niryuu/sni-each-converted huggingface dataset:
and { only { filter_not_eq { all_rows ; pos ; dnf } } ; eq { hop { filter_not_eq { all_rows ; pos ; dnf } ; year } ; 1989 } } = true
Output: select the rows whose pos record does not match to dnf . there is only one such row in the table . the year record of this unqiue row is 1989 .
eq { count { filter_eq { filter_greater_eq { all_rows ; year ; 2010 } ; genre ; action rpg } } ; 2 } = true
Output: select the rows whose year record is greater than or equal to 2010 . among these rows , select the rows whose genre record fuzzily matches to action rpg . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; engine ; v6 } } ; 4 } = true
Output: select the rows whose engine record fuzzily matches to v6 . the number of such rows is 4 .
eq { hop { nth_argmax { filter_eq { all_rows ; country ; united kingdom } ; seats ; 1 } ; model } ; bae atp } = true
Output: select the rows whose country record fuzzily matches to united kingdom . select the row whose seats record of these rows is 1st maximum . the model record of this row is bae atp .
and { only { filter_greater { all_rows ; scored ; 30 } } ; eq { hop { filter_greater { all_rows ; scored ; 30 } ; team } ; tembetary } } = true
Output: select the rows whose scored record is greater than 30 . there is only one such row in the table . the team record of this unqiue row is tembetary .
eq { count { filter_eq { filter_eq { all_rows ; tv time ; cbs 12:00 pm } ; result ; w } } ; 8 } = true
Output: select the rows whose tv time record fuzzily matches to cbs 12:00 pm . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 8 .
eq { hop { nth_argmin { all_rows ; time ; 2 } ; athlete } ; sophie balmary } = true
Output: select the row whose time record of all rows is 2nd minimum . the athlete record of this row is sophie balmary .
greater { hop { filter_eq { all_rows ; year ; 2011 } ; tournaments played } ; hop { filter_eq { all_rows ; year ; 2007 } ; tournaments played } } = true
Output: select the rows whose year record fuzzily matches to 2011 . take the tournaments played record of this row . select the rows whose year record fuzzily matches to 2007 . take the tournaments played record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; aurelija miseviciute } = true
Output: select the row whose date record of all rows is 1st minimum . the opponent record of this row is aurelija miseviciute .
eq { count { filter_eq { all_rows ; venue ; roker park } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to roker park . the number of such rows is 2 .
round_eq { sum { filter_eq { all_rows ; venue ; parken stadium , copenhagen } ; score } ; 7 } = true
Output: select the rows whose venue record fuzzily matches to parken stadium , copenhagen . the sum of the score record of these rows is 7 .
greater { hop { filter_eq { all_rows ; company ; bp } ; profits ( billion ) } ; hop { filter_eq { all_rows ; company ; hsbc } ; profits ( billion ) } } = true
Output: select the rows whose company record fuzzily matches to bp . take the profits  billion  record of this row . select the rows whose company record fuzzily matches to hsbc . take the profits  billion  record of this row . the first record is greater than the second record .
and { eq { diff { hop { filter_eq { all_rows ; artist ; ann breen } ; points } ; hop { filter_eq { all_rows ; artist ; fran meen } ; points } } ; 14 } ; and { eq { hop { filter_eq { all_rows ; artist ; ann breen } ; song } ; oh , darling } ; eq { hop { filter_eq { all_rows ; artist ; fran meen } ; song } ; say that you love me } } } = true
Output: select the rows whose artist record fuzzily matches to ann breen . take the points record of this row . select the rows whose artist record fuzzily matches to fran meen . take the points record of this row . the first record is 14 larger than the second record . the song record of the first row is oh , darling . the song record of the second row is say that you love me .
and { only { filter_eq { all_rows ; nationality ; slovakia } } ; eq { hop { filter_eq { all_rows ; nationality ; slovakia } ; distance } ; 500 m } } = true
Output: 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 ; silver ; 4 } } ; eq { hop { filter_eq { all_rows ; silver ; 4 } ; nation } ; pakistan ( pak ) } } = true
Output: select the rows whose silver record is equal to 4 . there is only one such row in the table . the nation record of this unqiue row is pakistan  pak  .
eq { count { filter_eq { all_rows ; venue ; home } } ; 4 } = true
Output: select the rows whose venue record fuzzily matches to home . the number of such rows is 4 .
and { only { filter_less { all_rows ; elevation ( m ) ; 2400 } } ; eq { hop { filter_less { all_rows ; elevation ( m ) ; 2400 } ; peak } ; pietrosul rodnei } } = true
Output: select the rows whose elevation  m  record is less than 2400 . there is only one such row in the table . the peak record of this unqiue row is pietrosul rodnei .
