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list of married ... with children episodes | https://en.wikipedia.org/wiki/List_of_Married..._with_Children_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2226817-2.html.csv | comparative | the pilot episode aired earlier than the nightmare on al 's street episode . | {'row_1': '1', 'row_2': '11', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'pilot'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to pilot .', 'tostr': 'filter_eq { all_rows ; title ; pilot }'}, 'no in series'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; pilot } ; no in series }', 'tointer': 'select the rows whose title record fuzzily matches to pilot . take the no in series record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', "nightmare on al 's street"], 'result': None, 'ind': 1, 'tointer': "select the rows whose title record fuzzily matches to nightmare on al 's street .", 'tostr': "filter_eq { all_rows ; title ; nightmare on al 's street }"}, 'no in series'], 'result': None, 'ind': 3, 'tostr': "hop { filter_eq { all_rows ; title ; nightmare on al 's street } ; no in series }", 'tointer': "select the rows whose title record fuzzily matches to nightmare on al 's street . take the no in series record of this row ."}], 'result': True, 'ind': 4, 'tostr': "less { hop { filter_eq { all_rows ; title ; pilot } ; no in series } ; hop { filter_eq { all_rows ; title ; nightmare on al 's street } ; no in series } } = true", 'tointer': "select the rows whose title record fuzzily matches to pilot . take the no in series record of this row . select the rows whose title record fuzzily matches to nightmare on al 's street . take the no in series record of this row . the first record is less than the second record ."} | less { hop { filter_eq { all_rows ; title ; pilot } ; no in series } ; hop { filter_eq { all_rows ; title ; nightmare on al 's street } ; no in series } } = true | select the rows whose title record fuzzily matches to pilot . take the no in series record of this row . select the rows whose title record fuzzily matches to nightmare on al 's street . take the no in series 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, 'title_7': 7, 'pilot_8': 8, 'no in series_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, "nightmare on al 's street_12": 12, 'no in series_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', 'title_7': 'title', 'pilot_8': 'pilot', 'no in series_9': 'no in series', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', "nightmare on al 's street_12": "nightmare on al 's street", 'no in series_13': 'no in series'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'pilot_8': [0], 'no in series_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], "nightmare on al 's street_12": [1], 'no in series_13': [3]} | ['no in series', 'title', 'directed by', 'written by', 'original air date', 'production code'] | [['1', 'pilot', 'linda day', 'ron leavitt & michael g moye', 'april 5 , 1987', '1.01'], ['2', 'thinergy', 'linda day', 'ron leavitt & michael g moye', 'april 12 , 1987', '1.02'], ['3', "but i did n't shoot the deputy", 'linda day', 'ron burla', 'april 19 , 1987', '1.03'], ['4', 'whose room is it anyway', 'zane buzby', 'marcy vosburgh & sandy sprung', 'april 26 , 1987', '1.04'], ['5', 'have you driven a ford lately', 'linda day', 'richard gurman & katherine green', 'may 3 , 1987', '1.05'], ['6', 'sixteen years and what do you get', 'linda day', 'katherine green & richard gurman', 'may 10 , 1987', '1.06'], ['7', 'married without children', 'linda day', 'ralph r farquhar', 'may 17 , 1987', '1.07'], ['8', 'the poker game', 'brian levant', 'ron leavitt & michael g moye', 'may 24 , 1987', '1.08'], ['9', 'peggy sue got work', 'linda day', 'ellen l fogle', 'may 31 , 1987', '1.09'], ['10', 'al loses his cherry', 'arlando smith', 'marcy vosburgh & sandy sprung', 'june 7 , 1987', '1.10'], ['11', "nightmare on al 's street", 'linda day', 'michael g moye', 'june 14 , 1987', '1.11'], ['12', "where 's the boss", 'linda day', 'marcy vosburgh & sandy sprung', 'june 21 , 1987', '1.12']] |
katja seizinger | https://en.wikipedia.org/wiki/Katja_Seizinger | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1489417-1.html.csv | ordinal | katja seizinger had her 3rd highest score in the slalom in the year 1996 . | {'row': '7', 'col': '3', 'order': '3', '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', 'slalom', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; slalom ; 3 }'}, 'season'], 'result': '1996', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; slalom ; 3 } ; season }'}, '1996'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; slalom ; 3 } ; season } ; 1996 } = true', 'tointer': 'select the row whose slalom record of all rows is 3rd maximum . the season record of this row is 1996 .'} | eq { hop { nth_argmax { all_rows ; slalom ; 3 } ; season } ; 1996 } = true | select the row whose slalom record of all rows is 3rd maximum . the season record of this row is 1996 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'slalom_5': 5, '3_6': 6, 'season_7': 7, '1996_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'slalom_5': 'slalom', '3_6': '3', 'season_7': 'season', '1996_8': '1996'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'slalom_5': [0], '3_6': [0], 'season_7': [1], '1996_8': [2]} | ['season', 'overall', 'slalom', 'giant slalom', 'super g', 'downhill', 'combined'] | [['1990', '44', '-', '39', '12', '-', '21'], ['1991', '15', '-', '29', '3', '13', '12'], ['1992', '3', '-', '10', '4', '1', '-'], ['1993', '2', '58', '7', '1', '1', '7'], ['1994', '3', '49', '6', '1', '1', '19'], ['1995', '2', '19', '9', '1', '3', '4'], ['1996', '1', '39', '2', '1', '2', '-'], ['1997', '2', '19', '2', '2', '5', '-'], ['1998', '1', '12', '6', '1', '1', '2']] |
2002 oakland raiders season | https://en.wikipedia.org/wiki/2002_Oakland_Raiders_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16353260-1.html.csv | superlative | the oakland raiders had their largest attendance of the 2002 season in their game on october 27th . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'date'], 'result': 'october 27 , 2002', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, 'october 27 , 2002'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; october 27 , 2002 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is october 27 , 2002 .'} | eq { hop { argmax { all_rows ; attendance } ; date } ; october 27 , 2002 } = true | select the row whose attendance record of all rows is maximum . the date record of this row is october 27 , 2002 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, 'october 27 , 2002_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', 'october 27 , 2002_7': 'october 27 , 2002'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], 'october 27 , 2002_7': [2]} | ['week', 'date', 'opponent', 'result', 'tv time', 'record', 'attendance'] | [['1', 'september 8 , 2002', 'seattle seahawks', 'w 31 - 17', 'fox 4:15 et', '1 - 0', '53260'], ['2', 'september 15 , 2002', 'pittsburgh steelers', 'w 30 - 17', 'espn 8:30 et', '2 - 0', '62260'], ['3', '-', '-', '-', '-', '-', ''], ['4', 'september 29 , 2002', 'tennessee titans', 'w 52 - 25', 'cbs 4:15 et', '3 - 0', '58719'], ['5', 'october 6 , 2002', 'buffalo bills', 'w 49 - 31', 'cbs 1:00 et', '4 - 0', '73038'], ['6', 'october 13 , 2002', 'st louis rams', 'l 28 - 13', 'cbs 4:15 et', '4 - 1', '66070'], ['7', 'october 20 , 2002', 'san diego chargers', 'l 27 - 21 ( ot )', 'cbs 4:05 et', '4 - 2', '60974'], ['8', 'october 27 , 2002', 'kansas city chiefs', 'l 20 - 10', 'cbs 1:00 et', '4 - 3', '78685'], ['9', 'november 3 , 2002', 'san francisco 49ers', 'l 23 - 20 ( ot )', 'fox 4:15 et', '4 - 4', '62660'], ['10', 'november 11 , 2002', 'denver broncos', 'w 34 - 10', 'abc 9:00 et', '5 - 4', '76643'], ['11', 'november 17 , 2002', 'new england patriots', 'w 27 - 20', 'espn 8:30 et', '6 - 4', '62552'], ['12', 'november 24 , 2002', 'arizona cardinals', 'w 41 - 20', 'cbs 1:05 et', '7 - 4', '58814'], ['13', 'december 2 , 2002', 'new york jets', 'w 26 - 20', 'abc 9:00 et', '8 - 4', '62257'], ['14', 'december 8 , 2002', 'san diego chargers', 'w 27 - 7', 'cbs 4:15 et', '9 - 4', '67968'], ['15', 'december 15 , 2002', 'miami dolphins', 'l 23 - 17', 'cbs 1:00 et', '9 - 5', '73572'], ['16', 'december 22 , 2002', 'denver broncos', 'w 28 - 16', 'cbs 4:15 et', '10 - 5', '62592'], ['17', 'december 28 , 2002', 'kansas city chiefs', 'w 24 - 0', 'cbs 5:00 et', '11 - 5', '62078']] |
united states house of representatives elections , 2000 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2000 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341423-46.html.csv | ordinal | frank wolf recorded the highest percentage ratio among all candidates of the 2000 house of representatives elections . | {'row': '9', 'col': '6', 'order': '1', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'candidates', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; candidates ; 1 }'}, 'incumbent'], 'result': 'frank wolf', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent }'}, 'frank wolf'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; frank wolf } = true', 'tointer': 'select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is frank wolf .'} | eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; frank wolf } = true | select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is frank wolf . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'candidates_5': 5, '1_6': 6, 'incumbent_7': 7, 'frank wolf_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', 'candidates_5': 'candidates', '1_6': '1', 'incumbent_7': 'incumbent', 'frank wolf_8': 'frank wolf'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'candidates_5': [0], '1_6': [0], 'incumbent_7': [1], 'frank wolf_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'results', 'candidates'] | [['virginia 2', 'owen b pickett', 'democratic', '1986', 'retired republican gain', 'ed schrock ( r ) 52 % jody wagner ( d ) 48 %'], ['virginia 3', 'bobby scott', 'democratic', '1992', 're - elected', 'bobby scott ( d ) unopposed'], ['virginia 4', 'norman sisisky', 'democratic', '1982', 're - elected', 'norman sisisky ( d ) unopposed'], ['virginia 5', 'virgil goode', 'independent', '1996', 're - elected , independent gain', 'virgil goode ( i ) 68 % john boyd ( d ) 31 %'], ['virginia 6', 'bob goodlatte', 'republican', '1992', 're - elected', 'bob goodlatte ( r ) unopposed'], ['virginia 7', 'thomas j bliley , jr', 'republican', '1980', 'retired republican hold', 'eric cantor ( r ) 67 % warren stewart ( d ) 33 %'], ['virginia 8', 'jim moran', 'democratic', '1990', 're - elected', 'jim moran ( d ) 64 % demaris h miller ( r ) 35 %'], ['virginia 9', 'rick boucher', 'democratic', '1982', 're - elected', 'rick boucher ( d ) 70 % michael osborne ( r ) 30 %'], ['virginia 10', 'frank wolf', 'republican', '1980', 're - elected', 'frank wolf ( r ) 85 %']] |
united states house of representatives elections , 1930 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1930 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342359-41.html.csv | count | four of the candidates were re-elected to their position in the house of representatives . | {'scope': 'all', 'criterion': 'not_equal', 'value': 'retired', 'result': '4', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'result', 'retired'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record does not match to retired .', 'tostr': 'filter_not_eq { all_rows ; result ; retired }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_not_eq { all_rows ; result ; retired } }', 'tointer': 'select the rows whose result record does not match to retired . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_not_eq { all_rows ; result ; retired } } ; 4 } = true', 'tointer': 'select the rows whose result record does not match to retired . the number of such rows is 4 .'} | eq { count { filter_not_eq { all_rows ; result ; retired } } ; 4 } = true | select the rows whose result record does not match to retired . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'retired_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'retired_6': 'retired', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_not_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'retired_6': [0], '4_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['tennessee 3', 'sam d mcreynolds', 'democratic', '1922', 're - elected', 'sam d mcreynolds ( d ) unopposed'], ['tennessee 4', 'cordell hull', 'democratic', '1922', 'retired to run for u s senate democratic hold', 'john ridley mitchell ( d ) unopposed'], ['tennessee 5', 'ewin l davis', 'democratic', '1918', 're - elected', 'ewin l davis ( d ) 92.0 % george motlow ( r ) 8.0 %'], ['tennessee 7', 'edward everett eslick', 'democratic', '1924', 're - elected', 'edward everett eslick ( d ) unopposed'], ['tennessee 8', 'gordon browning', 'democratic', '1922', 're - elected', 'gordon browning ( d ) unopposed']] |
2008 - 09 philadelphia 76ers season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Philadelphia_76ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17323042-8.html.csv | ordinal | the game against miami was the third game in february of the 2008-09 season . | {'row': '3', 'col': '1', 'order': '3', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'game', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; game ; 3 }'}, 'team'], 'result': 'miami', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; game ; 3 } ; team }'}, 'miami'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; game ; 3 } ; team } ; miami } = true', 'tointer': 'select the row whose game record of all rows is 3rd minimum . the team record of this row is miami .'} | eq { hop { nth_argmin { all_rows ; game ; 3 } ; team } ; miami } = true | select the row whose game record of all rows is 3rd minimum . the team record of this row is miami . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'game_5': 5, '3_6': 6, 'team_7': 7, 'miami_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', 'game_5': 'game', '3_6': '3', 'team_7': 'team', 'miami_8': 'miami'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'game_5': [0], '3_6': [0], 'team_7': [1], 'miami_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['47', 'february 3', 'boston', 'l 99 - 100 ( ot )', 'andre iguodala ( 22 )', 'reggie evans ( 10 )', 'andre miller ( 7 )', 'wachovia center 16831', '23 - 24'], ['48', 'february 5', 'indiana', 'w 99 - 94 ( ot )', 'willie green ( 23 )', 'samuel dalembert ( 20 )', 'andre miller ( 12 )', 'wachovia center 10699', '24 - 24'], ['49', 'february 7', 'miami', 'w 94 - 84 ( ot )', 'andre miller , marreese speights ( 15 )', 'samuel dalembert ( 10 )', 'andre miller , andre iguodala ( 5 )', 'wachovia center 17216', '25 - 24'], ['50', 'february 9', 'phoenix', 'w 108 - 91 ( ot )', 'thaddeus young ( 25 )', 'samuel dalembert ( 11 )', 'andre iguodala ( 7 )', 'wachovia center 16797', '26 - 24'], ['51', 'february 11', 'memphis', 'w 91 - 87 ( ot )', 'andre miller ( 24 )', 'samuel dalembert ( 7 )', 'andre miller ( 9 )', 'wachovia center 12812', '27 - 24'], ['52', 'february 17', 'indiana', 'l 91 - 100 ( ot )', 'andre iguodala ( 20 )', 'reggie evans ( 11 )', 'andre iguodala ( 9 )', 'conseco fieldhouse 13259', '27 - 25'], ['53', 'february 18', 'denver', 'l 89 - 101 ( ot )', 'andre miller ( 17 )', 'samuel dalembert , marreese speights ( 10 )', 'andre iguodala ( 4 )', 'wachovia center 15979', '27 - 26'], ['54', 'february 21', 'miami', 'l 91 - 97 ( ot )', 'andre miller ( 30 )', 'andre miller ( 9 )', 'andre iguodala ( 8 )', 'american airlines arena 19600', '27 - 27'], ['55', 'february 23', 'new jersey', 'l 96 - 98 ( ot )', 'andre iguodala ( 21 )', 'samuel dalembert ( 10 )', 'andre miller ( 10 )', 'izod center 13236', '27 - 28'], ['56', 'february 25', 'washington', 'w 106 - 98 ( ot )', 'andre iguodala ( 22 )', 'samuel dalembert ( 13 )', 'andre iguodala ( 11 )', 'verizon center 16505', '28 - 28'], ['57', 'february 27', 'new york', 'w 108 - 103 ( ot )', 'andre miller ( 25 )', 'samuel dalembert ( 14 )', 'andre miller ( 6 )', 'madison square garden 19763', '29 - 28'], ['58', 'february 28', 'orlando', 'l 100 - 106 ( ot )', 'andre miller ( 23 )', 'andre miller ( 8 )', 'andre miller ( 7 )', 'wachovia center 19703', '29 - 29']] |
floriana f.c | https://en.wikipedia.org/wiki/Floriana_F.C. | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1312112-6.html.csv | count | there are a total of 8 teams that played against floriana f.c. | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '8', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'club'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record is arbitrary .', 'tostr': 'filter_all { all_rows ; club }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; club } }', 'tointer': 'select the rows whose club record is arbitrary . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; club } } ; 8 } = true', 'tointer': 'select the rows whose club record is arbitrary . the number of such rows is 8 .'} | eq { count { filter_all { all_rows ; club } } ; 8 } = true | select the rows whose club record is arbitrary . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'club_5': 5, '8_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'club_5': 'club', '8_6': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'club_5': [0], '8_6': [2]} | ['season', 'competition', 'round', 'club', 'home', 'away', 'aggregate'] | [['1962 - 63', 'european cup', 'preliminary round', 'ipswich town', '1 - 4', '0 - 10', '1 - 14'], ['1968 - 69', 'european cup', '1 . round', 'lahti', '1 - 1', '0 - 2', '1 - 3'], ['1970 - 71', 'european cup', '1 . round', 'sporting cp', '0 - 4', '0 - 5', '0 - 9'], ['1973 - 74', 'european cup', '1 . round', 'club brugge', '0 - 2', '0 - 8', '0 - 10'], ['1975 - 76', 'european cup', '1 . round', 'hajduk split', '0 - 5', '0 - 3', '0 - 8'], ['1977 - 78', 'european cup', '1 . round', 'panathinaikos', '1 - 1', '0 - 4', '1 - 5'], ['1993 - 94', 'uefa champions league', 'preliminary round', 'ekranas', '1 - 0', '1 - 0', '2 - 0'], ['1993 - 94', 'uefa champions league', '1 . round', 'fc porto', '0 - 0', '0 - 2', '0 - 2']] |
christian dailly | https://en.wikipedia.org/wiki/Christian_Dailly | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1317736-1.html.csv | aggregation | the total number of international goals christian dailly scored in his career is eight . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '8', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'score'], 'result': '8', 'ind': 0, 'tostr': 'sum { all_rows ; score }'}, '8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; score } ; 8 } = true', 'tointer': 'the sum of the score record of all rows is 8 .'} | round_eq { sum { all_rows ; score } ; 8 } = true | the sum of the score record of all rows is 8 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'score_4': 4, '8_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'score_4': 'score', '8_5': '8'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'score_4': [0], '8_5': [1]} | ['goal', 'date', 'venue', 'score', 'result', 'competition'] | [['1', '1 june 1997', "ta ' qali , malta", '1 - 0', '3 - 2', 'friendly'], ['2', '17 april 2002', 'aberdeen , scotland', '1 - 0', '1 - 2', 'friendly'], ['3', '23 may 2002', 'hong kong , china', '3 - 0', '4 - 0', 'friendly'], ['4', '12 october 2002', 'reykjavík , iceland', '1 - 0', '2 - 0', 'uefa euro 2004 qualifying'], ['5', '4 june 2005', 'glasgow , scotland', '1 - 0', '2 - 0', 'fifa world cup 2006 qualifying'], ['6', '6 september 2006', 'kaunas , lithuania', '1 - 0', '2 - 1', 'uefa euro 2008 qualifying']] |
2006 tampa bay storm season | https://en.wikipedia.org/wiki/2006_Tampa_Bay_Storm_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11866255-1.html.csv | comparative | in 2006 , tampa bay storm played the soul before they played the force . | {'row_1': '1', 'row_2': '3', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'philadelphia soul'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to philadelphia soul .', 'tostr': 'filter_eq { all_rows ; opponent ; philadelphia soul }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; philadelphia soul } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to philadelphia soul . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'georgia force'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to georgia force .', 'tostr': 'filter_eq { all_rows ; opponent ; georgia force }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; georgia force } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to georgia force . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; philadelphia soul } ; date } ; hop { filter_eq { all_rows ; opponent ; georgia force } ; date } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to philadelphia soul . take the date record of this row . select the rows whose opponent record fuzzily matches to georgia force . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opponent ; philadelphia soul } ; date } ; hop { filter_eq { all_rows ; opponent ; georgia force } ; date } } = true | select the rows whose opponent record fuzzily matches to philadelphia soul . take the date record of this row . select the rows whose opponent record fuzzily matches to georgia force . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'philadelphia soul_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'georgia force_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'philadelphia soul_8': 'philadelphia soul', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'georgia force_12': 'georgia force', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'philadelphia soul_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'georgia force_12': [1], 'date_13': [3]} | ['week', 'date', 'opponent', 'home / away', 'result'] | [['1', 'january 29', 'philadelphia soul', 'away', 'l 52 - 34'], ['2', 'february 3', 'grand rapids rampage', 'away', 'w 51 - 43'], ['3', 'february 10', 'georgia force', 'home', 'w 61 - 60'], ['4', 'february 19', 'orlando predators', 'home', 'l 67 - 64 ( ot )'], ['5', 'february 25', 'austin wranglers', 'home', 'w 58 - 48'], ['6', 'march 5', 'kansas city brigade', 'away', 'w 69 - 59'], ['7', 'march 12', 'dallas desperados', 'home', 'l 64 - 35'], ['8', 'march 18', 'new york dragons', 'home', 'w 60 - 44'], ['9', 'march 26', 'georgia force', 'away', 'l 61 - 51'], ['10', 'april 1', 'utah blaze', 'home', 'w 56 - 41'], ['11', 'april 7', 'san jose sabercats', 'home', 'l 52 - 43'], ['12', 'april 15', 'austin wranglers', 'away', 'l 60 - 59'], ['13', 'april 22', 'orlando predators', 'away', 'l 52 - 13'], ['14', 'april 29', 'kansas city brigade', 'home', 'w 58 - 42'], ['15', 'may 6', 'columbus destroyers', 'away', 'l 51 - 48'], ['16', 'may 13', 'nashville kats', 'away', 'l 66 - 50']] |
list of people who took refuge in a diplomatic mission | https://en.wikipedia.org/wiki/List_of_people_who_took_refuge_in_a_diplomatic_mission | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18299148-1.html.csv | unique | josé manuel balmaceda is the only politican to take refuge in a diplomatic mission and commit suicide . | {'scope': 'all', 'row': '1', 'col': '9', 'col_other': '1', 'criterion': 'equal', 'value': 'committed suicide', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'resolution', 'committed suicide'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose resolution record fuzzily matches to committed suicide .', 'tostr': 'filter_eq { all_rows ; resolution ; committed suicide }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; resolution ; committed suicide } }', 'tointer': 'select the rows whose resolution record fuzzily matches to committed suicide . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'resolution', 'committed suicide'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose resolution record fuzzily matches to committed suicide .', 'tostr': 'filter_eq { all_rows ; resolution ; committed suicide }'}, 'name'], 'result': 'josé manuel balmaceda', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; resolution ; committed suicide } ; name }'}, 'josé manuel balmaceda'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; resolution ; committed suicide } ; name } ; josé manuel balmaceda }', 'tointer': 'the name record of this unqiue row is josé manuel balmaceda .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; resolution ; committed suicide } } ; eq { hop { filter_eq { all_rows ; resolution ; committed suicide } ; name } ; josé manuel balmaceda } } = true', 'tointer': 'select the rows whose resolution record fuzzily matches to committed suicide . there is only one such row in the table . the name record of this unqiue row is josé manuel balmaceda .'} | and { only { filter_eq { all_rows ; resolution ; committed suicide } } ; eq { hop { filter_eq { all_rows ; resolution ; committed suicide } ; name } ; josé manuel balmaceda } } = true | select the rows whose resolution record fuzzily matches to committed suicide . there is only one such row in the table . the name record of this unqiue row is josé manuel balmaceda . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'resolution_7': 7, 'committed suicide_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'josé manuel balmaceda_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'resolution_7': 'resolution', 'committed suicide_8': 'committed suicide', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'josé manuel balmaceda_10': 'josé manuel balmaceda'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'resolution_7': [0], 'committed suicide_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'josé manuel balmaceda_10': [3]} | ['name', 'notability', 'reason for seeking refuge', 'country', 'city', 'missions country', 'start date', 'end date', 'resolution'] | [['josé manuel balmaceda', 'president of chile', 'defeated in the chilean civil war', 'chile', 'santiago', 'argentina', 'august 29 , 1891', 'september 18 , 1891', 'committed suicide'], ['leonardo argüello barreto', 'president of nicaragua', 'ousted by anastasio somoza garcía', 'nicaragua', 'managua', 'mexico', 'may 26 , 1947', 'december 1947', 'negotiated exile in mexico'], ['jacobo arbenz guzmán', 'president of guatemala', 'ousted by carlos castillo armas', 'guatemala', 'guatemala city', 'mexico', 'june 27 , 1954', 'june 28 , 1954', 'negotiated exile in mexico'], ['józsef mindszenty', 'hungarian roman catholic church cardinal', 'soviet intervention', 'hungary', 'budapest', 'united states', 'november 4 , 1956', 'november 4 , 1971 ( total 15 years )', 'negotiated exile in austria'], ['reino häyhänen', 'soviet lieutenant colonel', 'defection', 'france', 'paris', 'united states', 'november 4 , 1956', 'may 1957', 'moved to the united states'], ['the siberian seven', 'siberian pentecostals', 'prevented from emigrating', 'soviet union', 'moscow', 'united states', 'june 27 , 1978', 'june 27 , 1983 ( total : 5 years ( last of them ) )', 'allowed to emigrate to israel and later the us'], ['ange patasse', 'central african opposition leader', 'opposing andre kolingba government', 'central african republic', 'bangui', 'france', 'february 27 , 1982', 'march 3 , 1982', 'negotiated exile to togo'], ['fang lizhi and his wife', 'dissident in tiananmen square protests of 1989', 'forced end of tiananmen square protests of 1989', 'china prc', 'beijing', 'united states', 'june 5 , 1989', 'june 25 , 1990', 'negotiated flight to the united states'], ['hou dejian', 'dissident in tiananmen square protests of 1989', 'forced end of tiananmen square protests of 1989', 'china prc', 'beijing', 'australia', 'june 1989', 'august 16 , 1989', 'negotiated exit and deported back to native taiwan'], ['manuel noriega', 'president of panama', 'united states invasion of panama', 'panama', 'ciudad de panama', 'holy see', 'december 24 , 1989', 'january 3 , 1990', 'negotiated arrest by united states forces'], ['michel aoun', 'lebanese army commander', 'defeated in lebanese civil war', 'lebanon', 'beirut', 'france', 'october 1990', 'august 27 , 1991', 'left to exile in france'], ['sylvestre ntibantunganya', 'president of burundi', "military coup d'état", 'burundi', 'bujumbura', 'united states', 'july 23 , 1996', 'june 1997', 'negotiated exit'], ['joão bernardo vieira', 'president of guinea - bissau', 'guinea - bissau civil war', 'guinea - bissau', 'bissau', 'portugal', 'may 1999', 'june 1999', 'negotiated exile in portugal'], ['alassane ouattara', "presidential candidate in côte d'ivoire", 'first ivorian civil war', "côte d'ivoire", 'abidjan', 'france', 'september 19 , 2002', 'november 2002', 'negotiated exile in gabon and france'], ['shahram amiri', 'ian iran nuclear scientist', 'disappeared from iran', 'united states', 'washington dc', 'pakistan', 'july 13 , 2010', 'july 14 , 2010', 'returned to iran']] |
1997 in paraguayan football | https://en.wikipedia.org/wiki/1997_in_Paraguayan_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18703133-6.html.csv | unique | the only time paraguay scored more than 30 points was against tembetary . | {'scope': 'all', 'row': '9', 'col': '7', 'col_other': '2', 'criterion': 'greater_than', 'value': '30', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'scored', '30'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose scored record is greater than 30 .', 'tostr': 'filter_greater { all_rows ; scored ; 30 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; scored ; 30 } }', 'tointer': 'select the rows whose scored record is greater than 30 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'scored', '30'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose scored record is greater than 30 .', 'tostr': 'filter_greater { all_rows ; scored ; 30 }'}, 'team'], 'result': 'tembetary', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; scored ; 30 } ; team }'}, 'tembetary'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; scored ; 30 } ; team } ; tembetary }', 'tointer': 'the team record of this unqiue row is tembetary .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; scored ; 30 } } ; eq { hop { filter_greater { all_rows ; scored ; 30 } ; team } ; tembetary } } = true', 'tointer': 'select the rows whose scored record is greater than 30 . there is only one such row in the table . the team record of this unqiue row is tembetary .'} | and { only { filter_greater { all_rows ; scored ; 30 } } ; eq { hop { filter_greater { all_rows ; scored ; 30 } ; team } ; tembetary } } = true | select the rows whose scored record is greater than 30 . there is only one such row in the table . the team record of this unqiue row is tembetary . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'scored_7': 7, '30_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'tembetary_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'scored_7': 'scored', '30_8': '30', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'tembetary_10': 'tembetary'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'scored_7': [0], '30_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'tembetary_10': [3]} | ['position', 'team', 'played', 'wins', 'draws pk wins / pk losses', 'losses', 'scored', 'conceded', 'points'] | [['1', 'cerro corá', '12', '8', '1 / 2', '1', '22', '11', '28'], ['2', 'guaraní', '12', '6', '1 / 4', '1', '22', '16', '24'], ['3', 'san lorenzo', '12', '5', '2 / 1', '4', '13', '10', '20'], ['4', 'sportivo luqueño', '12', '4', '3 / 2', '3', '18', '16', '20'], ['5', 'olimpia', '12', '4', '4 / 0', '4', '18', '16', '20'], ['6', 'atl colegiales', '12', '4', '3 / 2', '3', '18', '18', '20'], ['7', 'cerro porteño', '12', '4', '1 / 4', '3', '15', '13', '18'], ['8', 'nacional', '12', '4', '1 / 2', '5', '14', '23', '16'], ['9', 'tembetary', '12', '4', '1 / 2', '5', '31', '27', '16'], ['10', 'sport colombia', '12', '3', '1 / 3', '5', '20', '19', '14'], ['11', 'presidente hayes', '12', '3', '2 / 1', '6', '13', '18', '14'], ['12', 'sol de américa', '12', '3', '1 / 1', '7', '11', '15', '12'], ['13', 'libertad', '12', '2', '3 / 0', '7', '8', '21', '12']] |
