is maximum . the week record of this row is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'week_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'week_6': 'week', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'week_6': [1], '5_7': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'august 9 , 1968', 'los angeles rams', 'l 23 - 21', '64020'], ['2', 'august 18 , 1968', 'san francisco 49ers', 'w 31 - 17', '26801'], ['3', 'august 24 , 1968', 'new orleans saints', 'l 40 - 27', '70045'], ['4', 'august 30 , 1968', 'buffalo bills', 'w 22 - 12', '45448'], ['5', 'september 7 , 1968', 'green bay packers', 'l 31 - 9', '84918']] |
1998 australian touring car championship | https://en.wikipedia.org/wiki/1998_Australian_Touring_Car_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15284222-2.html.csv | count | russell ingall won a total of three races in the 1998 australian touring car championship . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'russell ingall', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'russell ingall'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to russell ingall .', 'tostr': 'filter_eq { all_rows ; winner ; russell ingall }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; winner ; russell ingall } }', 'tointer': 'select the rows whose winner record fuzzily matches to russell ingall . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; winner ; russell ingall } } ; 3 } = true', 'tointer': 'select the rows whose winner record fuzzily matches to russell ingall . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; winner ; russell ingall } } ; 3 } = true | select the rows whose winner record fuzzily matches to russell ingall . 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, 'winner_5': 5, 'russell ingall_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', 'winner_5': 'winner', 'russell ingall_6': 'russell ingall', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'winner_5': [0], 'russell ingall_6': [0], '3_7': [2]} | ['race title', 'circuit', 'location / state', 'date', 'winner', 'team'] | [['sandown', 'sandown international motor raceway', 'melbourne , victoria', '30 jan - 1 feb', 'craig lowndes', 'holden racing team'], ['launceston', 'symmons plains international raceway', 'launceston , tasmania', '6 - 8 feb', 'craig lowndes', 'holden racing team'], ['lakeside', 'lakeside international raceway', 'brisbane , queensland', '27 - 29 mar', 'russell ingall', 'castrol perkins motorsport'], ['phillip island', 'phillip island grand prix circuit', 'phillip island , victoria', '17 - 19 apr', 'craig lowndes', 'holden racing team'], ['winton', 'winton motor raceway', 'benalla , victoria', '1 - 3 may', 'john bowe', 'dick johnson racing'], ['mallala', 'mallala motor sport park', 'mallala , south australia', '22 - 24 may', 'russell ingall', 'castrol perkins motorsport'], ['vb 300', 'barbagallo raceway', 'perth , western australia', '29 - 31 may', 'craig lowndes', 'holden racing team'], ['calder', 'calder park raceway', 'melbourne , victoria', '19 - 21 jun', 'craig lowndes', 'holden racing team'], ['hidden valley', 'hidden valley raceway', 'darwin , northern territory', '17 - 19 jul', 'russell ingall', 'castrol perkins motorsport'], ['oran park', 'oran park international raceway', 'sydney , new south wales', '31 jul - 2 aug', 'craig lowndes', 'holden racing team']] |
tom kristensen | https://en.wikipedia.org/wiki/Tom_Kristensen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1802063-1.html.csv | count | there were four years that tom kristensen did not finish in his races . | {'scope': 'all', 'criterion': 'equal', 'value': 'dnf', 'result': '4', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pos', 'dnf'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pos record fuzzily matches to dnf .', 'tostr': 'filter_eq { all_rows ; pos ; dnf }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; pos ; dnf } }', 'tointer': 'select the rows whose pos record fuzzily matches to dnf . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; pos ; dnf } } ; 4 } = true', 'tointer': 'select the rows whose pos record fuzzily matches to dnf . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; pos ; dnf } } ; 4 } = true | select the rows whose pos record fuzzily matches to dnf . 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, 'pos_5': 5, 'dnf_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', 'pos_5': 'pos', 'dnf_6': 'dnf', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'pos_5': [0], 'dnf_6': [0], '4_7': [2]} | ['year', 'team', 'co - drivers', 'class', 'laps', 'pos'] | [['1997', 'joest racing', 'michele alboreto stefan johansson', 'lmp', '361', '1st'], ['1998', 'team bmw motorsport', 'hans joachim stuck steve soper', 'lmp1', '60', 'dnf'], ['1999', 'team bmw motorsport', 'jj lehto jörg müller', 'lmp', '304', 'dnf'], ['2000', 'audi sport team joest', 'frank biela emanuele pirro', 'lmp900', '368', '1st'], ['2001', 'audi sport team joest', 'frank biela emanuele pirro', 'lmp900', '321', '1st'], ['2002', 'audi sport team joest', 'frank biela emanuele pirro', 'lmp900', '375', '1st'], ['2003', 'team bentley', 'rinaldo capello guy smith', 'lmgtp', '377', '1st'], ['2004', 'audi sport japan team goh', 'seiji ara rinaldo capello', 'lmp1', '379', '1st'], ['2005', 'adt champion racing', 'jj lehto marco werner', 'lmp1', '370', '1st'], ['2006', 'audi sport team joest', 'rinaldo capello allan mcnish', 'lmp1', '367', '3rd'], ['2007', 'audi sport north america', 'rinaldo capello allan mcnish', 'lmp1', '262', 'dnf'], ['2008', 'audi sport north america', 'rinaldo capello allan mcnish', 'lmp1', '381', '1st'], ['2009', 'audi sport team joest', 'rinaldo capello allan mcnish', 'lmp1', '376', '3rd'], ['2010', 'audi sport team joest', 'rinaldo capello allan mcnish', 'lmp1', '394', '3rd'], ['2011', 'audi sport north america', 'rinaldo capello allan mcnish', 'lmp1', '14', 'dnf'], ['2012', 'audi sport team joest', 'allan mcnish rinaldo capello', 'lmp1', '377', '2nd'], ['2013', 'audi sport team joest', 'allan mcnish loïc duval', 'lmp1', '348', '1st']] |
miami valley conference | https://en.wikipedia.org/wiki/Miami_Valley_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13456202-1.html.csv | superlative | the school with the earliest founding date is lockland high school . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'founded'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; founded }'}, 'school'], 'result': 'lockland high school', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; founded } ; school }'}, 'lockland high school'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; founded } ; school } ; lockland high school } = true', 'tointer': 'select the row whose founded record of all rows is minimum . the school record of this row is lockland high school .'} | eq { hop { argmin { all_rows ; founded } ; school } ; lockland high school } = true | select the row whose founded record of all rows is minimum . the school record of this row is lockland high school . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'founded_5': 5, 'school_6': 6, 'lockland high school_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'founded_5': 'founded', 'school_6': 'school', 'lockland high school_7': 'lockland high school'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'founded_5': [0], 'school_6': [1], 'lockland high school_7': [2]} | ['school', 'location', 'founded', 'affiliation', 'mascot', 'division'] | [['cincinnati country day school', 'cincinnati , ohio', '1926', 'private', 'indians', 'gray'], ['cincinnati christian schools', 'fairfield , ohio', '1989', 'private christian', 'cougars', 'gray'], ['cincinnati hills christian academy', 'cincinnati , ohio', '1989', 'private christian', 'eagles', 'scarlet'], ['lockland high school', 'cincinnati , ohio', '1851', 'public / open enrollment', 'panthers', 'scarlet'], ['clark montessori high school', 'cincinnati , ohio', '1994', 'public', 'cougars', 'gray'], ['north college hill high school', 'cincinnati , ohio', '1901', 'public', 'trojans', 'scarlet'], ['new miami high school', 'new miami , ohio', '1972', 'public / open enrollment', 'vikings', 'gray'], ['seven hills school', 'cincinnati , ohio', '1906', 'private', 'stingers', 'scarlet'], ['st bernard - elmwood place high school', 'cincinnati , ohio', '1900', 'public / open enrollment', 'titans', 'gray']] |
the whole thing 's started | https://en.wikipedia.org/wiki/The_Whole_Thing%27s_Started | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17071146-1.html.csv | comparative | of the 7-inch single releases of the album the whole thing 's started , that 's how the whole thing started is longer than do what you do . | {'row_1': '3', 'row_2': '1', 'col': '3', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tracks', "that 's how the whole thing started"], 'result': None, 'ind': 0, 'tointer': "select the rows whose tracks record fuzzily matches to that 's how the whole thing started .", 'tostr': "filter_eq { all_rows ; tracks ; that 's how the whole thing started }"}, 'length'], 'result': None, 'ind': 2, 'tostr': "hop { filter_eq { all_rows ; tracks ; that 's how the whole thing started } ; length }", 'tointer': "select the rows whose tracks record fuzzily matches to that 's how the whole thing started . take the length record of this row ."}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tracks', 'do what you do'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tracks record fuzzily matches to do what you do .', 'tostr': 'filter_eq { all_rows ; tracks ; do what you do }'}, 'length'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tracks ; do what you do } ; length }', 'tointer': 'select the rows whose tracks record fuzzily matches to do what you do . take the length record of this row .'}], 'result': True, 'ind': 4, 'tostr': "greater { hop { filter_eq { all_rows ; tracks ; that 's how the whole thing started } ; length } ; hop { filter_eq { all_rows ; tracks ; do what you do } ; length } } = true", 'tointer': "select the rows whose tracks record fuzzily matches to that 's how the whole thing started . take the length record of this row . select the rows whose tracks record fuzzily matches to do what you do . take the length record of this row . the first record is greater than the second record ."} | greater { hop { filter_eq { all_rows ; tracks ; that 's how the whole thing started } ; length } ; hop { filter_eq { all_rows ; tracks ; do what you do } ; length } } = true | select the rows whose tracks record fuzzily matches to that 's how the whole thing started . take the length record of this row . select the rows whose tracks record fuzzily matches to do what you do . take the length 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, 'tracks_7': 7, "that 's how the whole thing started_8": 8, 'length_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tracks_11': 11, 'do what you do_12': 12, 'length_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', 'tracks_7': 'tracks', "that 's how the whole thing started_8": "that 's how the whole thing started", 'length_9': 'length', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tracks_11': 'tracks', 'do what you do_12': 'do what you do', 'length_13': 'length'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tracks_7': [0], "that 's how the whole thing started_8": [0], 'length_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tracks_11': [1], 'do what you do_12': [1], 'length_13': [3]} | ['date', 'tracks', 'length', 'label', 'catalog'] | [['1977', 'do what you do', '3:47', 'cbs', 'ba 222304'], ['1977', "it 's automatic", '2:57', 'cbs', 'ba 222304'], ['1977', "that 's how the whole thing started", '4:03', 'cbs', 'ba 222325'], ['1977', "there 's nothing i can do", '3:38', 'cbs', 'ba 222325'], ['1978', 'do it again', '3:35', 'columbia', 'c4 - 8217'], ['1978', 'empty pages', '4:20', 'columbia', 'c4 - 8217']] |
barbara boxer | https://en.wikipedia.org/wiki/Barbara_Boxer | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-197446-1.html.csv | count | barbara boxer was elected five times to the house of representatives . | {'scope': 'all', 'criterion': 'equal', 'value': 'representative', 'result': '5', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'office', 'representative'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose office record fuzzily matches to representative .', 'tostr': 'filter_eq { all_rows ; office ; representative }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; office ; representative } }', 'tointer': 'select the rows whose office record fuzzily matches to representative . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; office ; representative } } ; 5 } = true', 'tointer': 'select the rows whose office record fuzzily matches to representative . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; office ; representative } } ; 5 } = true | select the rows whose office record fuzzily matches to representative . 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, 'office_5': 5, 'representative_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', 'office_5': 'office', 'representative_6': 'representative', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'office_5': [0], 'representative_6': [0], '5_7': [2]} | ['office', 'branch', 'location', 'elected', 'term began', 'term ended'] | [['representative', 'legislative', 'washington , dc', '1982', 'january 3 , 1983', 'january 3 , 1985'], ['representative', 'legislative', 'washington , dc', '1984', 'january 3 , 1985', 'january 3 , 1987'], ['representative', 'legislative', 'washington , dc', '1986', 'january 3 , 1987', 'january 3 , 1989'], ['representative', 'legislative', 'washington , dc', '1988', 'january 3 , 1989', 'january 3 , 1991'], ['representative', 'legislative', 'washington , dc', '1990', 'january 3 , 1991', 'january 3 , 1993'], ['senator', 'legislative', 'washington , dc', '1992', 'january 3 , 1993', 'january 3 , 1999'], ['senator', 'legislative', 'washington , dc', '1998', 'january 3 , 1999', 'january 3 , 2005'], ['senator', 'legislative', 'washington , dc', '2004', 'january 3 , 2005', 'january 3 , 2011']] |
cleethorpes coast light railway | https://en.wikipedia.org/wiki/Cleethorpes_Coast_Light_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1158066-2.html.csv | unique | the ted railway train was the only one to be colored brown . | {'scope': 'all', 'row': '1', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'brown', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'colour', 'brown'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose colour record fuzzily matches to brown .', 'tostr': 'filter_eq { all_rows ; colour ; brown }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; colour ; brown } }', 'tointer': 'select the rows whose colour record fuzzily matches to brown . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'colour', 'brown'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose colour record fuzzily matches to brown .', 'tostr': 'filter_eq { all_rows ; colour ; brown }'}, 'name'], 'result': 'ted', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; colour ; brown } ; name }'}, 'ted'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; colour ; brown } ; name } ; ted }', 'tointer': 'the name record of this unqiue row is ted .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; colour ; brown } } ; eq { hop { filter_eq { all_rows ; colour ; brown } ; name } ; ted } } = true', 'tointer': 'select the rows whose colour record fuzzily matches to brown . there is only one such row in the table . the name record of this unqiue row is ted .'} | and { only { filter_eq { all_rows ; colour ; brown } } ; eq { hop { filter_eq { all_rows ; colour ; brown } ; name } ; ted } } = true | select the rows whose colour record fuzzily matches to brown . there is only one such row in the table . the name record of this unqiue row is ted . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'colour_7': 7, 'brown_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'ted_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'colour_7': 'colour', 'brown_8': 'brown', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'ted_10': 'ted'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'colour_7': [0], 'brown_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'ted_10': [3]} | ['name', 'built', 'wheels', 'fuel / trans', 'status', 'colour'] | [['ted', 'lister 1944', '0 - 4 - 0', 'diesel - mechanical', 'under rebuild', 'brown'], ['the cub / john', 'minirail 1954', '0 - 4 - 0 bo', 'diesel - hydraulic', 'stored', 'grey undercoat'], ['battison', 'battison 1958', '2 - 6 - 4de', 'diesel - electric', 'out of service', 'lner black'], ['dudley', 'g & s light engineering 1946', 'bo - bo', '4 petrol - mechanical', 'on display', 'grey & red'], ['da1', 'bush mill railway 1986', '0 - 4 - 0', 'diesel - mechanical', 'in service', 'royal blue with white linings'], ['kd1', 'unknown', 'articulated', 'diesel electric', 'long term restoration , stored', 'ran in a red livery previously']] |
indiana high school athletics conferences : ohio river valley - western indiana | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Ohio_River_Valley_%E2%80%93_Western_Indiana | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18974097-5.html.csv | superlative | the highest enrolment in the indiana high school athletics conferences : ohio river valley - western indiana was 580 . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'enrollment'], 'result': '580', 'ind': 0, 'tostr': 'max { all_rows ; enrollment }', 'tointer': 'the maximum enrollment record of all rows is 580 .'}, '580'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; enrollment } ; 580 } = true', 'tointer': 'the maximum enrollment record of all rows is 580 .'} | eq { max { all_rows ; enrollment } ; 580 } = true | the maximum enrollment record of all rows is 580 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'enrollment_4': 4, '580_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'enrollment_4': 'enrollment', '580_5': '580'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'enrollment_4': [0], '580_5': [1]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'county'] | [['boone grove', 'valparaiso', 'wolves', '543', 'aa', '64 porter'], ['hanover central', 'cedar lake', 'wildcats', '580', 'aaa', '45 lake'], ['hebron', 'hebron', 'hawks', '340', 'aa', '64 porter'], ['kouts', 'kouts', 'mustangs / fillies', '257', 'a', '64 porter'], ['lacrosse', 'lacrosse', 'tigers', '109', 'a', '46 laporte'], ['morgan township', 'valparaiso', 'cherokees', '220', 'a', '64 porter'], ['south central union mills', 'union mills', 'satellites', '297', 'a', '46 la porte'], ['washington township', 'valparaiso', 'senators', '264', 'a', '64 porter']] |
list of hartford whalers draft picks | https://en.wikipedia.org/wiki/List_of_Hartford_Whalers_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18278177-5.html.csv | superlative | the closest team hartford whalers came to getting the first draft pick was the 11th pick . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '5', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'pick'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; pick }'}, 'nhl team'], 'result': 'hartford whalers', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; pick } ; nhl team }'}, 'hartford whalers'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; pick } ; nhl team } ; hartford whalers } = true', 'tointer': 'select the row whose pick record of all rows is minimum . the nhl team record of this row is hartford whalers .'} | eq { hop { argmin { all_rows ; pick } ; nhl team } ; hartford whalers } = true | select the row whose pick record of all rows is minimum . the nhl team record of this row is hartford whalers . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, 'nhl team_6': 6, 'hartford whalers_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'pick_5': 'pick', 'nhl team_6': 'nhl team', 'hartford whalers_7': 'hartford whalers'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], 'nhl team_6': [1], 'hartford whalers_7': [2]} | ['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team'] | [['11', 'chris govedaris', 'left wing', 'canada', 'hartford whalers', 'toronto marlboros ( ohl )'], ['32', 'barry richter', 'defence', 'united states', 'hartford whalers', 'culver military academy ( ushs - in )'], ['74', 'dean dyer', 'centre', 'canada', 'hartford whalers', 'lake superior state university ( ncaa )'], ['95', 'scott morrow', 'left wing', 'united states', 'hartford whalers', 'northwood school ( ushs - ny )'], ['137', 'kerry russell', 'c', 'canada', 'hartford whalers', 'michigan state university ( ncaa )'], ['158', 'jim burke', 'd', 'united states', 'hartford whalers', 'university of maine ( ncaa )'], ['179', 'mark hirth', 'c', 'united states', 'hartford whalers', 'michigan state university ( ncaa )'], ['200', 'wayde bucsis', 'lw', 'canada', 'hartford whalers', 'prince albert raiders ( whl )'], ['221', 'rob white', 'd', 'canada', 'hartford whalers', 'st lawrence university ( ncaa )'], ['242', 'dan slatalla', 'c', 'united states', 'hartford whalers', 'deerfield academy ( ushs - ma )']] |
world figure skating championships cumulative medal count | https://en.wikipedia.org/wiki/World_Figure_Skating_Championships_cumulative_medal_count | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15409776-4.html.csv | majority | most of the countries in the world figure skating championships have won less than 10 total medals . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '10', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'total', '10'], 'result': True, 'ind': 0, 'tointer': 'for the total records of all rows , most of them are less than 10 .', 'tostr': 'most_less { all_rows ; total ; 10 } = true'} | most_less { all_rows ; total ; 10 } = true | for the total records of all rows , most of them are less than 10 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'total_3': 3, '10_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'total_3': 'total', '10_4': '10'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'total_3': [0], '10_4': [0]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'great britain', '17', '10', '7', '34'], ['2', 'soviet union', '16', '14', '8', '38'], ['3', 'russia', '12', '5', '5', '22'], ['4', 'czechoslovakia', '4', '0', '0', '4'], ['5', 'canada', '3', '10', '11', '24'], ['6', 'france', '3', '6', '4', '13'], ['7', 'united states', '2', '9', '17', '28'], ['8', 'bulgaria', '2', '1', '1', '4'], ['9', 'hungary', '1', '1', '1', '3'], ['9', 'italy', '1', '1', '1', '3'], ['11', 'germany', '0', '3', '2', '5'], ['12', 'finland', '0', '1', '1', '2'], ['13', 'israel', '0', '0', '1', '1'], ['13', 'lithuania', '0', '0', '1', '1'], ['13', 'ukraine', '0', '0', '1', '1']] |
list of bangladesh test wicket - keepers | https://en.wikipedia.org/wiki/List_of_Bangladesh_Test_wicket-keepers | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12415346-1.html.csv | aggregation | total dismissals among bangladesh test wicket - keepers between 2000 and 2007 with fewer than 10 dismissals each was 8 . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '8', 'subset': {'col': '6', 'criterion': 'less_than', 'value': '10'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'total dismissals', '10'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; total dismissals ; 10 }', 'tointer': 'select the rows whose total dismissals record is less than 10 .'}, 'total dismissals'], 'result': '8', 'ind': 1, 'tostr': 'sum { filter_less { all_rows ; total dismissals ; 10 } ; total dismissals }'}, '8'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_less { all_rows ; total dismissals ; 10 } ; total dismissals } ; 8 } = true', 'tointer': 'select the rows whose total dismissals record is less than 10 . the sum of the total dismissals record of these rows is 8 .'} | round_eq { sum { filter_less { all_rows ; total dismissals ; 10 } ; total dismissals } ; 8 } = true | select the rows whose total dismissals record is less than 10 . the sum of the total dismissals record of these rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'total dismissals_5': 5, '10_6': 6, 'total dismissals_7': 7, '8_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'total dismissals_5': 'total dismissals', '10_6': '10', 'total dismissals_7': 'total dismissals', '8_8': '8'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'total dismissals_5': [0], '10_6': [0], 'total dismissals_7': [1], '8_8': [2]} | ['player', 'test career', 'tests', 'catches', 'stumpings', 'total dismissals'] | [['khaled mashud', '2000 - 2007', '44', '78', '9', '87'], ['shahriar hossain', '2000 - 2004', '3', '0', '1', '1'], ['mehrab hossain', '2000 - 2003', '9', '2', '0', '2'], ['mohammad salim', '2003 - 2003', '2', '3', '1', '4'], ['rajin saleh', '2003 - 2007', '22', '1', '0', '1'], ['mushfiqur rahim', '2005 - 2007', '4', '0', '0', '0']] |
1938 cleveland rams season | https://en.wikipedia.org/wiki/1938_Cleveland_Rams_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11173623-1.html.csv | majority | most of the 1938 cleveland rams games had an attendance of 10,00 or more . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '10,00', 'subset': None} | {'func': 'most_greater_eq', 'args': ['all_rows', 'attendance', '10,00'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , most of them are greater than or equal to 10,00 .', 'tostr': 'most_greater_eq { all_rows ; attendance ; 10,00 } = true'} | most_greater_eq { all_rows ; attendance ; 10,00 } = true | for the attendance records of all rows , most of them are greater than or equal to 10,00 . | 1 | 1 | {'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '10,00_4': 4} | {'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '10,00_4': '10,00'} | {'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '10,00_4': [0]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 11 , 1938', 'green bay packers', 'l 26 - 17', '8247'], ['2', 'september 17 , 1938', 'chicago cardinals', 'l 7 - 6', '7500'], ['3', 'september 25 , 1938', 'washington redskins', 'l 37 - 13', '25000'], ['4', 'october 2 , 1938', 'detroit lions', 'w 21 - 17', '8012'], ['5', 'october 9 , 1938', 'chicago bears', 'w 14 - 7', '8024'], ['6', 'october 23 , 1938', 'chicago bears', 'w 23 - 21', '18705'], ['7', 'october 30 , 1938', 'green bay packers', 'l 28 - 7', '18483'], ['8', 'november 6 , 1938', 'detroit lions', 'l 6 - 0', '30140'], ['9', 'november 13 , 1938', 'new york giants', 'l 28 - 0', '25000'], ['10', 'november 27 , 1938', 'chicago cardinals', 'l 31 - 17', '10000'], ['11', 'december 4 , 1938', 'pittsburgh pirates', 'w 13 - 7', '7500']] |
