niryuu/sni-each-converted huggingface dataset:
greater { hop { filter_eq { all_rows ; tournament ; pga championship } ; top - 25 } ; hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 25 } } = true
Output: select the rows whose tournament record fuzzily matches to pga championship . take the top  25 record of this row . select the rows whose tournament record fuzzily matches to masters tournament . take the top  25 record of this row . the first record is greater than the second record .
eq { count { filter_eq { filter_less { all_rows ; played ; 10 } ; palace wins ; 0 } } ; 3 } = true
Output: select the rows whose played record is less than 10 . among these rows , select the rows whose palace wins record is equal to 0 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; edges } ; dual archimedean solid } ; truncated icosidodecahedron } = true
Output: select the row whose edges record of all rows is maximum . the dual archimedean solid record of this row is truncated icosidodecahedron .
eq { count { filter_eq { all_rows ; gp ; 56 } } ; 2 } = true
Output: select the rows whose gp record is equal to 56 . the number of such rows is 2 .
eq { count { filter_greater { filter_less { all_rows ; place ; 5 } ; result ; 30 % } } ; 2 } = true
Output: select the rows whose place record is less than 5 . among these rows , select the rows whose result record is greater than 30  . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; total matches ; 5 } } ; 6 } = true
Output: select the rows whose total matches record is equal to 5 . the number of such rows is 6 .
and { only { filter_eq { all_rows ; number of floors ; 0 } } ; eq { hop { filter_eq { all_rows ; number of floors ; 0 } ; building } ; torre entel } } = true
Output: select the rows whose number of floors record is equal to 0 . there is only one such row in the table . the building record of this unqiue row is torre entel .
eq { count { filter_eq { all_rows ; position ; guard } } ; 7 } = true
Output: select the rows whose position record fuzzily matches to guard . the number of such rows is 7 .
most_greater { all_rows ; wins ; 20 } = true
Output: for the wins records of all rows , most of them are greater than 20 .
greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; collingwood } ; away team score } } = true
Output: select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to collingwood . take the away team score record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; floors } ; name } ; place hauteville } = true
Output: select the row whose floors record of all rows is maximum . the name record of this row is place hauteville .
round_eq { avg { all_rows ; points } ; 5.3 } = true
Output: the average of the points record of all rows is 5.3 .
round_eq { avg { all_rows ; attendance } ; 15503 } = true
Output: the average of the attendance record of all rows is 15503 .
greater { hop { filter_eq { all_rows ; womens singles ; li xuerui } ; year } ; hop { filter_eq { all_rows ; womens singles ; juliane schenk } ; year } } = true
Output: select the rows whose womens singles record fuzzily matches to li xuerui . take the year record of this row . select the rows whose womens singles record fuzzily matches to juliane schenk . take the year record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; crazy for you } = true
Output: select the row whose us viewers  in millions  record of all rows is maximum . the title record of this row is crazy for you .
eq { hop { nth_argmax { all_rows ; established ; 2 } ; league } ; ahl } = true
Output: select the row whose established record of all rows is 2nd maximum . the league record of this row is ahl .
eq { hop { argmax { all_rows ; location attendance } ; date } ; january 11 } = true
Output: select the row whose location attendance record of all rows is maximum . the date record of this row is january 11 .
less { hop { filter_eq { all_rows ; incumbent ; horace f page } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; campbell p berry } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to horace f page . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to campbell p berry . take the first elected record of this row . the first record is less than the second record .
most_eq { all_rows ; result ; not nominated } = true
Output: for the result records of all rows , most of them fuzzily match to not nominated .
eq { nth_max { all_rows ; goals for ; 2 } ; 343 } = true
Output: the 2nd maximum goals for record of all rows is 343 .
less { hop { filter_eq { all_rows ; team ; dinamo minsk } ; position in 1992 } ; hop { filter_eq { all_rows ; team ; dinamo brest } ; position in 1992 } } = true
Output: select the rows whose team record fuzzily matches to dinamo minsk . take the position in 1992 record of this row . select the rows whose team record fuzzily matches to dinamo brest . take the position in 1992 record of this row . the first record is less than the second record .
most_eq { all_rows ; start ; 3 } = true
Output: for the start records of all rows , most of them are equal to 3 .
round_eq { avg { all_rows ; viewers ( in millions ) } ; 9.1 } = true
Output: the average of the viewers  in millions  record of all rows is 9.1 .
round_eq { avg { all_rows ; height } ; 1.99 } = true
Output: the average of the height record of all rows is 1.99 .
and { only { filter_eq { all_rows ; winner ; luke youlden } } ; eq { hop { filter_eq { all_rows ; winner ; luke youlden } ; race title } ; mallala } } = true
Output: select the rows whose winner record fuzzily matches to luke youlden . there is only one such row in the table . the race title record of this unqiue row is mallala .
most_eq { all_rows ; type ; transfer } = true
Output: for the type records of all rows , most of them fuzzily match to transfer .