1996 masters tournament | https://en.wikipedia.org/wiki/1996_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514480-5.html.csv | aggregation | in the 1996 masters tournament , the average strokes to par was -4.64 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '-4.64', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'to par'], 'result': '-4.64', 'ind': 0, 'tostr': 'avg { all_rows ; to par }'}, '-4.64'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; to par } ; -4.64 } = true', 'tointer': 'the average of the to par record of all rows is -4.64 .'} | round_eq { avg { all_rows ; to par } ; -4.64 } = true | the average of the to par record of all rows is -4.64 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'to par_4': 4, '-4.64_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'to par_4': 'to par', '-4.64_5': '-4.64'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'to par_4': [0], '-4.64_5': [1]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'nick faldo', 'england', '69 + 67 + 73 + 67 = 276', '- 12', '450000'], ['2', 'greg norman', 'australia', '63 + 69 + 71 + 78 = 281', '- 7', '270000'], ['3', 'phil mickelson', 'united states', '65 + 73 + 72 + 72 = 282', '- 6', '170000'], ['4', 'frank nobilo', 'new zealand', '71 + 71 + 72 + 69 = 283', '- 5', '120000'], ['t5', 'scott hoch', 'united states', '67 + 73 + 73 + 71 = 284', '- 4', '95000'], ['t5', 'duffy waldorf', 'united states', '72 + 71 + 69 + 72 = 284', '- 4', '95000'], ['t7', 'davis love iii', 'united states', '72 + 71 + 74 + 68 = 285', '- 3', '77933'], ['t7', 'jeff maggert', 'united states', '71 + 73 + 72 + 69 = 285', '- 3', '77933'], ['t7', 'corey pavin', 'united states', '75 + 66 + 73 + 71 = 285', '- 3', '77933'], ['t10', 'david frost', 'south africa', '70 + 68 + 74 + 74 = 286', '- 2', '65000'], ['t10', 'scott mccarron', 'united states', '70 + 70 + 72 + 74 = 286', '- 2', '65000']] |
1930 - 31 chicago black hawks season | https://en.wikipedia.org/wiki/1930%E2%80%9331_Chicago_Black_Hawks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12791739-5.html.csv | superlative | the april 11 game between the chicago black hawks and the montral canadiens had the highest total goals scored . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; score }'}, 'score'], 'result': '3 - 2', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; score } ; score }'}, '3 - 2'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; score } ; score } ; 3 - 2 } = true', 'tointer': 'select the row whose score record of all rows is maximum . the score record of this row is 3 - 2 .'} | eq { hop { argmax { all_rows ; score } ; score } ; 3 - 2 } = true | select the row whose score record of all rows is maximum . the score record of this row is 3 - 2 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'score_5': 5, 'score_6': 6, '3 - 2_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'score_5': 'score', 'score_6': 'score', '3 - 2_7': '3 - 2'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'score_5': [0], 'score_6': [1], '3 - 2_7': [2]} | ['date', 'visitor', 'score', 'home', 'record'] | [['april 3', 'montreal canadiens', '2 - 1', 'chicago black hawks', '0 - 1'], ['april 5', 'montreal canadiens', '1 - 2', 'chicago black hawks', '1 - 1'], ['april 9', 'chicago black hawks', '3 - 2', 'montreal canadiens', '2 - 1'], ['april 11', 'chicago black hawks', '2 - 4', 'montreal canadiens', '2 - 2'], ['april 13', 'chicago black hawks', '0 - 2', 'montreal canadiens', '2 - 3']] |
northumberland county , new brunswick | https://en.wikipedia.org/wiki/Northumberland_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-171354-1.html.csv | comparative | blackville encompasses about three times the area of rogersville in northumberland county , new brunswick . | {'row_1': '4', 'row_2': '3', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'official name', 'blackville'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose official name record fuzzily matches to blackville .', 'tostr': 'filter_eq { all_rows ; official name ; blackville }'}, 'area km 2'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; official name ; blackville } ; area km 2 }', 'tointer': 'select the rows whose official name record fuzzily matches to blackville . take the area km 2 record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'official name', 'rogersville'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose official name record fuzzily matches to rogersville .', 'tostr': 'filter_eq { all_rows ; official name ; rogersville }'}, 'area km 2'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; official name ; rogersville } ; area km 2 }', 'tointer': 'select the rows whose official name record fuzzily matches to rogersville . take the area km 2 record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; official name ; blackville } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; rogersville } ; area km 2 } } = true', 'tointer': 'select the rows whose official name record fuzzily matches to blackville . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to rogersville . take the area km 2 record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; official name ; blackville } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; rogersville } ; area km 2 } } = true | select the rows whose official name record fuzzily matches to blackville . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to rogersville . take the area km 2 record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'official name_7': 7, 'blackville_8': 8, 'area km 2_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'official name_11': 11, 'rogersville_12': 12, 'area km 2_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'official name_7': 'official name', 'blackville_8': 'blackville', 'area km 2_9': 'area km 2', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'official name_11': 'official name', 'rogersville_12': 'rogersville', 'area km 2_13': 'area km 2'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'official name_7': [0], 'blackville_8': [0], 'area km 2_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'official name_11': [1], 'rogersville_12': [1], 'area km 2_13': [3]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['miramichi', 'city', '179.84', '17811', '232 of 5008'], ['neguac', 'village', '26.69', '1678', '1500 of 5008'], ['rogersville', 'village', '7.23', '1170', '1875 of 5008'], ['blackville', 'village', '21.73', '990', '2086 of 5008'], ['doaktown', 'village', '28.74', '793', '2387 of 5008']] |
1937 in brazilian football | https://en.wikipedia.org/wiki/1937_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15352382-1.html.csv | aggregation | the top 5 teams in the 1937 brazilian football league had score about 31 goals for on average . | {'scope': 'all', 'col': '8', 'type': 'average', 'result': '31', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'for'], 'result': '31', 'ind': 0, 'tostr': 'avg { all_rows ; for }'}, '31'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; for } ; 31 } = true', 'tointer': 'the average of the for record of all rows is 31 .'} | round_eq { avg { all_rows ; for } ; 31 } = true | the average of the for record of all rows is 31 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'for_4': 4, '31_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'for_4': 'for', '31_5': '31'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'for_4': [0], '31_5': [1]} | ['position', 'team', 'points', 'played', 'won', 'drawn', 'lost', 'for', 'against', 'difference'] | [['1', 'corinthians', '22', '14', '10', '2', '2', '33', '14', '19'], ['2', 'palestra itã ¡ lia - sp', '21', '14', '10', '1', '3', '35', '12', '23'], ['3', 'portuguesa santista', '19', '14', '8', '3', '3', '27', '18', '9'], ['4', 'estudantes paulista', '15', '14', '7', '1', '6', '33', '22', '11'], ['5', 'santos', '14', '14', '5', '4', '5', '27', '20', '7']] |
1957 vfl season | https://en.wikipedia.org/wiki/1957_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10774891-18.html.csv | aggregation | the average crowd size for venues in the 1957 vfl season was 24248 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '24248', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '24248', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '24248'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 24248 } = true', 'tointer': 'the average of the crowd record of all rows is 24248 .'} | round_eq { avg { all_rows ; crowd } ; 24248 } = true | the average of the crowd record of all rows is 24248 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '24248_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '24248_5': '24248'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '24248_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '10.20 ( 80 )', 'south melbourne', '17.11 ( 113 )', 'arden street oval', '10000', '24 august 1957'], ['melbourne', '18.12 ( 120 )', 'richmond', '10.11 ( 71 )', 'mcg', '35751', '24 august 1957'], ['footscray', '8.11 ( 59 )', 'hawthorn', '7.15 ( 57 )', 'western oval', '25436', '24 august 1957'], ['fitzroy', '15.14 ( 104 )', 'geelong', '10.20 ( 80 )', 'brunswick street oval', '10000', '24 august 1957'], ['st kilda', '14.12 ( 96 )', 'collingwood', '7.14 ( 56 )', 'junction oval', '29300', '24 august 1957'], ['essendon', '17.21 ( 123 )', 'carlton', '9.8 ( 62 )', 'windy hill', '35000', '24 august 1957']] |
1995 men 's world ice hockey championships | https://en.wikipedia.org/wiki/1995_Men%27s_World_Ice_Hockey_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13771649-3.html.csv | superlative | 2 is the highest amount of drawn in the 1995 men 's world ice hockey championships . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'drawn'], 'result': '2', 'ind': 0, 'tostr': 'max { all_rows ; drawn }', 'tointer': 'the maximum drawn record of all rows is 2 .'}, '2'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; drawn } ; 2 } = true', 'tointer': 'the maximum drawn record of all rows is 2 .'} | eq { max { all_rows ; drawn } ; 2 } = true | the maximum drawn record of all rows is 2 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'drawn_4': 4, '2_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'drawn_4': 'drawn', '2_5': '2'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'drawn_4': [0], '2_5': [1]} | ['games', 'drawn', 'lost', 'points difference', 'points'] | [['5', '2', '0', '17 - 11', '8'], ['5', '1', '1', '22 - 14', '7'], ['5', '1', '1', '17 - 09', '7'], ['5', '0', '2', '14 - 09', '6'], ['5', '0', '4', '09 - 18', '2'], ['5', '0', '5', '09 - 27', '0']] |
nguyen tien minh | https://en.wikipedia.org/wiki/Nguyen_Tien_Minh | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12978997-1.html.csv | unique | vietnam open grand prix was the only tournament in 2008 . | {'scope': 'all', 'row': '11', 'col': '2', 'col_other': '3', 'criterion': 'equal', 'value': '2008', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2008'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 2008 .', 'tostr': 'filter_eq { all_rows ; year ; 2008 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; year ; 2008 } }', 'tointer': 'select the rows whose year record is equal to 2008 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2008'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record is equal to 2008 .', 'tostr': 'filter_eq { all_rows ; year ; 2008 }'}, 'tournament'], 'result': 'vietnam open grand prix', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 2008 } ; tournament }'}, 'vietnam open grand prix'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; year ; 2008 } ; tournament } ; vietnam open grand prix }', 'tointer': 'the tournament record of this unqiue row is vietnam open grand prix .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; year ; 2008 } } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; tournament } ; vietnam open grand prix } } = true', 'tointer': 'select the rows whose year record is equal to 2008 . there is only one such row in the table . the tournament record of this unqiue row is vietnam open grand prix .'} | and { only { filter_eq { all_rows ; year ; 2008 } } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; tournament } ; vietnam open grand prix } } = true | select the rows whose year record is equal to 2008 . there is only one such row in the table . the tournament record of this unqiue row is vietnam open grand prix . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '2008_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'vietnam open grand prix_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '2008_8': '2008', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'vietnam open grand prix_10': 'vietnam open grand prix'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'year_7': [0], '2008_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'vietnam open grand prix_10': [3]} | ['outcome', 'year', 'tournament', 'opponent in final', 'score'] | [['1', '2013', 'us open grand prix gold', 'wong wing ki', '18 - 21 21 - 17 21 - 18'], ['1', '2012', 'chinese taipei open grand prix gold', 'chou tien - chen', '21 - 11 21 - 17'], ['1', '2012', 'vietnam open grand prix', 'takuma ueda', '21 - 14 21 - 19'], ['2', '2012', 'australia open grand prix gold', 'jin chen', '11 - 21 12 - 21'], ['2', '2011', 'us open grand prix gold', 'sho sasaki', '17 - 21 18 - 21'], ['1', '2011', 'vietnam open grand prix', 'sho sasaki', '21 - 13 21 - 17'], ['1', '2010', 'australia open grand prix', 'krishnan yogendran', '21 - 14 21 - 11'], ['1', '2009', 'vietnam open grand prix', 'chong wei feng', '21 - 7 , 19 - 21 , 21 - 14'], ['1', '2009', 'chinese taipei open grand prix gold', 'wong choong hann', '21 - 11 21 - 14'], ['1', '2009', 'thailand open grand prix gold', 'boonsak ponsana', '21 - 16 21 - 13'], ['1', '2008', 'vietnam open grand prix', 'chan yan kit', '24 - 22 21 - 18']] |
mike di meglio | https://en.wikipedia.org/wiki/Mike_Di_Meglio | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16678131-2.html.csv | comparative | in 2008 mike di meglio had more wins than in 2005 . | {'row_1': '7', 'row_2': '5', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2008'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record fuzzily matches to 2008 .', 'tostr': 'filter_eq { all_rows ; season ; 2008 }'}, 'wins'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season ; 2008 } ; wins }', 'tointer': 'select the rows whose season record fuzzily matches to 2008 . take the wins record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2006'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record fuzzily matches to 2006 .', 'tostr': 'filter_eq { all_rows ; season ; 2006 }'}, 'wins'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; season ; 2006 } ; wins }', 'tointer': 'select the rows whose season record fuzzily matches to 2006 . take the wins record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; season ; 2008 } ; wins } ; hop { filter_eq { all_rows ; season ; 2006 } ; wins } } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2008 . take the wins record of this row . select the rows whose season record fuzzily matches to 2006 . take the wins record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; season ; 2008 } ; wins } ; hop { filter_eq { all_rows ; season ; 2006 } ; wins } } = true | select the rows whose season record fuzzily matches to 2008 . take the wins record of this row . select the rows whose season record fuzzily matches to 2006 . take the wins 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, 'season_7': 7, '2008_8': 8, 'wins_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'season_11': 11, '2006_12': 12, 'wins_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', 'season_7': 'season', '2008_8': '2008', 'wins_9': 'wins', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'season_11': 'season', '2006_12': '2006', 'wins_13': 'wins'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'season_7': [0], '2008_8': [0], 'wins_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'season_11': [1], '2006_12': [1], 'wins_13': [3]} | ['season', 'races', 'wins', 'podiums', 'poles', 'fastest laps'] | [['2003', '10', '0', '0', '0', '0'], ['2003', '5', '0', '0', '0', '0'], ['2004', '14', '0', '0', '0', '0'], ['2005', '16', '1', '2', '0', '0'], ['2006', '14', '0', '0', '0', '0'], ['2007', '15', '0', '0', '0', '0'], ['2008', '17', '4', '9', '2', '4'], ['2009', '16', '0', '2', '1', '0'], ['2010', '16', '0', '0', '0', '0'], ['2011', '17', '0', '0', '0', '0'], ['2012', '16', '0', '0', '0', '0'], ['2013', '10', '0', '0', '0', '0'], ['total', '166', '5', '13', '3', '4']] |
1973 nhl amateur draft | https://en.wikipedia.org/wiki/1973_NHL_Amateur_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1965650-4.html.csv | majority | more players from canada than the united states were chosen in picks 49-64 of the 1973 nhl amateur draft . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'canada', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nationality', 'canada'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , most of them fuzzily match to canada .', 'tostr': 'most_eq { all_rows ; nationality ; canada } = true'} | most_eq { all_rows ; nationality ; canada } = true | for the nationality records of all rows , most of them fuzzily match to canada . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'canada_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'canada_4': 'canada'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'canada_4': [0]} | ['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team'] | [['49', 'andre st laurent', 'centre', 'canada', 'new york islanders', 'montreal junior canadiens ( qmjhl )'], ['50', 'ron serafini', 'defence', 'united states', 'california golden seals', 'st catharines black hawks ( oha )'], ['51', 'keith mackie', 'defence', 'united kingdom canada', 'vancouver canucks', 'edmonton oil kings ( wchl )'], ['52', 'frank rochon', 'left wing', 'canada', 'toronto maple leafs', 'sherbrooke castors ( qmjhl )'], ['53', 'dean talafous', 'centre', 'united states', 'atlanta flames', 'university of wisconsin ( ncaa )'], ['54', 'jim mccrimmon', 'defence', 'canada', 'los angeles kings', 'medicine hat tigers ( wchl )'], ['55', 'dennis owchar', 'defence', 'canada', 'pittsburgh penguins', 'toronto marlboros ( oha )'], ['56', 'alan hangsleben', 'defence', 'united states', 'montreal canadiens', 'university of north dakota ( ncaa )'], ['57', 'tom colley', 'centre', 'canada', 'minnesota north stars', 'sudbury wolves ( oha )'], ['58', 'dale cook', 'left wing', 'canada', 'philadelphia flyers', 'victoria cougars ( wchl )'], ['59', 'mike korney', 'defence', 'canada', 'detroit red wings', 'winnipeg jets ( wchl )'], ['60', 'yvon dupuis', 'right wing', 'canada', 'buffalo sabres', 'quebec remparts ( qmjhl )'], ['61', 'dave elliott', 'left wing', 'canada', 'chicago black hawks', 'winnipeg jets ( wchl )'], ['62', 'brian molvik', 'defence', 'canada', 'new york rangers', 'calgary centennials ( wchl )'], ['63', 'steve langdon', 'left wing', 'canada', 'boston bruins', 'london knights ( oha )'], ['64', 'richard latulippe', 'centre', 'canada', 'montreal canadiens', 'quebec remparts ( qmjhl )']] |
tomasz sikora | https://en.wikipedia.org/wiki/Tomasz_Sikora | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1269400-2.html.csv | superlative | tomasz sikora had the best individual placing in the 1995 event . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', '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', 'event'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; event }'}, 'event'], 'result': '1995 antholz', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; event } ; event }'}, '1995 antholz'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; event } ; event } ; 1995 antholz } = true', 'tointer': 'select the row whose event record of all rows is minimum . the event record of this row is 1995 antholz .'} | eq { hop { argmin { all_rows ; event } ; event } ; 1995 antholz } = true | select the row whose event record of all rows is minimum . the event record of this row is 1995 antholz . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'event_5': 5, 'event_6': 6, '1995 antholz_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'event_5': 'event', 'event_6': 'event', '1995 antholz_7': '1995 antholz'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'event_5': [0], 'event_6': [1], '1995 antholz_7': [2]} | ['event', 'individual', 'sprint', 'pursuit', 'mass start', 'relay'] | [['1995 antholz', '1st', '29th', '-', '-', '7th'], ['1996 ruhpolding', '6th', '15th', '-', '-', '7th'], ['1997 brezno - osrblie', '28th', '17th', '21st', '-', '6th'], ['1998 pokljuka', '-', '-', '14th', '-', '-'], ['1999 kontiolahti', '14th', '59th', '-', '-', '14th'], ['2000 oslo', '32nd', '21st', '47th', '18th', '11th'], ['2001 pokljuka', '18th', '15th', '16th', '20th', '6th'], ['2002 oslo', '-', '-', '-', '25th', '-'], ['2003 khanty - mansiysk', '9th', '11th', '7th', '14th', '-'], ['2004 oberhof', '2nd', '11th', '7th', '14th', '-'], ['2005 hochfilzen', '-', '9th', '10th', '5th', '10th'], ['2005 khanty - mansiysk', '-', '-', '-', '-', '-'], ['2006 pokljuka', '-', '-', '-', '-', '-'], ['2007 antholz', '21st', '5th', '7th', '21st', '13th'], ['2008 östersund', '30th', '11th', '11th', '19th', '-'], ['2009 pyeongchang', '9th', '16th', '4th', '6th', '13th'], ['2010 khanty - mansiysk', '-', '-', '-', '-', '-']] |
list of new zealand warriors records | https://en.wikipedia.org/wiki/List_of_New_Zealand_Warriors_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13274816-9.html.csv | comparative | the margin of victory over south sydney was 22 points higher than western suburbs . | {'row_1': '1', 'row_2': '4', 'col': '1', 'col_other': '3', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '22', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'south sydney rabbitohs'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to south sydney rabbitohs .', 'tostr': 'filter_eq { all_rows ; opponent ; south sydney rabbitohs }'}, 'margin'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; south sydney rabbitohs } ; margin }', 'tointer': 'select the rows whose opponent record fuzzily matches to south sydney rabbitohs . take the margin record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'western suburbs'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to western suburbs .', 'tostr': 'filter_eq { all_rows ; opponent ; western suburbs }'}, 'margin'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; western suburbs } ; margin }', 'tointer': 'select the rows whose opponent record fuzzily matches to western suburbs . take the margin record of this row .'}], 'result': '22', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; opponent ; south sydney rabbitohs } ; margin } ; hop { filter_eq { all_rows ; opponent ; western suburbs } ; margin } }'}, '22'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; opponent ; south sydney rabbitohs } ; margin } ; hop { filter_eq { all_rows ; opponent ; western suburbs } ; margin } } ; 22 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to south sydney rabbitohs . take the margin record of this row . select the rows whose opponent record fuzzily matches to western suburbs . take the margin record of this row . the first record is 22 larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; opponent ; south sydney rabbitohs } ; margin } ; hop { filter_eq { all_rows ; opponent ; western suburbs } ; margin } } ; 22 } = true | select the rows whose opponent record fuzzily matches to south sydney rabbitohs . take the margin record of this row . select the rows whose opponent record fuzzily matches to western suburbs . take the margin record of this row . the first record is 22 larger than the second record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'opponent_8': 8, 'south sydney rabbitohs_9': 9, 'margin_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'opponent_12': 12, 'western suburbs_13': 13, 'margin_14': 14, '22_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'opponent_8': 'opponent', 'south sydney rabbitohs_9': 'south sydney rabbitohs', 'margin_10': 'margin', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'opponent_12': 'opponent', 'western suburbs_13': 'western suburbs', 'margin_14': 'margin', '22_15': '22'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'opponent_8': [0], 'south sydney rabbitohs_9': [0], 'margin_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'opponent_12': [1], 'western suburbs_13': [1], 'margin_14': [3], '22_15': [5]} | ['margin', 'score', 'opponent', 'venue', 'year'] | [['66', '66 - 0', 'south sydney rabbitohs', 'sydney football stadium', '2006'], ['58', '68 - 10', 'northern eagles', 'mt smart stadium', '2002'], ['46', '52 - 6', 'north queensland cowboys', 'mt smart stadium', '1996'], ['44', '60 - 16', 'western suburbs', 'campbelltown', '1999'], ['44', '52 - 8', 'penrith panthers', 'mt smart stadium', '2001']] |
world series of poker europe | https://en.wikipedia.org/wiki/World_Series_of_Poker_Europe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12454156-1.html.csv | comparative | elio fox won more money in the world series of poker europe than barry shulman . | {'row_1': '5', 'row_2': '3', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'elio fox'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to elio fox .', 'tostr': 'filter_eq { all_rows ; winner ; elio fox }'}, 'prize money'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winner ; elio fox } ; prize money }', 'tointer': 'select the rows whose winner record fuzzily matches to elio fox . take the prize money record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'barry shulman'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose winner record fuzzily matches to barry shulman .', 'tostr': 'filter_eq { all_rows ; winner ; barry shulman }'}, 'prize money'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; winner ; barry shulman } ; prize money }', 'tointer': 'select the rows whose winner record fuzzily matches to barry shulman . take the prize money record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; winner ; elio fox } ; prize money } ; hop { filter_eq { all_rows ; winner ; barry shulman } ; prize money } } = true', 'tointer': 'select the rows whose winner record fuzzily matches to elio fox . take the prize money record of this row . select the rows whose winner record fuzzily matches to barry shulman . take the prize money record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; winner ; elio fox } ; prize money } ; hop { filter_eq { all_rows ; winner ; barry shulman } ; prize money } } = true | select the rows whose winner record fuzzily matches to elio fox . take the prize money record of this row . select the rows whose winner record fuzzily matches to barry shulman . take the prize money record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winner_7': 7, 'elio fox_8': 8, 'prize money_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'winner_11': 11, 'barry shulman_12': 12, 'prize money_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winner_7': 'winner', 'elio fox_8': 'elio fox', 'prize money_9': 'prize money', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'winner_11': 'winner', 'barry shulman_12': 'barry shulman', 'prize money_13': 'prize money'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'winner_7': [0], 'elio fox_8': [0], 'prize money_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'winner_11': [1], 'barry shulman_12': [1], 'prize money_13': [3]} | ['year', 'winner', 'winning hand', 'prize money', 'entrants', 'runner - up', 'losing hand'] | [['2007', 'annette obrestad', '7h 7s', '1000000', '362', 'john tabatabai', '5s 6d'], ['2008', 'john juanda', 'ks 6c', '868800', '362', 'stanislav alekhin', 'ac 9s'], ['2009', 'barry shulman', '10s 10c', '801603', '334', 'daniel negreanu', '4s 4d'], ['2010', 'james bord', '10d 10h', '830401', '346', 'fabrizio baldassari', '5s 5h'], ['2011', 'elio fox', 'ad 10s', '1400000', '593', 'chris moorman', 'ah 7s'], ['2012', 'phil hellmuth', 'ah 10d', '1058403', '420', 'sergii baranov', 'as 4c'], ['2013', 'adrián mateos', 'as kc', '1000000', '375', 'fabrice soulier', '9d 8d']] |
kevin mirocha | https://en.wikipedia.org/wiki/Kevin_Mirocha | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25369796-1.html.csv | unique | the 2010 season was the only season in which kevin mirocha won a race . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '1', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; wins ; 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; wins ; 1 } }', 'tointer': 'select the rows whose wins record is equal to 1 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; wins ; 1 }'}, 'season'], 'result': '2010', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; wins ; 1 } ; season }'}, '2010'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; wins ; 1 } ; season } ; 2010 }', 'tointer': 'the season record of this unqiue row is 2010 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; wins ; 1 } } ; eq { hop { filter_eq { all_rows ; wins ; 1 } ; season } ; 2010 } } = true', 'tointer': 'select the rows whose wins record is equal to 1 . there is only one such row in the table . the season record of this unqiue row is 2010 .'} | and { only { filter_eq { all_rows ; wins ; 1 } } ; eq { hop { filter_eq { all_rows ; wins ; 1 } ; season } ; 2010 } } = true | select the rows whose wins record is equal to 1 . there is only one such row in the table . the season record of this unqiue row is 2010 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'wins_7': 7, '1_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'season_9': 9, '2010_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'wins_7': 'wins', '1_8': '1', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'season_9': 'season', '2010_10': '2010'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'wins_7': [0], '1_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'season_9': [2], '2010_10': [3]} | ['season', 'series', 'team', 'races', 'wins', 'poles', 'f / laps', 'podiums', 'points', 'position'] | [['2007', 'formula bmw adac', 'adac berlin - brandenburg', '18', '0', '0', '0', '1', '389', '8th'], ['2007', 'formula bmw world final', 'josef kaufmann racing', '1', '0', '0', '0', '0', 'n / a', '22nd'], ['2008', 'ats formel 3 cup', 'josef kaufmann racing', '18', '0', '0', '0', '4', '56', '6th'], ['2009', 'formula 3 euro series', 'hbr motorsport', '6', '0', '0', '0', '0', '0', '29th'], ['2010', 'formula renault 2.0 nec', 'sl formula racing', '8', '1', '0', '2', '3', '137', '9th'], ['2011', 'gp2 series', 'ocean racing technology', '14', '0', '0', '0', '0', '0', '22nd']] |
1932 vfl season | https://en.wikipedia.org/wiki/1932_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790099-6.html.csv | count | in the 1932 vfl season , when the away team had over 10 , there were 2 times the crowd was over 12000 . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '12000', 'result': '2', 'col': '6', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '10'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'away team score', '10'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; away team score ; 10 }', 'tointer': 'select the rows whose away team score record is greater than 10 .'}, 'crowd', '12000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 12000 .', 'tostr': 'filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 12000 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 12000 } }', 'tointer': 'select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 12000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 12000 } } ; 2 } = true', 'tointer': 'select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 12000 . the number of such rows is 2 .'} | eq { count { filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 12000 } } ; 2 } = true | select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 12000 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'away team score_6': 6, '10_7': 7, 'crowd_8': 8, '12000_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'away team score_6': 'away team score', '10_7': '10', 'crowd_8': 'crowd', '12000_9': '12000', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'away team score_6': [0], '10_7': [0], 'crowd_8': [1], '12000_9': [1], '2_10': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '11.14 ( 80 )', 'south melbourne', '16.13 ( 109 )', 'glenferrie oval', '12000', '4 june 1932'], ['geelong', '9.15 ( 69 )', 'richmond', '9.15 ( 69 )', 'corio oval', '17000', '4 june 1932'], ['essendon', '22.10 ( 142 )', 'fitzroy', '13.12 ( 90 )', 'windy hill', '15000', '4 june 1932'], ['collingwood', '12.19 ( 91 )', 'footscray', '9.21 ( 75 )', 'victoria park', '25000', '4 june 1932'], ['st kilda', '16.9 ( 105 )', 'north melbourne', '16.14 ( 110 )', 'junction oval', '13000', '4 june 1932'], ['carlton', '12.15 ( 87 )', 'melbourne', '9.9 ( 63 )', 'motordrome', '12500', '4 june 1932']] |