list of heads of state of albania | https://en.wikipedia.org/wiki/List_of_heads_of_state_of_Albania | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-167225-1.html.csv | count | the non-party political party held the office of head of state of albania three times . | {'scope': 'all', 'criterion': 'equal', 'value': 'non - party', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'political party', 'non - party'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose political party record fuzzily matches to non - party .', 'tostr': 'filter_eq { all_rows ; political party ; non - party }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; political party ; non - party } }', 'tointer': 'select the rows whose political party record fuzzily matches to non - party . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; political party ; non - party } } ; 3 } = true', 'tointer': 'select the rows whose political party record fuzzily matches to non - party . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; political party ; non - party } } ; 3 } = true | select the rows whose political party record fuzzily matches to non - party . 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, 'political party_5': 5, 'non - party_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', 'political party_5': 'political party', 'non - party_6': 'non - party', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'political party_5': [0], 'non - party_6': [0], '3_7': [2]} | ['name', 'born - died', 'term start', 'term end', 'political party'] | [['chairman of the national assembly 1912', 'chairman of the national assembly 1912', 'chairman of the national assembly 1912', 'chairman of the national assembly 1912', 'chairman of the national assembly 1912'], ['ismail qemali bej', '1844 - 1919', '28 november 1912', '29 november 1912', 'non - party'], ['chairman of the provisional government 1912 - 1914', 'chairman of the provisional government 1912 - 1914', 'chairman of the provisional government 1912 - 1914', 'chairman of the provisional government 1912 - 1914', 'chairman of the provisional government 1912 - 1914'], ['ismail qemali bej', '1844 - 1919', '29 november 1912', '22 january 1914', 'non - party'], ['chairman of the central government 1914', 'chairman of the central government 1914', 'chairman of the central government 1914', 'chairman of the central government 1914', 'chairman of the central government 1914'], ['fejzi bej alizoti', '1876 - 1945', '22 january 1914', '7 march 1914', 'non - party']] |
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 | superlative | galdhøpiggen has the highest elevation of all european ultra prominent peaks . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'elevation ( m )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; elevation ( m ) }'}, 'peak'], 'result': 'galdhøpiggen', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; elevation ( m ) } ; peak }'}, 'galdhøpiggen'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; elevation ( m ) } ; peak } ; galdhøpiggen } = true', 'tointer': 'select the row whose elevation ( m ) record of all rows is maximum . the peak record of this row is galdhøpiggen .'} | eq { hop { argmax { all_rows ; elevation ( m ) } ; peak } ; galdhøpiggen } = true | select the row whose elevation ( m ) record of all rows is maximum . the peak record of this row is galdhøpiggen . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'elevation (m)_5': 5, 'peak_6': 6, 'galdhøpiggen_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'elevation (m)_5': 'elevation ( m )', 'peak_6': 'peak', 'galdhøpiggen_7': 'galdhøpiggen'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'elevation (m)_5': [0], 'peak_6': [1], 'galdhøpiggen_7': [2]} | ['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']] |
1980 buffalo bills season | https://en.wikipedia.org/wiki/1980_Buffalo_Bills_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16677887-2.html.csv | count | in the 1980 buffalo bills season , there were two occasions where the miami dolphins were the opponent . | {'scope': 'all', 'criterion': 'equal', 'value': 'miami dolphins', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'miami dolphins'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to miami dolphins .', 'tostr': 'filter_eq { all_rows ; opponent ; miami dolphins }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; miami dolphins } }', 'tointer': 'select the rows whose opponent record fuzzily matches to miami dolphins . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; miami dolphins } } ; 2 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to miami dolphins . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; opponent ; miami dolphins } } ; 2 } = true | select the rows whose opponent record fuzzily matches to miami dolphins . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'miami dolphins_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'miami dolphins_6': 'miami dolphins', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'miami dolphins_6': [0], '2_7': [2]} | ['game', 'date', 'opponent', 'result', 'bills points', 'opponents', 'bills first downs', 'record', 'attendance'] | [['1', 'sept 7', 'miami dolphins', 'win', '17', '7', '22', '1 - 0', '79598'], ['2', 'sept 14', 'new york jets', 'win', '20', '10', '22', '2 - 0', '65315'], ['3', 'sept 21', 'new orleans saints', 'win', '35', '26', '26', '3 - 0', '51154'], ['4', 'sept 28', 'oakland raiders', 'win', '24', '7', '25', '4 - 0', '77259'], ['5', 'oct 5', 'san diego chargers', 'win', '26', '24', '14', '5 - 0', '51982'], ['6', 'oct 12', 'baltimore colts', 'loss', '12', '17', '24', '5 - 1', '73634'], ['7', 'oct 19', 'miami dolphins', 'loss', '14', '17', '18', '5 - 2', '41636'], ['8', 'oct 26', 'new england patriots', 'win', '31', '13', '21', '6 - 2', '75092'], ['9', 'nov 2', 'atlanta falcons', 'loss', '14', '30', '20', '6 - 3', '57959'], ['10', 'nov 9', 'new york jets', 'win', '31', '24', '17', '7 - 3', '45677'], ['11', 'nov 16', 'cincinnati bengals', 'win', '14', '0', '22', '8 - 3', '40836'], ['12', 'nov 23', 'pittsburgh steelers', 'win', '28', '13', '23', '9 - 3', '79659'], ['13', 'nov 30', 'baltimore colts', 'loss', '24', '28', '24', '9 - 4', '36184'], ['14', 'dec 7', 'los angeles rams', 'win', '10', '7', '15', '10 - 4', '77133'], ['15', 'dec 14', 'new england patriots', 'loss', '2', '24', '28', '10 - 5', '58324']] |
1998 pga championship | https://en.wikipedia.org/wiki/1998_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18048776-5.html.csv | count | in the 1998 pga championship , when the country is united states , 4 people had a score of 138 . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': '138', 'result': '4', 'col': '4', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'score', '138'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 138 .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 138 }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 138 } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 138 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 138 } } ; 4 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 138 . the number of such rows is 4 .'} | eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 138 } } ; 4 } = true | select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 138 . 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, 'country_6': 6, 'united states_7': 7, 'score_8': 8, '138_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', 'country_6': 'country', 'united states_7': 'united states', 'score_8': 'score', '138_9': '138', '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], 'country_6': [0], 'united states_7': [0], 'score_8': [1], '138_9': [1], '4_10': [3]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'vijay singh', 'fiji', '70 + 66 = 136', '4'], ['t2', 'scott gump', 'united states', '68 + 69 = 136', '3'], ['t2', 'colin montgomerie', 'scotland', '70 + 67 = 137', '3'], ['t2', 'steve stricker', 'united states', '69 + 68 = 137', '3'], ['t5', 'steve elkington', 'australia', '69 + 69 = 138', '2'], ['t5', 'brad faxon', 'united states', '70 + 68 = 138', '2'], ['t5', 'davis love iii', 'united states', '70 + 68 = 138', '2'], ['t5', 'andrew magee', 'united states', '70 + 68 = 138', '2'], ['t5', 'tiger woods', 'united states', '66 + 72 = 138', '2'], ['t10', 'john cook', 'united states', '71 + 68 = 139', '1'], ['t10', 'glen day', 'united states', '68 + 71 = 139', '1'], ['t10', 'david frost', 'south africa', '70 + 69 = 139', '1'], ['t10', 'frank lickliter', 'united states', '68 + 71 = 139', '1'], ['t10', "mark o'meara", 'united states', '69 + 70 = 139', '1']] |
dragon zakura ( tv series ) | https://en.wikipedia.org/wiki/Dragon_Zakura_%28TV_series%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25847911-1.html.csv | ordinal | hold out until you hit the wall was the third aired episode of dragon zakura among episodes 2 through 10 . | {'row': '3', 'col': '5', 'order': '3', 'col_other': '4', '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', 'broadcast date', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; broadcast date ; 3 }'}, 'translation of title'], 'result': 'hold out until you hit the wall', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; broadcast date ; 3 } ; translation of title }'}, 'hold out until you hit the wall'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; broadcast date ; 3 } ; translation of title } ; hold out until you hit the wall } = true', 'tointer': 'select the row whose broadcast date record of all rows is 3rd minimum . the translation of title record of this row is hold out until you hit the wall .'} | eq { hop { nth_argmin { all_rows ; broadcast date ; 3 } ; translation of title } ; hold out until you hit the wall } = true | select the row whose broadcast date record of all rows is 3rd minimum . the translation of title record of this row is hold out until you hit the wall . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'broadcast date_5': 5, '3_6': 6, 'translation of title_7': 7, 'hold out until you hit the wall_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', 'broadcast date_5': 'broadcast date', '3_6': '3', 'translation of title_7': 'translation of title', 'hold out until you hit the wall_8': 'hold out until you hit the wall'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'broadcast date_5': [0], '3_6': [0], 'translation of title_7': [1], 'hold out until you hit the wall_8': [2]} | ['', 'episode title', 'romanized title', 'translation of title', 'broadcast date', 'ratings'] | [['ep 2', '自分の弱さを知れ !', 'jibun no yowasa wo shire !', 'know your weaknesses !', 'july 15 , 2005', '16.5 %'], ['ep 3', '遊べ!受験はスポーツだ !', 'asobe ! juken wa supootsu da !', 'entrance exams are sports , so play !', 'july 22 , 2005', '13.8 %'], ['ep 4', '壁にぶつかるまで我慢しろ', 'kabe ni butsukaru made gaman shiro', 'hold out until you hit the wall', 'july 29 , 2005', '16.1 %'], ['ep 5', '泣くな!お前の人生だ !', 'nakuna ! omae no jinsei da !', "do n't cry ! it 's your life !", 'august 5 , 2005', '16.8 %'], ['ep 6', '英語対決!勝負だバカ6人', 'eigo taiketsu ! shoubu da baka rokunin', 'english showdown ! fight for stupid 6', 'august 12 , 2005', '17.9 %'], ['ep 7', '見返してやる!東大模試', 'mikaeshite yaru ! toudai moshi', 'vengeance ! mock exam for tokyo university', 'august 19 , 2005', '15.6 %'], ['ep 9', '信じろ!成績は必ず上がる', 'shinjiro ! seiseki wa kanarazu agaru', 'trust yourself ! your marks will surely improve', 'september 2 , 2005', '14.5 %'], ['ep 10', '友情か受験か最後の決断', 'yuujou ka juken ka saigo no ketsudan', 'friendship or entrance exam final decision', 'september 9 , 2005', '14.5 %']] |
2008 - 09 portland trail blazers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Portland_Trail_Blazers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17058178-11.html.csv | count | 3 portland trail blazers games were played at the rose garden . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'rose garden', 'result': '3', 'col': '8', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'rose garden'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location attendance record fuzzily matches to rose garden .', 'tostr': 'filter_eq { all_rows ; location attendance ; rose garden }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location attendance ; rose garden } }', 'tointer': 'select the rows whose location attendance record fuzzily matches to rose garden . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location attendance ; rose garden } } ; 3 } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to rose garden . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; location attendance ; rose garden } } ; 3 } = true | select the rows whose location attendance record fuzzily matches to rose garden . 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, 'location attendance_5': 5, 'rose garden_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', 'location attendance_5': 'location attendance', 'rose garden_6': 'rose garden', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], 'rose garden_6': [0], '3_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['75', 'april 3', 'oklahoma city', 'w 107 - 72 ( ot )', 'lamarcus aldridge ( 35 )', 'lamarcus aldridge ( 18 )', 'steve blake ( 10 )', 'ford center 19136', '48 - 27'], ['76', 'april 5', 'houston', 'l 88 - 102 ( ot )', 'lamarcus aldridge , brandon roy ( 22 )', 'lamarcus aldridge ( 9 )', 'brandon roy ( 6 )', 'toyota center 18214', '48 - 28'], ['77', 'april 7', 'memphis', 'w 96 - 93 ( ot )', 'brandon roy ( 24 )', 'lamarcus aldridge ( 8 )', 'brandon roy ( 4 )', 'fedexforum 10089', '49 - 28'], ['78', 'april 8', 'san antonio', 'w 95 - 83 ( ot )', 'brandon roy ( 26 )', 'joel przybilla ( 17 )', 'steve blake ( 7 )', 'at & t center 18797', '50 - 28'], ['79', 'april 10', 'la lakers', 'w 106 - 98 ( ot )', 'brandon roy ( 24 )', 'joel przybilla ( 13 )', 'brandon roy ( 8 )', 'rose garden 20681', '51 - 28'], ['80', 'april 11', 'la clippers', 'w 87 - 72 ( ot )', 'lamarcus aldridge ( 21 )', 'joel przybilla ( 14 )', 'steve blake ( 5 )', 'staples center 18321', '52 - 28'], ['81', 'april 13', 'oklahoma city', 'w 113 - 83 ( ot )', 'travis outlaw ( 21 )', 'joel przybilla ( 12 )', 'sergio rodríguez ( 8 )', 'rose garden 20655', '53 - 28'], ['82', 'april 15', 'denver', 'w 104 - 76 ( ot )', 'travis outlaw ( 21 )', 'joel przybilla ( 8 )', 'sergio rodríguez ( 12 )', 'rose garden 20652', '54 - 28']] |
john aldridge | https://en.wikipedia.org/wiki/John_Aldridge | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1208731-3.html.csv | majority | the majority of john aldridge 's international goals were in the lansdowne road , dublin , ireland venue . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'lansdowne road , dublin , ireland', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'venue', 'lansdowne road , dublin , ireland'], 'result': True, 'ind': 0, 'tointer': 'for the venue records of all rows , most of them fuzzily match to lansdowne road , dublin , ireland .', 'tostr': 'most_eq { all_rows ; venue ; lansdowne road , dublin , ireland } = true'} | most_eq { all_rows ; venue ; lansdowne road , dublin , ireland } = true | for the venue records of all rows , most of them fuzzily match to lansdowne road , dublin , ireland . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'venue_3': 3, 'lansdowne road , dublin , ireland_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'venue_3': 'venue', 'lansdowne road , dublin , ireland_4': 'lansdowne road , dublin , ireland'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'venue_3': [0], 'lansdowne road , dublin , ireland_4': [0]} | ['goal', 'date', 'venue', 'score', 'result', 'competition'] | [['1', '19 october 1988', 'lansdowne road , dublin , ireland', '3 - 0', '4 - 0', 'friendly'], ['2', '15 november 1989', "ta ' qali national stadium , attard , malta", '1 - 0', '2 - 0', '1990 world cup qual'], ['3', '15 november 1989', "ta ' qali national stadium , attard , malta", '2 - 0', '2 - 0', '1990 world cup qual'], ['4', '17 october 1990', 'lansdowne road , dublin , ireland', '1 - 0', '5 - 0', 'euro 1992 qual'], ['5', '17 october 1990', 'lansdowne road , dublin , ireland', '3 - 0', '5 - 0', 'euro 1992 qual'], ['6', '17 october 1990', 'lansdowne road , dublin , ireland', '5 - 0', '5 - 0', 'euro 1992 qual'], ['7', '25 march 1992', 'lansdowne road , dublin , ireland', '2 - 1', '2 - 1', 'friendly'], ['8', '26 may 1992', 'lansdowne road , dublin , ireland', '1 - 0', '2 - 0', '1994 world cup qual'], ['9', '9 september 1992', 'lansdowne road , dublin , ireland', '2 - 0', '4 - 0', '1994 world cup qual'], ['10', '9 september 1992', 'lansdowne road , dublin , ireland', '3 - 0', '4 - 0', '1994 world cup qual'], ['11', '9 september 1992', 'lansdowne road , dublin , ireland', '4 - 0', '4 - 0', '1994 world cup qual'], ['12', '9 june 1993', 'daugava stadium , riga , latvia', '1 - 0', '2 - 0', '1994 world cup qual'], ['13', '8 september 1993', 'lansdowne road , dublin , ireland', '1 - 0', '2 - 0', '1994 world cup qual'], ['14', '24 june 1994', 'citrus bowl , orlando , united states', '1 - 2', '1 - 2', 'world cup 1994'], ['15', '7 september 1994', 'daugava stadium , riga , latvia', '1 - 0', '3 - 0', 'euro 1996 qual'], ['16', '7 september 1994', 'daugava stadium , riga , latvia', '3 - 0', '3 - 0', 'euro 1996 qual'], ['17', '16 november 1994', 'windsor park , belfast , northern ireland', '1 - 0', '4 - 0', 'euro 1996 qual'], ['18', '11 october 1995', 'lansdowne road , dublin , ireland', '1 - 0', '2 - 0', 'euro 1996 qual'], ['19', '11 october 1995', 'lansdowne road , dublin , ireland', '2 - 0', '2 - 0', 'euro 1996 qual']] |
easyjet | https://en.wikipedia.org/wiki/EasyJet | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-180466-4.html.csv | count | three of easyjet 's aircrafts were manufactured by the boeing aviation company . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'boeing', 'result': '3', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'aircraft', 'boeing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose aircraft record fuzzily matches to boeing .', 'tostr': 'filter_eq { all_rows ; aircraft ; boeing }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; aircraft ; boeing } }', 'tointer': 'select the rows whose aircraft record fuzzily matches to boeing . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; aircraft ; boeing } } ; 3 } = true', 'tointer': 'select the rows whose aircraft record fuzzily matches to boeing . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; aircraft ; boeing } } ; 3 } = true | select the rows whose aircraft record fuzzily matches to boeing . 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, 'aircraft_5': 5, 'boeing_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', 'aircraft_5': 'aircraft', 'boeing_6': 'boeing', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'aircraft_5': [0], 'boeing_6': [0], '3_7': [2]} | ['aircraft', 'introduced', 'retired', 'seating', 'notes'] | [['airbus a319 - 100', '2004', '-', '156', 'in service'], ['airbus a320 - 200', '2008', '-', '180', 'in service'], ['airbus a321 - 200', '2008', '2010', '220', 'inherited from gb airways'], ['boeing 737 - 204', '1995', '1996', '115', 'replaced by 737 - 300s'], ['boeing 737 - 300', '1996', '2007', '148 / 9', 'replaced by a319s'], ['boeing 737 - 700', '2000', '2011', '149', 'replaced by a319s and a320s']] |
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 | aggregation | the average points scored across all years was around 2.5 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '2.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '2.5', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '2.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 2.5 } = true', 'tointer': 'the average of the points record of all rows is 2.5 .'} | round_eq { avg { all_rows ; points } ; 2.5 } = true | the average of the points record of all rows is 2.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '2.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '2.5_5': '2.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '2.5_5': [1]} | ['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']] |
orlando magic all - time roster | https://en.wikipedia.org/wiki/Orlando_Magic_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15621965-14.html.csv | ordinal | jawann oldham is the earliest player who joined orlando magic among those listed in their all - time roster . | {'row': '2', 'col': '5', 'order': '1', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'years in orlando', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; years in orlando ; 1 }'}, 'player'], 'result': 'jawann oldham', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; years in orlando ; 1 } ; player }'}, 'jawann oldham'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; years in orlando ; 1 } ; player } ; jawann oldham } = true', 'tointer': 'select the row whose years in orlando record of all rows is 1st minimum . the player record of this row is jawann oldham .'} | eq { hop { nth_argmin { all_rows ; years in orlando ; 1 } ; player } ; jawann oldham } = true | select the row whose years in orlando record of all rows is 1st minimum . the player record of this row is jawann oldham . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'years in orlando_5': 5, '1_6': 6, 'player_7': 7, 'jawann oldham_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', 'years in orlando_5': 'years in orlando', '1_6': '1', 'player_7': 'player', 'jawann oldham_8': 'jawann oldham'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'years in orlando_5': [0], '1_6': [0], 'player_7': [1], 'jawann oldham_8': [2]} | ['player', 'no', 'nationality', 'position', 'years in orlando', 'school / club team'] | [['victor oladipo', '5', 'united states', 'guard', '2013 - present', 'indiana'], ['jawann oldham', '55', 'united states', 'center', '1989 - 1990', 'seattle'], ['kevin ollie', '3', 'united states', 'guard', '1998', 'connecticut'], ["shaquille o'neal", '32', 'united states', 'center', '1992 - 1996', 'louisiana state'], ['daniel orton', '21', 'united states', 'center', '2010 - 2012', 'kentucky'], ['bo outlaw', '45', 'united states', 'forward - center', '1997 - 2001', 'houston'], ['bo outlaw', '45', 'united states', 'forward - center', '2005 - 2008', 'houston'], ['doug overton', '11', 'united states', 'guard', '1998 - 1999', 'la salle']] |
mark mccumber | https://en.wikipedia.org/wiki/Mark_McCumber | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1598242-1.html.csv | aggregation | for years when he was not in the playoffs , the average margin of victory was 2 strokes . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '2', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'margin of victory'], 'result': '2', 'ind': 0, 'tostr': 'avg { all_rows ; margin of victory }'}, '2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; margin of victory } ; 2 } = true', 'tointer': 'the average of the margin of victory record of all rows is 2 .'} | round_eq { avg { all_rows ; margin of victory } ; 2 } = true | the average of the margin of victory record of all rows is 2 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'margin of victory_4': 4, '2_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'margin of victory_4': 'margin of victory', '2_5': '2'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'margin of victory_4': [0], '2_5': [1]} | ['date', 'tournament', 'winning score', 'margin of victory', 'runner - up'] | [['mar 18 , 1979', 'doral - eastern open', '- 9 ( 67 + 71 + 69 + 72 = 279 )', '1 stroke', 'bill rogers'], ['jul 3 , 1983', 'western open', '- 4 ( 74 + 71 + 68 + 71 = 284 )', '1 stroke', 'tom watson'], ['oct 30 , 1983', 'pensacola open', '- 18 ( 68 + 68 + 65 + 65 = 266 )', '4 strokes', 'lon hinkle'], ['feb 24 , 1985', 'doral - eastern open', '- 4 ( 70 + 71 + 72 + 71 = 284 )', '1 stroke', 'tom kite'], ['jul 12 , 1987', 'anheuser - busch golf classic', '- 17 ( 65 + 69 + 67 + 66 = 267 )', '1 stroke', 'bobby clampett'], ['mar 27 , 1988', 'the players championship', '- 15 ( 65 + 72 + 67 + 69 = 273 )', '4 strokes', 'mike reid'], ['jul 3 , 1989', 'beatrice western open', '- 13 ( 68 + 67 + 71 + 69 = 275 )', 'playoff', 'peter jacobsen'], ['jul 10 , 1994', 'anheuser - busch golf classic', '- 17 ( 67 + 69 + 65 + 66 = 267 )', '3 strokes', 'glen day'], ['sep 25 , 1994', "hardee 's golf classic", '- 15 ( 66 + 67 + 65 + 67 = 265 )', '1 stroke', 'kenny perry'], ['oct 30 , 1994', 'the tour championship', '- 10 ( 66 + 71 + 69 + 68 = 274 )', 'playoff', 'fuzzy zoeller']] |
ramires | https://en.wikipedia.org/wiki/Ramires | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13211909-2.html.csv | ordinal | the second highest number of apps for ramires was in the season 2009-10 . | {'row': '2', 'col': '4', 'order': '2', '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', 'apps', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; apps ; 2 }'}, 'season'], 'result': '2009 - 10', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; apps ; 2 } ; season }'}, '2009 - 10'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; apps ; 2 } ; season } ; 2009 - 10 } = true', 'tointer': 'select the row whose apps record of all rows is 2nd maximum . the season record of this row is 2009 - 10 .'} | eq { hop { nth_argmax { all_rows ; apps ; 2 } ; season } ; 2009 - 10 } = true | select the row whose apps record of all rows is 2nd maximum . the season record of this row is 2009 - 10 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'apps_5': 5, '2_6': 6, 'season_7': 7, '2009 - 10_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', 'apps_5': 'apps', '2_6': '2', 'season_7': 'season', '2009 - 10_8': '2009 - 10'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'apps_5': [0], '2_6': [0], 'season_7': [1], '2009 - 10_8': [2]} | ['national team', 'club', 'season', 'apps', 'goals'] | [['brazil', 'cruzeiro', '2009', '7', '0'], ['brazil', 'benfica', '2009 - 10', '9', '2'], ['brazil', 'chelsea', '2010 - 11', '10', '0'], ['brazil', 'chelsea', '2011 - 12', '1', '0'], ['brazil', 'chelsea', '2012 - 13', '6', '1'], ['total', 'total', 'total', '33', '3']] |
piero taruffi | https://en.wikipedia.org/wiki/Piero_Taruffi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235869-1.html.csv | aggregation | piero taruffi earned 31 of his 50 career points with the ferrari straight - 4 engine . | {'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '31', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'ferrari straight-4'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'engine', 'ferrari straight-4'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; engine ; ferrari straight-4 }', 'tointer': 'select the rows whose engine record fuzzily matches to ferrari straight-4 .'}, 'points'], 'result': '31', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; engine ; ferrari straight-4 } ; points }'}, '31'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; engine ; ferrari straight-4 } ; points } ; 31 } = true', 'tointer': 'select the rows whose engine record fuzzily matches to ferrari straight-4 . the sum of the points record of these rows is 31 .'} | round_eq { sum { filter_eq { all_rows ; engine ; ferrari straight-4 } ; points } ; 31 } = true | select the rows whose engine record fuzzily matches to ferrari straight-4 . the sum of the points record of these rows is 31 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'engine_5': 5, 'ferrari straight-4_6': 6, 'points_7': 7, '31_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'engine_5': 'engine', 'ferrari straight-4_6': 'ferrari straight-4', 'points_7': 'points', '31_8': '31'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'engine_5': [0], 'ferrari straight-4_6': [0], 'points_7': [1], '31_8': [2]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1950', 'sa alfa romeo', 'alfa romeo 158', 'alfa romeo straight - 8', '0'], ['1951', 'scuderia ferrari', 'ferrari 375 f1', 'ferrari v12', '10'], ['1952', 'scuderia ferrari', 'ferrari 500', 'ferrari straight - 4', '22'], ['1954', 'scuderia ferrari', 'ferrari 625', 'ferrari straight - 4', '0'], ['1955', 'scuderia ferrari', 'ferrari 555', 'ferrari straight - 4', '9'], ['1955', 'daimler benz ag', 'mercedes - benz w196', 'mercedes - benz straight - 8', '9'], ['1956', 'officine alfieri maserati', 'maserati 250f', 'maserati straight - 6', '0'], ['1956', 'vandervell products ltd', 'vanwall', 'vanwall straight - 4', '0']] |