eq { hop { argmax { all_rows ; f bolt ( kgf ) } ; chambering } ; .50 bmg } = true
Output: select the row whose f bolt  kgf  record of all rows is maximum . the chambering record of this row is .50 bmg .
eq { hop { argmax { all_rows ; bronze } ; nation } ; spain } = true
Output: select the row whose bronze record of all rows is maximum . the nation record of this row is spain .
greater { hop { filter_eq { all_rows ; official name ; madawaska } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; baker brook } ; area km 2 } } = true
Output: select the rows whose official name record fuzzily matches to madawaska . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to baker brook . take the area km 2 record of this row . the first record is greater than the second record .
most_eq { all_rows ; podiums ; 0 } = true
Output: for the podiums records of all rows , most of them are equal to 0 .
eq { hop { nth_argmax { all_rows ; shot % ; 2 } ; skip } ; david murdoch } = true
Output: select the row whose shot  record of all rows is 2nd maximum . the skip record of this row is david murdoch .
most_eq { all_rows ; date ; 25 october } = true
Output: for the date records of all rows , most of them fuzzily match to 25 october .
eq { count { filter_greater_eq { filter_less { all_rows ; rank ; 4 } ; total ; 19.3 } } ; 2 } = true
Output: select the rows whose rank record is less than 4 . among these rows , select the rows whose total record is greater than or equal to 19.3 . the number of such rows is 2 .
eq { count { filter_greater { filter_less { all_rows ; crowd ; 10000 } ; home team score ; 10 } } ; 2 } = true
Output: select the rows whose crowd record is less than 10000 . among these rows , select the rows whose home team score record is greater than 10 . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; hop { filter_eq { all_rows ; opponent ; detroit lions } ; date } } = true
Output: 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 .
and { only { filter_eq { all_rows ; category ; best newcomer } } ; and { eq { hop { filter_eq { all_rows ; category ; best newcomer } ; year } ; 2008 } ; eq { hop { filter_eq { all_rows ; category ; best newcomer } ; film or series } ; eastenders } } } = true
Output: select the rows whose category record fuzzily matches to best newcomer . there is only one such row in the table . the year record of this unqiue row is 2008 . the film or series record of this unqiue row is eastenders .
round_eq { avg { all_rows ; wins } ; 1.1 } = true
Output: the average of the wins record of all rows is 1.1 .
eq { hop { argmax { all_rows ; attendance } ; date } ; march 14 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is march 14 .
round_eq { avg { filter_eq { all_rows ; loser ; kansas city chiefs } ; result } ; 25.5 } = true
Output: select the rows whose loser record fuzzily matches to kansas city chiefs . the average of the result record of these rows is 25.5 .
and { only { filter_eq { all_rows ; year ; 2008 } } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; tournament } ; vietnam open grand prix } } = true
Output: select the rows whose year record is equal to 2008 . there is only one such row in the table . the tournament record of this unqiue row is vietnam open grand prix .
round_eq { avg { all_rows ; central rate } ; 3.4 } = true
Output: the average of the central rate record of all rows is 3.4 .
eq { nth_max { all_rows ; no of episodes ; 3 } ; 135 } = true
Output: the 3rd maximum no of episodes record of all rows is 135 .
and { only { filter_less { all_rows ; apparent magnitude ; 10.0 } } ; and { eq { hop { filter_less { all_rows ; apparent magnitude ; 10.0 } ; object type } ; spiral galaxy } ; eq { hop { filter_less { all_rows ; apparent magnitude ; 10.0 } ; constellation } ; ursa major } } } = true
Output: select the rows whose apparent magnitude record is less than 10.0 . there is only one such row in the table . the object type record of this unqiue row is spiral galaxy . the constellation record of this unqiue row is ursa major .
most_eq { all_rows ; european cup ; fiba korać cup } = true
Output: for the european cup records of all rows , most of them fuzzily match to fiba korać cup .
all_eq { all_rows ; performer 1 ; john sessions } = true
Output: for the performer 1 records of all rows , all of them fuzzily match to john sessions .
eq { count { filter_eq { all_rows ; surface ; hard } } ; 4 } = true
Output: select the rows whose surface record fuzzily matches to hard . the number of such rows is 4 .