1948 ashes series | https://en.wikipedia.org/wiki/1948_Ashes_series | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16570286-4.html.csv | unique | ernie toshack was the only player to play four games in the 1948 ashes series . | {'scope': 'all', 'row': '5', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': '4', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'matches', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose matches record is equal to 4 .', 'tostr': 'filter_eq { all_rows ; matches ; 4 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; matches ; 4 } }', 'tointer': 'select the rows whose matches record is equal to 4 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'matches', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose matches record is equal to 4 .', 'tostr': 'filter_eq { all_rows ; matches ; 4 }'}, 'player'], 'result': 'ernie toshack', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; matches ; 4 } ; player }'}, 'ernie toshack'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; matches ; 4 } ; player } ; ernie toshack }', 'tointer': 'the player record of this unqiue row is ernie toshack .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; matches ; 4 } } ; eq { hop { filter_eq { all_rows ; matches ; 4 } ; player } ; ernie toshack } } = true', 'tointer': 'select the rows whose matches record is equal to 4 . there is only one such row in the table . the player record of this unqiue row is ernie toshack .'} | and { only { filter_eq { all_rows ; matches ; 4 } } ; eq { hop { filter_eq { all_rows ; matches ; 4 } ; player } ; ernie toshack } } = true | select the rows whose matches record is equal to 4 . there is only one such row in the table . the player record of this unqiue row is ernie toshack . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'matches_7': 7, '4_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'ernie toshack_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'matches_7': 'matches', '4_8': '4', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'ernie toshack_10': 'ernie toshack'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'matches_7': [0], '4_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'ernie toshack_10': [3]} | ['player', 'team', 'matches', 'wickets', 'average', 'best bowling'] | [['ray lindwall', 'australia', '5', '27', '19.62', '6 / 20'], ['norman yardley', 'england', '5', '9', '22.66', '2 / 32'], ['keith miller', 'australia', '5', '13', '23.15', '4 / 125'], ['bill johnston', 'australia', '5', '27', '23.33', '5 / 36'], ['ernie toshack', 'australia', '4', '11', '33.09', '5 / 40'], ['alec bedser', 'england', '5', '18', '38.22', '4 / 81']] |
2001 - 02 boston celtics season | https://en.wikipedia.org/wiki/2001%E2%80%9302_Boston_Celtics_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17622423-8.html.csv | majority | in the 2001 - 02 boston celtics season , most of the games after march 24th were at the fleetcenter . | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'fleetcenter', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': 'sun mar 24'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'date', 'sun mar 24'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; date ; sun mar 24 }', 'tointer': 'select the rows whose date record is greater than sun mar 24 .'}, 'location', 'fleetcenter'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record is greater than sun mar 24 . for the location records of these rows , most of them fuzzily match to fleetcenter .', 'tostr': 'most_eq { filter_greater { all_rows ; date ; sun mar 24 } ; location ; fleetcenter } = true'} | most_eq { filter_greater { all_rows ; date ; sun mar 24 } ; location ; fleetcenter } = true | select the rows whose date record is greater than sun mar 24 . for the location records of these rows , most of them fuzzily match to fleetcenter . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'date_4': 4, 'sun mar 24_5': 5, 'location_6': 6, 'fleetcenter_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'date_4': 'date', 'sun mar 24_5': 'sun mar 24', 'location_6': 'location', 'fleetcenter_7': 'fleetcenter'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'date_4': [0], 'sun mar 24_5': [0], 'location_6': [1], 'fleetcenter_7': [1]} | ['game', 'date', 'opponent', 'score', 'location', 'record'] | [['58', 'fri mar 1', 'charlotte hornets', '87 - 100', 'fleetcenter', '31 - 27'], ['59', 'mon mar 4', 'philadelphia 76ers', '100 - 94', 'first union center', '32 - 27'], ['60', 'wed mar 6', 'orlando magic', '130 - 110', 'fleetcenter', '33 - 27'], ['61', 'fri mar 8', 'detroit pistons', '117 - 92', 'fleetcenter', '34 - 27'], ['62', 'sun mar 10', 'washington wizards', '98 - 91', 'fleetcenter', '35 - 27'], ['63', 'mon mar 11', 'washington wizards', '104 - 99', 'mci center', '36 - 27'], ['64', 'wed mar 13', 'new jersey nets', '97 - 89', 'fleetcenter', '37 - 27'], ['65', 'fri mar 15', 'memphis grizzlies', '103 - 97', 'the pyramid', '38 - 27'], ['66', 'sat mar 16', 'san antonio spurs', '104 - 111', 'alamodome', '38 - 28'], ['67', 'mon mar 18', 'portland trail blazers', '91 - 100', 'fleetcenter', '38 - 29'], ['68', 'wed mar 20', 'cleveland cavaliers', '96 - 70', 'fleetcenter', '39 - 29'], ['69', 'fri mar 22', 'philadelphia 76ers', '91 - 96', 'fleetcenter', '39 - 30'], ['70', 'sun mar 24', 'detroit pistons', '101 - 109', 'the palace of auburn hills', '39 - 31'], ['71', 'mon mar 25', 'miami heat', '87 - 82', 'american airlines arena', '40 - 31'], ['72', 'wed mar 27', 'golden state warriors', '102 - 99', 'fleetcenter', '41 - 31'], ['73', 'fri mar 29', 'dallas mavericks', '82 - 108', 'fleetcenter', '41 - 32'], ['74', 'sun mar 31', 'milwaukee bucks', '110 - 80', 'fleetcenter', '42 - 32']] |
iran national football team results | https://en.wikipedia.org/wiki/Iran_national_football_team_results | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14960283-2.html.csv | majority | most of the matches by iran national football team were played in india . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'india', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'venue', 'india'], 'result': True, 'ind': 0, 'tointer': 'for the venue records of all rows , most of them fuzzily match to india .', 'tostr': 'most_eq { all_rows ; venue ; india } = true'} | most_eq { all_rows ; venue ; india } = true | for the venue records of all rows , most of them fuzzily match to india . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'venue_3': 3, 'india_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'venue_3': 'venue', 'india_4': 'india'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'venue_3': [0], 'india_4': [0]} | ['date', 'result', 'score', 'venue', 'competition'] | [['25 - aug - 41', 'd', '0 - 0', 'kabul , afghanistan', 'friendly'], ['26 - oct - 47', 'l', '1 - 3', 'tehran , iran', 'friendly'], ['28 - oct - 48', 'd', '1 - 1', 'tehran , iran', 'friendly'], ['28 - may - 50', 'l', '1 - 6', 'istanbul , turkey', 'friendly'], ['30 - may - 50', 'd', '2 - 2', 'ankara , turkey', 'friendly'], ['26 - oct - 50', 'w', '4 - 0', 'tehran , iran', 'friendly'], ['27 - oct - 50', 'w', '5 - 1', 'tehran , iran', 'friendly'], ['05 - mar - 51', 'w', '2 - 0', 'new delhi , india', '1951 asian games'], ['07 - mar - 51', 'd', '0 - 0', 'new delhi , india', '1951 asian games'], ['08 - mar - 51', 'w', '3 - 2', 'new delhi , india', '1951 asian games'], ['10 - may - 51', 'l', '0 - 1', 'new delhi , india', '1951 asian games'], ['02 - apr - 52', 'd', '0 - 0', 'karachi , pakistan', 'friendly'], ['26 - may - 58', 'l', '0 - 4', 'tokyo , japan', '1958 asian games'], ['28 - may - 58', 'l', '0 - 5', 'tokyo , japan', '1958 asian games'], ['05 - dec - 59', 'w', '3 - 0', 'kochi , india', '1960 asian cup qualifier'], ['09 - dec - 59', 'l', '1 - 4', 'kochi , india', '1960 asian cup qualifier'], ['11 - dec - 59', 'l', '1 - 3', 'kochi , india', '1960 asian cup qualifier'], ['12 - dec - 59', 'd', '1 - 1', 'kochi , india', '1960 asian cup qualifier'], ['14 - dec - 59', 'w', '4 - 1', 'kochi , india', '1960 asian cup qualifier'], ['18 - dec - 59', 'w', '2 - 1', 'kochi , india', '1960 asian cup qualifier']] |
andy dalton | https://en.wikipedia.org/wiki/Andy_Dalton | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12857517-1.html.csv | superlative | andy dalton had the highest completion percentage in 2010 than any other year with texas christian university . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'completion %'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; completion % }'}, 'year'], 'result': '2010', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; completion % } ; year }'}, '2010'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; completion % } ; year } ; 2010 } = true', 'tointer': 'select the row whose completion % record of all rows is maximum . the year record of this row is 2010 .'} | eq { hop { argmax { all_rows ; completion % } ; year } ; 2010 } = true | select the row whose completion % record of all rows is maximum . the year record of this row is 2010 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'completion %_5': 5, 'year_6': 6, '2010_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'completion %_5': 'completion %', 'year_6': 'year', '2010_7': '2010'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'completion %_5': [0], 'year_6': [1], '2010_7': [2]} | ['year', 'team', 'attempts', 'completions', 'completion %', 'yards'] | [['2006', 'tcu', 'redshirt', 'redshirt', 'redshirt', 'redshirt'], ['2007', 'tcu', '371', '222', '59.8 %', '2459'], ['2008', 'tcu', '307', '182', '59.3 %', '2242'], ['2009', 'tcu', '323', '199', '61.6 %', '2756'], ['2010', 'tcu', '316', '209', '66.1 %', '2857'], ['college totals', 'college totals', '1317', '812', '61.7 %', '10314']] |
law & order ( season 19 ) | https://en.wikipedia.org/wiki/Law_%26_Order_%28season_19%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19995378-1.html.csv | comparative | the episode " rumble " aired before the episode " sweetie " aired . | {'row_1': '1', 'row_2': '5', 'col': '6', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'rumble'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to rumble .', 'tostr': 'filter_eq { all_rows ; title ; rumble }'}, 'original air date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; rumble } ; original air date }', 'tointer': 'select the rows whose title record fuzzily matches to rumble . take the original air date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'sweetie'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to sweetie .', 'tostr': 'filter_eq { all_rows ; title ; sweetie }'}, 'original air date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; sweetie } ; original air date }', 'tointer': 'select the rows whose title record fuzzily matches to sweetie . take the original air date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; title ; rumble } ; original air date } ; hop { filter_eq { all_rows ; title ; sweetie } ; original air date } } = true', 'tointer': 'select the rows whose title record fuzzily matches to rumble . take the original air date record of this row . select the rows whose title record fuzzily matches to sweetie . take the original air date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; title ; rumble } ; original air date } ; hop { filter_eq { all_rows ; title ; sweetie } ; original air date } } = true | select the rows whose title record fuzzily matches to rumble . take the original air date record of this row . select the rows whose title record fuzzily matches to sweetie . take the original air date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'title_7': 7, 'rumble_8': 8, 'original air date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, 'sweetie_12': 12, 'original air date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'title_7': 'title', 'rumble_8': 'rumble', 'original air date_9': 'original air date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'sweetie_12': 'sweetie', 'original air date_13': 'original air date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'rumble_8': [0], 'original air date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], 'sweetie_12': [1], 'original air date_13': [3]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( millions )'] | [['412', '1', 'rumble', 'constantine makris', 'christopher ambrose & richard sweren', 'november 5 , 2008', '19003', '7.85'], ['413', '2', 'challenged', 'fred berner', 'ed zuckerman & renã balcer', 'november 12 , 2008', '19001', '7.91'], ['414', '3', 'lost boys', 'chris zalla', 'richard sweren & gina gionfriddo', 'november 19 , 2008', '19004', '7.58'], ['415', '4', 'falling', 'michael watkins', 'keith eisner & stephanie sengupta', 'november 26 , 2008', '19005', 'n / a'], ['417', '6', 'sweetie', 'mario van peebles', 'luke schelhaas & ed zuckerman', 'december 10 , 2008', '19007', '7.46'], ['418', '7', 'zero', 'marisol torres', 'luke schelhaas & ed zuckerman', 'december 17 , 2008', '19002', '6.95'], ['419', '8', 'chattel', 'jim mckay', 'matthew mcgough & william n fordes', 'january 7 , 2009', '19009', '10.11'], ['420', '9', 'by perjury', 'darnell martin', 'christopher ambrose & richard sweren', 'january 14 , 2009', '19010', '8.20'], ['421', '10', 'pledge', 'alex chapple', 'gina gionfriddo & richard sweren', 'january 21 , 2009', '19008', '8.49'], ['422', '11', 'lucky stiff', 'marc levin', 'matthew mcgough & ed zuckerman', 'january 28 , 2009', '19012', '8.89'], ['423', '12', 'illegitimate', 'josh marston', 'keith eisner & stephanie sengupta', 'february 4 , 2009', '19011', '8.69'], ['424', '13', 'crimebusters', 'alex chapple', 'richard sweren & gina gionfriddo', 'february 11 , 2009', '19013', '7.52'], ['425', '14', 'rapture', 'fred berner', 'luke schelhaas & ed zuckerman', 'february 18 , 2009', '19014', '7.15'], ['426', '15', 'bailout', 'jean de segonzac', 'christopher ambrose & richard sweren', 'march 11 , 2009', '19015', '7.58'], ['427', '16', 'take - out', 'jim mckay', 'keith eisner & william n fordes', 'march 18 , 2009', '19016', '7.07'], ['428', '17', 'anchors away', 'alex chapple', 'matthew mcgough & ed zuckerman', 'march 25 , 2009', '19017', '7.25'], ['429', '18', 'promote this !', 'michael watkins', 'christopher ambrose & richard sweren', 'april 29 , 2009', '19019', '7.69'], ['430', '19', 'all new', 'roger young', 'keith eisner & william n fordes', 'may 6 , 2009', '19020', '8.14'], ['431', '20', 'exchange', 'ernest dickerson', 'stephanie sengupta', 'may 13 , 2009', '19018', '7.82'], ['432', '21', 'skate or die', 'norberto barba', 'luke schelhaas & ed zuckerman', 'may 20 , 2009', '19021', '6.70']] |
1996 senior pga tour | https://en.wikipedia.org/wiki/1996_Senior_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11621873-4.html.csv | comparative | on the 1996 senior pga tour , chi chi rodriguez had more wins than dave stockton . | {'row_1': '5', 'row_2': '4', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'chi chi rodriguez'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to chi chi rodriguez .', 'tostr': 'filter_eq { all_rows ; player ; chi chi rodriguez }'}, 'wins'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; chi chi rodriguez } ; wins }', 'tointer': 'select the rows whose player record fuzzily matches to chi chi rodriguez . take the wins record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'dave stockton'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to dave stockton .', 'tostr': 'filter_eq { all_rows ; player ; dave stockton }'}, 'wins'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; dave stockton } ; wins }', 'tointer': 'select the rows whose player record fuzzily matches to dave stockton . take the wins record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; chi chi rodriguez } ; wins } ; hop { filter_eq { all_rows ; player ; dave stockton } ; wins } } = true', 'tointer': 'select the rows whose player record fuzzily matches to chi chi rodriguez . take the wins record of this row . select the rows whose player record fuzzily matches to dave stockton . take the wins record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; chi chi rodriguez } ; wins } ; hop { filter_eq { all_rows ; player ; dave stockton } ; wins } } = true | select the rows whose player record fuzzily matches to chi chi rodriguez . take the wins record of this row . select the rows whose player record fuzzily matches to dave stockton . take the wins 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, 'player_7': 7, 'chi chi rodriguez_8': 8, 'wins_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'dave stockton_12': 12, 'wins_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', 'player_7': 'player', 'chi chi rodriguez_8': 'chi chi rodriguez', 'wins_9': 'wins', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'dave stockton_12': 'dave stockton', 'wins_13': 'wins'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'chi chi rodriguez_8': [0], 'wins_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'dave stockton_12': [1], 'wins_13': [3]} | ['rank', 'player', 'country', 'earnings', 'wins'] | [['1', 'lee trevino', 'united states', '6715649', '27'], ['2', 'bob charles', 'new zealand', '6621207', '23'], ['3', 'jim colbert', 'united states', '6570797', '18'], ['4', 'dave stockton', 'united states', '5781417', '13'], ['5', 'chi chi rodriguez', 'puerto rico', '5696544', '22']] |
2010 world rally championship season | https://en.wikipedia.org/wiki/2010_World_Rally_Championship_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18812209-20.html.csv | majority | bp ford world rally team was the only constructor to have 34 starts in the 2010 world rally championship season . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '33', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'starts', '33'], 'result': True, 'ind': 0, 'tointer': 'for the starts records of all rows , most of them are greater than 33 .', 'tostr': 'most_greater { all_rows ; starts ; 33 } = true'} | most_greater { all_rows ; starts ; 33 } = true | for the starts records of all rows , most of them are greater than 33 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'starts_3': 3, '33_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'starts_3': 'starts', '33_4': '33'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'starts_3': [0], '33_4': [0]} | ['constructor', 'chassis', 'starts', 'finishes', 'wins', 'podiums', 'stage wins', 'points'] | [['citroën total world rally team', 'c4 wrc', '26', '24', '9', '19', '127', '456'], ['bp ford world rally team', 'focus rs wrc 08 and 09', '34', '28', '3', '8', '39', '337'], ['citroën junior team', 'c4 wrc', '23', '20', '1', '4', '26', '217'], ['stobart m - sport ford rally team', 'focus rs wrc 08', '31', '27', '0', '0', '2', '176'], ["munchi 's ford world rally team", 'focus rs wrc 08', '8', '8', '0', '0', '1', '58']] |
gabi rockmeier | https://en.wikipedia.org/wiki/Gabi_Rockmeier | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13252602-1.html.csv | unique | the only time that gabi rockmeir placed lower than 7th place was in 2002 . | {'scope': 'all', 'row': '10', 'col': '4', 'col_other': '1', 'criterion': 'greater_than', 'value': '7th', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'result', '7th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is greater than 7th .', 'tostr': 'filter_greater { all_rows ; result ; 7th }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; result ; 7th } }', 'tointer': 'select the rows whose result record is greater than 7th . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'result', '7th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is greater than 7th .', 'tostr': 'filter_greater { all_rows ; result ; 7th }'}, 'year'], 'result': '2002', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; result ; 7th } ; year }'}, '2002'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; result ; 7th } ; year } ; 2002 }', 'tointer': 'the year record of this unqiue row is 2002 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; result ; 7th } } ; eq { hop { filter_greater { all_rows ; result ; 7th } ; year } ; 2002 } } = true', 'tointer': 'select the rows whose result record is greater than 7th . there is only one such row in the table . the year record of this unqiue row is 2002 .'} | and { only { filter_greater { all_rows ; result ; 7th } } ; eq { hop { filter_greater { all_rows ; result ; 7th } ; year } ; 2002 } } = true | select the rows whose result record is greater than 7th . there is only one such row in the table . the year record of this unqiue row is 2002 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'result_7': 7, '7th_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '2002_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'result_7': 'result', '7th_8': '7th', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '2002_10': '2002'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], '7th_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '2002_10': [3]} | ['year', 'tournament', 'venue', 'result', 'extra'] | [['1991', 'european junior championships', 'thessaloniki , greece', '1st', '4x100 m relay'], ['1998', 'european championships', 'munich , germany', '7th', '200 m'], ['1998', 'european championships', 'munich , germany', '2nd', '4x100 m relay'], ['1998', 'world cup', 'johannesburg , south africa', '3rd', '4x100 m relay'], ['2000', 'olympic games', 'sydney , australia', '6th', '4x100 m relay'], ['2001', 'world championships', 'edmonton , canada', '1st', '4x100 m relay'], ['2002', 'european indoor championships', 'vienna , austria', '3rd', '200 m'], ['2002', 'european championships', 'munich , germany', '5th', '200 m'], ['2002', 'european championships', 'munich , germany', '2nd', '4x100 m relay'], ['2002', 'world cup', 'madrid , spain', '8th', '200 m'], ['2002', 'world cup', 'madrid , spain', '5th', '4x100 m relay']] |
1944 in brazilian football | https://en.wikipedia.org/wiki/1944_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15331540-1.html.csv | unique | portuguesa was the only team with more than 5 draws in the 1944 brazillian football league season . | {'scope': 'all', 'row': '9', 'col': '5', 'col_other': '2', 'criterion': 'greater_than', 'value': '5', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'drawn', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose drawn record is greater than 5 .', 'tostr': 'filter_greater { all_rows ; drawn ; 5 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; drawn ; 5 } }', 'tointer': 'select the rows whose drawn 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': ['all_rows', 'drawn', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose drawn record is greater than 5 .', 'tostr': 'filter_greater { all_rows ; drawn ; 5 }'}, 'team'], 'result': 'portuguesa', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; drawn ; 5 } ; team }'}, 'portuguesa'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; drawn ; 5 } ; team } ; portuguesa }', 'tointer': 'the team record of this unqiue row is portuguesa .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; drawn ; 5 } } ; eq { hop { filter_greater { all_rows ; drawn ; 5 } ; team } ; portuguesa } } = true', 'tointer': 'select the rows whose drawn record is greater than 5 . there is only one such row in the table . the team record of this unqiue row is portuguesa .'} | and { only { filter_greater { all_rows ; drawn ; 5 } } ; eq { hop { filter_greater { all_rows ; drawn ; 5 } ; team } ; portuguesa } } = true | select the rows whose drawn record is greater than 5 . there is only one such row in the table . the team record of this unqiue row is portuguesa . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'drawn_7': 7, '5_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'portuguesa_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'drawn_7': 'drawn', '5_8': '5', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'portuguesa_10': 'portuguesa'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'drawn_7': [0], '5_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'portuguesa_10': [3]} | ['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference'] | [['1', 'palmeiras', '32', '20', '2', '3', '19', '31'], ['2', 'são paulo', '29', '20', '3', '4', '32', '37'], ['3', 'corinthians', '28', '20', '4', '4', '35', '20'], ['4', 'ypiranga - sp', '23', '20', '3', '7', '29', '8'], ['5', 'são paulo railway', '21', '20', '3', '8', '48', '- 7'], ['6', 'santos', '20', '20', '4', '8', '41', '- 2'], ['7', 'juventus', '18', '20', '4', '9', '49', '- 10'], ['8', 'comercial - sp', '18', '20', '2', '10', '57', '- 15'], ['9', 'portuguesa', '12', '20', '6', '11', '47', '- 18'], ['10', 'jabaquara', '10', '20', '0', '15', '50', '- 12'], ['11', 'portuguesa santista', '9', '20', '3', '14', '69', '- 32']] |
icho larenas | https://en.wikipedia.org/wiki/Icho_Larenas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14712229-2.html.csv | aggregation | the matches of icho larenas lasted an average of 2 rounds . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '2', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'round'], 'result': '2', 'ind': 0, 'tostr': 'avg { all_rows ; round }'}, '2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; round } ; 2 } = true', 'tointer': 'the average of the round record of all rows is 2 .'} | round_eq { avg { all_rows ; round } ; 2 } = true | the average of the round record of all rows is 2 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'round_4': 4, '2_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'round_4': 'round', '2_5': '2'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'round_4': [0], '2_5': [1]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '6 - 4', 'pablo vargas', 'tko ( punches )', 'conviction mma 2 - consolidation', '1', '2:48', 'buenos aires , argentina'], ['win', '5 - 4', 'guido carlo', 'tko ( punches )', 'tko 34 - sims vs bosse', '1', '4:21', 'montreal , quebec , canada'], ['loss', '4 - 4', 'sebastien gauthier', 'decision ( majority )', 'xmma 4 - xtreme mma', '3', '5:00', 'saguenay , quebec , canada'], ['win', '4 - 3', 'steve bossã', 'tko ( punches )', 'tko 31 - young guns', '3', '3:31', 'montreal , quebec , canada'], ['loss', '3 - 3', 'krzysztof soszynski', 'tko ( doctor stoppage )', 'tko 27 - reincarnation', '3', '0:00', 'montreal , quebec , canada'], ['loss', '3 - 2', 'tom murphy', 'tko ( punches )', 'ufc 58', '3', '1:59', 'las vegas , nevada , united states'], ['win', '3 - 1', 'jacob conliffe', 'tko ( punches )', 'tko 20 - champion vs champion', '2', '1:38', 'montreal , quebec , canada'], ['win', '2 - 1', 'yan pellerin', 'tko ( corner stoppage )', 'tko 18 - impact', '1', '5:00', 'montreal , quebec , canada'], ['win', '1 - 1', 'brian magee', 'tko ( punches )', 'tko 17 - revenge', '1', '0:17', 'victoriaville , quebec , canada'], ['loss', '0 - 1', 'todd gouwenberg', 'tko ( punches )', 'tko 16 - infernal', '2', '2:23', 'quebec city , quebec , canada']] |
united council of christian fraternities & sororities | https://en.wikipedia.org/wiki/United_Council_of_Christian_Fraternities_%26_Sororities | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10054296-1.html.csv | majority | most of the uccfs were founded in 2006 by its members . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': '2006', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'uccfs', '2006'], 'result': True, 'ind': 0, 'tointer': 'for the uccfs records of all rows , most of them are equal to 2006 .', 'tostr': 'most_eq { all_rows ; uccfs ; 2006 } = true'} | most_eq { all_rows ; uccfs ; 2006 } = true | for the uccfs records of all rows , most of them are equal to 2006 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'uccfs_3': 3, '2006_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'uccfs_3': 'uccfs', '2006_4': '2006'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'uccfs_3': [0], '2006_4': [0]} | ['member', 'headquarters', 'classification', 'chapters', 'founded', 'uccfs'] | [['alpha nu omega', 'baltimore , maryland', 'fraternity & sorority', '26', '1988 at morgan state university', '2006'], ['men of god', 'san antonio , texas', 'fraternity', '5', '1999 at texas tech university', '2006'], ['delta psi epsilon', 'washington , dc', 'sorority', '12', '1999 in huntsville , alabama', '2006'], ['zeta phi zeta', 'chicago , illinois', 'fraternity & sorority', '7', '2001 at x - stream teens ministries', '2007'], ['gamma phi delta', 'austin , texas', 'fraternity', '16', '1988 at the university of texas at austin', '2011']] |
enernoc | https://en.wikipedia.org/wiki/EnerNOC | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12752072-1.html.csv | majority | most enernoc companies are in the united states . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; country ; united states } = true'} | most_eq { all_rows ; country ; united states } = true | for the country records of all rows , most of them fuzzily match to united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]} | ['date', 'company', 'business', 'country', 'value ( usd )'] | [['unknown', 'celerity energy partners san diego llc', 'energy', 'united states', 'unknown'], ['2009', 'cogent energy , inc', 'green building', 'united states', 'unknown'], ['unknown', 'enoc securities corporation', 'securities', 'united states', 'unknown'], ['unknown', 'enernoc ltd', 'energy', 'canada', 'unknown'], ['unknown', 'enernoc uk limited', 'energy', 'united kingdom', 'unknown'], ['unknown', 'mdenergy , llc', 'energy', 'united states', 'unknown'], ['unknown', 'pinpoint power dr llc', 'energy', 'united states', 'unknown'], ['unknown', 'south river consulting , llc', 'energy', 'united states', 'unknown'], ['unknown', 'global energy partners , inc', 'energy', 'united states', 'unknown'], ['2011', 'm2 m communications corporation', 'energy', 'united states', 'unknown'], ['unknown', 'enernoc australia pty ltd', 'energy', 'australia', 'unknown'], ['unknown', 'dmt energy pty ltd', 'energy', 'australia', 'unknown'], ['unknown', 'energy response holdings pty ltd', 'energy', 'australia', 'unknown'], ['unknown', 'enernoc pty ltd', 'energy', 'australia', 'unknown'], ['unknown', 'enernoc new zealand limited', 'energy', 'new zealand', 'unknown'], ['unknown', 'high street corporation pty ltd', 'energy', 'australia', 'unknown']] |
usa today all - usa high school basketball team | https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11677760-19.html.csv | superlative | among usa today 's all-usa high school basketball team for boys ' 2007 third team , anthony randolph is the tallest . | {'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', 'height'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; height }'}, 'player'], 'result': 'anthony randolph', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; height } ; player }'}, 'anthony randolph'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; height } ; player } ; anthony randolph } = true', 'tointer': 'select the row whose height record of all rows is maximum . the player record of this row is anthony randolph .'} | eq { hop { argmax { all_rows ; height } ; player } ; anthony randolph } = true | select the row whose height record of all rows is maximum . the player record of this row is anthony randolph . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'height_5': 5, 'player_6': 6, 'anthony randolph_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'height_5': 'height', 'player_6': 'player', 'anthony randolph_7': 'anthony randolph'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'height_5': [0], 'player_6': [1], 'anthony randolph_7': [2]} | ['player', 'height', 'school', 'hometown', 'college'] | [['anthony randolph', '6 - 10', 'woodrow wilson high school', 'dallas , tx', 'lsu'], ['nolan smith', '6 - 3', 'oak hill academy', 'washington , dc', 'duke'], ['corey fisher', '6 - 0', 'st patrick high school', 'elizabeth , nj', 'villanova'], ['nick calathes', '6 - 4', 'lake howell high school', 'winter park , fl', 'florida'], ['austin freeman', '6 - 4', 'dematha catholic high school', 'hyattsville , md', 'georgetown']] |
