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two miles
https://en.wikipedia.org/wiki/Two_miles
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18390957-5.html.csv
ordinal
craig virgin had the 5th shortest time for running two miles .
{'row': '4', 'col': '1', 'order': '5', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '5'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 5 }'}, 'athlete'], 'result': 'craig virgin', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 5 } ; athlete }'}, 'craig virgin'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 5 } ; athlete } ; craig virgin } = true', 'tointer': 'select the row whose time record of all rows is 5th minimum . the athlete record of this row is craig virgin .'}
eq { hop { nth_argmin { all_rows ; time ; 5 } ; athlete } ; craig virgin } = true
select the row whose time record of all rows is 5th minimum . the athlete record of this row is craig virgin .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '5_6': 6, 'athlete_7': 7, 'craig virgin_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', 'time_5': 'time', '5_6': '5', 'athlete_7': 'athlete', 'craig virgin_8': 'craig virgin'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '5_6': [0], 'athlete_7': [1], 'craig virgin_8': [2]}
['time', 'athlete', 'school', 'city', 'date']
[['8:57.8', 'mike ryan', 'wilcox high school', 'santa clara , california', '1965'], ['8:48.3', 'rick riley', 'ferris high school', 'spokane , washington', 'may 28 , 1966'], ['8:41.5', 'steve prefontaine', 'marshfield high school', 'coos bay , oregon', 'april 25 , 1969'], ['8:40.9', 'craig virgin', 'lebanon high school', 'lebanon , illinois', 'june 9 , 1973'], ['( 8:40.0 i )', 'gerry lindgren', 'rogers high school', 'spokane , washington', 'february 15 , 1964'], ['8:36.3', 'jeff nelson', 'burbank high school', 'burbank , california', 'may 6 , 1979'], ['8:34.23', 'german fernandez', 'riverbank high school', 'riverbank , california', 'june 20 , 2008'], ['8:29.46', 'lukas verzbicas', 'carl sandburg high school', 'orland hills , illinois', 'june 4 , 2011']]
list of virginia covered bridges
https://en.wikipedia.org/wiki/List_of_Virginia_covered_bridges
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14218015-1.html.csv
superlative
the covered bridge that was built the earliest was the humpback bridge .
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'built'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; built }'}, 'name'], 'result': 'humpback', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; built } ; name }'}, 'humpback'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; built } ; name } ; humpback } = true', 'tointer': 'select the row whose built record of all rows is minimum . the name record of this row is humpback .'}
eq { hop { argmin { all_rows ; built } ; name } ; humpback } = true
select the row whose built record of all rows is minimum . the name record of this row is humpback .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'built_5': 5, 'name_6': 6, 'humpback_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'built_5': 'built', 'name_6': 'name', 'humpback_7': 'humpback'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'built_5': [0], 'name_6': [1], 'humpback_7': [2]}
['name', 'county', 'location', 'built', 'length ( ft )', 'spans']
[['biedler farm', 'rockingham', 'broadway', '1896', '93', 'smith creek'], ['bob white', 'patrick', 'woolwine', '1921', '80', 'smith river'], ['ck reynolds', 'giles', 'newport', '1919', '36', 'sinking creek'], ['humpback', 'alleghany', 'covington', '1857', '109', 'dunlap creek'], ["jack 's creek", 'patrick', 'woolwine', '1914', '48', 'smith river'], ['link farm', 'giles', 'newport', '1912', '49', 'sinking creek'], ["meem 's bottom", 'shenandoah', 'mount jackson', '1894', '204', 'north fork of the shenandoah river'], ['sinking creek', 'giles', 'newport', 'ca 1916', '71', 'sinking creek']]
1979 new york jets season
https://en.wikipedia.org/wiki/1979_New_York_Jets_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13834389-1.html.csv
superlative
the game against the buffalo bills had the highest attendance of any game .
{'scope': 'all', 'col_superlative': '6', 'row_superlative': '4', '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', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'opponent'], 'result': 'buffalo bills', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; opponent }'}, 'buffalo bills'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; opponent } ; buffalo bills } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the opponent record of this row is buffalo bills .'}
eq { hop { argmax { all_rows ; attendance } ; opponent } ; buffalo bills } = true
select the row whose attendance record of all rows is maximum . the opponent record of this row is buffalo bills .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'opponent_6': 6, 'buffalo bills_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', 'opponent_6': 'opponent', 'buffalo bills_7': 'buffalo bills'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'opponent_6': [1], 'buffalo bills_7': [2]}
['week', 'date', 'opponent', 'result', 'game site', 'attendance']
[['1', '1979 - 09 - 02', 'cleveland browns', 'l 25 - 22 ( ot )', 'shea stadium', '48472'], ['2', '1979 - 09 - 09', 'new england patriots', 'l 56 - 3', 'schafer stadium', '53113'], ['3', '1979 - 09 - 16', 'detroit lions', 'w 31 - 10', 'shea stadium', '49612'], ['4', '1979 - 09 - 23', 'buffalo bills', 'l 46 - 31', 'rich stadium', '68731'], ['5', '1979 - 09 - 30', 'miami dolphins', 'w 33 - 27', 'shea stadium', '51496'], ['6', '1979 - 10 - 07', 'baltimore colts', 'l 10 - 8', 'memorial stadium', '32142'], ['7', '1979 - 10 - 15', 'minnesota vikings', 'w 14 - 7', 'shea stadium', '54479'], ['8', '1979 - 10 - 21', 'oakland raiders', 'w 28 - 19', 'shea stadium', '55802'], ['9', '1979 - 10 - 28', 'houston oilers', 'l 27 - 24 ( ot )', 'the astrodome', '45825'], ['10', '1979 - 11 - 04', 'green bay packers', 'w 27 - 22', 'lambeau field', '54201'], ['11', '1979 - 11 - 11', 'buffalo bills', 'l 14 - 12', 'shea stadium', '50647'], ['12', '1979 - 11 - 18', 'chicago bears', 'l 23 - 13', 'soldier field', '52635'], ['13', '1979 - 11 - 26', 'seattle seahawks', 'l 30 - 7', 'kingdome', '59977'], ['14', '1979 - 12 - 02', 'baltimore colts', 'w 30 - 17', 'shea stadium', '47744'], ['15', '1979 - 12 - 09', 'new england patriots', 'w 27 - 26', 'shea stadium', '45131'], ['16', '1979 - 12 - 15', 'miami dolphins', 'w 27 - 24', 'miami orange bowl', '49915']]
list of man v. food episodes
https://en.wikipedia.org/wiki/List_of_Man_v._Food_episodes
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24798489-2.html.csv
count
four of the episodes from 20-37 had food as the challenge winner .
{'scope': 'all', 'criterion': 'equal', 'value': 'food', 'result': '4', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'challenge winner', 'food'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose challenge winner record fuzzily matches to food .', 'tostr': 'filter_eq { all_rows ; challenge winner ; food }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; challenge winner ; food } }', 'tointer': 'select the rows whose challenge winner record fuzzily matches to food . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; challenge winner ; food } } ; 4 } = true', 'tointer': 'select the rows whose challenge winner record fuzzily matches to food . the number of such rows is 4 .'}
eq { count { filter_eq { all_rows ; challenge winner ; food } } ; 4 } = true
select the rows whose challenge winner record fuzzily matches to food . 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, 'challenge winner_5': 5, 'food_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', 'challenge winner_5': 'challenge winner', 'food_6': 'food', '4_7': '4'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'challenge winner_5': [0], 'food_6': [0], '4_7': [2]}
['episode number', 'location', 'original airdate', 'challenge winner', 'challenge']
[['20', 'las vegas , nevada', 'august 12 , 2009', 'food', '6 - pound big badass burrito'], ['21', 'charleston , south carolina', 'august 19 , 2009', 'man', 'spicy tuna sushi'], ['22', 'san francisco , california', 'august 26 , 2009', 'man', 'the kitchen sink ( 2 gallons of ice cream )'], ['23', 'durham , north carolina', 'september 2 , 2009', 'food', 'the doughman ( food & sport triathlon )'], ['24', 'honolulu , hawaii', 'september 9 , 2009', 'food', 'mac daddy pancake challenge'], ['26', 'philadelphia , pennsylvania', 'september 23 , 2009', 'man', '5 - pound ultimate cheesesteak'], ['28', 'springfield , illinois', 'october 7 , 2009', 'man', 'firebrand chili'], ['33', 'brooklyn , new york', 'november 11 , 2009', 'man', 'suicide six wings challenge ( spicy wings )'], ['37', 'new brunswick , new jersey', 'december 9 , 2009', 'food', 'fat sandwich challenge ( five stuffed sandwiches )']]
wybe
https://en.wikipedia.org/wiki/WYBE
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1882668-1.html.csv
majority
all of wybe channels had an aspect ratio of 4:3 .
{'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': '4:3', 'subset': None}
{'func': 'all_str_eq', 'args': ['all_rows', 'aspect', '4:3'], 'result': True, 'ind': 0, 'tointer': 'for the aspect records of all rows , all of them fuzzily match to 4:3 .', 'tostr': 'all_eq { all_rows ; aspect ; 4:3 } = true'}
all_eq { all_rows ; aspect ; 4:3 } = true
for the aspect records of all rows , all of them fuzzily match to 4:3 .
1
1
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'aspect_3': 3, '4:3_4': 4}
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'aspect_3': 'aspect', '4:3_4': '4:3'}
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'aspect_3': [0], '4:3_4': [0]}
['channel', 'video', 'aspect', 'psip short name', 'programming']
[['35.1', '480i', '4:3', 'mind', 'main wybe programming'], ['35.2', '480i', '4:3', 'nhkwrld', 'nhk world'], ['35.3', '480i', '4:3', 'f24', 'france24'], ['35.4', '480i', '4:3', 'rt', 'russia today'], ['35.66', '480i', '4:3', 'wnyj', 'simulcast of programming from wnyj - tv']]
handball at the asian games
https://en.wikipedia.org/wiki/Handball_at_the_Asian_Games
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14306965-3.html.csv
superlative
south korea dominated handball at the asian games , winning 11 gold medals .
{'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '2', 'subset': None}
{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'gold'], 'result': '11', 'ind': 0, 'tostr': 'max { all_rows ; gold }', 'tointer': 'the maximum gold record of all rows is 11 .'}, '11'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; gold } ; 11 }', 'tointer': 'the maximum gold record of all rows is 11 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'gold'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; gold }'}, 'nation'], 'result': 'south korea ( kor )', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; gold } ; nation }'}, 'south korea ( kor )'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; gold } ; nation } ; south korea ( kor ) }', 'tointer': 'the nation record of the row with superlative gold record is south korea ( kor ) .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; gold } ; 11 } ; eq { hop { argmax { all_rows ; gold } ; nation } ; south korea ( kor ) } } = true', 'tointer': 'the maximum gold record of all rows is 11 . the nation record of the row with superlative gold record is south korea ( kor ) .'}
and { eq { max { all_rows ; gold } ; 11 } ; eq { hop { argmax { all_rows ; gold } ; nation } ; south korea ( kor ) } } = true
the maximum gold record of all rows is 11 . the nation record of the row with superlative gold record is south korea ( kor ) .
6
6
{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'gold_8': 8, '11_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'gold_11': 11, 'nation_12': 12, 'south korea (kor)_13': 13}
{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'gold_8': 'gold', '11_9': '11', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'gold_11': 'gold', 'nation_12': 'nation', 'south korea (kor)_13': 'south korea ( kor )'}
{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'gold_8': [0], '11_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'gold_11': [2], 'nation_12': [3], 'south korea (kor)_13': [4]}
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
[['1', 'south korea ( kor )', '11', '0', '2', '13'], ['2', 'china ( chn )', '2', '2', '3', '7'], ['3', 'kuwait ( kuw )', '1', '2', '0', '3'], ['4', 'japan ( jpn )', '0', '5', '5', '10'], ['5', 'kazakhstan ( kaz )', '0', '2', '0', '2'], ['6', 'iran ( iri )', '0', '1', '1', '2'], ['6', 'qatar ( qat )', '0', '1', '1', '2'], ['8', 'north korea ( prk )', '0', '1', '0', '1'], ['9', 'chinese taipei ( tpe )', '0', '0', '1', '1'], ['9', 'saudi arabia ( ksa )', '0', '0', '1', '1'], ['total', 'total', '14', '14', '14', '42']]
athletics at the 2008 summer olympics - women 's 200 metres
https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_200_metres
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18569021-4.html.csv
unique
veronica campbell-brown was the only runner in the women 's 200 meters event in the 2008 summer olympics that finished faster than 22.20 .
{'scope': 'all', 'row': '1', 'col': '5', 'col_other': '3', 'criterion': 'less_than', 'value': '22.2', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'time', '22.2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record is less than 22.2 .', 'tostr': 'filter_less { all_rows ; time ; 22.2 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; time ; 22.2 } }', 'tointer': 'select the rows whose time record is less than 22.2 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'time', '22.2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record is less than 22.2 .', 'tostr': 'filter_less { all_rows ; time ; 22.2 }'}, 'athlete'], 'result': 'veronica campbell - brown', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; time ; 22.2 } ; athlete }'}, 'veronica campbell - brown'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; time ; 22.2 } ; athlete } ; veronica campbell - brown }', 'tointer': 'the athlete record of this unqiue row is veronica campbell - brown .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; time ; 22.2 } } ; eq { hop { filter_less { all_rows ; time ; 22.2 } ; athlete } ; veronica campbell - brown } } = true', 'tointer': 'select the rows whose time record is less than 22.2 . there is only one such row in the table . the athlete record of this unqiue row is veronica campbell - brown .'}
and { only { filter_less { all_rows ; time ; 22.2 } } ; eq { hop { filter_less { all_rows ; time ; 22.2 } ; athlete } ; veronica campbell - brown } } = true
select the rows whose time record is less than 22.2 . there is only one such row in the table . the athlete record of this unqiue row is veronica campbell - brown .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'time_7': 7, '22.2_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'athlete_9': 9, 'veronica campbell - brown_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'time_7': 'time', '22.2_8': '22.2', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'athlete_9': 'athlete', 'veronica campbell - brown_10': 'veronica campbell - brown'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'time_7': [0], '22.2_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'athlete_9': [2], 'veronica campbell - brown_10': [3]}
['rank', 'lane', 'athlete', 'country', 'time', 'react']
[['1', '5', 'veronica campbell - brown', 'jamaica', '22.19', '0.187'], ['2', '7', 'kerron stewart', 'jamaica', '22.29', '0.217'], ['3', '4', 'muna lee', 'united states', '22.29', '0.186'], ['4', '9', 'debbie ferguson - mckenzie', 'bahamas', '22.51', '0.165'], ['5', '6', 'yuliya chermoshanskaya', 'russia', '22.57', '0.204'], ['6', '3', 'nataliya pyhyda', 'ukraine', '22.95', '0.160'], ['7', '8', 'susanthika jayasinghe', 'sri lanka', '22.98', '0.245'], ['8', '2', 'roxana dã\xadaz', 'cuba', '23.12', '0.177']]
livonia cup
https://en.wikipedia.org/wiki/Livonia_Cup
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14157023-1.html.csv
majority
all seasons of the livonia cup had the matches held at the skonto hall , riga venue .
{'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'skonto hall , riga', 'subset': None}
{'func': 'all_str_eq', 'args': ['all_rows', 'venue', 'skonto hall , riga'], 'result': True, 'ind': 0, 'tointer': 'for the venue records of all rows , all of them fuzzily match to skonto hall , riga .', 'tostr': 'all_eq { all_rows ; venue ; skonto hall , riga } = true'}
all_eq { all_rows ; venue ; skonto hall , riga } = true
for the venue records of all rows , all of them fuzzily match to skonto hall , riga .
1
1
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'venue_3': 3, 'skonto hall , riga_4': 4}
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'venue_3': 'venue', 'skonto hall , riga_4': 'skonto hall , riga'}
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'venue_3': [0], 'skonto hall , riga_4': [0]}
['season', 'winner', 'score', 'runner - up', 'venue']
[['2011', 'fc flora tallinn', '2 - 0', 'skonto fc', 'skonto hall , riga'], ['2008', 'fk ventspils', '2 - 2 aet , 4 - 3 pen', 'fc levadia tallinn', 'skonto hall , riga'], ['2005', 'skonto fc', '4 - 3', 'fc levadia tallinn', 'skonto hall , riga'], ['2004', 'skonto fc', '3 - 3 aet , 4 - 3 pen', 'fc flora tallinn', 'skonto hall , riga'], ['2003', 'skonto fc', '2 - 2 aet , 12 - 11 pen', 'fc flora tallinn', 'skonto hall , riga']]
2008 indiana fever season
https://en.wikipedia.org/wiki/2008_Indiana_Fever_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17104539-10.html.csv
superlative
conseco fieldhouse was the first location used by indiana fever during the 2008 season .
{'scope': 'all', 'col_superlative': '8', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'location / attendance'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; location / attendance }'}, 'date'], 'result': 'july 2', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; location / attendance } ; date }'}, 'july 2'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; location / attendance } ; date } ; july 2 } = true', 'tointer': 'select the row whose location / attendance record of all rows is minimum . the date record of this row is july 2 .'}
eq { hop { argmin { all_rows ; location / attendance } ; date } ; july 2 } = true
select the row whose location / attendance record of all rows is minimum . the date record of this row is july 2 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'location / attendance_5': 5, 'date_6': 6, 'july 2_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'location / attendance_5': 'location / attendance', 'date_6': 'date', 'july 2_7': 'july 2'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'location / attendance_5': [0], 'date_6': [1], 'july 2_7': [2]}
['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record']
[['16', 'july 2', 'chicago', 'w 74 - 67', 'catchings ( 18 )', 'sutton - brown ( 12 )', 'catchings , douglas ( 3 )', 'conseco fieldhouse 6196', '8 - 8'], ['17', 'july 5', 'connecticut', 'w 81 - 74', 'douglas , sutton - brown ( 18 )', 'sutton - brown ( 9 )', 'douglas ( 5 )', 'conseco fieldhouse 6329', '9 - 8'], ['18', 'july 8', 'washington', 'l 50 - 48', 'hoffman ( 16 )', 'hoffman ( 9 )', 'bevilaqua ( 4 )', 'verizon center 7587', '9 - 9'], ['19', 'july 12', 'chicago', 'w 66 - 57', 'douglas ( 25 )', 'catchings ( 8 )', 'catchings ( 4 )', 'conseco fieldhouse 7134', '10 - 9'], ['20', 'july 16', 'atlanta', 'l 81 - 77', 'catchings ( 18 )', 'catchings ( 12 )', 'catchings ( 5 )', 'conseco fieldhouse 9303', '10 - 10'], ['21', 'july 18', 'seattle', 'l 65 - 59', 'sutton - brown ( 12 )', 'sutton - brown ( 7 )', 'bevilaqua , bond ( 3 )', 'conseco fieldhouse 7450', '10 - 11'], ['22', 'july 19', 'new york liberty outdoor classic', 'w 71 - 55', 'douglas ( 20 )', 'catchings , sutton - brown ( 9 )', 'catchings , douglas ( 4 )', 'arthur ashe stadium 19393', '11 - 11'], ['23', 'july 22', 'chicago', 'l 68 - 60', 'douglas , sutton - brown ( 14 )', 'sutton - brown ( 10 )', 'catchings ( 4 )', 'uic pavilion 3035', '11 - 12'], ['24', 'july 24', 'minnesota', 'l 84 - 80', 'catchings , hoffman ( 17 )', 'sutton - brown ( 9 )', 'catchings ( 9 )', 'conseco fieldhouse 6010', '11 - 13'], ['25', 'july 26', 'sacramento', 'l 70 - 62', 'douglas ( 23 )', 'hoffman ( 8 )', 'catchings , white ( 4 )', 'arco arena 7082', '11 - 14'], ['26', 'july 27', 'phoenix', 'w 84 - 80', 'catchings ( 25 )', 'hoffman ( 7 )', 'catchings ( 6 )', 'us airways center 7924', '12 - 14']]
1998 - 99 philadelphia flyers season
https://en.wikipedia.org/wiki/1998%E2%80%9399_Philadelphia_Flyers_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14344807-6.html.csv
aggregation
in games 48 to 60 of the 1998-99 philadelphia flyers ' season , the flyers scored an average of 70 points .
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '70', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '70', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '70'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 70 } = true', 'tointer': 'the average of the points record of all rows is 70 .'}
round_eq { avg { all_rows ; points } ; 70 } = true
the average of the points record of all rows is 70 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '70_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '70_5': '70'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '70_5': [1]}
['game', 'february', 'opponent', 'score', 'record', 'points']
[['48', '1', 'los angeles kings', '4 - 2', '27 - 10 - 11', '65'], ['49', '4', 'montreal canadiens', '5 - 2', '28 - 10 - 11', '67'], ['50', '6', 'boston bruins', '2 - 2 ot', '28 - 10 - 12', '68'], ['51', '10', 'mighty ducks of anaheim', '4 - 5', '28 - 11 - 12', '68'], ['52', '11', 'los angeles kings', '3 - 4', '28 - 12 - 12', '68'], ['53', '14', 'colorado avalanche', '4 - 4 ot', '28 - 12 - 13', '69'], ['54', '16', 'phoenix coyotes', '4 - 1', '29 - 12 - 13', '71'], ['55', '18', 'montreal canadiens', '1 - 3', '29 - 13 - 13', '71'], ['56', '20', 'ottawa senators', '1 - 4', '29 - 14 - 13', '71'], ['57', '21', 'pittsburgh penguins', '2 - 1', '30 - 14 - 13', '73'], ['58', '24', 'florida panthers', '3 - 5', '30 - 15 - 13', '73'], ['59', '26', 'tampa bay lightning', '1 - 4', '30 - 16 - 13', '73'], ['60', '28', 'new york rangers', '5 - 6', '30 - 17 - 13', '73']]
sports in st. louis
https://en.wikipedia.org/wiki/Sports_in_St._Louis
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21564794-3.html.csv
comparative
among former sports teams in st. louis , the st. louis ambush won more championships than the st. louis bombers .
{'row_1': '15', 'row_2': '9', 'col': '7', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'st louis ambush'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to st louis ambush .', 'tostr': 'filter_eq { all_rows ; team ; st louis ambush }'}, 'championships in st louis'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; st louis ambush } ; championships in st louis }', 'tointer': 'select the rows whose team record fuzzily matches to st louis ambush . take the championships in st louis record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'st louis bombers'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to st louis bombers .', 'tostr': 'filter_eq { all_rows ; team ; st louis bombers }'}, 'championships in st louis'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; st louis bombers } ; championships in st louis }', 'tointer': 'select the rows whose team record fuzzily matches to st louis bombers . take the championships in st louis record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team ; st louis ambush } ; championships in st louis } ; hop { filter_eq { all_rows ; team ; st louis bombers } ; championships in st louis } } = true', 'tointer': 'select the rows whose team record fuzzily matches to st louis ambush . take the championships in st louis record of this row . select the rows whose team record fuzzily matches to st louis bombers . take the championships in st louis record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; team ; st louis ambush } ; championships in st louis } ; hop { filter_eq { all_rows ; team ; st louis bombers } ; championships in st louis } } = true
select the rows whose team record fuzzily matches to st louis ambush . take the championships in st louis record of this row . select the rows whose team record fuzzily matches to st louis bombers . take the championships in st louis record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team_7': 7, 'st louis ambush_8': 8, 'championships in st louis_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'st louis bombers_12': 12, 'championships in st louis_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team_7': 'team', 'st louis ambush_8': 'st louis ambush', 'championships in st louis_9': 'championships in st louis', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'st louis bombers_12': 'st louis bombers', 'championships in st louis_13': 'championships in st louis'}
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'st louis ambush_8': [0], 'championships in st louis_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'st louis bombers_12': [1], 'championships in st louis_13': [3]}
['team', 'sport', 'league', 'established', 'began in st louis', 'venue', 'championships in st louis', 'left st louis']
[['st louis stampede', 'arena football', 'arena football league', '1987', '1994', 'scottrade center', '0', '1995'], ['st louis browns', 'baseball', 'american league', '1894', '1902', "sportsman 's park", '0', '1954'], ['st louis stars', 'baseball', 'negro american league', '1937', '1939', 'stars park', '0', '1939'], ['st louis terriers', 'baseball', 'federal league', '1914', '1914', "handlan 's park", '0', '1915'], ['st louis maroons', 'baseball', 'national league', '1884', '1884', 'union base ball park', '0', '1886'], ['st louis stars', 'baseball', 'negro national league', '1922', '1931', 'stars park', '3 ( 1928 , 1930 , 1931 )', '1931'], ['spirits of st louis', 'basketball', 'american basketball association', '1967', '1974', 'st louis arena', '0', '1976'], ['st louis hawks', 'basketball', 'national basketball association', '1946', '1955', 'kiel auditorium', '1 ( 1958 )', '1968'], ['st louis bombers', 'basketball', 'national basketball association', '1946', '1950', 'st louis arena', '0', '1950'], ['st louis cardinals', 'football', 'national football league', '1898', '1960', 'busch stadium', '0', '1988'], ['st louis all stars', 'football', 'national football league', '1923', '1923', "sportsman 's park", '0', '1923'], ['st louis gunners', 'football', 'national football league', '1931', '1931', 'st louis national guard armory', '0', '1934'], ['missouri river otters', 'hockey', 'united hockey league', '1991', '1999', 'family arena', '0', '2006'], ['st louis eagles', 'hockey', 'national hockey league', '1917', '1934', 'st louis arena', '0', '1936'], ['st louis ambush', 'indoor soccer', 'national professional soccer league', '1984', '1992', 'st louis arena / scottrade center', '1 ( 1995 )', '2000'], ['st louis steamers / st louis storm', 'indoor soccer', 'major indoor soccer league', '1977', '1979', 'st louis arena', '0', '1992']]
list of radio stations in tamaulipas
https://en.wikipedia.org/wiki/List_of_radio_stations_in_Tamaulipas
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17982829-17.html.csv
majority
the majority of the radio stations are of the norteño type .
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'norteño', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'type', 'norteño'], 'result': True, 'ind': 0, 'tointer': 'for the type records of all rows , most of them fuzzily match to norteño .', 'tostr': 'most_eq { all_rows ; type ; norteño } = true'}
most_eq { all_rows ; type ; norteño } = true
for the type records of all rows , most of them fuzzily match to norteño .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'type_3': 3, 'norteño_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'type_3': 'type', 'norteño_4': 'norteño'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'type_3': [0], 'norteño_4': [0]}
['frequency', 'callsign', 'brand', 'city of license', 'type']
[['580', 'xehp', 'la mas prendida', 'ciudad victoria', 'norteño'], ['640', 'xetam', 'la poderosa', 'santa elena', 'norteño'], ['970', 'xebj - am', 'radio 970', 'ciudad victoria', 'contemporary'], ['1340', 'xerpv - am', 'la cotorra', 'ciudad victoria', 'norteño'], ['1380', 'xegw', 'planeta w 1380', 'ciudad victoria', 'christian pop'], ['1480', 'xevic', 'radio tamaulipas', 'ciudad victoria', 'state government']]
chris wood ( golfer )
https://en.wikipedia.org/wiki/Chris_Wood_%28golfer%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18512893-3.html.csv
unique
the us open was the only tournament in which chris wood competed in zero events .
{'scope': 'all', 'row': '2', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'events', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose events record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; events ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; events ; 0 } }', 'tointer': 'select the rows whose events record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'events', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose events record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; events ; 0 }'}, 'tournament'], 'result': 'us open', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; events ; 0 } ; tournament }'}, 'us open'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; events ; 0 } ; tournament } ; us open }', 'tointer': 'the tournament record of this unqiue row is us open .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; events ; 0 } } ; eq { hop { filter_eq { all_rows ; events ; 0 } ; tournament } ; us open } } = true', 'tointer': 'select the rows whose events record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is us open .'}
and { only { filter_eq { all_rows ; events ; 0 } } ; eq { hop { filter_eq { all_rows ; events ; 0 } ; tournament } ; us open } } = true
select the rows whose events record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is us open .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'events_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'us open_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'events_7': 'events', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'us open_10': 'us open'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'events_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'us open_10': [3]}
['tournament', 'wins', 'top - 5', 'events', 'cuts made']
[['masters tournament', '0', '0', '1', '0'], ['us open', '0', '0', '0', '0'], ['the open championship', '0', '2', '4', '3'], ['pga championship', '0', '0', '3', '1'], ['totals', '0', '2', '8', '4']]
aro 10
https://en.wikipedia.org/wiki/ARO_10
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1371853-2.html.csv
superlative
the 1.2 petrol engine has the lowest capacity of all the engines listed .
{'scope': 'all', 'col_superlative': '2', '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', 'capacity'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; capacity }'}, 'name'], 'result': '1.2 petrol', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; capacity } ; name }'}, '1.2 petrol'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; capacity } ; name } ; 1.2 petrol } = true', 'tointer': 'select the row whose capacity record of all rows is minimum . the name record of this row is 1.2 petrol .'}
eq { hop { argmin { all_rows ; capacity } ; name } ; 1.2 petrol } = true
select the row whose capacity record of all rows is minimum . the name record of this row is 1.2 petrol .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'capacity_5': 5, 'name_6': 6, '1.2 petrol_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'capacity_5': 'capacity', 'name_6': 'name', '1.2 petrol_7': '1.2 petrol'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'capacity_5': [0], 'name_6': [1], '1.2 petrol_7': [2]}
['name', 'capacity', 'power', 'type', 'torque']
[['1.2 petrol', '1239 cc', 'renault', '5300 rpm', 'at 2800 rpm'], ['1.4 petrol', '1397 cc', 'dacia', '5500 rpm', 'at 3300 rpm'], ['1.6 petrol', '1557 cc', 'dacia', '5000 rpm', 'at 2500 rpm'], ['1.6 petrol', '1598 cc', 'daewoo', '5800 rpm', 'at 3400 rpm'], ['1.9 diesel', '1870 cc', 'renault', '4500 rpm', 'at 2250 rpm'], ['1.9 diesel', '1870 cc', 'renault', '4250 rpm', 'at 2250 rpm']]
2008 victoria cup
https://en.wikipedia.org/wiki/2008_Victoria_Cup
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17058856-1.html.csv
majority
the majority of goals happened during the 3rd period of the 2008 victoria cup .