gb railfreight | https://en.wikipedia.org/wiki/GB_Railfreight | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12085438-1.html.csv | count | for gb railfreight , for ones that were introduced before 2000 , there were 2 times that the fleet size was 2 . | {'scope': 'subset', 'criterion': 'equal', 'value': '2', 'result': '2', 'col': '4', 'subset': {'col': '3', 'criterion': 'less_than', 'value': '2000'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'introduced', '2000'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; introduced ; 2000 }', 'tointer': 'select the rows whose introduced record is less than 2000 .'}, 'fleet size', '2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose introduced record is less than 2000 . among these rows , select the rows whose fleet size record is equal to 2 .', 'tostr': 'filter_eq { filter_less { all_rows ; introduced ; 2000 } ; fleet size ; 2 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_less { all_rows ; introduced ; 2000 } ; fleet size ; 2 } }', 'tointer': 'select the rows whose introduced record is less than 2000 . among these rows , select the rows whose fleet size record is equal to 2 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less { all_rows ; introduced ; 2000 } ; fleet size ; 2 } } ; 2 } = true', 'tointer': 'select the rows whose introduced record is less than 2000 . among these rows , select the rows whose fleet size record is equal to 2 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_less { all_rows ; introduced ; 2000 } ; fleet size ; 2 } } ; 2 } = true | select the rows whose introduced record is less than 2000 . among these rows , select the rows whose fleet size record is equal to 2 . 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, 'introduced_6': 6, '2000_7': 7, 'fleet size_8': 8, '2_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', 'introduced_6': 'introduced', '2000_7': '2000', 'fleet size_8': 'fleet size', '2_9': '2', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'introduced_6': [0], '2000_7': [0], 'fleet size_8': [1], '2_9': [1], '2_10': [3]} | ['class', 'type', 'introduced', 'fleet size', 'numbers'] | [['class 08', 'shunter', '1953', '2', '08925 , 08934'], ['class 09', 'shunter', '1959', '2', '09002 , 09009'], ['class 20', 'diesel locomotive', '1957 - 1968', '9', '20096 , 107 , 142 , 189 , 227 311 , 314 , 901 , 905'], ['class 66', 'diesel locomotive', '2002', '48', '66701 - 733 , 735 - 751'], ['class 73', 'electro - diesel locomotive', '1966', '10', '73119 , 141 , 204 - 209 , 212 - 213'], ['class 92', 'electric locomotive', '1993', '7', '92020 , 021 , 032 , 040 , 044 - 046'], ['vanguard 0 - 6 - 0dh', 'diesel locomotive', '2011', '2', 'dh50 - 1 , dh50 - 2']] |
2008 baltimore ravens season | https://en.wikipedia.org/wiki/2008_Baltimore_Ravens_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15013564-4.html.csv | unique | the game in week four was the only time the game was at heinz field . | {'scope': 'all', 'row': '4', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'heinz field', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'stadium', 'heinz field'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose stadium record fuzzily matches to heinz field .', 'tostr': 'filter_eq { all_rows ; stadium ; heinz field }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; stadium ; heinz field } }', 'tointer': 'select the rows whose stadium record fuzzily matches to heinz field . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'stadium', 'heinz field'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose stadium record fuzzily matches to heinz field .', 'tostr': 'filter_eq { all_rows ; stadium ; heinz field }'}, 'week'], 'result': '4', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; stadium ; heinz field } ; week }'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; stadium ; heinz field } ; week } ; 4 }', 'tointer': 'the week record of this unqiue row is 4 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; stadium ; heinz field } } ; eq { hop { filter_eq { all_rows ; stadium ; heinz field } ; week } ; 4 } } = true', 'tointer': 'select the rows whose stadium record fuzzily matches to heinz field . there is only one such row in the table . the week record of this unqiue row is 4 .'} | and { only { filter_eq { all_rows ; stadium ; heinz field } } ; eq { hop { filter_eq { all_rows ; stadium ; heinz field } ; week } ; 4 } } = true | select the rows whose stadium record fuzzily matches to heinz field . there is only one such row in the table . the week record of this unqiue row is 4 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'stadium_7': 7, 'heinz field_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '4_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'stadium_7': 'stadium', 'heinz field_8': 'heinz field', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '4_10': '4'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'stadium_7': [0], 'heinz field_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '4_10': [3]} | ['week', 'opponent', 'date', 'tv network', 'time ( et )', 'stadium', 'location', 'results', 'record'] | [['1', 'cincinnati bengals', 'sunday , september 7 , 2008', 'cbs', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 17 - 10', '1 - 0'], ['2', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week', 'bye week'], ['3', 'cleveland browns', 'sunday , september 21 , 2008', 'cbs', '4:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 28 - 10', '2 - 0'], ['4', 'pittsburgh steelers', 'monday , september 29 , 2008', 'espn', '8:30 pm', 'heinz field', 'pittsburgh , pennsylvania', 'l 20 - 23 ot', '2 - 1'], ['5', 'tennessee titans', 'sunday , october 5 , 2008', 'cbs', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'l 10 - 13', '2 - 2'], ['6', 'indianapolis colts', 'sunday , october 12 , 2008', 'cbs', '1:00 pm', 'lucas oil stadium', 'indianapolis , indiana', 'l 3 - 31', '2 - 3'], ['7', 'miami dolphins', 'sunday , october 19 , 2008', 'cbs', '1:00 pm', 'dolphin stadium', 'miami , florida', 'w 27 - 13', '3 - 3'], ['8', 'oakland raiders', 'sunday , october 26 , 2008', 'cbs', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 29 - 10', '4 - 3'], ['9', 'cleveland browns', 'sunday , november 2 , 2008', 'cbs', '1:00 pm', 'cleveland browns stadium', 'cleveland , ohio', 'w 37 - 27', '5 - 3'], ['10', 'houston texans', 'sunday , november 9 , 2008', 'cbs', '1:00 pm', 'reliant stadium', 'houston , texas', 'w 41 - 13', '6 - 3'], ['11', 'new york giants', 'sunday , november 16 , 2008', 'cbs', '1:00 pm', 'giants stadium', 'east rutherford , new jersey', 'l 10 - 30', '6 - 4'], ['12', 'philadelphia eagles', 'sunday , november 23 , 2008', 'fox', '1:00 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 36 - 7', '7 - 4'], ['13', 'cincinnati bengals', 'sunday , november 30 , 2008', 'cbs', '1:00 pm', 'paul brown stadium', 'cincinnati , ohio', 'w 34 - 3', '8 - 4'], ['14', 'washington redskins', 'sunday , december 7 , 2008', 'nbc', '8:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 24 - 10', '9 - 4'], ['15', 'pittsburgh steelers', 'sunday , december 14 , 2008', 'cbs', '4:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'l 9 - 13', '9 - 5'], ['16', 'dallas cowboys', 'saturday , december 20 , 2008', 'nfl network', '8:00 pm', 'texas stadium', 'irving , texas', 'w 33 - 24', '10 - 5'], ['17', 'jacksonville jaguars', 'sunday , december 28 , 2008', 'cbs', '4:15 pm', 'm & t bank stadium', 'baltimore , maryland', 'w 27 - 7', '11 - 5']] |
peak uranium | https://en.wikipedia.org/wiki/Peak_uranium | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15624586-2.html.csv | count | four of the countries that have a demand for uranium had 0 indigenous mining production in 2006 . | {'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'indigenous mining production 2006', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose indigenous mining production 2006 record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; indigenous mining production 2006 ; 0 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; indigenous mining production 2006 ; 0 } }', 'tointer': 'select the rows whose indigenous mining production 2006 record is equal to 0 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; indigenous mining production 2006 ; 0 } } ; 4 } = true', 'tointer': 'select the rows whose indigenous mining production 2006 record is equal to 0 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; indigenous mining production 2006 ; 0 } } ; 4 } = true | select the rows whose indigenous mining production 2006 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, 'indigenous mining production 2006_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', 'indigenous mining production 2006_5': 'indigenous mining production 2006', '0_6': '0', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'indigenous mining production 2006_5': [0], '0_6': [0], '4_7': [2]} | ['country', 'uranium required 2006 - 08', '% of world demand', 'indigenous mining production 2006', 'deficit ( - surplus )'] | [['usa', 'tonnes ( 10 6lb )', '29.3 %', 'tonnes ( 10 6lb )', 'tonnes ( 10 6lb )'], ['france', 'tonnes ( 10 6lb )', '16.3 %', '0', 'tonnes ( 10 6lb )'], ['japan', 'tonnes ( 10 6lb )', '11.8 %', '0', 'tonnes ( 10 6lb )'], ['russia', 'tonnes ( 10 6lb )', '5.2 %', 'tonnes ( 10 6lb )', 'tonnes ( 10 6lb )'], ['germany', 'tonnes ( 10 6lb )', '5.2 %', 'tonnes ( 10 6lb )', 'tonnes ( 10 6lb )'], ['south korea', 'tonnes ( 10 6lb )', '4.8 %', '0', 'tonnes ( 10 6lb )'], ['uk', 'tonnes ( 10 6lb )', '3.4 %', '0', 'tonnes ( 10 6lb )'], ['rest of world', 'tonnes ( 10 6lb )', '24.0 %', 'tonnes ( 10 6lb )', 'tonnes ( 10 6lb )'], ['total', 'tonnes ( 10 6lb )', '100.0 %', 'tonnes ( 10 6lb )', 'tonnes ( 10 6lb )']] |
south asian canadian | https://en.wikipedia.org/wiki/South_Asian_Canadian | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1717824-1.html.csv | aggregation | in 2001 , the average number of south asians in all canadian provinces was 70544.6 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '70544.6', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'south asians 2001'], 'result': '70544.6', 'ind': 0, 'tostr': 'avg { all_rows ; south asians 2001 }'}, '70544.6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; south asians 2001 } ; 70544.6 } = true', 'tointer': 'the average of the south asians 2001 record of all rows is 70544.6 .'} | round_eq { avg { all_rows ; south asians 2001 } ; 70544.6 } = true | the average of the south asians 2001 record of all rows is 70544.6 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'south asians 2001_4': 4, '70544.6_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'south asians 2001_4': 'south asians 2001', '70544.6_5': '70544.6'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'south asians 2001_4': [0], '70544.6_5': [1]} | ['province', 'south asians 2001', '% 2001', 'south asians 2011', '% 2011'] | [['ontario', '554870', '4.9 %', '1003180', '7.9 %'], ['british columbia', '210295', '5.4 %', '311265', '7.2 %'], ['alberta', '69580', '2.4 %', '159055', '4.4 %'], ['quebec', '59510', '0.8 %', '91400', '1.2 %'], ['manitoba', '12875', '1.2 %', '26220', '2.2 %'], ['saskatchewan', '4090', '0.4 %', '12620', '1.3 %'], ['nova scotia', '2895', '0.3 %', '5935', '0.7 %'], ['new brunswick', '1415', '0.2 %', '3090', '0.4 %'], ['newfoundland and labrador', '1010', '0.2 %', '2005', '0.4 %'], ['prince edward island', '115', '0.1 %', '500', '0.4 %'], ['yukon', '205', '0.7 %', '340', '1.0 %'], ['northwest territories', '190', '0.5 %', '200', '0.5 %'], ['nunavut', '30', '0.1 %', '115', '0.4 %']] |
1947 in brazilian football | https://en.wikipedia.org/wiki/1947_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15318779-1.html.csv | unique | santos was the only brazilian football team in 1947 to have a difference of exactly 6 . | {'scope': 'all', 'row': '6', 'col': '10', 'col_other': '2', 'criterion': 'equal', 'value': '6', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'difference', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose difference record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; difference ; 6 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; difference ; 6 } }', 'tointer': 'select the rows whose difference record is equal to 6 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'difference', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose difference record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; difference ; 6 }'}, 'team'], 'result': 'santos', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; difference ; 6 } ; team }'}, 'santos'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; difference ; 6 } ; team } ; santos }', 'tointer': 'the team record of this unqiue row is santos .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; difference ; 6 } } ; eq { hop { filter_eq { all_rows ; difference ; 6 } ; team } ; santos } } = true', 'tointer': 'select the rows whose difference record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is santos .'} | and { only { filter_eq { all_rows ; difference ; 6 } } ; eq { hop { filter_eq { all_rows ; difference ; 6 } ; team } ; santos } } = true | select the rows whose difference record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is santos . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'difference_7': 7, '6_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'santos_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'difference_7': 'difference', '6_8': '6', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'santos_10': 'santos'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'difference_7': [0], '6_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'santos_10': [3]} | ['position', 'team', 'points', 'played', 'won', 'drawn', 'lost', 'for', 'against', 'difference'] | [['1', 'palmeiras', '36', '20', '17', '2', '1', '51', '16', '35'], ['2', 'corinthians', '32', '20', '14', '4', '2', '54', '19', '35'], ['3', 'portuguesa', '27', '20', '11', '5', '4', '43', '28', '15'], ['4', 'são paulo', '25', '20', '8', '9', '3', '48', '27', '21'], ['5', 'ypiranga - sp', '21', '20', '9', '3', '8', '36', '26', '10'], ['6', 'santos', '19', '20', '6', '7', '7', '33', '27', '6'], ['7', 'juventus', '16', '20', '5', '6', '9', '29', '45', '- 16'], ['8', 'portuguesa santista', '15', '20', '6', '3', '11', '27', '42', '- 15'], ['9', 'comercial - sp', '11', '20', '5', '1', '14', '25', '59', '- 34'], ['10', 'nacional - sp', '10', '20', '3', '4', '13', '25', '47', '- 22']] |
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/2-14175075-8.html.csv | majority | iowa was the winning team in the majority of sports played in the iowa corn cy - hawk series . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'iowa', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'series', 'iowa'], 'result': True, 'ind': 0, 'tointer': 'for the series records of all rows , most of them fuzzily match to iowa .', 'tostr': 'most_eq { all_rows ; series ; iowa } = true'} | most_eq { all_rows ; series ; iowa } = true | for the series records of all rows , most of them fuzzily match to iowa . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'series_3': 3, 'iowa_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'series_3': 'series', 'iowa_4': 'iowa'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'series_3': [0], 'iowa_4': [0]} | ['date', 'site', 'sport', 'winning team', 'series'] | [['september 10 , 2010', 'iowa city', 'volleyball', 'iowa state', 'iowa state 2 - 0'], ['september 11 , 2010', 'iowa city', 'football', 'iowa', 'iowa 3 - 2'], ['september 17 , 2010', 'ames', 'w soccer', 'iowa', 'iowa 5 - 2'], ['november 13 , 2010', 'springfield', 'm cross country', 'iowa state', 'iowa 5 - 4'], ['november 13 , 2010', 'springfield', 'w cross country', 'iowa state', 'iowa state 6 - 5'], ['december 3 , 2010', 'iowa city', 'wrestling', 'iowa', 'iowa 7 - 6'], ['december 9 , 2010', 'iowa city', 'w basketball', 'iowa', 'iowa 9 - 6'], ['december 10 , 2010', 'iowa city', 'm basketball', 'iowa state', 'iowa 9 - 8'], ['december 10 , 2010', 'iowa city', 'w swimming', 'iowa', 'iowa 11 - 8'], ['february 18 , 2011', 'ames', 'w gymnastics', 'iowa state', 'iowa 11 - 10'], ['february 25 , 2011', 'iowa city', 'w gymnastics', 'iowa', 'iowa 13 - 10'], ['april 20 , 2011', 'iowa city', 'softball', 'iowa', 'iowa 15 - 10'], ['may 5 , 2011', 'iowa city', 'academics', 'iowa state', 'iowa 15 - 11']] |
1976 - 77 segunda división | https://en.wikipedia.org/wiki/1976%E2%80%9377_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12239755-2.html.csv | superlative | the best team in the 1976 - 77 segunda división was sporting de gijon . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'position'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; position }'}, 'club'], 'result': 'sporting de gijón', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; position } ; club }'}, 'sporting de gijón'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; position } ; club } ; sporting de gijón } = true', 'tointer': 'select the row whose position record of all rows is minimum . the club record of this row is sporting de gijón .'} | eq { hop { argmin { all_rows ; position } ; club } ; sporting de gijón } = true | select the row whose position record of all rows is minimum . the club record of this row is sporting de gijón . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'position_5': 5, 'club_6': 6, 'sporting de gijón_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'position_5': 'position', 'club_6': 'club', 'sporting de gijón_7': 'sporting de gijón'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'position_5': [0], 'club_6': [1], 'sporting de gijón_7': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'sporting de gijón', '38', '47 + 9', '18', '11', '9', '62', '35', '+ 27'], ['2', 'cádiz cf', '38', '46 + 8', '17', '12', '9', '60', '42', '+ 18'], ['3', 'rayo vallecano', '38', '45 + 7', '17', '11', '10', '46', '34', '+ 12'], ['4', 'real jaén', '38', '43 + 5', '15', '13', '10', '42', '32', '+ 10'], ['5', 'real oviedo', '38', '43 + 5', '18', '7', '13', '48', '43', '+ 5'], ['6', 'cd tenerife', '38', '40 + 2', '15', '10', '13', '48', '48', '0'], ['7', 'terrassa fc', '38', '40 + 2', '13', '14', '11', '44', '34', '+ 10'], ['8', 'deportivo alavés', '38', '40 + 2', '14', '12', '12', '57', '42', '+ 15'], ['9', 'recreativo de huelva', '38', '38', '14', '10', '14', '42', '50', '- 8'], ['10', 'granada cf', '38', '36 - 2', '14', '8', '16', '42', '39', '+ 3'], ['11', 'deportivo de la coruña', '38', '36 - 2', '11', '14', '13', '40', '50', '- 10'], ['12', 'real valladolid', '38', '36 - 2', '14', '8', '16', '57', '56', '+ 1'], ['13', 'getafe deportivo', '38', '35 - 3', '12', '11', '15', '37', '48', '- 11'], ['14', 'cd castellón', '38', '35 - 3', '14', '7', '17', '46', '45', '+ 1'], ['15', 'córdoba cf', '38', '35 - 3', '10', '15', '13', '39', '45', '- 6'], ['16', 'cf calvo sotelo', '38', '34 - 4', '14', '6', '18', '39', '56', '- 17'], ['17', 'pontevedra cf', '38', '34 - 4', '10', '14', '14', '34', '44', '- 10'], ['18', 'levante ud', '38', '34 - 4', '12', '10', '16', '47', '61', '- 14'], ['19', 'ue sant andreu', '38', '33 - 5', '10', '13', '15', '39', '52', '- 13'], ['20', 'barcelona atlètic', '38', '30 - 8', '10', '10', '18', '39', '52', '- 13']] |
list of ultras of oceania | https://en.wikipedia.org/wiki/List_of_Ultras_of_Oceania | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18946749-3.html.csv | count | two of the ultras of oceania are on the island of hawaii . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'hawaii', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'island', 'hawaii'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose island record fuzzily matches to hawaii .', 'tostr': 'filter_eq { all_rows ; island ; hawaii }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; island ; hawaii } }', 'tointer': 'select the rows whose island record fuzzily matches to hawaii . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; island ; hawaii } } ; 2 } = true', 'tointer': 'select the rows whose island record fuzzily matches to hawaii . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; island ; hawaii } } ; 2 } = true | select the rows whose island record fuzzily matches to hawaii . 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, 'island_5': 5, 'hawaii_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', 'island_5': 'island', 'hawaii_6': 'hawaii', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'island_5': [0], 'hawaii_6': [0], '2_7': [2]} | ['rank', 'summit', 'country', 'island', 'col ( m )'] | [['1', 'mauna kea', 'united states', 'island of hawaii', '0'], ['2', 'haleakalā', 'united states', 'island of maui', '0'], ['3', 'mauna loa', 'united states', 'island of hawaii', '2005'], ['4', 'puu kukui', 'united states', 'island of maui', '33'], ['5', 'kawaikini', 'united states', 'island of kauai', '0'], ['6', 'kamakou', 'united states', 'island of molokai', '0']] |
keisuke honda | https://en.wikipedia.org/wiki/Keisuke_Honda | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14670286-3.html.csv | aggregation | keisuke honda had an average score during competition of 1.8 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '1.8', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '1.8', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '1.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 1.8 } = true', 'tointer': 'the average of the score record of all rows is 1.8 .'} | round_eq { avg { all_rows ; score } ; 1.8 } = true | the average of the score record of all rows is 1.8 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '1.8_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '1.8_5': '1.8'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '1.8_5': [1]} | ['date', 'venue', 'score', 'result', 'competition'] | [['7 august 2006', 'qinhuangdao olympic stadium , qinhuangdao', '1 - 0', '2 - 0', 'friendly match'], ['29 november 2006', 'qatar sc stadium , doha', '1 - 0', '3 - 2', '2006 asian games'], ['18 april 2007', 'abbasiyyin stadium , damascus', '1 - 0', '2 - 0', '2008 summer olympics qualification'], ['16 may 2007', 'hong kong stadium , hong kong', '3 - 0', '4 - 0', '2008 summer olympics qualification'], ['17 november 2007', 'my dinh national stadium , hanoi', '3 - 0', '4 - 0', '2008 summer olympics qualification']] |
1986 formula one season | https://en.wikipedia.org/wiki/1986_Formula_One_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140067-2.html.csv | ordinal | the san marino grand prix was the third earliest race in the 1986 formula one season . | {'row': '3', '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', 'date', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 3 }'}, 'race'], 'result': 'san marino grand prix', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 3 } ; race }'}, 'san marino grand prix'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 3 } ; race } ; san marino grand prix } = true', 'tointer': 'select the row whose date record of all rows is 3rd minimum . the race record of this row is san marino grand prix .'} | eq { hop { nth_argmin { all_rows ; date ; 3 } ; race } ; san marino grand prix } = true | select the row whose date record of all rows is 3rd minimum . the race record of this row is san marino grand prix . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '3_6': 6, 'race_7': 7, 'san marino grand prix_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '3_6': '3', 'race_7': 'race', 'san marino grand prix_8': 'san marino grand prix'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '3_6': [0], 'race_7': [1], 'san marino grand prix_8': [2]} | ['race', 'date', 'location', 'pole position', 'fastest lap', 'race winner', 'constructor', 'report'] | [['brazilian grand prix', '23 march', 'jacarepaguá', 'ayrton senna', 'nelson piquet', 'nelson piquet', 'williams - honda', 'report'], ['spanish grand prix', '13 april', 'jerez', 'ayrton senna', 'nigel mansell', 'ayrton senna', 'lotus - renault', 'report'], ['san marino grand prix', '27 april', 'imola', 'ayrton senna', 'nelson piquet', 'alain prost', 'mclaren - tag', 'report'], ['monaco grand prix', '11 may', 'monaco', 'alain prost', 'alain prost', 'alain prost', 'mclaren - tag', 'report'], ['belgian grand prix', '25 may', 'spa - francorchamps', 'nelson piquet', 'alain prost', 'nigel mansell', 'williams - honda', 'report'], ['canadian grand prix', '15 june', 'circuit gilles villeneuve', 'nigel mansell', 'nelson piquet', 'nigel mansell', 'williams - honda', 'report'], ['detroit grand prix', '22 june', 'detroit', 'ayrton senna', 'nelson piquet', 'ayrton senna', 'lotus - renault', 'report'], ['french grand prix', '6 july', 'paul ricard', 'ayrton senna', 'nigel mansell', 'nigel mansell', 'williams - honda', 'report'], ['british grand prix', '13 july', 'brands hatch', 'nelson piquet', 'nigel mansell', 'nigel mansell', 'williams - honda', 'report'], ['german grand prix', '27 july', 'hockenheimring', 'keke rosberg', 'gerhard berger', 'nelson piquet', 'williams - honda', 'report'], ['hungarian grand prix', '10 august', 'hungaroring', 'ayrton senna', 'nelson piquet', 'nelson piquet', 'williams - honda', 'report'], ['austrian grand prix', '17 august', 'österreichring', 'teo fabi', 'gerhard berger', 'alain prost', 'mclaren - tag', 'report'], ['italian grand prix', '7 september', 'monza', 'teo fabi', 'teo fabi', 'nelson piquet', 'williams - honda', 'report'], ['portuguese grand prix', '21 september', 'estoril', 'ayrton senna', 'nigel mansell', 'nigel mansell', 'williams - honda', 'report'], ['mexican grand prix', '12 october', 'hermanos rodriguez', 'ayrton senna', 'nelson piquet', 'gerhard berger', 'benetton - bmw', 'report'], ['australian grand prix', '26 october', 'adelaide', 'nigel mansell', 'nelson piquet', 'alain prost', 'mclaren - tag', 'report']] |
indiana high school athletics conferences : ohio river valley - western indiana | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Ohio_River_Valley_%E2%80%93_Western_Indiana | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18974097-16.html.csv | aggregation | the average student enrollment of ohio river valley - western indiana high schools is 772 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '772', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'enrollment'], 'result': '772', 'ind': 0, 'tostr': 'avg { all_rows ; enrollment }'}, '772'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; enrollment } ; 772 } = true', 'tointer': 'the average of the enrollment record of all rows is 772 .'} | round_eq { avg { all_rows ; enrollment } ; 772 } = true | the average of the enrollment record of all rows is 772 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'enrollment_4': 4, '772_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'enrollment_4': 'enrollment', '772_5': '772'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'enrollment_4': [0], '772_5': [1]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['brown county', 'nashville', 'eagles', '755', 'aaa', 'aaa', '7 brown'], ['edgewood', 'ellettsville', 'mustangs', '833', 'aaa', 'aaa', '53 monroe'], ['northview', 'brazil', 'knights', '1142', 'aaaa', 'aaaa', '11 clay'], ['owen valley', 'spencer', 'patriots', '908', 'aaa', 'aaaa', '60 owen'], ['south vermillion', 'clinton', 'wildcats', '583', 'aaa', 'aa', '83 vermillion'], ['sullivan', 'sullivan', 'golden arrows', '543', 'aaa', 'aa', '77 sullivan'], ['west vigo', 'west terre haute', 'vikings', '640', 'aaa', 'aaa', '84 vigo']] |