saulo roston | https://en.wikipedia.org/wiki/Saulo_Roston | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27614707-1.html.csv | unique | the week that the top 9 contestants on the reality tv show ídolos brazil performed , was the only week that saulo roston ended up in the bottom three . | {'scope': 'all', 'row': '7', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'bottom 3', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'bottom 3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to bottom 3 .', 'tostr': 'filter_eq { all_rows ; result ; bottom 3 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; bottom 3 } }', 'tointer': 'select the rows whose result record fuzzily matches to bottom 3 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'bottom 3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to bottom 3 .', 'tostr': 'filter_eq { all_rows ; result ; bottom 3 }'}, 'week'], 'result': 'top 9', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; bottom 3 } ; week }'}, 'top 9'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; bottom 3 } ; week } ; top 9 }', 'tointer': 'the week record of this unqiue row is top 9 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; bottom 3 } } ; eq { hop { filter_eq { all_rows ; result ; bottom 3 } ; week } ; top 9 } } = true', 'tointer': 'select the rows whose result record fuzzily matches to bottom 3 . there is only one such row in the table . the week record of this unqiue row is top 9 .'} | and { only { filter_eq { all_rows ; result ; bottom 3 } } ; eq { hop { filter_eq { all_rows ; result ; bottom 3 } ; week } ; top 9 } } = true | select the rows whose result record fuzzily matches to bottom 3 . there is only one such row in the table . the week record of this unqiue row is top 9 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'bottom 3_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'week_9': 9, 'top 9_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'bottom 3_8': 'bottom 3', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'week_9': 'week', 'top 9_10': 'top 9'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'bottom 3_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'week_9': [2], 'top 9_10': [3]} | ['week', 'theme', 'song choice', 'original artist', 'order', 'result'] | [['audition', "auditioner 's choice", 'bem que se quis', 'marisa monte', 'n / a', 'advanced'], ['theater', 'first solo', 'n / a', 'n / a', 'n / a', 'advanced'], ['top 24', 'top 12 men', 'como vai você', 'roberto carlos', '7', 'advanced'], ['top 12', 'sing your idol', 'beija eu', 'marisa monte', '4', 'safe'], ['top 11', '70s night', 'mania de você', 'rita lee', '10', 'safe'], ['top 10', 'the roguish', 'já tive mulheres', 'martinho da vila', '6', 'safe'], ['top 9', 'broken heart songs', 'tem que ser você', 'victor & léo', '4', 'bottom 3'], ['top 7', '80s night', 'você é linda', 'caetano veloso', '2', 'safe'], ['top 6', 'cult trash', 'aguenta coração', 'josé augusto', '1', 'safe'], ['top 5', 'kings of the pop', 'amor i love you', 'marisa monte', '4', 'safe'], ['top 5', 'kings of the pop', 'your song', 'elton john', '9', 'safe'], ['top 4', 'dedicate a song', 'monalisa', 'jorge vercilo', '1', 'safe'], ['top 4', 'my soundtrack', 'eu sei que vou te amar', 'tom jobim', '5', 'safe'], ['top 3', "judge 's choice", 'pro dia nascer feliz', 'cazuza', '1', 'safe'], ['top 3', "judge 's choice", 'fácil', 'jota quest', '4', 'safe'], ['top 3', "judge 's choice", 'o portão', 'roberto carlos', '7', 'safe'], ['top 2', "winner 's single 1", 'nova paixão', 'saulo roston', '1', 'winner'], ['top 2', 'best of the season', 'your song', 'elton john', '3', 'winner']] |
2002 french motorcycle grand prix | https://en.wikipedia.org/wiki/2002_French_motorcycle_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17036702-1.html.csv | count | two riders did not finish due to accidents in the 2002 french motorcycle grand prix . | {'scope': 'all', 'criterion': 'equal', 'value': 'accident', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time / retired', 'accident'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time / retired record fuzzily matches to accident .', 'tostr': 'filter_eq { all_rows ; time / retired ; accident }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; time / retired ; accident } }', 'tointer': 'select the rows whose time / retired record fuzzily matches to accident . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; time / retired ; accident } } ; 2 } = true', 'tointer': 'select the rows whose time / retired record fuzzily matches to accident . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; time / retired ; accident } } ; 2 } = true | select the rows whose time / retired record fuzzily matches to accident . 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, 'time / retired_5': 5, 'accident_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', 'time / retired_5': 'time / retired', 'accident_6': 'accident', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'time / retired_5': [0], 'accident_6': [0], '2_7': [2]} | ['rider', 'manufacturer', 'laps', 'time / retired', 'grid'] | [['valentino rossi', 'honda', '21', '34:22.335', '1'], ['tohru ukawa', 'honda', '21', '+ 0.217', '4'], ['max biaggi', 'yamaha', '21', '+ 0.604', '3'], ['norifumi abe', 'yamaha', '21', '+ 1.701', '11'], ['kenny roberts , jr', 'suzuki', '21', '+ 8.464', '9'], ['nobuatsu aoki', 'proton kr', '21', '+ 10.212', '10'], ['loris capirossi', 'honda', '21', '+ 12.437', '7'], ['alex barros', 'honda', '21', '+ 15.231', '15'], ['régis laconi', 'aprilia', '21', '+ 17.155', '14'], ['jeremy mcwilliams', 'proton kr', '21', '+ 21.847', '6'], ['john hopkins', 'yamaha', '21', '+ 25.121', '19'], ['sete gibernau', 'suzuki', '21', '+ 25.919', '16'], ['shinya nakano', 'yamaha', '21', '+ 26.227', '13'], ['jean - michel bayle', 'yamaha', '21', '+ 27.011', '18'], ['jurgen vd goorbergh', 'honda', '21', '+ 30.342', '17'], ['josé luis cardoso', 'yamaha', '21', '+ 36.574', '20'], ['daijiro kato', 'honda', '11', 'accident', '5'], ['olivier jacque', 'yamaha', '10', 'retirement', '12'], ['tetsuya harada', 'honda', '10', 'retirement', '8'], ['carlos checa', 'yamaha', '8', 'accident', '2']] |
rochester , new york | https://en.wikipedia.org/wiki/Rochester%2C_New_York | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-126641-5.html.csv | majority | in rochester , new york , of the teams that began play in the 1900s , the majority of the clubs have the word " rochester " in their name . | {'scope': 'subset', 'col': '1', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'rochester', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': '19'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'began play', '19'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; began play ; 19 }', 'tointer': 'select the rows whose began play record fuzzily matches to 19 .'}, 'club', 'rochester'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose began play record fuzzily matches to 19 . for the club records of these rows , most of them fuzzily match to rochester .', 'tostr': 'most_eq { filter_eq { all_rows ; began play ; 19 } ; club ; rochester } = true'} | most_eq { filter_eq { all_rows ; began play ; 19 } ; club ; rochester } = true | select the rows whose began play record fuzzily matches to 19 . for the club records of these rows , most of them fuzzily match to rochester . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'began play_4': 4, '19_5': 5, 'club_6': 6, 'rochester_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'began play_4': 'began play', '19_5': '19', 'club_6': 'club', 'rochester_7': 'rochester'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'began play_4': [0], '19_5': [0], 'club_6': [1], 'rochester_7': [1]} | ['club', 'sport', 'began play', 'league', 'venue'] | [['rochester red wings', 'baseball', '1899', 'international league', 'frontier field'], ['rochester americans', 'ice hockey', '1956', 'ahl', 'blue cross arena'], ['rochester knighthawks', 'indoor lacrosse', '1995', 'nll', 'blue cross arena'], ['rochester rhinos', 'soccer', '1996', 'usl pro', "sahlen 's stadium"], ['rochester rattlers', 'outdoor lacrosse', '2001 ( 2011 )', 'mll', "sahlen 's stadium"], ['rochester razorsharks', 'basketball', '2005', 'pbl', 'blue cross arena'], ['western new york flash', 'soccer', '2011', 'nwsl', "sahlen 's stadium"], ['rochester lancers', 'indoor soccer', '2011', 'misl', 'blue cross arena'], ['roc city thunder', 'indoor football', '2013', 'aif', 'rit gordon field house']] |
list of interplanetary voyages | https://en.wikipedia.org/wiki/List_of_interplanetary_voyages | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13698001-8.html.csv | count | of the interplanetary voyages that were passing flights in 1973 , three of them went to mars . | {'scope': 'subset', 'criterion': 'equal', 'value': 'mars', 'result': '3', 'col': '2', 'subset': {'col': '3', 'criterion': 'fuzzily_match', 'value': '1973'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'launched', '1973'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; launched ; 1973 }', 'tointer': 'select the rows whose launched record fuzzily matches to 1973 .'}, 'destination', 'mars'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose launched record fuzzily matches to 1973 . among these rows , select the rows whose destination record fuzzily matches to mars .', 'tostr': 'filter_eq { filter_eq { all_rows ; launched ; 1973 } ; destination ; mars }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; launched ; 1973 } ; destination ; mars } }', 'tointer': 'select the rows whose launched record fuzzily matches to 1973 . among these rows , select the rows whose destination record fuzzily matches to mars . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; launched ; 1973 } ; destination ; mars } } ; 3 } = true', 'tointer': 'select the rows whose launched record fuzzily matches to 1973 . among these rows , select the rows whose destination record fuzzily matches to mars . the number of such rows is 3 .'} | eq { count { filter_eq { filter_eq { all_rows ; launched ; 1973 } ; destination ; mars } } ; 3 } = true | select the rows whose launched record fuzzily matches to 1973 . among these rows , select the rows whose destination record fuzzily matches to mars . the number of such rows is 3 . | 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, 'launched_6': 6, '1973_7': 7, 'destination_8': 8, 'mars_9': 9, '3_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', 'launched_6': 'launched', '1973_7': '1973', 'destination_8': 'destination', 'mars_9': 'mars', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'launched_6': [0], '1973_7': [0], 'destination_8': [1], 'mars_9': [1], '3_10': [3]} | ['spacecraft', 'destination', 'launched', 'closest approach', 'time elapsed'] | [['pioneer 10', 'jupiter', '3 march 1972', '3 december 1973', '641 days ( 1 yr , 9 mos , 1 d )'], ['pioneer 11', 'jupiter', '6 april 1973', '4 december 1974', '608 days ( 1 yr , 7 mo , 29 d )'], ['pioneer 11', 'saturn', '6 april 1973', '1 september 1979', '2340 days ( 6 yr , 4 mo , 27 d )'], ['mars 4', 'mars', '21 july 1973', '10 february 1974', '205 days ( 6 months , 21 days )'], ['mars 6', 'mars', '5 august 1973', '12 march 1974', '220 days ( 7 months , 8 days )'], ['mars 7', 'mars', '9 august 1973', '9 march 1974', '213 days ( 7 months , 1 day )'], ['mariner 10', 'venus', '3 november 1973', '5 february 1974', '95 days ( 3 months , 3 days )'], ['mariner 10', 'mercury', '3 november 1973', '29 march 1974', '147 days ( 4 months , 27 days )'], ['mariner 10', 'mercury', '3 november 1973', '21 september 1974', '323 days ( 10 months , 19 days )'], ['mariner 10', 'mercury', '3 november 1973', '16 march 1975', '499 days ( 1 yr , 4 mo , 14 d )'], ['voyager 2', 'jupiter', '20 august 1977', '9 july 1979', '689 days ( 1 yr , 10 mo , 20 d )'], ['voyager 2', 'saturn', '20 august 1977', '5 august 1981', '1447 days ( 3 yr , 11 mo , 17 d )'], ['voyager 2', 'uranus', '20 august 1977', '24 january 1986', '3080 days ( 8 yr , 5 mo , 5 d )'], ['voyager 2', 'neptune', '20 august 1977', '25 august 1989', '4389 days ( 12 yr , 6 days )'], ['voyager 1', 'jupiter', '5 september 1977', '5 march 1979', '547 days ( 1 yr , 6 mo , 1 d )'], ['voyager 1', 'saturn', '5 september 1977', '12 november 1980', '1165 days ( 3 yr , 2 mo , 8 d )'], ['ice', 'comet 21p / giacobini - zinner', '12 august 1978', '11 september 1985', '2588 days ( 7 yr , 1 mo )'], ['ice', 'comet 1p / halley', '12 august 1978', '28 march 1986', '2786 days ( 7 yr , 7 mo , 17 d )'], ['venera 11', 'venus', '9 september 1978', '25 december 1978', '108 days ( 3 months , 17 days )'], ['venera 12', 'venus', '14 september 1978', '19 december 1978', '97 days ( 3 months , 6 days )']] |
list of how it 's made episodes | https://en.wikipedia.org/wiki/List_of_How_It%27s_Made_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15187735-6.html.csv | comparative | the netflix tv series segment three wheeled vehicles recorded 66 episodes while the netflix tv series segment air filters recorded 70 episodes . | {'row_1': '1', 'row_2': '5', 'col': '2', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'three wheeled vehicles'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles .', 'tostr': 'filter_eq { all_rows ; segment a ; three wheeled vehicles }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'air filters'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment a record fuzzily matches to air filters .', 'tostr': 'filter_eq { all_rows ; segment a ; air filters }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment a ; air filters } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } }', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'three wheeled vehicles'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles .', 'tostr': 'filter_eq { all_rows ; segment a ; three wheeled vehicles }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row .'}, '66'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 }', 'tointer': 'the episode record of the first row is 66 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment a', 'air filters'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment a record fuzzily matches to air filters .', 'tostr': 'filter_eq { all_rows ; segment a ; air filters }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment a ; air filters } ; episode }', 'tointer': 'select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row .'}, '70'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 }', 'tointer': 'the episode record of the second row is 70 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } }', 'tointer': 'the episode record of the first row is 66 . the episode record of the second row is 70 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } } = true', 'tointer': 'select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 .'} | and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } } = true | select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'less_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'segment a_11': 11, 'three wheeled vehicles_12': 12, 'episode_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'segment a_15': 15, 'air filters_16': 16, 'episode_17': 17, 'and_7': 7, 'eq_5': 5, '66_18': 18, 'eq_6': 6, '70_19': 19} | {'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'segment a_11': 'segment a', 'three wheeled vehicles_12': 'three wheeled vehicles', 'episode_13': 'episode', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'segment a_15': 'segment a', 'air filters_16': 'air filters', 'episode_17': 'episode', 'and_7': 'and', 'eq_5': 'eq', '66_18': '66', 'eq_6': 'eq', '70_19': '70'} | {'and_8': [9], 'result_9': [], 'less_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'segment a_11': [0], 'three wheeled vehicles_12': [0], 'episode_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'segment a_15': [1], 'air filters_16': [1], 'episode_17': [3], 'and_7': [8], 'eq_5': [7], '66_18': [5], 'eq_6': [7], '70_19': [6]} | ['series ep', 'episode', 'netflix', 'segment a', 'segment b', 'segment c', 'segment d'] | [['6 - 01', '66', 's03e14', 'three wheeled vehicles', 'baseball bats', 'artificial bonsai', 's trombone'], ['6 - 02', '67', 's03e15', 's spring', 's paver', 's piano ( part 1 )', 's piano ( part 2 )'], ['6 - 03', '68', 's03e16', 's rope', 's billiard table', 's sailboard', 's cymbal'], ['6 - 04', '69', 's03e17', 's seatbelt', 's window', 'wax figurines', 'hot air balloons'], ['6 - 05', '70', 's03e18', 'air filters', 'billiard cues', 'ice sculptures', 's suit'], ['6 - 06', '71', 's03e19', 'escalator s handrail', 's highlighter', 'guitar s string', 'wigs'], ['6 - 07', '72', 's03e20', 'traditional bows', 's coffee machine', 's mascot', 's hammock'], ['6 - 08', '73', 's03e21', 'fibreglass insulation', 's wooden duck', 'gumball machines', 'exhaust systems'], ['6 - 09', '74', 's03e22', 's chain', 's bagel', 'vinyl records ( part 1 )', 'vinyl records ( part 2 )'], ['6 - 10', '75', 's03e23', 's windshield', 'english saddles', 'butter', 'post clocks'], ['6 - 11', '76', 's03e24', 'individual transporters', 'cedar canoes', 'electric guitars ( part 1 )', 'electric guitars ( part 2 )'], ['6 - 12', '77', 's03e25', 'residential water heaters', 'air bags', 'jelly beans', 'ice resurfacers']] |
1934 vfl season | https://en.wikipedia.org/wiki/1934_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790510-6.html.csv | ordinal | in the 1934 vfl season , the game with the second highest attendance was in victoria park . | {'scope': 'all', 'row': '3', 'col': '6', 'order': '2', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 2 }'}, 'venue'], 'result': 'victoria park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 2 } ; venue }'}, 'victoria park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park } = true', 'tointer': 'select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park .'} | eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park } = true | select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '2_6': 6, 'venue_7': 7, 'victoria park_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '2_6': '2', 'venue_7': 'venue', 'victoria park_8': 'victoria park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '2_6': [0], 'venue_7': [1], 'victoria park_8': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '14.17 ( 101 )', 'st kilda', '20.14 ( 134 )', 'mcg', '18102', '9 june 1934'], ['essendon', '14.11 ( 95 )', 'geelong', '11.16 ( 82 )', 'windy hill', '15000', '9 june 1934'], ['collingwood', '16.15 ( 111 )', 'fitzroy', '13.18 ( 96 )', 'victoria park', '22000', '9 june 1934'], ['carlton', '20.25 ( 145 )', 'north melbourne', '12.11 ( 83 )', 'princes park', '15000', '9 june 1934'], ['south melbourne', '9.10 ( 64 )', 'richmond', '16.12 ( 108 )', 'lake oval', '32000', '9 june 1934'], ['hawthorn', '11.11 ( 77 )', 'footscray', '10.11 ( 71 )', 'glenferrie oval', '8000', '9 june 1934']] |
1999 cfl draft | https://en.wikipedia.org/wiki/1999_CFL_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25085059-3.html.csv | superlative | in the 1999 cfl draft , of the players in the running back position , the last player drafted was from mount allison college . | {'scope': 'subset', 'col_superlative': '1', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '4,5', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'rb'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'rb'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; position ; rb }', 'tointer': 'select the rows whose position record fuzzily matches to rb .'}, 'pick'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; position ; rb } ; pick }'}, 'college'], 'result': 'mount allison', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; position ; rb } ; pick } ; college }'}, 'mount allison'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; position ; rb } ; pick } ; college } ; mount allison } = true', 'tointer': 'select the rows whose position record fuzzily matches to rb . select the row whose pick record of these rows is maximum . the college record of this row is mount allison .'} | eq { hop { argmax { filter_eq { all_rows ; position ; rb } ; pick } ; college } ; mount allison } = true | select the rows whose position record fuzzily matches to rb . select the row whose pick record of these rows is maximum . the college record of this row is mount allison . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'position_6': 6, 'rb_7': 7, 'pick_8': 8, 'college_9': 9, 'mount allison_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'position_6': 'position', 'rb_7': 'rb', 'pick_8': 'pick', 'college_9': 'college', 'mount allison_10': 'mount allison'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'position_6': [0], 'rb_7': [0], 'pick_8': [1], 'college_9': [2], 'mount allison_10': [3]} | ['pick', 'cfl team', 'player', 'position', 'college'] | [['17', 'winnipeg blue bombers', 'jeff pilon', 'ol', 'syracuse'], ['18', 'saskatchewan', 'kennedy nkeyasen', 'rb', 'idaho state university'], ['19', 'bc', 'jason kralt', 'db', 'carleton'], ['20', 'edmonton', 'ã ‰ ric lapointe', 'rb', 'mount allison'], ['21', 'toronto', 'jean - phillipe darche', 'lb', 'mcgill'], ['22', 'montreal', 'yannic sermanou', 'dl', 'howard'], ['23', 'hamilton', 'morty bryce', 'db', 'bowling green']] |
1987 - 88 bradford city a.f.c. season | https://en.wikipedia.org/wiki/1987%E2%80%9388_Bradford_City_A.F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18998832-5.html.csv | majority | in the 1987 - 88 bradford city a. f. c. season , there were at least 10000 people in attendance at most of the november games . | {'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '10000', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'november'}} | {'func': 'most_greater_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; november }', 'tointer': 'select the rows whose date record fuzzily matches to november .'}, 'attendance', '10000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november . for the attendance records of these rows , most of them are greater than or equal to 10000 .', 'tostr': 'most_greater_eq { filter_eq { all_rows ; date ; november } ; attendance ; 10000 } = true'} | most_greater_eq { filter_eq { all_rows ; date ; november } ; attendance ; 10000 } = true | select the rows whose date record fuzzily matches to november . for the attendance records of these rows , most of them are greater than or equal to 10000 . | 2 | 2 | {'most_greater_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'november_5': 5, 'attendance_6': 6, '10000_7': 7} | {'most_greater_eq_1': 'most_greater_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'november_5': 'november', 'attendance_6': 'attendance', '10000_7': '10000'} | {'most_greater_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'november_5': [0], 'attendance_6': [1], '10000_7': [1]} | ['round ( leg )', 'date', 'opponent', 'venue', 'result', 'attendance'] | [['2 ( 1 )', '22 september 1987', 'fulham', 'away', '5 - 1', '4357'], ['2 ( 2 )', '7 october 1987', 'fulham', 'home', '2 - 1', '6408'], ['3', '27 october 1987', 'charlton athletic', 'away', '1 - 0', '3629'], ['4', '18 november 1987', 'reading', 'away', '0 - 0', '6784'], ['4r', '24 november 1987', 'reading', 'home', '1 - 0', '10448'], ['5', '19 january 1988', 'luton town', 'away', '0 - 2', '11022']] |
1972 - 73 philadelphia flyers season | https://en.wikipedia.org/wiki/1972%E2%80%9373_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14294324-2.html.csv | superlative | in the 1972-78 philadelphia flyers season , the highest number of points was in game 10 . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '10', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'game'], 'result': '10', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; game }'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; game } ; 10 } = true', 'tointer': 'select the row whose points record of all rows is maximum . the game record of this row is 10 .'} | eq { hop { argmax { all_rows ; points } ; game } ; 10 } = true | select the row whose points record of all rows is maximum . the game record of this row is 10 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'game_6': 6, '10_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'game_6': 'game', '10_7': '10'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'game_6': [1], '10_7': [2]} | ['game', 'october', 'opponent', 'score', 'record', 'points'] | [['1', '7', 'st louis blues', '4 - 4', '0 - 0 - 1', '1'], ['2', '12', 'vancouver canucks', '7 - 3', '1 - 0 - 1', '3'], ['3', '14', 'detroit red wings', '0 - 5', '1 - 1 - 1', '3'], ['4', '15', 'california golden seals', '1 - 4', '1 - 2 - 1', '3'], ['5', '18', 'los angeles kings', '4 - 3', '2 - 2 - 1', '5'], ['6', '20', 'california golden seals', '3 - 3', '2 - 2 - 2', '6'], ['7', '25', 'new york rangers', '1 - 6', '2 - 3 - 2', '6'], ['8', '26', 'detroit red wings', '2 - 1', '3 - 3 - 2', '8'], ['9', '28', 'minnesota north stars', '1 - 2', '3 - 4 - 2', '8'], ['10', '29', 'toronto maple leafs', '5 - 2', '4 - 4 - 2', '10']] |
list of kent first - class cricket records | https://en.wikipedia.org/wiki/List_of_Kent_first-class_cricket_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11204543-17.html.csv | majority | the county ground was used twice in season 1907 and 1922 more than any other venue . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'county ground', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'venue', 'county ground'], 'result': True, 'ind': 0, 'tointer': 'for the venue records of all rows , most of them fuzzily match to county ground .', 'tostr': 'most_eq { all_rows ; venue ; county ground } = true'} | most_eq { all_rows ; venue ; county ground } = true | for the venue records of all rows , most of them fuzzily match to county ground . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'venue_3': 3, 'county ground_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'venue_3': 'venue', 'county ground_4': 'county ground'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'venue_3': [0], 'county ground_4': [0]} | ['bowling', 'player', 'opponent', 'venue', 'season'] | [['17 - 48', 'colin blythe', 'v northamptonshire', 'county ground , northampton', '1907'], ['17 - 67', 'tich freeman', 'v sussex', 'county ground , hove', '1922'], ['17 - 92', 'tich freeman', 'v warwickshire', 'cheriton road , folkestone', '1932'], ['16 - 80', 'doug wright', 'v somerset', 'recreation ground , bath', '1939'], ['16 - 82', 'tich freeman', 'v northamptonshire', 'nevill ground , tunbridge wells', '1932']] |
2009 - 10 atlanta hawks season | https://en.wikipedia.org/wiki/2009%E2%80%9310_Atlanta_Hawks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23248910-9.html.csv | ordinal | the atlanta hawks ' game against the knicks team recorded their highest attendance of the 2009 - 10 season . | {'row': '5', 'col': '8', 'order': '1', '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', 'location attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; location attendance ; 1 }'}, 'team'], 'result': 'knicks', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location attendance ; 1 } ; team }'}, 'knicks'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; knicks } = true', 'tointer': 'select the row whose location attendance record of all rows is 1st maximum . the team record of this row is knicks .'} | eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; knicks } = true | select the row whose location attendance record of all rows is 1st maximum . the team record of this row is knicks . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, '1_6': 6, 'team_7': 7, 'knicks_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', 'location attendance_5': 'location attendance', '1_6': '1', 'team_7': 'team', 'knicks_8': 'knicks'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], '1_6': [0], 'team_7': [1], 'knicks_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['60', 'march 1', 'bulls', 'w 116 - 92 ( ot )', 'j crawford ( 21 )', 'j smith ( 18 )', 'm williams ( 4 ) j smith ( 4 ) a horford ( 4 )', 'united center 19011', '38 - 21'], ['61', 'march 3', '76ers', 'w 112 - 93 ( ot )', 'm williams ( 21 )', 'm williams ( 8 ) a horford ( 8 ) j smith ( 8 )', 'j johnson ( 5 ) j smith ( 5 )', 'philips arena 15408', '39 - 21'], ['62', 'march 5', 'warriors', 'w 127 - 122 ( ot )', 'j smith ( 29 )', 'a horford ( 15 )', 'j johnson ( 8 )', 'philips arena 14066', '40 - 21'], ['63', 'march 6', 'heat', 'l 94 - 100 ( ot )', 'j crawford ( 24 )', 'a horford ( 9 )', 'j smith ( 5 )', 'american airlines arena 19600', '40 - 22'], ['64', 'march 8', 'knicks', 'l 98 - 99 ( ot )', 'j smith ( 25 )', 'a horford ( 12 )', 'j smith ( 6 )', 'madison square garden 19763', '40 - 23'], ['50', 'march 11', 'wizards', 'w 105 - 99 ( ot )', 'j crawford ( 29 )', 'j johnson ( 7 )', 'j johnson ( 5 ) j smith ( 5 ) m bibby ( 5 )', 'verizon center 13625', '41 - 23'], ['65', 'march 13', 'pistons', 'w 112 - 99 ( ot )', 'j crawford ( 29 )', 'j johnson ( 7 )', 'j johnson ( 5 ) j smith ( 5 ) m bibby ( 5 )', 'philips arena 18214', '42 - 23'], ['66', 'march 16', 'nets', 'w 108 - 84 ( ot )', 'j crawford ( 25 )', 'a horford ( 11 )', 'a horford ( 7 )', 'izod center 11128', '43 - 23'], ['67', 'march 17', 'raptors', 'l 105 - 106 ( ot )', 'j crawford ( 33 )', 'a horford ( 14 )', 'j smith ( 7 )', 'air canada centre 18441', '43 - 24'], ['68', 'march 19', 'bobcats', 'w 93 - 92 ( ot ) ot', 'j johnson ( 18 ) j smith ( 18 )', 'm williams ( 14 )', 'j smith ( 5 )', 'philips arena 17697', '44 - 24'], ['69', 'march 21', 'spurs', 'w 119 - 114 ( ot ) ot', 'm williams ( 26 )', 'a horford ( 18 )', 'j johnson ( 13 )', 'philips arena 18729', '45 - 24'], ['70', 'march 22', 'bucks', 'l 95 - 98 ( ot )', 'j johnson ( 27 )', 'a horford ( 12 )', 'a horford ( 4 )', 'bradley center 14186', '45 - 25'], ['71', 'march 24', 'magic', 'w 86 - 84 ( ot )', 'j johnson ( 17 )', 'a horford ( 11 )', 'j johnson ( 8 )', 'philips arena 16887', '46 - 25'], ['72', 'march 26', '76ers', 'l 98 - 105 ( ot )', 'j johnson ( 20 ) j smith ( 20 )', 'a horford ( 10 )', 'j johnson ( 6 )', 'wachovia center 13293', '46 - 26'], ['73', 'march 28', 'pacers', 'w 94 - 84 ( ot )', 'j smith ( 21 )', 'j smith ( 13 )', 'm bibby ( 8 )', 'philips arena 16646', '47 - 26']] |
1975 oakland raiders season | https://en.wikipedia.org/wiki/1975_Oakland_Raiders_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18207285-2.html.csv | count | the oakland raiders only lost three games during the 1975 season . | {'scope': 'all', 'criterion': 'equal', 'value': 'loss', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'loss'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to loss .', 'tostr': 'filter_eq { all_rows ; result ; loss }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; loss } }', 'tointer': 'select the rows whose result record fuzzily matches to loss . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; loss } } ; 3 } = true', 'tointer': 'select the rows whose result record fuzzily matches to loss . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; result ; loss } } ; 3 } = true | select the rows whose result record fuzzily matches to loss . 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, 'result_5': 5, 'loss_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', 'result_5': 'result', 'loss_6': 'loss', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'loss_6': [0], '3_7': [2]} | ['game', 'date', 'opponent', 'result', 'raiders points', 'opponents', 'raiders first downs', 'record', 'attendance'] | [['1', 'sept 22', 'miami dolphins', 'win', '31', '21', '17', '1 - 0', '78744'], ['2', 'sept 28', 'baltimore colts', 'win', '31', '20', '18', '2 - 0', '40657'], ['3', 'oct 5', 'san diego chargers', 'win', '6', '0', '17', '3 - 0', '31095'], ['4', 'oct 12', 'kansas city chiefs', 'loss', '10', '42', '23', '3 - 1', '60425'], ['5', 'oct 19', 'cincinnati bengals', 'loss', '10', '14', '18', '3 - 2', '48122'], ['6', 'oct 26', 'san diego chargers', 'win', '25', '0', '23', '4 - 2', '42796'], ['7', 'nov 2', 'denver broncos', 'win', '42', '17', '21', '5 - 2', '52505'], ['8', 'nov 9', 'new orleans saints', 'win', '48', '10', '34', '6 - 2', '51267'], ['9', 'nov 16', 'cleveland browns', 'win', '38', '17', '22', '7 - 2', '50461'], ['10', 'nov 23', 'washington redskins', 'win', '26', '23', '26', '8 - 2', '53582'], ['11', 'nov 30', 'atlanta falcons', 'win', '37', '34', '33', '9 - 2', '50860'], ['12', 'dec 8', 'denver broncos', 'win', '17', '10', '16', '10 - 2', '51075'], ['13', 'dec 14', 'houston oilers', 'loss', '26', '27', '23', '10 - 3', '50719'], ['14', 'dec 21', 'kansas city chiefs', 'win', '28', '20', '24', '11 - 3', '48604']] |