and { eq { diff { hop { filter_eq { all_rows ; winner ; thomas wassberg } ; fis nordic world ski championships } ; hop { filter_eq { all_rows ; winner ; gunde svan } ; fis nordic world ski championships } } ; -9 } ; and { eq { hop { filter_eq { all_rows ; winner ; thomas wassberg } ; fis nordic world ski championships } ; 1980 } ; eq { hop { filter_eq { all_rows ; winner ; gunde svan } ; fis nordic world ski championships } ; 1989 } } } = true
Output: select the rows whose winner record fuzzily matches to thomas wassberg . take the fis nordic world ski championships record of this row . select the rows whose winner record fuzzily matches to gunde svan . take the fis nordic world ski championships record of this row . the second record is 9 larger than the first record . the fis nordic world ski championships record of the first row is 1980 . the fis nordic world ski championships record of the second row is 1989 .
and { only { filter_greater { filter_eq { all_rows ; position ; running back } ; overall ; 100th } } ; eq { hop { filter_greater { filter_eq { all_rows ; position ; running back } ; overall ; 100th } ; name } ; david tolomu } } = true
Output: select the rows whose position record fuzzily matches to running back . among these rows , select the rows whose overall record is greater than 100th . there is only one such row in the table . the name record of this unqiue row is david tolomu .
eq { hop { nth_argmin { all_rows ; record ; 2 } ; nationality } ; russia } = true
Output: select the row whose record record of all rows is 2nd minimum . the nationality record of this row is russia .
eq { hop { argmax { all_rows ; upper index kcal / nm 3 } ; fuel gas } ; n - butane } = true
Output: select the row whose upper index kcal  nm 3 record of all rows is maximum . the fuel gas record of this row is n  butane .
round_eq { avg { filter_not_eq { all_rows ; release price ( usd ) ; n/a } ; release price ( usd ) } ; 114.7 } = true
Output: select the rows whose release price  usd  record does not match to na . the average of the release price  usd  record of these rows is 114.7 .
eq { hop { argmax { all_rows ; crowd } ; venue } ; windy hill } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is windy hill .
eq { count { filter_eq { all_rows ; country ; sweden } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to sweden . the number of such rows is 2 .
and { only { filter_eq { all_rows ; colors ; green , white } } ; eq { hop { filter_eq { all_rows ; colors ; green , white } ; school } ; mogadore } } = true
Output: select the rows whose colors record fuzzily matches to green , white . there is only one such row in the table . the school record of this unqiue row is mogadore .
eq { diff { hop { filter_eq { all_rows ; school ; perry central } ; size } ; hop { filter_eq { all_rows ; school ; springs valley } ; size } } ; 15 } = true
Output: select the rows whose school record fuzzily matches to perry central . take the size record of this row . select the rows whose school record fuzzily matches to springs valley . take the size record of this row . the first record is 15 larger than the second record .
round_eq { avg { all_rows ; attendance } ; 60644 } = true
Output: the average of the attendance record of all rows is 60644 .
eq { count { filter_eq { all_rows ; event ; ufc 12 } } ; 2 } = true
Output: select the rows whose event record fuzzily matches to ufc 12 . the number of such rows is 2 .
eq { max { all_rows ; away team score } ; 15.9 ( 99 ) } = true
Output: the maximum away team score record of all rows is 15.9  99  .
most_eq { all_rows ; authority ; state } = true
Output: for the authority records of all rows , most of them fuzzily match to state .
eq { hop { argmax { all_rows ; 2012 } ; location } ; lake buena vista , florida , usa } = true
Output: select the row whose 2012 record of all rows is maximum . the location record of this row is lake buena vista , florida , usa .
eq { hop { argmax { all_rows ; top 10 } ; year } ; 1996 } = true
Output: select the row whose top 10 record of all rows is maximum . the year record of this row is 1996 .
eq { hop { argmax { all_rows ; viewers ( millions ) } ; episode } ; episode 5 } = true
Output: select the row whose viewers  millions  record of all rows is maximum . the episode record of this row is episode 5 .
most_greater { all_rows ; date ; 1930 } = true
Output: for the date records of all rows , most of them are greater than 1930 .
and { only { filter_eq { all_rows ; races ; 16 } } ; and { eq { hop { filter_eq { all_rows ; races ; 16 } ; year } ; 2012 } ; eq { hop { filter_eq { all_rows ; races ; 16 } ; riders } ; alex de angelis } } } = true
Output: select the rows whose races record is equal to 16 . there is only one such row in the table . the year record of this unqiue row is 2012 . the riders record of this unqiue row is alex de angelis .
round_eq { avg { all_rows ; score } ; 2.3 } = true
Output: the average of the score record of all rows is 2.3 .