1958 - 59 segunda división | https://en.wikipedia.org/wiki/1958%E2%80%9359_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17695272-2.html.csv | count | 4 clubs had 7 draws in the 1958 - 59 segunda división . | {'scope': 'all', 'criterion': 'equal', 'value': '7', 'result': '4', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'draws', '7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose draws record is equal to 7 .', 'tostr': 'filter_eq { all_rows ; draws ; 7 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; draws ; 7 } }', 'tointer': 'select the rows whose draws record is equal to 7 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; draws ; 7 } } ; 4 } = true', 'tointer': 'select the rows whose draws record is equal to 7 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; draws ; 7 } } ; 4 } = true | select the rows whose draws record is equal to 7 . 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, 'draws_5': 5, '7_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'draws_5': 'draws', '7_6': '7', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'draws_5': [0], '7_6': [0], '4_7': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'real valladolid', '30', '40', '19', '2', '9', '70', '38', '+ 32'], ['2', 'cd sabadell cf', '30', '39', '16', '7', '7', '55', '35', '+ 20'], ['3', 'sd indautxu', '30', '35', '14', '7', '9', '46', '35', '+ 11'], ['4', 'cd condal', '30', '32', '14', '4', '12', '51', '41', '+ 10'], ['5', 'cd basconia', '30', '32', '12', '8', '10', '37', '43', '- 6'], ['6', 'baracaldo ah', '30', '31', '12', '7', '11', '38', '36', '+ 2'], ['7', 'deportivo la coruña', '30', '30', '13', '4', '13', '54', '49', '+ 5'], ['8', 'club sestao', '30', '30', '11', '8', '11', '41', '36', '+ 5'], ['9', 'real santander', '30', '30', '13', '4', '13', '39', '35', '+ 4'], ['10', 'club ferrol', '30', '27', '11', '5', '14', '43', '47', '- 4'], ['11', 'real avilés cf', '30', '27', '11', '5', '14', '40', '43', '- 3'], ['12', 'cd tarrasa', '30', '27', '12', '3', '15', '40', '56', '- 16'], ['13', 'deportivo alavés', '30', '27', '10', '7', '13', '34', '43', '- 9'], ['14', 'rayo vallecano', '30', '26', '11', '4', '15', '39', '46', '- 7'], ['15', 'gerona cf', '30', '25', '11', '3', '16', '43', '64', '- 21'], ['16', 'real unión club', '30', '22', '8', '6', '16', '34', '57', '- 23']] |
daren kagasoff | https://en.wikipedia.org/wiki/Daren_Kagasoff | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18381900-1.html.csv | majority | all of daren kagasoff 's awards were for the secret life of the american teenager . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'the secret life of the american teenager', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'work', 'the secret life of the american teenager'], 'result': True, 'ind': 0, 'tointer': 'for the work records of all rows , all of them fuzzily match to the secret life of the american teenager .', 'tostr': 'all_eq { all_rows ; work ; the secret life of the american teenager } = true'} | all_eq { all_rows ; work ; the secret life of the american teenager } = true | for the work records of all rows , all of them fuzzily match to the secret life of the american teenager . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'work_3': 3, 'the secret life of the american teenager_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'work_3': 'work', 'the secret life of the american teenager_4': 'the secret life of the american teenager'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'work_3': [0], 'the secret life of the american teenager_4': [0]} | ['year', 'award', 'work', 'category', 'result'] | [['2009', 'teen choice awards', 'the secret life of the american teenager', 'choice summer tv star : male', 'won'], ['2009', 'teen choice awards', 'the secret life of the american teenager', 'choice tv breakout star : male', 'nominated'], ['2010', 'teen choice awards', 'the secret life of the american teenager', 'choice tv actor : drama', 'nominated'], ['2010', 'teen choice awards', 'the secret life of the american teenager', 'choice summer tv star : male', 'nominated'], ['2011', 'teen choice awards', 'the secret life of the american teenager', 'choice tv actor : drama', 'nominated'], ['2012', 'teen choice awards', 'the secret life of the american teenager', 'choice summer tv star : male', 'nominated']] |
list of swat kats : the radical squadron episodes | https://en.wikipedia.org/wiki/List_of_SWAT_Kats%3A_The_Radical_Squadron_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17810099-3.html.csv | majority | of the swat kats : the radical squadron episodes , all of them were directed by robert alvarez . | {'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'robert alvarez', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'director', 'robert alvarez'], 'result': True, 'ind': 0, 'tointer': 'for the director records of all rows , all of them fuzzily match to robert alvarez .', 'tostr': 'all_eq { all_rows ; director ; robert alvarez } = true'} | all_eq { all_rows ; director ; robert alvarez } = true | for the director records of all rows , all of them fuzzily match to robert alvarez . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'director_3': 3, 'robert alvarez_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'director_3': 'director', 'robert alvarez_4': 'robert alvarez'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'director_3': [0], 'robert alvarez_4': [0]} | ['episode', 'season', 'title', 'writer ( s )', 'director', 'originalairdate'] | [['14', '2', 'mutation city', 'glenn leopold', 'robert alvarez', 'september 10 , 1994'], ['15', '2', 'a bright and shiny future', 'glenn leopold', 'robert alvarez', 'september 17 , 1994'], ['16', '2', 'when mutilor strikes', 'lance falk', 'robert alvarez', 'september 24 , 1994'], ['17', '2', "razor 's edge", 'mark saraceni', 'robert alvarez', 'october 29 , 1994'], ['18a', '2', 'cry turmoil', 'lance falk', 'robert alvarez', 'november 5 , 1994'], ['18b', '2', 'swat kats unplugged', 'glenn leopold', 'robert alvarez', 'november 5 , 1994'], ['19', '2', 'the deadly pyramid', 'glenn leopold', 'robert alvarez', 'november 12 , 1994'], ['20', '2', 'caverns of horror', 'glenn leopold', 'robert alvarez', 'november 19 , 1994'], ['21a', '2', 'volcanus erupts !', 'glenn leopold', 'robert alvarez', 'november 26 , 1994'], ['21b', '2', 'the origin of dr viper', 'glenn leopold', 'robert alvarez', 'november 26 , 1994'], ['22', '2', 'the dark side of the swat kats', 'jim katz', 'robert alvarez', 'december 10 , 1994']] |
robby gordon | https://en.wikipedia.org/wiki/Robby_Gordon | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1507423-4.html.csv | ordinal | the second highest amount of winnings that robby gordon had was in 2003 . | {'row': '10', 'col': '9', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'winnings', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; winnings ; 2 }'}, 'year'], 'result': '2003', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; winnings ; 2 } ; year }'}, '2003'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; winnings ; 2 } ; year } ; 2003 } = true', 'tointer': 'select the row whose winnings record of all rows is 2nd maximum . the year record of this row is 2003 .'} | eq { hop { nth_argmax { all_rows ; winnings ; 2 } ; year } ; 2003 } = true | select the row whose winnings record of all rows is 2nd maximum . the year record of this row is 2003 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'winnings_5': 5, '2_6': 6, 'year_7': 7, '2003_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'winnings_5': 'winnings', '2_6': '2', 'year_7': 'year', '2003_8': '2003'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'winnings_5': [0], '2_6': [0], 'year_7': [1], '2003_8': [2]} | ['year', 'starts', 'wins', 'top 5', 'top 10', 'poles', 'avg start', 'avg finish', 'winnings', 'position'] | [['1991', '2', '0', '0', '0', '0', '35.0', '22.0', '27625', '55th'], ['1993', '1', '0', '0', '0', '0', '14.0', '42.0', '17665', '93rd'], ['1994', '1', '0', '0', '0', '0', '38.0', '38.0', '7965', '76th'], ['1996', '3', '0', '0', '0', '0', '17.3', '40.7', '33915', '57th'], ['1997', '20', '0', '1', '1', '1', '25.3', '29.6', '622439', '40th'], ['1998', '1', '0', '0', '0', '0', '18.0', '37.0', '24765', '67th'], ['2000', '17', '0', '1', '2', '0', '29.9', '29.2', '620781', '43rd'], ['2001', '17', '1', '2', '3', '0', '32.4', '24.8', '1371900', '44th'], ['2002', '36', '0', '1', '5', '0', '18.4', '21.1', '3342703', '20th'], ['2003', '36', '2', '4', '10', '0', '23.1', '19.7', '4157064', '16th'], ['2004', '36', '0', '2', '6', '0', '23.2', '21.2', '4225719', '23rd'], ['2005', '29', '0', '1', '2', '0', '27.0', '30.1', '2271313', '37th'], ['2006', '36', '0', '1', '3', '0', '27.5', '25.3', '3143787', '30th'], ['2007', '35', '0', '1', '2', '0', '33.9', '25.8', '3090004', '26th'], ['2008', '36', '0', '0', '3', '0', '30.9', '29.0', '3816362', '33rd'], ['2009', '35', '0', '1', '1', '0', '30.1', '28.5', '3860582', '34th'], ['2010', '27', '0', '1', '1', '0', '33.8', '29.1', '2913816', '34th'], ['2011', '25', '0', '0', '0', '0', '36.5', '33.4', '2271891', '34th'], ['2012', '3', '0', '0', '0', '0', '30.0', '40.3', '405300', '52nd']] |
french west african cup | https://en.wikipedia.org/wiki/French_West_African_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12444503-1.html.csv | unique | the only team to score more than 5 goals when asec abidjan were runners up in the french west african cup was us gorée . | {'scope': 'subset', 'row': '9', 'col': '3', 'col_other': '2', 'criterion': 'greater_than', 'value': '5', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'asec abidjan'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'runner - up', 'asec abidjan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; runner - up ; asec abidjan }', 'tointer': 'select the rows whose runner - up record fuzzily matches to asec abidjan .'}, 'score', '5'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose runner - up record fuzzily matches to asec abidjan . among these rows , select the rows whose score record is greater than 5 .', 'tostr': 'filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } }', 'tointer': 'select the rows whose runner - up record fuzzily matches to asec abidjan . among these rows , select the rows whose score record is greater than 5 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'runner - up', 'asec abidjan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; runner - up ; asec abidjan }', 'tointer': 'select the rows whose runner - up record fuzzily matches to asec abidjan .'}, 'score', '5'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose runner - up record fuzzily matches to asec abidjan . among these rows , select the rows whose score record is greater than 5 .', 'tostr': 'filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 }'}, 'winner'], 'result': 'us gorée', 'ind': 3, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } ; winner }'}, 'us gorée'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } ; winner } ; us gorée }', 'tointer': 'the winner record of this unqiue row is us gorée .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } } ; eq { hop { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } ; winner } ; us gorée } } = true', 'tointer': 'select the rows whose runner - up record fuzzily matches to asec abidjan . among these rows , select the rows whose score record is greater than 5 . there is only one such row in the table . the winner record of this unqiue row is us gorée .'} | and { only { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } } ; eq { hop { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } ; winner } ; us gorée } } = true | select the rows whose runner - up record fuzzily matches to asec abidjan . among these rows , select the rows whose score record is greater than 5 . there is only one such row in the table . the winner record of this unqiue row is us gorée . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'runner - up_8': 8, 'asec abidjan_9': 9, 'score_10': 10, '5_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'winner_12': 12, 'us gorée_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'runner - up_8': 'runner - up', 'asec abidjan_9': 'asec abidjan', 'score_10': 'score', '5_11': '5', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'winner_12': 'winner', 'us gorée_13': 'us gorée'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_greater_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'runner - up_8': [0], 'asec abidjan_9': [0], 'score_10': [1], '5_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'winner_12': [3], 'us gorée_13': [4]} | ['season', 'winner', 'score', 'runner - up', 'lost to eventual winner', 'lost to eventual runner - up'] | [['1947', 'us gorée', '2 - 1', "asc jeanne d'arc", 'espoir saint - louis', 'espérance rufisque'], ['1948', 'foyer france sénégal', '4 - 0', "jeunesse club d'abidjan", 'saint - louisienne', 'racing club de conakry'], ['1949', 'racing club de dakar', '3 - 0', 'racing club de conakry', 'espoir saint - louis', 'usc bassam'], ['1949 / 50', 'racing club de conakry', '4 - 2', 'espoir saint - louis', 'usc bassam', "jeanne d'arc ( bamako )"], ['1950 / 51', "asc jeanne d'arc", '3 - 1', "jeanne d'arc ( bamako )", 'africa sports', 'us indigène'], ['1951 / 52', "asc jeanne d'arc", '2 - 0', 'etoile sportive porto novo', 'africa sports', 'foyer france sénégal'], ['1952 / 53', "jeanne d'arc ( bamako )", '3 - 1', 'racing club de conakry', 'us gorée', "jeunesse club d'abidjan"], ['1953 / 54', 'us gorée', '1 - 0', 'foyer du soudan', 'etoile sportive porto - novo', 'racing club de conakry'], ['1954 / 55', 'us gorée', '7 - 0', 'asec abidjan', 'as porto - novo', 'avenir saint - louis'], ['1955 / 56', "jeanne d'arc ( bamako )", '3 - 0', 'asec abidjan', 'foyer france sénégal', 'essor'], ['1956 / 57', 'réveil de saint - louis', '4 - 1', 'africa sports', 'etoile filante ( lomé )', "jeanne d'arc ( bamako )"], ['1957 / 58', 'africa sports', '5 - 0', 'asec abidjan', 'foyer france sénégal', 'société sportive de guinée'], ['1958 / 59', 'saint - louisienne', '2 - 1', 'modèle lomé', "stella d'abidjan", "asc jeanne d'arc"]] |
1968 vfl season | https://en.wikipedia.org/wiki/1968_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10808933-2.html.csv | comparative | in the 1968 vfl season , the glenferrie oval venue had a smaller crowd than the mcg venue . | {'row_1': '1', 'row_2': '6', 'col': '6', 'col_other': '5', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'glenferrie oval'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to glenferrie oval .', 'tostr': 'filter_eq { all_rows ; venue ; glenferrie oval }'}, 'crowd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; glenferrie oval } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to glenferrie oval . take the crowd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'mcg'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to mcg .', 'tostr': 'filter_eq { all_rows ; venue ; mcg }'}, 'crowd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; venue ; mcg } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; venue ; glenferrie oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; mcg } ; crowd } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to glenferrie oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; venue ; glenferrie oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; mcg } ; crowd } } = true | select the rows whose venue record fuzzily matches to glenferrie oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to mcg . take the crowd 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, 'venue_7': 7, 'glenferrie oval_8': 8, 'crowd_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'venue_11': 11, 'mcg_12': 12, 'crowd_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', 'venue_7': 'venue', 'glenferrie oval_8': 'glenferrie oval', 'crowd_9': 'crowd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'venue_11': 'venue', 'mcg_12': 'mcg', 'crowd_13': 'crowd'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'venue_7': [0], 'glenferrie oval_8': [0], 'crowd_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'venue_11': [1], 'mcg_12': [1], 'crowd_13': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '17.24 ( 126 )', 'south melbourne', '19.12 ( 126 )', 'glenferrie oval', '13536', '20 april 1968'], ['st kilda', '16.22 ( 118 )', 'melbourne', '9.8 ( 62 )', 'moorabbin oval', '21758', '20 april 1968'], ['geelong', '9.17 ( 71 )', 'footscray', '6.11 ( 47 )', 'kardinia park', '14589', '20 april 1968'], ['north melbourne', '9.9 ( 63 )', 'essendon', '10.22 ( 82 )', 'arden street oval', '14810', '20 april 1968'], ['fitzroy', '14.16 ( 100 )', 'collingwood', '10.11 ( 71 )', 'princes park', '17149', '20 april 1968'], ['richmond', '17.16 ( 118 )', 'carlton', '10.12 ( 72 )', 'mcg', '51889', '20 april 1968']] |
australian national bl class | https://en.wikipedia.org/wiki/Australian_National_BL_class | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11373937-1.html.csv | ordinal | the bl28 australian national bl class locomotive is the third earliest to enter service . | {'row': '3', 'col': '3', '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', 'entered service', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; entered service ; 3 }'}, 'locomotive'], 'result': 'bl28', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive }'}, 'bl28'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; bl28 } = true', 'tointer': 'select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is bl28 .'} | eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; bl28 } = true | select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is bl28 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'entered service_5': 5, '3_6': 6, 'locomotive_7': 7, 'bl28_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', 'entered service_5': 'entered service', '3_6': '3', 'locomotive_7': 'locomotive', 'bl28_8': 'bl28'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'entered service_5': [0], '3_6': [0], 'locomotive_7': [1], 'bl28_8': [2]} | ['locomotive', 'serial no', 'entered service', 'gauge', 'livery'] | [['bl26', '83 - 1010', 'march 1983', 'standard', 'pacific national blue & yellow'], ['bl27', '83 - 1011', 'august 1983', 'standard', 'pacific national blue & yellow'], ['bl28', '83 - 1012', 'september 1983', 'standard', 'pacific national blue & yellow'], ['bl29', '83 - 1013', 'october 1983', 'broad', 'pacific national blue & yellow'], ['bl30', '83 - 1014', 'december 1983', 'standard', 'pacific national blue & yellow'], ['bl31', '83 - 1015', 'november 1983', 'standard', 'pacific national blue & yellow'], ['bl32', '83 - 1016', 'february 1984', 'broad', 'national rail orange & grey'], ['bl33', '83 - 1017', 'april 1984', 'standard', 'pacific national blue & yellow'], ['bl34', '83 - 1018', 'june 1984', 'broad', 'pacific national blue & yellow'], ['bl35', '83 - 1019', 'july 1984', 'standard', 'pacific national blue & yellow']] |
munkedals if | https://en.wikipedia.org/wiki/Munkedals_IF | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12220421-1.html.csv | unique | the 1946 - 47 season was the only season that munkedals if finished in 8th place . | {'scope': 'all', 'row': '15', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '8th', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '8th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 8th .', 'tostr': 'filter_eq { all_rows ; position ; 8th }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; position ; 8th } }', 'tointer': 'select the rows whose position record fuzzily matches to 8th . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '8th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 8th .', 'tostr': 'filter_eq { all_rows ; position ; 8th }'}, 'season'], 'result': '1946 - 47', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; 8th } ; season }'}, '1946 - 47'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; 8th } ; season } ; 1946 - 47 }', 'tointer': 'the season record of this unqiue row is 1946 - 47 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; position ; 8th } } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; season } ; 1946 - 47 } } = true', 'tointer': 'select the rows whose position record fuzzily matches to 8th . there is only one such row in the table . the season record of this unqiue row is 1946 - 47 .'} | and { only { filter_eq { all_rows ; position ; 8th } } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; season } ; 1946 - 47 } } = true | select the rows whose position record fuzzily matches to 8th . there is only one such row in the table . the season record of this unqiue row is 1946 - 47 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, '8th_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'season_9': 9, '1946 - 47_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'position_7': 'position', '8th_8': '8th', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'season_9': 'season', '1946 - 47_10': '1946 - 47'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], '8th_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'season_9': [2], '1946 - 47_10': [3]} | ['season', 'level', 'division', 'section', 'position'] | [['1932 - 33', 'tier 3', 'division 3', 'västsvenska', '7th'], ['1933 - 34', 'tier 3', 'division 3', 'västsvenska', '6th'], ['1934 - 35', 'tier 3', 'division 3', 'västsvenska norra', '7th'], ['1935 - 36', 'tier 3', 'division 3', 'västsvenska norra', '6th'], ['1936 - 37', 'tier 3', 'division 3', 'västsvenska norra', '7th'], ['1937 - 38', 'tier 3', 'division 3', 'västsvenska norra', '6th'], ['1938 - 39', 'tier 3', 'division 3', 'västsvenska norra', '7th'], ['1939 - 40', 'tier 3', 'division 3', 'västsvenska norra', '9th'], ['1940 - 41', 'tier 3', 'division 3', 'västsvenska södra', '3rd'], ['1941 - 42', 'tier 3', 'division 3', 'västsvenska södra , bohus', '2nd'], ['1942 - 43', 'tier 3', 'division 3', 'västsvenska södra , bohus', '1st'], ['1943 - 44', 'tier 2', 'division 2', 'västra', '9th'], ['1944 - 45', 'tier 3', 'division 3', 'västsvenska södra , bohus', '1st'], ['1945 - 46', 'tier 3', 'division 3', 'västsvenska södra , bohus', '4th'], ['1946 - 47', 'tier 3', 'division 3', 'västsvenska södra', '8th']] |
bosnia and herzegovina davis cup team | https://en.wikipedia.org/wiki/Bosnia_and_Herzegovina_Davis_Cup_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10295599-5.html.csv | unique | the only time the davis cup tennis team from bosnia and herzegovina lost in 2010 was when they played in cruz quebrada , portugal . | {'scope': 'subset', 'row': '3', 'col': '7', 'col_other': '5', 'criterion': 'equal', 'value': 'lost', 'subset': {'col': '1', 'criterion': 'equal', 'value': '2010'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2010'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 2010 }', 'tointer': 'select the rows whose year record is equal to 2010 .'}, 'result', 'lost'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record is equal to 2010 . among these rows , select the rows whose result record fuzzily matches to lost .', 'tostr': 'filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost } }', 'tointer': 'select the rows whose year record is equal to 2010 . 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_eq', 'args': ['all_rows', 'year', '2010'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 2010 }', 'tointer': 'select the rows whose year record is equal to 2010 .'}, 'result', 'lost'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record is equal to 2010 . among these rows , select the rows whose result record fuzzily matches to lost .', 'tostr': 'filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost }'}, 'location'], 'result': 'cruz quebrada , portugal', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost } ; location }'}, 'cruz quebrada , portugal'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost } ; location } ; cruz quebrada , portugal }', 'tointer': 'the location record of this unqiue row is cruz quebrada , portugal .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost } } ; eq { hop { filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost } ; location } ; cruz quebrada , portugal } } = true', 'tointer': 'select the rows whose year record is equal to 2010 . among these rows , select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the location record of this unqiue row is cruz quebrada , portugal .'} | and { only { filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost } } ; eq { hop { filter_eq { filter_eq { all_rows ; year ; 2010 } ; result ; lost } ; location } ; cruz quebrada , portugal } } = true | select the rows whose year record is equal to 2010 . among these rows , select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the location record of this unqiue row is cruz quebrada , portugal . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_eq_0': 0, 'all_rows_7': 7, 'year_8': 8, '2010_9': 9, 'result_10': 10, 'lost_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'location_12': 12, 'cruz quebrada , portugal_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_eq_0': 'filter_eq', 'all_rows_7': 'all_rows', 'year_8': 'year', '2010_9': '2010', 'result_10': 'result', 'lost_11': 'lost', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'location_12': 'location', 'cruz quebrada , portugal_13': 'cruz quebrada , portugal'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_eq_0': [1], 'all_rows_7': [0], 'year_8': [0], '2010_9': [0], 'result_10': [1], 'lost_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'location_12': [3], 'cruz quebrada , portugal_13': [4]} | ['year', 'competition', 'date', 'surface', 'location', 'score', 'result'] | [['2010', 'europe / africa zone group ii first round', '5 - 7 march', 'clay', 'veles , macedonia', '3 - 2', 'won'], ['2010', 'europe / africa zone group ii quarterfinals', '9 - 11 july', 'clay', 'tallinn , estonia', '3 - 2', 'won'], ['2010', 'europe / africa zone group ii semifinals', '17 - 19 september', 'clay', 'cruz quebrada , portugal', '2 - 3', 'lost'], ['2011', 'europe / africa zone group ii first round', '4 - 6 march', 'clay', 'marrakesh , morocco', '3 - 2', 'won'], ['2011', 'europe / africa zone group ii quarterfinals', '8 - 10 july', 'hard', 'tuzla , bosnia and herzegovina', '3 - 2', 'won'], ['2011', 'europe / africa zone group ii semifinals', '16 - 18 september', 'hard', 'hillerød , denmark', '2 - 3', 'lost'], ['2012', 'europe / africa zone group ii first round', '10 - 12 february', 'hard', 'ankara , turkey', '3 - 1', 'won'], ['2012', 'europe / africa zone group ii quarterfinals', '6 - 8 april', 'hard', 'minsk , belarus', '1 - 4', 'lost'], ['2013', 'europe / africa zone group ii first round', '1 - 3 february', 'hard', 'sarajevo , bosnia and herzegovina', '4 - 1', 'won'], ['2013', 'europe / africa zone group ii quarterfinals', '5 - 7 april', 'clay', 'mostar , bosnia and herzegovina', '1 - 3', 'lost']] |