list of cities , towns and villages in vojvodina | https://en.wikipedia.org/wiki/List_of_cities%2C_towns_and_villages_in_Vojvodina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2562572-50.html.csv | count | there are 2 settlements in vojvodina where the dominant religion is catholic christianity . | {'scope': 'all', 'criterion': 'equal', 'value': 'catholic christianity', 'result': '2', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'dominant religion ( 2002 )', 'catholic christianity'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose dominant religion ( 2002 ) record fuzzily matches to catholic christianity .', 'tostr': 'filter_eq { all_rows ; dominant religion ( 2002 ) ; catholic christianity }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; dominant religion ( 2002 ) ; catholic christianity } }', 'tointer': 'select the rows whose dominant religion ( 2002 ) record fuzzily matches to catholic christianity . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; dominant religion ( 2002 ) ; catholic christianity } } ; 2 } = true', 'tointer': 'select the rows whose dominant religion ( 2002 ) record fuzzily matches to catholic christianity . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; dominant religion ( 2002 ) ; catholic christianity } } ; 2 } = true | select the rows whose dominant religion ( 2002 ) record fuzzily matches to catholic christianity . 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, 'dominant religion (2002)_5': 5, 'catholic christianity_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', 'dominant religion (2002)_5': 'dominant religion ( 2002 )', 'catholic christianity_6': 'catholic christianity', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'dominant religion (2002)_5': [0], 'catholic christianity_6': [0], '2_7': [2]} | ['settlement', 'cyrillic name other names', 'type', 'population ( 2011 )', 'largest ethnic group ( 2002 )', 'dominant religion ( 2002 )'] | [['irig', 'ириг', 'town', '4415', 'serbs', 'orthodox christianity'], ['dobrodol', 'добродол ( hungarian : dobradópuszta )', 'village', '107', 'hungarians', 'catholic christianity'], ['grgetek', 'гргетек', 'village', '76', 'serbs', 'orthodox christianity'], ['jazak', 'јазак', 'village', '960', 'serbs', 'orthodox christianity'], ['krušedol prnjavor', 'крушедол прњавор', 'village', '234', 'serbs', 'orthodox christianity'], ['krušedol selo', 'крушедол село', 'village', '340', 'serbs', 'orthodox christianity'], ['mala remeta', 'мала ремета', 'village', '130', 'serbs', 'orthodox christianity'], ['neradin', 'нерадин', 'village', '475', 'serbs', 'orthodox christianity'], ['rivica', 'ривица', 'village', '620', 'serbs', 'orthodox christianity'], ['šatrinci', 'шатринци ( hungarian : satrinca )', 'village', '373', 'hungarians', 'catholic christianity'], ['velika remeta', 'велика ремета', 'village', '44', 'serbs', 'orthodox christianity']] |
1996 pga championship | https://en.wikipedia.org/wiki/1996_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18096431-2.html.csv | unique | in the 1996 pga championship , for the players from the united states , the only one who finished 4 over par was payne stewart . | {'scope': 'subset', 'row': '6', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '+4', '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', '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 .'}, 'to par', '+4'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record fuzzily matches to +4 .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record fuzzily matches to +4 . 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', '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 .'}, 'to par', '+4'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record fuzzily matches to +4 .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 }'}, 'player'], 'result': 'payne stewart', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 } ; player }'}, 'payne stewart'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 } ; player } ; payne stewart }', 'tointer': 'the player record of this unqiue row is payne stewart .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 } ; player } ; payne stewart } } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record fuzzily matches to +4 . there is only one such row in the table . the player record of this unqiue row is payne stewart .'} | and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; +4 } ; player } ; payne stewart } } = true | select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record fuzzily matches to +4 . there is only one such row in the table . the player record of this unqiue row is payne stewart . | 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, 'country_8': 8, 'united states_9': 9, 'to par_10': 10, '+4_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'player_12': 12, 'payne stewart_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', 'country_8': 'country', 'united states_9': 'united states', 'to par_10': 'to par', '+4_11': '+4', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'player_12': 'player', 'payne stewart_13': 'payne stewart'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'country_8': [0], 'united states_9': [0], 'to par_10': [1], '+4_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'player_12': [3], 'payne stewart_13': [4]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['steve elkington', 'australia', '1995', '278', '- 10', 't3'], ['nick price', 'zimbabwe', '1992 , 1994', '280', '- 8', 't13'], ['paul azinger', 'united states', '1993', '285', '- 3', 't29'], ['jeff sluman', 'united states', '1988', '287', '- 1', 't41'], ['wayne grady', 'australia', '1990', '291', '+ 3', 't65'], ['payne stewart', 'united states', '1989', '292', '+ 4', 't69'], ['larry nelson', 'united states', '1981 , 1987', '295', '+ 15', 't71']] |
1999 senior pga tour | https://en.wikipedia.org/wiki/1999_Senior_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11621747-4.html.csv | unique | in the 1999 senior pga tour , bob charles was the only golfer not from the united states that was ranked in the top five . | {'scope': 'all', 'row': '5', 'col': '3', 'col_other': '2', 'criterion': 'not_equal', 'value': 'united states', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record does not match to united states .', 'tostr': 'filter_not_eq { all_rows ; country ; united states }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; country ; united states } }', 'tointer': 'select the rows whose country record does not match to united states . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record does not match to united states .', 'tostr': 'filter_not_eq { all_rows ; country ; united states }'}, 'player'], 'result': 'bob charles', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; country ; united states } ; player }'}, 'bob charles'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; country ; united states } ; player } ; bob charles }', 'tointer': 'the player record of this unqiue row is bob charles .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; country ; united states } } ; eq { hop { filter_not_eq { all_rows ; country ; united states } ; player } ; bob charles } } = true', 'tointer': 'select the rows whose country record does not match to united states . there is only one such row in the table . the player record of this unqiue row is bob charles .'} | and { only { filter_not_eq { all_rows ; country ; united states } } ; eq { hop { filter_not_eq { all_rows ; country ; united states } ; player } ; bob charles } } = true | select the rows whose country record does not match to united states . there is only one such row in the table . the player record of this unqiue row is bob charles . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'united states_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'bob charles_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'united states_8': 'united states', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'bob charles_10': 'bob charles'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_not_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'united states_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'bob charles_10': [3]} | ['rank', 'player', 'country', 'earnings', 'wins'] | [['1', 'hale irwin', 'united states', '9645485', '25'], ['2', 'jim colbert', 'united states', '8887831', '19'], ['3', 'lee trevino', 'united states', '8666030', '28'], ['4', 'dave stockton', 'united states', '8104786', '14'], ['5', 'bob charles', 'new zealand', '8001710', '23']] |
2004 scottish claymores season | https://en.wikipedia.org/wiki/2004_Scottish_Claymores_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29679510-2.html.csv | superlative | in the 2004 scottish claymores season , the game with the largest attendance was the game at waldstadion . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '7', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'game site'], 'result': 'waldstadion', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; game site }'}, 'waldstadion'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; game site } ; waldstadion } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the game site record of this row is waldstadion .'} | eq { hop { argmax { all_rows ; attendance } ; game site } ; waldstadion } = true | select the row whose attendance record of all rows is maximum . the game site record of this row is waldstadion . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'game site_6': 6, 'waldstadion_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'game site_6': 'game site', 'waldstadion_7': 'waldstadion'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'game site_6': [1], 'waldstadion_7': [2]} | ['week', 'date', 'kickoff', 'opponent', 'final score', 'team record', 'game site', 'attendance'] | [['1', 'sunday , april 4', '4:00 pm', 'berlin thunder', 'l 14 - 20', '0 - 1', 'olympic stadium', '14257'], ['2', 'saturday , april 10', '7:00 pm', 'rhein fire', 'l 3 - 31', '0 - 2', 'arena aufschalke', '17176'], ['3', 'sunday , april 18', '2:00 pm', 'amsterdam admirals', 'l 0 - 3', '0 - 3', 'hampden park', '10971'], ['4', 'saturday , april 24', '7:00 pm', 'cologne centurions', 'l 3 - 17', '0 - 4', 'rheinenergiestadion', '8761'], ['5', 'sunday , may 2', '2:00 pm', 'rhein fire', 'w 13 - 12', '1 - 4', 'hampden park', '9165'], ['6', 'sunday , may 9', '2:00 pm', 'frankfurt galaxy', 'l 13 - 15', '1 - 5', 'hampden park', '9017'], ['7', 'sunday , may 16', '4:00 pm', 'frankfurt galaxy', 'l 24 - 27', '1 - 6', 'waldstadion', '26879'], ['8', 'friday , may 21', '8:00 pm', 'amsterdam admirals', 'w 19 - 17', '2 - 6', 'amsterdam arena', '10738'], ['9', 'saturday , may 29', '2:00 pm', 'berlin thunder', 'l 19 - 27', '2 - 7', 'hampden park', '9153']] |
1979 new orleans saints season | https://en.wikipedia.org/wiki/1979_New_Orleans_Saints_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18842968-2.html.csv | unique | the lowest attendance for a game against san francisco 49ers in the 1979 new orleans saints season was 39727 . | {'scope': 'subset', 'row': '4', 'col': '5', 'col_other': 'n/a', 'criterion': 'equal', 'value': '39727', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'san francisco 49ers'}} | {'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'san francisco 49ers'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; san francisco 49ers }', 'tointer': 'select the rows whose opponent record fuzzily matches to san francisco 49ers .'}, 'attendance', '39727'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to san francisco 49ers . among these rows , select the rows whose attendance record is equal to 39727 .', 'tostr': 'filter_eq { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance ; 39727 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance ; 39727 } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to san francisco 49ers . among these rows , select the rows whose attendance record is equal to 39727 . there is only one such row in the table .'} | only { filter_eq { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance ; 39727 } } = true | select the rows whose opponent record fuzzily matches to san francisco 49ers . among these rows , select the rows whose attendance record is equal to 39727 . there is only one such row in the table . | 3 | 3 | {'only_2': 2, 'result_3': 3, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'san francisco 49ers_6': 6, 'attendance_7': 7, '39727_8': 8} | {'only_2': 'only', 'result_3': 'true', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'san francisco 49ers_6': 'san francisco 49ers', 'attendance_7': 'attendance', '39727_8': '39727'} | {'only_2': [3], 'result_3': [], 'filter_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'san francisco 49ers_6': [0], 'attendance_7': [1], '39727_8': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 2 , 1979', 'atlanta falcons', 'l 40 - 34', '70940'], ['2', 'september 9 , 1979', 'green bay packers', 'l 28 - 19', '53184'], ['3', 'september 16 , 1979', 'philadelphia eagles', 'l 26 - 14', '54212'], ['4', 'september 23 , 1979', 'san francisco 49ers', 'w 30 - 21', '39727'], ['5', 'september 30 , 1979', 'new york giants', 'w 24 - 14', '51543'], ['6', 'october 7 , 1979', 'los angeles rams', 'l 35 - 17', '68986'], ['7', 'october 14 , 1979', 'tampa bay buccaneers', 'w 42 - 14', '67640'], ['8', 'october 21 , 1979', 'detroit lions', 'w 17 - 7', '57428'], ['9', 'october 28 , 1979', 'washington redskins', 'w 14 - 10', '52133'], ['10', 'november 4 , 1979', 'denver broncos', 'l 10 - 3', '74482'], ['11', 'november 11 , 1979', 'san francisco 49ers', 'w 31 - 20', '65551'], ['12', 'november 18 , 1979', 'seattle seahawks', 'l 38 - 24', '60055'], ['13', 'november 25 , 1979', 'atlanta falcons', 'w 37 - 6', '42815'], ['14', 'december 3 , 1979', 'oakland raiders', 'l 42 - 35', '65541'], ['15', 'december 9 , 1979', 'san diego chargers', 'l 35 - 0', '61059'], ['16', 'december 16 , 1979', 'los angeles rams', 'w 29 - 14', '53879']] |
jean - karl vernay | https://en.wikipedia.org/wiki/Jean-Karl_Vernay | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13808686-1.html.csv | aggregation | jean - karl vernay participated in a total of 123 races throughout the series presented on this table . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '123', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'races'], 'result': '123', 'ind': 0, 'tostr': 'sum { all_rows ; races }'}, '123'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; races } ; 123 } = true', 'tointer': 'the sum of the races record of all rows is 123 .'} | round_eq { sum { all_rows ; races } ; 123 } = true | the sum of the races record of all rows is 123 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'races_4': 4, '123_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'races_4': 'races', '123_5': '123'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'races_4': [0], '123_5': [1]} | ['season', 'series', 'team', 'races', 'wins', 'poles', 'points', 'position'] | [['2005', 'formula campus france', 'la filière', '14', '4', '3', '205', '1st'], ['2006', 'french formula renault 2.0', 'sg formula', '13', '2', '3', '108', '2nd'], ['2006', 'eurocup formula renault 2.0', 'sg formula', '2', '0', '0', '0', 'nc'], ['2006 - 07', 'a1 grand prix', 'a1 team france', '4', '0', '0', '3', '4th'], ['2007', 'formula 3 euro series', 'signature - plus', '20', '0', '0', '23', '10th'], ['2008', 'formula 3 euro series', 'signature - plus', '20', '0', '0', '35', '8th'], ['2009', 'formula 3 euro series', 'signature', '20', '2', '0', '47', '5th'], ['2009', 'macau grand prix', 'signature', '1', '0', '0', 'n / a', '2nd'], ['2010', 'indy lights', 'sam schmidt motorsports', '13', '5', '3', '494', '1st'], ['2011', 'formula renault 3.5 series', 'pons racing', '2', '0', '0', '0', 'nc'], ['2012', 'porsche carrera cup france', 'sébastien loeb racing', '14', '6', '4', '253', '1st']] |
list of one - day cricket records for new zealand | https://en.wikipedia.org/wiki/List_of_one-day_cricket_records_for_New_Zealand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13322378-10.html.csv | majority | the majority of players of the list of one - day cricket records for new zealand , had participated in more than 100 matches . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '100', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'matches', '100'], 'result': True, 'ind': 0, 'tointer': 'for the matches records of all rows , most of them are greater than 100 .', 'tostr': 'most_greater { all_rows ; matches ; 100 } = true'} | most_greater { all_rows ; matches ; 100 } = true | for the matches records of all rows , most of them are greater than 100 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'matches_3': 3, '100_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'matches_3': 'matches', '100_4': '100'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'matches_3': [0], '100_4': [0]} | ['', 'matches', 'runs', 'wickets', 'average', 'economy rate', 'best bowling', '4wi', '5wi'] | [['shane bond', '82', '3070', '147', '20.88', '4.28', '6 / 19', '7', '4'], ['richard hadlee', '115', '3407', '158', '21.56', '4.20', '5 / 25', '1', '5'], ['chris pringle', '64', '2459', '103', '23.87', '4.45', '5 / 45', '2', '1'], ['ewen chatfield', '114', '3618', '140', '25.84', '3.57', '5 / 34', '3', '1'], ['kyle mills', '129', '4998', '192', '26.03', '4.73', '5 / 25', '7', '1']] |
taniec z gwiazdami | https://en.wikipedia.org/wiki/Taniec_z_gwiazdami | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15988037-12.html.csv | count | for taniec z gwiazdami , when they came in after 1st place , there were 4 times they had over 30 points . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '30', 'result': '4', 'col': '4', 'subset': {'col': '5', 'criterion': 'greater_than', 'value': '1'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'place', '1'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; place ; 1 }', 'tointer': 'select the rows whose place record is greater than 1 .'}, 'points jury', '30'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose place record is greater than 1 . among these rows , select the rows whose points jury record is greater than 30 .', 'tostr': 'filter_greater { filter_greater { all_rows ; place ; 1 } ; points jury ; 30 }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; place ; 1 } ; points jury ; 30 } }', 'tointer': 'select the rows whose place record is greater than 1 . among these rows , select the rows whose points jury record is greater than 30 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; place ; 1 } ; points jury ; 30 } } ; 4 } = true', 'tointer': 'select the rows whose place record is greater than 1 . among these rows , select the rows whose points jury record is greater than 30 . the number of such rows is 4 .'} | eq { count { filter_greater { filter_greater { all_rows ; place ; 1 } ; points jury ; 30 } } ; 4 } = true | select the rows whose place record is greater than 1 . among these rows , select the rows whose points jury record is greater than 30 . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'place_6': 6, '1_7': 7, 'points jury_8': 8, '30_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'place_6': 'place', '1_7': '1', 'points jury_8': 'points jury', '30_9': '30', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'place_6': [0], '1_7': [0], 'points jury_8': [1], '30_9': [1], '4_10': [3]} | ['team', 'dance', 'music', 'points jury', 'place'] | [['rafał bryndal & diana staniszewska', 'jive', 'i get around - beach boys', '18 ( 5 , 5 , 4 , 4 )', '4 . place'], ['rafał bryndal & diana staniszewska', 'pop', 'thriller - michael jackson', '31 ( 5 , 6 , 10 , 10 )', '4 . place'], ['anna guzik & rafał kamiński', 'tango', 'libertango - ástor piazzolla', '24 ( 7 , 5 , 6 , 6 )', '1 . place'], ['anna guzik & rafał kamiński', 'hip - hop', 'yeah - usher', '39 ( 9 , 10 , 10 , 10 )', '1 . place'], ['mateusz damięcki & anna bosak', 'waltz', 'imagine - john lennon', '34 ( 7 , 8 , 9 , 10 )', '2 . place'], ['mateusz damięcki & anna bosak', 'jazz', "when you 're gone - avril lavigne", '33 ( 8 , 10 , 7 , 8 )', '2 . place'], ['justyna steczkowska & maciej florek', 'tango', "et si tu n'existais pas - toto cutugno & delanoë", '17 ( 4 , 5 , 5 , 3 )', '3 . place'], ['justyna steczkowska & maciej florek', 'modern', 'bring me to life - evanescence', '39 ( 9 , 10 , 10 , 10 )', '3 . place']] |
lindsay davenport career statistics | https://en.wikipedia.org/wiki/Lindsay_Davenport_career_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22858557-1.html.csv | count | lindsay davenport has been able to play at wimbleton two times . | {'scope': 'all', 'criterion': 'equal', 'value': 'wimbledon', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'championship', 'wimbledon'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose championship record fuzzily matches to wimbledon .', 'tostr': 'filter_eq { all_rows ; championship ; wimbledon }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; championship ; wimbledon } }', 'tointer': 'select the rows whose championship record fuzzily matches to wimbledon . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; championship ; wimbledon } } ; 2 } = true', 'tointer': 'select the rows whose championship record fuzzily matches to wimbledon . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; championship ; wimbledon } } ; 2 } = true | select the rows whose championship record fuzzily matches to wimbledon . 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, 'championship_5': 5, 'wimbledon_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', 'championship_5': 'championship', 'wimbledon_6': 'wimbledon', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'championship_5': [0], 'wimbledon_6': [0], '2_7': [2]} | ['outcome', 'year', 'championship', 'surface', 'opponent in final', 'score in final'] | [['winner', '1998', 'us open', 'hard', 'martina hingis', '6 - 3 , 7 - 5'], ['winner', '1999', 'wimbledon', 'grass', 'steffi graf', '6 - 4 , 7 - 5'], ['winner', '2000', 'australian open', 'hard', 'martina hingis', '6 - 1 , 7 - 5'], ['runner - up', '2000', 'wimbledon', 'grass', 'venus williams', '6 - 3 , 7 - 6'], ['runner - up', '2000', 'us open', 'hard', 'venus williams', '6 - 4 , 7 - 5'], ['runner - up', '2005', 'australian open', 'hard', 'serena williams', '2 - 6 , 6 - 3 , 6 - 0']] |
1990 fei world equestrian games | https://en.wikipedia.org/wiki/1990_FEI_World_Equestrian_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11871903-2.html.csv | count | there were 3 nations that won a single gold medal in the 1990 fei world equestrian games . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '1', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'gold', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record fuzzily matches to 1 .', 'tostr': 'filter_eq { all_rows ; gold ; 1 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; gold ; 1 } }', 'tointer': 'select the rows whose gold record fuzzily matches to 1 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; gold ; 1 } } ; 3 } = true', 'tointer': 'select the rows whose gold record fuzzily matches to 1 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; gold ; 1 } } ; 3 } = true | select the rows whose gold record fuzzily matches to 1 . 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, 'gold_5': 5, '1_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', 'gold_5': 'gold', '1_6': '1', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'gold_5': [0], '1_6': [0], '3_7': [2]} | ['nation', 'gold', 'silver', 'bronze', 'total'] | [['west germany', '4', '4', '4', '12'], ['france', '2', '-', '1', '3'], ['new zealand', '2', '-', '-', '2'], ['sweden', '2', '-', '-', '2'], ['united kingdom', '1', '4', '1', '6'], ['united states', '1', '-', '2', '3'], ['switzerland', '1', '-', '1', '2'], ['hungary', '-', '1', '1', '2'], ['netherlands', '-', '1', '1', '2'], ['belgium', '-', '1', '-', '1'], ['finland', '-', '1', '-', '1'], ['soviet union', '-', '1', '-', '1'], ['australia', '-', '-', '1', '1'], ['spain', '-', '-', '1', '1']] |
lara gut | https://en.wikipedia.org/wiki/Lara_Gut | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15556757-2.html.csv | count | there were five times when lara gut finished in third place . | {'scope': 'all', 'criterion': 'equal', 'value': '3rd', 'result': '5', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'place', '3rd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose place record fuzzily matches to 3rd .', 'tostr': 'filter_eq { all_rows ; place ; 3rd }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; place ; 3rd } }', 'tointer': 'select the rows whose place record fuzzily matches to 3rd . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; place ; 3rd } } ; 5 } = true', 'tointer': 'select the rows whose place record fuzzily matches to 3rd . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; place ; 3rd } } ; 5 } = true | select the rows whose place record fuzzily matches to 3rd . 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, 'place_5': 5, '3rd_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', 'place_5': 'place', '3rd_6': '3rd', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'place_5': [0], '3rd_6': [0], '5_7': [2]} | ['season', 'date', 'location', 'race', 'place'] | [['2008', '2 feb 2008', 'st moritz , switzerland', 'downhill', '3rd'], ['2009', '20 dec 2008', 'st moritz , switzerland', 'super - g', '1st'], ['2009', '28 dec 2008', 'semmering , austria', 'giant slalom', '3rd'], ['2011', '18 dec 2010', "val d'isère , france", 'downhill', '3rd'], ['2011', '9 jan 2011', 'altenmarkt - zauchensee , austria', 'super - g', '1st'], ['2011', '23 jan 2011', "cortina d'ampezzo , italy", 'super - g', '3rd'], ['2011', '16 mar 2011', 'lenzerheide , switzerland', 'downhill', '2nd'], ['2013', '14 dec 2012', "val - d'isère , france", 'downhill', '1st'], ['2013', '17 mar 2013', 'lenzerheide , switzerland', 'giant slalom', '3rd'], ['2014', '26 oct 2013', 'sölden , austria', 'giant slalom', '1st']] |
list of open - source films | https://en.wikipedia.org/wiki/List_of_open-source_films | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10597959-2.html.csv | majority | the majority of open-source films do not have a planned release date . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'not yet', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'planned release', 'not yet'], 'result': True, 'ind': 0, 'tointer': 'for the planned release records of all rows , most of them fuzzily match to not yet .', 'tostr': 'most_eq { all_rows ; planned release ; not yet } = true'} | most_eq { all_rows ; planned release ; not yet } = true | for the planned release records of all rows , most of them fuzzily match to not yet . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'planned release_3': 3, 'not yet_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'planned release_3': 'planned release', 'not yet_4': 'not yet'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'planned release_3': [0], 'not yet_4': [0]} | ['name', 'type', 'planned release', 'cc license', 'sources available', 'open source movie'] | [['the last drug', 'full feature', '2009', 'by - sa 3.0', 'with the release', 'yes'], ['a swarm of angels', 'full feature', 'not yet', 'by - nc - sa 2.0', 'intended', 'partially'], ['sanctuary', 'short', 'not yet', 'by - nc - sa 2.5', 'no', 'no'], ['the digital tipping point', 'documentary', 'not yet released', 'by - sa', 'yes', 'yes'], ['collision', 'short', 'not yet', 'by - sa 3.0', 'yes', 'yes'], ['the green sight', 'documentary', 'not yet', 'tbd', 'yes', 'yes'], ['the beautiful queen marya morevna - underground', 'animated feature', 'not yet', 'by 3.0', 'yes', 'yes'], ['lunatics ! - no children in space', 'animated series pilot', '2012', 'by - sa 3.0', 'yes', 'yes'], ['moon dog', 'feature film', '2011', 'by 3.0', 'no', 'no'], ['tomã', 'documentary', '2012', 'by 3.0', 'intended - looking for hosting', 'yes'], ['this way to denmark hill', 'feature film', '2013', 'by 3.0', 'no', 'no']] |
list of fc barcelona records and statistics | https://en.wikipedia.org/wiki/List_of_FC_Barcelona_records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14707564-2.html.csv | count | two players have played more than 700 games for fc barcelona . | {'scope': 'all', 'criterion': 'greater_than', 'value': '700', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'games', '700'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose games record is greater than 700 .', 'tostr': 'filter_greater { all_rows ; games ; 700 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; games ; 700 } }', 'tointer': 'select the rows whose games record is greater than 700 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; games ; 700 } } ; 2 } = true', 'tointer': 'select the rows whose games record is greater than 700 . the number of such rows is 2 .'} | eq { count { filter_greater { all_rows ; games ; 700 } } ; 2 } = true | select the rows whose games record is greater than 700 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'games_5': 5, '700_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'games_5': 'games', '700_6': '700', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'games_5': [0], '700_6': [0], '2_7': [2]} | ['ranking', 'nationality', 'name', 'games', 'years'] | [['1', 'spain', 'xavi', '833', '1997 -'], ['2', 'spain', 'carles puyol', '724', '1996 -'], ['3', 'spain', 'migueli', '664', '1973 - 1989'], ['4', 'spain', 'carles rexach', '656', '1965 - 1981'], ['5', 'spain', 'víctor valdés', '639', '2000 -'], ['6', 'spain', 'guillermo amor', '550', '1988 - 1998'], ['7', 'spain', 'joaquim rifé', '535', '1964 - 1976'], ['8', 'spain', 'joan segarra', '528', '1949 - 1964']] |
russian football premier league | https://en.wikipedia.org/wiki/Russian_Football_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1167698-5.html.csv | superlative | oleg veretennikov was the top goal scorer in the russian football premier league . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'goals'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; goals }'}, 'player'], 'result': 'oleg veretennikov', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; goals } ; player }'}, 'oleg veretennikov'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; goals } ; player } ; oleg veretennikov } = true', 'tointer': 'select the row whose goals record of all rows is maximum . the player record of this row is oleg veretennikov .'} | eq { hop { argmax { all_rows ; goals } ; player } ; oleg veretennikov } = true | select the row whose goals record of all rows is maximum . the player record of this row is oleg veretennikov . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'goals_5': 5, 'player_6': 6, 'oleg veretennikov_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'goals_5': 'goals', 'player_6': 'player', 'oleg veretennikov_7': 'oleg veretennikov'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'goals_5': [0], 'player_6': [1], 'oleg veretennikov_7': [2]} | ['rank', 'player', 'goals', 'apps', 'avg / game'] | [['1', 'oleg veretennikov', '143', '274', '0.52'], ['2', 'aleksandr kerzhakov', '129', '293', '0.44'], ['3', 'dmitri kirichenko', '129', '377', '0.34'], ['4', 'dmitri loskov', '120', '452', '0.27'], ['5', 'sergei semak', '102', '456', '0.22'], ['6', 'andrey tikhonov', '98', '346', '0.28'], ['7', 'igor semshov', '97', '419', '0.23'], ['8', 'roman pavlyuchenko', '89', '234', '0.38'], ['9', 'yegor titov', '88', '336', '0.26'], ['10', 'valery yesipov', '88', '390', '0.23']] |