eq { hop { argmin { all_rows ; date } ; circuit } ; calder park raceway } = true
Output: select the row whose date record of all rows is minimum . the circuit record of this row is calder park raceway .
eq { hop { nth_argmin { all_rows ; individual ; 2 } ; event } ; 2004 oberhof } = true
Output: select the row whose individual record of all rows is 2nd minimum . the event record of this row is 2004 oberhof .
eq { hop { nth_argmax { all_rows ; cultural and educational panel ; 2 } ; party } ; fianna fáil } = true
Output: select the row whose cultural and educational panel record of all rows is 2nd maximum . the party record of this row is fianna fáil .
eq { count { filter_greater { all_rows ; crowd ; 25000 } } ; 3 } = true
Output: select the rows whose crowd record is greater than 25000 . the number of such rows is 3 .
eq { count { filter_all { all_rows ; official name } } ; 9 } = true
Output: select the rows whose official name record is arbitrary . the number of such rows is 9 .
round_eq { sum { all_rows ; worcs f - c matches } ; 1239 } = true
Output: the sum of the worcs f  c matches record of all rows is 1239 .
eq { hop { argmax { all_rows ; area } ; rr romaja } ; gyeongsangbuk } = true
Output: select the row whose area record of all rows is maximum . the rr romaja record of this row is gyeongsangbuk .
eq { count { filter_eq { all_rows ; position ; defensive tackle } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to defensive tackle . the number of such rows is 3 .
round_eq { avg { filter_eq { all_rows ; country ; south africa } ; overall rank } ; 8.5 } = true
Output: select the rows whose country record fuzzily matches to south africa . the average of the overall rank record of these rows is 8.5 .
eq { hop { nth_argmin { all_rows ; finish ; 2 } ; year } ; 1967 } = true
Output: select the row whose finish record of all rows is 2nd minimum . the year record of this row is 1967 .
eq { diff { hop { filter_eq { all_rows ; name ; benito lorenzi } ; rank } ; hop { filter_eq { all_rows ; name ; christian vieri } ; rank } } ; -2 } = true
Output: select the rows whose name record fuzzily matches to benito lorenzi . take the rank record of this row . select the rows whose name record fuzzily matches to christian vieri . take the rank record of this row . the second record is 2 larger than the first record .
eq { count { filter_eq { all_rows ; fuel / trans ; diesel } } ; 5 } = true
Output: select the rows whose fuel  trans record fuzzily matches to diesel . the number of such rows is 5 .
and { eq { max { all_rows ; result } ; kuwait won by 72 runs scorecard } ; eq { hop { argmax { all_rows ; result } ; details } ; 2011 division seven } } = true
Output: the maximum result record of all rows is kuwait won by 72 runs scorecard . the details record of the row with superlative result record is 2011 division seven .
and { only { filter_eq { filter_eq { all_rows ; label ; alfa records } ; format ; stereo lp } } ; eq { hop { filter_eq { filter_eq { all_rows ; label ; alfa records } ; format ; stereo lp } ; date } ; october 21 , 1981 } } = true
Output: select the rows whose label record fuzzily matches to alfa records . among these rows , select the rows whose format record fuzzily matches to stereo lp . there is only one such row in the table . the date record of this unqiue row is october 21 , 1981 .
and { only { filter_eq { filter_eq { all_rows ; location ; tokyo } ; res ; loss } } ; eq { hop { filter_eq { filter_eq { all_rows ; location ; tokyo } ; res ; loss } ; opponent } ; hitomi akano } } = true
Output: select the rows whose location record fuzzily matches to tokyo . among these rows , select the rows whose res record fuzzily matches to loss . there is only one such row in the table . the opponent record of this unqiue row is hitomi akano .
and { only { filter_eq { all_rows ; result ; anti - jacksonian gain } } ; eq { hop { filter_eq { all_rows ; result ; anti - jacksonian gain } ; incumbent } ; robert ramsey } } = true
Output: select the rows whose result record fuzzily matches to anti  jacksonian gain . there is only one such row in the table . the incumbent record of this unqiue row is robert ramsey .
round_eq { avg { all_rows ; us viewers ( millions ) } ; 8.55 million } = true
Output: the average of the us viewers  millions  record of all rows is 8.55 million .
eq { max { filter_greater { all_rows ; date ; december 16 } ; high points } ; zach randolph ( 34 ) } = true
Output: select the rows whose date record is greater than december 16 . the maximum high points record of these rows is zach randolph  34  .