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': '3rd', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'period', '3rd'], 'result': True, 'ind': 0, 'tointer': 'for the period records of all rows , most of them fuzzily match to 3rd .', 'tostr': 'most_eq { all_rows ; period ; 3rd } = true'}
most_eq { all_rows ; period ; 3rd } = true
for the period records of all rows , most of them fuzzily match to 3rd .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'period_3': 3, '3rd_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'period_3': 'period', '3rd_4': '3rd'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'period_3': [0], '3rd_4': [0]}
['period', 'team', 'goal', 'time', 'score']
[['1st', 'met', 'denis platonov ( 1 )', '1:28', '0 - 1 met'], ['1st', 'met', 'vladimir malenkikh ( 1 ) ( pp )', '18:27', '0 - 2 met'], ['2nd', 'met', 'nikolai zavarukhin ( 1 )', '30:20', '0 - 3 met'], ['2nd', 'nyr', 'chris drury ( 1 ) ( pp )', '39:37', '1 - 3 nyr'], ['3rd', 'nyr', 'dan fritsche ( 1 )', '45:45', '2 - 3 nyr'], ['3rd', 'nyr', 'chris drury ( 2 ) ( pp )', '50:13', '3 - 3 nyr'], ['3rd', 'nyr', 'ryan callahan ( 1 )', '59:40', '4 - 3 nyr']]
marine pharmacognosy
https://en.wikipedia.org/wiki/Marine_pharmacognosy
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12715053-1.html.csv
majority
the majority of marine sourced drugs are used in the cancer disease area .
{'scope': 'all', 'col': '8', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'cancer', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'disease area', 'cancer'], 'result': True, 'ind': 0, 'tointer': 'for the disease area records of all rows , most of them fuzzily match to cancer .', 'tostr': 'most_eq { all_rows ; disease area ; cancer } = true'}
most_eq { all_rows ; disease area ; cancer } = true
for the disease area records of all rows , most of them fuzzily match to cancer .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'disease area_3': 3, 'cancer_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'disease area_3': 'disease area', 'cancer_4': 'cancer'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'disease area_3': [0], 'cancer_4': [0]}
['clinical status', 'compound name', 'trademark', 'marine organism α', 'chemical class', 'molecular target', 'clinical trials β', 'disease area']
[['fda - approved', 'cytarabine ( ara - c )', 'cytosar - u ®', 'sponge', 'nucleoside', 'dna polymerase', '> 50 / 711', 'cancer'], ['fda - approved', 'vidarabine ( ara - a )', 'vira - a ®', 'sponge', 'nucleoside', 'viral dna polymerase', '0', 'antiviral'], ['fda - approved', 'ziconotide', 'prialt ®', 'cone snail', 'peptide', 'n - type ca 2 + channel', '2 / 5', 'analgesic'], ['fda - approved', 'eribulin mesylate ( e7389 )', 'halaven ®', 'sponge', 'macrolide', 's microtubule', '19 / 27', 'cancer'], ['fda - approved', 'omega - 3 - fatty acid ethyl esters', 'lovaza ®', 'fish', 'omega - 3 fatty acids', 'triglyceride - synthesizing enzymes', '45 / 94', 'hypertriglyceridemia'], ['fda - approved', 'trabectedin ( et - 743 ) eu approved only', 'yondelis ®', 'tunicate', 'alkaloid', 'minor groove of dna', '17 / 34', 'cancer'], ['phase iii', 'brentuximab vedotin ( sgn - 35 )', 'adcetris ®', 'mollusk', 'antibody - drug conjugate ( mm auristatin e )', 'cd30 and microtubules', '9 / 19', 'cancer'], ['phase iii', 'plitidepsin', 'aplidin ®', 'tunicate', 'depsipeptide', 'rac1 and jnk activation', '1 / 7', 'cancer'], ['phase ii', 'dmxba ( gts - 21 )', 'n / a', 'worm', 'alkaloid', 'alpha - 7 nicotinic acetylcholine receptor', '0 / 3', 'congnition , schizophrenia'], ['phase ii', 'plinabulin ( npi 2358 )', 'n / a', 'fungus', 'diketopiperazine', 'microtubules and jnk stress protein', '1 / 2', 'cancer'], ['phase ii', 'elisidepsin', 'irvalec ®', 'mollusk', 'depsipeptide', 'plasma membrane fluidity', '1 / 2', 'cancer'], ['phase ii', 'pm00104', 'zalypsis ®', 'nudibranch', 'alkaloid', 'dna - binding', '2 / 3', 'cancer'], ['phase ii', 'glembatumumab vedotin ( cdx - 011 )', 'n / a', 'mollusk', 'antibody drug conjugate ( mm auristatin e )', 'glycoprotein nmb and microtubules', '1 / 3', 'cancer'], ['phase i', 'marizomib ( salinosporamide a )', 'n / a', 'bacterium', 'beta - lactone - gamma lactam', '20s proteasome', '4 / 4', 'cancer'], ['phase i', 'pm01183', 'n / a', 'tunicate', 'alkaloid', 'minor groove of dna', 'n / a', 'cancer'], ['phase i', 'sgn - 75', 'n / a', 'mollusk', 'antibody drug conjugate ( mm auristatin f )', 'cd70 and microtubules', '2 / 2', 'cancer'], ['phase i', 'asg - 5 me', 'n / a', 'mollusk', 'antibody drug conjugate ( mm auristatin e )', 'asg - 5 and microtubules', '2 / 2', 'cancer'], ['phase i', 'hemiasterlin ( e7974 )', 'n / a', 'sponge', 'tripeptide', 'microtubules', '0 / 3', 'cancer'], ['phase i', 'bryostatin 1', 'n / a', 'bryozoa', 'polyketide', 'protein kinase c', '0 / 38', 'cancer , alzheimers'], ['phase i', 's pseudopterosin', 'n / a', 'soft coral', 'diterpene glycoside', 'eicosanoid metabolism', 'n / a', 'wound healing']]
communist party of india ( maoist )
https://en.wikipedia.org/wiki/Communist_Party_of_India_%28Maoist%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1074011-2.html.csv
majority
most of the incidents resulted in 0 people being injured .
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None}
{'func': 'most_eq', 'args': ['all_rows', 'injured', '0'], 'result': True, 'ind': 0, 'tointer': 'for the injured records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; injured ; 0 } = true'}
most_eq { all_rows ; injured ; 0 } = true
for the injured records of all rows , most of them are equal to 0 .
1
1
{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'injured_3': 3, '0_4': 4}
{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'injured_3': 'injured', '0_4': '0'}
{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'injured_3': [0], '0_4': [0]}
['incident no', 'date', 'place', 'killed', 'injured']
[['1', 'february', 'tumkur , karnataka', '6', '0'], ['2', 'august', 'dantewada , chattisgarh', '350', '00'], ['3', '17 august', 'andhra pradesh', '0', '0'], ['4', '11 november', 'giridih , jharkhand', '00', '00'], ['5', '11 november', 'giridih , jharkhand', '5', '16'], ['6', '13 november', 'jehanabad , bihar', '4', '5'], ['7', '30 december', 'dantewada , chhattisgarh', '2', '0'], ['total', 'total', 'total', '367', '21']]
statistics relating to enlargement of the european union
https://en.wikipedia.org/wiki/Statistics_relating_to_enlargement_of_the_European_Union
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1307842-7.html.csv
majority
the majority of member countries in the european union have a total gdp of under 100 billion .
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '100 billion', 'subset': None}
{'func': 'most_less', 'args': ['all_rows', 'gdp ( billion us )', '100 billion'], 'result': True, 'ind': 0, 'tointer': 'for the gdp ( billion us ) records of all rows , most of them are less than 100 billion .', 'tostr': 'most_less { all_rows ; gdp ( billion us ) ; 100 billion } = true'}
most_less { all_rows ; gdp ( billion us ) ; 100 billion } = true
for the gdp ( billion us ) records of all rows , most of them are less than 100 billion .
1
1
{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'gdp (billion us)_3': 3, '100 billion_4': 4}
{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'gdp (billion us)_3': 'gdp ( billion us )', '100 billion_4': '100 billion'}
{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'gdp (billion us)_3': [0], '100 billion_4': [0]}
['member countries', 'population', 'area ( km square )', 'gdp ( billion us )', 'gdp per capita ( us )']
[['cyprus', '775927', '9250', '11.681', '15054'], ['czech republic', '10246178', '78866', '105.248', '10272'], ['estonia', '1341664', '45226', '22.384', '16684'], ['hungary', '10032375', '93030', '102183', '10185'], ['latvia', '2306306', '64589', '24.826', '10764'], ['lithuania', '3607899', '65200', '31.971', '8861'], ['malta', '396851', '316', '5.097', '12843'], ['poland', '38580445', '311904', '316.438', '8202'], ['slovakia', '5423567', '49036', '42.800', '7810'], ['slovenia', '2011473', '20273', '29.633', '14732'], ['accession countries', '74722685', '737690', '685.123', '9169'], ['existing members ( 2004 )', '381781620', '3367154', '7711.871', '20200']]
list of ngc objects ( 7001 - 7840 )
https://en.wikipedia.org/wiki/List_of_NGC_objects_%287001%E2%80%937840%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11051840-8.html.csv
aggregation
the average apparent magnitude of ngc objects for the 7001 - 7840 range is 12.01 .
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '12.01', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'apparent magnitude'], 'result': '12.01', 'ind': 0, 'tostr': 'avg { all_rows ; apparent magnitude }'}, '12.01'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; apparent magnitude } ; 12.01 } = true', 'tointer': 'the average of the apparent magnitude record of all rows is 12.01 .'}
round_eq { avg { all_rows ; apparent magnitude } ; 12.01 } = true
the average of the apparent magnitude record of all rows is 12.01 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'apparent magnitude_4': 4, '12.01_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'apparent magnitude_4': 'apparent magnitude', '12.01_5': '12.01'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'apparent magnitude_4': [0], '12.01_5': [1]}
['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )', 'apparent magnitude']
[['7714', 'spiral galaxy', 'pisces', '23h36 m14 .1 s', 'degree9 ′ 17 ″', '12.2'], ['7742', 'spiral galaxy', 'pegasus', '23h44 m15 .9 s', 'degree46 ′ 01 ″', '12.5'], ['7752', 'irregular galaxy', 'pegasus', '23h46 m58 .5 s', 'degree27 ′ 32 ″', '14.3'], ['7753', 'spiral galaxy', 'pegasus', '23h47 m05 .0 s', 'degree29 ′ 00 ″', '13.2'], ['7777', 'lenticular galaxy', 'pegasus', '23h53 m12 .6 s', 'degree16 ′ 59 ″', '14.5'], ['7789', 'open cluster', 'cassiopeia', '23h57 m24s', 'degree43 ′', '7.7'], ['7793', 'spiral galaxy', 'sculptor', '23h57 m49 .7 s', 'degree35 ′ 30 ″', '9.7']]
jason leffler
https://en.wikipedia.org/wiki/Jason_Leffler
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1637041-2.html.csv
superlative
during the years jason leffler drove for haas cnc racing , his best average finish was 29.2 .
{'scope': 'subset', 'col_superlative': '6', 'row_superlative': '3', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '9', 'subset': {'col': '9', 'criterion': 'equal', 'value': 'haas cnc racing'}}
{'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team ( s )', 'haas cnc racing'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team ( s ) ; haas cnc racing }', 'tointer': 'select the rows whose team ( s ) record fuzzily matches to haas cnc racing .'}, 'avg finish'], 'result': '29.2', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; team ( s ) ; haas cnc racing } ; avg finish }', 'tointer': 'select the rows whose team ( s ) record fuzzily matches to haas cnc racing . the minimum avg finish record of these rows is 29.2 .'}, '29.2'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; team ( s ) ; haas cnc racing } ; avg finish } ; 29.2 } = true', 'tointer': 'select the rows whose team ( s ) record fuzzily matches to haas cnc racing . the minimum avg finish record of these rows is 29.2 .'}
eq { min { filter_eq { all_rows ; team ( s ) ; haas cnc racing } ; avg finish } ; 29.2 } = true
select the rows whose team ( s ) record fuzzily matches to haas cnc racing . the minimum avg finish record of these rows is 29.2 .
3
3
{'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'team (s)_5': 5, 'haas cnc racing_6': 6, 'avg finish_7': 7, '29.2_8': 8}
{'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'team (s)_5': 'team ( s )', 'haas cnc racing_6': 'haas cnc racing', 'avg finish_7': 'avg finish', '29.2_8': '29.2'}
{'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'team (s)_5': [0], 'haas cnc racing_6': [0], 'avg finish_7': [1], '29.2_8': [2]}
['year', 'starts', 'wins', 'top 10', 'avg start', 'avg finish', 'winnings', 'position', 'team ( s )']
[['2001', '30', '0', '1', '28.7', '27.7', '1724692', '37th', '01 chip ganassi racing'], ['2002', '2', '0', '0', '32.5', '33.0', '78500', '63rd', '7 ultra motorsports'], ['2003', '10', '0', '0', '28.0', '29.2', '594500', '47th', '0 haas cnc racing'], ['2004', '1', '0', '0', '25.0', '43.0', '116359', '88th', '60 haas cnc racing'], ['2005', '19', '0', '0', '25.7', '27.5', '1663868', '38th', '11 joe gibbs racing'], ['2008', '3', '0', '0', '30.0', '33.0', '286450', '59th', '70 haas cnc racing'], ['2010', '2', '0', '0', '34.0', '43.0', '135984', '70th', '32 braun racing 66 prism motorsports']]
most daring
https://en.wikipedia.org/wiki/Most_Daring
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18821196-1.html.csv
majority
almost all of the series premiere 's of most daring are unknown .
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'unknown', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'series premiere', 'unknown'], 'result': True, 'ind': 0, 'tointer': 'for the series premiere records of all rows , most of them fuzzily match to unknown .', 'tostr': 'most_eq { all_rows ; series premiere ; unknown } = true'}
most_eq { all_rows ; series premiere ; unknown } = true
for the series premiere records of all rows , most of them fuzzily match to unknown .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'series premiere_3': 3, 'unknown_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'series premiere_3': 'series premiere', 'unknown_4': 'unknown'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'series premiere_3': [0], 'unknown_4': [0]}
['country', 'tv network ( s )', 'series premiere', 'weekly schedule', 'status']
[['australia', 'fox8', 'unknown', 'weekdays 2:30 pm', 'currently airing'], ['belgium', '2be', 'unknown', 'mondays 8:00 pm', 'currently airing'], ['brazil', 'trutv', 'unknown', 'saturdays 11:00 pm', 'currently airing'], ['estonia', 'kanal 12', 'unknown', 'weekends', 'currently airing'], ['greece', 'skai tv', 'unknown', 'weekends 3:00 pm', 'currently airing'], ['india', 'axn india', 'season 5 & 6', 'monday to thursday 11:00 pm', 'currently airing'], ['italy', 'sky italia', 'unknown', 'unknown', 'currently airing'], ['norway', 'viasat 4', 'unknown', 'fridays 8:35 pm', 'currently airing'], ['pakistan', 'axn', 'unknown', 'unknown', 'currently airing'], ['poland', 'polsat play', 'season 3 & 4', 'every day 7:00 pm', 'currently airing'], ['united arab emirates', 'mbc action', 'unknown', 'thursday 4:00 pm', 'currently airing']]
christian population growth
https://en.wikipedia.org/wiki/Christian_population_growth
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28137918-5.html.csv
unique
out of the religions with new adherents per year over a million , the only one with a growth rate over 1.70 % is islam .
{'scope': 'subset', 'row': '2', 'col': '5', 'col_other': '1', 'criterion': 'greater_than', 'value': '1.70 %', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '1000000'}}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'new adherents per year', '1000000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; new adherents per year ; 1000000 }', 'tointer': 'select the rows whose new adherents per year record is greater than 1000000 .'}, 'growth rate', '1.70 %'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % .', 'tostr': 'filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } }', 'tointer': 'select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'new adherents per year', '1000000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; new adherents per year ; 1000000 }', 'tointer': 'select the rows whose new adherents per year record is greater than 1000000 .'}, 'growth rate', '1.70 %'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % .', 'tostr': 'filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % }'}, 'religion'], 'result': 'islam', 'ind': 3, 'tostr': 'hop { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } ; religion }'}, 'islam'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } ; religion } ; islam }', 'tointer': 'the religion record of this unqiue row is islam .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } } ; eq { hop { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } ; religion } ; islam } } = true', 'tointer': 'select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % . there is only one such row in the table . the religion record of this unqiue row is islam .'}
and { only { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } } ; eq { hop { filter_greater { filter_greater { all_rows ; new adherents per year ; 1000000 } ; growth rate ; 1.70 % } ; religion } ; islam } } = true
select the rows whose new adherents per year record is greater than 1000000 . among these rows , select the rows whose growth rate record is greater than 1.70 % . there is only one such row in the table . the religion record of this unqiue row is islam .
8
6
{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_7': 7, 'new adherents per year_8': 8, '1000000_9': 9, 'growth rate_10': 10, '1.70%_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'religion_12': 12, 'islam_13': 13}
{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_7': 'all_rows', 'new adherents per year_8': 'new adherents per year', '1000000_9': '1000000', 'growth rate_10': 'growth rate', '1.70%_11': '1.70 %', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'religion_12': 'religion', 'islam_13': 'islam'}
{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_greater_1': [2, 3], 'filter_greater_0': [1], 'all_rows_7': [0], 'new adherents per year_8': [0], '1000000_9': [0], 'growth rate_10': [1], '1.70%_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'religion_12': [3], 'islam_13': [4]}
['religion', 'births', 'conversions', 'new adherents per year', 'growth rate']
[['christianity', '22708799', '2501396', '25210195', '1.56 %'], ['islam', '21951118', '865558', '22588676', '1.84 %'], ['hinduism', '13194111', '- 660377', '12533734', '1.69 %'], ['buddhism', '3530918', '156609', '3687527', '1.09 %'], ['sikhism', '363677', '28961', '392638', '1.87 %'], ['judaism', '194962', '70447', '124515', '0.91 %'], ["bahá ' í", '117158', '26333', '143491', '2.28 %'], ['confucianism', '55739', '11434', '44305', '0.73 %'], ['jainism', '74539', '39588', '34951', '0.87 %'], ['shinto', '8534', '40527', '- 31993', '1.09 %'], ['taoism', '25397', '155', '25242', '1.00 %'], ['zoroastrianism', '45391', '13080', '58471', '2.65 %']]
2006 - 07 coventry city f.c. season
https://en.wikipedia.org/wiki/2006%E2%80%9307_Coventry_City_F.C._season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12808457-2.html.csv
count
18 players participated in the 2006 - 07 coventry city f.c. season games .
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '18', 'col': '1', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'name'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record is arbitrary .', 'tostr': 'filter_all { all_rows ; name }'}], 'result': '18', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; name } }', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 18 .'}, '18'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; name } } ; 18 } = true', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 18 .'}
eq { count { filter_all { all_rows ; name } } ; 18 } = true
select the rows whose name record is arbitrary . the number of such rows is 18 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'name_5': 5, '18_6': 6}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'name_5': 'name', '18_6': '18'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'name_5': [0], '18_6': [2]}
['name', 'championship', 'league cup', 'fa cup', 'total']
[['kevin kyle', '11', '0', '1', '12'], ['robert page', '10', '0', '0', '10'], ['michael doyle', '8', '0', '2', '10'], ['andrew whing', '6', '1', '0', '7'], ['david mcnamee', '6', '0', '0', '6'], ['marcus hall', '5', '0', '0', '5'], ['leon mckenzie', '5', '0', '0', '5'], ['jay tabb', '5', '0', '0', '5'], ['elliott ward', '5', '0', '0', '5'], ['richard duffy', '4', '0', '0', '4'], ['stephen hughes', '3', '1', '0', '3'], ['dele adebola', '3', '0', '0', '3'], ['isaac osbourne', '3', '0', '0', '3'], ['kevin thornton', '3', '0', '0', '3'], ['adam virgo', '2', '0', '1', '3'], ['colin cameron', '1', '0', '0', '1'], ['colin hawkins', '1', '0', '0', '1'], ['stern john', '1', '0', '0', '1']]
canoeing at the 2008 summer olympics - men 's c - 1 1000 metres
https://en.wikipedia.org/wiki/Canoeing_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_C-1_1000_metres
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18646220-4.html.csv
ordinal
mathieu goubel had the second fastest time in the men 's 1000 meter canoeing at the 2008 olympics .
{'row': '2', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 2 }'}, 'athletes'], 'result': 'mathieu goubel', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 2 } ; athletes }'}, 'mathieu goubel'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 2 } ; athletes } ; mathieu goubel } = true', 'tointer': 'select the row whose time record of all rows is 2nd minimum . the athletes record of this row is mathieu goubel .'}
eq { hop { nth_argmin { all_rows ; time ; 2 } ; athletes } ; mathieu goubel } = true
select the row whose time record of all rows is 2nd minimum . the athletes record of this row is mathieu goubel .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '2_6': 6, 'athletes_7': 7, 'mathieu goubel_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', 'time_5': 'time', '2_6': '2', 'athletes_7': 'athletes', 'mathieu goubel_8': 'mathieu goubel'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '2_6': [0], 'athletes_7': [1], 'mathieu goubel_8': [2]}
['rank', 'athletes', 'country', 'time', 'notes']
[['1', 'vadim menkov', 'uzbekistan', '3:56.793', 'qf'], ['2', 'mathieu goubel', 'france', '3:56.972', 'qs'], ['3', 'marián ostrčil', 'slovakia', '4:00.191', 'qs'], ['4', 'aliaksandr zhukouski', 'belarus', '4:01.380', 'qs'], ['5', 'viktor melantiev', 'russia', '4:03.316', 'qs'], ['6', 'nivalter santos', 'brazil', '4:17.407', 'qs'], ['7', 'mikhail yemelyanov', 'kazakhstan', '4:19.259', 'qs']]
2008 - 09 bundesliga
https://en.wikipedia.org/wiki/2008%E2%80%9309_Bundesliga
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17182686-3.html.csv
count
five managers departed on june 30 , 2008 during 2008 - 09 bundesliga .
{'scope': 'all', 'criterion': 'equal', 'value': '30 june 2008', 'result': '5', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date of vacancy', '30 june 2008'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date of vacancy record fuzzily matches to 30 june 2008 .', 'tostr': 'filter_eq { all_rows ; date of vacancy ; 30 june 2008 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date of vacancy ; 30 june 2008 } }', 'tointer': 'select the rows whose date of vacancy record fuzzily matches to 30 june 2008 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date of vacancy ; 30 june 2008 } } ; 5 } = true', 'tointer': 'select the rows whose date of vacancy record fuzzily matches to 30 june 2008 . the number of such rows is 5 .'}
eq { count { filter_eq { all_rows ; date of vacancy ; 30 june 2008 } } ; 5 } = true
select the rows whose date of vacancy record fuzzily matches to 30 june 2008 . 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, 'date of vacancy_5': 5, '30 june 2008_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', 'date of vacancy_5': 'date of vacancy', '30 june 2008_6': '30 june 2008', '5_7': '5'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date of vacancy_5': [0], '30 june 2008_6': [0], '5_7': [2]}
['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment', 'position in table']
[['bayer 04 leverkusen', 'michael skibbe', 'sacked', '30 june 2008', 'bruno labbadia', '1 july 2008', 'pre - season'], ['fc bayern munich', 'ottmar hitzfeld', 'end of contract', '30 june 2008', 'jürgen klinsmann', '1 july 2008', 'pre - season'], ['borussia dortmund', 'thomas doll', 'resigned', '30 june 2008', 'jürgen klopp', '1 july 2008', 'pre - season'], ['hamburger sv', 'huub stevens', 'end of contract', '30 june 2008', 'martin jol', '1 july 2008', 'pre - season'], ['fc schalke 04', 'mike büskens & youri mulder', 'stepped down to assistant position', '30 june 2008', 'fred rutten', '1 july 2008', 'pre - season'], ['borussia mönchengladbach', 'jos luhukay', 'sacked', '5 october 2008', 'hans meyer', '18 october 2008', '18th'], ['vfb stuttgart', 'armin veh', 'sacked', '23 november 2008', 'markus babbel', '23 november 2008', '11th'], ['fc schalke 04', 'fred rutten', 'sacked', '26 march 2009', 'mike büskens , youri mulder and oliver reck', '1 april 2009', '8th'], ['fc bayern munich', 'jürgen klinsmann', 'sacked', '27 april 2009', 'jupp heynckes', '27 april 2009', '3rd'], ['arminia bielefeld', 'michael frontzeck', 'sacked', '17 may 2009', 'jörg berger', '19 may 2009', '16th']]
giorgio mazza
https://en.wikipedia.org/wiki/Giorgio_Mazza
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11881236-1.html.csv
unique
giorgio mazza only finished in 2nd place in 1963 .
{'scope': 'all', 'row': '4', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '2nd', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '2nd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 2nd .', 'tostr': 'filter_eq { all_rows ; result ; 2nd }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; 2nd } }', 'tointer': 'select the rows whose result record fuzzily matches to 2nd . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '2nd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 2nd .', 'tostr': 'filter_eq { all_rows ; result ; 2nd }'}, 'year'], 'result': '1963', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; 2nd } ; year }'}, '1963'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; 2nd } ; year } ; 1963 }', 'tointer': 'the year record of this unqiue row is 1963 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; 2nd } } ; eq { hop { filter_eq { all_rows ; result ; 2nd } ; year } ; 1963 } } = true', 'tointer': 'select the rows whose result record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 1963 .'}
and { only { filter_eq { all_rows ; result ; 2nd } } ; eq { hop { filter_eq { all_rows ; result ; 2nd } ; year } ; 1963 } } = true
select the rows whose result record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 1963 .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, '2nd_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1963_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', '2nd_8': '2nd', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1963_10': '1963'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], '2nd_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1963_10': [3]}
['year', 'tournament', 'venue', 'result', 'extra']
[['1958', 'european championships', 'stockholm , sweden', '5th', '110 m hurdles'], ['1959', 'universiade', 'turin , italy', '3rd', '110 m hurdles'], ['1962', 'european championships', 'belgrade , yugoslavia', '5th', '110 m hurdles'], ['1963', 'universiade', 'pãrto alegre , brazil', '2nd', '110 m hurdles'], ['1963', 'mediterranean games', 'naples , italy', '3rd', '110 m hurdles'], ['1964', 'olympic games', 'tokyo , japan', '8th', '110 m hurdles']]
2001 in paraguayan football
https://en.wikipedia.org/wiki/2001_in_Paraguayan_football
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16788123-5.html.csv
count
during 2001 in paraguayan football , among the teams that had 4 wins , two of them had less than 4 draws .
{'scope': 'subset', 'criterion': 'less_than', 'value': '4', 'result': '2', 'col': '5', 'subset': {'col': '4', 'criterion': 'equal', 'value': '4'}}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'wins', '4'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; wins ; 4 }', 'tointer': 'select the rows whose wins record is equal to 4 .'}, 'draws', '4'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose wins record is equal to 4 . among these rows , select the rows whose draws record is less than 4 .', 'tostr': 'filter_less { filter_eq { all_rows ; wins ; 4 } ; draws ; 4 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_less { filter_eq { all_rows ; wins ; 4 } ; draws ; 4 } }', 'tointer': 'select the rows whose wins record is equal to 4 . among these rows , select the rows whose draws record is less than 4 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_eq { all_rows ; wins ; 4 } ; draws ; 4 } } ; 2 } = true', 'tointer': 'select the rows whose wins record is equal to 4 . among these rows , select the rows whose draws record is less than 4 . the number of such rows is 2 .'}
eq { count { filter_less { filter_eq { all_rows ; wins ; 4 } ; draws ; 4 } } ; 2 } = true
select the rows whose wins record is equal to 4 . among these rows , select the rows whose draws record is less than 4 . the number of such rows is 2 .
4
4
{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'wins_6': 6, '4_7': 7, 'draws_8': 8, '4_9': 9, '2_10': 10}
{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'wins_6': 'wins', '4_7': '4', 'draws_8': 'draws', '4_9': '4', '2_10': '2'}
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'wins_6': [0], '4_7': [0], 'draws_8': [1], '4_9': [1], '2_10': [3]}
['position', 'team', 'played', 'wins', 'draws', 'losses', 'scored', 'conceded', 'points']
[['1', 'cerro porteño', '9', '5', '2', '2', '14', '7', '17'], ['2', 'libertad', '9', '4', '4', '1', '12', '4', '16'], ['3', '12 de octubre', '9', '5', '1', '3', '15', '10', '16'], ['4', 'cerro corá', '9', '4', '2', '3', '15', '14', '14'], ['5', 'san lorenzo', '9', '4', '1', '4', '11', '11', '13'], ['6', 'sportivo luqueño', '9', '3', '4', '2', '11', '12', '13'], ['7', 'guaraní', '9', '3', '1', '5', '6', '9', '10'], ['8', 'sol de américa', '9', '2', '3', '4', '11', '16', '9'], ['9', 'atl colegiales', '9', '2', '3', '4', '6', '11', '9']]
kharkov governorate
https://en.wikipedia.org/wiki/Kharkov_Governorate
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17051786-1.html.csv
comparative
in the kharkov governorate 's census of 1897 , there were more belarusian speakers than polish ones .