myrtle beach 250 | https://en.wikipedia.org/wiki/Myrtle_Beach_250 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23015396-1.html.csv | comparative | mark martin had a higher average speed than chuck bown in the myrtle beach 250 . | {'row_1': '3', 'row_2': '4', 'col': '8', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'driver', 'mark martin'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose driver record fuzzily matches to mark martin .', 'tostr': 'filter_eq { all_rows ; driver ; mark martin }'}, 'average speed ( mph )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; driver ; mark martin } ; average speed ( mph ) }', 'tointer': 'select the rows whose driver record fuzzily matches to mark martin . take the average speed ( mph ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'driver', 'chuck bown'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose driver record fuzzily matches to chuck bown .', 'tostr': 'filter_eq { all_rows ; driver ; chuck bown }'}, 'average speed ( mph )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; driver ; chuck bown } ; average speed ( mph ) }', 'tointer': 'select the rows whose driver record fuzzily matches to chuck bown . take the average speed ( mph ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; driver ; mark martin } ; average speed ( mph ) } ; hop { filter_eq { all_rows ; driver ; chuck bown } ; average speed ( mph ) } } = true', 'tointer': 'select the rows whose driver record fuzzily matches to mark martin . take the average speed ( mph ) record of this row . select the rows whose driver record fuzzily matches to chuck bown . take the average speed ( mph ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; driver ; mark martin } ; average speed ( mph ) } ; hop { filter_eq { all_rows ; driver ; chuck bown } ; average speed ( mph ) } } = true | select the rows whose driver record fuzzily matches to mark martin . take the average speed ( mph ) record of this row . select the rows whose driver record fuzzily matches to chuck bown . take the average speed ( mph ) 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, 'driver_7': 7, 'mark martin_8': 8, 'average speed (mph)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'driver_11': 11, 'chuck bown_12': 12, 'average speed (mph)_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', 'driver_7': 'driver', 'mark martin_8': 'mark martin', 'average speed (mph)_9': 'average speed ( mph )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'driver_11': 'driver', 'chuck bown_12': 'chuck bown', 'average speed (mph)_13': 'average speed ( mph )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'driver_7': [0], 'mark martin_8': [0], 'average speed (mph)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'driver_11': [1], 'chuck bown_12': [1], 'average speed (mph)_13': [3]} | ['year', 'date', 'driver', 'manufacturer', 'laps', '-', 'race time', 'average speed ( mph )'] | [['1988', 'july 2', 'rob moroso', 'oldsmobile', '200', '107.6 ( 173.165 )', '1:36:04', '66.971'], ['1989', 'july 4', 'jimmy spencer', 'buick', '200', '107.6 ( 173.165 )', '1:25:01', '75.938'], ['1990', 'june 30', 'mark martin', 'ford', '200', '107.6 ( 173.165 )', '1:24:52', '76.072'], ['1991', 'june 22', 'chuck bown', 'pontiac', '250', '134.5 ( 216.456 )', '1:49:15', '73.867'], ['1992', 'june 20', 'jimmy spencer', 'oldsmobile', '250', '134.5 ( 216.456 )', '2:21:14', '57.139'], ['1993', 'june 12', 'jeff burton', 'ford', '250', '134.5 ( 216.456 )', '1:56:59', '68.984'], ['1994', 'june 11', 'elton sawyer', 'ford', '250', '134.5 ( 216.456 )', '2:01:18', '66.529'], ['1995', 'june 10', 'larry pearson', 'chevrolet', '250', '134.5 ( 216.456 )', '1:41:23', '79.599'], ['1996', 'june 22', 'david green', 'chevrolet', '250', '134.5 ( 216.456 )', '1:53:35', '71.049'], ['1997', 'july 12', 'elliott sadler', 'chevrolet', '250', '134.5 ( 216.456 )', '1:39:07', '81.419'], ['1998', 'july 11', 'randy lajoie', 'chevrolet', '250', '134.5 ( 216.456 )', '1:36:56', '80.754'], ['1999', 'july 17', 'jeff green', 'chevrolet', '250', '134.5 ( 216.456 )', '1:35:52', '84.179']] |
wind power in the republic of ireland | https://en.wikipedia.org/wiki/Wind_power_in_the_Republic_of_Ireland | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14101606-2.html.csv | ordinal | codling wind farm has the highest wind power capacity ( mw ) in the republic of ireland . | {'row': '1', 'col': '3', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'capacity ( mw )', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; capacity ( mw ) ; 1 }'}, 'wind farm'], 'result': 'codling', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; capacity ( mw ) ; 1 } ; wind farm }'}, 'codling'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; capacity ( mw ) ; 1 } ; wind farm } ; codling } = true', 'tointer': 'select the row whose capacity ( mw ) record of all rows is 1st maximum . the wind farm record of this row is codling .'} | eq { hop { nth_argmax { all_rows ; capacity ( mw ) ; 1 } ; wind farm } ; codling } = true | select the row whose capacity ( mw ) record of all rows is 1st maximum . the wind farm record of this row is codling . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'capacity (mw)_5': 5, '1_6': 6, 'wind farm_7': 7, 'codling_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'capacity (mw)_5': 'capacity ( mw )', '1_6': '1', 'wind farm_7': 'wind farm', 'codling_8': 'codling'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'capacity (mw)_5': [0], '1_6': [0], 'wind farm_7': [1], 'codling_8': [2]} | ['wind farm', 'scheduled', 'capacity ( mw )', 'turbines', 'type', 'location'] | [['codling', 'unknown', '1100', '220', 'unknown', 'county wicklow'], ['carrowleagh', '2012', '36.8', '16', 'enercon e - 70 2.3', 'county cork'], ['dublin array', '2015', '364', '145', 'unknown', 'county dublin'], ['glenmore', '2009 summer', '30', '10', 'vestas v90', 'county clare'], ['glenough', '2010 winter', '32.5', '13', 'nordex n80 / n90', 'county tipperary'], ['gortahile', '2010 autumn', '20', '8', 'nordex n90', 'county laois'], ['grouse lodge', '2011 summer', '20', '8', 'nordex n90', 'county tipperary'], ['moneypoint', 'unknown', '22.5', '9', 'unknown', 'county clare'], ['mount callan', 'unknown', '90', '30', '3 mw', 'county clare'], ['oriel', '2013', '330', '55', 'unknown', 'county louth'], ['skerd rocks', 'unknown', '100', '20', '5 mw', 'county galway'], ['shragh', 'planning submitted oct 2011', '135', '45', 'enercon e82 3.0 mw', 'county clare'], ['garracummer', '2012', '42.5', '17', 'nordex n90 2.5 mw', 'county tipperary'], ['knockacummer', '2013', '87.5', '35', 'nordex n90 2.5 mw', 'county cork'], ['monaincha', '2013', '36', '15', 'nordex n117 2.4 mw', 'county tipperary'], ['gibbet hill', '2013', '15', '6', 'nordex n90 2.5 mw', 'county wexford'], ['glenough extension', '2013', '2.5', '1', 'nordex n90 2.5 mw', 'county tipperary']] |
1971 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1971_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17245565-4.html.csv | majority | the majority of the top finishers at the 1971 us open golf tournament scored a 70 and finished even . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '70', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'score', '70'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , most of them are equal to 70 .', 'tostr': 'most_eq { all_rows ; score ; 70 } = true'} | most_eq { all_rows ; score ; 70 } = true | for the score records of all rows , most of them are equal to 70 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, '70_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', '70_4': '70'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], '70_4': [0]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'labron harris', 'united states', '67', '- 3'], ['t2', 'bob goalby', 'united states', '68', '- 2'], ['t2', 'doug sanders', 'united states', '68', '- 2'], ['t2', 'lanny wadkins ( a )', 'united states', '68', '- 2'], ['t5', 'jim colbert', 'united states', '69', '- 1'], ['t5', 'jack nicklaus', 'united states', '69', '- 1'], ['t5', 'bobby nichols', 'united states', '69', '- 1'], ['t8', 'gay brewer', 'united states', '70', 'e'], ['t8', 'charles coody', 'united states', '70', 'e'], ['t8', 'dale douglass', 'united states', '70', 'e'], ['t8', 'ralph johnston', 'united states', '70', 'e'], ['t8', 'johnny miller', 'united states', '70', 'e'], ['t8', 'chi - chi rodríguez', 'united states', '70', 'e'], ['t8', 'john schlee', 'united states', '70', 'e'], ['t8', 'leonard thompson', 'united states', '70', 'e'], ['t8', 'lee trevino', 'united states', '70', 'e'], ['t8', 'tom weiskopf', 'united states', '70', 'e']] |
2002 pga tour | https://en.wikipedia.org/wiki/2002_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14583241-4.html.csv | aggregation | in 2002 , the top 5 players on the pga tour earned an average of $ 22,046,805 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '22046805', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'earnings'], 'result': '22046805', 'ind': 0, 'tostr': 'avg { all_rows ; earnings }'}, '22046805'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; earnings } ; 22046805 } = true', 'tointer': 'the average of the earnings record of all rows is 22046805 .'} | round_eq { avg { all_rows ; earnings } ; 22046805 } = true | the average of the earnings record of all rows is 22046805 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'earnings_4': 4, '22046805_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'earnings_4': 'earnings', '22046805_5': '22046805'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'earnings_4': [0], '22046805_5': [1]} | ['rank', 'player', 'country', 'earnings', 'wins'] | [['1', 'tiger woods', 'united states', '33103852', '34'], ['2', 'phil mickelson', 'united states', '22149969', '21'], ['3', 'davis love iii', 'united states', '20050850', '14'], ['4', 'vijay singh', 'fiji', '18281015', '11'], ['5', 'nick price', 'zimbabwe', '16648337', '18']] |
ricardo páez | https://en.wikipedia.org/wiki/Ricardo_P%C3%A1ez | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14394530-1.html.csv | count | 5 of the matches took place in venezuela . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'venezuela', 'result': '5', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'venezuela'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to venezuela .', 'tostr': 'filter_eq { all_rows ; venue ; venezuela }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; venue ; venezuela } }', 'tointer': 'select the rows whose venue record fuzzily matches to venezuela . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; venue ; venezuela } } ; 5 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to venezuela . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; venue ; venezuela } } ; 5 } = true | select the rows whose venue record fuzzily matches to venezuela . 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, 'venue_5': 5, 'venezuela_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', 'venue_5': 'venue', 'venezuela_6': 'venezuela', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'venezuela_6': [0], '5_7': [2]} | ['goal', 'date', 'venue', 'score', 'result', 'competition'] | [['1', 'september 4 , 2001', 'estadio nacional de chile , santiago , chile', '0 - 1', '0 - 2', '2002 world cup qualification'], ['2', 'november 20 , 2002', 'brígido iriarte , caracas , venezuela', '1 - 0', '1 - 0', 'friendly'], ['3', 'april 2 , 2003', 'brígido iriarte , caracas , venezuela', '2 - 0', '2 - 0', 'friendly'], ['4', 'february 9 , 2005', 'josé pachencho romero , maracaibo , venezuela', '1 - 0', '3 - 0', 'friendly'], ['5', 'march 28 , 2007', 'josé pachencho romero , maracaibo , venezuela', '1 - 0', '5 - 0', 'friendly'], ['6', 'june 26 , 2007', 'pueblo nuevo , san cristóbal , venezuela', '2 - 1', '2 - 2', '2007 copa américa']] |
united states house of representatives elections , 1962 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1962 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341884-11.html.csv | superlative | during united states house of representatives elections in 1962 , charles edward bennett was the incumbent from democratic party that has been first elected the longest time ago . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2,3', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'democratic'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; democratic }', 'tointer': 'select the rows whose party record fuzzily matches to democratic .'}, 'first elected'], 'result': None, 'ind': 1, 'tostr': 'argmin { filter_eq { all_rows ; party ; democratic } ; first elected }'}, 'incumbent'], 'result': 'charles edward bennett', 'ind': 2, 'tostr': 'hop { argmin { filter_eq { all_rows ; party ; democratic } ; first elected } ; incumbent }'}, 'charles edward bennett'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmin { filter_eq { all_rows ; party ; democratic } ; first elected } ; incumbent } ; charles edward bennett } = true', 'tointer': 'select the rows whose party record fuzzily matches to democratic . select the row whose first elected record of these rows is minimum . the incumbent record of this row is charles edward bennett .'} | eq { hop { argmin { filter_eq { all_rows ; party ; democratic } ; first elected } ; incumbent } ; charles edward bennett } = true | select the rows whose party record fuzzily matches to democratic . select the row whose first elected record of these rows is minimum . the incumbent record of this row is charles edward bennett . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'party_6': 6, 'democratic_7': 7, 'first elected_8': 8, 'incumbent_9': 9, 'charles edward bennett_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmin_1': 'argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'party_6': 'party', 'democratic_7': 'democratic', 'first elected_8': 'first elected', 'incumbent_9': 'incumbent', 'charles edward bennett_10': 'charles edward bennett'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'party_6': [0], 'democratic_7': [0], 'first elected_8': [1], 'incumbent_9': [2], 'charles edward bennett_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['florida 2', 'charles edward bennett', 'democratic', '1948', 're - elected', 'charles edward bennett ( d ) unopposed'], ['florida 6', 'paul rogers', 'democratic', '1954', 're - elected', 'paul rogers ( d ) 64.2 % frederick a kibbe ( r ) 35.8 %'], ['florida 7', 'james a haley', 'democratic', '1952', 're - elected', 'james a haley ( d ) 66.8 % f onell rogers ( r ) 33.2 %'], ['florida 8', 'donald ray matthews', 'democratic', '1952', 're - elected', 'donald ray matthews ( d ) unopposed'], ['florida 9', 'none ( district created )', 'none ( district created )', 'none ( district created )', 'new seat democratic gain', 'don fuqua ( d ) 75.4 % wilfred c varn ( r ) 24.6 %']] |
independent girls ' schools sports association ( south australia ) | https://en.wikipedia.org/wiki/Independent_Girls%27_Schools_Sports_Association_%28South_Australia%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22043925-1.html.csv | aggregation | the average student enrollment for schools in the independent girls ' schools sports association is 831 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '831', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'enrolment'], 'result': '831', 'ind': 0, 'tostr': 'avg { all_rows ; enrolment }'}, '831'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; enrolment } ; 831 } = true', 'tointer': 'the average of the enrolment record of all rows is 831 .'} | round_eq { avg { all_rows ; enrolment } ; 831 } = true | the average of the enrolment record of all rows is 831 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'enrolment_4': 4, '831_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'enrolment_4': 'enrolment', '831_5': '831'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'enrolment_4': [0], '831_5': [1]} | ['school', 'location', 'enrolment', 'founded', 'denomination', 'boys / girls', 'day / boarding', 'school colors'] | [['annesley college', 'wayville', '530', '1902', 'uniting church', 'girls', 'day & boarding', 'maroon & white'], ['concordia college', 'highgate', '700', '1890', 'lutheran', 'boys & girls', 'day', 'blue & gold'], ['immanuel college', 'novar gardens', '800', '1895', 'lutheran', 'boys & girls', 'day & boarding', 'blue , gold & white'], ['pembroke school', 'kensington park', '1545', '1915', 'non - denominational', 'boys & girls', 'day & boarding', 'royal blue , green & gold'], ['pulteney grammar school', 'adelaide', '820', '1847', 'anglican', 'boys & girls', 'day', 'navy blue , white & gold'], ["st peter 's collegiate girls ' school", 'stonyfell', '550', '1894', 'anglican', 'girls', 'day', 'navy blue & white'], ['scotch college', 'mitcham', '850', '1919', 'uniting church', 'boys & girls', 'day & boarding', 'blue & gold'], ['seymour college', 'glen osmond', '765', '1922', 'uniting church', 'girls', 'day & boarding', 'green , navy & white'], ['walford anglican school for girls', 'hyde park', '650', '1893', 'anglican', 'girls', 'day & boarding', 'navy blue , light blue & gold'], ['westminster school', 'marion', '1100', '1961', 'uniting church', 'boys & girls', 'day & boarding', 'green & white']] |
1986 open championship | https://en.wikipedia.org/wiki/1986_Open_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18150723-3.html.csv | count | in the 1986 open championship , among the players from england , 3 of them had a score of 71 . | {'scope': 'subset', 'criterion': 'equal', 'value': '71', 'result': '3', 'col': '4', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'england'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'england'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; england }', 'tointer': 'select the rows whose country record fuzzily matches to england .'}, 'score', '71'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to england . among these rows , select the rows whose score record is equal to 71 .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; england } ; score ; 71 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; country ; england } ; score ; 71 } }', 'tointer': 'select the rows whose country record fuzzily matches to england . among these rows , select the rows whose score record is equal to 71 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; country ; england } ; score ; 71 } } ; 3 } = true', 'tointer': 'select the rows whose country record fuzzily matches to england . among these rows , select the rows whose score record is equal to 71 . the number of such rows is 3 .'} | eq { count { filter_eq { filter_eq { all_rows ; country ; england } ; score ; 71 } } ; 3 } = true | select the rows whose country record fuzzily matches to england . among these rows , select the rows whose score record is equal to 71 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'england_7': 7, 'score_8': 8, '71_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'england_7': 'england', 'score_8': 'score', '71_9': '71', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'england_7': [0], 'score_8': [1], '71_9': [1], '3_10': [3]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'ian woosnam', 'wales', '70', 'e'], ['t2', 'gordon j brand', 'england', '71', '+ 1'], ['t2', 'nick faldo', 'england', '71', '+ 1'], ['t2', 'anders forsbrand', 'sweden', '71', '+ 1'], ['t2', 'robert lee', 'england', '71', '+ 1'], ['t6', 'andrew brooks', 'scotland', '72', '+ 2'], ['t6', 'ron commans', 'united states', '72', '+ 2'], ['t6', 'derrick cooper', 'england', '72', '+ 2'], ['t6', 'bernhard langer', 'west germany', '72', '+ 2'], ['t6', 'sam randolph', 'united states', '72', '+ 2'], ['t6', 'ian stanley', 'australia', '72', '+ 2']] |
1958 formula one season | https://en.wikipedia.org/wiki/1958_Formula_One_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140110-6.html.csv | count | the 1958 formula one season had 5 circuits put into use . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '5', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'circuit'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose circuit record is arbitrary .', 'tostr': 'filter_all { all_rows ; circuit }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; circuit } }', 'tointer': 'select the rows whose circuit record is arbitrary . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; circuit } } ; 5 } = true', 'tointer': 'select the rows whose circuit record is arbitrary . the number of such rows is 5 .'} | eq { count { filter_all { all_rows ; circuit } } ; 5 } = true | select the rows whose circuit record is arbitrary . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'circuit_5': 5, '5_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'circuit_5': 'circuit', '5_6': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'circuit_5': [0], '5_6': [2]} | ['race name', 'circuit', 'date', 'winning driver', 'constructor', 'report'] | [['vi glover trophy', 'goodwood', '7 april', 'mike hawthorn', 'ferrari', 'report'], ['viii gran premio di siracusa', 'syracuse', '13 april', 'luigi musso', 'ferrari', 'report'], ['xiii barc aintree 200', 'aintree', '19 april', 'stirling moss', 'cooper - climax', 'report'], ['x brdc international trophy', 'silverstone', '3 may', 'peter collins', 'ferrari', 'report'], ['vi grand prix de caen', 'caen', '20 july', 'stirling moss', 'cooper - climax', 'report']] |
gabriela navrátilová | https://en.wikipedia.org/wiki/Gabriela_Navr%C3%A1tilov%C3%A1 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14935689-2.html.csv | majority | all of gabriela navrátilová 's tournaments took place after the year 2000 . | {'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '2000', 'subset': None} | {'func': 'all_greater', 'args': ['all_rows', 'date', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them are greater than 2000 .', 'tostr': 'all_greater { all_rows ; date ; 2000 } = true'} | all_greater { all_rows ; date ; 2000 } = true | for the date records of all rows , all of them are greater than 2000 . | 1 | 1 | {'all_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '2000_4': 4} | {'all_greater_0': 'all_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '2000_4': '2000'} | {'all_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '2000_4': [0]} | ['date', 'tournament', 'surface', 'partnering', 'opponents in the final', 'score'] | [['march 1 , 2004', 'acapulco , mexico', 'clay', 'olga blahotová', 'lisa mcshea milagros sequera', '2 - 6 , 7 - 6 ( 7 - 5 ) , 6 - 4'], ['march 12 , 2004', 'estoril , portugal', 'clay', 'olga blahotová', 'emmanuelle gagliardi janette husárová', '6 - 3 , 6 - 2'], ['january 10 , 2005', 'canberra , australia', 'hard', 'michaela paštiková', 'tathiana garbin tina križan', '7 - 5 , 1 - 6 , 6 - 4'], ['july 11 , 2005', 'modena , italy', 'clay', 'michaela paštiková', 'yulia beygelzimer mervana jugić - salkić', '6 - 2 , 6 - 0'], ['february 5 , 2007', 'paris , france', 'carpet ( i )', 'vladimíra uhlířová', 'cara black liezel huber', '6 - 2 , 6 - 0'], ['april 23 , 2007', 'budapest , hungary', 'clay', 'martina müller', 'ágnes szávay vladimíra uhlířová', '7 - 5 , 6 - 2']] |
east kent mavericks | https://en.wikipedia.org/wiki/East_Kent_Mavericks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16994082-1.html.csv | aggregation | the east kent mavericks had a total of 45 wins overall . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '45', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'wins'], 'result': '45', 'ind': 0, 'tostr': 'sum { all_rows ; wins }'}, '45'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; wins } ; 45 } = true', 'tointer': 'the sum of the wins record of all rows is 45 .'} | round_eq { sum { all_rows ; wins } ; 45 } = true | the sum of the wins record of all rows is 45 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'wins_4': 4, '45_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '45_5': '45'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'wins_4': [0], '45_5': [1]} | ['season', 'division', 'wins', 'ties', 'final position'] | [['2001', 'british senior flag league , southern', '3', '1', '2 / 4'], ['2002', 'british senior flag league , nine - man league', '5', '3', '2 / 7'], ['2003 to 2005', 'did not compete', 'did not compete', 'did not compete', 'did not compete'], ['2006', 'bafl division two south', '0', '0', '4 / 4'], ['2007', 'bafl division two south east', '5', '0', '3 / 6'], ['2008', 'bafl division two south east', '6', '0', '3 / 5'], ['2009', 'bafl division two south east', '8', '1', '1 / 4'], ['2010', 'bafl division one south east', '8', '1', '1 / 4'], ['2011', 'bafl division one south east', '2', '6', ''], ['2012', 'bafl division one south and central', '8', '2', '8 / 2']] |
1966 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1966_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17277136-4.html.csv | superlative | in the 1966 u.s. open ( golf ) , billy casper ranks the highest . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'place'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; place }'}, 'player'], 'result': 'billy casper', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; place } ; player }'}, 'billy casper'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; place } ; player } ; billy casper } = true', 'tointer': 'select the row whose place record of all rows is minimum . the player record of this row is billy casper .'} | eq { hop { argmin { all_rows ; place } ; player } ; billy casper } = true | select the row whose place record of all rows is minimum . the player record of this row is billy casper . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'place_5': 5, 'player_6': 6, 'billy casper_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'place_5': 'place', 'player_6': 'player', 'billy casper_7': 'billy casper'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'place_5': [0], 'player_6': [1], 'billy casper_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['t1', 'billy casper', 'united states', '69 + 68 = 137', '- 3'], ['t1', 'arnold palmer', 'united states', '71 + 66 = 137', '- 3'], ['t3', 'phil rodgers', 'united states', '70 + 70 = 140', 'e'], ['t3', 'rives mcbee', 'united states', '76 + 64 = 140', 'e'], ['t5', 'jack nicklaus', 'united states', '71 + 71 = 142', '+ 2'], ['t5', 'johnny miller ( a )', 'united states', '70 + 72 = 142', '+ 2'], ['t7', 'julius boros', 'united states', '74 + 69 = 143', '+ 3'], ['t7', 'dave hill', 'united states', '72 + 71 = 143', '+ 3'], ['t7', 'kel nagle', 'australia', '70 + 73 = 143', '+ 3'], ['t10', 'bob goalby', 'united states', '71 + 73 = 144', '+ 4'], ['t10', 'al mengert', 'united states', '67 + 77 = 144', '+ 4']] |
ainsi soit je ... ( song ) | https://en.wikipedia.org/wiki/Ainsi_soit_je..._%28song%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15146625-2.html.csv | comparative | the maxi remix version of ainsi soit je ... is longer than the single version . | {'row_1': '3', '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', 'version', 'maxi remix'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose version record fuzzily matches to maxi remix .', 'tostr': 'filter_eq { all_rows ; version ; maxi remix }'}, 'length'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; version ; maxi remix } ; length }', 'tointer': 'select the rows whose version record fuzzily matches to maxi remix . take the length record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'version', 'single version'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose version record fuzzily matches to single version .', 'tostr': 'filter_eq { all_rows ; version ; single version }'}, 'length'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; version ; single version } ; length }', 'tointer': 'select the rows whose version record fuzzily matches to single version . take the length record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; version ; maxi remix } ; length } ; hop { filter_eq { all_rows ; version ; single version } ; length } } = true', 'tointer': 'select the rows whose version record fuzzily matches to maxi remix . take the length record of this row . select the rows whose version record fuzzily matches to single version . take the length record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; version ; maxi remix } ; length } ; hop { filter_eq { all_rows ; version ; single version } ; length } } = true | select the rows whose version record fuzzily matches to maxi remix . take the length record of this row . select the rows whose version record fuzzily matches to single version . take the length 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, 'version_7': 7, 'maxi remix_8': 8, 'length_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'version_11': 11, 'single version_12': 12, 'length_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', 'version_7': 'version', 'maxi remix_8': 'maxi remix', 'length_9': 'length', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'version_11': 'version', 'single version_12': 'single version', 'length_13': 'length'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'version_7': [0], 'maxi remix_8': [0], 'length_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'version_11': [1], 'single version_12': [1], 'length_13': [3]} | ['version', 'length', 'album', 'remixed by', 'year'] | [['single version', '4:30', '-', '-', '1988'], ['album version', '6:18', 'ainsi soit je', 'laurent boutonnat', '1988'], ['maxi remix', '7:10', 'dance remixes', 'thierry rogen', '1988'], ['classic bonus beat', '6:22', '-', 'thierry rogen', '1988'], ['lamentations', '4:45', '-', 'thierry rogen', '1988'], ['music video', '5:23', 'les clips vol ii , music videos i', '-', '1988'], ['live version ( recorded in 1989 )', '7:42', 'en concert', '-', '1989'], ['live version ( recorded in 1996 )', '5:00', 'live à bercy', '-', '1996'], ['live music video', '5:04', 'music videos ii & iii', '-', '1996'], ['album version', '4:45', 'les mots', 'laurent boutonnat', '2001'], ['live version ( recorded in 2006 )', '5:00', '-', '-', '2006'], ['live version ( recorded in 2009 )', '4:18', 'n degree5 on tour', '-', '2009']] |