greater { hop { filter_eq { all_rows ; artist ; mariana popova } ; televote / sms } ; hop { filter_eq { all_rows ; artist ; stefan dobrev } ; televote / sms } } = true
Output: select the rows whose artist record fuzzily matches to mariana popova . take the televote  sms record of this row . select the rows whose artist record fuzzily matches to stefan dobrev . take the televote  sms record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; player ; juan theron } ; overs } ; hop { filter_eq { all_rows ; player ; makhaya ntini } ; overs } } = true
Output: select the rows whose player record fuzzily matches to juan theron . take the overs record of this row . select the rows whose player record fuzzily matches to makhaya ntini . take the overs record of this row . the first record is less than the second record .
only { filter_eq { all_rows ; nba draft ; clippers } } = true
Output: select the rows whose nba draft record fuzzily matches to clippers . there is only one such row in the table .
and { only { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } } ; eq { hop { filter_eq { all_rows ; tournament ; mercedescup , stuttgart , germany } ; date } ; july 17 , 2011 } } = true
Output: select the rows whose tournament record fuzzily matches to mercedescup , stuttgart , germany . there is only one such row in the table . the date record of this unqiue row is july 17 , 2011 .
and { only { filter_eq { all_rows ; 1 usd = ; 1 } } ; eq { hop { filter_eq { all_rows ; 1 usd = ; 1 } ; country } ; ecuador } } = true
Output: select the rows whose 1 usd = record is equal to 1 . there is only one such row in the table . the country record of this unqiue row is ecuador .
round_eq { avg { all_rows ; cowboys points } ; 24.2 } = true
Output: the average of the cowboys points record of all rows is 24.2 .
eq { hop { argmax { all_rows ; market value ( usd million ) } ; name } ; microsoft } = true
Output: select the row whose market value  usd million  record of all rows is maximum . the name record of this row is microsoft .
round_eq { sum { all_rows ; points } ; 167 } = true
Output: the sum of the points record of all rows is 167 .
eq { nth_min { all_rows ; days held ; 3 } ; 196 } = true
Output: the 3rd minimum days held record of all rows is 196 .
round_eq { avg { all_rows ; innings } ; 22 } = true
Output: the average of the innings record of all rows is 22 .
eq { count { filter_eq { all_rows ; nationality ; united states } } ; 8 } = true
Output: select the rows whose nationality record fuzzily matches to united states . the number of such rows is 8 .
eq { count { filter_eq { all_rows ; manufacturer ; ford } } ; 3 } = true
Output: select the rows whose manufacturer record fuzzily matches to ford . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; 9 april 2000 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is 9 april 2000 .
eq { hop { argmin { all_rows ; finished } ; horse } ; big brown } = true
Output: select the row whose finished record of all rows is minimum . the horse record of this row is big brown .
round_eq { avg { filter_eq { all_rows ; written by ; robert king & michelle king } ; us viewers ( million ) } ; 12.47 } = true
Output: select the rows whose written by record fuzzily matches to robert king  michelle king . the average of the us viewers  million  record of these rows is 12.47 .
eq { count { filter_eq { all_rows ; laps ; 68 } } ; 5 } = true
Output: select the rows whose laps record is equal to 68 . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; series ; 10 - 13 } } ; 2 } = true
Output: select the rows whose series record fuzzily matches to 10  13 . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; name ; keith benson } ; weight } ; hop { filter_eq { all_rows ; name ; tim williams } ; weight } } ; 10 } = true
Output: select the rows whose name record fuzzily matches to keith benson . take the weight record of this row . select the rows whose name record fuzzily matches to tim williams . take the weight record of this row . the first record is 10 larger than the second record .
greater { hop { filter_eq { all_rows ; team ; wauters motorsports } ; rounds } ; hop { filter_eq { all_rows ; team ; turner scott motorsports } ; rounds } } = true
Output: select the rows whose team record fuzzily matches to wauters motorsports . take the rounds record of this row . select the rows whose team record fuzzily matches to turner scott motorsports . take the rounds record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; east carolina university } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is east carolina university .
less { hop { filter_eq { all_rows ; name ; bong hawkins } ; season } ; hop { filter_eq { all_rows ; name ; bakari hendrix } ; season } } = true
Output: select the rows whose name record fuzzily matches to bong hawkins . take the season record of this row . select the rows whose name record fuzzily matches to bakari hendrix . take the season record of this row . the first record is less than the second record .
most_eq { all_rows ; episodes ; 11 } = true
Output: for the episodes records of all rows , most of them are equal to 11 .
eq { hop { nth_argmin { all_rows ; series ep ; 2 } ; netflix } ; s07e02 } = true
Output: select the row whose series ep record of all rows is 2nd minimum . the netflix record of this row is s07e02 .
most_eq { all_rows ; power kw ; 10 kw } = true
Output: for the power kw records of all rows , most of them fuzzily match to 10 kw .
and { only { filter_eq { all_rows ; director ; benedict andrews } } ; eq { hop { filter_eq { all_rows ; director ; benedict andrews } ; production } ; the war of the roses } } = true
Output: select the rows whose director record fuzzily matches to benedict andrews . there is only one such row in the table . the production record of this unqiue row is the war of the roses .
eq { count { filter_less { all_rows ; win % ; 50 } } ; 4 } = true
Output: select the rows whose win  record is less than 50 . the number of such rows is 4 .