{'row_1': '4', 'row_2': '6', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'language', 'belarusian'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose language record fuzzily matches to belarusian .', 'tostr': 'filter_eq { all_rows ; language ; belarusian }'}, 'number'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; language ; belarusian } ; number }', 'tointer': 'select the rows whose language record fuzzily matches to belarusian . take the number record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'language', 'polish'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose language record fuzzily matches to polish .', 'tostr': 'filter_eq { all_rows ; language ; polish }'}, 'number'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; language ; polish } ; number }', 'tointer': 'select the rows whose language record fuzzily matches to polish . take the number record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; language ; belarusian } ; number } ; hop { filter_eq { all_rows ; language ; polish } ; number } } = true', 'tointer': 'select the rows whose language record fuzzily matches to belarusian . take the number record of this row . select the rows whose language record fuzzily matches to polish . take the number record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; language ; belarusian } ; number } ; hop { filter_eq { all_rows ; language ; polish } ; number } } = true
select the rows whose language record fuzzily matches to belarusian . take the number record of this row . select the rows whose language record fuzzily matches to polish . take the number record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'language_7': 7, 'belarusian_8': 8, 'number_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'language_11': 11, 'polish_12': 12, 'number_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'language_7': 'language', 'belarusian_8': 'belarusian', 'number_9': 'number', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'language_11': 'language', 'polish_12': 'polish', 'number_13': 'number'}
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'language_7': [0], 'belarusian_8': [0], 'number_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'language_11': [1], 'polish_12': [1], 'number_13': [3]}
['language', 'number', 'percentage ( % )', 'males', 'females']
[['ukrainian', '2 009 411', '80.62', '1 004 372', '1 005 039'], ['russian', '440 936', '17.69', '225 803', '215 133'], ['yiddish', '12 650', '0.5', '7 007', '5 643'], ['belarusian', '10 258', '0.41', '4 936', '5 322'], ['german', '9 080', '0.36', '4 504', '4 576'], ['polish', '5 910', '0.23', '4 056', '1 854'], ['tatar', '1 358', '> 0.1', '1 221', '137'], ["persons that did n't name their native language", '44', '> 0.01', '23', '21'], ['other', '2 669', '0.1', '1 700', '969'], ['total', '2 492 316', '100', '1 253 759', '1 238 557']]
2009 nll season
https://en.wikipedia.org/wiki/2009_NLL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14132239-3.html.csv
count
in the 2009 nll season , matt disher was named best defensive player three times .
{'scope': 'all', 'criterion': 'equal', 'value': 'matt disher', 'result': '3', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'defensive', 'matt disher'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose defensive record fuzzily matches to matt disher .', 'tostr': 'filter_eq { all_rows ; defensive ; matt disher }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; defensive ; matt disher } }', 'tointer': 'select the rows whose defensive record fuzzily matches to matt disher . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; defensive ; matt disher } } ; 3 } = true', 'tointer': 'select the rows whose defensive record fuzzily matches to matt disher . the number of such rows is 3 .'}
eq { count { filter_eq { all_rows ; defensive ; matt disher } } ; 3 } = true
select the rows whose defensive record fuzzily matches to matt disher . 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, 'defensive_5': 5, 'matt disher_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', 'defensive_5': 'defensive', 'matt disher_6': 'matt disher', '3_7': '3'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'defensive_5': [0], 'matt disher_6': [0], '3_7': [2]}
['month', 'week', 'overall', 'offensive', 'defensive', 'transition', 'rookie']
[['january', '1', 'blaine manning', 'casey powell', 'kevin croswell', 'scott stewart', 'andrew watt'], ['january', '2', 'gary gait', 'pat maddalena', 'ken montour', 'brodie merrill', 'sean thomson'], ['january', '3', 'mark steenhuis', 'mark steenhuis', 'ken montour', 'greg peyser', 'daryl veltman'], ['january', '4', 'dan teat', 'dan dawson', 'michael thompson', 'tyler codron', 'daryl veltman'], ['january', '5', 'matt disher', 'mike accursi', 'matt disher', 'curtis hodgson', 'matt danowski'], ['february', '6', 'gary bining', 'tracey kelusky', 'pat campbell', 'chris driscoll', 'gary bining'], ['february', '7', 'mark steenhuis', 'mark steenhuis', 'anthony cosmo', 'jason bloom', 'tyler crompton'], ['february', '8', 'dan dawson', 'mark steenhuis', 'jon harnett', 'bobby mcbride', 'rhys duch'], ['february', '9', 'shawn evans', 'shawn evans', 'matt disher', 'kyle ross', 'kevin buchanan'], ['march', '10', 'shawn evans', 'shawn evans', 'sandy chapman', 'pat mccready', 'kevin buchanan'], ['march', '11', 'bob watson', 'john tavares', 'ken montour', 'paul rabil', 'tyler crompton'], ['march', '12', 'athan iannucci', 'andy secore', 'matt vinc', 'brodie merrill', 'rhys duch'], ['march', '13', 'john tavares', 'colin doyle', 'tyler richards', 'brodie merrill', 'rhys duch'], ['april', '14', 'anthony cosmo', 'merrick thomson', 'matt disher', 'scott stewart', 'rhys duch']]
eurovision dance contest 2008
https://en.wikipedia.org/wiki/Eurovision_Dance_Contest_2008
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13053979-1.html.csv
unique
jason roditis & tonia kosovich were the only dancers who danced latin dances in the eurovision dance contest 2008 .
{'scope': 'all', 'row': '11', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'latin dances', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'dance styles', 'latin dances'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose dance styles record fuzzily matches to latin dances .', 'tostr': 'filter_eq { all_rows ; dance styles ; latin dances }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; dance styles ; latin dances } }', 'tointer': 'select the rows whose dance styles record fuzzily matches to latin dances . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'dance styles', 'latin dances'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose dance styles record fuzzily matches to latin dances .', 'tostr': 'filter_eq { all_rows ; dance styles ; latin dances }'}, 'competing dancers'], 'result': 'jason roditis & tonia kosovich', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; dance styles ; latin dances } ; competing dancers }'}, 'jason roditis & tonia kosovich'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; dance styles ; latin dances } ; competing dancers } ; jason roditis & tonia kosovich }', 'tointer': 'the competing dancers record of this unqiue row is jason roditis & tonia kosovich .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; dance styles ; latin dances } } ; eq { hop { filter_eq { all_rows ; dance styles ; latin dances } ; competing dancers } ; jason roditis & tonia kosovich } } = true', 'tointer': 'select the rows whose dance styles record fuzzily matches to latin dances . there is only one such row in the table . the competing dancers record of this unqiue row is jason roditis & tonia kosovich .'}
and { only { filter_eq { all_rows ; dance styles ; latin dances } } ; eq { hop { filter_eq { all_rows ; dance styles ; latin dances } ; competing dancers } ; jason roditis & tonia kosovich } } = true
select the rows whose dance styles record fuzzily matches to latin dances . there is only one such row in the table . the competing dancers record of this unqiue row is jason roditis & tonia kosovich .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'dance styles_7': 7, 'latin dances_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'competing dancers_9': 9, 'jason roditis & tonia kosovich_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'dance styles_7': 'dance styles', 'latin dances_8': 'latin dances', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'competing dancers_9': 'competing dancers', 'jason roditis & tonia kosovich_10': 'jason roditis & tonia kosovich'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'dance styles_7': [0], 'latin dances_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'competing dancers_9': [2], 'jason roditis & tonia kosovich_10': [3]}
['draw', 'competing dancers', 'dance styles', 'rank', 'points']
[['01', 'danny saucedo & jeanette carlsson', 'cha - cha', '12', '38'], ['02', 'dorian steidl & nicole kuntner', 'slowfox / jive / hip - hop', '13', '29'], ['03', 'patrick spiegelberg & katja svensson', 'samba / tango / paso doble / jazz dance', '6', '102'], ['04', 'eldar dzhafarov & anna sazhina', 'paso doble / rumba / tango / azeri folk dance', '5', '106'], ['05', 'gavin ó fearraigh & dearbhla lennon', 'paso doble / rumba / hard shoe irish dance', '11', '40'], ['06', 'maria lund & mikko ahti', 'tango', '10', '44'], ['07', 'thomas berge & roemjana de haan', 'rumba / show dance', '14', '1'], ['08', 'karina krysko & saulius skambinas', 'rumba / cha - cha / acrobatic elements', '4', '110'], ['09', 'louisa lytton & vincent simone', 'paso doble / jive / tango', '9', '47'], ['10', 'tatiana navka & alexander litvinenko', 'cha - cha / samba / rumba / paso doble / russian folk dance', '2', '121'], ['11', 'jason roditis & tonia kosovich', 'latin dances', '7', '72'], ['12', 'raquel tavares & joão tiago', 'rumba / tango', '8', '61'], ['13', 'edyta herbuś & marcin mroczek', 'rumba / cha - cha / jazz dance', '1', '154'], ['14', 'lilia podkopayeva & sergey kostetskiy', "jive / ukrainian folk dance / rock 'n' roll", '3', '119']]
charlotte county , new brunswick
https://en.wikipedia.org/wiki/Charlotte_County%2C_New_Brunswick
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-170969-2.html.csv
superlative
saint george has the most people in charlotte county , new brunswick .
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'population'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population }'}, 'official name'], 'result': 'saint george', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population } ; official name }'}, 'saint george'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population } ; official name } ; saint george } = true', 'tointer': 'select the row whose population record of all rows is maximum . the official name record of this row is saint george .'}
eq { hop { argmax { all_rows ; population } ; official name } ; saint george } = true
select the row whose population record of all rows is maximum . the official name record of this row is saint george .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, 'official name_6': 6, 'saint george_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population_5': 'population', 'official name_6': 'official name', 'saint george_7': 'saint george'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], 'official name_6': [1], 'saint george_7': [2]}
['official name', 'status', 'area km 2', 'population', 'census ranking']
[['saint george', 'parish', '499.51', '2476', '1146 of 5008'], ['pennfield', 'parish', '363.88', '2322', '1206 of 5008'], ['saint stephen', 'parish', '104.41', '2113', '1268 of 5008'], ['saint david', 'parish', '189.91', '1499', '1592 of 5008'], ['saint james', 'parish', '555.99', '1350', '1706 of 5008'], ['campobello', 'parish', '39.59', '1056', '1986 of 5008'], ['lepreau', 'parish', '209.40', '824', '2319 of 5008'], ['west isles', 'parish', '37.93', '824', '2319 of 5008'], ['saint patrick', 'parish', '236.76', '721', '2525 of 5008'], ['saint croix', 'parish', '78.67', '670', '2630 of 5008'], ['saint andrews', 'parish', '24.38', '592', '2797 of 5008'], ['dufferin', 'parish', '12.40', '535', '2919 of 5008'], ['dumbarton', 'parish', '375.06', '356', '3474 of 5008'], ['grand manan', 'parish', '6.20', '190', '4057 of 5008'], ['clarendon', 'parish', '492.84', '71', '4565 of 5008']]
ugly betty ( season 4 )
https://en.wikipedia.org/wiki/Ugly_Betty_%28season_4%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22570439-1.html.csv
majority
most of the ugly betty episodes in series 4 directed by paul holahan gt more than 4.5 million us viewers .
{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '4.5', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'paul holahan'}}
{'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'paul holahan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; directed by ; paul holahan }', 'tointer': 'select the rows whose directed by record fuzzily matches to paul holahan .'}, 'us viewers ( millions )', '4.5'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose directed by record fuzzily matches to paul holahan . for the us viewers ( millions ) records of these rows , most of them are greater than 4.5 .', 'tostr': 'most_greater { filter_eq { all_rows ; directed by ; paul holahan } ; us viewers ( millions ) ; 4.5 } = true'}
most_greater { filter_eq { all_rows ; directed by ; paul holahan } ; us viewers ( millions ) ; 4.5 } = true
select the rows whose directed by record fuzzily matches to paul holahan . for the us viewers ( millions ) records of these rows , most of them are greater than 4.5 .
2
2
{'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'directed by_4': 4, 'paul holahan_5': 5, 'us viewers (millions)_6': 6, '4.5_7': 7}
{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'directed by_4': 'directed by', 'paul holahan_5': 'paul holahan', 'us viewers (millions)_6': 'us viewers ( millions )', '4.5_7': '4.5'}
{'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'directed by_4': [0], 'paul holahan_5': [0], 'us viewers (millions)_6': [1], '4.5_7': [1]}
['series', 'season', 'episode title', 'written by', 'directed by', 'us viewers ( millions )', 'original air date']
[['66', '1', 'the butterfly effect ( part 1 )', 'sheila lawrence & henry alonso myers', 'john terlesky', '5.01', 'october 16 , 2009'], ['67', '2', 'the butterfly effect ( part 2 )', 'sheila lawrence & henry alonso myers', 'victor nelli , jr', '5.18', 'october 16 , 2009'], ['68', '3', 'blue on blue', 'abraham higginbotham', 'victor nelli , jr', '4.55', 'october 23 , 2009'], ['69', '4', 'the weiner , the bun , and the boob', 'brian tanen', 'wendey stanzler', '4.50', 'october 30 , 2009'], ['70', '5', 'plus none', 'cara dipaolo', 'paul holahan', '4.76', 'november 6 , 2009'], ['71', '6', 'backseat betty', 'tracy poust & jon kinnally', 'john putch', '4.46', 'november 13 , 2009'], ['72', '7', 'level ( 7 ) with me', 'chris black', 'john fortenberry', '3.39', 'november 27 , 2009'], ['73', '8', 'the bahamas triangle', 'sheila lawrence', 'victor neili , jr', '4.23', 'december 4 , 2009'], ['74', '9', 'be - shure', 'gail lerner', 'david dworetzky', '4.80', 'december 11 , 2009'], ['75', '10', 'the passion of the betty', 'david grubstick & chris black', 'sj clarkson', '5.13', 'january 6 , 2010'], ['76', '11', 'back in her place', 'abraham higginbotham', 'richard heus', '4.67', 'january 13 , 2010'], ['77', '12', 'blackout !', 'cara dipoulo', 'john putch', '4.59', 'january 20 , 2010'], ['78', '13', 'chica and the man', 'gail lerner', 'victor nelli , jr', '4.34', 'february 3 , 2010'], ['79', '14', "smokin ' hot", 'brian tanen', 'john scott', '4.68', 'february 10 , 2010'], ['80', '15', 'fire and nice', 'erika johnson', 'john terlesky', '4.10', 'march 10 , 2010'], ['81', '16', "all the world 's a stage", 'abraham higginbotham & david grubstick', 'andy wolk', '3.33', 'march 17 , 2010'], ['82', '17', 'million dollar smile', 'henry alonso myers & chris black', 'paul holahan', '4.56', 'march 24 , 2010'], ['83', '18', 'london calling', 'david grubstick & sheila lawrence', 'mark worthington', '4.01', 'march 31 , 2010'], ['84', '19', 'the past presents the future', 'jon kinnaly & tracy poust', 'paul holahan', '4.03', 'april 7 , 2010']]
2002 - 03 toronto raptors season
https://en.wikipedia.org/wiki/2002%E2%80%9303_Toronto_Raptors_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15780718-8.html.csv
ordinal
the toronto raptors ' game against washington recorded their highest attendance of the 2002 - 03 season .
{'row': '2', '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': 'washington', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location attendance ; 1 } ; team }'}, 'washington'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; washington } = true', 'tointer': 'select the row whose location attendance record of all rows is 1st maximum . the team record of this row is washington .'}
eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; washington } = true
select the row whose location attendance record of all rows is 1st maximum . the team record of this row is washington .
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, 'washington_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', 'washington_8': 'washington'}
{'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], 'washington_8': [2]}
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
[['56', 'march 2', 'boston', 'w 104 - 92 ( ot )', 'antonio davis ( 19 )', 'michael bradley ( 13 )', 'alvin williams ( 6 )', 'air canada centre 19800', '18 - 38'], ['57', 'march 4', 'washington', 'w 89 - 86 ( ot )', 'vince carter ( 24 )', 'michael bradley , jerome williams ( 7 )', 'antonio davis ( 5 )', 'mci center 20173', '19 - 38'], ['58', 'march 5', 'houston', 'l 95 - 97 ( ot )', 'vince carter ( 21 )', 'jerome williams ( 10 )', 'antonio davis ( 6 )', 'air canada centre 20171', '19 - 39'], ['59', 'march 8', 'atlanta', 'w 107 - 98 ( ot )', 'vince carter ( 43 )', 'jerome williams ( 15 )', 'antonio davis ( 8 )', 'philips arena 19445', '20 - 39'], ['60', 'march 9', 'memphis', 'l 106 - 119 ( ot )', 'vince carter ( 26 )', 'antonio davis ( 8 )', 'alvin williams ( 9 )', 'air canada centre 19138', '20 - 40'], ['61', 'march 11', 'denver', 'l 87 - 95 ( ot )', 'vince carter ( 21 )', 'michael bradley ( 12 )', 'alvin williams ( 6 )', 'pepsi center 13409', '20 - 41'], ['62', 'march 12', 'portland', 'l 103 - 125 ( ot )', 'vince carter ( 21 )', 'michael bradley ( 10 )', 'rafer alston ( 6 )', 'rose garden 19991', '20 - 42'], ['63', 'march 14', 'sacramento', 'l 84 - 119 ( ot )', 'vince carter , morris peterson ( 16 )', "mamadou n'diaye ( 10 )", 'rafer alston ( 7 )', 'arco arena 17317', '20 - 43'], ['64', 'march 16', 'la clippers', 'l 110 - 111 ( ot )', 'vince carter ( 28 )', 'antonio davis , jerome williams ( 8 )', 'alvin williams ( 5 )', 'staples center 18268', '20 - 44'], ['65', 'march 17', 'phoenix', 'l 91 - 95 ( ot )', 'morris peterson ( 17 )', 'antonio davis ( 15 )', 'alvin williams ( 7 )', 'america west arena 15326', '20 - 45'], ['66', 'march 19', 'atlanta', 'w 87 - 86 ( ot )', 'vince carter ( 27 )', 'jerome williams ( 10 )', 'alvin williams ( 6 )', 'air canada centre 17885', '21 - 45'], ['67', 'march 21', 'miami', 'l 98 - 107 ( ot )', 'vince carter ( 30 )', 'jerome williams ( 9 )', 'alvin williams ( 7 )', 'american airlines arena 14492', '21 - 46'], ['68', 'march 23', 'philadelphia', 'l 95 - 112 ( ot )', 'vince carter ( 22 )', 'antonio davis ( 9 )', 'vince carter ( 9 )', 'air canada centre 19800', '21 - 47'], ['69', 'march 24', 'new york', 'l 90 - 100 ( ot )', 'antonio davis ( 23 )', 'antonio davis ( 12 )', 'alvin williams ( 8 )', 'madison square garden 18824', '21 - 48'], ['70', 'march 26', 'cleveland', 'w 89 - 83 ( ot )', 'morris peterson ( 21 )', 'jelani mccoy ( 8 )', 'rafer alston ( 6 )', 'air canada centre 16832', '22 - 48'], ['71', 'march 28', 'new orleans', 'l 92 - 101 ( ot )', 'vince carter ( 21 )', 'michael bradley ( 11 )', 'alvin williams ( 5 )', 'air canada centre 18773', '22 - 49']]
lamine ouahab
https://en.wikipedia.org/wiki/Lamine_Ouahab
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16981551-2.html.csv
ordinal
the 2nd earliest tournament that lamine ouahab participated in was the kish island tournament .
{'row': '2', 'col': '1', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'tournament'], 'result': 'kish island', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; tournament }'}, 'kish island'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; kish island } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the tournament record of this row is kish island .'}
eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; kish island } = true
select the row whose date record of all rows is 2nd minimum . the tournament record of this row is kish island .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'tournament_7': 7, 'kish island_8': 8}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '2_6': '2', 'tournament_7': 'tournament', 'kish island_8': 'kish island'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'tournament_7': [1], 'kish island_8': [2]}
['date', 'tournament', 'surface', 'opponent in the final', 'score']
[['11 may 2003', 'sidi fredj', 'clay', 'sasa tuksar', '6 - 4 , 6 - 2'], ['21 december 2003', 'kish island', 'clay', 'sebastian fitz', '6 - 4 , 5 - 7 , 6 - 1'], ['4 april 2004', 'syros', 'hard', 'pavel šnobel', '6 - 4 , 6 - 4'], ['21 may 2005', 'agadir', 'clay', 'tres davis', '6 - 1 , 6 - 2'], ['28 may 2005', 'marrakech', 'clay', 'lukáš lacko', '4 - 6 , 6 - 3 , 6 - 2'], ['4 june 2005', 'khemisset', 'clay', 'talal ouahabi', '7 - 6 , 6 - 1'], ['9 september 2005', 'algiers', 'clay', 'filip polášek', '6 - 3 , 6 - 0'], ['16 september 2005', 'algiers', 'clay', 'slimane saoudi', '6 - 4 , 6 - 3'], ['22 april 2006', 'rabat', 'clay', 'frederico gil', '6 - 4 , 6 - 3'], ['7 may 2006', 'tunis', 'clay', 'younes el aynaoui', 'w / o'], ['9 july 2006', 'montauban', 'clay', 'marc gicquel', '7 - 5 , 3 - 6 , 7 - 6'], ['19 may 2007', 'algiers', 'clay', 'reda el amrani', '6 - 4 , 6 - 3'], ['11 october 2008', 'khemisset', 'clay', 'jan mertl', '6 - 4 , 6 - 4'], ['18 october 2008', 'casablanca', 'clay', 'jonathan dasnières de veigy', '6 - 4 , 6 - 3'], ['31 january 2009', 'casablanca', 'clay', 'éric prodon', '6 - 3 , 6 - 1'], ['7 february 2009', 'rabat', 'clay', 'éric prodon', '7 - 5 , 7 - 5'], ['1 february 2010', 'rabat', 'clay', 'laurent rochette', '6 - 3 , 6 - 3'], ['28 may 2012', 'rabat', 'clay', 'yannik reuter', '6 - 2 , 6 - 3'], ['4 june 2012', 'casablanca', 'clay', 'mehdi ziadi', '6 - 0 , 6 - 2']]
list of world records in canoeing
https://en.wikipedia.org/wiki/List_of_world_records_in_canoeing
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14884844-1.html.csv
unique
the 500 meter distance is the only distance in which slovakia holds a canoeing world record .
{'scope': 'all', 'row': '6', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'slovakia', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'slovakia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to slovakia .', 'tostr': 'filter_eq { all_rows ; nationality ; slovakia }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; slovakia } }', 'tointer': 'select the rows whose nationality record fuzzily matches to slovakia . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'slovakia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to slovakia .', 'tostr': 'filter_eq { all_rows ; nationality ; slovakia }'}, 'distance'], 'result': '500 m', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nationality ; slovakia } ; distance }'}, '500 m'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nationality ; slovakia } ; distance } ; 500 m }', 'tointer': 'the distance record of this unqiue row is 500 m .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nationality ; slovakia } } ; eq { hop { filter_eq { all_rows ; nationality ; slovakia } ; distance } ; 500 m } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to slovakia . there is only one such row in the table . the distance record of this unqiue row is 500 m .'}
and { only { filter_eq { all_rows ; nationality ; slovakia } } ; eq { hop { filter_eq { all_rows ; nationality ; slovakia } ; distance } ; 500 m } } = true
select the rows whose nationality record fuzzily matches to slovakia . there is only one such row in the table . the distance record of this unqiue row is 500 m .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'slovakia_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'distance_9': 9, '500 m_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nationality_7': 'nationality', 'slovakia_8': 'slovakia', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'distance_9': 'distance', '500 m_10': '500 m'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'slovakia_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'distance_9': [2], '500 m_10': [3]}
['distance', 'event', 'record', 'nationality', 'year', 'location']
[['200 m', 'k1', '33.8 s', 'canada', '2012', 'montreal , canada'], ['200 m', 'k2', '30.962 s', 'russia', '2012', 'duisburg , germany'], ['200 m', 'k4', '29.023 s', 'hungary', '1997', 'plovdiv , bulgaria'], ['500 m', 'k1', '1:35.554 s', 'canada', '2008', 'beijing , china'], ['500 m', 'k2', '1:26.873 s', 'belarus', '2008', 'poznan , poland'], ['500 m', 'k4', '1:19.650 s', 'slovakia', '2002', 'szeged , hungary'], ['1000 m', 'k1', '3:22.485 s', 'germany', '2011', 'belgrade , serbia'], ['1000 m', 'k2', '3:09.190 s', 'italy', '1996', 'atlanta , usa'], ['1000 m', 'k4', '2:47.734 s', 'germany', '2011', 'szeged , hungary']]
dancing on ice ( series 4 )
https://en.wikipedia.org/wiki/Dancing_on_Ice_%28series_4%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19744915-4.html.csv
superlative
coleen & stuart were the couple with the highest share of public vote in the show dancing on ice series 4 .
{'scope': 'all', 'col_superlative': '11', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'public vote'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; public vote }'}, 'couple'], 'result': 'coleen & stuart', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; public vote } ; couple }'}, 'coleen & stuart'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; public vote } ; couple } ; coleen & stuart } = true', 'tointer': 'select the row whose public vote record of all rows is maximum . the couple record of this row is coleen & stuart .'}
eq { hop { argmax { all_rows ; public vote } ; couple } ; coleen & stuart } = true
select the row whose public vote record of all rows is maximum . the couple record of this row is coleen & stuart .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'public vote_5': 5, 'couple_6': 6, 'coleen & stuart_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'public vote_5': 'public vote', 'couple_6': 'couple', 'coleen & stuart_7': 'coleen & stuart'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'public vote_5': [0], 'couple_6': [1], 'coleen & stuart_7': [2]}
['order', 'couple', 'karen', 'nicky', 'jason', 'ruthie', 'robin', 'total', 'skating song', 'scoreboard', 'public vote']
[['1', 'roxanne & daniel', '3.5', '3.0', '2.5', '3.0', '3.5', '15.0', 'take a bow - rihanna', '4th', '13.563 %'], ['2', 'melinda & fred', '3.5', '3.5', '2.5', '3.5', '3.5', '15.5', 'love song - sara bareilles', '3rd', '2.922 %'], ['3', 'coleen & stuart', '2.5', '2.5', '2.0', '2.5', '3.0', '12.5', 'dream a little dream of me - ella fitzgerald', '6th', '61.801 %'], ['4', 'zöe & matt', '4.0', '4.0', '3.0', '3.5', '4.0', '18.5', 'i wan na dance with somebody - whitney houston', '2nd', '7.593 %'], ['5', 'gemma & andrei', '2.5', '3.0', '2.0', '2.5', '3.0', '13.5', 'the power of love - jennifer rush', '5th', '3.901 %']]
media in sherbrooke
https://en.wikipedia.org/wiki/Media_in_Sherbrooke
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18409243-1.html.csv
majority
regarding the media in sherbrooke , most of the fm radio stations are broadcast in french .
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'french', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'notes', 'french'], 'result': True, 'ind': 0, 'tointer': 'for the notes records of all rows , most of them fuzzily match to french .', 'tostr': 'most_eq { all_rows ; notes ; french } = true'}
most_eq { all_rows ; notes ; french } = true
for the notes records of all rows , most of them fuzzily match to french .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'notes_3': 3, 'french_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'notes_3': 'notes', 'french_4': 'french'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'notes_3': [0], 'french_4': [0]}
['frequency', 'call sign', 'format', 'owner', 'notes']
[['fm 88.1', 'cfpp - fm', 'christian radio', 'fabrique notre - dame du perpétuel - secours', 'french'], ['fm 88.3', 'cfak - fm', 'campus radio', 'université de sherbrooke', 'french'], ['fm 88.9', 'cjmq - fm', 'community radio', "bishop 's university", 'english'], ['fm 89.7', 'cbm - fm - 1', 'public music', 'canadian broadcasting corporation', 'english'], ['fm 90.7', 'cbfx - fm - 2', 'public music', 'société radio - canada', 'french'], ['fm 91.7', 'cbmb - fm', 'public news / talk', 'canadian broadcasting corporation', 'english'], ['fm 93.7', 'cfge - fm', 'adult contemporary', 'cogeco', 'french'], ['fm 95.5', 'cflx - fm', 'community radio', "radio communautaire de l'estrie", 'french'], ['fm 100.3', 'cira - fm - 1', 'christian radio', 'radio ville - marie', 'french'], ['fm 101.1', 'cbf - fm - 10', 'public news / talk', 'société radio - canada', 'french'], ['fm 102.7', 'cite - fm - 1', 'soft adult contemporary', 'bell media radio', 'french'], ['fm 106.1', 'cimo - fm', 'contemporary hit radio', 'bell media radio', 'french'], ['fm 107.7', 'ckoy - fm', 'talk radio', 'cogeco', 'french']]
1963 vfl season
https://en.wikipedia.org/wiki/1963_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10783853-3.html.csv
aggregation
the average crowd was 24303 on the 4th may of the 1963 vfl season .