uruguayan air force | https://en.wikipedia.org/wiki/Uruguayan_Air_Force | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1015521-1.html.csv | unique | the aerospatiale as 365 dauphin is the only aircraft in the uruguayan air force that originated in france . | {'scope': 'all', 'row': '14', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'france', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'origin', 'france'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose origin record fuzzily matches to france .', 'tostr': 'filter_eq { all_rows ; origin ; france }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; origin ; france } }', 'tointer': 'select the rows whose origin record fuzzily matches to france . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'origin', 'france'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose origin record fuzzily matches to france .', 'tostr': 'filter_eq { all_rows ; origin ; france }'}, 'aircraft'], 'result': 'aerospatiale as 365 dauphin', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; origin ; france } ; aircraft }'}, 'aerospatiale as 365 dauphin'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; origin ; france } ; aircraft } ; aerospatiale as 365 dauphin }', 'tointer': 'the aircraft record of this unqiue row is aerospatiale as 365 dauphin .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; origin ; france } } ; eq { hop { filter_eq { all_rows ; origin ; france } ; aircraft } ; aerospatiale as 365 dauphin } } = true', 'tointer': 'select the rows whose origin record fuzzily matches to france . there is only one such row in the table . the aircraft record of this unqiue row is aerospatiale as 365 dauphin .'} | and { only { filter_eq { all_rows ; origin ; france } } ; eq { hop { filter_eq { all_rows ; origin ; france } ; aircraft } ; aerospatiale as 365 dauphin } } = true | select the rows whose origin record fuzzily matches to france . there is only one such row in the table . the aircraft record of this unqiue row is aerospatiale as 365 dauphin . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'origin_7': 7, 'France_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'aircraft_9': 9, 'aerospatiale as 365 dauphin_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'origin_7': 'origin', 'France_8': 'france', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'aircraft_9': 'aircraft', 'aerospatiale as 365 dauphin_10': 'aerospatiale as 365 dauphin'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'origin_7': [0], 'France_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'aircraft_9': [2], 'aerospatiale as 365 dauphin_10': [3]} | ['aircraft', 'origin', 'type', 'versions', 'in service'] | [['cessna a - 37 dragonfly', 'united states', 'attack / fighter', 'a - 37b', '12 ( 16 delivered )'], ['fma ia 58 pucarã ¡', 'argentina', 'attack', 'a - 58', '5 ( 6 delivered )'], ['lockheed c - 130 hercules', 'united states', 'transport / utility', 'c - 130b', '2'], ['embraer emb 110 bandeirante', 'brazil', 'transport / utility', 'c - 95', '3'], ['beechcraft twin bonanza', 'united states', 'transport / utility', 'd50', '1'], ['casa c - 212 aviocar', 'spain', 'transport', 'c - 212 - 200', '2'], ['embraer emb 120 brasilia', 'brazil', 'transport', 'emb 120', '1'], ['cessna 206 stationair', 'united states', 'utility / liaison', 'u206h', '10'], ['beechcraft b58 baron', 'united states', 'trainer / liaison', 'b - 58', '2'], ['british aerospace 125', 'united kingdom', 'vip transport', '700a 600a', '2'], ['aermacchi sf260', 'italy', 'trainer', 't - 260 eu', '12'], ['pilatus pc - 7 turbo trainer', 'switzerland', 'trainer', '- 92', '5 ( 6 delivered )'], ['cessna t - 41 mescalero', 'united states', 'trainer', 't - 41d', '7'], ['aerospatiale as 365 dauphin', 'france', 'liaison / transport', 'as 365', '1'], ['bell 212 twin huey', 'united states', 'transport / utility', 'bell 212', '4'], ['bell uh - 1 iroquois', 'united states', 'transport / utility', 'uh - 1h', '13']] |
safee sali | https://en.wikipedia.org/wiki/Safee_Sali | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16972055-1.html.csv | comparative | safee sali won more matches against liverpool than they did against chelsea . | {'row_1': '2', 'row_2': '5', 'col': '5', 'col_other': '4', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'liverpool'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to liverpool .', 'tostr': 'filter_eq { all_rows ; opponent ; liverpool }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; liverpool } ; result }', 'tointer': 'select the rows whose opponent record fuzzily matches to liverpool . take the result record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'chelsea'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to chelsea .', 'tostr': 'filter_eq { all_rows ; opponent ; chelsea }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; chelsea } ; result }', 'tointer': 'select the rows whose opponent record fuzzily matches to chelsea . take the result record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; liverpool } ; result } ; hop { filter_eq { all_rows ; opponent ; chelsea } ; result } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to liverpool . take the result record of this row . select the rows whose opponent record fuzzily matches to chelsea . take the result record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opponent ; liverpool } ; result } ; hop { filter_eq { all_rows ; opponent ; chelsea } ; result } } = true | select the rows whose opponent record fuzzily matches to liverpool . take the result record of this row . select the rows whose opponent record fuzzily matches to chelsea . take the result 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, 'opponent_7': 7, 'liverpool_8': 8, 'result_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'chelsea_12': 12, 'result_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', 'opponent_7': 'opponent', 'liverpool_8': 'liverpool', 'result_9': 'result', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'chelsea_12': 'chelsea', 'result_13': 'result'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'liverpool_8': [0], 'result_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'chelsea_12': [1], 'result_13': [3]} | ['date', 'location', 'head coach', 'opponent', 'result'] | [['29 july 2008', 'shah alam stadium', 'b sathianathan', 'chelsea', '0 - 2 ( l )'], ['16 july 2011', 'national stadium , bukit jalil', 'k rajagopal', 'liverpool', '3 - 6 ( l )'], ['24 july 2012', 'national stadium , bukit jalil', 'k rajagopal', 'arsenal', '0 - 2 ( l )'], ['30 july 2012', 'national stadium , bukit jalil', 'k rajagopal', 'manchester city', '0 - 3 ( l )'], ['21 july 2013', 'shah alam stadium', 'k rajagopal', 'chelsea', '1 - 4 ( l )']] |
2010 mls superdraft | https://en.wikipedia.org/wiki/2010_MLS_SuperDraft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25518547-2.html.csv | ordinal | in the second round of picks in the mls 2010 superdraft , toni stãhl was the first pick . | {'row': '1', 'col': '1', 'order': '1', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'pick', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; pick ; 1 }'}, 'player'], 'result': 'toni stãhl', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; pick ; 1 } ; player }'}, 'toni stãhl'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; pick ; 1 } ; player } ; toni stãhl } = true', 'tointer': 'select the row whose pick record of all rows is 1st minimum . the player record of this row is toni stãhl .'} | eq { hop { nth_argmin { all_rows ; pick ; 1 } ; player } ; toni stãhl } = true | select the row whose pick record of all rows is 1st minimum . the player record of this row is toni stãhl . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, '1_6': 6, 'player_7': 7, 'toni stãhl_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', 'pick_5': 'pick', '1_6': '1', 'player_7': 'player', 'toni stãhl_8': 'toni stãhl'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], '1_6': [0], 'player_7': [1], 'toni stãhl_8': [2]} | ['pick', 'mls team', 'player', 'position', 'affiliation'] | [['17', 'philadelphia union', 'toni stãhl', 'midfielder', 'university of connecticut'], ['18', 'new york red bulls', 'tim ream', 'defender', 'saint louis university chicago fire premier'], ['19', 'san jose earthquakes', 'michael thomas', 'midfielder', 'university of notre dame kansas city brass'], ['20', 'kansas city wizards', 'korede aiyegbusi', 'defender', 'north carolina state university cary clarets'], ['21', 'fc dallas', 'andrew wiedeman', 'forward', 'university of california norcal lamorinda united'], ['22', 'colorado rapids', 'andre akpan', 'forward', 'harvard university chicago fire premier'], ['23', 'colorado rapids', 'ross labauex', 'midfielder', 'university of virginia chicago fire premier'], ['24', 'toronto fc', 'zachary herold', 'defender', 'west pines united club'], ['25', 'new england revolution', 'seth sinovic', 'defender', 'creighton university chicago fire premier'], ['26', 'chicago fire', 'kwame watson - siriboe', 'defender', 'university of connecticut westchester flames'], ['27', 'seattle sounders fc', 'mike seamon', 'midfielder', 'villanova university'], ['28', 'san jose earthquakes', 'justin morrow', 'defender', 'university of notre dame chicago fire premier'], ['29', 'chicago fire', 'drew yates', 'midfielder', 'university of maryland'], ['30', 'san jose earthquakes', 'steven beitashour', 'defender', 'san diego state university san jose frogs'], ['31', 'new england revolution', 'zak boggs', 'forward', 'university of south florida bradenton academics']] |
2007 - 08 commonwealth bank series statistics | https://en.wikipedia.org/wiki/2007%E2%80%9308_Commonwealth_Bank_Series_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15700367-3.html.csv | count | three of the players are listed as playing eight innings . | {'scope': 'all', 'criterion': 'equal', 'value': '8', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'innings', '8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose innings record is equal to 8 .', 'tostr': 'filter_eq { all_rows ; innings ; 8 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; innings ; 8 } }', 'tointer': 'select the rows whose innings record is equal to 8 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; innings ; 8 } } ; 3 } = true', 'tointer': 'select the rows whose innings record is equal to 8 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; innings ; 8 } } ; 3 } = true | select the rows whose innings record is equal to 8 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'innings_5': 5, '8_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'innings_5': 'innings', '8_6': '8', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'innings_5': [0], '8_6': [0], '3_7': [2]} | ['name', 'innings', 'runs scored', 'balls faced', 'average', 'sr'] | [['adam gilchrist ( wk )', '8', '313', '318', '39.13', '98.43'], ['matthew hayden', '6', '161', '231', '26.83', '69.70'], ['ricky ponting ( c )', '8', '189', '256', '23.63', '73.83'], ['michael clarke', '7', '293', '416', '48.83', '70.43'], ['andrew symonds', '8', '100', '125', '14.29', '80.00'], ['michael hussey', '7', '189', '283', '47.25', '66.78'], ['james hopes', '7', '115', '125', '16.43', '92.00'], ['brett lee', '5', '49', '102', '12.25', '48.04'], ['mitchell johnson', '5', '21', '44', '7.00', '47.73'], ['nathan bracken', '4', '16', '43', '5.33', '37.21'], ['stuart clark', '2', '8', '10', '8.00', '80.00'], ['brad haddin', '2', '12', '44', '6.00', '27.27'], ['brad hogg', '4', '62', '100', '15.50', '62.00']] |
list of ottawa senators draft picks | https://en.wikipedia.org/wiki/List_of_Ottawa_Senators_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11803648-20.html.csv | comparative | stefan noesen was selected in an earlier round than jean - gabriel pageau . | {'row_1': '2', 'row_2': '5', 'col': '1', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'stefan noesen'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to stefan noesen .', 'tostr': 'filter_eq { all_rows ; player ; stefan noesen }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; stefan noesen } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to stefan noesen . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'jean - gabriel pageau'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to jean - gabriel pageau .', 'tostr': 'filter_eq { all_rows ; player ; jean - gabriel pageau }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; jean - gabriel pageau } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to jean - gabriel pageau . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; stefan noesen } ; round } ; hop { filter_eq { all_rows ; player ; jean - gabriel pageau } ; round } } = true', 'tointer': 'select the rows whose player record fuzzily matches to stefan noesen . take the round record of this row . select the rows whose player record fuzzily matches to jean - gabriel pageau . take the round record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; stefan noesen } ; round } ; hop { filter_eq { all_rows ; player ; jean - gabriel pageau } ; round } } = true | select the rows whose player record fuzzily matches to stefan noesen . take the round record of this row . select the rows whose player record fuzzily matches to jean - gabriel pageau . take the round record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'stefan noesen_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'jean - gabriel pageau_12': 12, 'round_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'stefan noesen_8': 'stefan noesen', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'jean - gabriel pageau_12': 'jean - gabriel pageau', 'round_13': 'round'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'stefan noesen_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'jean - gabriel pageau_12': [1], 'round_13': [3]} | ['round', 'overall', 'player', 'position', 'nationality', 'club team'] | [['1', '6', 'mika zibanejad', 'centre', 'sweden', 'djurgårdens if hockey ( sel )'], ['1', '21 ( from nashville )', 'stefan noesen', 'right wing', 'united states', 'plymouth whalers ( ohl )'], ['1', '24 ( from detroit )', 'matthew puempel', 'left wing', 'canada', 'peterborough petes ( ohl )'], ['2', '61 ( from boston )', 'shane prince', 'left wing', 'united states', "ottawa 67 's ( ohl )"], ['4', '96', 'jean - gabriel pageau', 'centre', 'canada', 'gatineau olympiques ( qmjhl )'], ['5', '126', 'fredrik claesson', 'defense', 'sweden', 'djurgårdens if hockey ( sel )'], ['6', '156', 'darren kramer', 'centre', 'canada', 'spokane chiefs ( whl )'], ['6', '171 ( from phoenix )', 'max mccormick', 'left wing', 'united states', 'sioux city musketeers ( ushl )'], ['7', '186', 'jordan fransoo', 'defense', 'canada', 'brandon wheat kings ( whl )']] |
lewis black 's root of all evil | https://en.wikipedia.org/wiki/Lewis_Black%27s_Root_of_All_Evil | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15781170-1.html.csv | aggregation | in lewis black 's root of all evil , of the advocates , the total number of nonpoll wins , excl . ties , is 17 . | {'scope': 'all', 'col': '2', 'type': 'sum', 'result': '17', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'wins'], 'result': '17', 'ind': 0, 'tostr': 'sum { all_rows ; wins }'}, '17'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; wins } ; 17 } = true', 'tointer': 'the sum of the wins record of all rows is 17 .'} | round_eq { sum { all_rows ; wins } ; 17 } = true | the sum of the wins record of all rows is 17 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'wins_4': 4, '17_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '17_5': '17'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'wins_4': [0], '17_5': [1]} | ['advocate', 'wins', 'losses', 'ties', 'poll wins', 'poll losses'] | [['andrew daly', '4', '2', '0', '2', '4'], ['andy kindler', '3', '1', '0', '1', '3'], ['patton oswalt', '3', '2', '1', '3', '3'], ['kathleen madigan', '2', '1', '0', '1', '2'], ['greg giraldo', '2', '7', '0', '6', '3'], ['paul f tompkins', '1', '4', '1', '3', '3'], ['jerry minor', '1', '0', '0', '1', '0'], ['andrea savage', '1', '0', '0', '1', '0']] |
1995 - 96 atlanta hawks season | https://en.wikipedia.org/wiki/1995%E2%80%9396_Atlanta_Hawks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18493036-6.html.csv | count | in the 1995 - 96 atlanta hawks season , among the games played in the omni , 2 of them were lost by atlanta hawks . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'l', 'result': '2', 'col': '4', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'the omni'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location / attendance', 'the omni'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location / attendance ; the omni }', 'tointer': 'select the rows whose location / attendance record fuzzily matches to the omni .'}, 'score', 'l'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location / attendance record fuzzily matches to the omni . among these rows , select the rows whose score record fuzzily matches to l .', 'tostr': 'filter_eq { filter_eq { all_rows ; location / attendance ; the omni } ; score ; l }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; location / attendance ; the omni } ; score ; l } }', 'tointer': 'select the rows whose location / attendance record fuzzily matches to the omni . among these rows , select the rows whose score record fuzzily matches to l . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; location / attendance ; the omni } ; score ; l } } ; 2 } = true', 'tointer': 'select the rows whose location / attendance record fuzzily matches to the omni . among these rows , select the rows whose score record fuzzily matches to l . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; location / attendance ; the omni } ; score ; l } } ; 2 } = true | select the rows whose location / attendance record fuzzily matches to the omni . among these rows , select the rows whose score record fuzzily matches to l . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'location / attendance_6': 6, 'the omni_7': 7, 'score_8': 8, 'l_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'location / attendance_6': 'location / attendance', 'the omni_7': 'the omni', 'score_8': 'score', 'l_9': 'l', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'location / attendance_6': [0], 'the omni_7': [0], 'score_8': [1], 'l_9': [1], '2_10': [3]} | ['game', 'date', 'opponent', 'score', 'location / attendance', 'record'] | [['29', 'january 2', 'seattle supersonics', 'l 88 - 111', 'the omni', '13 - 16'], ['game', 'date', 'opponent', 'score', 'location / attendance', 'record'], ['30', 'january 4', 'toronto raptors', 'w 104 - 101 ( ot )', 'the omni', '14 - 16'], ['31', 'january 6', 'charlotte hornets', 'l 90 - 96', 'charlotte coliseum', '14 - 17'], ['32', 'january 9', 'sacramento kings', 'w 104 - 88', 'the omni', '15 - 17'], ['33', 'january 11', 'toronto raptors', 'w 87 - 79', 'skydome', '16 - 17'], ['34', 'january 13', 'boston celtics', 'w 108 - 105', 'the omni', '17 - 17'], ['35', 'january 15', 'detroit pistons', 'w 96 - 88', 'the omni', '18 - 17'], ['36', 'january 17', 'indiana pacers', 'w 102 - 93', 'the omni', '19 - 17'], ['37', 'january 19', 'philadelphia 76ers', 'w 82 - 77', 'the spectrum', '20 - 17'], ['38', 'january 20', 'miami heat', 'w 98 - 78', 'the omni', '21 - 17'], ['39', 'january 22', 'houston rockets', 'w 105 - 96', 'the omni', '22 - 17'], ['40', 'january 23', 'cleveland cavaliers', 'w 84 - 72', 'gund arena', '23 - 17'], ['41', 'january 26', 'orlando magic', 'w 96 - 84', 'the omni', '24 - 17'], ['42', 'january 30', 'indiana pacers', 'l 90 - 107', 'market square arena', '24 - 18'], ['43', 'january 31', 'phoenix suns', 'l 84 - 120', 'the omni', '24 - 19']] |
list of superleague formula drivers and teams | https://en.wikipedia.org/wiki/List_of_Superleague_Formula_drivers_and_teams | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19312274-2.html.csv | superlative | france is the country with the highest number of superleague formula teams . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total }'}, 'country'], 'result': 'france', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total } ; country }'}, 'france'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total } ; country } ; france } = true', 'tointer': 'select the row whose total record of all rows is maximum . the country record of this row is france .'} | eq { hop { argmax { all_rows ; total } ; country } ; france } = true | select the row whose total record of all rows is maximum . the country record of this row is france . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, 'country_6': 6, 'france_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total_5': 'total', 'country_6': 'country', 'france_7': 'france'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], 'country_6': [1], 'france_7': [2]} | ['country', 'total', 'champions', 'current', 'first driver ( s )', 'last / current driver ( s )'] | [['argentina', '1', '0', '0', 'esteban guerrieri ( 2009 )', 'esteban guerrieri ( 2010 )'], ['australia', '1', '0', '1', 'john martin ( 2009 )', 'john martin'], ['belgium', '2', '0', '1', 'bertrand baguette ( 2008 )', 'frédéric vervisch'], ['brazil', '3', '0', '1', 'tuka rocha ( 2008 )', 'antônio pizzonia'], ['china', '3', '0', '1', 'ho - pin tung ( 2009 )', 'ho - pin tung'], ['czech republic', '1', '0', '1', 'filip salaquarda ( 2011 )', 'filip salaquarda'], ['denmark', '1', '0', '0', 'kasper andersen ( 2008 )', 'kasper andersen ( 2009 )'], ['france', '7', '0', '1', 'tristan gommendy , nelson philippe ( 2008 )', 'tristan gommendy'], ['germany', '1', '0', '0', 'max wissel ( 2008 )', 'max wissel ( 2010 )'], ['greece', '1', '0', '0', 'stamatis katsimis ( 2008 )', 'stamatis katsimis ( 2008 )'], ['india', '1', '0', '0', 'narain karthikeyan ( 2010 )', 'narain karthikeyan ( 2010 )'], ['netherlands', '6', '0', '2', 'yelmer buurman , robert doornbos ( 2008 )', 'yelmer buurman , robert doornbos'], ['new zealand', '2', '0', '1', 'chris van der drift ( 2010 )', 'earl bamber'], ['portugal', '2', '0', '0', 'pedro petiz ( 2009 )', 'álvaro parente ( 2010 )'], ['switzerland', '1', '0', '1', 'neel jani ( 2010 )', 'neel jani'], ['united arab emirates', '1', '0', '0', 'andreas zuber ( 2008 )', 'andreas zuber ( 2008 )']] |
1982 - 83 north west counties football league | https://en.wikipedia.org/wiki/1982%E2%80%9383_North_West_Counties_Football_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17716055-3.html.csv | superlative | the colne dynamoes team had the most points in the 1982 - 83 north west counties football league . | {'scope': 'all', 'col_superlative': '9', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'points 1'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points 1 }'}, 'team'], 'result': 'colne dynamoes', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points 1 } ; team }'}, 'colne dynamoes'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points 1 } ; team } ; colne dynamoes } = true', 'tointer': 'select the row whose points 1 record of all rows is maximum . the team record of this row is colne dynamoes .'} | eq { hop { argmax { all_rows ; points 1 } ; team } ; colne dynamoes } = true | select the row whose points 1 record of all rows is maximum . the team record of this row is colne dynamoes . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points 1_5': 5, 'team_6': 6, 'colne dynamoes_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points 1_5': 'points 1', 'team_6': 'team', 'colne dynamoes_7': 'colne dynamoes'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points 1_5': [0], 'team_6': [1], 'colne dynamoes_7': [2]} | ['position', 'team', 'played', 'drawn', 'lost', 'goals for', 'goals against', 'goal difference', 'points 1'] | [['1', 'colne dynamoes', '34', '5', '4', '95', '37', '+ 58', '55'], ['2', 'warrington town', '34', '6', '4', '83', '33', '+ 50', '54'], ['3', 'clitheroe', '34', '7', '5', '87', '35', '+ 52', '51'], ['4', 'prestwich heys', '34', '11', '5', '70', '37', '+ 33', '47'], ['5', 'vulcan newton', '34', '10', '11', '70', '65', '+ 5', '36'], ['6', 'blackpool mechanics', '34', '13', '10', '67', '56', '+ 11', '35'], ['7', 'bacup borough', '34', '7', '13', '53', '45', '+ 8', '35'], ['8', 'atherton collieries', '34', '11', '11', '55', '57', '2', '35'], ['9', 'whitworth valley', '34', '9', '12', '54', '65', '11', '35'], ['10', 'nelson', '34', '16', '11', '49', '56', '7', '28'], ['11', 'daisy hill', '34', '10', '14', '47', '58', '11', '30'], ['12', 'maghull', '34', '9', '15', '56', '61', '5', '29'], ['13', 'ashton town', '34', '5', '17', '53', '73', '20', '29'], ['14', 'newton', '34', '12', '14', '59', '62', '3', '28'], ['15', 'oldham dew', '34', '8', '16', '48', '61', '13', '28'], ['16', 'bolton st', '34', '6', '19', '50', '84', '34', '24'], ['17', 'wigan rovers', '34', '7', '22', '35', '72', '37', '17'], ['18', 'ashton athletic', '34', '8', '23', '18', '92', '74', '14']] |
1974 icf canoe sprint world championships | https://en.wikipedia.org/wiki/1974_ICF_Canoe_Sprint_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18607938-4.html.csv | ordinal | hungary earned the 2nd highest amount of silver medals during the 1974 icf canoe sprint world championships . | {'row': '3', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'silver', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; silver ; 2 }'}, 'nation'], 'result': 'hungary', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; silver ; 2 } ; nation }'}, 'hungary'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; hungary } = true', 'tointer': 'select the row whose silver record of all rows is 2nd maximum . the nation record of this row is hungary .'} | eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; hungary } = true | select the row whose silver record of all rows is 2nd maximum . the nation record of this row is hungary . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'silver_5': 5, '2_6': 6, 'nation_7': 7, 'hungary_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', 'silver_5': 'silver', '2_6': '2', 'nation_7': 'nation', 'hungary_8': 'hungary'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'silver_5': [0], '2_6': [0], 'nation_7': [1], 'hungary_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'soviet union', '6', '7', '1', '14'], ['2', 'romania', '3', '2', '6', '11'], ['3', 'hungary', '3', '4', '3', '10'], ['4', 'east germany', '4', '2', '2', '8'], ['5', 'poland', '1', '2', '3', '6'], ['6', 'italy', '1', '0', '1', '2'], ['7', 'belgium', '0', '1', '0', '1'], ['8', 'czechoslovakia', '0', '0', '1', '1'], ['9', 'france', '0', '0', '1', '1'], ['total', 'total', '18', '18', '18', '54']] |
water polo at the 2004 summer olympics - men 's team rosters | https://en.wikipedia.org/wiki/Water_polo_at_the_2004_Summer_Olympics_%E2%80%93_Men%27s_team_rosters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17759945-7.html.csv | comparative | of the men 's water polo players at the 2004 summer olympics , gergely kiss was born 5 years after tibor benedek . | {'row_1': '7', 'row_2': '8', 'col': '5', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '5 years', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'gergely kiss'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to gergely kiss .', 'tostr': 'filter_eq { all_rows ; name ; gergely kiss }'}, 'date of birth'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; gergely kiss } ; date of birth }', 'tointer': 'select the rows whose name record fuzzily matches to gergely kiss . take the date of birth record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'tibor benedek'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to tibor benedek .', 'tostr': 'filter_eq { all_rows ; name ; tibor benedek }'}, 'date of birth'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; tibor benedek } ; date of birth }', 'tointer': 'select the rows whose name record fuzzily matches to tibor benedek . take the date of birth record of this row .'}], 'result': '5 years', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; name ; gergely kiss } ; date of birth } ; hop { filter_eq { all_rows ; name ; tibor benedek } ; date of birth } }'}, '5 years'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; name ; gergely kiss } ; date of birth } ; hop { filter_eq { all_rows ; name ; tibor benedek } ; date of birth } } ; 5 years } = true', 'tointer': 'select the rows whose name record fuzzily matches to gergely kiss . take the date of birth record of this row . select the rows whose name record fuzzily matches to tibor benedek . take the date of birth record of this row . the first record is 5 years larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; name ; gergely kiss } ; date of birth } ; hop { filter_eq { all_rows ; name ; tibor benedek } ; date of birth } } ; 5 years } = true | select the rows whose name record fuzzily matches to gergely kiss . take the date of birth record of this row . select the rows whose name record fuzzily matches to tibor benedek . take the date of birth record of this row . the first record is 5 years larger than the second record . | 6 | 6 | {'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'name_8': 8, 'gergely kiss_9': 9, 'date of birth_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'name_12': 12, 'tibor benedek_13': 13, 'date of birth_14': 14, '5 years_15': 15} | {'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'name_8': 'name', 'gergely kiss_9': 'gergely kiss', 'date of birth_10': 'date of birth', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'name_12': 'name', 'tibor benedek_13': 'tibor benedek', 'date of birth_14': 'date of birth', '5 years_15': '5 years'} | {'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'name_8': [0], 'gergely kiss_9': [0], 'date of birth_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'name_12': [1], 'tibor benedek_13': [1], 'date of birth_14': [3], '5 years_15': [5]} | ['name', 'pos', 'height', 'weight', 'date of birth', 'club'] | [['zoltán szécsi', 'gk', 'm ( ft 6in )', '-', '1977 - 12 - 22', 'bvsc vízilabda'], ['tamás varga', 'cb', 'm ( ft 4in )', '-', '1975 - 07 - 14', 'vasas sc'], ['norbert madaras', 'cf', 'm ( ft 3in )', '-', '1979 - 12 - 01', 'vasas sc'], ['ádám steinmetz', 'cf', 'm ( ft 6in )', '-', '1980 - 08 - 11', 'vasas sc'], ['tamás kásás', 'd', 'm ( ft 7in )', '-', '1976 - 07 - 20', 'vasas sc'], ['attila vári', 'cb', 'm ( ft 7in )', '-', '1976 - 02 - 26', 'domino bhse'], ['gergely kiss', 'cf', 'm ( ft 6in )', '-', '1977 - 09 - 21', 'domino bhse'], ['tibor benedek', 'cf', 'm ( ft 3in )', '-', '1972 - 07 - 12', 'pro recco'], ['rajmund fodor', 'd', 'm ( ft 3in )', '-', '1976 - 02 - 21', 'domino bhse'], ['istván gergely', 'gk', 'm ( ft 8in )', '-', '1976 - 08 - 20', 'domino bhse'], ['barnabás steinmetz', 'cb', 'm ( ft 5in )', '-', '1975 - 10 - 06', 'vasas sc'], ['tamás molnár', 'cf', 'm ( ft 5in )', '-', '1975 - 08 - 02', 'domino bhse'], ['péter biros', 'cf', 'm ( ft 4in )', '-', '1976 - 04 - 05', 'domino bhse']] |