and { only { filter_eq { all_rows ; wins ; 18 } } ; eq { hop { filter_eq { all_rows ; wins ; 18 } ; club } ; rc celta de vigo } } = true
Output: select the rows whose wins record is equal to 18 . there is only one such row in the table . the club record of this unqiue row is rc celta de vigo .
and { only { filter_eq { all_rows ; score ; 0 } } ; eq { hop { filter_eq { all_rows ; score ; 0 } ; visitor } ; los angeles } } = true
Output: select the rows whose score record fuzzily matches to 0 . there is only one such row in the table . the visitor record of this unqiue row is los angeles .
eq { count { filter_eq { all_rows ; team name ; isport international } } ; 4 } = true
Output: select the rows whose team name record fuzzily matches to isport international . the number of such rows is 4 .
round_eq { avg { all_rows ; maidens } ; 2 } = true
Output: the average of the maidens record of all rows is 2 .
round_eq { avg { all_rows ; pick } ; 15 } = true
Output: the average of the pick record of all rows is 15 .
eq { count { filter_greater { all_rows ; points ; 100 } } ; 6 } = true
Output: select the rows whose points record is greater than 100 . the number of such rows is 6 .
and { eq { hop { argmax { all_rows ; home team score } ; home team } ; richmond } ; eq { hop { argmax { all_rows ; home team score } ; away team score } ; 14.10 ( 94 ) } } = true
Output: select the row whose home team score record of all rows is maximum . the home team record of this row is richmond . the away team score record of this row is 14.10  94  .
eq { hop { argmax { all_rows ; points } ; opponent } ; vancouver canucks } = true
Output: select the row whose points record of all rows is maximum . the opponent record of this row is vancouver canucks .
eq { count { filter_greater { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 } = true
Output: select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .
round_eq { avg { all_rows ; attendance } ; 30349 } = true
Output: the average of the attendance record of all rows is 30349 .
greater { hop { filter_eq { all_rows ; competition ; 2014 world cup qualifier } ; goal } ; hop { filter_eq { all_rows ; competition ; 2011 copa américa } ; goal } } = true
Output: select the rows whose competition record fuzzily matches to 2014 world cup qualifier . take the goal record of this row . select the rows whose competition record fuzzily matches to 2011 copa américa . take the goal record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; opponent ; east fife } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to east fife . the number of such rows is 2 .
eq { count { filter_all { all_rows ; player } } ; 12 } = true
Output: select the rows whose player record is arbitrary . the number of such rows is 12 .
less { hop { filter_eq { all_rows ; 1st member ; william hall } ; elected } ; hop { filter_eq { all_rows ; 1st member ; george gascoigne } ; elected } } = true
Output: select the rows whose 1st member record fuzzily matches to william hall . take the elected record of this row . select the rows whose 1st member record fuzzily matches to george gascoigne . take the elected record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; points } ; 84 } = true
Output: the average of the points record of all rows is 84 .
eq { count { filter_eq { all_rows ; publisher ; bethesda softworks } } ; 2 } = true
Output: select the rows whose publisher record fuzzily matches to bethesda softworks . the number of such rows is 2 .
round_eq { avg { all_rows ; assets 2013 ( bil ) } ; 18.9 } = true
Output: the average of the assets 2013  bil  record of all rows is 18.9 .
round_eq { sum { filter_greater_eq { all_rows ; date ; october 29 } ; score } ; 9 } = true
Output: select the rows whose date record is greater than or equal to october 29 . the sum of the score record of these rows is 9 .
eq { count { filter_eq { all_rows ; reason for change ; died } } ; 2 } = true
Output: select the rows whose reason for change record fuzzily matches to died . the number of such rows is 2 .
round_eq { avg { all_rows ; attendance } ; 16261 } = true
Output: the average of the attendance record of all rows is 16261 .
eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; cyprus } = true
Output: select the row whose silver record of all rows is 2nd maximum . the nation record of this row is cyprus .
most_greater { all_rows ; commenced operations ; 2000 } = true
Output: for the commenced operations records of all rows , most of them are greater than 2000 .
most_eq { filter_greater { all_rows ; date ; 2007 } ; competition ; friendly } = true
Output: select the rows whose date record is greater than 2007 . for the competition records of these rows , most of them fuzzily match to friendly .
eq { count { filter_eq { all_rows ; cardinalatial order and title ; bishop } } ; 3 } = true
Output: select the rows whose cardinalatial order and title record fuzzily matches to bishop . the number of such rows is 3 .
and { only { filter_eq { all_rows ; type ; mill } } ; eq { hop { filter_eq { all_rows ; type ; mill } ; name } ; shore mill } } = true
Output: select the rows whose type record fuzzily matches to mill . there is only one such row in the table . the name record of this unqiue row is shore mill .
most_eq { all_rows ; party ; democratic - republican } = true
Output: for the party records of all rows , most of them fuzzily match to democratic  republican .
and { only { filter_eq { filter_eq { all_rows ; position ; center } ; nationality ; canada } } ; eq { hop { filter_eq { filter_eq { all_rows ; position ; center } ; nationality ; canada } ; player } ; julien cayer } } = true
Output: select the rows whose position record fuzzily matches to center . among these rows , select the rows whose nationality record fuzzily matches to canada . there is only one such row in the table . the player record of this unqiue row is julien cayer .