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '24303', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '24303', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '24303'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 24303 } = true', 'tointer': 'the average of the crowd record of all rows is 24303 .'}
round_eq { avg { all_rows ; crowd } ; 24303 } = true
the average of the crowd record of all rows is 24303 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '24303_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '24303_5': '24303'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '24303_5': [1]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['fitzroy', '9.13 ( 67 )', 'st kilda', '11.11 ( 77 )', 'brunswick street oval', '18544', '4 may 1963'], ['essendon', '9.14 ( 68 )', 'melbourne', '9.9 ( 63 )', 'windy hill', '27283', '4 may 1963'], ['collingwood', '8.16 ( 64 )', 'hawthorn', '13.22 ( 100 )', 'victoria park', '27419', '4 may 1963'], ['richmond', '6.12 ( 48 )', 'north melbourne', '10.9 ( 69 )', 'punt road oval', '23200', '4 may 1963'], ['geelong', '10.25 ( 85 )', 'footscray', '10.4 ( 64 )', 'kardinia park', '26523', '4 may 1963'], ['south melbourne', '10.14 ( 74 )', 'carlton', '16.15 ( 111 )', 'lake oval', '22850', '4 may 1963']]
how i met your mother ( season 3 )
https://en.wikipedia.org/wiki/How_I_Met_Your_Mother_%28season_3%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26702078-1.html.csv
count
in season 3 of how i met your mother , when the director was pamela fryman , 3 of the episodes aired in april .
{'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'april', 'result': '3', 'col': '6', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'pamela fryman'}}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'pamela fryman'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; director ; pamela fryman }', 'tointer': 'select the rows whose director record fuzzily matches to pamela fryman .'}, 'original air date', 'april'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose director record fuzzily matches to pamela fryman . among these rows , select the rows whose original air date record fuzzily matches to april .', 'tostr': 'filter_eq { filter_eq { all_rows ; director ; pamela fryman } ; original air date ; april }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; director ; pamela fryman } ; original air date ; april } }', 'tointer': 'select the rows whose director record fuzzily matches to pamela fryman . among these rows , select the rows whose original air date record fuzzily matches to april . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; director ; pamela fryman } ; original air date ; april } } ; 3 } = true', 'tointer': 'select the rows whose director record fuzzily matches to pamela fryman . among these rows , select the rows whose original air date record fuzzily matches to april . the number of such rows is 3 .'}
eq { count { filter_eq { filter_eq { all_rows ; director ; pamela fryman } ; original air date ; april } } ; 3 } = true
select the rows whose director record fuzzily matches to pamela fryman . among these rows , select the rows whose original air date record fuzzily matches to april . 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, 'director_6': 6, 'pamela fryman_7': 7, 'original air date_8': 8, 'april_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', 'director_6': 'director', 'pamela fryman_7': 'pamela fryman', 'original air date_8': 'original air date', 'april_9': 'april', '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], 'director_6': [0], 'pamela fryman_7': [0], 'original air date_8': [1], 'april_9': [1], '3_10': [3]}
['no in series', 'no in season', 'title', 'director', 'writer ( s )', 'original air date', 'production code', 'us viewers ( million )']
[['45', '1', 'wait for it', 'pamela fryman', 'carter bays & craig thomas', 'september 24 , 2007', '3alh01', '8.12'], ['46', '2', "we 're not from here", 'pamela fryman', 'chris harris', 'october 1 , 2007', '3alh02', '7.88'], ['47', '3', 'third wheel', 'pamela fryman', 'david hemingson', 'october 8 , 2007', '3alh04', '7.96'], ['48', '4', 'little boys', 'rob greenberg', 'kourtney kang', 'october 15 , 2007', '3alh03', '7.71'], ['49', '5', 'how i met everyone else', 'pamela fryman', 'gloria calderon kellett', 'october 22 , 2007', '3alh05', '8.50'], ['50', '6', "i 'm not that guy", 'pamela fryman', 'jonathan groff', 'october 29 , 2007', '3alh06', '8.55'], ['51', '7', 'dowisetrepla', 'pamela fryman', 'brenda hsueh', 'november 5 , 2007', '3alh07', '8.77'], ['52', '8', 'spoiler alert', 'pamela fryman', 'stephen lloyd', 'november 12 , 2007', '3alh08', '8.58'], ['53', '9', 'slapsgiving', 'pamela fryman', 'matt kuhn', 'november 19 , 2007', '3alh09', '8.06'], ['54', '10', 'the yips', 'pamela fryman', 'jamie rhonheimer', 'november 26 , 2007', '3alh10', '7.91'], ['55', '11', 'the platinum rule', 'pamela fryman', 'carter bays & craig thomas', 'december 10 , 2007', '3alh11', '8.49'], ['56', '12', 'no tomorrow', 'pamela fryman', 'carter bays & craig thomas', 'march 17 , 2008', '3alh12', '9.73'], ['57', '13', 'ten sessions', 'pamela fryman', 'chris harris , carter bays & craig thomas', 'march 24 , 2008', '3alh14', '10.67'], ['58', '14', 'the bracket', 'pamela fryman', 'joe kelly', 'march 31 , 2008', '3alh13', '9.50'], ['59', '15', 'the chain of screaming', 'pamela fryman', 'carter bays & craig thomas', 'april 14 , 2008', '3alh15', '7.99'], ['60', '16', 'sandcastles in the sand', 'pamela fryman', 'kourtney kang', 'april 21 , 2008', '3alh16', '8.45'], ['61', '17', 'the goat', 'pamela fryman', 'stephen lloyd', 'april 28 , 2008', '3alh17', '8.84'], ['62', '18', 'rebound bro', 'pamela fryman', 'jamie rhonheimer', 'may 5 , 2008', '3alh18', '8.36'], ['63', '19', 'everything must go', 'pamela fryman', 'jonathan groff & chris harris', 'may 12 , 2008', '3alh19', '8.93']]
1985 - 86 boston celtics season
https://en.wikipedia.org/wiki/1985%E2%80%9386_Boston_Celtics_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13821848-6.html.csv
aggregation
during the 1985 - 86 boston celtics season , the boston celtics scored a total of 843 points at boston garden .
{'scope': 'subset', 'col': '4', 'type': 'sum', 'result': '843', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'boston garden'}}
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'boston garden'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; boston garden }', 'tointer': 'select the rows whose location record fuzzily matches to boston garden .'}, 'score'], 'result': '843', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; location ; boston garden } ; score }'}, '843'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; location ; boston garden } ; score } ; 843 } = true', 'tointer': 'select the rows whose location record fuzzily matches to boston garden . the sum of the score record of these rows is 843 .'}
round_eq { sum { filter_eq { all_rows ; location ; boston garden } ; score } ; 843 } = true
select the rows whose location record fuzzily matches to boston garden . the sum of the score record of these rows is 843 .
3
3
{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'boston garden_6': 6, 'score_7': 7, '843_8': 8}
{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location_5': 'location', 'boston garden_6': 'boston garden', 'score_7': 'score', '843_8': '843'}
{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'boston garden_6': [0], 'score_7': [1], '843_8': [2]}
['game', 'date', 'opponent', 'score', 'location', 'record']
[['31', 'thu jan 2', 'indiana pacers', '122 - 104', 'market square arena', '24 - 7'], ['32', 'fri jan 3', 'new jersey nets', '129 - 117', 'boston garden', '25 - 7'], ['33', 'tue jan 7', 'detroit pistons', '109 - 113', 'pontiac silverdome', '25 - 8'], ['34', 'wed jan 8', 'cleveland cavaliers', '126 - 95', 'boston garden', '26 - 8'], ['35', 'fri jan 10', 'atlanta hawks', '115 - 108', 'boston garden', '27 - 8'], ['36', 'wed jan 15', 'denver nuggets', '123 - 100', 'boston garden', '28 - 8'], ['37', 'fri jan 17', 'indiana pacers', '123 - 105', 'market square arena', '29 - 8'], ['38', 'sat jan 18', 'atlanta hawks', '125 - 122 ( ot )', 'the omni', '30 - 8'], ['39', 'wed jan 22', 'los angeles lakers', '110 - 95', 'boston garden', '31 - 8'], ['40', 'fri jan 24', 'golden state warriors', '135 - 114', 'boston garden', '32 - 8'], ['41', 'sun jan 26', 'philadelphia 76ers', '105 - 103', 'boston garden', '33 - 8'], ['42', 'thu jan 30', 'chicago bulls', '101 - 91', 'chicago stadium', '34 - 8'], ['43', 'fri jan 31', 'washington bullets', '97 - 88', 'capital centre', '35 - 8']]
pirveli liga
https://en.wikipedia.org/wiki/Pirveli_Liga
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18009885-2.html.csv
aggregation
the stadiums in the pirveli liga have a combined total capacity of 185080 .
{'scope': 'all', 'col': '5', 'type': 'sum', 'result': '185080', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'capacity'], 'result': '185080', 'ind': 0, 'tostr': 'sum { all_rows ; capacity }'}, '185080'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; capacity } ; 185080 } = true', 'tointer': 'the sum of the capacity record of all rows is 185080 .'}
round_eq { sum { all_rows ; capacity } ; 185080 } = true
the sum of the capacity record of all rows is 185080 .
2
2
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'capacity_4': 4, '185080_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'capacity_4': 'capacity', '185080_5': '185080'}
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'capacity_4': [0], '185080_5': [1]}
['clubs', 'position 2010 - 11', 'region', 'stadium', 'capacity']
[['samtredia', 'umaglesi liga', 'imereti', 'erosi manjgaladze stadium', '15000'], ['chikhura sachkhere', '4', 'imereti', 'tsentral stadium ( sachkhere )', '2000'], ['dinamo batumi', '5', 'adjara', 'batumi stadium', '30000'], ['guria lanchkhuti', '6', 'guria', 'evgrapi shevardnadze stadium', '22000'], ['kolkheti khobi', '7', 'samegrelo', 'tsentral stadium ( khobi )', '12000'], ['imereti khoni', '8', 'imereti', 'tsentral stadium ( khoni )', '2000'], ['meshakhte tkibuli', '9', 'imereti', 'vladimer bochorishvili stadium', '11700'], ['norchi dinamoeli tbilisi', '10', 'tbilisi', 'sport - kompleksi shatili', '2000'], ['chkherimela kharagauli', '11', 'imereti', 'kharagauli stadium', '6000'], ['adeli batumi', '12', 'adjara', 'tsentral stadium ( batumi )', '15000'], ['mertskhali ozurgeti', '13', 'guria', 'megobroba stadium', '3500'], ['samgurali tskhaltubo', '14', 'imereti', '26 may stadium', '12000'], ['skuri tsalenjikha', '15', 'samegrelo', 'sasha kvaratskhelia stadium', '4000'], ['chiatura sachkhere', '16', 'imereti', 'temur maghradze stadium', '11700'], ['lokomotivi tbilisi', '17', 'tbilisi', 'mikheil meskhi stadium', '24680'], ['sulori vani', 'meore liga', 'imereti', 'grigol nikoleishvili stadium', '2500'], ['stu tbilisi', 'meore liga', 'tbilisi', 'sport - kompleksi shatili', '2000'], ['meskheri akhaltsikhe', 'meore liga', 'samtskhe - javakheti', 'tsentral stadium ( akhaltsikhe )', '4000'], ['aeti sokhumi', 'meore liga', 'abkhazia', 'sport - kompleksi shatili', '2000'], ['zooveti tbilisi', 'meore liga', 'tbilisi', 'sportis akademiis stadioni', '1000']]
lori chalupny
https://en.wikipedia.org/wiki/Lori_Chalupny
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12265902-2.html.csv
majority
most of lori chalupny 's goals took place from a lineup of 90 . start .
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '90 . start', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'lineup', '90 . start'], 'result': True, 'ind': 0, 'tointer': 'for the lineup records of all rows , most of them fuzzily match to 90 . start .', 'tostr': 'most_eq { all_rows ; lineup ; 90 . start } = true'}
most_eq { all_rows ; lineup ; 90 . start } = true
for the lineup records of all rows , most of them fuzzily match to 90 . start .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'lineup_3': 3, '90. start_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'lineup_3': 'lineup', '90. start_4': '90 . start'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'lineup_3': [0], '90. start_4': [0]}
['goal', 'location', 'lineup', 'assist / pass', 'score', 'result', 'competition']
[['1', 'usa albuquerque nm', "on 70 ' ( off lilly )", 'tarpley', '3 - 0', '3 - 0', 'friendly'], ['2', 'usa virginia beach', '90 . start', 'unassisted', '1 - 0', '2 - 0', 'friendly'], ['3', 'chn guangzhou', '90 . start', 'unassisted', '1 - 0', '2 - 0', 'four nations tournament'], ['4', 'usa frisco tx', "off 72 ' ( on wagner )", 'tarpley', '3 - 1', '6 - 2', 'friendly'], ['5', 'chn shanghai', '90 . start', 'wambach', '1 - 0', '1 - 0', 'world cup group b'], ['6', 'chn shanghai', '90 . start', 'unassisted', '3 - 0', '4 - 1', 'world cup final - third place playoff'], ['7', 'chn beijing', '90 . start', 'rodriguez', '2 - 1', '4 - 2', 'olympics tournament'], ['8', 'usa bridgeview il', '90 . start', 'tarpley', '1 - 0', '2 - 0', 'friendly']]
1978 san francisco 49ers season
https://en.wikipedia.org/wiki/1978_San_Francisco_49ers_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18674332-1.html.csv
comparative
the san francisco 49ers had a game with the tampa bay buccaneers earlier than detroit lions .
{'row_1': '15', 'row_2': '16', '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', 'tampa bay buccaneers'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to tampa bay buccaneers .', 'tostr': 'filter_eq { all_rows ; opponent ; tampa bay buccaneers }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to tampa bay buccaneers . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'detroit lions'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to detroit lions .', 'tostr': 'filter_eq { all_rows ; opponent ; detroit lions }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; detroit lions } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit lions . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; hop { filter_eq { all_rows ; opponent ; detroit lions } ; date } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to tampa bay buccaneers . take the date record of this row . select the rows whose opponent record fuzzily matches to detroit lions . take the date record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; hop { filter_eq { all_rows ; opponent ; detroit lions } ; date } } = true
select the rows whose opponent record fuzzily matches to tampa bay buccaneers . take the date record of this row . select the rows whose opponent record fuzzily matches to detroit lions . 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, 'tampa bay buccaneers_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'detroit lions_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', 'tampa bay buccaneers_8': 'tampa bay buccaneers', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'detroit lions_12': 'detroit lions', 'date_13': 'date'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'tampa bay buccaneers_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'detroit lions_12': [1], 'date_13': [3]}
['week', 'date', 'opponent', 'result', 'attendance']
[['1', 'september 3 , 1978', 'cleveland browns', 'l 24 - 7', '68973'], ['2', 'september 10 , 1978', 'chicago bears', 'l 16 - 13', '49502'], ['3', 'september 17 , 1978', 'houston oilers', 'l 20 - 19', '46161'], ['4', 'september 24 , 1978', 'new york giants', 'l 27 - 10', '71536'], ['5', 'october 1 , 1978', 'cincinnati bengals', 'w 28 - 12', '41107'], ['6', 'october 8 , 1978', 'los angeles rams', 'l 27 - 10', '59337'], ['7', 'october 15 , 1978', 'new orleans saints', 'l 14 - 7', '37671'], ['8', 'october 22 , 1978', 'atlanta falcons', 'l 20 - 17', '44235'], ['9', 'october 29 , 1978', 'washington redskins', 'l 38 - 20', '53706'], ['10', 'november 5 , 1978', 'atlanta falcons', 'l 21 - 10', '55468'], ['11', 'november 12 , 1978', 'st louis cardinals', 'l 16 - 10', '33155'], ['12', 'november 19 , 1978', 'los angeles rams', 'l 31 - 28', '45022'], ['13', 'november 27 , 1978', 'pittsburgh steelers', 'l 24 - 7', '51657'], ['14', 'december 3 , 1978', 'new orleans saints', 'l 24 - 13', '50068'], ['15', 'december 10 , 1978', 'tampa bay buccaneers', 'w 6 - 3', '30931'], ['16', 'december 17 , 1978', 'detroit lions', 'l 33 - 14', '56674']]
2010 - 11 uefa champions league
https://en.wikipedia.org/wiki/2010%E2%80%9311_UEFA_Champions_League
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18255941-28.html.csv
comparative
barcelona scored more goals than manchester united scored in the 2010 - 11 uefa champions league .
{'row_1': '6', 'row_2': '7', 'col': '2', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team 2', 'barcelona'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team 2 record fuzzily matches to barcelona .', 'tostr': 'filter_eq { all_rows ; team 2 ; barcelona }'}, 'agg'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team 2 ; barcelona } ; agg }', 'tointer': 'select the rows whose team 2 record fuzzily matches to barcelona . take the agg record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team 2', 'manchester united'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team 2 record fuzzily matches to manchester united .', 'tostr': 'filter_eq { all_rows ; team 2 ; manchester united }'}, 'agg'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team 2 ; manchester united } ; agg }', 'tointer': 'select the rows whose team 2 record fuzzily matches to manchester united . take the agg record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team 2 ; barcelona } ; agg } ; hop { filter_eq { all_rows ; team 2 ; manchester united } ; agg } } = true', 'tointer': 'select the rows whose team 2 record fuzzily matches to barcelona . take the agg record of this row . select the rows whose team 2 record fuzzily matches to manchester united . take the agg record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; team 2 ; barcelona } ; agg } ; hop { filter_eq { all_rows ; team 2 ; manchester united } ; agg } } = true
select the rows whose team 2 record fuzzily matches to barcelona . take the agg record of this row . select the rows whose team 2 record fuzzily matches to manchester united . take the agg record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team 2_7': 7, 'barcelona_8': 8, 'agg_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team 2_11': 11, 'manchester united_12': 12, 'agg_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team 2_7': 'team 2', 'barcelona_8': 'barcelona', 'agg_9': 'agg', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team 2_11': 'team 2', 'manchester united_12': 'manchester united', 'agg_13': 'agg'}
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team 2_7': [0], 'barcelona_8': [0], 'agg_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team 2_11': [1], 'manchester united_12': [1], 'agg_13': [3]}
['team 1', 'agg', 'team 2', '1st leg', '2nd leg']
[['roma', '2 - 6', 'shakhtar donetsk', '2 - 3', '0 - 3'], ['milan', '0 - 1', 'tottenham hotspur', '0 - 1', '0 - 0'], ['valencia', '2 - 4', 'schalke 04', '1 - 1', '1 - 3'], ['internazionale', '( a ) 3 - 3', 'bayern munich', '0 - 1', '3 - 2'], ['lyon', '1 - 4', 'real madrid', '1 - 1', '0 - 3'], ['arsenal', '3 - 4', 'barcelona', '2 - 1', '1 - 3'], ['marseille', '1 - 2', 'manchester united', '0 - 0', '1 - 2'], ['copenhagen', '0 - 2', 'chelsea', '0 - 2', '0 - 0']]
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
unique
only stefan noesen belonged to the club team plymouth whalers .
{'scope': 'all', 'row': '2', 'col': '6', 'col_other': '3', 'criterion': 'equal', 'value': 'plymouth whalers ( ohl )', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club team', 'plymouth whalers ( ohl )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club team record fuzzily matches to plymouth whalers ( ohl ) .', 'tostr': 'filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } }', 'tointer': 'select the rows whose club team record fuzzily matches to plymouth whalers ( ohl ) . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club team', 'plymouth whalers ( ohl )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club team record fuzzily matches to plymouth whalers ( ohl ) .', 'tostr': 'filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) }'}, 'player'], 'result': 'stefan noesen', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } ; player }'}, 'stefan noesen'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } ; player } ; stefan noesen }', 'tointer': 'the player record of this unqiue row is stefan noesen .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } } ; eq { hop { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } ; player } ; stefan noesen } } = true', 'tointer': 'select the rows whose club team record fuzzily matches to plymouth whalers ( ohl ) . there is only one such row in the table . the player record of this unqiue row is stefan noesen .'}
and { only { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } } ; eq { hop { filter_eq { all_rows ; club team ; plymouth whalers ( ohl ) } ; player } ; stefan noesen } } = true
select the rows whose club team record fuzzily matches to plymouth whalers ( ohl ) . there is only one such row in the table . the player record of this unqiue row is stefan noesen .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'club team_7': 7, 'plymouth whalers (ohl)_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'stefan noesen_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'club team_7': 'club team', 'plymouth whalers (ohl)_8': 'plymouth whalers ( ohl )', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'stefan noesen_10': 'stefan noesen'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'club team_7': [0], 'plymouth whalers (ohl)_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'stefan noesen_10': [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 )']]
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/1-15187735-12.html.csv
ordinal
of the how it 's made episodes , the 2nd to last episode was the one where segment b was compact track loaders .
{'row': '11', 'col': '2', 'order': '2', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'episode', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; episode ; 2 }'}, 'segment b'], 'result': 'compact track loaders', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; episode ; 2 } ; segment b }'}, 'compact track loaders'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; episode ; 2 } ; segment b } ; compact track loaders } = true', 'tointer': 'select the row whose episode record of all rows is 2nd maximum . the segment b record of this row is compact track loaders .'}
eq { hop { nth_argmax { all_rows ; episode ; 2 } ; segment b } ; compact track loaders } = true
select the row whose episode record of all rows is 2nd maximum . the segment b record of this row is compact track loaders .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'episode_5': 5, '2_6': 6, 'segment b_7': 7, 'compact track loaders_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', 'episode_5': 'episode', '2_6': '2', 'segment b_7': 'segment b', 'compact track loaders_8': 'compact track loaders'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'episode_5': [0], '2_6': [0], 'segment b_7': [1], 'compact track loaders_8': [2]}
['series ep', 'episode', 'netflix', 'segment a', 'segment b', 'segment c', 'segment d']
[['12 - 01', '144', 's06e14', 'pneumatic impact wrenches', 'cultured marble sinks', 'plantain chips', 'nascar stock cars'], ['12 - 02', '145', 's06e15', 'jaws of life', 'artificial christmas trees', 'soda crackers', 'ratchets'], ['12 - 03', '146', 's06e16', 's thermometer', 'produce scales', 'aircraft painting', 'luxury s chocolate'], ['12 - 04', '147', 's06e17', 'carburetors', 'air conditioners', 'sugar ( part 1 )', 'sugar ( part 2 )'], ['12 - 05', '148', 's06e18', 'combination wrenches', 'deli meats', 'golf cars', 'airships'], ['12 - 06', '149', 's06e19', 'carbon fibre car parts', 'hand dryers', 'recycled polyester yarn', 'fleece'], ['12 - 07', '150', 's06e20', 'police badges', 'muffins', 'car washes', 'pressure gauges'], ['12 - 08', '151', 's06e21', 'metal detectors', 'rum', 'tiffany reproductions', 'aircraft engines'], ['12 - 09', '152', 's06e22', 'riding mowers', 'popcorn', 'adjustable beds', 'cultured diamonds'], ['12 - 10', '153', 's06e23', 'airstream trailers', 'horseradish', 'industrial steam s boiler', 'deodorant'], ['12 - 11', '154', 's06e24', 's screwdriver', 'compact track loaders', 'physician scales', 'carbon fibre bats'], ['12 - 12', '155', 's06e25', 's escalator', 'kevlar s canoe', 'goat cheese', 'disc music boxes']]
new england women 's and men 's athletic conference
https://en.wikipedia.org/wiki/New_England_Women%27s_and_Men%27s_Athletic_Conference
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1974782-1.html.csv
comparative
more people attend smith college than attend the united states coast guard academy .
{'row_1': '6', 'row_2': '8', 'col': '6', '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', 'institution', 'smith college'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose institution record fuzzily matches to smith college .', 'tostr': 'filter_eq { all_rows ; institution ; smith college }'}, 'enrollment'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; institution ; smith college } ; enrollment }', 'tointer': 'select the rows whose institution record fuzzily matches to smith college . take the enrollment record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'institution', 'united states coast guard academy'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose institution record fuzzily matches to united states coast guard academy .', 'tostr': 'filter_eq { all_rows ; institution ; united states coast guard academy }'}, 'enrollment'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; institution ; united states coast guard academy } ; enrollment }', 'tointer': 'select the rows whose institution record fuzzily matches to united states coast guard academy . take the enrollment record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; institution ; smith college } ; enrollment } ; hop { filter_eq { all_rows ; institution ; united states coast guard academy } ; enrollment } } = true', 'tointer': 'select the rows whose institution record fuzzily matches to smith college . take the enrollment record of this row . select the rows whose institution record fuzzily matches to united states coast guard academy . take the enrollment record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; institution ; smith college } ; enrollment } ; hop { filter_eq { all_rows ; institution ; united states coast guard academy } ; enrollment } } = true
select the rows whose institution record fuzzily matches to smith college . take the enrollment record of this row . select the rows whose institution record fuzzily matches to united states coast guard academy . take the enrollment record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'institution_7': 7, 'smith college_8': 8, 'enrollment_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'institution_11': 11, 'united states coast guard academy_12': 12, 'enrollment_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'institution_7': 'institution', 'smith college_8': 'smith college', 'enrollment_9': 'enrollment', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'institution_11': 'institution', 'united states coast guard academy_12': 'united states coast guard academy', 'enrollment_13': 'enrollment'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'institution_7': [0], 'smith college_8': [0], 'enrollment_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'institution_11': [1], 'united states coast guard academy_12': [1], 'enrollment_13': [3]}
['institution', 'location', 'nickname', 'founded', 'type', 'enrollment', 'joined']
[['babson college', 'wellesley , massachusetts', 'beavers', '1919', 'private / non - sectarian', '3200', '1985'], ['clark university', 'worcester , massachusetts', 'cougars', '1887', 'private / non - sectarian', '2780', '1995'], ['emerson college', 'boston , massachusetts', 'lions', '1880', 'private / non - sectarian', '4290', '2013'], ['massachusetts institute of technology', 'cambridge , massachusetts', 'engineers', '1861', 'private / non - sectarian', '10253', '1985'], ['mount holyoke college', 'south hadley , massachusetts', 'lyons', '1837', 'private / non - sectarian', '2100', '1987'], ['smith college', 'northampton , massachusetts', 'pioneers', '1871', 'private / non - sectarian', '2600', '1985'], ['springfield college', 'springfield , massachusetts', 'pride', '1885', 'private / non - sectarian', '5062', '1998'], ['united states coast guard academy', 'new london , connecticut', 'bears', '1876', 'federal / military', '990', '1998'], ['wellesley college', 'wellesley , massachusetts', 'blue', '1870', 'private / non - sectarian', '2300', '1985'], ['wheaton college', 'norton , massachusetts', 'lyons', '1834', 'private / non - sectarian', '1550', '1985']]
hit 'n run tour
https://en.wikipedia.org/wiki/Hit_%27n_Run_Tour
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12946465-1.html.csv
unique
the concert at the blackcomb mountain was the only cancelled event of the hit 'n run tour .
{'scope': 'all', 'row': '5', 'col': '5', 'col_other': '4', 'criterion': 'equal', 'value': 'canceled', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'attendance', 'canceled'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record fuzzily matches to canceled .', 'tostr': 'filter_eq { all_rows ; attendance ; canceled }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; attendance ; canceled } }', 'tointer': 'select the rows whose attendance record fuzzily matches to canceled . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'attendance', 'canceled'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record fuzzily matches to canceled .', 'tostr': 'filter_eq { all_rows ; attendance ; canceled }'}, 'venue'], 'result': 'blackcomb mountain', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; attendance ; canceled } ; venue }'}, 'blackcomb mountain'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; attendance ; canceled } ; venue } ; blackcomb mountain }', 'tointer': 'the venue record of this unqiue row is blackcomb mountain .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; attendance ; canceled } } ; eq { hop { filter_eq { all_rows ; attendance ; canceled } ; venue } ; blackcomb mountain } } = true', 'tointer': 'select the rows whose attendance record fuzzily matches to canceled . there is only one such row in the table . the venue record of this unqiue row is blackcomb mountain .'}
and { only { filter_eq { all_rows ; attendance ; canceled } } ; eq { hop { filter_eq { all_rows ; attendance ; canceled } ; venue } ; blackcomb mountain } } = true
select the rows whose attendance record fuzzily matches to canceled . there is only one such row in the table . the venue record of this unqiue row is blackcomb mountain .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'attendance_7': 7, 'canceled_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'venue_9': 9, 'blackcomb mountain_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'attendance_7': 'attendance', 'canceled_8': 'canceled', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'venue_9': 'venue', 'blackcomb mountain_10': 'blackcomb mountain'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'attendance_7': [0], 'canceled_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'venue_9': [2], 'blackcomb mountain_10': [3]}
['date', 'city', 'country', 'venue', 'attendance']
[['july 20 , 2007', 'sault ste marie , michigan', 'united states', 'kewadin casino', '10000'], ['july 21 , 2007', 'cadott , wisconsin', 'united states', 'cadott rock fest', '35000'], ['july 25 , 2007', 'anaheim , california', 'united states', 'cisco customer appreciation event', '1000'], ['july 27 , 2007', 'san jacinto , california', 'united states', 'soboba casino arena', '3500'], ['september 15 , 2007', 'whistler , british columbia', 'canada', 'blackcomb mountain', 'canceled'], ['october 26 , 2007', 'paradise , nevada', 'united states', 'mandalay bay resort', '1500']]
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/1-1965650-6.html.csv
unique
david lee is the only player of the 1973 nhl amateur draft with the nationality of united kingdom canada .