midland railway - butterley | https://en.wikipedia.org/wiki/Midland_Railway_%E2%80%93_Butterley | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1167462-1.html.csv | unique | the no 1163 whitehead is the only midland railway - butterley locomotive that has a private owner . | {'scope': 'all', 'row': '1', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'private owner', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'owner ( s )', 'private owner'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose owner ( s ) record fuzzily matches to private owner .', 'tostr': 'filter_eq { all_rows ; owner ( s ) ; private owner }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; owner ( s ) ; private owner } }', 'tointer': 'select the rows whose owner ( s ) record fuzzily matches to private owner . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'owner ( s )', 'private owner'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose owner ( s ) record fuzzily matches to private owner .', 'tostr': 'filter_eq { all_rows ; owner ( s ) ; private owner }'}, 'number & name'], 'result': 'no 1163 whitehead', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; owner ( s ) ; private owner } ; number & name }'}, 'no 1163 whitehead'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; owner ( s ) ; private owner } ; number & name } ; no 1163 whitehead }', 'tointer': 'the number & name record of this unqiue row is no 1163 whitehead .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; owner ( s ) ; private owner } } ; eq { hop { filter_eq { all_rows ; owner ( s ) ; private owner } ; number & name } ; no 1163 whitehead } } = true', 'tointer': 'select the rows whose owner ( s ) record fuzzily matches to private owner . there is only one such row in the table . the number & name record of this unqiue row is no 1163 whitehead .'} | and { only { filter_eq { all_rows ; owner ( s ) ; private owner } } ; eq { hop { filter_eq { all_rows ; owner ( s ) ; private owner } ; number & name } ; no 1163 whitehead } } = true | select the rows whose owner ( s ) record fuzzily matches to private owner . there is only one such row in the table . the number & name record of this unqiue row is no 1163 whitehead . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'owner (s)_7': 7, 'private owner_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'number & name_9': 9, 'no 1163 whitehead_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'owner (s)_7': 'owner ( s )', 'private owner_8': 'private owner', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'number & name_9': 'number & name', 'no 1163 whitehead_10': 'no 1163 whitehead'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'owner (s)_7': [0], 'private owner_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'number & name_9': [2], 'no 1163 whitehead_10': [3]} | ['number & name', 'description', 'livery', 'owner ( s )', 'date'] | [['no 1163 whitehead', 'peckett 0 - 4 - 0st', 'green', 'private owner', '1908'], ['no 47327 / 23', 'lms fowler class 3f 0 - 6 - 0t', 's & djr prussian blue', 'derby city council', '1926'], ['castle donington power station no 1', 'rsh 0 - 4 - 0st', 'dark blue', 'midland railway trust', '1954'], ['no 80080', 'br 2 - 6 - 4t class 4 mt', 'br lined black with early crest', 'princess royal class locomotive trust', '1954'], ['no 73129', 'br 4 - 6 - 0 class 5 mt', 'br unlined black with the early crest', 'derby city council', '1956'], ['no46233 duchess of sutherland', 'lms coronation class', 'br green with early crest', 'princess royal class locomotive trust', '1938']] |
miss andretti | https://en.wikipedia.org/wiki/Miss_Andretti | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14981555-1.html.csv | unique | in the shortest races of 1000 meters , miss andretti weighed the most at 59 kilograms . | {'scope': 'all', 'row': '5', 'col': '7', 'col_other': '6', 'criterion': 'equal', 'value': '59', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'weight ( kg )', '59'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose weight ( kg ) record is equal to 59 .', 'tostr': 'filter_eq { all_rows ; weight ( kg ) ; 59 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; weight ( kg ) ; 59 } }', 'tointer': 'select the rows whose weight ( kg ) record is equal to 59 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'weight ( kg )', '59'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose weight ( kg ) record is equal to 59 .', 'tostr': 'filter_eq { all_rows ; weight ( kg ) ; 59 }'}, 'distance'], 'result': '1000 m', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; weight ( kg ) ; 59 } ; distance }'}, '1000 m'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; weight ( kg ) ; 59 } ; distance } ; 1000 m }', 'tointer': 'the distance record of this unqiue row is 1000 m .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; weight ( kg ) ; 59 } } ; eq { hop { filter_eq { all_rows ; weight ( kg ) ; 59 } ; distance } ; 1000 m } } = true', 'tointer': 'select the rows whose weight ( kg ) record is equal to 59 . there is only one such row in the table . the distance record of this unqiue row is 1000 m .'} | and { only { filter_eq { all_rows ; weight ( kg ) ; 59 } } ; eq { hop { filter_eq { all_rows ; weight ( kg ) ; 59 } ; distance } ; 1000 m } } = true | select the rows whose weight ( kg ) record is equal to 59 . there is only one such row in the table . the distance record of this unqiue row is 1000 m . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'weight (kg)_7': 7, '59_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'distance_9': 9, '1000 m_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'weight (kg)_7': 'weight ( kg )', '59_8': '59', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'distance_9': 'distance', '1000 m_10': '1000 m'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'weight (kg)_7': [0], '59_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'distance_9': [2], '1000 m_10': [3]} | ['result', 'date', 'race', 'venue', 'group', 'distance', 'weight ( kg )', 'jockey', 'winner / 2nd'] | [['won', '29 dec 2004', '3yo hcp restricted maiden', 'pinjarra', 'na', '1200 m', '55', 'k forrester', '2nd - hello doctor'], ['2nd', '26 jan 2005', '3yo hcp restricted fillies', 'belmont', 'na', '1200 m', '52.5', 'k forrester', '1st - lust for dust'], ['won', '12 feb 2005', '3yo hcp restricted', 'belmont', 'na', '1200 m', '52', 'k forrester', "2nd - key 's ace"], ['won', '27 feb 2005', '3yo hcp restricted fillies & mares', 'pinjarra', 'na', '1400 m', '53.5', 'k forrester', '2nd - blondelle'], ['won', '25 apr 2005', '3yo hcp restricted fillies', 'belmont', 'na', '1000 m', '59', 'k forrester', '2nd - final effect'], ['won', '14 may 2005', '3yo hcp restricted', 'belmont', 'na', '1200 m', '52.5', 'k forrester', '2nd - zed power'], ['won', '28 may 2005', '3yo hcp restricted fillies & mares', 'belmont', 'na', '1200 m', '55.5', 'k forrester', '2nd - eroded']] |
1963 detroit lions season | https://en.wikipedia.org/wiki/1963_Detroit_Lions_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16983245-2.html.csv | ordinal | for the 1963 detroit lions season , the second highest attendance at a game was 55,400 . | {'row': '3', 'col': '5', 'order': '2', 'col_other': 'n/a', 'max_or_min': 'max_to_min', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'nth_max', 'args': ['all_rows', 'attendance', '2'], 'result': '55400', 'ind': 0, 'tostr': 'nth_max { all_rows ; attendance ; 2 }', 'tointer': 'the 2nd maximum attendance record of all rows is 55400 .'}, '55400'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_max { all_rows ; attendance ; 2 } ; 55400 } = true', 'tointer': 'the 2nd maximum attendance record of all rows is 55400 .'} | eq { nth_max { all_rows ; attendance ; 2 } ; 55400 } = true | the 2nd maximum attendance record of all rows is 55400 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'nth_max_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '2_5': 5, '55400_6': 6} | {'eq_1': 'eq', 'result_2': 'true', 'nth_max_0': 'nth_max', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '2_5': '2', '55400_6': '55400'} | {'eq_1': [2], 'result_2': [], 'nth_max_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '2_5': [0], '55400_6': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 14 , 1963', 'los angeles rams', 'w 23 - 2', '49342'], ['2', 'september 22 , 1963', 'green bay packers', 'l 31 - 10', '45912'], ['3', 'september 29 , 1963', 'chicago bears', 'l 37 - 21', '55400'], ['4', 'october 6 , 1963', 'san francisco 49ers', 'w 26 - 3', '44088'], ['5', 'october 13 , 1963', 'dallas cowboys', 'l 17 - 14', '27264'], ['6', 'october 20 , 1963', 'baltimore colts', 'l 25 - 21', '51901'], ['7', 'october 27 , 1963', 'minnesota vikings', 'w 28 - 10', '44509'], ['8', 'november 3 , 1963', 'san francisco 49ers', 'w 45 - 7', '33511'], ['9', 'november 10 , 1963', 'baltimore colts', 'l 24 - 21', '59758'], ['10', 'november 17 , 1963', 'los angeles rams', 'l 28 - 21', '44951'], ['11', 'november 24 , 1963', 'minnesota vikings', 'l 34 - 31', '28763'], ['12', 'november 28 , 1963', 'green bay packers', 't 13 - 13', '54016'], ['13', 'december 8 , 1963', 'cleveland browns', 'w 38 - 10', '51382'], ['14', 'december 15 , 1963', 'chicago bears', 'l 24 - 14', '45317']] |
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 | majority | all clubs played 30 games in the 1958 - 59 segunda división . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': '30', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'played', '30'], 'result': True, 'ind': 0, 'tointer': 'for the played records of all rows , all of them are equal to 30 .', 'tostr': 'all_eq { all_rows ; played ; 30 } = true'} | all_eq { all_rows ; played ; 30 } = true | for the played records of all rows , all of them are equal to 30 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'played_3': 3, '30_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'played_3': 'played', '30_4': '30'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'played_3': [0], '30_4': [0]} | ['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']] |
ariana afghan airlines | https://en.wikipedia.org/wiki/Ariana_Afghan_Airlines | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-158904-1.html.csv | count | there were four incidents of ariana afghan airlines , where a boeing 727 was involved . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'boeing 727', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'aircraft', 'boeing 727'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose aircraft record fuzzily matches to boeing 727 .', 'tostr': 'filter_eq { all_rows ; aircraft ; boeing 727 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; aircraft ; boeing 727 } }', 'tointer': 'select the rows whose aircraft record fuzzily matches to boeing 727 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; aircraft ; boeing 727 } } ; 4 } = true', 'tointer': 'select the rows whose aircraft record fuzzily matches to boeing 727 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; aircraft ; boeing 727 } } ; 4 } = true | select the rows whose aircraft record fuzzily matches to boeing 727 . 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, 'aircraft_5': 5, 'boeing 727_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', 'aircraft_5': 'aircraft', 'boeing 727_6': 'boeing 727', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'aircraft_5': [0], 'boeing 727_6': [0], '4_7': [2]} | ['location', 'aircraft', 'tail number', 'aircraft damage', 'fatalities'] | [['greece', 'douglas c - 47a', 'ya - aad', 'w / o', 'unknown'], ['off beirut', 'dc - 4', 'ya - bag', 'w / o', '24 / 27'], ['london', 'boeing 727 - 100c', 'ya - far', 'w / o', '50'], ['kabul', 'douglas c - 47dl', 'ya - bad', 'w / o', 'unknown'], ['pakistan', 'an - 26', 'unknown', 'w / o', '25 / 25'], ['zabol', 'an - 26', 'ya - bak', 'w / o', '6 / 39'], ['kabul', 'tu - 154 m', 'ya - tap', 'w / o', '0 / 0'], ['kabul', 'an - 26', 'ya - ban', 'w / o', 'unknown'], ['jalalabad', 'an - 26b', 'ya - bao', 'w / o', '3 / 46'], ['jalalabad', 'yak - 40', 'ya - kae', 'w / o', '1'], ['charasyab', 'boeing 727 - 200', 'ya - faz', 'w / o', '45 / 45'], ['kabul', 'an - 12b', 'ya - daa', 'w / o', '0 / 0'], ['kabul', 'an - 12bk', 'ya - dab', 'w / o', '0 / 0'], ['kabul', 'an - 24', 'unknown', 'w / o', '0 / 0'], ['kabul', 'an - 24b', 'ya - dah', 'w / o', '0 / 0'], ['kabul', 'an - 24rv', 'ya - daj', 'w / o', '0 / 0'], ['kabul', 'boeing 727 - 100c', 'ya - fau', 'w / o', '0 / 0'], ['kabul', 'boeing 727 - 100c', 'ya - faw', 'w / o', '0 / 0'], ['istanbul', 'a300b4 - 200', 'ya - bad', 'w / o', '0']] |
athletics at the 1966 central american and caribbean games | https://en.wikipedia.org/wiki/Athletics_at_the_1966_Central_American_and_Caribbean_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10139327-3.html.csv | comparative | cuba has double the amount of total medals that jamaica has . | {'row_1': '1', 'row_2': '2', 'col': '6', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'cuba'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to cuba .', 'tostr': 'filter_eq { all_rows ; nation ; cuba }'}, 'total'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; cuba } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to cuba . take the total record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'jamaica'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to jamaica .', 'tostr': 'filter_eq { all_rows ; nation ; jamaica }'}, 'total'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; jamaica } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to jamaica . take the total record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; cuba } ; total } ; hop { filter_eq { all_rows ; nation ; jamaica } ; total } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to cuba . take the total record of this row . select the rows whose nation record fuzzily matches to jamaica . take the total record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; nation ; cuba } ; total } ; hop { filter_eq { all_rows ; nation ; jamaica } ; total } } = true | select the rows whose nation record fuzzily matches to cuba . take the total record of this row . select the rows whose nation record fuzzily matches to jamaica . take the total 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, 'nation_7': 7, 'cuba_8': 8, 'total_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'jamaica_12': 12, 'total_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', 'nation_7': 'nation', 'cuba_8': 'cuba', 'total_9': 'total', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'jamaica_12': 'jamaica', 'total_13': 'total'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'cuba_8': [0], 'total_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'jamaica_12': [1], 'total_13': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'cuba', '9', '11', '12', '32'], ['2', 'jamaica', '7', '5', '4', '16'], ['3', 'colombia', '4', '2', '3', '9'], ['4', 'puerto rico', '4', '2', '1', '7'], ['5', 'mexico', '3', '3', '4', '10'], ['6', 'trinidad and tobago', '2', '5', '1', '8'], ['7', 'barbados', '1', '1', '1', '3'], ['8', 'guatemala', '1', '0', '0', '1'], ['8', 'bahamas', '1', '0', '0', '1'], ['10', 'venezuela', '0', '2', '5', '7'], ['11', 'us virgin islands', '0', '1', '1', '2']] |
1993 new york jets season | https://en.wikipedia.org/wiki/1993_New_York_Jets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10918196-1.html.csv | comparative | in the 1993 new york jets season , the attendance for the game against the dallas cowboys was the higher than the game against the philadelphia eagles . | {'row_1': '14', 'row_2': '4', 'col': '6', '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', 'opponent', 'dallas cowboys'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to dallas cowboys .', 'tostr': 'filter_eq { all_rows ; opponent ; dallas cowboys }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'philadelphia eagles'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to philadelphia eagles .', 'tostr': 'filter_eq { all_rows ; opponent ; philadelphia eagles }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to philadelphia eagles . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } ; hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; attendance } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . select the rows whose opponent record fuzzily matches to philadelphia eagles . take the attendance record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } ; hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; attendance } } = true | select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . select the rows whose opponent record fuzzily matches to philadelphia eagles . take the attendance record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'dallas cowboys_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'philadelphia eagles_12': 12, 'attendance_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'dallas cowboys_8': 'dallas cowboys', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'philadelphia eagles_12': 'philadelphia eagles', 'attendance_13': 'attendance'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'dallas cowboys_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'philadelphia eagles_12': [1], 'attendance_13': [3]} | ['week', 'date', 'opponent', 'result', 'game site', 'attendance'] | [['1', '1993 - 09 - 05', 'denver broncos', 'l 26 - 20', 'the meadowlands', '68130'], ['2', '1993 - 09 - 12', 'miami dolphins', 'w 24 - 14', 'joe robbie stadium', '70314'], ['4', '1993 - 09 - 26', 'new england patriots', 'w 45 - 7', 'the meadowlands', '64836'], ['5', '1993 - 10 - 03', 'philadelphia eagles', 'l 35 - 30', 'the meadowlands', '72593'], ['6', '1993 - 10 - 10', 'los angeles raiders', 'l 24 - 20', 'los angeles memorial coliseum', '41627'], ['8', '1993 - 10 - 24', 'buffalo bills', 'l 19 - 10', 'the meadowlands', '71541'], ['9', '1993 - 10 - 31', 'new york giants', 'w 10 - 6', 'giants stadium', '71659'], ['10', '1993 - 11 - 07', 'miami dolphins', 'w 27 - 10', 'the meadowlands', '71306'], ['11', '1993 - 11 - 14', 'indianapolis colts', 'w 31 - 17', 'rca dome', '47351'], ['12', '1993 - 11 - 21', 'cincinnati bengals', 'w 17 - 12', 'the meadowlands', '64264'], ['13', '1993 - 11 - 28', 'new england patriots', 'w 6 - 0', 'foxboro stadium', '42810'], ['14', '1993 - 12 - 05', 'indianapolis colts', 'l 9 - 6', 'the meadowlands', '45799'], ['15', '1993 - 12 - 11', 'washington redskins', 'w 3 - 0', 'robert f kennedy memorial stadium', '47970'], ['16', '1993 - 12 - 18', 'dallas cowboys', 'l 28 - 7', 'the meadowlands', '73233'], ['17', '1993 - 12 - 26', 'buffalo bills', 'l 16 - 14', 'rich stadium', '70817'], ['18', '1994 - 01 - 02', 'houston oilers', 'l 24 - 0', 'houston astrodome', '61040']] |
list of australia one day international cricket records | https://en.wikipedia.org/wiki/List_of_Australia_One_Day_International_cricket_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21100348-11.html.csv | comparative | mitchell johnson had a higher number of innings compared to the player shane lee regarding their games for australia in an one day international cricket game . | {'row_1': '2', 'row_2': '3', 'col': '4', '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', 'player', 'mitchell johnson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to mitchell johnson .', 'tostr': 'filter_eq { all_rows ; player ; mitchell johnson }'}, 'innings'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; mitchell johnson } ; innings }', 'tointer': 'select the rows whose player record fuzzily matches to mitchell johnson . take the innings record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'shane lee'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to shane lee .', 'tostr': 'filter_eq { all_rows ; player ; shane lee }'}, 'innings'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; shane lee } ; innings }', 'tointer': 'select the rows whose player record fuzzily matches to shane lee . take the innings record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; mitchell johnson } ; innings } ; hop { filter_eq { all_rows ; player ; shane lee } ; innings } } = true', 'tointer': 'select the rows whose player record fuzzily matches to mitchell johnson . take the innings record of this row . select the rows whose player record fuzzily matches to shane lee . take the innings record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; mitchell johnson } ; innings } ; hop { filter_eq { all_rows ; player ; shane lee } ; innings } } = true | select the rows whose player record fuzzily matches to mitchell johnson . take the innings record of this row . select the rows whose player record fuzzily matches to shane lee . take the innings 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, 'player_7': 7, 'mitchell johnson_8': 8, 'innings_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'shane lee_12': 12, 'innings_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', 'player_7': 'player', 'mitchell johnson_8': 'mitchell johnson', 'innings_9': 'innings', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'shane lee_12': 'shane lee', 'innings_13': 'innings'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'mitchell johnson_8': [0], 'innings_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'shane lee_12': [1], 'innings_13': [3]} | ['rank', 'strike rate', 'player', 'innings', 'balls faced', 'runs', 'period'] | [['1', '96.89', 'adam gilchrist', '278', '9902', '9595', '1996 - 2008'], ['2', '96.12', 'mitchell johnson', '64', '749', '720', '2005 -'], ['3', '95.40', 'shane lee', '35', '500', '477', '1995 - 2001'], ['4', '93.71', 'james hopes', '61', '1415', '1326', '2005 - 2010'], ['5', '92.44', 'andrew symonds', '161', '5504', '5088', '1998 - 2009'], ['6', '91.67', 'david hussey', '57', '1885', '1728', '2008 -'], ['7', '88.27', 'shane watson', '134', '5169', '4563', '2002 -'], ['8', '88.16', 'ian harvey', '51', '811', '715', '1997 - 2004'], ['9', '87.51', 'brad hodge', '21', '657', '575', '2005 - 2007']] |
nhpc limited | https://en.wikipedia.org/wiki/NHPC_Limited | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18145877-2.html.csv | unique | the only one among those whose state is jammu & kashmir who has a total capacity of 44 is chutak . | {'scope': 'subset', 'row': '7', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': '44', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'jammu & kashmir'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'jammu & kashmir'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; state ; jammu & kashmir }', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir .'}, 'total capacity ( mw )', '44'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir . among these rows , select the rows whose total capacity ( mw ) record is equal to 44 .', 'tostr': 'filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } }', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir . among these rows , select the rows whose total capacity ( mw ) record is equal to 44 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'jammu & kashmir'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; state ; jammu & kashmir }', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir .'}, 'total capacity ( mw )', '44'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir . among these rows , select the rows whose total capacity ( mw ) record is equal to 44 .', 'tostr': 'filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 }'}, 'power plant'], 'result': 'chutak', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } ; power plant }'}, 'chutak'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } ; power plant } ; chutak }', 'tointer': 'the power plant record of this unqiue row is chutak .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } ; power plant } ; chutak } } = true', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir . among these rows , select the rows whose total capacity ( mw ) record is equal to 44 . there is only one such row in the table . the power plant record of this unqiue row is chutak .'} | and { only { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } ; power plant } ; chutak } } = true | select the rows whose state record fuzzily matches to jammu & kashmir . among these rows , select the rows whose total capacity ( mw ) record is equal to 44 . there is only one such row in the table . the power plant record of this unqiue row is chutak . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'state_8': 8, 'jammu & kashmir_9': 9, 'total capacity (mw)_10': 10, '44_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'power plant_12': 12, 'chutak_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'state_8': 'state', 'jammu & kashmir_9': 'jammu & kashmir', 'total capacity (mw)_10': 'total capacity ( mw )', '44_11': '44', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'power plant_12': 'power plant', 'chutak_13': 'chutak'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'state_8': [0], 'jammu & kashmir_9': [0], 'total capacity (mw)_10': [1], '44_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'power plant_12': [3], 'chutak_13': [4]} | ['sno', 'power plant', 'state', 'total capacity ( mw )', 'completion schedule'] | [['1', 'kishenganga', 'jammu & kashmir', '330', '2016'], ['2', 'parbati - ii', 'himachal pradesh', '800', '2013'], ['3', 'subansiri ( lower )', 'assam', '2000', '2014'], ['4', 'teesta low dam - iv', 'west bengal', '160', '2011'], ['5', 'parbati - iii', 'himachal pradesh', '520', '2012'], ['6', 'nimmo - bazgo', 'jammu & kashmir', '45', '2011'], ['7', 'chutak', 'jammu & kashmir', '44', '2011'], ['8', 'uri - ii', 'jammu & kashmir', '240', '2011']] |
list of ittf pro tour winners | https://en.wikipedia.org/wiki/List_of_ITTF_Pro_Tour_winners | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28138035-27.html.csv | count | zhang yining won the womens singles a total of four times in the ittf pro tour . | {'scope': 'all', 'criterion': 'equal', 'value': 'zhang yining', 'result': '4', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'womens singles', 'zhang yining'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose womens singles record fuzzily matches to zhang yining .', 'tostr': 'filter_eq { all_rows ; womens singles ; zhang yining }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; womens singles ; zhang yining } }', 'tointer': 'select the rows whose womens singles record fuzzily matches to zhang yining . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; womens singles ; zhang yining } } ; 4 } = true', 'tointer': 'select the rows whose womens singles record fuzzily matches to zhang yining . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; womens singles ; zhang yining } } ; 4 } = true | select the rows whose womens singles record fuzzily matches to zhang yining . 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, 'womens singles_5': 5, 'zhang yining_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', 'womens singles_5': 'womens singles', 'zhang yining_6': 'zhang yining', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'womens singles_5': [0], 'zhang yining_6': [0], '4_7': [2]} | ['year location', 'mens singles', 'womens singles', 'mens doubles', 'womens doubles'] | [['2012 doha', 'xu xin', 'chen meng', 'ma lin xu xin', 'li xiaodan wen jia'], ['2011 doha', 'xu xin', 'liu shiwen', 'wang liqin xu xin', 'guo yue li xiaoxia'], ['2010 doha', 'wang liqin', 'guo yue', 'ma lin wang hao', 'ding ning liu shiwen'], ['2009 doha', 'timo boll', 'zhang yining', 'ma long xu xin', 'guo yue zhang yining'], ['2008 doha', 'ma lin', 'zhang yining', 'chen qi ma lin', 'guo yue zhang yining'], ['2007 doha', 'ma lin', 'li xiaoxia', 'cho eon - rae lee jung - woo', 'li xiaoxia wang nan'], ['2006 doha', 'wang liqin', 'zhang yining', 'wang hao wang liqin', 'wang nan zhang yining'], ['2005 doha', 'wang liqin', 'zhang yining', 'kong linghui wang hao', 'guo yue niu jianfeng'], ['2003 doha', 'vladimir samsonov', 'tamara boros', 'kalinikos kreanga jörg roßkopf', 'tatyana logatzkaya veronika pavlovich'], ['2002 doha', 'jean - michel saive', 'wang nan', 'wang liqin yan sen', 'li jia niu jianfeng'], ['2001 doha', 'zoran primorac', 'kim hyon - hui', 'chang yen - shu chiang peng - lung', 'kim moo - kyo ryu ji - hae'], ['1999 doha', 'zoran primorac', 'jing tian - zorner', 'chang yen - shu chiang peng - lung', 'csilla bátorfi krisztina tóth'], ['1998 doha', 'zoran primorac', 'li ju', 'wang liqin yan sen', 'li ju wang nan']] |
toronto , grey and bruce railway | https://en.wikipedia.org/wiki/Toronto%2C_Grey_and_Bruce_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15339223-1.html.csv | count | baldwin locomotive works was the builder for eight railways . | {'scope': 'all', 'criterion': 'equal', 'value': 'baldwin locomotive works', 'result': '8', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'builder', 'baldwin locomotive works'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose builder record fuzzily matches to baldwin locomotive works .', 'tostr': 'filter_eq { all_rows ; builder ; baldwin locomotive works }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; builder ; baldwin locomotive works } }', 'tointer': 'select the rows whose builder record fuzzily matches to baldwin locomotive works . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; builder ; baldwin locomotive works } } ; 8 } = true', 'tointer': 'select the rows whose builder record fuzzily matches to baldwin locomotive works . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; builder ; baldwin locomotive works } } ; 8 } = true | select the rows whose builder record fuzzily matches to baldwin locomotive works . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'builder_5': 5, 'baldwin locomotive works_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'builder_5': 'builder', 'baldwin locomotive works_6': 'baldwin locomotive works', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'builder_5': [0], 'baldwin locomotive works_6': [0], '8_7': [2]} | ['number', 'name', 'builder', 'type', 'date', 'works number'] | [['1', 'gordon', 'avonside engine company', '4 - 6 - 0', 'aug 1870', '799'], ['2', 'ar mcmaster', 'avonside engine company', '4 - 4 - 0', 'aug 1870', '800'], ['3', 'kincardine', 'avonside engine company', '4 - 4 - 0', 'september 1870', '809'], ['4', 'r walker & sons', 'avonside engine company', '4 - 4 - 0', 'may 1871', '838'], ['5', 'albion', 'avonside engine company', '4 - 4 - 0', 'july 1871', '839'], ['6', 'rice lewis & son', 'avonside engine company', '4 - 4 - 0', 'mid 1871', '840'], ['7', 'caledon', 'avonside engine company', '0 - 6 - 6 - 0 fairlie type', 'late 1872', '862 & 863'], ['8', 'mono', 'avonside engine company', '4 - 6 - 0', 'late 1871', '866'], ['9', 'toronto', 'baldwin locomotive works', '2 - 6 - 0', 'september 1871', '2534'], ['10', 'amaranth', 'baldwin locomotive works', '2 - 6 - 0', 'september 1871', '2538'], ['11', 'holland', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 935 - 939'], ['12', 'sydenham', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 935 - 939'], ['13', 'artemisia', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 935 - 939'], ['14', 'owen sound', 'avonside engine company', '4 - 6 - 0', 'early 1873', 'one of 931932933 , or 934'], ['15', 'mount forest', 'baldwin locomotive works', '2 - 8 - 0', 'february 1874', '3524'], ['16', 'orangeville', 'baldwin locomotive works', '2 - 8 - 0', 'february 1874', '3525'], ['17', 'sarawak', 'baldwin locomotive works', '2 - 8 - 0', 'april 1874', '3551'], ['18', 'melancthon', 'baldwin locomotive works', '2 - 8 - 0', 'april 1874', '3552'], ['19', 'howick', 'baldwin locomotive works', '2 - 8 - 0', 'september 1874', '3636'], ['20', 'culross', 'baldwin locomotive works', '2 - 8 - 0', 'september 1874', '3640']] |