and { greater { hop { filter_eq { all_rows ; date ; sept 15 } ; attendance } ; hop { filter_eq { all_rows ; date ; nov 3 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers } ; eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears } } } = true
Output: select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row . select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row . the first record is greater than the second record . the opponent record of the first row is green bay packers . the opponent record of the second row is chicago bears .
all_eq { all_rows ; played ; 18 } = true
Output: for the played records of all rows , all of them are equal to 18 .
less { hop { filter_eq { all_rows ; school ; mogadore } ; tenure } ; hop { filter_eq { all_rows ; school ; field } ; tenure } } = true
Output: select the rows whose school record fuzzily matches to mogadore . take the tenure record of this row . select the rows whose school record fuzzily matches to field . take the tenure record of this row . the first record is less than the second record .
most_eq { all_rows ; prothrombin time ; unaffected } = true
Output: for the prothrombin time records of all rows , most of them fuzzily match to unaffected .
eq { hop { argmax { all_rows ; catholic } ; region } ; southeast asia } = true
Output: select the row whose catholic record of all rows is maximum . the region record of this row is southeast asia .
less { hop { filter_eq { all_rows ; venue ; sydney cricket ground } ; date } ; hop { filter_eq { all_rows ; venue ; waca ground } ; date } } = true
Output: select the rows whose venue record fuzzily matches to sydney cricket ground . take the date record of this row . select the rows whose venue record fuzzily matches to waca ground . take the date record of this row . the first record is less than the second record .
most_eq { filter_eq { all_rows ; result ; won } ; distance ; 1400 m } = true
Output: select the rows whose result record fuzzily matches to won . for the distance records of these rows , most of them fuzzily match to 1400 m .
eq { count { filter_eq { all_rows ; time / retired ; accident } } ; 2 } = true
Output: select the rows whose time  retired record fuzzily matches to accident . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; venue ; waverley park } } ; 4 } = true
Output: select the rows whose venue record fuzzily matches to waverley park . the number of such rows is 4 .
round_eq { avg { all_rows ; attendance } ; 52,519.93 } = true
Output: the average of the attendance record of all rows is 52,519.93 .
most_eq { all_rows ; network ; abc 's wwos } = true
Output: for the network records of all rows , most of them fuzzily match to abc s wwos .
round_eq { avg { filter_eq { all_rows ; lane ; 1 } ; mark } ; 1:48.84 } = true
Output: select the rows whose lane record is equal to 1 . the average of the mark record of these rows is 1:48.84 .
round_eq { sum { all_rows ; mintage } ; 418918 } = true
Output: the sum of the mintage record of all rows is 418918 .
and { greater { hop { filter_eq { all_rows ; date ; 19 november 1997 } ; score } ; hop { filter_eq { all_rows ; date ; 19 november 2003 } ; score } } ; and { eq { hop { filter_eq { all_rows ; date ; 19 november 1997 } ; score } ; 1 - 0 } ; eq { hop { filter_eq { all_rows ; date ; 19 november 2003 } ; score } ; 0 - 3 } } } = true
Output: select the rows whose date record fuzzily matches to 19 november 1997 . take the score record of this row . select the rows whose date record fuzzily matches to 19 november 2003 . take the score record of this row . the first record is greater than the second record . the score record of the first row is 1  0 . the score record of the second row is 0  3 .
most_eq { all_rows ; country of origin ; united states } = true
Output: for the country of origin records of all rows , most of them fuzzily match to united states .
round_eq { sum { all_rows ; goals } ; 1158 } = true
Output: the sum of the goals record of all rows is 1158 .
round_eq { avg { all_rows ; grüne } ; 10.58 % } = true
Output: the average of the grüne record of all rows is 10.58  .
only { filter_eq { all_rows ; opponent ; st louis blues } } = true
Output: select the rows whose opponent record fuzzily matches to st louis blues . there is only one such row in the table .
most_eq { all_rows ; previous conference ; independents } = true
Output: for the previous conference records of all rows , most of them fuzzily match to independents .
eq { hop { nth_argmax { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 1 } ; home } ; jazz } = true
Output: select the rows whose visitor record fuzzily matches to spurs . select the row whose attendance record of these rows is 1st maximum . the home record of this row is jazz .
and { only { filter_greater { all_rows ; speed ; 70 mph } } ; eq { hop { filter_greater { all_rows ; speed ; 70 mph } ; model } ; gyro drop } } = true
Output: select the rows whose speed record is greater than 70 mph . there is only one such row in the table . the model record of this unqiue row is gyro drop .
eq { hop { argmax { all_rows ; balls } ; player } ; sanath jayasuriya } = true
Output: select the row whose balls record of all rows is maximum . the player record of this row is sanath jayasuriya .
eq { count { filter_eq { all_rows ; runner - up ; aus } } ; 7 } = true
Output: select the rows whose runner  up record fuzzily matches to aus . the number of such rows is 7 .
eq { max { all_rows ; score } ; 8 - 0 } = true
Output: the maximum score record of all rows is 8  0 .