{'scope': 'all', 'row': '9', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'united kingdom canada', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united kingdom canada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to united kingdom canada .', 'tostr': 'filter_eq { all_rows ; nationality ; united kingdom canada }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; united kingdom canada } }', 'tointer': 'select the rows whose nationality record fuzzily matches to united kingdom canada . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united kingdom canada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to united kingdom canada .', 'tostr': 'filter_eq { all_rows ; nationality ; united kingdom canada }'}, 'player'], 'result': 'david lee', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nationality ; united kingdom canada } ; player }'}, 'david lee'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nationality ; united kingdom canada } ; player } ; david lee }', 'tointer': 'the player record of this unqiue row is david lee .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nationality ; united kingdom canada } } ; eq { hop { filter_eq { all_rows ; nationality ; united kingdom canada } ; player } ; david lee } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to united kingdom canada . there is only one such row in the table . the player record of this unqiue row is david lee .'}
and { only { filter_eq { all_rows ; nationality ; united kingdom canada } } ; eq { hop { filter_eq { all_rows ; nationality ; united kingdom canada } ; player } ; david lee } } = true
select the rows whose nationality record fuzzily matches to united kingdom canada . there is only one such row in the table . the player record of this unqiue row is david lee .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'united kingdom canada_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'david lee_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nationality_7': 'nationality', 'united kingdom canada_8': 'united kingdom canada', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'david lee_10': 'david lee'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'united kingdom canada_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'david lee_10': [3]}
['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team']
[['81', 'keith smith', 'defence', 'canada', 'new york islanders', 'brown university ( ecac )'], ['82', 'willie trognitz', 'left wing', 'canada', 'california golden seals', 'thunder bay vulcans ( tbjhl )'], ['83', 'jim cowell', 'centre', 'canada', 'vancouver canucks', "ottawa 67 's ( oha )"], ['84', 'doug marit', 'defence', 'canada', 'toronto maple leafs', 'regina pats ( wchl )'], ['85', 'ken houston', 'defence', 'canada', 'atlanta flames', 'chatham maroons sojhl'], ['86', 'blair macdonald', 'right wing', 'canada', 'los angeles kings', 'cornwall royals ( qmjhl )'], ['87', 'don seiling', 'left wing', 'canada', 'pittsburgh penguins', 'oshawa generals ( oha )'], ['88', 'randy smith', 'left wing', 'canada', 'st louis blues', 'edmonton oil kings ( wchl )'], ['89', 'david lee', 'left wing', 'united kingdom canada', 'minnesota north stars', "ottawa 67 's ( oha )"], ['90', 'doug ferguson', 'defence', 'canada', 'philadelphia flyers', 'hamilton red wings ( oha )'], ['91', 'glenn cickello', 'defence', 'canada', 'detroit red wings', 'hamilton red wings ( oha )'], ['92', 'neil korzack', 'left wing', 'canada', 'buffalo sabres', 'peterborough petes ( oha )'], ['93', 'garry doerksen', 'centre', 'canada', 'chicago black hawks', 'winnipeg jets ( wchl )'], ['94', 'dwayne pentland', 'defence', 'canada', 'new york rangers', 'brandon wheat kings ( wchl )'], ['95', 'jp burgoyne', 'defence', 'canada', 'boston bruins', 'shawinigan dynamos ( qmjhl )'], ['96', 'denis patry', 'right wing', 'canada', 'montreal canadiens', 'drummondville rangers ( qmjhl )']]
1978 san francisco 49ers season
https://en.wikipedia.org/wiki/1978_San_Francisco_49ers_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18674332-1.html.csv
ordinal
the san francisco 49ers ' game against the new york giants recorded their highest attendance of the 1978 season .
{'row': '4', 'col': '5', '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', 'attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 1 }'}, 'opponent'], 'result': 'new york giants', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent }'}, 'new york giants'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants } = true', 'tointer': 'select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants .'}
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants } = true
select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '1_6': 6, 'opponent_7': 7, 'new york giants_8': 8}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '1_6': '1', 'opponent_7': 'opponent', 'new york giants_8': 'new york giants'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '1_6': [0], 'opponent_7': [1], 'new york giants_8': [2]}
['week', 'date', 'opponent', 'result', 'attendance']
[['1', 'september 3 , 1978', 'cleveland browns', 'l 24 - 7', '68973'], ['2', 'september 10 , 1978', 'chicago bears', 'l 16 - 13', '49502'], ['3', 'september 17 , 1978', 'houston oilers', 'l 20 - 19', '46161'], ['4', 'september 24 , 1978', 'new york giants', 'l 27 - 10', '71536'], ['5', 'october 1 , 1978', 'cincinnati bengals', 'w 28 - 12', '41107'], ['6', 'october 8 , 1978', 'los angeles rams', 'l 27 - 10', '59337'], ['7', 'october 15 , 1978', 'new orleans saints', 'l 14 - 7', '37671'], ['8', 'october 22 , 1978', 'atlanta falcons', 'l 20 - 17', '44235'], ['9', 'october 29 , 1978', 'washington redskins', 'l 38 - 20', '53706'], ['10', 'november 5 , 1978', 'atlanta falcons', 'l 21 - 10', '55468'], ['11', 'november 12 , 1978', 'st louis cardinals', 'l 16 - 10', '33155'], ['12', 'november 19 , 1978', 'los angeles rams', 'l 31 - 28', '45022'], ['13', 'november 27 , 1978', 'pittsburgh steelers', 'l 24 - 7', '51657'], ['14', 'december 3 , 1978', 'new orleans saints', 'l 24 - 13', '50068'], ['15', 'december 10 , 1978', 'tampa bay buccaneers', 'w 6 - 3', '30931'], ['16', 'december 17 , 1978', 'detroit lions', 'l 33 - 14', '56674']]
makoto takimoto
https://en.wikipedia.org/wiki/Makoto_Takimoto
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14601654-2.html.csv
unique
makoto takimoto 's fight against gegard mousasi was the only fight to end with a tko method .
{'scope': 'all', 'row': '7', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'tko', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'method', 'tko'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose method record fuzzily matches to tko .', 'tostr': 'filter_eq { all_rows ; method ; tko }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; method ; tko } }', 'tointer': 'select the rows whose method record fuzzily matches to tko . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'method', 'tko'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose method record fuzzily matches to tko .', 'tostr': 'filter_eq { all_rows ; method ; tko }'}, 'opponent'], 'result': 'gegard mousasi', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; method ; tko } ; opponent }'}, 'gegard mousasi'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; method ; tko } ; opponent } ; gegard mousasi }', 'tointer': 'the opponent record of this unqiue row is gegard mousasi .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; method ; tko } } ; eq { hop { filter_eq { all_rows ; method ; tko } ; opponent } ; gegard mousasi } } = true', 'tointer': 'select the rows whose method record fuzzily matches to tko . there is only one such row in the table . the opponent record of this unqiue row is gegard mousasi .'}
and { only { filter_eq { all_rows ; method ; tko } } ; eq { hop { filter_eq { all_rows ; method ; tko } ; opponent } ; gegard mousasi } } = true
select the rows whose method record fuzzily matches to tko . there is only one such row in the table . the opponent record of this unqiue row is gegard mousasi .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'method_7': 7, 'tko_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'opponent_9': 9, 'gegard mousasi_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'method_7': 'method', 'tko_8': 'tko', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'opponent_9': 'opponent', 'gegard mousasi_10': 'gegard mousasi'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'method_7': [0], 'tko_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'opponent_9': [2], 'gegard mousasi_10': [3]}
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
[['win', '6 - 5', 'jae sun lee', 'decision ( unanimous )', 'sengoku 10', '3', '5:00', 'saitama , japan'], ['win', '5 - 5', 'michael costa', 'submission ( heel hook )', 'sengoku 8', '1', '3:31', 'tokyo , japan'], ['loss', '4 - 5', 'frank trigg', 'decision ( unanimous )', 'sengoku 4', '3', '5:00', 'saitama , japan'], ['loss', '4 - 4', 'evangelista santos', 'submission ( achilles lock )', 'sengoku 1', '1', '4:51', 'tokyo , japan'], ['win', '4 - 3', 'murilo bustamante', 'decision ( split )', 'yarennoka !', '2', '5:00', 'saitama , japan'], ['win', '3 - 3', 'zelg galešić', 'submission ( kimura )', 'pride 34', '1', '5:40', 'saitama , japan'], ['loss', '2 - 3', 'gegard mousasi', 'tko ( broken eye socket )', 'pride bushido 11', '1', '5:34', 'saitama , japan'], ['loss', '2 - 2', 'sanae kikuta', 'decision ( unanimous )', 'pride shockwave 2005', '3', '5:00', 'saitama , japan'], ['win', '2 - 1', 'dong - sik yoon', 'decision ( unanimous )', 'pride 30', '3', '5:00', 'saitama , japan'], ['loss', '1 - 1', 'kiyoshi tamura', 'decision ( unanimous )', 'pride critical countdown 2005', '3', '5:00', 'saitama , japan'], ['win', '1 - 0', 'henry miller', 'decision ( unanimous )', 'pride shockwave 2004', '3', '5:00', 'saitama , japan']]
gymnastics at the 2007 pan american games
https://en.wikipedia.org/wiki/Gymnastics_at_the_2007_Pan_American_Games
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12320552-17.html.csv
comparative
canada won more silver medals than puerto rico in gymnastics at the 2007 pan american games .
{'row_1': '3', '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', 'nation', 'canada ( can )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to canada ( can ) .', 'tostr': 'filter_eq { all_rows ; nation ; canada ( can ) }'}, 'silver'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; canada ( can ) } ; silver }', 'tointer': 'select the rows whose nation record fuzzily matches to canada ( can ) . take the silver record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'puerto rico ( pur )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to puerto rico ( pur ) .', 'tostr': 'filter_eq { all_rows ; nation ; puerto rico ( pur ) }'}, 'silver'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; puerto rico ( pur ) } ; silver }', 'tointer': 'select the rows whose nation record fuzzily matches to puerto rico ( pur ) . take the silver record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; canada ( can ) } ; silver } ; hop { filter_eq { all_rows ; nation ; puerto rico ( pur ) } ; silver } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to canada ( can ) . take the silver record of this row . select the rows whose nation record fuzzily matches to puerto rico ( pur ) . take the silver record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; nation ; canada ( can ) } ; silver } ; hop { filter_eq { all_rows ; nation ; puerto rico ( pur ) } ; silver } } = true
select the rows whose nation record fuzzily matches to canada ( can ) . take the silver record of this row . select the rows whose nation record fuzzily matches to puerto rico ( pur ) . take the silver 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, 'canada (can)_8': 8, 'silver_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'puerto rico (pur)_12': 12, 'silver_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', 'canada (can)_8': 'canada ( can )', 'silver_9': 'silver', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'puerto rico (pur)_12': 'puerto rico ( pur )', 'silver_13': 'silver'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'canada (can)_8': [0], 'silver_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'puerto rico (pur)_12': [1], 'silver_13': [3]}
['nation', 'gold', 'silver', 'bronze', 'total']
[['united states ( usa )', '9', '10', '4', '23'], ['brazil ( bra )', '7', '2', '7', '16'], ['canada ( can )', '4', '2', '3', '9'], ['venezuela ( ven )', '3', '0', '1', '4'], ['puerto rico ( pur )', '2', '0', '3', '5'], ['colombia ( col )', '0', '3', '0', '3'], ['cuba ( cub )', '0', '3', '0', '3'], ['mexico ( mex )', '0', '2', '5', '7'], ['chile ( chi )', '0', '1', '1', '2'], ['total', '25', '23', '24', '72']]
allen county conference
https://en.wikipedia.org/wiki/Allen_County_Conference
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18765101-1.html.csv
ordinal
in the allen county conference , the school with the 2nd highest enrollment is heritage .
{'row': '4', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'enrollment 08 - 09', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; enrollment 08 - 09 ; 2 }'}, 'school'], 'result': 'heritage', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; school }'}, 'heritage'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; school } ; heritage } = true', 'tointer': 'select the row whose enrollment 08 - 09 record of all rows is 2nd maximum . the school record of this row is heritage .'}
eq { hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; school } ; heritage } = true
select the row whose enrollment 08 - 09 record of all rows is 2nd maximum . the school record of this row is heritage .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'enrollment 08 - 09_5': 5, '2_6': 6, 'school_7': 7, 'heritage_8': 8}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'enrollment 08 - 09_5': 'enrollment 08 - 09', '2_6': '2', 'school_7': 'school', 'heritage_8': 'heritage'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'enrollment 08 - 09_5': [0], '2_6': [0], 'school_7': [1], 'heritage_8': [2]}
['school', 'location', 'mascot', 'enrollment 08 - 09', 'ihsaa class / football class', 'county', 'year joined', 'previous conference']
[['adams central', 'monroe', 'flying jets', '404', '2a / 1a', '01 adams', '1969', 'independent'], ['bluffton', 'bluffton', 'tigers', '467', '2a / 2a', '90 wells', '1989', 'northeastern indiana'], ['garrett', 'garrett', 'railroaders', '598', '3a / 3a', '17 dekalb', '2005', 'northeast corner'], ['heritage', 'monroeville', 'patriots', '734', '3a / 3a', '02 allen', '1969', 'independent'], ['leo', 'leo', 'lions', '980', '3a / 4a', '02 allen', '1969', 'independent'], ['south adams', 'berne', 'starfires', '398', '2a / 1a', '01 adams', '1989', 'northeastern indiana'], ['southern wells', 'poneto', 'raiders', '227', '1a / 1a', '90 wells', '1971', 'none ( new school )'], ['woodlan', 'woodburn', 'warriors', '591', '3a / 2a', '02 allen', '1969', 'independents']]
indiana high school athletics conferences : allen county - metropolitan
https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Allen_County_%E2%80%93_Metropolitan
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13986492-1.html.csv
ordinal
the second highest enrollment for schools in allen county , in the indiana high school athletics conferences , is at heritage school .
{'row': '4', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'enrollment ( 2010 )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; enrollment ( 2010 ) ; 2 }'}, 'school'], 'result': 'heritage', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; enrollment ( 2010 ) ; 2 } ; school }'}, 'heritage'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; enrollment ( 2010 ) ; 2 } ; school } ; heritage } = true', 'tointer': 'select the row whose enrollment ( 2010 ) record of all rows is 2nd maximum . the school record of this row is heritage .'}
eq { hop { nth_argmax { all_rows ; enrollment ( 2010 ) ; 2 } ; school } ; heritage } = true
select the row whose enrollment ( 2010 ) record of all rows is 2nd maximum . the school record of this row is heritage .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'enrollment (2010)_5': 5, '2_6': 6, 'school_7': 7, 'heritage_8': 8}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'enrollment (2010)_5': 'enrollment ( 2010 )', '2_6': '2', 'school_7': 'school', 'heritage_8': 'heritage'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'enrollment (2010)_5': [0], '2_6': [0], 'school_7': [1], 'heritage_8': [2]}
['school', 'location', 'mascot', 'enrollment ( 2010 )', 'ihsaa class', 'ihsaa football class', 'county']
[['adams central', 'monroe', 'flying jets', '404', 'aa', 'a', '01 adams'], ['bluffton', 'bluffton', 'tigers', '467', 'aa', 'aa', '90 wells'], ['garrett', 'garrett', 'railroaders', '598', 'aaa', 'aaa', '17 de kalb'], ['heritage', 'monroeville', 'patriots', '734', 'aaa', 'aaa', '02 allen'], ['leo', 'leo - cedarville', 'lions', '980', 'aaa', 'aaaa', '02 allen'], ['south adams', 'berne', 'starfires', '398', 'aa', 'a', '01 adams'], ['southern wells', 'poneto', 'raiders', '277', 'a', 'a', '90 wells'], ['woodlan', 'woodburn', 'warriors', '591', 'aaa', 'aa', '02 allen']]
2007 - 08 four hills tournament
https://en.wikipedia.org/wiki/2007%E2%80%9308_Four_Hills_Tournament
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14948647-5.html.csv
comparative
martin schmitt managed to accumulate more points than denis kornilov .
{'row_1': '4', 'row_2': '5', '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', 'name', 'martin schmitt'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to martin schmitt .', 'tostr': 'filter_eq { all_rows ; name ; martin schmitt }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; martin schmitt } ; points }', 'tointer': 'select the rows whose name record fuzzily matches to martin schmitt . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'denis kornilov'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to denis kornilov .', 'tostr': 'filter_eq { all_rows ; name ; denis kornilov }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; denis kornilov } ; points }', 'tointer': 'select the rows whose name record fuzzily matches to denis kornilov . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; martin schmitt } ; points } ; hop { filter_eq { all_rows ; name ; denis kornilov } ; points } } = true', 'tointer': 'select the rows whose name record fuzzily matches to martin schmitt . take the points record of this row . select the rows whose name record fuzzily matches to denis kornilov . take the points record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; name ; martin schmitt } ; points } ; hop { filter_eq { all_rows ; name ; denis kornilov } ; points } } = true
select the rows whose name record fuzzily matches to martin schmitt . take the points record of this row . select the rows whose name record fuzzily matches to denis kornilov . take the points record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'martin schmitt_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'denis kornilov_12': 12, 'points_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'martin schmitt_8': 'martin schmitt', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'denis kornilov_12': 'denis kornilov', 'points_13': 'points'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'martin schmitt_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'denis kornilov_12': [1], 'points_13': [3]}
['rank', 'name', 'nationality', '1st ( m )', '2nd ( m )', 'points', 'overall fht points', 'overall wc points ( rank )']
[['1', 'janne ahonen', 'fin', '126.0', '136.0', '251.6', '1085.8 ( 1 )', '615 ( 3 )'], ['2', 'anders bardal', 'nor', '132.5', '124.5', '243.6', '958.7 ( 7 )', '245 ( 13 )'], ['3', 'thomas morgenstern', 'aut', '121.0', '135.5', '242.7', '1066.0 ( 2 )', '940 ( 1 )'], ['4', 'martin schmitt', 'ger', '121.5', '132.5', '235.7', '955.9 ( 8 )', '115 ( 20 )'], ['5', 'denis kornilov', 'rus', '120.0', '132.0', '232.6', '685.0 ( 24 )', '140 ( 18 )']]
list of solar car teams
https://en.wikipedia.org/wiki/List_of_solar_car_teams
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1688640-4.html.csv
majority
in the list of solar car teams most of the websites are english only .
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'english', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'website', 'english'], 'result': True, 'ind': 0, 'tointer': 'for the website records of all rows , most of them fuzzily match to english .', 'tostr': 'most_eq { all_rows ; website ; english } = true'}
most_eq { all_rows ; website ; english } = true
for the website records of all rows , most of them fuzzily match to english .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'website_3': 3, 'english_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'website_3': 'website', 'english_4': 'english'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'website_3': [0], 'english_4': [0]}
['year started', 'number of cars', 'current car', 'car', 'website']
[['1998', '7', 'b - 7', '77', 'english'], ['1992', '7', 'ã ‰ clipse 7', '92', 'french english'], ['1998', '6', 'esteban vi', '55', 'french english'], ['1992', '3', 'isun', '66', 'french english'], ['1997', '4', 'phoenix ii', '116', 'english'], ['1990', '10', 'midnight sun x', '24', 'english'], ['2008', '1', 'arctic sun', 'none', 'english'], ['1988', '11', 'aurum', '100', 'english'], ['1991', '6', 'sunstang', '96', 'english'], ['2008', '1', 'raven', 'none', 'english'], ['2004', '4', 'schulich delta', '65', 'english'], ['1989', '2', 'ralos ii', '125', 'english'], ['1999', '1', 'xof1', '125', 'english french']]
ethnic groups in london
https://en.wikipedia.org/wiki/Ethnic_groups_in_London
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19149550-9.html.csv
unique
newham is the only london borough with a total asian population over 130000 .
{'scope': 'all', 'row': '1', 'col': '8', 'col_other': '2', 'criterion': 'greater_than', 'value': '130000', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'total asian population', '130000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total asian population record is greater than 130000 .', 'tostr': 'filter_greater { all_rows ; total asian population ; 130000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; total asian population ; 130000 } }', 'tointer': 'select the rows whose total asian population record is greater than 130000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'total asian population', '130000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total asian population record is greater than 130000 .', 'tostr': 'filter_greater { all_rows ; total asian population ; 130000 }'}, 'london borough'], 'result': 'newham', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; total asian population ; 130000 } ; london borough }'}, 'newham'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; total asian population ; 130000 } ; london borough } ; newham }', 'tointer': 'the london borough record of this unqiue row is newham .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; total asian population ; 130000 } } ; eq { hop { filter_greater { all_rows ; total asian population ; 130000 } ; london borough } ; newham } } = true', 'tointer': 'select the rows whose total asian population record is greater than 130000 . there is only one such row in the table . the london borough record of this unqiue row is newham .'}
and { only { filter_greater { all_rows ; total asian population ; 130000 } } ; eq { hop { filter_greater { all_rows ; total asian population ; 130000 } ; london borough } ; newham } } = true
select the rows whose total asian population record is greater than 130000 . there is only one such row in the table . the london borough record of this unqiue row is newham .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'total asian population_7': 7, '130000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'london borough_9': 9, 'newham_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'total asian population_7': 'total asian population', '130000_8': '130000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'london borough_9': 'london borough', 'newham_10': 'newham'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'total asian population_7': [0], '130000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'london borough_9': [2], 'newham_10': [3]}
['rank', 'london borough', 'indian population', 'pakistani population', 'bangladeshi population', 'chinese population', 'other asian population', 'total asian population']
[['1', 'newham', '42484', '30307', '37262', '3930', '19912', '133895'], ['2', 'redbridge', '45660', '31051', '16011', '3000', '20781', '116503'], ['3', 'brent', '58017', '14381', '1749', '3250', '28589', '105986'], ['4', 'tower hamlets', '6787', '2442', '81377', '8109', '5786', '104501'], ['5', 'harrow', '63051', '7797', '1378', '2629', '26953', '101808'], ['6', 'ealing', '48240', '14711', '1786', '4132', '31570', '100439'], ['7', 'hounslow', '48161', '13676', '2189', '2405', '20826', '87257'], ['8', 'hillingdon', '36795', '9200', '2639', '2889', '17730', '69253'], ['9', 'haringey', '36795', '9200', '2639', '2889', '17730', '69253'], ['10', 'barnet', '27920', '5344', '2215', '8259', '22180', '65918'], ['11', 'croydon', '24660', '10865', '2570', '3925', '17607', '59627'], ['12', 'waltham forest', '9134', '26347', '4632', '2579', '11697', '54389'], ['13', 'merton', '8106', '7337', '2216', '2618', '15866', '36143'], ['14', 'camden', '6083', '1489', '12503', '6493', '8878', '35446'], ['15', 'enfield', '11648', '2594', '5599', '2588', '12464', '34893'], ['16', 'wandsworth', '8642', '9718', '1493', '3715', '9770', '33338'], ['17', 'westminster', '7213', '2328', '6299', '5917', '10105', '31862'], ['18', 'greenwich', '7836', '2594', '1645', '5061', '12758', '29894'], ['19', 'barking and dagenham', '7436', '8007', '7701', '1315', '5135', '29594']]
wru division one west
https://en.wikipedia.org/wiki/WRU_Division_One_West
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12792876-3.html.csv
aggregation
in wru division one west , for teams with over 500 points against , the total number of losses is 44 .
{'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '44', 'subset': {'col': '7', 'criterion': 'greater_than', 'value': '500'}}
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'points against', '500'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; points against ; 500 }', 'tointer': 'select the rows whose points against record is greater than 500 .'}, 'lost'], 'result': '44', 'ind': 1, 'tostr': 'sum { filter_greater { all_rows ; points against ; 500 } ; lost }'}, '44'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_greater { all_rows ; points against ; 500 } ; lost } ; 44 } = true', 'tointer': 'select the rows whose points against record is greater than 500 . the sum of the lost record of these rows is 44 .'}
round_eq { sum { filter_greater { all_rows ; points against ; 500 } ; lost } ; 44 } = true
select the rows whose points against record is greater than 500 . the sum of the lost record of these rows is 44 .
3
3
{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'points against_5': 5, '500_6': 6, 'lost_7': 7, '44_8': 8}
{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'points against_5': 'points against', '500_6': '500', 'lost_7': 'lost', '44_8': '44'}
{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'points against_5': [0], '500_6': [0], 'lost_7': [1], '44_8': [2]}
['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points']
[['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['tonmawr rfc', '22', '20', '0', '2', '714', '269', '91', '21', '13', '2', '95'], ['whitland rfc', '22', '13', '2', '7', '449', '330', '51', '36', '7', '3', '66'], ['corus ( port talbot ) rfc', '22', '12', '1', '9', '496', '450', '54', '49', '6', '3', '59'], ['bonymaen rfc', '22', '12', '0', '10', '477', '372', '49', '36', '4', '7', '59'], ['bridgend athletic rfc', '22', '12', '1', '9', '413', '415', '49', '48', '4', '4', '58'], ['narberth rfc', '22', '11', '0', '11', '407', '445', '54', '52', '6', '4', '54'], ['felinfoel rfc', '22', '11', '0', '11', '402', '563', '46', '69', '3', '3', '50'], ['llangennech rfc', '22', '11', '0', '11', '410', '431', '41', '45', '3', '3', '50'], ['bridgend ravens', '22', '8', '1', '13', '448', '442', '54', '42', '5', '7', '46'], ['carmarthen athletic rfc', '22', '9', '0', '13', '398', '436', '42', '51', '3', '6', '45'], ['builth wells rfc', '22', '7', '1', '14', '412', '583', '45', '73', '3', '4', '37'], ['cwmllynfell rfc', '22', '3', '0', '19', '360', '650', '35', '89', '0', '5', '17']]
2006 - 07 serie d
https://en.wikipedia.org/wiki/2006%E2%80%9307_Serie_D
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11125453-25.html.csv
majority
most of the aggregates of the 2 legs had less than 4 as its score .
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '4', 'subset': None}
{'func': 'most_less', 'args': ['all_rows', 'agg', '4'], 'result': True, 'ind': 0, 'tointer': 'for the agg records of all rows , most of them are less than 4 .', 'tostr': 'most_less { all_rows ; agg ; 4 } = true'}
most_less { all_rows ; agg ; 4 } = true
for the agg records of all rows , most of them are less than 4 .
1
1
{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'agg_3': 3, '4_4': 4}
{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'agg_3': 'agg', '4_4': '4'}
{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'agg_3': [0], '4_4': [0]}
['team 1', 'agg', 'team 2', '1st leg', '2nd leg']
[['canelli ( a16 )', '1 - 1', '( a13 ) vado', '1 - 0', '0 - 1'], ['saluzzo ( a15 )', '3 - 3', '( a14 ) sestri levante', '2 - 1', '1 - 2'], ['calangianus ( b16 )', '4 - 3', '( b13 ) renate', '3 - 0', '1 - 3'], ['palazzolo ( b15 )', '1 - 1', '( b14 ) fanfulla', '0 - 1', '1 - 0'], ['rivignano ( c16 )', '1 - 1', '( c13 ) trento', '1 - 1', '0 - 0'], ['bolzano ( c15 )', '4 - 4', '( c14 ) sanvitese', '3 - 0', '1 - 4'], ['renocentese ( d16 )', '1 - 2', '( d13 ) este', '0 - 1', '1 - 1'], ['santarcangelo ( d15 )', '4 - 2', '( d14 ) fidenza', '2 - 2', '2 - 0'], ['sestese ( e16 )', '4 - 4', '( e13 ) sansepolcro', '1 - 0', '3 - 4'], ['aglianese ( e15 )', '2 - 2', '( e14 ) fortisjuventus', '1 - 1', '1 - 1'], ['penne ( f16 )', '0 - 1', '( f13 ) centobuchi', '0 - 1', '0 - 0'], ['verucchio ( f15 )', '4 - 3', '( f14 ) pergolese', '1 - 1', '3 - 2'], ['pisoniano ( g16 )', '1 - 3', '( g13 ) venafro', '0 - 1', '1 - 2'], ['tivoli ( g15 )', '2 - 4', '( g14 ) morolo', '1 - 0', '1 - 4'], ['sportinggenzano ( h16 )', '1 - 1', '( h13 ) bitonto', '0 - 0', '1 - 1'], ['lavello ( h15 )', '2 - 1', '( h14 ) ebolitana', '2 - 1', '0 - 0'], ['campobello ( i16 )', '3 - 1', '( i13 ) giarre', '2 - 0', '1 - 1'], ['licata ( i15 )', '1 - 4', '( i14 ) acicatena', '1 - 1', '0 - 3']]
2007 - 08 colorado avalanche season
https://en.wikipedia.org/wiki/2007%E2%80%9308_Colorado_Avalanche_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11786147-3.html.csv
count
the colorado avalanche were the visitor team a total of three times .
{'scope': 'all', 'criterion': 'equal', 'value': 'colorado', 'result': '3', 'col': '2', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visitor', 'colorado'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose visitor record fuzzily matches to colorado .', 'tostr': 'filter_eq { all_rows ; visitor ; colorado }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; visitor ; colorado } }', 'tointer': 'select the rows whose visitor record fuzzily matches to colorado . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; visitor ; colorado } } ; 3 } = true', 'tointer': 'select the rows whose visitor record fuzzily matches to colorado . the number of such rows is 3 .'}
eq { count { filter_eq { all_rows ; visitor ; colorado } } ; 3 } = true
select the rows whose visitor record fuzzily matches to colorado . 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, 'visitor_5': 5, 'colorado_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', 'visitor_5': 'visitor', 'colorado_6': 'colorado', '3_7': '3'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'visitor_5': [0], 'colorado_6': [0], '3_7': [2]}
['date', 'visitor', 'score', 'home', 'decision', 'record']
[['september 17', 'colorado', '4 - 3', 'phoenix', 'weiman', '1 - 0'], ['september 19', 'los angeles', '3 - 6', 'colorado', 'budaj', '2 - 0'], ['september 20', 'colorado', '6 - 3', 'dallas', 'wall', '3 - 0'], ['september 22', 'colorado', '2 - 3', 'los angeles', 'weiman', '3 - 1'], ['september 25', 'dallas', '5 - 4', 'colorado', 'budaj', '3 - 2'], ['september 29', 'phoenix', '2 - 3', 'colorado', 'budaj', '4 - 2']]
tampa bay rowdies ( 1975 - 1993 )
https://en.wikipedia.org/wiki/Tampa_Bay_Rowdies_%281975%E2%80%931993%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1428018-3.html.csv
unique
the only year the tampa bay rowdies had average attendance below 4000 was in 1983-84 .