1983 - 84 fa cup | https://en.wikipedia.org/wiki/1983%E2%80%9384_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17437287-6.html.csv | unique | the only match played on 14 march 1984 was a replay between derby county and plymouth argyle . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': '14 march 1984', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '14 march 1984'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 14 march 1984 .', 'tostr': 'filter_eq { all_rows ; date ; 14 march 1984 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; date ; 14 march 1984 } }', 'tointer': 'select the rows whose date record fuzzily matches to 14 march 1984 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '14 march 1984'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 14 march 1984 .', 'tostr': 'filter_eq { all_rows ; date ; 14 march 1984 }'}, 'home team'], 'result': 'derby county', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 14 march 1984 } ; home team }'}, 'derby county'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; 14 march 1984 } ; home team } ; derby county }', 'tointer': 'the home team record of this unqiue row is derby county .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; date ; 14 march 1984 } } ; eq { hop { filter_eq { all_rows ; date ; 14 march 1984 } ; home team } ; derby county } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 14 march 1984 . there is only one such row in the table . the home team record of this unqiue row is derby county .'} | and { only { filter_eq { all_rows ; date ; 14 march 1984 } } ; eq { hop { filter_eq { all_rows ; date ; 14 march 1984 } ; home team } ; derby county } } = true | select the rows whose date record fuzzily matches to 14 march 1984 . there is only one such row in the table . the home team record of this unqiue row is derby county . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, '14 march 1984_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'home team_9': 9, 'derby county_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', '14 march 1984_8': '14 march 1984', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'home team_9': 'home team', 'derby county_10': 'derby county'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'date_7': [0], '14 march 1984_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'home team_9': [2], 'derby county_10': [3]} | ['tie no', 'home team', 'score', 'away team', 'date'] | [['1', 'notts county', '1 - 2', 'everton', '10 march 1984'], ['2', 'sheffield wednesday', '0 - 0', 'southampton', '11 march 1984'], ['replay', 'southampton', '5 - 1', 'sheffield wednesday', '20 march 1984'], ['3', 'plymouth argyle', '0 - 0', 'derby county', '10 march 1984'], ['replay', 'derby county', '0 - 1', 'plymouth argyle', '14 march 1984'], ['4', 'birmingham city', '1 - 3', 'watford', '10 march 1984']] |
united states house of representatives elections , 2006 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1805191-2.html.csv | unique | robert cramer was the only incumbent to the united states house of representatives who was a democrat . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'democratic', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to democratic .', 'tostr': 'filter_eq { all_rows ; party ; democratic }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; party ; democratic } }', 'tointer': 'select the rows whose party record fuzzily matches to democratic . 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', 'democratic'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to democratic .', 'tostr': 'filter_eq { all_rows ; party ; democratic }'}, 'incumbent'], 'result': 'robert cramer', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; party ; democratic } ; incumbent }'}, 'robert cramer'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; robert cramer }', 'tointer': 'the incumbent record of this unqiue row is robert cramer .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; robert cramer } } = true', 'tointer': 'select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is robert cramer .'} | and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; robert cramer } } = true | select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is robert cramer . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'party_7': 7, 'democratic_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'robert cramer_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', 'democratic_8': 'democratic', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'robert cramer_10': 'robert cramer'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'party_7': [0], 'democratic_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'robert cramer_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'results', 'candidates'] | [['alabama 1', 'jo bonner', 'republican', '2002', 're - elected', 'jo bonner ( r ) 68.1 % vivian beckerle ( d ) 31.8 %'], ['alabama 2', 'terry everett', 'republican', '1992', 're - elected', 'terry everett ( r ) 69.5 % chuck james ( d ) 30.4 %'], ['alabama 4', 'robert aderholt', 'republican', '1996', 're - elected', 'robert aderholt ( r ) 70.2 % barbara bobo ( d ) 29.7 %'], ['alabama 5', 'robert cramer', 'democratic', '1990', 're - elected', 'robert cramer ( d ) unopposed'], ['alabama 6', 'spencer bachus', 'republican', '1992', 're - elected', 'spencer bachus ( r ) unopposed']] |
1995 miami dolphins season | https://en.wikipedia.org/wiki/1995_Miami_Dolphins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16023832-2.html.csv | aggregation | during the 1995 miami dolphins season , the average attendance each week was about 66000 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '66000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '66000', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '66000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 66000 } = true', 'tointer': 'the average of the attendance record of all rows is 66000 .'} | round_eq { avg { all_rows ; attendance } ; 66000 } = true | the average of the attendance record of all rows is 66000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '66000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '66000_5': '66000'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '66000_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 3 , 1995', 'new york jets', 'w 52 - 14', '71317'], ['2', 'september 10 , 1995', 'new england patriots', 'w 20 - 3', '60239'], ['3', 'september 18 , 1995', 'pittsburgh steelers', 'w 23 - 10', '72874'], ['5', 'october 1 , 1995', 'cincinnati bengals', 'w 26 - 23', '52671'], ['6', 'october 8 , 1995', 'indianapolis colts', 'l 27 - 24', '68471'], ['7', 'october 15 , 1995', 'new orleans saints', 'l 33 - 30', '55628'], ['8', 'october 22 , 1995', 'new york jets', 'l 17 - 16', '67228'], ['9', 'october 29 , 1995', 'buffalo bills', 'w 23 - 6', '71060'], ['10', 'november 6 , 1995', 'san diego chargers', 'w 24 - 14', '61966'], ['11', 'november 12 , 1995', 'new england patriots', 'l 34 - 17', '70399'], ['12', 'november 20 , 1995', 'san francisco 49ers', 'l 44 - 20', '73080'], ['13', 'november 26 , 1995', 'indianapolis colts', 'l 36 - 28', '60414'], ['14', 'december 3 , 1995', 'atlanta falcons', 'w 21 - 20', '63395'], ['15', 'december 11 , 1995', 'kansas city chiefs', 'w 13 - 6', '70321'], ['16', 'december 17 , 1995', 'buffalo bills', 'l 23 - 20', '79531'], ['17', 'december 24 , 1995', 'st louis rams', 'w 41 - 22', '63876']] |
chad little | https://en.wikipedia.org/wiki/Chad_Little | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1875157-1.html.csv | count | chad little was in top 5 for one time . | {'scope': 'all', 'criterion': 'equal', 'value': '1', 'result': '1', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'top 5', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose top 5 record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; top 5 ; 1 }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; top 5 ; 1 } }', 'tointer': 'select the rows whose top 5 record is equal to 1 . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; top 5 ; 1 } } ; 1 } = true', 'tointer': 'select the rows whose top 5 record is equal to 1 . the number of such rows is 1 .'} | eq { count { filter_eq { all_rows ; top 5 ; 1 } } ; 1 } = true | select the rows whose top 5 record is equal to 1 . the number of such rows is 1 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'top 5_5': 5, '1_6': 6, '1_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'top 5_5': 'top 5', '1_6': '1', '1_7': '1'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'top 5_5': [0], '1_6': [0], '1_7': [2]} | ['year', 'starts', 'wins', 'top 5', 'top 10', 'poles', 'avg start', 'avg finish', 'winnings', 'position', 'team ( s )'] | [['1986', '2', '0', '0', '0', '0', '31.0', '24.0', '6065', '70th', '28 jefferson racing'], ['1987', '2', '0', '0', '0', '0', '32.5', '15.0', '8810', '60th', '95 jefferson racing'], ['1989', '8', '0', '0', '0', '0', '29.9', '29.2', '44690', '38th', '90 donlavey racing'], ['1990', '18', '0', '0', '0', '0', '29.1', '24.1', '80140', '33rd', '19 little racing 20 moroso racing'], ['1991', '28', '0', '0', '1', '0', '24.3', '22.6', '184190', '27th', '19 little racing'], ['1992', '19', '0', '0', '1', '0', '26.9', '25.1', '145805', '31st', '66 cale yarborough motorsports 9 melling racing'], ['1993', '3', '0', '0', '0', '0', '31.3', '30.3', '41140', '51st', '9 melling racing 19 mark rypien motorsports'], ['1994', '1', '0', '0', '0', '0', '17.0', '29.0', '30805', '68th', '97 mark rypien motorsports'], ['1995', '2', '0', '0', '0', '0', '19.5', '30.0', '22775', '53rd', '97 mark rypien motorsports'], ['1997', '27', '0', '0', '1', '0', '29.1', '28.7', '555914', '36th', '97 mark rypien motorsports 97 roush racing'], ['1998', '32', '0', '1', '7', '0', '27.1', '19.4', '1449659', '15th', '97 roush racing'], ['1999', '34', '0', '0', '5', '0', '28.7', '23.4', '1623976', '23rd', '97 roush racing'], ['2000', '27', '0', '0', '1', '0', '31.7', '22.2', '1418884', '32nd', '97 roush racing']] |
2008 nascar craftsman truck series | https://en.wikipedia.org/wiki/2008_NASCAR_Craftsman_Truck_Series | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14292964-20.html.csv | unique | among the top 10 , only one car is made by dodge . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'dodge', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'make', 'dodge'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose make record fuzzily matches to dodge .', 'tostr': 'filter_eq { all_rows ; make ; dodge }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; make ; dodge } } = true', 'tointer': 'select the rows whose make record fuzzily matches to dodge . there is only one such row in the table .'} | only { filter_eq { all_rows ; make ; dodge } } = true | select the rows whose make record fuzzily matches to dodge . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'make_4': 4, 'dodge_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'make_4': 'make', 'dodge_5': 'dodge'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'make_4': [0], 'dodge_5': [0]} | ['pos', 'car', 'driver', 'make', 'team'] | [['1', '33', 'ron hornaday', 'chevrolet', 'kevin harvick incorporated'], ['2', '18', 'dennis setzer', 'dodge', 'bobby hamilton racing - virginia'], ['3', '23', 'johnny benson', 'toyota', 'bill davis racing'], ['4', '30', 'todd bodine', 'toyota', 'germian racing'], ['5', '2', 'jack sprague', 'chevy', 'kevin harvick incorporated'], ['6', '99', 'erik darnell', 'ford', 'roush fenway racing'], ['7', '5', 'mike skinner', 'toyota', 'bill davis racing'], ['8', '14', 'rick crawford', 'ford', 'circle bar racing'], ['9', '6', 'colin braun r', 'ford', 'roush fenway racing'], ['10', '59', 'ted musgrave', 'toyota', 'ht motorsports']] |
1926 vfl season | https://en.wikipedia.org/wiki/1926_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10746808-1.html.csv | majority | all the matches of the 1926 vfl season were played on 1 may 1926 . | {'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': '1 may 1926', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', '1 may 1926'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to 1 may 1926 .', 'tostr': 'all_eq { all_rows ; date ; 1 may 1926 } = true'} | all_eq { all_rows ; date ; 1 may 1926 } = true | for the date records of all rows , all of them fuzzily match to 1 may 1926 . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '1 may 1926_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '1 may 1926_4': '1 may 1926'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '1 may 1926_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '13.14 ( 92 )', 'st kilda', '8.15 ( 63 )', 'mcg', '18742', '1 may 1926'], ['essendon', '15.14 ( 104 )', 'north melbourne', '6.17 ( 53 )', 'windy hill', '15000', '1 may 1926'], ['south melbourne', '11.11 ( 77 )', 'richmond', '12.13 ( 85 )', 'lake oval', '20000', '1 may 1926'], ['geelong', '13.15 ( 93 )', 'footscray', '3.9 ( 27 )', 'corio oval', '15000', '1 may 1926'], ['fitzroy', '7.13 ( 55 )', 'collingwood', '14.10 ( 94 )', 'brunswick street oval', '25000', '1 may 1926'], ['hawthorn', '6.14 ( 50 )', 'carlton', '9.16 ( 70 )', 'glenferrie oval', '16000', '1 may 1926']] |
chinese jia - a league 2003 | https://en.wikipedia.org/wiki/Chinese_Jia-A_League_2003 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18983113-2.html.csv | aggregation | the average total position for chinese jia - a league 2003 is 10.73 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '10.73', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total position'], 'result': '10.73', 'ind': 0, 'tostr': 'avg { all_rows ; total position }'}, '10.73'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total position } ; 10.73 } = true', 'tointer': 'the average of the total position record of all rows is 10.73 .'} | round_eq { avg { all_rows ; total position } ; 10.73 } = true | the average of the total position record of all rows is 10.73 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total position_4': 4, '10.73_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total position_4': 'total position', '10.73_5': '10.73'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total position_4': [0], '10.73_5': [1]} | ['team', '2002 position', '2003 position', 'total position', 'qualification'] | [['dalian shide', '0.5', '3.0', '3.5', 'entry to the 2004 chinese super league'], ['shenzhen jianlibao', '1.0', '4.0', '5.0', 'entry to the 2004 chinese super league'], ['shanghai international', '4.5', '2.0', '6.5', 'entry to the 2004 chinese super league'], ['shanghai shenhua', '6.0', '1.0', '7.0', 'entry to the 2004 chinese super league'], ['liaoning zhongshun', '2.5', '6.0', '8.5', 'entry to the 2004 chinese super league'], ['beijing hyundai cars', '1.5', '9.0', '10.5', 'entry to the 2004 chinese super league'], ['shenyang ginde', '5.5', '5.0', '10.5', 'entry to the 2004 chinese super league'], ['yunnan hongta', '3.5', '7.0', '10.5', 'entry to the 2004 chinese super league'], ['shandong luneng', '2.0', '12.0', '14.0', 'entry to the 2004 chinese super league'], ['qingdao beilaite', '4.0', '11.0', '15.0', 'entry to the 2004 chinese super league'], ['sichuan guancheng', '7.0', '8.0', '15.0', 'entry to the 2004 chinese super league'], ['tianjin kangshifu', '5.0', '10.0', '15.0', 'entry to the 2004 chinese super league'], ['chongqing lifan', '3.0', '13.0', '16.0', 'relegated to the jia league'], ['august 1st', '6.5', '14.0', '20.5', 'relegated to the jia league'], ['shaanxi guoli', '7.5', '15.0', '22.5', 'relegated to the jia league']] |
seattle supersonics all - time roster | https://en.wikipedia.org/wiki/Seattle_SuperSonics_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16772687-9.html.csv | unique | on the seattle supersonics all - time roster , of the players from the united states , the only one who went to college at idaho state wears jersey number 41 . | {'scope': 'subset', 'row': '9', 'col': '6', 'col_other': '2,3', 'criterion': 'equal', 'value': 'idaho state', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nationality ; united states }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states .'}, 'from', 'idaho state'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose from record fuzzily matches to idaho state .', 'tostr': 'filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose from record fuzzily matches to idaho state . 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', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nationality ; united states }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states .'}, 'from', 'idaho state'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose from record fuzzily matches to idaho state .', 'tostr': 'filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state }'}, 'jersey number ( s )'], 'result': '41', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } ; jersey number ( s ) }'}, '41'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } ; jersey number ( s ) } ; 41 }', 'tointer': 'the jersey number ( s ) record of this unqiue row is 41 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } } ; eq { hop { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } ; jersey number ( s ) } ; 41 } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose from record fuzzily matches to idaho state . there is only one such row in the table . the jersey number ( s ) record of this unqiue row is 41 .'} | and { only { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } } ; eq { hop { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } ; jersey number ( s ) } ; 41 } } = true | select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose from record fuzzily matches to idaho state . there is only one such row in the table . the jersey number ( s ) record of this unqiue row is 41 . | 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, 'nationality_8': 8, 'united states_9': 9, 'from_10': 10, 'idaho state_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'jersey number (s)_12': 12, '41_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', 'nationality_8': 'nationality', 'united states_9': 'united states', 'from_10': 'from', 'idaho state_11': 'idaho state', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'jersey number (s)_12': 'jersey number ( s )', '41_13': '41'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'nationality_8': [0], 'united states_9': [0], 'from_10': [1], 'idaho state_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'jersey number (s)_12': [3], '41_13': [4]} | ['player', 'nationality', 'jersey number ( s )', 'position', 'years', 'from'] | [['al hairston', 'united states', '25', 'pg', '1968 - 1969', 'bowling green state'], ['lars hansen', 'denmark canada', '22', 'c', '1978 - 1979', 'washington'], ['bill hanzlik', 'united states', '22', 'sg / sf', '1980 - 1982', 'notre dame'], ['art harris', 'united states', '12', 'g', '1968 - 1969', 'stanford'], ['antonio harvey', 'united states', '24 , 4', 'power forward / center', '1996 - 1997 2001', 'pfeiffer'], ['joe hassett', 'united states', '10', 'sg', '1977 - 1978', 'providence'], ['steve hawes', 'united states', '10', 'c', '1983 - 1984', 'washington'], ['hersey hawkins', 'united states', '33', 'sg', '1995 - 1998', 'bradley'], ['steve hayes', 'united states', '41', 'c', '1983 - 1984', 'idaho state'], ['spencer haywood', 'united states', '24', 'pf / c', '1970 - 1975', 'detroit'], ['gar heard', 'united states', '40', 'pf', '1970 - 1972', 'oklahoma'], ['gerald henderson', 'united states', '15', 'pg', '1984 - 1986', 'vcu'], ['rod higgins', 'united states', '55', 'pf / c', '1985', 'fresno state'], ['armond hill', 'united states', '24', 'pg', '1980 - 1982', 'princeton'], ['byron houston', 'united states', '21', 'pf', '1994 - 1995', 'oklahoma state'], ['stephen howard', 'united states', '44', 'pf', '1997 - 1998', 'depaul'], ['john hummer', 'united states', '14 , 42', 'pf / c', '1974 - 1976', 'princeton']] |
2008 - 09 tampa bay lightning season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Tampa_Bay_Lightning_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17360840-9.html.csv | aggregation | in ' 08 - '09 season of tampa bay lightning , their games against toronto maple leafs got 38,002 total attendance . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '38,002', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'toronto maple leafs'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'toronto maple leafs'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; toronto maple leafs }', 'tointer': 'select the rows whose opponent record fuzzily matches to toronto maple leafs .'}, 'attendance'], 'result': '38,002', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; attendance }'}, '38,002'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; attendance } ; 38,002 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the attendance record of these rows is 38,002 .'} | round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; attendance } ; 38,002 } = true | select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the attendance record of these rows is 38,002 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'toronto maple leafs_6': 6, 'attendance_7': 7, '38,002_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'toronto maple leafs_6': 'toronto maple leafs', 'attendance_7': 'attendance', '38,002_8': '38,002'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'toronto maple leafs_6': [0], 'attendance_7': [1], '38,002_8': [2]} | ['game', 'date', 'opponent', 'score', 'location', 'attendance', 'record', 'points'] | [['63', 'march 1', 'calgary flames', '8 - 6', 'pengrowth saddledome', '19289', '21 - 30 - 12', '54'], ['64', 'march 3', 'pittsburgh penguins', '1 - 3', 'st pete times forum', '19908', '21 - 31 - 12', '54'], ['65', 'march 6', 'st louis blues', '3 - 4 ot', 'st pete times forum', '13831', '21 - 31 - 13', '55'], ['66', 'march 7', 'carolina hurricanes', '3 - 9', 'st pete times forum', '15692', '21 - 32 - 13', '55'], ['67', 'march 11', 'ottawa senators', '2 - 3 ot', 'scotiabank place', '19231', '21 - 32 - 14', '56'], ['68', 'march 12', 'toronto maple leafs', '4 - 1', 'air canada centre', '19209', '22 - 32 - 14', '58'], ['69', 'march 14', 'florida panthers', '4 - 3 so', 'bankatlantic center', '17734', '23 - 32 - 14', '60'], ['70', 'march 17', 'toronto maple leafs', '3 - 4 so', 'st pete times forum', '18793', '23 - 32 - 15', '61'], ['71', 'march 19', 'washington capitals', '2 - 5', 'st pete times forum', '16541', '23 - 33 - 15', '61'], ['72', 'march 21', 'atlanta thrashers', '3 - 4 so', 'st pete times forum', '15391', '23 - 33 - 16', '62'], ['73', 'march 24', 'columbus blue jackets', '2 - 1 ot', 'st pete times forum', '14454', '24 - 33 - 16', '64'], ['74', 'march 26', 'montreal canadiens', '2 - 3 ot', 'bell centre', '21273', '24 - 33 - 17', '65'], ['75', 'march 27', 'washington capitals', '3 - 5', 'verizon center', '18277', '24 - 34 - 17', '65'], ['76', 'march 29', 'ottawa senators', '0 - 3', 'st pete times forum', '16427', '24 - 35 - 17', '65']] |
easyjet | https://en.wikipedia.org/wiki/EasyJet | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-180466-4.html.csv | ordinal | the boeing 737 - 300 is the second oldest aircraft to be introduced by easyjet . | {'row': '5', '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', 'introduced', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; introduced ; 2 }'}, 'aircraft'], 'result': 'boeing 737 - 300', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; introduced ; 2 } ; aircraft }'}, 'boeing 737 - 300'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; introduced ; 2 } ; aircraft } ; boeing 737 - 300 } = true', 'tointer': 'select the row whose introduced record of all rows is 2nd minimum . the aircraft record of this row is boeing 737 - 300 .'} | eq { hop { nth_argmin { all_rows ; introduced ; 2 } ; aircraft } ; boeing 737 - 300 } = true | select the row whose introduced record of all rows is 2nd minimum . the aircraft record of this row is boeing 737 - 300 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'introduced_5': 5, '2_6': 6, 'aircraft_7': 7, 'boeing 737 - 300_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', 'introduced_5': 'introduced', '2_6': '2', 'aircraft_7': 'aircraft', 'boeing 737 - 300_8': 'boeing 737 - 300'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'introduced_5': [0], '2_6': [0], 'aircraft_7': [1], 'boeing 737 - 300_8': [2]} | ['aircraft', 'introduced', 'retired', 'seating', 'notes'] | [['airbus a319 - 100', '2004', '-', '156', 'in service'], ['airbus a320 - 200', '2008', '-', '180', 'in service'], ['airbus a321 - 200', '2008', '2010', '220', 'inherited from gb airways'], ['boeing 737 - 204', '1995', '1996', '115', 'replaced by 737 - 300s'], ['boeing 737 - 300', '1996', '2007', '148 / 9', 'replaced by a319s'], ['boeing 737 - 700', '2000', '2011', '149', 'replaced by a319s and a320s']] |
yanina wickmayer | https://en.wikipedia.org/wiki/Yanina_Wickmayer | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15100199-11.html.csv | unique | her only us open semi-final ( sf ) appearance was in 2009 . | {'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'sf', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2009', 'sf'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2009 record fuzzily matches to sf .', 'tostr': 'filter_eq { all_rows ; 2009 ; sf }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 2009 ; sf } }', 'tointer': 'select the rows whose 2009 record fuzzily matches to sf . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2009', 'sf'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2009 record fuzzily matches to sf .', 'tostr': 'filter_eq { all_rows ; 2009 ; sf }'}, 'tournament'], 'result': 'us open', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 2009 ; sf } ; tournament }'}, 'us open'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 2009 ; sf } ; tournament } ; us open }', 'tointer': 'the tournament record of this unqiue row is us open .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; 2009 ; sf } } ; eq { hop { filter_eq { all_rows ; 2009 ; sf } ; tournament } ; us open } } = true', 'tointer': 'select the rows whose 2009 record fuzzily matches to sf . there is only one such row in the table . the tournament record of this unqiue row is us open .'} | and { only { filter_eq { all_rows ; 2009 ; sf } } ; eq { hop { filter_eq { all_rows ; 2009 ; sf } ; tournament } ; us open } } = true | select the rows whose 2009 record fuzzily matches to sf . there is only one such row in the table . the tournament record of this unqiue row is us open . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, '2009_7': 7, 'sf_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'us open_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', '2009_7': '2009', 'sf_8': 'sf', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'us open_10': 'us open'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], '2009_7': [0], 'sf_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'us open_10': [3]} | ['tournament', '2006', '2007', '2008', '2009', '2010', '2011', '2012'] | [['australian open', 'a', 'a', 'q2', '1r', '4r', '2r', '1r'], ['french open', 'a', 'a', '1r', '2r', '3r', '3r', '1r'], ['wimbledon', 'a', 'a', '1r', '1r', '3r', '4r', '3r'], ['us open', 'a', 'a', '1r', 'sf', '4r', '2r', '2r'], ['win - loss', '0 - 0', '0 - 0', '0 - 3', '6 - 4', '10 - 4', '7 - 4', '3 - 4'], ['wta premier mandatory tournaments', 'wta premier mandatory tournaments', 'wta premier mandatory tournaments', 'wta premier mandatory tournaments', 'wta premier mandatory tournaments', 'wta premier mandatory tournaments', 'wta premier mandatory tournaments', 'wta premier mandatory tournaments'], ['indian wells', 'a', 'a', 'a', '2r', '4r', 'sf', '2r'], ['miami', 'a', 'a', 'a', '1r', 'qf', '2r', '4r'], ['madrid', 'not held', 'not held', 'not held', 'a', 'a', '1r', '2r'], ['beijing', 'not held', 'not held', 'not held', 'a', '1r', 'a', '1r'], ['wta premier 5 tournaments', 'wta premier 5 tournaments', 'wta premier 5 tournaments', 'wta premier 5 tournaments', 'wta premier 5 tournaments', 'wta premier 5 tournaments', 'wta premier 5 tournaments', 'wta premier 5 tournaments'], ['dubai', 'not held', 'not held', 'not held', 'a', '1r', '3r', 'np5'], ['doha', 'not tier i', 'not tier i', 'a', 'not held', 'not held', 'np5', 'qf'], ['rome', 'a', 'a', 'a', 'a', '3r', '3r', '1r'], ['cincinnati', 'not held', 'not held', 'not held', '2r', 'qf', '2r', '1r'], ['canada', 'a', 'a', 'a', '1r', '2r', '1r', '1r'], ['tokyo', 'a', 'a', 'a', 'a', '1r', 'a', '1r'], ['career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics', 'career statistics'], ['year end ranking', '534', '221', '69', '16', '23', '26', '23']] |