eq { hop { nth_argmax { all_rows ; winter olympics ; 2 } ; winner } ; thomas wassberg } = true
Output: select the row whose winter olympics record of all rows is 2nd maximum . the winner record of this row is thomas wassberg .
eq { hop { nth_argmin { all_rows ; original air date ; 2 } ; title } ; heroes } = true
Output: select the row whose original air date record of all rows is 2nd minimum . the title record of this row is heroes .
greater { hop { filter_eq { all_rows ; winner ; chuck arnason } ; goals } ; hop { filter_eq { all_rows ; winner ; reggie leach } ; goals } } = true
Output: select the rows whose winner record fuzzily matches to chuck arnason . take the goals record of this row . select the rows whose winner record fuzzily matches to reggie leach . take the goals record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; date ; october 7 , 2011 } ; score } ; hop { filter_eq { all_rows ; date ; june 8 , 2012 } ; score } } = true
Output: select the rows whose date record fuzzily matches to october 7 , 2011 . take the score record of this row . select the rows whose date record fuzzily matches to june 8 , 2012 . take the score record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; length } ; name } ; northern eagle } = true
Output: select the row whose length record of all rows is maximum . the name record of this row is northern eagle .
eq { hop { nth_argmax { all_rows ; market value ( billion ) ; 1 } ; company } ; general electric } = true
Output: select the row whose market value  billion  record of all rows is 1st maximum . the company record of this row is general electric .
and { only { filter_eq { all_rows ; venue ; victoria park } } ; and { eq { hop { filter_eq { all_rows ; venue ; victoria park } ; home team } ; collingwood } ; eq { hop { filter_eq { all_rows ; venue ; victoria park } ; away team } ; north melbourne } } } = true
Output: select the rows whose venue record fuzzily matches to victoria park . there is only one such row in the table . the home team record of this unqiue row is collingwood . the away team record of this unqiue row is north melbourne .
eq { hop { nth_argmax { all_rows ; apps ; 2 } ; season } ; 2009 - 10 } = true
Output: select the row whose apps record of all rows is 2nd maximum . the season record of this row is 2009  10 .
round_eq { avg { all_rows ; high points } ; 29.5 } = true
Output: the average of the high points record of all rows is 29.5 .
eq { count { filter_eq { all_rows ; margin ; 3 strokes } } ; 2 } = true
Output: select the rows whose margin record fuzzily matches to 3 strokes . the number of such rows is 2 .
eq { hop { argmin { all_rows ; date } ; location attendance } ; energysolutions arena 18732 } = true
Output: select the row whose date record of all rows is minimum . the location attendance record of this row is energysolutions arena 18732 .
round_eq { avg { all_rows ; viewers } ; 44 } = true
Output: the average of the viewers record of all rows is 44 .
eq { count { filter_eq { all_rows ; champions ; guy forget } } ; 2 } = true
Output: select the rows whose champions record fuzzily matches to guy forget . the number of such rows is 2 .
round_eq { avg { all_rows ; crowd } ; 13916 } = true
Output: the average of the crowd record of all rows is 13916 .
less { hop { filter_eq { all_rows ; name ; still standing } ; launched } ; hop { filter_eq { all_rows ; name ; falling angel } ; launched } } = true
Output: select the rows whose name record fuzzily matches to still standing . take the launched record of this row . select the rows whose name record fuzzily matches to falling angel . take the launched record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; ch 1 ; 59 ( uhf ) } ; on - air date } ; hop { filter_eq { all_rows ; ch 1 ; 32 ( uhf ) } ; on - air date } } = true
Output: select the rows whose ch 1 record fuzzily matches to 59  uhf  . take the on  air date record of this row . select the rows whose ch 1 record fuzzily matches to 32  uhf  . take the on  air date record of this row . the first record is less than the second record .
eq { hop { argmax { filter_greater { all_rows ; gold ; 100 } ; silver } ; country } ; indonesia } = true
Output: select the rows whose gold record is greater than 100 . select the row whose silver record of these rows is maximum . the country record of this row is indonesia .
most_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true
Output: select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to w .
eq { count { filter_eq { all_rows ; outcome ; winner } } ; 2 } = true
Output: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 2 .
most_greater { all_rows ; runs ; 100 } = true
Output: for the runs records of all rows , most of them are greater than 100 .
eq { count { filter_all { all_rows ; home team } } ; 6 } = true
Output: select the rows whose home team record is arbitrary . the number of such rows is 6 .
eq { hop { nth_argmin { all_rows ; date first lit ; 1 } ; lighthouse } ; pasig river light ( 1 ) } = true
Output: select the row whose date first lit record of all rows is 1st minimum . the lighthouse record of this row is pasig river light  1  .
eq { hop { argmin { all_rows ; time } ; year } ; 2006 } = true
Output: select the row whose time record of all rows is minimum . the year record of this row is 2006 .
eq { count { filter_eq { all_rows ; sport ; wrestling } } ; 2 } = true
Output: select the rows whose sport record fuzzily matches to wrestling . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; qual 1 ; 2 } ; name } ; sébastien bourdais } = true
Output: select the row whose qual 1 record of all rows is 2nd minimum . the name record of this row is sébastien bourdais .
eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; orlando quintana } = true
Output: select the row whose matches record of all rows is maximum . the goalkeeper record of this row is orlando quintana .