{'scope': 'all', 'row': '10', 'col': '5', 'col_other': '1', 'criterion': 'less_than', 'value': '4000', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'avg attend', '4000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose avg attend record is less than 4000 .', 'tostr': 'filter_less { all_rows ; avg attend ; 4000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; avg attend ; 4000 } }', 'tointer': 'select the rows whose avg attend record is less than 4000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'avg attend', '4000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose avg attend record is less than 4000 .', 'tostr': 'filter_less { all_rows ; avg attend ; 4000 }'}, 'indoor year'], 'result': '1983 - 1984', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; avg attend ; 4000 } ; indoor year }'}, '1983 - 1984'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; avg attend ; 4000 } ; indoor year } ; 1983 - 1984 }', 'tointer': 'the indoor year record of this unqiue row is 1983 - 1984 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; avg attend ; 4000 } } ; eq { hop { filter_less { all_rows ; avg attend ; 4000 } ; indoor year } ; 1983 - 1984 } } = true', 'tointer': 'select the rows whose avg attend record is less than 4000 . there is only one such row in the table . the indoor year record of this unqiue row is 1983 - 1984 .'}
and { only { filter_less { all_rows ; avg attend ; 4000 } } ; eq { hop { filter_less { all_rows ; avg attend ; 4000 } ; indoor year } ; 1983 - 1984 } } = true
select the rows whose avg attend record is less than 4000 . there is only one such row in the table . the indoor year record of this unqiue row is 1983 - 1984 .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'avg attend_7': 7, '4000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'indoor year_9': 9, '1983 - 1984_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'avg attend_7': 'avg attend', '4000_8': '4000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'indoor year_9': 'indoor year', '1983 - 1984_10': '1983 - 1984'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'avg attend_7': [0], '4000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'indoor year_9': [2], '1983 - 1984_10': [3]}
['indoor year', 'record', 'regular season finish', 'playoffs', 'avg attend']
[['1975', '3 - 1', '1st , region 3', 'runners - up', '4235'], ['1976', '4 - 0', '1st , eastern region', 'nasl champions', '5458'], ['1977', '1 - 1', '( friendlies only )', 'none', '5685'], ['1978', '6 - 2', '( friendlies only )', 'none', '5901'], ['1979', '3 - 2', '2nd , budweiser invitational ( 2 - 0 )', 'runners - up', '6181'], ['1979 - 1980', '8 - 4', '2nd , eastern division', 'nasl champions', '5712'], ['1980 - 1981', '9 - 9', '2nd , eastern division', 'did not qualify', '5175'], ['1981 - 1982', '11 - 7', '2nd , cent division , american conference', 'runners - up', '5372'], ['1983', '10 - 2', '( 2nd , in grand prix preliminaries )', 'nasl grand prix champions', '4771'], ['1983 - 1984', '9 - 23', '7th', 'did not qualify', '2334']]
1981 vfl season
https://en.wikipedia.org/wiki/1981_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823950-1.html.csv
comparative
the crowd at junction oval was 8099 people higher than the crowd at western oval .
{'row_1': '5', 'row_2': '2', 'col': '6', 'col_other': '5', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '8099', 'bigger': 'row1'}}
{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'junction oval'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to junction oval .', 'tostr': 'filter_eq { all_rows ; venue ; junction oval }'}, 'crowd'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; junction oval } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to junction oval . take the crowd record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'western oval'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to western oval .', 'tostr': 'filter_eq { all_rows ; venue ; western oval }'}, 'crowd'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; venue ; western oval } ; crowd }', 'tointer': 'select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row .'}], 'result': '8099', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; western oval } ; crowd } }'}, '8099'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; western oval } ; crowd } } ; 8099 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to junction oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . the first record is 8099 larger than the second record .'}
eq { diff { hop { filter_eq { all_rows ; venue ; junction oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; western oval } ; crowd } } ; 8099 } = true
select the rows whose venue record fuzzily matches to junction oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . the first record is 8099 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, 'venue_8': 8, 'junction oval_9': 9, 'crowd_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'venue_12': 12, 'western oval_13': 13, 'crowd_14': 14, '8099_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', 'venue_8': 'venue', 'junction oval_9': 'junction oval', 'crowd_10': 'crowd', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'venue_12': 'venue', 'western oval_13': 'western oval', 'crowd_14': 'crowd', '8099_15': '8099'}
{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'venue_8': [0], 'junction oval_9': [0], 'crowd_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'venue_12': [1], 'western oval_13': [1], 'crowd_14': [3], '8099_15': [5]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['north melbourne', '21.19 ( 145 )', 'south melbourne', '12.25 ( 97 )', 'arden street oval', '19437', '28 march 1981'], ['footscray', '16.12 ( 108 )', 'st kilda', '23.19 ( 157 )', 'western oval', '19101', '28 march 1981'], ['melbourne', '16.16 ( 112 )', 'hawthorn', '23.15 ( 153 )', 'mcg', '32202', '28 march 1981'], ['geelong', '10.17 ( 77 )', 'essendon', '10.11 ( 71 )', 'kardinia park', '37303', '28 march 1981'], ['fitzroy', '20.13 ( 133 )', 'collingwood', '22.27 ( 159 )', 'junction oval', '27200', '28 march 1981'], ['carlton', '22.12 ( 144 )', 'richmond', '12.10 ( 82 )', 'vfl park', '56372', '28 march 1981']]
1969 oakland raiders season
https://en.wikipedia.org/wiki/1969_Oakland_Raiders_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12828987-1.html.csv
majority
most of the games during the 1969 season , the oakland raiders won .
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'w'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to w .', 'tostr': 'most_eq { all_rows ; result ; w } = true'}
most_eq { all_rows ; result ; w } = true
for the result records of all rows , most of them fuzzily match to w .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'w_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'w_4': 'w'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'w_4': [0]}
['week', 'date', 'opponent', 'result', 'attendance']
[['1', 'september 14 , 1969', 'houston oilers', 'w 21 - 17', '49361'], ['2', 'september 20 , 1969', 'miami dolphins', 'w 20 - 17', '50277'], ['3', 'september 28 , 1969', 'boston patriots', 'w 38 - 23', '19069'], ['4', 'october 4 , 1969', 'miami dolphins', 't 20 - 20', '35614'], ['5', 'october 12 , 1969', 'denver broncos', 'w 24 - 14', '49511'], ['6', 'october 19 , 1969', 'buffalo bills', 'w 50 - 21', '54418'], ['7', 'october 26 , 1969', 'san diego chargers', 'w 24 - 12', '54008'], ['8', 'november 2 , 1969', 'cincinnati bengals', 'l 31 - 17', '27927'], ['9', 'november 9 , 1969', 'denver broncos', 'w 41 - 10', '54416'], ['10', 'november 16 , 1969', 'san diego chargers', 'w 21 - 16', '54372'], ['11', 'november 23 , 1969', 'kansas city chiefs', 'w 27 - 24', '51982'], ['12', 'november 30 , 1969', 'new york jets', 'w 27 - 14', '63865'], ['13', 'december 7 , 1969', 'cincinnati bengals', 'w 37 - 17', '54427'], ['14', 'december 13 , 1969', 'kansas city chiefs', 'w 10 - 6', '54443']]
orchid stakes
https://en.wikipedia.org/wiki/Orchid_Stakes
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14863959-1.html.csv
majority
christophe clement trained most of the winners of the orchard stakes .
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'christophe clement', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'trainer', 'christophe clement'], 'result': True, 'ind': 0, 'tointer': 'for the trainer records of all rows , most of them fuzzily match to christophe clement .', 'tostr': 'most_eq { all_rows ; trainer ; christophe clement } = true'}
most_eq { all_rows ; trainer ; christophe clement } = true
for the trainer records of all rows , most of them fuzzily match to christophe clement .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'trainer_3': 3, 'christophe clement_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'trainer_3': 'trainer', 'christophe clement_4': 'christophe clement'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'trainer_3': [0], 'christophe clement_4': [0]}
['year', 'winner', 'jockey', 'trainer', 'owner', 'time']
[['2013', 'regalo mia', 'luis contreras', 'michelle nihei', 'steven w ciccarone', '2:23.48'], ['2012', 'hit it rich', 'javier castellano', 'shug mcgaughey', 'stuart s janney iii', '2:28.06'], ['2011', 'la luna de miel', 'john velazquez', 'h graham motion', 'rashit shaykhutdinov', '2:25.76'], ['2010', 'speak easy gal', 'elvis trujillo', 'marty wolfson', 'farnsworth stables', '2:28.46'], ['2009', 'dress rehearsal', 'kent desormeaux', 'william i mott', 'swettenham stud', '2:29.77'], ['2008', 'hostess', 'john velazquez', 'h james bond', 'william clifton jr', '2:25.83'], ['2007', 'safari queen', 'chris decarlo', 'todd a pletcher', 'arindel farm', '2:25.17'], ['2006', 'honey ryder', 'john r velazquez', 'todd a pletcher', 'glencrest farm llc', '2:23.07'], ['2005', 'honey ryder', 'john r velazquez', 'todd a pletcher', 'glencrest farm llc', '2:27.15'], ['2004', 'meridiana', 'edgar prado', 'christophe clement', 'jon & sarah kelly', '2:26.99'], ['2003', 'tweedside', 'rene douglas', 'todd a pletcher', 'e & l melnyk', '2:32.36'], ['2002', 'julie jalouse', 'jose a santos', 'christophe clement', 'skymarc farm', '2:25.89'], ['2001', 'innuendo', 'jerry d bailey', 'christophe clement', 'gerald w leigh', '2:25.24'], ['2000', 'lisieux rose', 'jose a santos', 'christophe clement', 'moyglare stud farm', '2:25.64'], ['1999', 'coretta', 'jose a santos', 'christophe clement', 'gerald w leigh', '2:23.85']]
1942 vfl season
https://en.wikipedia.org/wiki/1942_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807990-7.html.csv
majority
most of the games on june 20 in the 1942 vfl season had at least 5000 people attending .
{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '5000', 'subset': None}
{'func': 'most_greater_eq', 'args': ['all_rows', 'crowd', '5000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are greater than or equal to 5000 .', 'tostr': 'most_greater_eq { all_rows ; crowd ; 5000 } = true'}
most_greater_eq { all_rows ; crowd ; 5000 } = true
for the crowd records of all rows , most of them are greater than or equal to 5000 .
1
1
{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '5000_4': 4}
{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '5000_4': '5000'}
{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '5000_4': [0]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['richmond', '10.9 ( 69 )', 'south melbourne', '11.20 ( 86 )', 'punt road oval', '18000', '20 june 1942'], ['fitzroy', '16.14 ( 110 )', 'hawthorn', '14.10 ( 94 )', 'brunswick street oval', '5000', '20 june 1942'], ['north melbourne', '11.10 ( 76 )', 'melbourne', '12.11 ( 83 )', 'arden street oval', '4000', '20 june 1942'], ['st kilda', '11.14 ( 80 )', 'collingwood', '9.14 ( 68 )', 'toorak park', '5000', '20 june 1942'], ['footscray', '12.19 ( 91 )', 'carlton', '10.12 ( 72 )', 'yarraville oval', '8500', '20 june 1942']]
the chicago code
https://en.wikipedia.org/wiki/The_Chicago_Code
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27401228-1.html.csv
majority
the majority of episodes had over 6 million viewers in the us .
{'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '6', 'subset': None}
{'func': 'most_greater', 'args': ['all_rows', 'us viewers ( million )', '6'], 'result': True, 'ind': 0, 'tointer': 'for the us viewers ( million ) records of all rows , most of them are greater than 6 .', 'tostr': 'most_greater { all_rows ; us viewers ( million ) ; 6 } = true'}
most_greater { all_rows ; us viewers ( million ) ; 6 } = true
for the us viewers ( million ) records of all rows , most of them are greater than 6 .
1
1
{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'us viewers (million)_3': 3, '6_4': 4}
{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'us viewers (million)_3': 'us viewers ( million )', '6_4': '6'}
{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'us viewers (million)_3': [0], '6_4': [0]}
['no', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( million )']
[['1', 'pilot', 'charles mcdougall', 'shawn ryan', 'february 7 , 2011', '1ata79', '9.43'], ['2', 'hog butcher', 'clark johnson', 'patrick massett & john zinman', 'february 14 , 2011', '1ata01', '7.35'], ['3', 'gillis , chase & babyface', 'guy ferland', 'davey holmes', 'february 21 , 2011', '1ata09', '7.87'], ['4', 'cabrini - green', 'jean de segonzac', 'tim minear & jon worley', 'february 28 , 2011', '1ata10', '8.04'], ['5', "o'leary 's cow", 'clark johnson', 'kevin townsley', 'march 7 , 2011', '1ata03', '7.46'], ['6', 'the gold coin kid', 'lesli linka glatter', 'heather mitchell', 'march 14 , 2011', '1ata02', '7.30'], ['7', 'black hand and the shotgun man', 'billy gierhart', 'davey holmes', 'march 21 , 2011', '1ata04', '6.16'], ['8', 'wild onions', 'adam arkin', 'virgil williams', 'april 11 , 2011', '1ata05', '5.94'], ['9', "st valentine 's day massacre", 'michael offer', 'christal henry', 'april 18 , 2011', '1ata06', '6.38'], ['10', 'bathhouse & hinky dink', "terrence o'hara", 'patrick massett & john zinman', 'may 2 , 2011', '1ata07', '5.60'], ['11', 'black sox', 'michael offer', 'heather mitchell & kevin townsley', 'may 9 , 2011', '1ata08', '5.67'], ['12', 'greylord & gambat', 'paris barclay', 'virgil williams', 'may 16 , 2011', '1ata11', '5.86']]
2002 world series
https://en.wikipedia.org/wiki/2002_World_Series
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1103715-1.html.csv
majority
most of the games in the 2002 world series were played at the edison international field of anaheim .
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'edison international field of anaheim', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'location', 'edison international field of anaheim'], 'result': True, 'ind': 0, 'tointer': 'for the location records of all rows , most of them fuzzily match to edison international field of anaheim .', 'tostr': 'most_eq { all_rows ; location ; edison international field of anaheim } = true'}
most_eq { all_rows ; location ; edison international field of anaheim } = true
for the location records of all rows , most of them fuzzily match to edison international field of anaheim .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'location_3': 3, 'edison international field of anaheim_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'location_3': 'location', 'edison international field of anaheim_4': 'edison international field of anaheim'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'location_3': [0], 'edison international field of anaheim_4': [0]}
['game', 'date', 'score', 'location', 'time', 'attendance']
[['1', 'october 19', 'san francisco giants - 4 , anaheim angels - 3', 'edison international field of anaheim', '3:44', '44603'], ['2', 'october 20', 'san francisco giants - 10 , anaheim angels - 11', 'edison international field of anaheim', '3:57', '44584'], ['3', 'october 22', 'anaheim angels - 10 , san francisco giants - 4', 'pacific bell park', '3:37', '42707'], ['4', 'october 23', 'anaheim angels - 3 , san francisco giants - 4', 'pacific bell park', '3:02', '42703'], ['5', 'october 24', 'anaheim angels - 4 , san francisco giants - 16', 'pacific bell park', '3:53', '42713'], ['6', 'october 26', 'san francisco giants - 5 , anaheim angels - 6', 'edison international field of anaheim', '3:48', '44506'], ['7', 'october 27', 'san francisco giants - 1 , anaheim angels - 4', 'edison international field of anaheim', '3:16', '44598']]
conference carolinas
https://en.wikipedia.org/wiki/Conference_Carolinas
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11658094-3.html.csv
majority
the majority of institutions in the conference carolinas are private institutions .
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'private', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'type', 'private'], 'result': True, 'ind': 0, 'tointer': 'for the type records of all rows , most of them fuzzily match to private .', 'tostr': 'most_eq { all_rows ; type ; private } = true'}
most_eq { all_rows ; type ; private } = true
for the type records of all rows , most of them fuzzily match to private .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'type_3': 3, 'private_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'type_3': 'type', 'private_4': 'private'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'type_3': [0], 'private_4': [0]}
['institution', 'location', 'founded', 'type', 'enrollment', 'nickname', 'joined', 'left', 'current conference']
[['anderson university', 'anderson , south carolina', '1911', 'private', '2907', 'trojans', '1998', '2010', 'sac'], ['appalachian state university', 'boone , north carolina', '1899', 'public', '17589', 'mountaineers', '1930', '1967', 'socon ( sun belt in 2014 ) ( ncaa division i )'], ['catawba college', 'salisbury , north carolina', '1851', 'private', '1300', 'indians', '1930', '1975', 'sac'], ['coker college', 'hartsville , south carolina', '1908', 'private', '1200', 'cobras', '1991', '2013', 'sac'], ['east carolina university', 'greenville , north carolina', '1907', 'public', '27386', 'pirates', '1947', '1962', 'c - usa ( the american in 2014 ) ( ncaa division i )'], ['elon university', 'elon , north carolina', '1889', 'private', '6720', 'phoenix', '1930', '1975', 'socon ( caa in 2014 ) ( ncaa division i )'], ['guilford college', 'greensboro , north carolina', '1837', 'private', '2706', 'quakers', '1930', '1988', 'odac ( ncaa division iii )'], ['high point university', 'high point , north carolina', '1924', 'private', '4519', 'panthers', '1930', '1997', 'big south ( ncaa division i )'], ['lenoirrhyne university', 'hickory , north carolina', '1891', 'private', '1983', 'bears', '1930', '1975', 'sac'], ['longwood university', 'farmville , virginia', '1839', 'public', '4800', 'lancers', '1995', '2003', 'big south ( ncaa division i )'], ['mars hill college', 'mars hill , north carolina', '1856', 'private', '1370', 'lions', '1973', '1975', 'sac'], ['newberry college', 'newberry , south carolina', '1856', 'private', '949', 'wolves', '1961', '1972', 'sac'], ['university of north carolina at pembroke', 'pembroke , north carolina', '1887', 'public', '6433', 'braves', '1976', '1992', 'peach belt ( pbc )'], ['presbyterian college', 'clinton , south carolina', '1880', 'private', '1300', 'blue hose', '1965', '1972', 'big south ( ncaa division i )'], ['queens university of charlotte', 'charlotte , north carolina', '1857', 'private', '2386', 'royals', '1995', '2013', 'sac'], ['st andrews university', 'laurinburg , north carolina', '1958', 'private', '600', 'knights', '1988', '2012', 'aac ( naia )'], ['western carolina university', 'cullowhee , north carolina', '1889', 'public', '9608', 'catamounts', '1933', '1975', 'socon ( ncaa division i )'], ['wingate university', 'wingate , north carolina', '1896', 'private', '2700', 'bulldogs', '1979', '1989', 'sac']]
1977 washington redskins season
https://en.wikipedia.org/wiki/1977_Washington_Redskins_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15085862-2.html.csv
majority
most of the games of the 1977 season of the washington redskins resulted in wins .
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'w'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to w .', 'tostr': 'most_eq { all_rows ; result ; w } = true'}
most_eq { all_rows ; result ; w } = true
for the result records of all rows , most of them fuzzily match to w .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'w_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'w_4': 'w'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'w_4': [0]}
['week', 'date', 'opponent', 'result', 'attendance']
[['1', 'september 18 , 1977', 'new york giants', 'l 20 - 17', '76086'], ['2', 'september 25 , 1977', 'atlanta falcons', 'w 10 - 6', '55031'], ['3', 'october 2 , 1977', 'st louis cardinals', 'w 24 - 14', '55031'], ['4', 'october 9 , 1977', 'tampa bay buccaneers', 'w 10 - 0', '58571'], ['5', 'october 16 , 1977', 'dallas cowboys', 'l 34 - 16', '62115'], ['6', 'october 23 , 1977', 'new york giants', 'l 17 - 6', '53903'], ['7', 'october 30 , 1977', 'philadelphia eagles', 'w 23 - 17', '55031'], ['8', 'november 7 , 1977', 'baltimore colts', 'l 10 - 3', '57740'], ['9', 'november 13 , 1977', 'philadelphia eagles', 'w 17 - 14', '60702'], ['10', 'november 21 , 1977', 'green bay packers', 'w 10 - 9', '51498'], ['11', 'november 27 , 1977', 'dallas cowboys', 'l 14 - 7', '55031'], ['12', 'december 4 , 1977', 'buffalo bills', 'w 10 - 0', '22975'], ['13', 'december 10 , 1977', 'st louis cardinals', 'w 26 - 20', '36067'], ['14', 'december 17 , 1977', 'los angeles rams', 'w 17 - 14', '54208']]
list of tvb series ( 1998 )
https://en.wikipedia.org/wiki/List_of_TVB_series_%281998%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18493407-2.html.csv
aggregation
the tvb series aired in 1998 had an average of 30 episodes apiece .
{'scope': 'all', 'col': '3', 'type': 'average', 'result': '29.87', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'number of episodes'], 'result': '29.87', 'ind': 0, 'tostr': 'avg { all_rows ; number of episodes }'}, '29.87'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; number of episodes } ; 29.87 } = true', 'tointer': 'the average of the number of episodes record of all rows is 29.87 .'}
round_eq { avg { all_rows ; number of episodes } ; 29.87 } = true
the average of the number of episodes record of all rows is 29.87 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'number of episodes_4': 4, '29.87_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'number of episodes_4': 'number of episodes', '29.87_5': '29.87'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'number of episodes_4': [0], '29.87_5': [1]}
['airing date', 'english title ( chinese title )', 'number of episodes', 'genre', 'official website']
[['19 jan - 13 feb', 'a measure of love 緣來沒法擋', '20', 'modern drama', 'official website'], ['16 feb - 9 may', 'secret of the heart 天地豪情', '62', 'costume drama', 'official website'], ['11 may - 9 jun', 'crimes of passion 掃黃先鋒', '22', 'modern action', 'official website'], ['6 jul - 31 jul', 'armed reaction 陀槍師姐', '20', 'modern action', 'official website'], ['3 aug - 28 aug', 'rural hero 離島特警', '20', 'modern action', 'official website'], ['31 aug - 10 oct', 'healing hands 妙手仁心', '32', 'modern drama', 'official website'], ['12 oct - 4 dec', 'burning flame 烈火雄心', '43', 'modern action', 'official website'], ['7 dec 1998 - 1 jan 1999', 'till when do us part 冤家宜結不宜解', '20', 'modern drama', 'website']]
newfoundland and labrador general election , 2011
https://en.wikipedia.org/wiki/Newfoundland_and_Labrador_general_election%2C_2011
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24778847-2.html.csv
unique
the poll of the 2011 newfoundland and labrador general election taken from september 29 - october 4 , 2011 was the only one done by the polling firm environics .
{'scope': 'all', 'row': '2', 'col': '1', 'col_other': '2', 'criterion': 'equal', 'value': 'environics', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'polling firm', 'environics'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose polling firm record fuzzily matches to environics .', 'tostr': 'filter_eq { all_rows ; polling firm ; environics }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; polling firm ; environics } }', 'tointer': 'select the rows whose polling firm record fuzzily matches to environics . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'polling firm', 'environics'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose polling firm record fuzzily matches to environics .', 'tostr': 'filter_eq { all_rows ; polling firm ; environics }'}, 'date of polling'], 'result': 'september 29 - october 4 , 2011', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; polling firm ; environics } ; date of polling }'}, 'september 29 - october 4 , 2011'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; polling firm ; environics } ; date of polling } ; september 29 - october 4 , 2011 }', 'tointer': 'the date of polling record of this unqiue row is september 29 - october 4 , 2011 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; polling firm ; environics } } ; eq { hop { filter_eq { all_rows ; polling firm ; environics } ; date of polling } ; september 29 - october 4 , 2011 } } = true', 'tointer': 'select the rows whose polling firm record fuzzily matches to environics . there is only one such row in the table . the date of polling record of this unqiue row is september 29 - october 4 , 2011 .'}
and { only { filter_eq { all_rows ; polling firm ; environics } } ; eq { hop { filter_eq { all_rows ; polling firm ; environics } ; date of polling } ; september 29 - october 4 , 2011 } } = true
select the rows whose polling firm record fuzzily matches to environics . there is only one such row in the table . the date of polling record of this unqiue row is september 29 - october 4 , 2011 .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'polling firm_7': 7, 'environics_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date of polling_9': 9, 'september 29 - october 4 , 2011_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'polling firm_7': 'polling firm', 'environics_8': 'environics', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date of polling_9': 'date of polling', 'september 29 - october 4 , 2011_10': 'september 29 - october 4 , 2011'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'polling firm_7': [0], 'environics_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date of polling_9': [2], 'september 29 - october 4 , 2011_10': [3]}
['polling firm', 'date of polling', 'link', 'progressive conservative', 'liberal', 'new democratic']
[['corporate research associates', 'september 29 - october 3 , 2011', 'html', '59', '16', '25'], ['environics', 'september 29 - october 4 , 2011', 'html', '54', '13', '33'], ['marketquest omnifacts research', 'september 28 - 30 , 2011', 'html', '54', '13', '33'], ['marketquest omnifacts research', 'september 16 - 19 , 2011', 'html', '53', '18', '29'], ['corporate research associates', 'august 15 - 31 , 2011', 'pdf', '54', '22', '24'], ['corporate research associates', 'may 11 - 28 , 2011', 'pdf', '57', '22', '20'], ['corporate research associates', 'february 10 - 28 , 2011', 'pdf', '73', '18', '8'], ['corporate research associates', 'november 9 - 30 , 2010', 'pdf', '75', '16', '8'], ['corporate research associates', 'august 10 - 30 , 2010', 'pdf', '76', '17', '7'], ['corporate research associates', 'may 11 - 31 , 2010', 'pdf', '75', '16', '8'], ['corporate research associates', 'february 9 - 25 , 2010', 'pdf', '80', '15', '5'], ['corporate research associates', 'november 5 - 22 , 2009', 'pdf', '77', '16', '7'], ['corporate research associates', 'august 11 - 29 , 2009', 'pdf', '77', '15', '8'], ['corporate research associates', 'may 12 - 30 , 2009', 'pdf', '72', '19', '8'], ['corporate research associates', 'february 11 - 28 , 2009', 'pdf', '71', '22', '7'], ['corporate research associates', 'november 5 - december 2 , 2008', 'pdf', '72', '19', '9'], ['corporate research associates', 'august 12 - 30 , 2008', 'pdf', '78', '14', '7'], ['corporate research associates', 'may 8 - june 1 , 2008', 'pdf', '77', '13', '8'], ['corporate research associates', 'february 12 - march 4 , 2008', 'pdf', '79', '14', '6'], ['corporate research associates', 'november 9 - december 3 , 2007', 'pdf', '82', '12', '7']]
phoenix film critics society award for best foreign language film
https://en.wikipedia.org/wiki/Phoenix_Film_Critics_Society_Award_for_Best_Foreign_Language_Film
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14830632-1.html.csv
count
among the films not from china / hong kong awarded by phoenix film critics society for best foreign language film , 4 of them were awarded before 2004 .
{'scope': 'subset', 'criterion': 'less_than', 'value': '2004', 'result': '4', 'col': '1', 'subset': {'col': '4', 'criterion': 'not_equal', 'value': 'china / hong kong'}}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'country', 'china / hong kong'], 'result': None, 'ind': 0, 'tostr': 'filter_not_eq { all_rows ; country ; china / hong kong }', 'tointer': 'select the rows whose country record does not match to china / hong kong .'}, 'year', '2004'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record does not match to china / hong kong . among these rows , select the rows whose year record is less than 2004 .', 'tostr': 'filter_less { filter_not_eq { all_rows ; country ; china / hong kong } ; year ; 2004 }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_less { filter_not_eq { all_rows ; country ; china / hong kong } ; year ; 2004 } }', 'tointer': 'select the rows whose country record does not match to china / hong kong . among these rows , select the rows whose year record is less than 2004 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_not_eq { all_rows ; country ; china / hong kong } ; year ; 2004 } } ; 4 } = true', 'tointer': 'select the rows whose country record does not match to china / hong kong . among these rows , select the rows whose year record is less than 2004 . the number of such rows is 4 .'}
eq { count { filter_less { filter_not_eq { all_rows ; country ; china / hong kong } ; year ; 2004 } } ; 4 } = true
select the rows whose country record does not match to china / hong kong . among these rows , select the rows whose year record is less than 2004 . the number of such rows is 4 .
4
4
{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'china / hong kong_7': 7, 'year_8': 8, '2004_9': 9, '4_10': 10}
{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'china / hong kong_7': 'china / hong kong', 'year_8': 'year', '2004_9': '2004', '4_10': '4'}
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_str_not_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'china / hong kong_7': [0], 'year_8': [1], '2004_9': [1], '4_10': [3]}
['year', 'english title', 'original title', 'country', 'director']
[['2000', 'crouching tiger , hidden dragon', 'wo hu cang long', 'taiwan', 'ang lee'], ['2001', 'amélie', "le fabuleux destin d'amélie poulain", 'france / germany', 'jean - pierre jeunet'], ['2002', 'the fast runner', 'atanarjuat', 'canada', 'zacharias kunuk'], ['2003', 'city of god', 'cidade de deus', 'brazil', 'fernando meirelles'], ['2004', 'hero', 'ying xiong', 'china / hong kong', 'zhang yimou'], ['2005', 'kung fu hustle', 'kung fu', 'china / hong kong', 'stephen chow'], ['2006', 'letters from iwo jima', 'letters from iwo jima', 'usa / japan', 'clint eastwood'], ['2007', 'the diving bell and the butterfly', 'le scaphandre et le papillon', 'france / usa', 'julian schnabel'], ['2008', 'let the right one in', 'låt den rätte komma in', 'sweden', 'tomas alfredson'], ['2009', 'broken embraces', 'los abrazos rotos', 'spain', 'pedro almodóvar']]
list of sri lanka one day international cricket records
https://en.wikipedia.org/wiki/List_of_Sri_Lanka_One_Day_International_cricket_records
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26041144-10.html.csv
count
there are 5 players listed in the sri lanka one day international cricket records .