list of prussian locomotives and railbuses | https://en.wikipedia.org/wiki/List_of_Prussian_locomotives_and_railbuses | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17951246-2.html.csv | superlative | according to the list of prussian locomotives and railbuses , p 2 class that has the least quantity was built in 1886 . | {'scope': 'subset', 'col_superlative': '3', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1,4', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'p 2'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'p 2'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; class ; p 2 }', 'tointer': 'select the rows whose class record fuzzily matches to p 2 .'}, 'quantity'], 'result': None, 'ind': 1, 'tostr': 'argmin { filter_eq { all_rows ; class ; p 2 } ; quantity }'}, 'year ( s ) built'], 'result': '1886', 'ind': 2, 'tostr': 'hop { argmin { filter_eq { all_rows ; class ; p 2 } ; quantity } ; year ( s ) built }'}, '1886'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmin { filter_eq { all_rows ; class ; p 2 } ; quantity } ; year ( s ) built } ; 1886 } = true', 'tointer': 'select the rows whose class record fuzzily matches to p 2 . select the row whose quantity record of these rows is minimum . the year ( s ) built record of this row is 1886 .'} | eq { hop { argmin { filter_eq { all_rows ; class ; p 2 } ; quantity } ; year ( s ) built } ; 1886 } = true | select the rows whose class record fuzzily matches to p 2 . select the row whose quantity record of these rows is minimum . the year ( s ) built record of this row is 1886 . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'class_6': 6, 'p 2_7': 7, 'quantity_8': 8, 'year (s) built_9': 9, '1886_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmin_1': 'argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'class_6': 'class', 'p 2_7': 'p 2', 'quantity_8': 'quantity', 'year (s) built_9': 'year ( s ) built', '1886_10': '1886'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'class_6': [0], 'p 2_7': [0], 'quantity_8': [1], 'year (s) built_9': [2], '1886_10': [3]} | ['class', 'number range', 'quantity', 'year ( s ) built', 'type'] | [['p 1 ( de )', '1501 - 1550', '56', '1885 - 1891', '1 ′ b n2'], ['p 2', '1551 - 1600', '166', '1877 - 1884', '1b n2'], ['p 2', '1551 - 1600', '76', '1878 - 1883', '1b n2'], ['p 2', '1551 - 1600', '5', '1886', '2 ′ b n2'], ['p 3 ( de )', '1601 - 1700', '3', '1891', '2 ′ b n2v'], ['p 3 1 ( de )', '1601 - 1700', '685', '1885 - 1899', '1b n2'], ['p 3 2 ( de )', '1701 - 1800', '131', '1887 - 1903', '1b n2v'], ['p 4 1 ( de )', '1801 - 1900', '2', '1891', '2 ′ b n2'], ['p 4 1 ( de )', '1801 - 1900', '55', '1891 - 1892', '2 ′ b n2'], ['p 4 1 ( de )', '1801 - 1900', '424', '1893 - 1901', '2 ′ b n2'], ['p 4 1 ( de )', '1801 - 1900', '1', '1898', '2 ′ b h2'], ['p 4 2 ( de )', '1901 - 2000', '2', '1891', '2 ′ b n2v'], ['p 4 2 ( de )', '1901 - 2000', '707', '1898 - 1910', '2 ′ b n2v'], ['p 4 2 ( de )', '1901 - 2000', '( 5 )', '( 1907 )', '2 ′ b n2v'], ['p 6', '2101 - 2300', '272', '1901 - 1910', '1 ′ c h2'], ['p 7 ( de )', '2301 - 2400', '18', '1899 - 1902', '2 ′ c n4v'], ['p 8', '2401 - 2800', '3498', '1906 - 1923', '2 ′ c h2'], ['( p 10 )', '( from 2801 )', '260', '1922 - 1925', '1 ′ d1 ′ h3']] |
1945 vfl season | https://en.wikipedia.org/wiki/1945_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809271-20.html.csv | comparative | in the games of the 1945 vfl season shown south melbourne scored more points than north melbourne . | {'row_1': '3', 'row_2': '4', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'south melbourne'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to south melbourne .', 'tostr': 'filter_eq { all_rows ; home team ; south melbourne }'}, 'home team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'north melbourne'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to north melbourne .', 'tostr': 'filter_eq { all_rows ; home team ; north melbourne }'}, 'home team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } } = true | select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'home team_7': 7, 'south melbourne_8': 8, 'home team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'home team_11': 11, 'north melbourne_12': 12, 'home team score_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'home team_7': 'home team', 'south melbourne_8': 'south melbourne', 'home team score_9': 'home team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'home team_11': 'home team', 'north melbourne_12': 'north melbourne', 'home team score_13': 'home team score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'home team_7': [0], 'south melbourne_8': [0], 'home team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'home team_11': [1], 'north melbourne_12': [1], 'home team score_13': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '13.14 ( 92 )', 'st kilda', '9.11 ( 65 )', 'kardinia park', '7500', '1 september 1945'], ['fitzroy', '14.22 ( 106 )', 'melbourne', '15.11 ( 101 )', 'brunswick street oval', '5000', '1 september 1945'], ['south melbourne', '16.16 ( 112 )', 'hawthorn', '11.10 ( 76 )', 'junction oval', '12000', '1 september 1945'], ['north melbourne', '14.17 ( 101 )', 'essendon', '13.17 ( 95 )', 'arden street oval', '12000', '1 september 1945'], ['richmond', '8.19 ( 67 )', 'collingwood', '12.15 ( 87 )', 'punt road oval', '23000', '1 september 1945'], ['footscray', '8.14 ( 62 )', 'carlton', '16.19 ( 115 )', 'western oval', '30000', '1 september 1945']] |
1969 world judo championships | https://en.wikipedia.org/wiki/1969_World_Judo_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15807914-2.html.csv | count | in the 1969 world judo championships , for nations that won 0 gold medals , two also won 0 silver medals . | {'scope': 'subset', 'criterion': 'equal', 'value': '0', 'result': '2', 'col': '4', 'subset': {'col': '3', 'criterion': 'equal', 'value': '0'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '0'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; gold ; 0 }', 'tointer': 'select the rows whose gold record is equal to 0 .'}, 'silver', '0'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 0 .', 'tostr': 'filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 0 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 0 } }', 'tointer': 'select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 0 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 0 } } ; 2 } = true', 'tointer': 'select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 0 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 0 } } ; 2 } = true | select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 0 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'gold_6': 6, '0_7': 7, 'silver_8': 8, '0_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'gold_6': 'gold', '0_7': '0', 'silver_8': 'silver', '0_9': '0', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'gold_6': [0], '0_7': [0], 'silver_8': [1], '0_9': [1], '2_10': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'japan', '6', '3', '3', '12'], ['2', 'germany', '0', '2', '0', '2'], ['3', 'netherlands', '0', '1', '2', '3'], ['4', 'soviet union', '0', '0', '4', '4'], ['5', 'south korea', '0', '0', '3', '3']] |
atlanta falcons draft history | https://en.wikipedia.org/wiki/Atlanta_Falcons_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15198842-20.html.csv | ordinal | reggie pleasant was the fourth highest overall player drafted by the atlanta falcons . | {'row': '4', 'col': '3', 'order': '4', 'col_other': '4', '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', 'overall', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; overall ; 4 }'}, 'name'], 'result': 'reggie pleasant', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; overall ; 4 } ; name }'}, 'reggie pleasant'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; overall ; 4 } ; name } ; reggie pleasant } = true', 'tointer': 'select the row whose overall record of all rows is 4th minimum . the name record of this row is reggie pleasant .'} | eq { hop { nth_argmin { all_rows ; overall ; 4 } ; name } ; reggie pleasant } = true | select the row whose overall record of all rows is 4th minimum . the name record of this row is reggie pleasant . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'overall_5': 5, '4_6': 6, 'name_7': 7, 'reggie pleasant_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', 'overall_5': 'overall', '4_6': '4', 'name_7': 'name', 'reggie pleasant_8': 'reggie pleasant'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'overall_5': [0], '4_6': [0], 'name_7': [1], 'reggie pleasant_8': [2]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '2', '2', 'bill fralic', 'guard', 'pittsburgh'], ['2', '17', '45', 'mike gann', 'defensive end', 'notre dame'], ['4', '5', '89', 'emile harry', 'wide receiver', 'stanford'], ['6', '12', '152', 'reggie pleasant', 'defensive back', 'clemson'], ['8', '5', '201', 'ashley lee', 'defensive back', 'virginia tech'], ['8', '19', '215', 'ronnie washington', 'linebacker', 'northeast louisiana'], ['9', '4', '228', 'micah moon', 'linebacker', 'north carolina'], ['10', '5', '257', 'brent martin', 'center', 'stanford'], ['11', '4', '284', 'john ayres', 'defensive back', 'illinois'], ['12', '5', '313', 'ken whisenhunt', 'tight end', 'georgia tech']] |
1953 masters tournament | https://en.wikipedia.org/wiki/1953_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13073611-2.html.csv | unique | ben hogan was the only player to achieve 5 under par . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': '-5', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'to par', '-5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose to par record is equal to -5 .', 'tostr': 'filter_eq { all_rows ; to par ; -5 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; to par ; -5 } }', 'tointer': 'select the rows whose to par record is equal to -5 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'to par', '-5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose to par record is equal to -5 .', 'tostr': 'filter_eq { all_rows ; to par ; -5 }'}, 'player'], 'result': 'ben hogan', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; to par ; -5 } ; player }'}, 'ben hogan'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; to par ; -5 } ; player } ; ben hogan }', 'tointer': 'the player record of this unqiue row is ben hogan .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; to par ; -5 } } ; eq { hop { filter_eq { all_rows ; to par ; -5 } ; player } ; ben hogan } } = true', 'tointer': 'select the rows whose to par record is equal to -5 . there is only one such row in the table . the player record of this unqiue row is ben hogan .'} | and { only { filter_eq { all_rows ; to par ; -5 } } ; eq { hop { filter_eq { all_rows ; to par ; -5 } ; player } ; ben hogan } } = true | select the rows whose to par record is equal to -5 . there is only one such row in the table . the player record of this unqiue row is ben hogan . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'to par_7': 7, '-5_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'ben hogan_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'to par_7': 'to par', '-5_8': '-5', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'ben hogan_10': 'ben hogan'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'to par_7': [0], '-5_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'ben hogan_10': [3]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'ben hogan', 'united states', '70 + 69 = 139', '- 5'], ['2', 'bob hamilton', 'united states', '71 + 69 = 140', '- 4'], ['t3', 'chick harbert', 'united states', '68 + 73 = 141', '- 3'], ['t3', 'ted kroll', 'united states', '71 + 70 = 141', '- 3'], ['t5', 'lloyd mangrum', 'united states', '74 + 68 = 142', '- 2'], ['t5', 'milan marusic', 'united states', '70 + 72 = 142', '- 2'], ['t5', 'ed oliver', 'united states', '69 + 73 = 142', '- 2'], ['t8', 'al besselink', 'united states', '69 + 75 = 144', 'e'], ['t8', 'julius boros', 'united states', '73 + 71 = 144', 'e'], ['t8', 'lew worsham', 'united states', '74 + 70 = 144', 'e']] |
list of tallest buildings in houston | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Houston | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11530524-3.html.csv | count | 10 buildings are listed as being the tallest in houston city . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '10', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'name'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record is arbitrary .', 'tostr': 'filter_all { all_rows ; name }'}], 'result': '10', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; name } }', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 10 .'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; name } } ; 10 } = true', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 10 .'} | eq { count { filter_all { all_rows ; name } } ; 10 } = true | select the rows whose name record is arbitrary . the number of such rows is 10 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'name_5': 5, '10_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'name_5': 'name', '10_6': '10'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'name_5': [0], '10_6': [2]} | ['name', 'street address', 'years as tallest', 'height ft / m', 'floors'] | [['lomas & nettleton building', '201 main street', '1904 - 1908', 'n / a', '8'], ['711 main', '711 main street', '1908 - 1910', '134 / 41', '10'], ['806 main', '806 main street', '1910 - 1926', '302 / 92', '23'], ['magnolia hotel', '1100 texas avenue', '1926 - 1927', '325 / 99', '22'], ['niels esperson building', '808 travis street', '1927 - 1929', '410 / 125', '32'], ['jpmorgan chase building', '712 main street', '1929 - 1963', '428 / 131', '36'], ['exxon building', '800 bell avenue', '1963 - 1971', '607 / 185', '44'], ['one shell plaza', '910 louisiana street', '1971 - 1980', '714 / 218', '50'], ['enterprise plaza', '1100 louisiana street', '1980 - 1982', '756 / 230', '55'], ['jpmorgan chase tower', '600 travis street', '1982 - present', '1002 / 305', '75']] |
equestrian at the 1980 summer olympics | https://en.wikipedia.org/wiki/Equestrian_at_the_1980_Summer_Olympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1461487-1.html.csv | superlative | for the 1980 summer olympics the soviet union was the clear winner at the games for equestrian events with 8 total medal 3 each being gold and silver . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '2,3,4', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'total'], 'result': '8', 'ind': 0, 'tostr': 'max { all_rows ; total }', 'tointer': 'the maximum total record of all rows is 8 .'}, '8'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; total } ; 8 }', 'tointer': 'the maximum total record of all rows is 8 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; total }'}, 'nation'], 'result': 'soviet union ( urs )', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; total } ; nation }'}, 'soviet union ( urs )'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; total } ; nation } ; soviet union ( urs ) }', 'tointer': 'the nation record of the row with superlative total record is soviet union ( urs ) .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; total }'}, 'gold'], 'result': '3', 'ind': 5, 'tostr': 'hop { argmax { all_rows ; total } ; gold }'}, '3'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { argmax { all_rows ; total } ; gold } ; 3 }', 'tointer': 'the gold record of the row with superlative total record is 3 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; total }'}, 'silver'], 'result': '3', 'ind': 7, 'tostr': 'hop { argmax { all_rows ; total } ; silver }'}, '3'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { argmax { all_rows ; total } ; silver } ; 3 }', 'tointer': 'the silver record of the row with superlative total record is 3 .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { argmax { all_rows ; total } ; gold } ; 3 } ; eq { hop { argmax { all_rows ; total } ; silver } ; 3 } }', 'tointer': 'the gold record of the row with superlative total record is 3 . the silver record of the row with superlative total record is 3 .'}], 'result': True, 'ind': 10, 'tostr': 'and { eq { hop { argmax { all_rows ; total } ; nation } ; soviet union ( urs ) } ; and { eq { hop { argmax { all_rows ; total } ; gold } ; 3 } ; eq { hop { argmax { all_rows ; total } ; silver } ; 3 } } }', 'tointer': 'the nation record of the row with superlative total record is soviet union ( urs ) . the gold record of the row with superlative total record is 3 . the silver record of the row with superlative total record is 3 .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { max { all_rows ; total } ; 8 } ; and { eq { hop { argmax { all_rows ; total } ; nation } ; soviet union ( urs ) } ; and { eq { hop { argmax { all_rows ; total } ; gold } ; 3 } ; eq { hop { argmax { all_rows ; total } ; silver } ; 3 } } } } = true', 'tointer': 'the maximum total record of all rows is 8 . the nation record of the row with superlative total record is soviet union ( urs ) . the gold record of the row with superlative total record is 3 . the silver record of the row with superlative total record is 3 .'} | and { eq { max { all_rows ; total } ; 8 } ; and { eq { hop { argmax { all_rows ; total } ; nation } ; soviet union ( urs ) } ; and { eq { hop { argmax { all_rows ; total } ; gold } ; 3 } ; eq { hop { argmax { all_rows ; total } ; silver } ; 3 } } } } = true | the maximum total record of all rows is 8 . the nation record of the row with superlative total record is soviet union ( urs ) . the gold record of the row with superlative total record is 3 . the silver record of the row with superlative total record is 3 . | 14 | 12 | {'and_11': 11, 'result_12': 12, 'eq_1': 1, 'max_0': 0, 'all_rows_13': 13, 'total_14': 14, '8_15': 15, 'and_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_16': 16, 'total_17': 17, 'nation_18': 18, 'soviet union (urs)_19': 19, 'and_9': 9, 'eq_6': 6, 'num_hop_5': 5, 'gold_20': 20, '3_21': 21, 'eq_8': 8, 'num_hop_7': 7, 'silver_22': 22, '3_23': 23} | {'and_11': 'and', 'result_12': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_13': 'all_rows', 'total_14': 'total', '8_15': '8', 'and_10': 'and', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_16': 'all_rows', 'total_17': 'total', 'nation_18': 'nation', 'soviet union (urs)_19': 'soviet union ( urs )', 'and_9': 'and', 'eq_6': 'eq', 'num_hop_5': 'num_hop', 'gold_20': 'gold', '3_21': '3', 'eq_8': 'eq', 'num_hop_7': 'num_hop', 'silver_22': 'silver', '3_23': '3'} | {'and_11': [12], 'result_12': [], 'eq_1': [11], 'max_0': [1], 'all_rows_13': [0], 'total_14': [0], '8_15': [1], 'and_10': [11], 'str_eq_4': [10], 'str_hop_3': [4], 'argmax_2': [3, 5, 7], 'all_rows_16': [2], 'total_17': [2], 'nation_18': [3], 'soviet union (urs)_19': [4], 'and_9': [10], 'eq_6': [9], 'num_hop_5': [6], 'gold_20': [5], '3_21': [6], 'eq_8': [9], 'num_hop_7': [8], 'silver_22': [7], '3_23': [8]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'soviet union ( urs )', '3', '3', '2', '8'], ['2', 'italy ( ita )', '1', '1', '0', '2'], ['2', 'poland ( pol )', '1', '1', '0', '2'], ['4', 'austria ( aut )', '1', '0', '0', '1'], ['5', 'bulgaria ( bul )', '0', '1', '0', '1'], ['6', 'mexico ( mex )', '0', '0', '3', '3'], ['7', 'romania ( rou )', '0', '0', '1', '1']] |
2010 - 11 rugby - bundesliga | https://en.wikipedia.org/wiki/2010%E2%80%9311_Rugby-Bundesliga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-30153446-1.html.csv | majority | the majority of the teams won less than 10 games in this league in the year 2010-2011 . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '10', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'won', '10'], 'result': True, 'ind': 0, 'tointer': 'for the won records of all rows , most of them are less than 10 .', 'tostr': 'most_less { all_rows ; won ; 10 } = true'} | most_less { all_rows ; won ; 10 } = true | for the won records of all rows , most of them are less than 10 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'won_3': 3, '10_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'won_3': 'won', '10_4': '10'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'won_3': [0], '10_4': [0]} | ['', 'club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'difference', 'bonus points', 'points'] | [['1', 'heidelberger rk', '16', '15', '0', '1', '924', '120', '804', '15', '75'], ['2', 'sc 1880 frankfurt', '16', '14', '0', '2', '849', '237', '612', '12', '68'], ['3', 'tsv handschuhsheim', '16', '11', '0', '5', '468', '439', '29', '9', '53'], ['4', 'rg heidelberg', '16', '9', '0', '7', '512', '264', '248', '8', '44'], ['5', 'sc neuenheim', '16', '9', '0', '7', '380', '395', '- 15', '8', '44'], ['6', 'berliner rugby club', '16', '7', '0', '9', '281', '471', '- 190', '6', '34'], ['7', 'dsv 78 hannover', '16', '4', '0', '12', '265', '594', '- 329', '4', '20'], ['8', 'rk 03 berlin', '16', '2', '0', '14', '195', '688', '- 493', '2', '10']] |
1940 vfl season | https://en.wikipedia.org/wiki/1940_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807253-5.html.csv | majority | all games of the 1940 vfl season were played on the 25th of may . | {'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': '25 may 1940', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', '25 may 1940'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to 25 may 1940 .', 'tostr': 'all_eq { all_rows ; date ; 25 may 1940 } = true'} | all_eq { all_rows ; date ; 25 may 1940 } = true | for the date records of all rows , all of them fuzzily match to 25 may 1940 . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '25 may 1940_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '25 may 1940_4': '25 may 1940'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '25 may 1940_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '12.21 ( 93 )', 'south melbourne', '10.12 ( 72 )', 'corio oval', '5000', '25 may 1940'], ['fitzroy', '8.14 ( 62 )', 'richmond', '12.11 ( 83 )', 'brunswick street oval', '14000', '25 may 1940'], ['essendon', '12.18 ( 90 )', 'hawthorn', '9.19 ( 73 )', 'windy hill', '12000', '25 may 1940'], ['north melbourne', '7.11 ( 53 )', 'footscray', '9.12 ( 66 )', 'arden street oval', '13000', '25 may 1940'], ['melbourne', '17.12 ( 114 )', 'collingwood', '13.20 ( 98 )', 'mcg', '20043', '25 may 1940'], ['st kilda', '6.18 ( 54 )', 'carlton', '11.19 ( 85 )', 'junction oval', '21000', '25 may 1940']] |
weightlifting at the 1999 pan american games | https://en.wikipedia.org/wiki/Weightlifting_at_the_1999_Pan_American_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11279593-14.html.csv | unique | nelly rivera was the only one at the 1999 pan american games who had a bodyweight of 69.73 . | {'scope': 'all', 'row': '7', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '69.73', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'bodyweight', '69.73'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bodyweight record is equal to 69.73 .', 'tostr': 'filter_eq { all_rows ; bodyweight ; 69.73 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; bodyweight ; 69.73 } }', 'tointer': 'select the rows whose bodyweight record is equal to 69.73 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'bodyweight', '69.73'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose bodyweight record is equal to 69.73 .', 'tostr': 'filter_eq { all_rows ; bodyweight ; 69.73 }'}, 'name'], 'result': 'nelly rivera ( dom )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; bodyweight ; 69.73 } ; name }'}, 'nelly rivera ( dom )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; bodyweight ; 69.73 } ; name } ; nelly rivera ( dom ) }', 'tointer': 'the name record of this unqiue row is nelly rivera ( dom ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; bodyweight ; 69.73 } } ; eq { hop { filter_eq { all_rows ; bodyweight ; 69.73 } ; name } ; nelly rivera ( dom ) } } = true', 'tointer': 'select the rows whose bodyweight record is equal to 69.73 . there is only one such row in the table . the name record of this unqiue row is nelly rivera ( dom ) .'} | and { only { filter_eq { all_rows ; bodyweight ; 69.73 } } ; eq { hop { filter_eq { all_rows ; bodyweight ; 69.73 } ; name } ; nelly rivera ( dom ) } } = true | select the rows whose bodyweight record is equal to 69.73 . there is only one such row in the table . the name record of this unqiue row is nelly rivera ( dom ) . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'bodyweight_7': 7, '69.73_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'nelly rivera ( dom )_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'bodyweight_7': 'bodyweight', '69.73_8': '69.73', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'nelly rivera ( dom )_10': 'nelly rivera ( dom )'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'bodyweight_7': [0], '69.73_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'nelly rivera ( dom )_10': [3]} | ['name', 'bodyweight', 'snatch', 'clean & jerk', 'total ( kg )'] | [['wanda rijo ( dom )', '73.68', '100.0', '120.0', '220.0'], ['cara heads ( usa )', '73.26', '97.5', '120.0', '217.5'], ['jean lassen ( can )', '73.73', '92.5', '117.5', '210.0'], ['theresa brick ( can )', '74.80', '95.0', '115.0', '210.0'], ['mayra martínez ( ven )', '73.60', '87.5', '112.5', '200.0'], ['maría ruiz obando ( nca )', '73.28', '75.0', '107.5', '182.5'], ['nelly rivera ( dom )', '69.73', '70.0', '82.5', '152.5']] |