most_eq { all_rows ; competition ; 2002 world cup qualifier } = true
Output: for the competition records of all rows , most of them fuzzily match to 2002 world cup qualifier .
all_greater { filter_greater { all_rows ; gold ; 200 } ; total ; 1000 } = true
Output: select the rows whose gold record is greater than 200 . for the total records of these rows , all of them are greater than 1000 .
most_eq { all_rows ; year ; 1986 } = true
Output: for the year records of all rows , most of them are equal to 1986 .
less { hop { filter_eq { all_rows ; athlete ; philomena mensah } ; time } ; hop { filter_eq { all_rows ; athlete ; marita koch } ; time } } = true
Output: select the rows whose athlete record fuzzily matches to philomena mensah . take the time record of this row . select the rows whose athlete record fuzzily matches to marita koch . take the time record of this row . the first record is less than the second record .
and { eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } } ; and { eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; retired democratic hold } ; eq { hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } ; retired democratic hold } } } = true
Output: select the rows whose district record fuzzily matches to arkansas 5 . take the result record of this row . select the rows whose district record fuzzily matches to arkansas 6 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is retired democratic hold . the result record of the second row is retired democratic hold .
most_greater { filter_greater { all_rows ; area ( km square ) ; 1000000 } ; population ( millions , 2011 ) ; 6 } = true
Output: select the rows whose area  km square  record is greater than 1000000 . for the population  millions , 2011  records of these rows , most of them are greater than 6 .
eq { hop { nth_argmin { all_rows ; gdp per capita ( us ) ; 2 } ; member countries } ; poland } = true
Output: select the row whose gdp per capita  us  record of all rows is 2nd minimum . the member countries record of this row is poland .
round_eq { avg { all_rows ; issue price } ; 59.95 } = true
Output: the average of the issue price record of all rows is 59.95 .
and { only { filter_eq { all_rows ; country ; west germany } } ; eq { hop { filter_eq { all_rows ; country ; west germany } ; player } ; bernhard langer } } = true
Output: select the rows whose country record fuzzily matches to west germany . there is only one such row in the table . the player record of this unqiue row is bernhard langer .
eq { count { filter_eq { all_rows ; team name ; blue } } ; 3 } = true
Output: select the rows whose team name record fuzzily matches to blue . the number of such rows is 3 .
round_eq { sum { all_rows ; enrollment } ; 11100 } = true
Output: the sum of the enrollment record of all rows is 11100 .
eq { hop { nth_argmin { filter_eq { all_rows ; state ; oregon } ; year ; 2 } ; result } ; resigned } = true
Output: select the rows whose state record fuzzily matches to oregon . select the row whose year record of these rows is 2nd minimum . the result record of this row is resigned .
and { only { filter_eq { all_rows ; class / type ; passenger ship } } ; eq { hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder } ; john roach and son } } = true
Output: select the rows whose class  type record fuzzily matches to passenger ship . there is only one such row in the table . the builder record of this unqiue row is john roach and son .
most_greater { all_rows ; gained university status ; 1920 } = true
Output: for the gained university status records of all rows , most of them are greater than 1920 .
most_eq { filter_eq { all_rows ; largest ethnic group ( 2002 ) ; serbs } ; type ; village } = true
Output: select the rows whose largest ethnic group  2002  record fuzzily matches to serbs . for the type records of these rows , most of them fuzzily match to village .
most_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } = true
Output: for the current affiliation records of all rows , most of them fuzzily match to cbs owned  and  operated  o  o  .
round_eq { avg { filter_eq { all_rows ; high points ; jason richardson } ; high points } ; 31 } = true
Output: select the rows whose high points record fuzzily matches to jason richardson . the average of the high points record of these rows is 31 .
and { only { filter_eq { all_rows ; height ; 6 - 10 } } ; eq { hop { filter_eq { all_rows ; height ; 6 - 10 } ; player } ; tony latham } } = true
Output: select the rows whose height record fuzzily matches to 6  10 . there is only one such row in the table . the player record of this unqiue row is tony latham .
most_less { all_rows ; birth ; 1800 } = true
Output: for the birth records of all rows , most of them are less than 1800 .
and { only { filter_eq { all_rows ; years for jazz ; present } } ; eq { hop { filter_eq { all_rows ; years for jazz ; present } ; player } ; jamaal tinsley } } = true
Output: select the rows whose years for jazz record fuzzily matches to present . there is only one such row in the table . the player record of this unqiue row is jamaal tinsley .
round_eq { avg { all_rows ; enrollment } ; 20944 } = true
Output: the average of the enrollment record of all rows is 20944 .
greater { hop { filter_eq { all_rows ; date ; december 30 , 2007 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 23 , 2007 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to december 30 , 2007 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 23 , 2007 . take the attendance record of this row . the first record is greater than the second record .
and { only { filter_less { all_rows ; year ( s ) of manufacture ; 1900 } } ; eq { hop { filter_less { all_rows ; year ( s ) of manufacture ; 1900 } ; railway number ( s ) } ; xi - xxii , xxviii } } = true
Output: select the rows whose year  s  of manufacture record is less than 1900 . there is only one such row in the table . the railway number  s  record of this unqiue row is xi  xxii , xxviii .