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '5', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'player'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record is arbitrary .', 'tostr': 'filter_all { all_rows ; player }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; player } }', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; player } } ; 5 } = true', 'tointer': 'select the rows whose player record is arbitrary . the number of such rows is 5 .'}
eq { count { filter_all { all_rows ; player } } ; 5 } = true
select the rows whose player record is arbitrary . the number of such rows is 5 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'player_5': 5, '5_6': 6}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'player_5': 'player', '5_6': '5'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'player_5': [0], '5_6': [2]}
['rank', 'runs', 'average', 'strike rate', 'player', 'matches', 'innings', 'period']
[['1', '13430', '32.3', '91.21', 'sanath jayasuriya', '445', '433', '1989 - 2011'], ['2', '10591', '33.62', '78.11', 'mahela jayawardene', '372', '350', '1998 - pre'], ['3', '10466', '38.33', '75.78', 'kumar sangakkara', '324', '305', '2000 - pre'], ['4', '9284', '34.90', '81.13', 'aravinda de silva', '308', '296', '19842003'], ['5', '8529', '37.57', '67.72', 'marvan atapattu', '268', '259', '19902007']]
1994 minnesota vikings season
https://en.wikipedia.org/wiki/1994_Minnesota_Vikings_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10362172-2.html.csv
superlative
the 1994 minnesota vikings ' game against the new york giants was their most highly attended .
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'opponent'], 'result': 'new york giants', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; opponent }'}, 'new york giants'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants .'}
eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants } = true
select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'opponent_6': 6, 'new york giants_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', 'opponent_6': 'opponent', 'new york giants_7': 'new york giants'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'opponent_6': [1], 'new york giants_7': [2]}
['week', 'date', 'opponent', 'result', 'attendance']
[['1', 'september 4 , 1994', 'green bay packers', 'l 16 - 10', '59487'], ['2', 'september 11 , 1994', 'detroit lions', 'w 10 - 3', '57349'], ['3', 'september 18 , 1994', 'chicago bears', 'w 42 - 14', '61073'], ['4', 'september 25 , 1994', 'miami dolphins', 'w 38 - 35', '64035'], ['5', 'october 2 , 1994', 'arizona cardinals', 'l 17 - 7', '67950'], ['6', 'october 10 , 1994', 'new york giants', 'w 27 - 10', '77294'], ['8', 'october 20 , 1994', 'green bay packers', 'w 13 - 10 ( ot )', '63041'], ['9', 'october 30 , 1994', 'tampa bay buccaneers', 'w 36 - 13', '42110'], ['10', 'november 6 , 1994', 'new orleans saints', 'w 21 - 20', '57564'], ['11', 'november 13 , 1994', 'new england patriots', 'l 26 - 20 ( ot )', '58382'], ['12', 'november 20 , 1994', 'new york jets', 'l 31 - 21', '60687'], ['13', 'november 27 , 1994', 'tampa bay buccaneers', 'l 20 - 17 ( ot )', '47259'], ['14', 'december 1 , 1994', 'chicago bears', 'w 33 - 27 ( ot )', '61483'], ['15', 'december 11 , 1994', 'buffalo bills', 'w 21 - 17', '66501'], ['16', 'december 17 , 1994', 'detroit lions', 'l 41 - 19', '73881'], ['17', 'december 26 , 1994', 'san francisco 49ers', 'w 21 - 14', '63326']]
list of serbian submissions for the academy award for best foreign language film
https://en.wikipedia.org/wiki/List_of_Serbian_submissions_for_the_Academy_Award_for_Best_Foreign_Language_Film
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22265716-1.html.csv
majority
all of the results for the serbian submissions for the academy award for best foreign language film , were " not nominated " .
{'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'not nominated', 'subset': None}
{'func': 'all_str_eq', 'args': ['all_rows', 'result', 'not nominated'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , all of them fuzzily match to not nominated .', 'tostr': 'all_eq { all_rows ; result ; not nominated } = true'}
all_eq { all_rows ; result ; not nominated } = true
for the result records of all rows , all of them fuzzily match to not nominated .
1
1
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'not nominated_4': 4}
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'not nominated_4': 'not nominated'}
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'not nominated_4': [0]}
['year ( ceremony )', 'film title used in nomination', 'original title', 'director', 'result']
[['1994 ( 67th )', 'vukovar poste restante', 'вуковар , једна прича', 'boro drašković', 'not nominated'], ['1995 ( 68th )', 'underground', 'подземље', 'emir kusturica', 'not nominated'], ['1996 ( 69th )', 'pretty village , pretty flame', 'лепа села лепо горе', 'srđan dragojević', 'not nominated'], ['1997 ( 70th )', 'three summer days', 'три летња дана', 'mirjana vukomanović', 'not nominated'], ['1998 ( 71st )', 'powder keg', 'буре барута', 'goran paskaljević', 'not nominated'], ['1999 ( 72nd )', 'the white suit', 'бело одело', 'lazar ristovski', 'not nominated'], ['2000 ( 73rd )', 'sky hook', 'небеска удица', 'ljubiša samardžić', 'not nominated'], ['2001 ( 74th )', 'war live', 'рат уживо', 'darko bajić', 'not nominated']]
2008 - 09 football league two
https://en.wikipedia.org/wiki/2008%E2%80%9309_Football_League_Two
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18795125-6.html.csv
comparative
kevin bond vacated his position fourteen days before alan buckley .
{'row_1': '1', 'row_2': '2', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outgoing manager', 'kevin bond'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outgoing manager record fuzzily matches to kevin bond .', 'tostr': 'filter_eq { all_rows ; outgoing manager ; kevin bond }'}, 'date of vacancy'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; outgoing manager ; kevin bond } ; date of vacancy }', 'tointer': 'select the rows whose outgoing manager record fuzzily matches to kevin bond . take the date of vacancy record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outgoing manager', 'alan buckley'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose outgoing manager record fuzzily matches to alan buckley .', 'tostr': 'filter_eq { all_rows ; outgoing manager ; alan buckley }'}, 'date of vacancy'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; outgoing manager ; alan buckley } ; date of vacancy }', 'tointer': 'select the rows whose outgoing manager record fuzzily matches to alan buckley . take the date of vacancy record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; outgoing manager ; kevin bond } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; alan buckley } ; date of vacancy } } = true', 'tointer': 'select the rows whose outgoing manager record fuzzily matches to kevin bond . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to alan buckley . take the date of vacancy record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; outgoing manager ; kevin bond } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; alan buckley } ; date of vacancy } } = true
select the rows whose outgoing manager record fuzzily matches to kevin bond . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to alan buckley . take the date of vacancy 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, 'outgoing manager_7': 7, 'kevin bond_8': 8, 'date of vacancy_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'outgoing manager_11': 11, 'alan buckley_12': 12, 'date of vacancy_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', 'outgoing manager_7': 'outgoing manager', 'kevin bond_8': 'kevin bond', 'date of vacancy_9': 'date of vacancy', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'outgoing manager_11': 'outgoing manager', 'alan buckley_12': 'alan buckley', 'date of vacancy_13': 'date of vacancy'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'outgoing manager_7': [0], 'kevin bond_8': [0], 'date of vacancy_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'outgoing manager_11': [1], 'alan buckley_12': [1], 'date of vacancy_13': [3]}
['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment', 'position in table']
[['bournemouth', 'kevin bond', 'contract terminated', '1 september 2008', 'jimmy quinn', '2 september 2008', '23rd'], ['grimsby town', 'alan buckley', 'contract terminated', '15 september 2008', 'mike newell', '6 october 2008', '20th'], ['port vale', 'lee sinnott', 'mutual consent', '22 september 2008', 'dean glover', '6 october 2008', '16th'], ['chester city', 'simon davies', 'contract terminated', '11 november 2008', 'mark wright', '14 november 2008', '19th'], ['barnet', 'paul fairclough', 'resigned', '28 december 2008', 'ian hendon', '21 april 2009', '16th']]
athletics at the 2008 summer olympics - men 's 200 metres
https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_200_metres
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18569011-4.html.csv
majority
most of the athletes in the 2008 men 's 200 metres competition took less than 21 seconds to finish the run .
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '21.0', 'subset': None}
{'func': 'most_less', 'args': ['all_rows', 'time', '21.0'], 'result': True, 'ind': 0, 'tointer': 'for the time records of all rows , most of them are less than 21.0 .', 'tostr': 'most_less { all_rows ; time ; 21.0 } = true'}
most_less { all_rows ; time ; 21.0 } = true
for the time records of all rows , most of them are less than 21.0 .
1
1
{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'time_3': 3, '21.0_4': 4}
{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'time_3': 'time', '21.0_4': '21.0'}
{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'time_3': [0], '21.0_4': [0]}
['rank', 'lane', 'athlete', 'nationality', 'time', 'react']
[['1', '3', 'brian dzingai', 'zimbabwe', '20.25', '0.172'], ['2', '5', 'christian malcolm', 'great britain', '20.42', '0.178'], ['3', '4', 'christopher williams', 'jamaica', '20.53', '0.166'], ['4', '8', 'shinji takahira', 'japan', '20.58', '0.175'], ['5', '2', 'amr ibrahim mostafa seoud', 'egypt', '20.75', '0.172'], ['6', '9', 'thuso mpuang', 'south africa', '20.87', '0.166'], ['7', '6', 'daniel grueso', 'colombia', '21.15', '0.232'], ['8', '7', 'arnaldo abrantes', 'portugal', '21.46', '0.173']]
asean club championship
https://en.wikipedia.org/wiki/ASEAN_Club_Championship
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12303563-1.html.csv
aggregation
there was a combined total of 3 results for 3rd place in the asean club championship .
{'scope': 'all', 'col': '5', 'type': 'sum', 'result': '3', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', '3rd place'], 'result': '3', 'ind': 0, 'tostr': 'sum { all_rows ; 3rd place }'}, '3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; 3rd place } ; 3 } = true', 'tointer': 'the sum of the 3rd place record of all rows is 3 .'}
round_eq { sum { all_rows ; 3rd place } ; 3 } = true
the sum of the 3rd place record of all rows is 3 .
2
2
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, '3rd place_4': 4, '3_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', '3rd place_4': '3rd place', '3_5': '3'}
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], '3rd place_4': [0], '3_5': [1]}
['', 'nation', 'winners', 'runners - up', '3rd place', '4th place']
[['1', 'kingfisher east bengal fc', '1', '0', '0', '0'], ['2', 'tampines rovers fc', '1', '0', '0', '0'], ['3', 'bec tero sasana', '0', '1', '0', '0'], ['4', 'pahang fa', '0', '1', '0', '0'], ['5', 'dpmm fc ( duli pengiran muda mahkota fc )', '0', '0', '1', '0'], ['6', 'hoang anh gia lai', '0', '0', '1', '0'], ['7', 'petrokimia putra fc', '0', '0', '1', '0']]
eurovision dance contest 2008
https://en.wikipedia.org/wiki/Eurovision_Dance_Contest_2008
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13053979-1.html.csv
count
six competing couples scored more than 100 points in the eurovision dance contest 2008 .
{'scope': 'all', 'criterion': 'greater_than', 'value': '100', 'result': '6', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'points', '100'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is greater than 100 .', 'tostr': 'filter_greater { all_rows ; points ; 100 }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; points ; 100 } }', 'tointer': 'select the rows whose points record is greater than 100 . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; points ; 100 } } ; 6 } = true', 'tointer': 'select the rows whose points record is greater than 100 . the number of such rows is 6 .'}
eq { count { filter_greater { all_rows ; points ; 100 } } ; 6 } = true
select the rows whose points record is greater than 100 . the number of such rows is 6 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'points_5': 5, '100_6': 6, '6_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'points_5': 'points', '100_6': '100', '6_7': '6'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'points_5': [0], '100_6': [0], '6_7': [2]}
['draw', 'competing dancers', 'dance styles', 'rank', 'points']
[['01', 'danny saucedo & jeanette carlsson', 'cha - cha', '12', '38'], ['02', 'dorian steidl & nicole kuntner', 'slowfox / jive / hip - hop', '13', '29'], ['03', 'patrick spiegelberg & katja svensson', 'samba / tango / paso doble / jazz dance', '6', '102'], ['04', 'eldar dzhafarov & anna sazhina', 'paso doble / rumba / tango / azeri folk dance', '5', '106'], ['05', 'gavin ó fearraigh & dearbhla lennon', 'paso doble / rumba / hard shoe irish dance', '11', '40'], ['06', 'maria lund & mikko ahti', 'tango', '10', '44'], ['07', 'thomas berge & roemjana de haan', 'rumba / show dance', '14', '1'], ['08', 'karina krysko & saulius skambinas', 'rumba / cha - cha / acrobatic elements', '4', '110'], ['09', 'louisa lytton & vincent simone', 'paso doble / jive / tango', '9', '47'], ['10', 'tatiana navka & alexander litvinenko', 'cha - cha / samba / rumba / paso doble / russian folk dance', '2', '121'], ['11', 'jason roditis & tonia kosovich', 'latin dances', '7', '72'], ['12', 'raquel tavares & joão tiago', 'rumba / tango', '8', '61'], ['13', 'edyta herbuś & marcin mroczek', 'rumba / cha - cha / jazz dance', '1', '154'], ['14', 'lilia podkopayeva & sergey kostetskiy', "jive / ukrainian folk dance / rock 'n' roll", '3', '119']]
1990 minnesota vikings season
https://en.wikipedia.org/wiki/1990_Minnesota_Vikings_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10362095-2.html.csv
majority
the minnesota vikings won all games in the month of november during the 1990 season .
{'scope': 'subset', 'col': '4', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'november'}}
{'func': 'all_str_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 .'}, 'result', 'w'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november . for the result records of these rows , all of them fuzzily match to w .', 'tostr': 'all_eq { filter_eq { all_rows ; date ; november } ; result ; w } = true'}
all_eq { filter_eq { all_rows ; date ; november } ; result ; w } = true
select the rows whose date record fuzzily matches to november . for the result records of these rows , all of them fuzzily match to w .
2
2
{'all_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'november_5': 5, 'result_6': 6, 'w_7': 7}
{'all_str_eq_1': 'all_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'november_5': 'november', 'result_6': 'result', 'w_7': 'w'}
{'all_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'november_5': [0], 'result_6': [1], 'w_7': [1]}
['week', 'date', 'opponent', 'result', 'attendance']
[['1', 'september 9 , 1990', 'kansas city chiefs', 'l 24 - 21', '68363'], ['2', 'september 16 , 1990', 'new orleans saints', 'w 32 - 3', '56272'], ['3', 'september 23 , 1990', 'chicago bears', 'l 19 - 16', '65420'], ['4', 'september 30 , 1990', 'tampa bay buccaneers', 'l 23 - 20 ( ot )', '54462'], ['5', 'october 7 , 1990', 'detroit lions', 'l 34 - 27', '57586'], ['6', 'october 15 , 1990', 'philadelphia eagles', 'l 32 - 24', '66296'], ['8', 'october 28 , 1990', 'green bay packers ( milw )', 'l 24 - 10', '55125'], ['9', 'november 4 , 1990', 'denver broncos', 'w 27 - 22', '57331'], ['10', 'november 11 , 1990', 'detroit lions', 'w 17 - 7', '68264'], ['11', 'november 18 , 1990', 'seattle seahawks', 'w 24 - 21', '59735'], ['12', 'november 25 , 1990', 'chicago bears', 'w 41 - 13', '58866'], ['13', 'december 2 , 1990', 'green bay packers', 'w 23 - 7', '62058'], ['14', 'december 9 , 1990', 'new york giants', 'l 23 - 15', '76121'], ['15', 'december 16 , 1990', 'tampa bay buccaneers', 'l 26 - 13', '47272'], ['16', 'december 22 , 1990', 'los angeles raiders', 'l 28 - 24', '53899'], ['17', 'december 30 , 1990', 'san francisco 49ers', 'l 20 - 17', '51590']]
1980 vfl season
https://en.wikipedia.org/wiki/1980_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809823-19.html.csv
comparative
as the home team , carlton scored less points than st kilda .
{'row_1': '2', 'row_2': '5', 'col': '2', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'carlton'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to carlton .', 'tostr': 'filter_eq { all_rows ; home team ; carlton }'}, 'home team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; carlton } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'st kilda'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to st kilda .', 'tostr': 'filter_eq { all_rows ; home team ; st kilda }'}, 'home team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; st kilda } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to st kilda . take the home team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; st kilda } ; home team score } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to st kilda . take the home team score record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; st kilda } ; home team score } } = true
select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to st kilda . take the home team score record of this row . the first record is less than the second record .
5
5
{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'home team_7': 7, 'carlton_8': 8, 'home team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'home team_11': 11, 'st kilda_12': 12, 'home team score_13': 13}
{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'home team_7': 'home team', 'carlton_8': 'carlton', 'home team score_9': 'home team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'home team_11': 'home team', 'st kilda_12': 'st kilda', 'home team score_13': 'home team score'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'home team_7': [0], 'carlton_8': [0], 'home team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'home team_11': [1], 'st kilda_12': [1], 'home team score_13': [3]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['essendon', '18.8 ( 116 )', 'footscray', '11.8 ( 74 )', 'windy hill', '16952', '9 august 1980'], ['carlton', '12.19 ( 91 )', 'richmond', '10.10 ( 70 )', 'princes park', '30051', '9 august 1980'], ['south melbourne', '12.13 ( 85 )', 'north melbourne', '11.7 ( 73 )', 'lake oval', '13681', '9 august 1980'], ['melbourne', '9.10 ( 64 )', 'hawthorn', '19.27 ( 141 )', 'mcg', '15447', '9 august 1980'], ['st kilda', '13.10 ( 88 )', 'geelong', '11.17 ( 83 )', 'moorabbin oval', '13236', '9 august 1980'], ['collingwood', '19.10 ( 124 )', 'fitzroy', '16.19 ( 115 )', 'vfl park', '31013', '9 august 1980']]
united states house of representatives elections , 1926
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1926
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342379-20.html.csv
count
three candidates were unopposed in their election races .
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'unopposed', 'result': '3', 'col': '6', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'candidates', 'unopposed'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose candidates record fuzzily matches to unopposed .', 'tostr': 'filter_eq { all_rows ; candidates ; unopposed }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; candidates ; unopposed } }', 'tointer': 'select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 3 } = true', 'tointer': 'select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 3 .'}
eq { count { filter_eq { all_rows ; candidates ; unopposed } } ; 3 } = true
select the rows whose candidates record fuzzily matches to unopposed . 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, 'candidates_5': 5, 'unopposed_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', 'candidates_5': 'candidates', 'unopposed_6': 'unopposed', '3_7': '3'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'candidates_5': [0], 'unopposed_6': [0], '3_7': [2]}
['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']
[['massachusetts 2', 'henry l bowles', 'republican', '1925', 're - elected', 'henry l bowles ( r ) 64.0 % john hall ( d ) 36.0 %'], ['massachusetts 3', 'frank h foss', 'republican', '1924', 're - elected', 'frank h foss ( r ) 62.8 % joseph e casey ( d ) 37.2 %'], ['massachusetts 6', 'abram andrew', 'republican', '1921', 're - elected', 'abram andrew ( r ) 76.9 % james mcpherson ( d ) 23.1 %'], ['massachusetts 10', 'john j douglass', 'democratic', '1924', 're - elected', 'john j douglass ( d ) unopposed'], ['massachusetts 11', 'george h tinkham', 'republican', '1914', 're - elected', 'george h tinkham ( r ) unopposed'], ['massachusetts 12', 'james a gallivan', 'democratic', '1914', 're - elected', 'james a gallivan ( d ) unopposed']]
solids with icosahedral symmetry
https://en.wikipedia.org/wiki/Solids_with_icosahedral_symmetry
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13727381-6.html.csv
aggregation
the average number of faces for all dual archimedean solids is 66 .
{'scope': 'all', 'col': '3', 'type': 'average', 'result': '66', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'faces'], 'result': '66', 'ind': 0, 'tostr': 'avg { all_rows ; faces }'}, '66'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; faces } ; 66 } = true', 'tointer': 'the average of the faces record of all rows is 66 .'}
round_eq { avg { all_rows ; faces } ; 66 } = true
the average of the faces record of all rows is 66 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'faces_4': 4, '66_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'faces_4': 'faces', '66_5': '66'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'faces_4': [0], '66_5': [1]}
['picture', 'dual archimedean solid', 'faces', 'edges', 'vertices', 'face polygon']
[['( video )', 'icosidodecahedron', '30', '60', '32', 'rhombus'], ['( video )', 'truncated dodecahedron', '60', '90', '32', 'isosceles triangle'], ['( video )', 'truncated icosahedron', '60', '90', '32', 'isosceles triangle'], ['( video )', 'rhombicosidodecahedron', '60', '120', '62', 'kite'], ['( video )', 'truncated icosidodecahedron', '120', '180', '62', 'scalene triangle']]
weightlifting at the 2007 pan american games
https://en.wikipedia.org/wiki/Weightlifting_at_the_2007_Pan_American_Games
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17703223-5.html.csv
unique
among those lifted at least 330 kg total in the 2007 pan-am games , juan quiterio was only one who did n't snatch at least 150 .
{'scope': 'subset', 'row': '5', 'col': '3', 'col_other': '1', 'criterion': 'less_than', 'value': '150', 'subset': {'col': '5', 'criterion': 'greater_than_eq', 'value': '330'}}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'total ( kg )', '330'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; total ( kg ) ; 330 }', 'tointer': 'select the rows whose total ( kg ) record is greater than or equal to 330 .'}, 'snatch', '150'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 .', 'tostr': 'filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } }', 'tointer': 'select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'total ( kg )', '330'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; total ( kg ) ; 330 }', 'tointer': 'select the rows whose total ( kg ) record is greater than or equal to 330 .'}, 'snatch', '150'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 .', 'tostr': 'filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 }'}, 'name'], 'result': 'juan quiterio ( dom )', 'ind': 3, 'tostr': 'hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name }'}, 'juan quiterio ( dom )'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name } ; juan quiterio ( dom ) }', 'tointer': 'the name record of this unqiue row is juan quiterio ( dom ) .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } } ; eq { hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name } ; juan quiterio ( dom ) } } = true', 'tointer': 'select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 . there is only one such row in the table . the name record of this unqiue row is juan quiterio ( dom ) .'}
and { only { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } } ; eq { hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name } ; juan quiterio ( dom ) } } = true
select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 . there is only one such row in the table . the name record of this unqiue row is juan quiterio ( dom ) .
8
6
{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_less_1': 1, 'filter_greater_eq_0': 0, 'all_rows_7': 7, 'total (kg)_8': 8, '330_9': 9, 'snatch_10': 10, '150_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'name_12': 12, 'juan quiterio ( dom )_13': 13}
{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_less_1': 'filter_less', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_7': 'all_rows', 'total (kg)_8': 'total ( kg )', '330_9': '330', 'snatch_10': 'snatch', '150_11': '150', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'name_12': 'name', 'juan quiterio ( dom )_13': 'juan quiterio ( dom )'}
{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_less_1': [2, 3], 'filter_greater_eq_0': [1], 'all_rows_7': [0], 'total (kg)_8': [0], '330_9': [0], 'snatch_10': [1], '150_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'name_12': [3], 'juan quiterio ( dom )_13': [4]}
['name', 'bodyweight', 'snatch', 'clean & jerk', 'total ( kg )']
[['josé oliver ruíz ( col )', '84.45', '160.0', '203.0', '363.0'], ['jadier valladares ( cub )', '84.50', '161.0', '202.0', '363.0'], ['herbys márquez ( ven )', '84.75', '155.0', '195.0', '350.0'], ['kendrick farris ( usa )', '84.15', '158.0', '191.0', '349.0'], ['juan quiterio ( dom )', '84.35', '145.0', '185.0', '330.0'], ['buck ramsay ( can )', '84.75', '140.0', '178.0', '318.0'], ['rafael andrade ( bra )', '83.75', '140.0', '175.0', '315.0'], ['edward silva ( uru )', '84.10', '120.0', '150.0', '270.0']]
1907 michigan wolverines football team
https://en.wikipedia.org/wiki/1907_Michigan_Wolverines_football_team
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25724294-2.html.csv
unique
octy graham was the only player on the 1907 michigan wolverines football team with any field goals .
{'scope': 'all', 'row': '3', 'col': '4', 'col_other': '1', 'criterion': 'not_equal', 'value': '0', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', 'field goals', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose field goals record is not equal to 0 .', 'tostr': 'filter_not_eq { all_rows ; field goals ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; field goals ; 0 } }', 'tointer': 'select the rows whose field goals record is not equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', 'field goals', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose field goals record is not equal to 0 .', 'tostr': 'filter_not_eq { all_rows ; field goals ; 0 }'}, 'player'], 'result': 'octy graham', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; field goals ; 0 } ; player }'}, 'octy graham'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; field goals ; 0 } ; player } ; octy graham }', 'tointer': 'the player record of this unqiue row is octy graham .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; field goals ; 0 } } ; eq { hop { filter_not_eq { all_rows ; field goals ; 0 } ; player } ; octy graham } } = true', 'tointer': 'select the rows whose field goals record is not equal to 0 . there is only one such row in the table . the player record of this unqiue row is octy graham .'}
and { only { filter_not_eq { all_rows ; field goals ; 0 } } ; eq { hop { filter_not_eq { all_rows ; field goals ; 0 } ; player } ; octy graham } } = true
select the rows whose field goals record is not equal to 0 . there is only one such row in the table . the player record of this unqiue row is octy graham .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_not_eq_0': 0, 'all_rows_6': 6, 'field goals_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'octy graham_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_not_eq_0': 'filter_not_eq', 'all_rows_6': 'all_rows', 'field goals_7': 'field goals', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'octy graham_10': 'octy graham'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_not_eq_0': [1, 2], 'all_rows_6': [0], 'field goals_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'octy graham_10': [3]}
['player', 'touchdowns', 'extra points', 'field goals', 'points']
[['paul magoffin', '7', '0', '0', '35'], ['walter rheinschild', '5', '0', '0', '25'], ['octy graham', '0', '7', '4', '24'], ['jack loell', '3', '0', '0', '15'], ['prentiss douglass', '1', '0', '0', '5'], ['dave allerdice', '0', '3', '0', '3'], ['harry s hammond', '0', '1', '0', '1']]
sc freiburg
https://en.wikipedia.org/wiki/SC_Freiburg
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1438835-1.html.csv
superlative
the season in which sc freiburg had the most away wins was in 2001-2002 .
{'scope': 'all', 'col_superlative': '6', 'row_superlative': '3', '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', 'away'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; away }'}, 'season'], 'result': '2001 - 02', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; away } ; season }'}, '2001 - 02'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; away } ; season } ; 2001 - 02 } = true', 'tointer': 'select the row whose away record of all rows is maximum . the season record of this row is 2001 - 02 .'}
eq { hop { argmax { all_rows ; away } ; season } ; 2001 - 02 } = true
select the row whose away record of all rows is maximum . the season record of this row is 2001 - 02 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'away_5': 5, 'season_6': 6, '2001 - 02_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'away_5': 'away', 'season_6': 'season', '2001 - 02_7': '2001 - 02'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'away_5': [0], 'season_6': [1], '2001 - 02_7': [2]}
['season', 'competition', 'round', 'club', 'home', 'away']
[['1995 - 96', 'uefa cup', 'first round', 'slavia prague', '1 - 2', '0 - 0'], ['2001 - 02', 'uefa cup', 'first round', 'matador púchov', '2 - 1', '0 - 0'], ['2001 - 02', 'uefa cup', 'second round', 'st gallen', '0 - 1', '4 - 1'], ['2001 - 02', 'uefa cup', 'third round', 'feyenoord', '2 - 2', '0 - 1'], ['2013 - 14', 'uefa europa league', 'group h', 'sevilla', '-', '0 - 2'], ['2013 - 14', 'uefa europa league', 'group h', 'estoril', '-', '-'], ['2013 - 14', 'uefa europa league', 'group h', 'slovan liberec', '2 - 2', '-']]
1975 - 76 boston celtics season
https://en.wikipedia.org/wiki/1975%E2%80%9376_Boston_Celtics_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17342278-4.html.csv
superlative
chicago stadium was the first location to be used during the 1975 - 76 boston celtics season .