religion in eritrea | https://en.wikipedia.org/wiki/Religion_in_Eritrea | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16806446-2.html.csv | unique | kunama is the only ethnic group where 41 % of the people are christians . | {'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '41 %', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'christians', '41 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose christians record fuzzily matches to 41 % .', 'tostr': 'filter_eq { all_rows ; christians ; 41 % }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; christians ; 41 % } }', 'tointer': 'select the rows whose christians record fuzzily matches to 41 % . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'christians', '41 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose christians record fuzzily matches to 41 % .', 'tostr': 'filter_eq { all_rows ; christians ; 41 % }'}, 'ethnic group'], 'result': 'kunama', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; christians ; 41 % } ; ethnic group }'}, 'kunama'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; christians ; 41 % } ; ethnic group } ; kunama }', 'tointer': 'the ethnic group record of this unqiue row is kunama .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; christians ; 41 % } } ; eq { hop { filter_eq { all_rows ; christians ; 41 % } ; ethnic group } ; kunama } } = true', 'tointer': 'select the rows whose christians record fuzzily matches to 41 % . there is only one such row in the table . the ethnic group record of this unqiue row is kunama .'} | and { only { filter_eq { all_rows ; christians ; 41 % } } ; eq { hop { filter_eq { all_rows ; christians ; 41 % } ; ethnic group } ; kunama } } = true | select the rows whose christians record fuzzily matches to 41 % . there is only one such row in the table . the ethnic group record of this unqiue row is kunama . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'christians_7': 7, '41%_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'ethnic group_9': 9, 'kunama_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'christians_7': 'christians', '41%_8': '41 %', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'ethnic group_9': 'ethnic group', 'kunama_10': 'kunama'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'christians_7': [0], '41%_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'ethnic group_9': [2], 'kunama_10': [3]} | ['ethnic group', 'main regions', 'population', 'percentage of total population', 'christians', 'muslims', 'other'] | [['tigrigna', 'maekel region , debub region', '3319680', '57 %', '53 %', '44 %', '1 %'], ['tigre', 'gash - barka region , anseba region , maekel region', '1630720', '28 %', '6 %', '90 %', '4 %'], ['saho', 'northern red sea region , debub region', '232960', '4 %', '7 %', '93 %', 'n / a'], ['kunama', 'gash - barka region', '174720', '3 %', '41 %', '23 %', '36 %'], ['afar', 'southern red sea region', '174720', '3 %', '2 %', '98 %', 'n / a'], ['bilen', 'anseba region', '116480', '2 %', '48 %', '47 %', '5 %'], ['nara', 'gash - barka region', '58240', '1 %', '14 %', '85 %', '1 %'], ['beja', 'gash - barka region , anseba region', '58240', '1 %', '1 %', '98 %', '1 %'], ['rashaida', 'northern red sea region', '58.240', '1 %', 'n / a', '99 %', 'na']] |
llanberis lake railway | https://en.wikipedia.org/wiki/Llanberis_Lake_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1281645-1.html.csv | count | four of the llanberis lake railway locomotives were built by hunslet . | {'scope': 'all', 'criterion': 'equal', 'value': 'hunslet', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'builder', 'hunslet'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose builder record fuzzily matches to hunslet .', 'tostr': 'filter_eq { all_rows ; builder ; hunslet }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; builder ; hunslet } }', 'tointer': 'select the rows whose builder record fuzzily matches to hunslet . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; builder ; hunslet } } ; 4 } = true', 'tointer': 'select the rows whose builder record fuzzily matches to hunslet . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; builder ; hunslet } } ; 4 } = true | select the rows whose builder record fuzzily matches to hunslet . 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, 'builder_5': 5, 'hunslet_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', 'builder_5': 'builder', 'hunslet_6': 'hunslet', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'builder_5': [0], 'hunslet_6': [0], '4_7': [2]} | ['number', 'name', 'builder', 'type', 'works number', 'date'] | [['1', 'elidir', 'hunslet', '0 - 4 - 0 st', '493', '1889'], ['2', 'thomas bach', 'hunslet', '0 - 4 - 0 st', '894', '1904'], ['3', 'dolbadarn', 'hunslet', '0 - 4 - 0 st', '1430', '1922'], ['3', 'maid marian', 'hunslet', '0 - 4 - 0 st', '822', '1903'], ['7', 'topsy', 'ruston hornsby', '4wdm', '441427', '1961'], ['8', 'twll coed', 'ruston hornsby', '4wdm', '268878', '1952'], ['11', 'garrett', 'ruston hornsby', '4wdm', '198286', '1939'], ['12', 'llanelli', 'ruston hornsby', '4wdm', '451901', '1961']] |
1970 isle of man tt | https://en.wikipedia.org/wiki/1970_Isle_of_Man_TT | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10301911-1.html.csv | majority | most of the riders had speeds that were over 90 miles per hour . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '90', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'speed', '90'], 'result': True, 'ind': 0, 'tointer': 'for the speed records of all rows , most of them are greater than 90 .', 'tostr': 'most_greater { all_rows ; speed ; 90 } = true'} | most_greater { all_rows ; speed ; 90 } = true | for the speed records of all rows , most of them are greater than 90 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'speed_3': 3, '90_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'speed_3': 'speed', '90_4': '90'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'speed_3': [0], '90_4': [0]} | ['rank', 'rider', 'team', 'speed', 'time'] | [['1', 'malcolm uphill', 'triumph', '97.71 mph', '1:55.51.4'], ['2', 'peter williams', 'norton', '97.69 mph', '1:55.52.6'], ['3', 'ray pickrell', 'norton', '95.86 mph', '1:58.05.2'], ['4', 'tom dickie', 'triumph', '94.14 mph', '2:00.15.0'], ['5', 'bob heath', 'bsa', '94.09 mph', '2:00.19.0'], ['6', 'hans - otto butenuth', 'bmw', '93.54 mph', '2:01.01.8'], ['7', 'steve spencer', 'norton', '93.18 mph', '2:01.29.2'], ['8', 'tommy robb', 'honda', '92.26 mph', '2:02.42.0'], ['9', 'john cooper', 'honda', '91.32 mph', '2:03.57.6'], ['10', 'pat mahoney', 'norton', '89.77 mph', '2:06.06.0']] |
somerset county cricket club in 2009 | https://en.wikipedia.org/wiki/Somerset_County_Cricket_Club_in_2009 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27922491-8.html.csv | aggregation | the members of the somerset county cricket club in 2009 played in 84 matches . | {'scope': 'all', 'col': '2', 'type': 'sum', 'result': '84', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'matches'], 'result': '84', 'ind': 0, 'tostr': 'sum { all_rows ; matches }'}, '84'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; matches } ; 84 } = true', 'tointer': 'the sum of the matches record of all rows is 84 .'} | round_eq { sum { all_rows ; matches } ; 84 } = true | the sum of the matches record of all rows is 84 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'matches_4': 4, '84_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'matches_4': 'matches', '84_5': '84'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'matches_4': [0], '84_5': [1]} | ['player', 'matches', 'innings', 'wickets', 'average', 'bbi', 'bbm', '5wi'] | [['charl willoughby', '16', '26', '54', '30.03', '5 / 56', '7 / 170', '3'], ['david stiff', '10', '18', '31', '36.12', '5 / 91', '5 / 93', '1'], ['alfonso thomas', '14', '22', '35', '37.62', '5 / 53', '8 / 152', '1'], ['ben phillips', '7', '11', '12', '38.00', '4 / 46', '4 / 73', '0'], ['arul suppiah', '16', '19', '15', '45.46', '3 / 58', '5 / 85', '0'], ['peter trego', '16', '25', '19', '46.78', '3 / 53', '3 / 74', '0'], ['andrew caddick', '5', '8', '10', '52.50', '3 / 53', '4 / 95', '0']] |
lancashire county council election , 2009 | https://en.wikipedia.org/wiki/Lancashire_County_Council_election%2C_2009 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18992950-1.html.csv | count | in the lancashire county council election in 2009 , there were two parties that had 4 votes in west lancashire . | {'scope': 'all', 'criterion': 'equal', 'value': '4', 'result': '2', 'col': '12', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'west lancashire', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose west lancashire record is equal to 4 .', 'tostr': 'filter_eq { all_rows ; west lancashire ; 4 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; west lancashire ; 4 } }', 'tointer': 'select the rows whose west lancashire record is equal to 4 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; west lancashire ; 4 } } ; 2 } = true', 'tointer': 'select the rows whose west lancashire record is equal to 4 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; west lancashire ; 4 } } ; 2 } = true | select the rows whose west lancashire record is equal to 4 . 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, 'west lancashire_5': 5, '4_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'west lancashire_5': 'west lancashire', '4_6': '4', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'west lancashire_5': [0], '4_6': [0], '2_7': [2]} | ['party', 'burnley', 'chorley', 'fylde', 'hyndburn', 'lancaster', 'pendle', 'preston', 'ribble valley', 'rossendale', 'south ribble', 'west lancashire', 'wyre', 'total'] | [['labour', '6', '4', '0', '6', '6', '1', '6', '0', '3', '5', '4', '3', '44'], ['conservative', '0', '3', '5', '0', '3', '2', '3', '3', '2', '1', '4', '5', '31'], ['liberal democrat', '0', '0', '0', '0', '0', '3', '1', '1', '0', '1', '0', '0', '6'], ['green', '0', '0', '0', '0', '1', '0', '0', '0', '0', '0', '0', '0', '1'], ['idle toad', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '1'], ['independent', '0', '0', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1']] |
pete sampras career statistics | https://en.wikipedia.org/wiki/Pete_Sampras_career_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22834834-3.html.csv | count | five of the masters series finals singles matches pete sampras competed in were on a carpeted surface . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'carpet', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'carpet'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to carpet .', 'tostr': 'filter_eq { all_rows ; surface ; carpet }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; carpet } }', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; carpet } } ; 5 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; surface ; carpet } } ; 5 } = true | select the rows whose surface record fuzzily matches to carpet . 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, 'carpet_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', 'carpet_6': 'carpet', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'carpet_6': [0], '5_7': [2]} | ['outcome', 'year', 'championship', 'surface', 'opponent in the final', 'score in the final'] | [['runner - up', '1991', 'cincinnati', 'hard', 'guy forget', '6 - 2 , 6 - 7 ( 4 - 7 ) , 4 - 6'], ['runner - up', '1991', 'paris', 'carpet ( i )', 'guy forget', '6 - 7 ( 9 - 11 ) , 6 - 4 , 7 - 5 , 4 - 6 , 4 - 6'], ['winner', '1992', 'cincinnati', 'hard', 'ivan lendl', '6 - 3 , 3 - 6 , 6 - 3'], ['winner', '1993', 'miami', 'hard', 'malivai washington', '6 - 3 , 6 - 2'], ['winner', '1994', 'indian wells', 'hard', 'petr korda', '4 - 6 , 6 - 3 , 3 - 6 , 6 - 3 , 6 - 2'], ['winner', '1994', 'miami ( 2 )', 'hard', 'andre agassi', '5 - 7 , 6 - 3 , 6 - 3'], ['winner', '1994', 'rome', 'clay', 'boris becker', '6 - 1 , 6 - 2 , 6 - 2'], ['winner', '1995', 'indian wells ( 2 )', 'hard', 'andre agassi', '7 - 5 , 6 - 3 , 7 - 5'], ['runner - up', '1995', 'miami', 'hard', 'andre agassi', '6 - 3 , 2 - 6 , 6 - 7 ( 6 - 8 )'], ['runner - up', '1995', 'canada ( montreal )', 'hard', 'andre agassi', '6 - 3 , 2 - 6 , 3 - 6'], ['winner', '1995', 'paris', 'carpet ( i )', 'boris becker', '7 - 6 ( 7 - 5 ) , 6 - 4 , 6 - 4'], ['runner - up', '1996', 'stuttgart', 'carpet ( i )', 'boris becker', '6 - 3 , 3 - 6 , 6 - 3 , 3 - 6 , 4 - 6'], ['winner', '1997', 'cincinnati ( 2 )', 'hard', 'thomas muster', '6 - 3 , 6 - 4'], ['winner', '1997', 'paris ( 2 )', 'carpet ( i )', 'jonas björkman', '6 - 3 , 4 - 6 , 6 - 3 , 6 - 1'], ['runner - up', '1998', 'cincinnati ( 2 )', 'hard', 'patrick rafter', '6 - 1 , 6 - 7 ( 2 - 7 ) , 4 - 6'], ['runner - up', '1998', 'paris ( 2 )', 'carpet ( i )', 'greg rusedski', '4 - 6 , 6 - 7 ( 4 - 7 ) , 3 - 6'], ['winner', '1999', 'cincinnati ( 3 )', 'hard', 'patrick rafter', '7 - 6 ( 9 - 7 ) , 6 - 3'], ['winner', '2000', 'miami ( 3 )', 'hard', 'gustavo kuerten', '6 - 1 , 6 - 7 ( 2 - 7 ) , 7 - 6 ( 7 - 5 ) , 7 - 6 ( 10 - 8 )']] |
tasmania cricket team first - class records | https://en.wikipedia.org/wiki/Tasmania_cricket_team_first-class_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14412861-10.html.csv | ordinal | dene hills has the 2nd highest number of runs in the tasmania cricket team first - class records . | {'row': '2', 'col': '2', 'order': '2', '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', 'runs', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; runs ; 2 }'}, 'player'], 'result': 'dene hills', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; runs ; 2 } ; player }'}, 'dene hills'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; runs ; 2 } ; player } ; dene hills } = true', 'tointer': 'select the row whose runs record of all rows is 2nd maximum . the player record of this row is dene hills .'} | eq { hop { nth_argmax { all_rows ; runs ; 2 } ; player } ; dene hills } = true | select the row whose runs record of all rows is 2nd maximum . the player record of this row is dene hills . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'runs_5': 5, '2_6': 6, 'player_7': 7, 'dene hills_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', 'runs_5': 'runs', '2_6': '2', 'player_7': 'player', 'dene hills_8': 'dene hills'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'runs_5': [0], '2_6': [0], 'player_7': [1], 'dene hills_8': [2]} | ['rank', 'runs', 'player', 'opponent', 'venue', 'season'] | [['1', '274', 'jack badcock', 'victoria', 'ntca ground , launceston', '1933 - 34'], ['2', '265', 'dene hills', 'south australia', 'bellerive oval , hobart', '1997 - 98'], ['3', '245', 'jamie cox', 'new south wales', 'bellerive oval , hobart', '1999 - 2000'], ['4', '233', 'ricky ponting', 'queensland', 'albion', '2000 - 01'], ['5', '227', 'david boon', 'victoria', 'mcg , melbourne', '1983 - 84']] |
1956 formula one season | https://en.wikipedia.org/wiki/1956_Formula_One_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140112-5.html.csv | unique | the i brscc formula 1 race is the only race won by archie scott brown in the 1956 formula one season . | {'scope': 'all', 'row': '11', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'archie scott brown', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning driver', 'archie scott brown'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning driver record fuzzily matches to archie scott brown .', 'tostr': 'filter_eq { all_rows ; winning driver ; archie scott brown }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winning driver ; archie scott brown } }', 'tointer': 'select the rows whose winning driver record fuzzily matches to archie scott brown . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning driver', 'archie scott brown'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning driver record fuzzily matches to archie scott brown .', 'tostr': 'filter_eq { all_rows ; winning driver ; archie scott brown }'}, 'race name'], 'result': 'i brscc formula 1 race', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winning driver ; archie scott brown } ; race name }'}, 'i brscc formula 1 race'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; winning driver ; archie scott brown } ; race name } ; i brscc formula 1 race }', 'tointer': 'the race name record of this unqiue row is i brscc formula 1 race .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; winning driver ; archie scott brown } } ; eq { hop { filter_eq { all_rows ; winning driver ; archie scott brown } ; race name } ; i brscc formula 1 race } } = true', 'tointer': 'select the rows whose winning driver record fuzzily matches to archie scott brown . there is only one such row in the table . the race name record of this unqiue row is i brscc formula 1 race .'} | and { only { filter_eq { all_rows ; winning driver ; archie scott brown } } ; eq { hop { filter_eq { all_rows ; winning driver ; archie scott brown } ; race name } ; i brscc formula 1 race } } = true | select the rows whose winning driver record fuzzily matches to archie scott brown . there is only one such row in the table . the race name record of this unqiue row is i brscc formula 1 race . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winning driver_7': 7, 'archie scott brown_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'race name_9': 9, 'i brscc formula 1 race_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winning driver_7': 'winning driver', 'archie scott brown_8': 'archie scott brown', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'race name_9': 'race name', 'i brscc formula 1 race_10': 'i brscc formula 1 race'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'winning driver_7': [0], 'archie scott brown_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'race name_9': [2], 'i brscc formula 1 race_10': [3]} | ['race name', 'circuit', 'date', 'winning driver', 'constructor', 'report'] | [['x gran premio ciudad de buenos aires', 'mendoza', '5 february', 'juan manuel fangio', 'lancia - ferrari', 'report'], ['iv glover trophy', 'goodwood', '2 april', 'stirling moss', 'maserati', 'report'], ['vi gran premio di siracusa', 'syracuse', '15 april', 'juan manuel fangio', 'lancia - ferrari', 'report'], ['xi barc aintree 200', 'aintree', '21 april', 'stirling moss', 'maserati', 'report'], ['vii brdc international trophy', 'silverstone', '5 may', 'stirling moss', 'vanwall', 'report'], ['ix gran premio di napoli', 'posillipo', '6 may', 'robert manzon', 'gordini', 'report'], ['i aintree 100', 'aintree', '24 june', 'horace gould', 'maserati', 'report'], ['i vanwall trophy', 'snetterton', '22 july', 'roy salvadori', 'maserati', 'report'], ['iv grand prix de caen', 'caen', '26 august', 'harry schell', 'maserati', 'report'], ['ii sussex trophy', 'goodwood', '8 september', 'roy salvadori', 'cooper - climax', 'report'], ['i brscc formula 1 race', 'brands hatch', '14 october', 'archie scott brown', 'connaught - alta', 'report']] |
2007 volta a catalunya | https://en.wikipedia.org/wiki/2007_Volta_a_Catalunya | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11355733-20.html.csv | majority | víctor hugo peña won the sprints classification in the majority of stages at the 2007 volta a catalunya where relax - gam won the team classification . | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'víctor hugo peña', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'relax - gam'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team classification', 'relax - gam'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team classification ; relax - gam }', 'tointer': 'select the rows whose team classification record fuzzily matches to relax - gam .'}, 'sprints classification', 'víctor hugo peña'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose team classification record fuzzily matches to relax - gam . for the sprints classification records of these rows , most of them fuzzily match to víctor hugo peña .', 'tostr': 'most_eq { filter_eq { all_rows ; team classification ; relax - gam } ; sprints classification ; víctor hugo peña } = true'} | most_eq { filter_eq { all_rows ; team classification ; relax - gam } ; sprints classification ; víctor hugo peña } = true | select the rows whose team classification record fuzzily matches to relax - gam . for the sprints classification records of these rows , most of them fuzzily match to víctor hugo peña . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'team classification_4': 4, 'relax - gam_5': 5, 'sprints classification_6': 6, 'víctor hugo peña_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'team classification_4': 'team classification', 'relax - gam_5': 'relax - gam', 'sprints classification_6': 'sprints classification', 'víctor hugo peña_7': 'víctor hugo peña'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'team classification_4': [0], 'relax - gam_5': [0], 'sprints classification_6': [1], 'víctor hugo peña_7': [1]} | ['stage ( winner )', 'general classification', 'mountains classification', 'points classification', 'sprints classification', 'team classification'] | [["0 stage 1 ( ttt ) ( caisse d'epargne )", 'vladimir karpets', 'no award', 'no award', 'no award', "caisse d'epargne"], ['0 stage 2 ( mark cavendish )', 'imanol erviti', 'francisco josé martinez', 'mark cavendish', 'víctor hugo peña', "caisse d'epargne"], ['0 stage 3 ( allan davis )', 'imanol erviti', 'francisco josé martinez', 'baden cooke', 'víctor hugo peña', "caisse d'epargne"], ['0 stage 4 ( óscar sevilla )', 'óscar sevilla', 'luis pasamontes', 'baden cooke', 'luis pasamontes', 'relax - gam'], ['0 stage 5 ( itt ) ( denis menchov )', 'vladimir karpets', 'luis pasamontes', 'denis menchov', 'luis pasamontes', 'relax - gam'], ['0 stage 6 ( mark cavendish )', 'vladimir karpets', 'luis pasamontes', 'mark cavendish', 'víctor hugo peña', 'relax - gam'], ['0 stage 7 ( samuel sánchez )', 'vladimir karpets', 'luis pasamontes', 'denis menchov', 'víctor hugo peña', 'relax - gam'], ['0 final', 'vladimir karpets', 'luis pasamontes', 'denis menchov', 'víctor hugo peña', 'relax - gam']] |
2004 - 05 greek cup | https://en.wikipedia.org/wiki/2004%E2%80%9305_Greek_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19130829-4.html.csv | comparative | kastoria did better than illsiakos in the 2004-05 greek cup . | {'row_1': '2', 'row_2': '5', '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', 'team 1', 'kastoria'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team 1 record fuzzily matches to kastoria .', 'tostr': 'filter_eq { all_rows ; team 1 ; kastoria }'}, 'agg score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team 1 ; kastoria } ; agg score }', 'tointer': 'select the rows whose team 1 record fuzzily matches to kastoria . take the agg score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team 1', 'ilisiakos'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team 1 record fuzzily matches to ilisiakos .', 'tostr': 'filter_eq { all_rows ; team 1 ; ilisiakos }'}, 'agg score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team 1 ; ilisiakos } ; agg score }', 'tointer': 'select the rows whose team 1 record fuzzily matches to ilisiakos . take the agg score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team 1 ; kastoria } ; agg score } ; hop { filter_eq { all_rows ; team 1 ; ilisiakos } ; agg score } } = true', 'tointer': 'select the rows whose team 1 record fuzzily matches to kastoria . take the agg score record of this row . select the rows whose team 1 record fuzzily matches to ilisiakos . take the agg score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team 1 ; kastoria } ; agg score } ; hop { filter_eq { all_rows ; team 1 ; ilisiakos } ; agg score } } = true | select the rows whose team 1 record fuzzily matches to kastoria . take the agg score record of this row . select the rows whose team 1 record fuzzily matches to ilisiakos . take the agg score record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team 1_7': 7, 'kastoria_8': 8, 'agg score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team 1_11': 11, 'ilisiakos_12': 12, 'agg score_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team 1_7': 'team 1', 'kastoria_8': 'kastoria', 'agg score_9': 'agg score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team 1_11': 'team 1', 'ilisiakos_12': 'ilisiakos', 'agg score_13': 'agg score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team 1_7': [0], 'kastoria_8': [0], 'agg score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team 1_11': [1], 'ilisiakos_12': [1], 'agg score_13': [3]} | ['team 1', 'agg score', 'team 2', '1st leg', '2nd leg'] | [['iraklis', '1 - 2', 'olympiacos', '1 - 0', '0 - 2'], ['kastoria', '4 - 2', 'ptolemaida - lignitorikhi', '2 - 0', '2 - 3'], ['aris', '4 - 2', 'ethnikos', '2 - 1', '2 - 1'], ['skoda xanthi', '1 - 0', 'egaleo', '1 - 0', '0 - 0'], ['ilisiakos', '0 - 2', 'panionios', '0 - 1', '0 - 1'], ['larissa', '3 - 2', 'chalkidon near east', '3 - 1', '0 - 1'], ['ofi', '1 - 1', 'apollon kalamaria', '1 - 1', '0 - 0']] |
houston rockets all - time roster | https://en.wikipedia.org/wiki/Houston_Rockets_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11734041-15.html.csv | superlative | curtis perry is the first player that joined the houston rockets . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'years for rockets'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; years for rockets }'}, 'player'], 'result': 'perry , curtis curtis perry', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; years for rockets } ; player }'}, 'perry , curtis curtis perry'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; years for rockets } ; player } ; perry , curtis curtis perry } = true', 'tointer': 'select the row whose years for rockets record of all rows is minimum . the player record of this row is perry , curtis curtis perry .'} | eq { hop { argmin { all_rows ; years for rockets } ; player } ; perry , curtis curtis perry } = true | select the row whose years for rockets record of all rows is minimum . the player record of this row is perry , curtis curtis perry . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'years for rockets_5': 5, 'player_6': 6, 'perry , curtis curtis perry_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'years for rockets_5': 'years for rockets', 'player_6': 'player', 'perry , curtis curtis perry_7': 'perry , curtis curtis perry'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'years for rockets_5': [0], 'player_6': [1], 'perry , curtis curtis perry_7': [2]} | ['player', 'no ( s )', 'height in ft', 'position', 'years for rockets', 'school / club team / country'] | [['padgett , scott scott padgett', '35', '6 - 9', 'forward', '2003 - 05 , 2006 - 07', 'kentucky'], ['patterson , patrick patrick patterson', '54', '6 - 9', 'forward', '2010 - 2013', 'kentucky'], ['paultz , billy billy paultz', '5', '6 - 11', 'center', '1979 - 83', "st john 's"], ['perry , curtis curtis perry', '54', '6 - 7', 'forward', '1970 - 71', 'sw missouri state'], ['petersen , jim jim petersen', '43', '6 - 10', 'forward / center', '1984 - 88', 'minnesota'], ['petruska , richard richard petruska', '3', '6 - 10', 'center', '1993 - 94', 'ucla'], ['piatkowski , eric eric piatkowski', '52', '6 - 7', 'guard', '2003 - 04', 'nebraska'], ['pippen , scottie scottie pippen', '33', '6 - 8', 'forward', '1998 - 99', 'central arkansas'], ['posey , james james posey', '55', '6 - 8', 'forward', '2002 - 03', 'xavier ( ohio )']] |
płock governorate | https://en.wikipedia.org/wiki/P%C5%82ock_Governorate | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12333984-1.html.csv | superlative | polish is the most spoken language in the plock governorate . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'number'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; number }'}, 'language'], 'result': 'polish', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; number } ; language }'}, 'polish'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; number } ; language } ; polish } = true', 'tointer': 'select the row whose number record of all rows is maximum . the language record of this row is polish .'} | eq { hop { argmax { all_rows ; number } ; language } ; polish } = true | select the row whose number record of all rows is maximum . the language record of this row is polish . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'number_5': 5, 'language_6': 6, 'polish_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'number_5': 'number', 'language_6': 'language', 'polish_7': 'polish'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'number_5': [0], 'language_6': [1], 'polish_7': [2]} | ['language', 'number', 'percentage ( % )', 'males', 'females'] | [['polish', '447 685', '80.86', '216 794', '230 891'], ['yiddish', '51 215', '9.25', '24 538', '26 677'], ['german', '35 931', '6.49', '17 409', '18 522'], ['russian', '15 137', '2.73', '13 551', '1 586'], ['ukrainian', '2 350', '0.42', '2 302', '48'], ['other', '1 285', '0.23', '1 041', '244'], ["persons that did n't name their native language", '27', '> 0.01', '14', '13'], ['total', '553 633', '100', '275 652', '277 981']] |
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