most_eq { all_rows ; competition ; division 2 } = true
Output: for the competition records of all rows , most of them fuzzily match to division 2 .
round_eq { avg { all_rows ; wins } ; 3.5 } = true
Output: the average of the wins record of all rows is 3.5 .
most_eq { all_rows ; league ; aviva premiership } = true
Output: for the league records of all rows , most of them fuzzily match to aviva premiership .
round_eq { sum { all_rows ; rounds } ; 122 } = true
Output: the sum of the rounds record of all rows is 122 .
most_eq { filter_eq { all_rows ; listed ; 1997 } ; built ; 1894 } = true
Output: select the rows whose listed record fuzzily matches to 1997 . for the built records of these rows , most of them fuzzily match to 1894 .
eq { count { filter_eq { filter_eq { all_rows ; english ( streamline ) ; unknown } ; english ( pioneer / geneon ) ; richard cansino } } ; 2 } = true
Output: select the rows whose english  streamline  record fuzzily matches to unknown . among these rows , select the rows whose english  pioneer  geneon  record fuzzily matches to richard cansino . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; surface ; hard } } ; 3 } = true
Output: select the rows whose surface record fuzzily matches to hard . the number of such rows is 3 .
all_eq { all_rows ; issue price ; 24.95 } = true
Output: for the issue price records of all rows , all of them are equal to 24.95 .
greater { hop { filter_eq { all_rows ; city ; seongnam } ; population ( 2010 ) } ; hop { filter_eq { all_rows ; city ; namyangju } ; population ( 2010 ) } } = true
Output: select the rows whose city record fuzzily matches to seongnam . take the population  2010  record of this row . select the rows whose city record fuzzily matches to namyangju . take the population  2010  record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; length ; 1 } ; title } ; my man and the devil on his shoulder } = true
Output: select the row whose length record of all rows is 1st maximum . the title record of this row is my man and the devil on his shoulder .
eq { hop { argmax { all_rows ; pieces } ; release } ; 1981 } = true
Output: select the row whose pieces record of all rows is maximum . the release record of this row is 1981 .
round_eq { avg { all_rows ; score } ; 3.625 } = true
Output: the average of the score record of all rows is 3.625 .
less { hop { filter_eq { all_rows ; vacator ; william f norrell ( d ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; walter m mumma ( r ) } ; reason for change } } = true
Output: select the rows whose vacator record fuzzily matches to william f norrell  d  . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to walter m mumma  r  . take the reason for change record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; 3rd place } ; 3 } = true
Output: the sum of the 3rd place record of all rows is 3 .
greater { hop { filter_eq { all_rows ; municipality ; san miguel } ; no of barangays } ; hop { filter_eq { all_rows ; municipality ; gigmoto } ; no of barangays } } = true
Output: select the rows whose municipality record fuzzily matches to san miguel . take the no of barangays record of this row . select the rows whose municipality record fuzzily matches to gigmoto . take the no of barangays record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; date ; 14 jul } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to 14 jul . the number of such rows is 2 .
eq { nth_min { filter_eq { all_rows ; drivers ; romain grosjean } ; year ; 1 } ; 2010 } = true
Output: select the rows whose drivers record fuzzily matches to romain grosjean . the 1st minimum year record of these rows is 2010 .
most_eq { all_rows ; competition or tour ; friendly } = true
Output: for the competition or tour records of all rows , most of them fuzzily match to friendly .
and { only { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } } ; eq { hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date } ; 27 april } } = true
Output: select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello . there is only one such row in the table . the date record of this unqiue row is 27 april .
eq { hop { argmax { filter_eq { all_rows ; debut in europe ; 1995 } ; games } ; player } ; raãl } = true
Output: select the rows whose debut in europe record is equal to 1995 . select the row whose games record of these rows is maximum . the player record of this row is raãl .
and { only { filter_eq { all_rows ; studio ( s ) ; red chillies entertainment } } ; eq { hop { filter_eq { all_rows ; studio ( s ) ; red chillies entertainment } ; movie } ; chennai express } } = true
Output: select the rows whose studio  s  record fuzzily matches to red chillies entertainment . there is only one such row in the table . the movie record of this unqiue row is chennai express .
most_greater { all_rows ; home team score ; 10 } = true
Output: for the home team score records of all rows , most of them are greater than 10 .
eq { hop { nth_argmin { filter_eq { all_rows ; tournament ; japan } ; date ; 2 } ; surface } ; carpet ( i ) } = true
Output: select the rows whose tournament record fuzzily matches to japan . select the row whose date record of these rows is 2nd minimum . the surface record of this row is carpet  i  .
eq { hop { argmax { all_rows ; margin of victory } ; tournament } ; aberdeen ladies scottish open } = true
Output: select the row whose margin of victory record of all rows is maximum . the tournament record of this row is aberdeen ladies scottish open .
most_eq { all_rows ; high points ; jason richardson } = true
Output: for the high points records of all rows , most of them fuzzily match to jason richardson .
eq { hop { argmax { all_rows ; first - team goals } ; player } ; gareth barry } = true
Output: select the row whose first  team goals record of all rows is maximum . the player record of this row is gareth barry .