washington redskins draft history | https://en.wikipedia.org/wiki/Washington_Redskins_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17100961-20.html.csv | unique | dewey nelson was the only player the washington redskins drafted from utah college . | {'scope': 'all', 'row': '9', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'utah', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'utah'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to utah .', 'tostr': 'filter_eq { all_rows ; college ; utah }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; utah } }', 'tointer': 'select the rows whose college record fuzzily matches to utah . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'utah'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to utah .', 'tostr': 'filter_eq { all_rows ; college ; utah }'}, 'name'], 'result': 'dewey nelson', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; utah } ; name }'}, 'dewey nelson'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; utah } ; name } ; dewey nelson }', 'tointer': 'the name record of this unqiue row is dewey nelson .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; utah } } ; eq { hop { filter_eq { all_rows ; college ; utah } ; name } ; dewey nelson } } = true', 'tointer': 'select the rows whose college record fuzzily matches to utah . there is only one such row in the table . the name record of this unqiue row is dewey nelson .'} | and { only { filter_eq { all_rows ; college ; utah } } ; eq { hop { filter_eq { all_rows ; college ; utah } ; name } ; dewey nelson } } = true | select the rows whose college record fuzzily matches to utah . there is only one such row in the table . the name record of this unqiue row is dewey nelson . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'utah_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'dewey nelson_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'utah_8': 'utah', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'dewey nelson_10': 'dewey nelson'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'utah_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'dewey nelson_10': [3]} | ['round', 'pick', 'name', 'position', 'college', 'aafc team'] | [['1', '5', 'jim spavital', 'fb', 'oklahoma a & m', 'los angeles dons'], ['2', '18', 'chuck drazenovich', 'fb', 'penn state', 'los angeles dons'], ['3', '31', 'roland dale', 'ot', 'mississippi', 'brooklyn dodgers'], ['4', '48', 'lloyd eisenberg', 'ot', 'duke', 'los angeles dons'], ['5', '61', 'hardy brown', 'fb', 'tulsa', 'chicago hornets'], ['6', '76', 'ed hirsch', 'lb', 'northwestern', 'buffalo bills'], ['7', '89', 'ed smith', 'hb', 'texas mines', 'new york yanks'], ['9', '117', 'murray alexander', 'e', 'mississippi state', 'brooklyn dodgers'], ['10', '132', 'dewey nelson', 'hb', 'utah', 'new york bulldogs']] |
list of chilean submissions for the academy award for best foreign language film | https://en.wikipedia.org/wiki/List_of_Chilean_submissions_for_the_Academy_Award_for_Best_Foreign_Language_Film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20404716-1.html.csv | majority | a majority of the chilean submissions for the academy award for a foreign film resulted in not nominated . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'not nominated', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'not nominated'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to not nominated .', 'tostr': 'most_eq { all_rows ; result ; not nominated } = true'} | most_eq { all_rows ; result ; not nominated } = true | for the result records of all rows , most of them fuzzily match to not nominated . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'not nominated_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'not nominated_4': 'not nominated'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'not nominated_4': [0]} | ['year ( ceremony )', 'film title used in nomination', 'spanish title', 'director', 'result'] | [['1990 : ( 63rd )', 'the moon in the mirror', 'la luna en el espejo', 'silvio caiozzi', 'not nominated'], ['1991 : ( 64th )', 'the frontier', 'la frontera', 'ricardo larraín', 'not nominated'], ['1993 : ( 66th )', 'johnny 100 pesos', 'johnny 100 pesos', 'gustavo graef - marino', 'not nominated'], ['1994 : ( 67th )', 'amnesia', 'amnesia', 'gonzalo justiniano', 'not nominated'], ['2000 : ( 73rd )', 'coronation', 'coronación', 'silvio caiozzi', 'not nominated'], ['2001 : ( 74th )', 'a cab for three', 'taxi para tres', 'orlando lubbert', 'not nominated'], ['2002 : ( 75th )', 'ogu and mampato in rapa nui', 'ogu y mampato en rapa nui', 'alejandro rojas', 'not nominated'], ['2003 : ( 76th )', 'los debutantes', 'los debutantes', 'andres waissbluth', 'not nominated'], ['2004 : ( 77th )', 'machuca', 'machuca', 'andrés wood', 'not nominated'], ['2005 : ( 78th )', 'play', 'play', 'alicia scherson', 'not nominated'], ['2006 : ( 79th )', 'in bed', 'en la cama', 'matías bize', 'not nominated'], ['2007 : ( 80th )', 'padre nuestro', 'padre nuestro', 'rodrigo sepúlveda', 'not nominated'], ['2008 : ( 81st )', 'tony manero', 'tony manero', 'pablo larrain', 'not nominated'], ['2009 : ( 82nd )', 'dawson , island 10', 'dawson , isla 10', 'miguel littín', 'not nominated'], ['2010 : ( 83rd )', 'the life of fish', 'la vida de los peces', 'matías bize', 'not nominated'], ['2011 : ( 84th )', 'violeta went to heaven', 'violeta se fue a los cielos', 'andrés wood', 'not nominated'], ['2012 : ( 85th )', 'no', 'no', 'pablo larraín', 'nominee']] |
list of people in playboy 2000 - 09 | https://en.wikipedia.org/wiki/List_of_people_in_Playboy_2000%E2%80%9309 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1566852-7.html.csv | majority | most of the pictorials feature groups of girls in addition to one person . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'girls', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'pictorials', 'girls'], 'result': True, 'ind': 0, 'tointer': 'for the pictorials records of all rows , most of them fuzzily match to girls .', 'tostr': 'most_eq { all_rows ; pictorials ; girls } = true'} | most_eq { all_rows ; pictorials ; girls } = true | for the pictorials records of all rows , most of them fuzzily match to girls . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'pictorials_3': 3, 'girls_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'pictorials_3': 'pictorials', 'girls_4': 'girls'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'pictorials_3': [0], 'girls_4': [0]} | ['date', 'cover model', 'centerfold model', 'interview subject', '20 questions', 'pictorials'] | [['1 - 06', 'lisa guerrero', 'athena lundberg', 'mark cuban', 'kate beckinsale', 'lisa guerrero'], ['2 - 06', 'adrianne curry', 'cassandra lynn', 'al franken', 'hugh laurie', 'adrianne curry , girls of tuscany'], ['3 - 06', 'jessica alba', 'monica leigh', 'kanye west', 'franz ferdinand', 'willa ford'], ['4 - 06', 'candice michelle', 'holley ann dorrough', 'keanu reeves', 'craig ferguson', 'candice michelle , cyber girls in print'], ['5 - 06', 'alison waite', 'alison waite', 'ozzie guillãn', 'rebecca romijn', 'girls of the top 10 party schools , rachel sterling'], ['6 - 06', 'kara monaco', 'stephanie larimore', 'shepard smith', 'jason lee', 'pmoy - kara monaco , girls of myspace'], ['7 - 06', 'vida guerra', 'sara jean underwood', 'jerry bruckheimer', 'dana white', 'vida guerra'], ['8 - 06', 'monica leigh', 'nicole voss', 'denis leary', 'luke wilson', 'girls of orange county , stacey dash'], ['10 - 06', 'tamara witmer', 'jordan monroe', 'ludacris', 'johnny knoxville', 'girls of the big 12 , christine dolce'], ['11 - 06', 'mercedes mcnab', 'sarah elizabeth', 'arianna huffington', 'tenacious d', 'mercedes mcnab , girls of hawaiian tropic']] |
national league 1 | https://en.wikipedia.org/wiki/National_League_1 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23927423-4.html.csv | count | in the national league 1 , when the season is after 2005 , there are 4 times that there were 16 teams . | {'scope': 'subset', 'criterion': 'equal', 'value': '16', 'result': '4', 'col': '3', 'subset': {'col': '1', 'criterion': 'greater_than', 'value': '2005'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'season', '2005'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; season ; 2005 }', 'tointer': 'select the rows whose season record is greater than 2005 .'}, 'teams', '16'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 .', 'tostr': 'filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 } }', 'tointer': 'select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 } } ; 4 } = true', 'tointer': 'select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 . the number of such rows is 4 .'} | eq { count { filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 } } ; 4 } = true | select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'season_6': 6, '2005_7': 7, 'teams_8': 8, '16_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'season_6': 'season', '2005_7': '2005', 'teams_8': 'teams', '16_9': '16', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'season_6': [0], '2005_7': [0], 'teams_8': [1], '16_9': [1], '4_10': [3]} | ['season', 'name', 'teams', 'relegated to league', 'promoted to league', 'promoted from league', 'relegated from league'] | [['2005 - 06', 'national division two', '14', 'henley hawks orrell', 'barking halifax redruth', 'moseley waterloo', 'orrell'], ['2006 - 07', 'national division two', '14', 'none', 'bradford & bingley cambridge nuneaton', 'esher cornish all blacks', 'bradford & bingley harrogate'], ['2007 - 08', 'national division two', '14', 'waterloo otley', 'blaydon southend westcombe park', 'manchester otley', 'halifax henley hawks nuneaton'], ['2008 - 09', 'national division two', '14', 'cornish all blacks pertemps bees', "cinderford mount 's bay tynedale", 'birmingham & solihull', "westcombe park southend mount 's bay waterloo"], ['2009 - 10', 'national league 1', '16', 'esher newbury sedgley park otley manchester', 'london scottish nuneaton', 'esher', 'newbury nuneaton manchester'], ['2010 - 11', 'national league 1', '16', 'coventry', 'barking macclesfield rosslyn park', 'london scottish', 'cornish all blacks otley redruth'], ['2011 - 12', 'national league 1', '16', 'birmingham & solihull', 'ealing trailfinders fylde jersey', 'jersey', 'barking birmingham & solihull stourbridge'], ['2012 - 13', 'national league 1', '16', 'esher', 'loughborough students old albanians richmond', 'ealing trailfinders', 'cambridge sedgley park macclesfield']] |
2007 - 08 new orleans hornets season | https://en.wikipedia.org/wiki/2007%E2%80%9308_New_Orleans_Hornets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11963536-11.html.csv | majority | for the first six games in the 2007-08 new orleans hornet 's season , chris paul was the high scorer for the majority of the games . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'paul', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high points', 'paul'], 'result': True, 'ind': 0, 'tointer': 'for the high points records of all rows , most of them fuzzily match to paul .', 'tostr': 'most_eq { all_rows ; high points ; paul } = true'} | most_eq { all_rows ; high points ; paul } = true | for the high points records of all rows , most of them fuzzily match to paul . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high points_3': 3, 'paul_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high points_3': 'high points', 'paul_4': 'paul'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high points_3': [0], 'paul_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'may 3', 'san antonio', '101 - 82', 'west ( 30 )', 'chandler ( 15 )', 'paul ( 13 )', 'new orleans arena 18040', '1 - 0'], ['2', 'may 5', 'san antonio', '102 - 84', 'paul ( 30 )', 'chandler ( 11 )', 'paul ( 12 )', 'new orleans arena 17927', '2 - 0'], ['3', 'may 8', 'san antonio', '99 - 110', 'paul ( 35 )', 'west ( 12 )', 'paul ( 9 )', 'at & t center 18797', '2 - 1'], ['4', 'may 11', 'san antonio', '80 - 100', 'paul ( 23 )', 'armstrong , paul ( 6 )', 'paul ( 5 )', 'at & t center 18797', '2 - 2'], ['5', 'may 13', 'san antonio', '101 - 79', 'west ( 38 )', 'west ( 14 )', 'paul ( 14 )', 'new orleans arena 18246', '3 - 2'], ['6', 'may 15', 'san antonio', '80 - 99', 'paul ( 21 )', 'five - way tie ( 6 )', 'paul ( 8 )', 'at & t center 18797', '3 - 3']] |
augusta lynx | https://en.wikipedia.org/wiki/Augusta_Lynx | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1888157-1.html.csv | aggregation | the average point score for augusta lynx when in the southeast division is 75.5 . | {'scope': 'subset', 'col': '10', 'type': 'average', 'result': '75.5', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'southeast'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'division', 'southeast'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; division ; southeast }', 'tointer': 'select the rows whose division record fuzzily matches to southeast .'}, 'pts'], 'result': '75.5', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; division ; southeast } ; pts }'}, '75.5'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; division ; southeast } ; pts } ; 75.5 } = true', 'tointer': 'select the rows whose division record fuzzily matches to southeast . the average of the pts record of these rows is 75.5 .'} | round_eq { avg { filter_eq { all_rows ; division ; southeast } ; pts } ; 75.5 } = true | select the rows whose division record fuzzily matches to southeast . the average of the pts record of these rows is 75.5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'division_5': 5, 'southeast_6': 6, 'pts_7': 7, '75.5_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'division_5': 'division', 'southeast_6': 'southeast', 'pts_7': 'pts', '75.5_8': '75.5'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'division_5': [0], 'southeast_6': [0], 'pts_7': [1], '75.5_8': [2]} | ['season', 'league', 'division', 'gp', 'w', 'l', 't', 'otl', 'sol', 'pts', 'pct', 'gf', 'ga', 'pim', 'coach ( es )', 'result'] | [['1998 - 99', 'echl', 'southeast', '70', '38', '27', '5', '0', '0', '81', '0.579', '235', '233', '1984', 'dan wiebe', 'lost in round 1 ( baton rouge ) 2 - 0'], ['2000 - 01', 'echl', 'southeast', '72', '36', '29', '7', '0', '0', '79', '0.549', '259', '253', '1579', 'scott macpherson , jim burton', 'lost in round 1 ( new orleans ) 2 - 1'], ['2001 - 02', 'echl', 'southeast', '72', '36', '26', '10', '0', '0', '82', '0.569', '218', '224', '1834', 'jim burton', 'out of playoffs'], ['2002 - 03', 'echl', 'southeast', '72', '27', '39', '6', '0', '0', '60', '0.417', '203', '256', '1623', 'jim burton , david wilkie', 'out of playoffs'], ['2003 - 04', 'echl', 'central', '72', '32', '33', '7', '0', '0', '71', '0.493', '203', '234', '1510', 'stan drulia', 'out of playoffs'], ['2004 - 05', 'echl', 'east', '72', '28', '35', '9', '0', '0', '65', '0.451', '188', '237', '1232', 'stan drulia', 'out of playoffs'], ['2005 - 06', 'echl', 'south', '72', '30', '36', '6', '0', '0', '66', '0.458', '216', '255', '1475', 'bob ferguson', 'lost in round 1 ( greenville ) 2 - 0'], ['2006 - 07', 'echl', 'south', '72', '39', '29', '0', '1', '3', '82', '0.569', '258', '265', '1225', 'bob ferguson', 'lost in round 1 ( charlotte ) 2 - 0'], ['2007 - 08', 'echl', 'south', '72', '32', '35', '0', '1', '4', '69', '0.479', '200', '223', '1173', 'bob ferguson', 'lost in round 1 ( south carolina ) 3 - 2'], ['2008 - 09', 'echl', 'south', '18', '6', '10', '0', '1', '1', '14', '0.389', '39', '70', '471', 'john marks', 'team ceased operations december 2']] |
aquatics at the 1982 commonwealth games | https://en.wikipedia.org/wiki/Aquatics_at_the_1982_Commonwealth_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13049944-3.html.csv | count | there are two nations that did not win any gold metals in the aquatics at the 1982 commonwealth games . | {'scope': 'all', 'criterion': 'equal', 'value': '-', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is equal to - .', 'tostr': 'filter_eq { all_rows ; gold ; - }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; gold ; - } }', 'tointer': 'select the rows whose gold record is equal to - . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; gold ; - } } ; 2 } = true', 'tointer': 'select the rows whose gold record is equal to - . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; gold ; - } } ; 2 } = true | select the rows whose gold record is equal to - . 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, 'gold_5': 5, '-_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'gold_5': 'gold', '-_6': '-', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'gold_5': [0], '-_6': [0], '2_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'australia', '13', '13', '8', '34'], ['2', 'canada', '9', '6', '9', '24'], ['3', 'england', '7', '7', '8', '22'], ['4', 'scotland', '-', '3', '3', '6'], ['5', 'new zealand', '-', '13', '9', '1'], ['total', 'total', '29', '29', '29', '87']] |
wru division three east | https://en.wikipedia.org/wiki/WRU_Division_Three_East | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12886178-3.html.csv | aggregation | the wru division three east clubs scored a total of 636 points . | {'scope': 'all', 'col': '11', 'type': 'sum', 'result': '636', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'points'], 'result': '636', 'ind': 0, 'tostr': 'sum { all_rows ; points }'}, '636'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; points } ; 636 } = true', 'tointer': 'the sum of the points record of all rows is 636 .'} | round_eq { sum { all_rows ; points } ; 636 } = true | the sum of the points record of all rows is 636 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'points_4': 4, '636_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'points_4': 'points', '636_5': '636'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'points_4': [0], '636_5': [1]} | ['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'] | [['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['fleur de lys rfc', '22', '0', '1', '618', '335', '70', '45', '9', '0', '93'], ['abergavenny rfc', '22', '0', '4', '611', '318', '89', '40', '12', '4', '88'], ['rhymney rfc', '22', '0', '9', '452', '406', '55', '50', '5', '4', '61'], ['pill harriers rfc', '22', '1', '9', '590', '442', '79', '58', '8', '3', '61'], ['croesyceiliog rfc', '22', '1', '10', '423', '497', '51', '67', '4', '3', '53'], ['tredegar ironsides rfc', '22', '0', '12', '382', '399', '46', '45', '5', '5', '50'], ['newport hsob rfc', '22', '0', '13', '418', '529', '53', '65', '7', '2', '45'], ['pontypool united rfc', '22', '3', '12', '442', '498', '54', '61', '4', '6', '44'], ['gwernyfed rfc', '22', '0', '13', '338', '386', '41', '46', '2', '5', '43'], ['blaina rfc', '22', '1', '13', '383', '452', '39', '56', '1', '7', '42'], ['nelson rfc', '22', '0', '14', '383', '478', '49', '54', '2', '4', '38'], ['cwmbran rfc', '22', '2', '18', '286', '586', '38', '77', '1', '5', '18']] |
list of how it 's made episodes | https://en.wikipedia.org/wiki/List_of_How_It%27s_Made_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15187735-16.html.csv | count | there were thirteen episodes produced in season 16 . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '13', 'col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'series ep'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose series ep record is arbitrary .', 'tostr': 'filter_all { all_rows ; series ep }'}], 'result': '13', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; series ep } }', 'tointer': 'select the rows whose series ep record is arbitrary . the number of such rows is 13 .'}, '13'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; series ep } } ; 13 } = true', 'tointer': 'select the rows whose series ep record is arbitrary . the number of such rows is 13 .'} | eq { count { filter_all { all_rows ; series ep } } ; 13 } = true | select the rows whose series ep record is arbitrary . the number of such rows is 13 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'series ep_5': 5, '13_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'series ep_5': 'series ep', '13_6': '13'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'series ep_5': [0], '13_6': [2]} | ['series ep', 'episode', 'netflix', 'segment a', 'segment b', 'segment c', 'segment d'] | [['16 - 01', '196', 's08e14', 'millefiori glass paperweights', 'road salt', 's nutcracker', 'car doors'], ['16 - 02', '197', 's08e15', 'straight razors', 'black pudding', 'steering wheels', 'inorganic pigments'], ['16 - 03', '198', 's08e16', 'cast iron cookware', 'biodiesel', 'clothes hangers', 'stone wool insulation'], ['16 - 04', '199', 's08e17', 'needles & pins', 'architectural mouldings', 's locomotive', 's clothespin'], ['16 - 05', '200', 's08e18', 'filigree glass', 'fish food', 's motor home ( part 1 )', 's motor home ( part 2 )'], ['16 - 06', '201', 's08e19', 'surgical instruments', 'ketchup', 'double - decker buses', 'walking sticks'], ['16 - 07', '202', 's08e20', 'audio vacuum tubes', 'light bars', 'wood model aircraft', 'metal s snare drum'], ['16 - 08', '203', 's08e21', 'kitchen accessories', 'central vacuums', 'papier - mché animals', 'hydraulic cylinders'], ['16 - 09', '204', 's08e22', 'clay liquor jugs', 'poultry deli meats', 'nascar engines ( part 1 )', 'nascar engines ( part 2 )'], ['16 - 10', '205', 's08e23', 'digital dentistry', 's nail clipper', 'poster restoration', 'canola oil'], ['16 - 11', '206', 's08e24', 'dial thermometers', 'hummus', 'spent fuel containers', 'straw s sombrero'], ['16 - 12', '207', 's08e25', 'tequila', 's waterbed', 's flip flop', 'silver'], ['16 - 13', '208', 's08e26', 'composite propane cylinders', 'salsa', 'water - pumping s windmill', 'dragsters']] |
c - class destroyer ( 1943 ) | https://en.wikipedia.org/wiki/C-class_destroyer_%281943%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1206583-1.html.csv | comparative | cavendish ( ex - sibyl ) was first launched at a later date than caesar ( ex - ranger ) . | {'row_1': '4', 'row_2': '3', 'col': '5', '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', 'name', 'cavendish ( ex - sibyl )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to cavendish ( ex - sibyl ) .', 'tostr': 'filter_eq { all_rows ; name ; cavendish ( ex - sibyl ) }'}, 'launched'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; cavendish ( ex - sibyl ) } ; launched }', 'tointer': 'select the rows whose name record fuzzily matches to cavendish ( ex - sibyl ) . take the launched record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'caesar ( ex - ranger )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to caesar ( ex - ranger ) .', 'tostr': 'filter_eq { all_rows ; name ; caesar ( ex - ranger ) }'}, 'launched'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; caesar ( ex - ranger ) } ; launched }', 'tointer': 'select the rows whose name record fuzzily matches to caesar ( ex - ranger ) . take the launched record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; cavendish ( ex - sibyl ) } ; launched } ; hop { filter_eq { all_rows ; name ; caesar ( ex - ranger ) } ; launched } } = true', 'tointer': 'select the rows whose name record fuzzily matches to cavendish ( ex - sibyl ) . take the launched record of this row . select the rows whose name record fuzzily matches to caesar ( ex - ranger ) . take the launched record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; name ; cavendish ( ex - sibyl ) } ; launched } ; hop { filter_eq { all_rows ; name ; caesar ( ex - ranger ) } ; launched } } = true | select the rows whose name record fuzzily matches to cavendish ( ex - sibyl ) . take the launched record of this row . select the rows whose name record fuzzily matches to caesar ( ex - ranger ) . take the launched 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, 'name_7': 7, 'cavendish (ex - sibyl)_8': 8, 'launched_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'caesar (ex - ranger)_12': 12, 'launched_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', 'name_7': 'name', 'cavendish (ex - sibyl)_8': 'cavendish ( ex - sibyl )', 'launched_9': 'launched', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'caesar (ex - ranger)_12': 'caesar ( ex - ranger )', 'launched_13': 'launched'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'cavendish (ex - sibyl)_8': [0], 'launched_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'caesar (ex - ranger)_12': [1], 'launched_13': [3]} | ['name', 'pennant', 'builder', 'laid down', 'launched', 'commissioned'] | [['caprice ( ex - swallow )', 'r01 later d01', 'yarrow , scotstoun', '24 september 1942', '16 september 1943', '5 april 1944'], ['cassandra ( ex - tourmaline )', 'r62 later d10', 'yarrow , scotstoun', '30 january 1943', '29 november 1943', '28 july 1944'], ['caesar ( ex - ranger )', 'r07 later d07', 'john brown , clydebank', '3 april 1943', '14 february 1944', '5 october 1944'], ['cavendish ( ex - sibyl )', 'r15 later d15', 'john brown , clydebank', '19 may 1943', '12 april 1944', '13 december 1944'], ['cambrian ( ex - spitfire )', 'r85 later d85', 'scotts , greenock', '14 august 1942', '10 december 1943', '17 july 1944 by john brown'], ['carron ( ex - strenuous )', 'r30 later d30', 'scotts , greenock', '26 november 1942', '28 march 1944', '6 november 1944'], ['cavalier ( ex - pellew )', 'r73 later d73', 'white , cowes', '28 february 1943', '7 april 1944', '22 november 1944'], ['carysfort ( ex - pique )', 'r25 later d25', 'white , cowes', '12 may 1943', '25 july 1944', '20 february 1945']] |
2007 - 08 guildford flames season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Guildford_Flames_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15213262-10.html.csv | majority | the flames won most of their games during the 07-08 season . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'won', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'won'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to won .', 'tostr': 'most_eq { all_rows ; result ; won } = true'} | most_eq { all_rows ; result ; won } = true | for the result records of all rows , most of them fuzzily match to won . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'won_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'won_4': 'won'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'won_4': [0]} | ['date', 'opponent', 'venue', 'result', 'attendance', 'competition'] | [['1', 'bracknell bees', 'home', 'won 4 - 0', '1467', 'league'], ['5', 'telford tigers', 'home', 'won 5 - 4', '1634', 'league'], ['6', 'swindon wildcats', 'away', 'won 4 - 3 ( so )', '670', 'league'], ['12', 'slough jets', 'away', 'lost 5 - 7', '702', 'league'], ['13', 'milton keynes lightning', 'home', 'lost 5 - 6 ( so )', '1443', 'league'], ['19', 'wightlink raiders', 'away', 'won 10 - 4', '506', 'league'], ['20', 'bracknell bees', 'away', 'lost 1 - 4', '1110', 'league'], ['26', 'chelmsford chieftains', 'home', 'won 10 - 5', '1619', 'league']] |
los angeles lakers all - time roster | https://en.wikipedia.org/wiki/Los_Angeles_Lakers_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10560886-12.html.csv | majority | all of the players on the los angeles lakers all - time roster have united states nationality . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; nationality ; united states } = true'} | all_eq { all_rows ; nationality ; united states } = true | for the nationality records of all rows , all of them fuzzily match to united states . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'united states_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]} | ['player', 'nationality', 'position', 'from', 'school / country'] | [['edwin kachan', 'united states', 'guard', '1948', 'depaul'], ['ed kalafat', 'united states', 'forward / center', '1954', 'minnesota'], ['jason kapono', 'united states', 'forward', '2011', 'ucla'], ['coby karl', 'united states', 'guard', '2007', 'boise state'], ['jerome kersey', 'united states', 'forward', '1996', 'longwood'], ['randolph keys', 'united states', 'guard / forward', '1994', 'southern mississippi'], ['earnie killum', 'united states', 'guard', '1970', 'stetson'], ['frankie king', 'united states', 'guard', '1995', 'western carolina'], ['jim king', 'united states', 'guard', '1963', 'tulsa'], ['joe kleine', 'united states', 'center', '1996', 'arkansas'], ['travis knight', 'united states', 'forward / center', '1996 , 1999', 'connecticut'], ['jim krebs', 'united states', 'forward / center', '1957', 'southern methodist'], ['larry krystkowiak', 'united states', 'forward / center', '1996', 'montana'], ['mitch kupchak', 'united states', 'forward / center', '1981', 'north carolina'], ['cj kupec', 'united states', 'forward / center', '1975', 'michigan']] |
2005 pba draft | https://en.wikipedia.org/wiki/2005_PBA_draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11779131-3.html.csv | unique | rey mendoza was the only pick that was from nu college . | {'scope': 'all', 'row': '7', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'nu', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'nu'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to nu .', 'tostr': 'filter_eq { all_rows ; college ; nu }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; nu } }', 'tointer': 'select the rows whose college record fuzzily matches to nu . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'nu'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to nu .', 'tostr': 'filter_eq { all_rows ; college ; nu }'}, 'player'], 'result': 'rey mendoza', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; nu } ; player }'}, 'rey mendoza'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; nu } ; player } ; rey mendoza }', 'tointer': 'the player record of this unqiue row is rey mendoza .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; nu } } ; eq { hop { filter_eq { all_rows ; college ; nu } ; player } ; rey mendoza } } = true', 'tointer': 'select the rows whose college record fuzzily matches to nu . there is only one such row in the table . the player record of this unqiue row is rey mendoza .'} | and { only { filter_eq { all_rows ; college ; nu } } ; eq { hop { filter_eq { all_rows ; college ; nu } ; player } ; rey mendoza } } = true | select the rows whose college record fuzzily matches to nu . there is only one such row in the table . the player record of this unqiue row is rey mendoza . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'nu_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'rey mendoza_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'nu_8': 'nu', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'rey mendoza_10': 'rey mendoza'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'nu_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'rey mendoza_10': [3]} | ['pick', 'player', 'country of origin', 'pba team', 'college'] | [['10', 'cesar catli', 'philippines', 'sta lucia realtors', 'feu'], ['11', 'neil raã ± eses', 'philippines', 'coca - cola tigers', 'uv'], ['12', 'al magpayo', 'philippines', 'coca - cola tigers', 'st benilde'], ['13', 'bj manalo', 'philippines', 'purefoods chunkee giants', 'de la salle'], ['14', 'larry fonacier', 'philippines', 'red bull barako', 'ateneo'], ['15', 'mark joseph kong', 'philippines', 'alaska aces', 'adamson'], ['16', 'rey mendoza', 'philippines', 'sta lucia realtors', 'nu'], ['17', 'paolo bugia', 'philippines', 'red bull barako', 'ateneo'], ['18', 'mark macapagal', 'philippines', "talk n ' text phone pals", 'san sebastian']] |
a gift from a flower to a garden | https://en.wikipedia.org/wiki/A_Gift_from_a_Flower_to_a_Garden | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1793227-2.html.csv | majority | most of the titles were released in the usa region . | {'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'usa', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'region', 'usa'], 'result': True, 'ind': 0, 'tointer': 'for the region records of all rows , most of them fuzzily match to usa .', 'tostr': 'most_eq { all_rows ; region ; usa } = true'} | most_eq { all_rows ; region ; usa } = true | for the region records of all rows , most of them fuzzily match to usa . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'region_3': 3, 'usa_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'region_3': 'region', 'usa_4': 'usa'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'region_3': [0], 'usa_4': [0]} | ['region', 'title', 'label', 'format', 'catalog - nr'] | [['usa', 'a gift from a flower to a garden', 'epic', 'mono lp', 'l2n6071'], ['usa', 'a gift from a flower to a garden', 'epic', 'stereo lp', 'b2n171'], ['uk', 'a gift from a flower to a garden', 'pye', 'mono lp', 'npl20000'], ['uk', 'a gift from a flower to a garden', 'pye', 'stereo lp', 'nspl 20000'], ['usa', 'wear your love like heaven', 'epic', 'monaural lp', 'ln 24349'], ['usa', 'wear your love like heaven', 'epic', 'stereo lp', 'bn 26349 ( stereo )'], ['usa', 'for little ones', 'epic', 'monaural lp', 'ln24350'], ['usa', 'for little ones', 'epic', 'stereo lp', 'bn26350 ( stereo )']] |
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