{'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '5', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'game'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; game }'}, 'location attendance'], 'result': 'chicago stadium', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; game } ; location attendance }'}, 'chicago stadium'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; game } ; location attendance } ; chicago stadium } = true', 'tointer': 'select the row whose game record of all rows is minimum . the location attendance record of this row is chicago stadium .'}
eq { hop { argmin { all_rows ; game } ; location attendance } ; chicago stadium } = true
select the row whose game record of all rows is minimum . the location attendance record of this row is chicago stadium .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'game_5': 5, 'location attendance_6': 6, 'chicago stadium_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'game_5': 'game', 'location attendance_6': 'location attendance', 'chicago stadium_7': 'chicago stadium'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'game_5': [0], 'location attendance_6': [1], 'chicago stadium_7': [2]}
['game', 'date', 'team', 'score', 'location attendance', 'record']
[['4', 'november 1', 'chicago', 'l 82 - 84', 'chicago stadium', '3 - 1'], ['5', 'november 5', 'buffalo', 'w 105 - 95', 'boston garden', '4 - 1'], ['6', 'november 7', 'milwaukee', 'l 101 - 104', 'mecca arena', '4 - 2'], ['7', 'november 8', 'detroit', 'w 118 - 104', 'cobo arena', '5 - 2'], ['8', 'november 11', 'atlanta', 'l 91 - 100', 'hartford civic center', '5 - 3'], ['9', 'november 13', 'washington', 'l 107 - 110', 'capital centre', '5 - 4'], ['10', 'november 14', 'philadelphia', 'l 109 - 119', 'boston garden', '5 - 5'], ['11', 'november 15', 'buffalo', 'w 112 - 110', 'buffalo memorial auditorium', '6 - 5'], ['12', 'november 21', 'new york', 'w 110 - 101', 'boston garden', '7 - 5'], ['13', 'november 23', 'cleveland', 'w 105 - 90', 'richfield coliseum', '8 - 5'], ['14', 'november 26', 'seattle', 'l 109 - 110', 'boston garden', '8 - 6'], ['15', 'november 28', 'atlanta', 'w 114 - 107', 'boston garden', '9 - 6']]
united states house of representatives elections , 1942
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1942
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342256-40.html.csv
majority
of the incumbents in the 1942 election for the united states house of representatives , all of them were from the democratic party .
{'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'democratic', 'subset': None}
{'func': 'all_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , all of them fuzzily match to democratic .', 'tostr': 'all_eq { all_rows ; party ; democratic } = true'}
all_eq { all_rows ; party ; democratic } = true
for the party records of all rows , all of them fuzzily match to democratic .
1
1
{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic_4': 4}
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic_4': 'democratic'}
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic_4': [0]}
['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']
[['south carolina 1', 'l mendel rivers', 'democratic', '1940', 're - elected', 'l mendel rivers ( d ) unopposed'], ['south carolina 2', 'hampton p fulmer', 'democratic', '1920', 're - elected', 'hampton p fulmer ( d ) unopposed'], ['south carolina 3', 'butler b hare', 'democratic', '1938', 're - elected', 'butler b hare ( d ) unopposed'], ['south carolina 4', 'joseph r bryson', 'democratic', '1938', 're - elected', 'joseph r bryson ( d ) unopposed'], ['south carolina 5', 'james p richards', 'democratic', '1932', 're - elected', 'james p richards ( d ) unopposed']]
eagles - giants rivalry
https://en.wikipedia.org/wiki/Eagles%E2%80%93Giants_rivalry
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16900662-10.html.csv
count
the eagles and giants faced off at metlife stadium three times between 2010 and 2013 .
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'metlife stadium', 'result': '3', 'col': '6', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'metlife stadium'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to metlife stadium .', 'tostr': 'filter_eq { all_rows ; location ; metlife stadium }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; metlife stadium } }', 'tointer': 'select the rows whose location record fuzzily matches to metlife stadium . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; metlife stadium } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to metlife stadium . the number of such rows is 3 .'}
eq { count { filter_eq { all_rows ; location ; metlife stadium } } ; 3 } = true
select the rows whose location record fuzzily matches to metlife stadium . the number of such rows is 3 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'metlife stadium_6': 6, '3_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location_5': 'location', 'metlife stadium_6': 'metlife stadium', '3_7': '3'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'metlife stadium_6': [0], '3_7': [2]}
['year', 'date', 'winner', 'result', 'loser', 'location']
[['2010', 'november 21', 'philadelphia eagles', '27 - 17', 'new york giants', 'lincoln financial field'], ['2010', 'december 19', 'philadelphia eagles', '38 - 31', 'new york giants', 'new meadowlands stadium'], ['2011', 'september 25', 'new york giants', '29 - 16', 'philadelphia eagles', 'lincoln financial field'], ['2011', 'november 20', 'philadelphia eagles', '17 - 10', 'new york giants', 'metlife stadium'], ['2012', 'september 30', 'philadelphia eagles', '19 - 17', 'new york giants', 'lincoln financial field'], ['2012', 'december 30', 'new york giants', '42 - 7', 'philadelphia eagles', 'metlife stadium'], ['2013', 'october 6', 'philadelphia eagles', '36 - 21', 'new york giants', 'metlife stadium'], ['2013', 'october 27', 'new york giants', '15 - 7', 'philadelphia eagles', 'lincoln financial field']]
ufc 94
https://en.wikipedia.org/wiki/UFC_94
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16023995-1.html.csv
majority
most of the cards in ufc 94 were part of the main event .
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'main', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'card', 'main'], 'result': True, 'ind': 0, 'tointer': 'for the card records of all rows , most of them fuzzily match to main .', 'tostr': 'most_eq { all_rows ; card ; main } = true'}
most_eq { all_rows ; card ; main } = true
for the card records of all rows , most of them fuzzily match to main .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'card_3': 3, 'main_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'card_3': 'card', 'main_4': 'main'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'card_3': [0], 'main_4': [0]}
['card', 'weight class', 'round', 'time', 'method']
[['preliminary', 'welterweight', '3', '5:00', 'decision ( split )'], ['preliminary', 'light heavyweight', '3', '5:00', 'decision ( split )'], ['preliminary', 'lightweight', '3', '5:00', 'decision ( unanimous )'], ['preliminary', 'welterweight', '3', '5:00', 'decision ( unanimous )'], ['main', 'lightweight', '3', '5:00', 'decision ( split )'], ['main', 'welterweight', '3', '5:00', 'no contest'], ['main', 'light heavyweight', '3', '5:00', 'decision ( unanimous )'], ['main', 'light heavyweight', '1', '4:59', 'ko ( punch )'], ['main', 'welterweight', '4', '5:00', 'tko ( doctor stoppage )']]
baltimore city delegation
https://en.wikipedia.org/wiki/Baltimore_City_Delegation
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11873520-1.html.csv
superlative
nina r harper is the newest baltimore city delegate to take office .
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '15', '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', 'took office'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; took office }'}, 'delegate'], 'result': 'nina r harper', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; took office } ; delegate }'}, 'nina r harper'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; took office } ; delegate } ; nina r harper } = true', 'tointer': 'select the row whose took office record of all rows is maximum . the delegate record of this row is nina r harper .'}
eq { hop { argmax { all_rows ; took office } ; delegate } ; nina r harper } = true
select the row whose took office record of all rows is maximum . the delegate record of this row is nina r harper .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'took office_5': 5, 'delegate_6': 6, 'nina r harper_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'took office_5': 'took office', 'delegate_6': 'delegate', 'nina r harper_7': 'nina r harper'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'took office_5': [0], 'delegate_6': [1], 'nina r harper_7': [2]}
['district', 'place of birth', 'delegate', 'party', 'took office', 'committee']
[['40', 'baltimore city', 'frank conaway', 'democratic', '2006', 'judiciary'], ['40', 'alexandria city , alabama', 'barbara robinson', 'democratic', '2006', 'appropriations'], ['40', 'freeport , ny', 'shawn z tarrant', 'democratic', '2006', 'health and government operations'], ['41', 'baltimore city', 'jill p carter', 'democratic', '2002', 'judiciary'], ['41', 'baltimore city', 'nathaniel t oaks', 'democratic', '1982', 'health and government operations'], ['41', 'baltimore city', 'sandy rosenberg', 'democratic', '1982', 'ways and means ( vice - chair )'], ['43', 'chicago , illinois', 'curt anderson , chair', 'democratic', '1982', 'judiciary'], ['43', 'philadelphia , pennsylvania', 'mary l washington', 'democratic', '2011', 'appropriations'], ['43', 'quinter , kansas', 'maggie mcintosh', 'democratic', '1992', 'environmental matters ( chair )'], ['44', 'shelby , north carolina', 'keith e haynes', 'democratic', '2002', 'appropriations'], ['44', 'baltimore city', 'keiffer mitchell', 'democratic', '2011', 'judiciary'], ['44', 'baltimore city', 'melvin l stukes', 'democratic', '2006', 'ways and means'], ['45', 'northampton co , north carolina', 'talmadge branch', 'democratic', '1994', 'appropriations'], ['45', 'baltimore city', 'cheryl glenn', 'democratic', '2006', 'environmental matters'], ['45', 'baltimore city', 'nina r harper', 'democratic', '2013', 'ways and means'], ['46', 'baltimore city', 'peter a hammen', 'democratic', '1994', 'health and government operations ( chair )'], ['46', 'baltimore city', 'luke clippinger', 'democratic', '2011', 'judiciary'], ['46', 'baltimore city', 'brian k mchale', 'democratic', '1990', 'economic matters']]
maria jo \ xc3 \ xa3o koehler
https://en.wikipedia.org/wiki/Maria_Jo%C3%A3o_Koehler
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22656187-9.html.csv
count
two of maria joão koehler 's fed cup europe / africa group games were on a carpet surface .
{'scope': 'all', 'criterion': 'equal', 'value': 'carpet', 'result': '2', 'col': '6', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'carpet'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to carpet .', 'tostr': 'filter_eq { all_rows ; surface ; carpet }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; carpet } }', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; carpet } } ; 2 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; surface ; carpet } } ; 2 } = true
select the rows whose surface record fuzzily matches to carpet . 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, 'surface_5': 5, 'carpet_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', 'surface_5': 'surface', 'carpet_6': 'carpet', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'carpet_6': [0], '2_7': [2]}
['edition', 'round', 'date', 'partnering', 'against', 'surface', 'opponents', 'w - l', 'result']
[['2008 fed cup europe / africa group i', 'rr', '30 january - 3 february 2008', 'magali de lattre', 'bulgaria', 'carpet', 'dia evtimova tsvetana pironkova', 'loss', '1 - 6 , 2 - 6'], ['2008 fed cup europe / africa group i', 'rr', '30 january - 3 february 2008', 'magali de lattre', 'the netherlands', 'carpet', 'nicole thijssen pauline wong', 'loss', '2 - 6 , 4 - 6'], ['2010 fed cup europe / africa group i', 'rr', '4 - 5 february 2010', 'frederica piedade', 'switzerland', 'hard', 'sarah moundir amra sadikovic', 'loss', '5 - 7 , 7 - 5 , 6 - 4'], ['2010 fed cup europe / africa group i', 'rr', '4 - 5 february 2010', 'neuza silva', 'romania', 'hard', 'irina - camelia begu ioana raluca olaru', 'win', '7 - 5 , 7 - 5'], ['2011 fed cup europe / africa group ii', 'rr', '4 - 6 may 2011', 'michelle larcher de brito', 'morocco', 'clay', 'fatima el allami nadia lalami', 'win', '6 - 3 , 6 - 2'], ['2011 fed cup europe / africa group ii', 'rr', '4 - 6 may 2011', 'michelle larcher de brito', 'finland', 'clay', 'emma laine piia suomalainen', 'win', '6 - 3 , 6 - 2'], ['2012 fed cup europe / africa group i', 'rr', '1 - 3 february 2012', 'michelle larcher de brito', 'great britain', 'hard', 'laura robson heather watson', 'loss', '5 - 7 , 0 - 6']]
spain men 's national volleyball team
https://en.wikipedia.org/wiki/Spain_men%27s_national_volleyball_team
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13312864-1.html.csv
count
two players on the spain men 's national volleyball team have a weight of 95 kg .
{'scope': 'all', 'criterion': 'equal', 'value': '95', 'result': '2', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'weight', '95'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose weight record is equal to 95 .', 'tostr': 'filter_eq { all_rows ; weight ; 95 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; weight ; 95 } }', 'tointer': 'select the rows whose weight record is equal to 95 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; weight ; 95 } } ; 2 } = true', 'tointer': 'select the rows whose weight record is equal to 95 . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; weight ; 95 } } ; 2 } = true
select the rows whose weight record is equal to 95 . 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, 'weight_5': 5, '95_6': 6, '2_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'weight_5': 'weight', '95_6': '95', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'weight_5': [0], '95_6': [0], '2_7': [2]}
['shirt no', 'player', 'birth date', 'weight', 'height']
[['1', 'rafael pascual', '16 march 1970', '94', '194'], ['2', 'ibán pérez', '13 november 1983', '89', '198'], ['3', 'josé luis lobato', '19 february 1977', '81', '186'], ['4', 'manuel sevillano', '2 july 1981', '90', '194'], ['7', 'guillermo hernán', '25 july 1982', '68', '181'], ['10', 'miguel ángel falasca', '29 april 1973', '92', '195'], ['11', 'javier subiela', '22 march 1984', '88', '198'], ['12', 'guillermo falasca', '24 october 1977', '104', '200'], ['14', 'josé luis moltó', '29 june 1975', '95', '207'], ['16', 'julián garcía - torres', '8 november 1980', '93', '202'], ['17', 'enrique de la fuente', '11 august 1975', '95', '195']]
napa auto parts 200
https://en.wikipedia.org/wiki/NAPA_Auto_Parts_200
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10716893-3.html.csv
count
marty reid was the lap-by-lap commentator for the napa auto parts 200 race a total of four times .
{'scope': 'all', 'criterion': 'equal', 'value': 'marty reid', 'result': '4', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'lap - by - lap', 'marty reid'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose lap - by - lap record fuzzily matches to marty reid .', 'tostr': 'filter_eq { all_rows ; lap - by - lap ; marty reid }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; lap - by - lap ; marty reid } }', 'tointer': 'select the rows whose lap - by - lap record fuzzily matches to marty reid . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; lap - by - lap ; marty reid } } ; 4 } = true', 'tointer': 'select the rows whose lap - by - lap record fuzzily matches to marty reid . the number of such rows is 4 .'}
eq { count { filter_eq { all_rows ; lap - by - lap ; marty reid } } ; 4 } = true
select the rows whose lap - by - lap record fuzzily matches to marty reid . 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, 'lap - by - lap_5': 5, 'marty reid_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', 'lap - by - lap_5': 'lap - by - lap', 'marty reid_6': 'marty reid', '4_7': '4'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'lap - by - lap_5': [0], 'marty reid_6': [0], '4_7': [2]}
['year', 'network', 'host', 'pre - race analyst', 'lap - by - lap', 'color commentator ( s )', 'pit reporters']
[['2012', 'espn', 'shannon spake', 'n / a', 'marty reid', 'ricky craven', 'rick debruhl jim noble shannon spake'], ['2011', 'espn', 'marty reid', 'n / a', 'marty reid', 'rusty wallace ricky craven', 'rick debruhl jim noble shannon spake'], ['2010', 'espn2', 'allen bestwick', 'n / a', 'allen bestwick', 'andy petree rusty wallace', 'mike massaro vince welch shannon spake'], ['2009', 'espn2', 'shannon spake', 'n / a', 'marty reid', 'andy petree rusty wallace', 'dave burns jamie little shannon spake'], ['2008', 'espn2', 'jack arute', 'n / a', 'marty reid', 'randy lajoie rusty wallace', 'jack arute vince welch mike massaro']]
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
unique
1990 was the only year in which katja seizinger did not compete in the downhill event .
{'scope': 'all', 'row': '1', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': '-', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'downhill', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose downhill record is equal to - .', 'tostr': 'filter_eq { all_rows ; downhill ; - }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; downhill ; - } }', 'tointer': 'select the rows whose downhill record is equal to - . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'downhill', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose downhill record is equal to - .', 'tostr': 'filter_eq { all_rows ; downhill ; - }'}, 'season'], 'result': '1990', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; downhill ; - } ; season }'}, '1990'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; downhill ; - } ; season } ; 1990 }', 'tointer': 'the season record of this unqiue row is 1990 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; downhill ; - } } ; eq { hop { filter_eq { all_rows ; downhill ; - } ; season } ; 1990 } } = true', 'tointer': 'select the rows whose downhill record is equal to - . there is only one such row in the table . the season record of this unqiue row is 1990 .'}
and { only { filter_eq { all_rows ; downhill ; - } } ; eq { hop { filter_eq { all_rows ; downhill ; - } ; season } ; 1990 } } = true
select the rows whose downhill record is equal to - . there is only one such row in the table . the season record of this unqiue row is 1990 .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'downhill_7': 7, '-_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'season_9': 9, '1990_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'downhill_7': 'downhill', '-_8': '-', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'season_9': 'season', '1990_10': '1990'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'downhill_7': [0], '-_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'season_9': [2], '1990_10': [3]}
['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']]
83rd united states congress
https://en.wikipedia.org/wiki/83rd_United_States_Congress
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1652224-5.html.csv
comparative
clifford p. case vacated his seat in congress before paul w. shafer vacated his seat .
{'row_1': '5', 'row_2': '8', 'col': '3', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'vacator', 'clifford p case ( r )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose vacator record fuzzily matches to clifford p case ( r ) .', 'tostr': 'filter_eq { all_rows ; vacator ; clifford p case ( r ) }'}, 'reason for change'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; vacator ; clifford p case ( r ) } ; reason for change }', 'tointer': 'select the rows whose vacator record fuzzily matches to clifford p case ( r ) . take the reason for change record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'vacator', 'paul w shafer ( r )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose vacator record fuzzily matches to paul w shafer ( r ) .', 'tostr': 'filter_eq { all_rows ; vacator ; paul w shafer ( r ) }'}, 'reason for change'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; vacator ; paul w shafer ( r ) } ; reason for change }', 'tointer': 'select the rows whose vacator record fuzzily matches to paul w shafer ( r ) . take the reason for change record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; vacator ; clifford p case ( r ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; paul w shafer ( r ) } ; reason for change } } = true', 'tointer': 'select the rows whose vacator record fuzzily matches to clifford p case ( r ) . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to paul w shafer ( r ) . take the reason for change record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; vacator ; clifford p case ( r ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; paul w shafer ( r ) } ; reason for change } } = true
select the rows whose vacator record fuzzily matches to clifford p case ( r ) . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to paul w shafer ( r ) . take the reason for change 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, 'vacator_7': 7, 'clifford p case (r)_8': 8, 'reason for change_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'vacator_11': 11, 'paul w shafer (r)_12': 12, 'reason for change_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', 'vacator_7': 'vacator', 'clifford p case (r)_8': 'clifford p case ( r )', 'reason for change_9': 'reason for change', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'vacator_11': 'vacator', 'paul w shafer (r)_12': 'paul w shafer ( r )', 'reason for change_13': 'reason for change'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'vacator_7': [0], 'clifford p case (r)_8': [0], 'reason for change_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'vacator_11': [1], 'paul w shafer (r)_12': [1], 'reason for change_13': [3]}
['district', 'vacator', 'reason for change', 'successor', 'date successor seated']
[['georgia 2nd', 'vacant', 'rep edward e cox died during previous congress', 'j l pilcher ( d )', 'february 4 , 1953'], ['south carolina 4th', 'joseph r bryson ( d )', 'died march 10 , 1953', 'robert t ashmore ( d )', 'june 2 , 1953'], ['kentucky 2nd', 'garrett l withers ( d )', 'died april 30 , 1953', 'william h natcher ( d )', 'august 1 , 1953'], ['wisconsin 9th', 'merlin hull ( r )', 'died may 17 , 1953', 'lester johnson ( d )', 'october 13 , 1953'], ['new jersey 6th', 'clifford p case ( r )', 'resigned august 16 , 1953', 'harrison a williams ( d )', 'november 3 , 1953'], ['hawaii territory at - large', 'joseph r farrington ( r )', 'resigned june 19 , 1954', 'elizabeth p farrington ( r )', 'july 31 , 1954'], ['georgia 4th', 'a sidney camp ( d )', 'died july 24 , 1954', 'john j flynt , jr ( d )', 'november 2 , 1954'], ['michigan 3rd', 'paul w shafer ( r )', 'died august 17 , 1954', 'vacant', 'not filled this term'], ['ohio 15th', 'robert t secrest ( d )', 'resigned september 26 , 1954', 'vacant', 'not filled this term']]
christian heritage party of canada candidates , 2008 canadian federal election
https://en.wikipedia.org/wiki/Christian_Heritage_Party_of_Canada_candidates%2C_2008_Canadian_federal_election
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12890254-6.html.csv
comparative
in the christian heritage party micheal mackay ranks higher , than joe larkin .
{'row_1': '1', 'row_2': '5', 'col': '7', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', "candidate 's name", 'michael mackay'], 'result': None, 'ind': 0, 'tointer': "select the rows whose candidate 's name record fuzzily matches to michael mackay .", 'tostr': "filter_eq { all_rows ; candidate 's name ; michael mackay }"}, 'rank'], 'result': None, 'ind': 2, 'tostr': "hop { filter_eq { all_rows ; candidate 's name ; michael mackay } ; rank }", 'tointer': "select the rows whose candidate 's name record fuzzily matches to michael mackay . take the rank record of this row ."}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', "candidate 's name", 'joe larkin'], 'result': None, 'ind': 1, 'tointer': "select the rows whose candidate 's name record fuzzily matches to joe larkin .", 'tostr': "filter_eq { all_rows ; candidate 's name ; joe larkin }"}, 'rank'], 'result': None, 'ind': 3, 'tostr': "hop { filter_eq { all_rows ; candidate 's name ; joe larkin } ; rank }", 'tointer': "select the rows whose candidate 's name record fuzzily matches to joe larkin . take the rank record of this row ."}], 'result': True, 'ind': 4, 'tostr': "less { hop { filter_eq { all_rows ; candidate 's name ; michael mackay } ; rank } ; hop { filter_eq { all_rows ; candidate 's name ; joe larkin } ; rank } } = true", 'tointer': "select the rows whose candidate 's name record fuzzily matches to michael mackay . take the rank record of this row . select the rows whose candidate 's name record fuzzily matches to joe larkin . take the rank record of this row . the first record is less than the second record ."}
less { hop { filter_eq { all_rows ; candidate 's name ; michael mackay } ; rank } ; hop { filter_eq { all_rows ; candidate 's name ; joe larkin } ; rank } } = true
select the rows whose candidate 's name record fuzzily matches to michael mackay . take the rank record of this row . select the rows whose candidate 's name record fuzzily matches to joe larkin . take the rank 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, "candidate 's name_7": 7, 'michael mackay_8': 8, 'rank_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, "candidate 's name_11": 11, 'joe larkin_12': 12, 'rank_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', "candidate 's name_7": "candidate 's name", 'michael mackay_8': 'michael mackay', 'rank_9': 'rank', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', "candidate 's name_11": "candidate 's name", 'joe larkin_12': 'joe larkin', 'rank_13': 'rank'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], "candidate 's name_7": [0], 'michael mackay_8': [0], 'rank_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], "candidate 's name_11": [1], 'joe larkin_12': [1], 'rank_13': [3]}
['riding', "candidate 's name", 'gender', 'residence', 'occupation', 'votes', 'rank']
[['central nova', 'michael mackay', 'm', 'west river station', 'retail', '427', '4th'], ['dartmouth-cole harbour', 'george campbell', 'm', 'dartmouth', 'minister', '219', '5th'], ['halifax west', 'trevor ennis', 'm', 'halifax', 'swimming pool salesman', '257', '5th'], ['kings-hants', 'jim hnatiuk', 'm', 'enfield', 'combat systems technician', '528', '5th'], ["south shore-st margaret 's", 'joe larkin', 'm', 'shag harbour', 'retired', '513', '5th']]
1957 vfl season
https://en.wikipedia.org/wiki/1957_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10774891-14.html.csv
superlative
the game played at the princes park venue drew the largest crowd size .
{'scope': 'all', 'col_superlative': '6', 'row_superlative': '5', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'venue'], 'result': 'princes park', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; venue }'}, 'princes park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; venue } ; princes park } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the venue record of this row is princes park .'}
eq { hop { argmax { all_rows ; crowd } ; venue } ; princes park } = true
select the row whose crowd record of all rows is maximum . the venue record of this row is princes park .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'venue_6': 6, 'princes park_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'venue_6': 'venue', 'princes park_7': 'princes park'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'venue_6': [1], 'princes park_7': [2]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['richmond', '10.13 ( 73 )', 'south melbourne', '11.12 ( 78 )', 'punt road oval', '21000', '27 july 1957'], ['hawthorn', '7.13 ( 55 )', 'north melbourne', '5.4 ( 34 )', 'glenferrie oval', '10000', '27 july 1957'], ['essendon', '9.18 ( 72 )', 'melbourne', '11.7 ( 73 )', 'windy hill', '22500', '27 july 1957'], ['collingwood', '13.14 ( 92 )', 'geelong', '6.8 ( 44 )', 'victoria park', '21316', '27 july 1957'], ['carlton', '11.13 ( 79 )', 'footscray', '7.10 ( 52 )', 'princes park', '31810', '27 july 1957'], ['st kilda', '15.18 ( 108 )', 'fitzroy', '8.8 ( 56 )', 'junction oval', '14500', '27 july 1957']]
list of top association football goal scorers by country
https://en.wikipedia.org/wiki/List_of_top_association_football_goal_scorers_by_country
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1590321-74.html.csv
majority
all of the players with a rank of 1 through 5 have more than 100 goals each .
{'scope': 'subset', 'col': '5', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '100', 'subset': {'col': '1', 'criterion': 'less_than_eq', 'value': '5'}}
{'func': 'all_greater', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'rank', '5'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; rank ; 5 }', 'tointer': 'select the rows whose rank record is less than or equal to 5 .'}, 'goals', '100'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose rank record is less than or equal to 5 . for the goals records of these rows , all of them are greater than 100 .', 'tostr': 'all_greater { filter_less_eq { all_rows ; rank ; 5 } ; goals ; 100 } = true'}
all_greater { filter_less_eq { all_rows ; rank ; 5 } ; goals ; 100 } = true
select the rows whose rank record is less than or equal to 5 . for the goals records of these rows , all of them are greater than 100 .
2
2
{'all_greater_1': 1, 'result_2': 2, 'filter_less_eq_0': 0, 'all_rows_3': 3, 'rank_4': 4, '5_5': 5, 'goals_6': 6, '100_7': 7}
{'all_greater_1': 'all_greater', 'result_2': 'true', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_3': 'all_rows', 'rank_4': 'rank', '5_5': '5', 'goals_6': 'goals', '100_7': '100'}
{'all_greater_1': [2], 'result_2': [], 'filter_less_eq_0': [1], 'all_rows_3': [0], 'rank_4': [0], '5_5': [0], 'goals_6': [1], '100_7': [1]}
['rank', 'name', 'years', 'matches', 'goals']
[['1', 'jeff cunningham', '1998 - 2011', '365', '134'], ['2', 'jaime moreno', '1996 - 2010', '340', '133'], ['3', 'landon donovan', '2001 - present', '281', '124'], ['4', 'ante razov', '1996 - 2009', '262', '114'], ['5', 'jason kreis', '1996 - 2007', '305', '108'], ['6', 'taylor twellman', '2002 - 2010', '174', '101'], ['7', 'dwayne de rosario', '2001 - current', '300', '100'], ['8', 'edson buddle', '2001 - 2010', '231', '90'], ['9', 'carlos ruiz', '2002 - 2008 , 2011', '169', '88'], ['10', 'roy lassiter', '1996 - 2002', '179', '88']]
idaho vandals football
https://en.wikipedia.org/wiki/Idaho_Vandals_football
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15164733-4.html.csv
unique
jim norton was the only idaho vandals player to be selected in the 7th round .
{'scope': 'all', 'row': '8', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '7th', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'round', '7th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round record fuzzily matches to 7th .', 'tostr': 'filter_eq { all_rows ; round ; 7th }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; round ; 7th } }', 'tointer': 'select the rows whose round record fuzzily matches to 7th . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'round', '7th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round record fuzzily matches to 7th .', 'tostr': 'filter_eq { all_rows ; round ; 7th }'}, 'player'], 'result': 'jim norton', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; round ; 7th } ; player }'}, 'jim norton'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; round ; 7th } ; player } ; jim norton }', 'tointer': 'the player record of this unqiue row is jim norton .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; round ; 7th } } ; eq { hop { filter_eq { all_rows ; round ; 7th } ; player } ; jim norton } } = true', 'tointer': 'select the rows whose round record fuzzily matches to 7th . there is only one such row in the table . the player record of this unqiue row is jim norton .'}
and { only { filter_eq { all_rows ; round ; 7th } } ; eq { hop { filter_eq { all_rows ; round ; 7th } ; player } ; jim norton } } = true
select the rows whose round record fuzzily matches to 7th . there is only one such row in the table . the player record of this unqiue row is jim norton .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'round_7': 7, '7th_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'jim norton_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'round_7': 'round', '7th_8': '7th', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'jim norton_10': 'jim norton'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'round_7': [0], '7th_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'jim norton_10': [3]}
['player', 'position', 'overall pick', 'round', 'nfl draft', 'franchise']
[['ray mcdonald', 'rb', '13', '1st', '1967', 'washington redskins'], ['mike iupati', 'g', '17', '1st', '2010', 'san francisco 49ers'], ['jerry kramer', 'g / pk', '39', '4th', '1958', 'green bay packers'], ['wayne walker', 'lb / pk', '44', '4th', '1958', 'detroit lions'], ['carl kiilsgaard', 't', '61', '5th', '1950', 'chicago cardinals'], ['ryan phillips', 'lb', '68', '3rd', '1997', 'new york giants'], ['jim prestel', 'dt', '70', '6th', '1959', 'cleveland browns'], ['jim norton', 's / p', '75', '7th', '1960', 'detroit lions'], ['john yarno', 'c', '87', '4th', '1977', 'seattle seahawks'], ['jeff robinson', 'de / te / ls', '98', '4th', '1993', 'denver broncos']]