most_eq { all_rows ; decision ; theodore } = true
Output: for the decision records of all rows , most of them fuzzily match to theodore .
and { only { filter_eq { all_rows ; method ; rear naked choke } } ; eq { hop { filter_eq { all_rows ; method ; rear naked choke } ; opponent } ; helio dipp } } = true
Output: select the rows whose method record fuzzily matches to rear naked choke . there is only one such row in the table . the opponent record of this unqiue row is helio dipp .
and { only { filter_eq { all_rows ; school ; hawaii } } ; eq { hop { filter_eq { all_rows ; school ; hawaii } ; player } ; darrick branch } } = true
Output: select the rows whose school record fuzzily matches to hawaii . there is only one such row in the table . the player record of this unqiue row is darrick branch .
and { only { filter_greater { filter_eq { all_rows ; laps ; 64 } ; points ; 30 } } ; eq { hop { filter_greater { filter_eq { all_rows ; laps ; 64 } ; points ; 30 } ; driver } ; sébastien bourdais } } = true
Output: select the rows whose laps record is equal to 64 . among these rows , select the rows whose points record is greater than 30 . there is only one such row in the table . the driver record of this unqiue row is sébastien bourdais .
eq { count { filter_eq { all_rows ; competition ; world championships } } ; 3 } = true
Output: select the rows whose competition record fuzzily matches to world championships . the number of such rows is 3 .
only { filter_greater { all_rows ; points ; 90 } } = true
Output: select the rows whose points record is greater than 90 . there is only one such row in the table .
most_eq { all_rows ; competition ; european championships } = true
Output: for the competition records of all rows , most of them fuzzily match to european championships .
eq { count { filter_eq { filter_greater { all_rows ; points ; 10 } ; drawn ; 2 } } ; 2 } = true
Output: select the rows whose points record is greater than 10 . among these rows , select the rows whose drawn record is equal to 2 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; drawn ; 1 } } ; 2 } = true
Output: select the rows whose drawn record is equal to 1 . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; height ( f ) ; 2 } ; player } ; yuval naimi } = true
Output: select the row whose height  f  record of all rows is 2nd minimum . the player record of this row is yuval naimi .
and { only { filter_eq { all_rows ; easa ( eu ) ; yes } } ; eq { hop { filter_eq { all_rows ; easa ( eu ) ; yes } ; airline } ; premiair } } = true
Output: select the rows whose easa  eu  record fuzzily matches to yes . there is only one such row in the table . the airline record of this unqiue row is premiair .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; team } ; germnia } = true
Output: select the row whose points record of all rows is 2nd maximum . the team record of this row is germnia .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; stanford } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is stanford .
eq { count { filter_eq { all_rows ; group position ; 1st } } ; 3 } = true
Output: select the rows whose group position record fuzzily matches to 1st . the number of such rows is 3 .
and { eq { hop { filter_eq { all_rows ; event ; 2004 summer paralympics } ; total } ; hop { filter_eq { all_rows ; event ; 2008 summer paralympics } ; total } } ; and { eq { hop { filter_eq { all_rows ; event ; 2004 summer paralympics } ; total } ; 1 } ; eq { hop { filter_eq { all_rows ; event ; 2008 summer paralympics } ; total } ; 1 } } } = true
Output: select the rows whose event record fuzzily matches to 2004 summer paralympics . take the total record of this row . select the rows whose event record fuzzily matches to 2008 summer paralympics . take the total record of this row . the first record is equal to the second record . the total record of the first row is 1 . the total record of the second row is 1 .
eq { hop { argmax { all_rows ; owgr points } ; tournament } ; ballantine 's championship } = true
Output: select the row whose owgr points record of all rows is maximum . the tournament record of this row is ballantine s championship .
most_eq { all_rows ; visitor ; new jersey devils } = true
Output: for the visitor records of all rows , most of them fuzzily match to new jersey devils .
all_eq { all_rows ; transfer window ; winter } = true
Output: for the transfer window records of all rows , all of them fuzzily match to winter .
eq { hop { argmax { all_rows ; crowd } ; date } ; friday , 1 august } = true
Output: select the row whose crowd record of all rows is maximum . the date record of this row is friday , 1 august .
and { only { filter_not_eq { all_rows ; afc cup ; 0 } } ; eq { hop { filter_not_eq { all_rows ; afc cup ; 0 } ; member association } ; uzbekistan } } = true
Output: select the rows whose afc cup record is not equal to 0 . there is only one such row in the table . the member association record of this unqiue row is uzbekistan .
and { only { filter_eq { all_rows ; wins ; 10 } } ; eq { hop { filter_eq { all_rows ; wins ; 10 } ; club } ; deportivo alavés } } = true
Output: select the rows whose wins record is equal to 10 . there is only one such row in the table . the club record of this unqiue row is deportivo alavés .
eq { count { filter_eq { filter_eq { all_rows ; playoffs ; did not qualify } ; regular season finish ; 3rd , southern division } } ; 2 } = true
Output: select the rows whose playoffs record fuzzily matches to did not qualify . among these rows , select the rows whose regular season finish record fuzzily matches to 3rd , southern division . the number of such rows is 2 .