less { hop { filter_eq { all_rows ; builder ; north british } ; date } ; hop { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; date } } = true
Output: select the rows whose builder record fuzzily matches to north british . take the date record of this row . select the rows whose builder record fuzzily matches to g  swr kilmarnock . take the date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; college ; detroit mercy } } ; eq { hop { filter_eq { all_rows ; college ; detroit mercy } ; player } ; joe kopicki } } = true
Output: select the rows whose college record fuzzily matches to detroit mercy . there is only one such row in the table . the player record of this unqiue row is joe kopicki .
eq { hop { argmax { all_rows ; quantity } ; make } ; gm new look } = true
Output: select the row whose quantity record of all rows is maximum . the make record of this row is gm new look .
less { hop { filter_eq { all_rows ; player ; sam torrance } ; to par } ; hop { filter_eq { all_rows ; player ; ben crenshaw } ; to par } } = true
Output: select the rows whose player record fuzzily matches to sam torrance . take the to par record of this row . select the rows whose player record fuzzily matches to ben crenshaw . take the to par record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; batting partners ; tim curtis and tom moody } ; runs } ; hop { filter_eq { all_rows ; batting partners ; chris read and andrew harris } ; runs } } = true
Output: select the rows whose batting partners record fuzzily matches to tim curtis and tom moody . take the runs record of this row . select the rows whose batting partners record fuzzily matches to chris read and andrew harris . take the runs record of this row . the first record is greater than the second record .
eq { count { filter_less { all_rows ; away team score ; 10.0 } } ; 2 } = true
Output: select the rows whose away team score record is less than 10.0 . the number of such rows is 2 .
round_eq { avg { filter_eq { all_rows ; high rebounds ; wallace } ; high rebounds } ; 11 } = true
Output: select the rows whose high rebounds record fuzzily matches to wallace . the average of the high rebounds record of these rows is 11 .
eq { count { filter_eq { filter_eq { all_rows ; title ; ( part } ; directed by ; tom tataranowicz } } ; 3 } = true
Output: select the rows whose title record fuzzily matches to  part . among these rows , select the rows whose directed by record fuzzily matches to tom tataranowicz . the number of such rows is 3 .
eq { hop { argmax { all_rows ; won } ; team } ; corinthians } = true
Output: select the row whose won record of all rows is maximum . the team record of this row is corinthians .
round_eq { avg { all_rows ; scored } ; .23 } = true
Output: the average of the scored record of all rows is .23 .
eq { hop { nth_argmin { all_rows ; game ; 2 } ; date } ; march 6 } = true
Output: select the row whose game record of all rows is 2nd minimum . the date record of this row is march 6 .
round_eq { avg { all_rows ; wkts } ; 12.91 } = true
Output: the average of the wkts record of all rows is 12.91 .
less { hop { filter_eq { all_rows ; location ; ullswater } ; date } ; hop { filter_eq { all_rows ; location ; lake dumbleyung } ; date } } = true
Output: select the rows whose location record fuzzily matches to ullswater . take the date record of this row . select the rows whose location record fuzzily matches to lake dumbleyung . take the date record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; status ; five nations } } ; 4 } = true
Output: select the rows whose status record fuzzily matches to five nations . the number of such rows is 4 .
round_eq { avg { all_rows ; capacity } ; 10300 } = true
Output: the average of the capacity record of all rows is 10300 .
and { only { filter_less { all_rows ; laps ; 7 } } ; eq { hop { filter_less { all_rows ; laps ; 7 } ; year } ; 1980 } } = true
Output: select the rows whose laps record is less than 7 . there is only one such row in the table . the year record of this unqiue row is 1980 .
greater { hop { filter_eq { all_rows ; season ; 2012 } ; season } ; hop { filter_eq { all_rows ; season ; 2011 } ; season } } = true
Output: select the rows whose season record fuzzily matches to 2012 . take the season record of this row . select the rows whose season record fuzzily matches to 2011 . take the season record of this row . the first record is greater than the second record .
round_eq { avg { filter_eq { all_rows ; location attendance ; at & t center } ; location attendance } ; 18581 } = true
Output: select the rows whose location attendance record fuzzily matches to at  t center . the average of the location attendance record of these rows is 18581 .
eq { count { filter_eq { all_rows ; college ; notre dame } } ; 2 } = true
Output: select the rows whose college record fuzzily matches to notre dame . the number of such rows is 2 .
and { eq { nth_min { all_rows ; year ; 2 } ; 1994 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; men 's singles } ; juraj brestovský } } = true
Output: the 2nd minimum year record of all rows is 1994 . the men s singles record of the row with 2nd minimum year record is juraj brestovský .
and { only { filter_eq { all_rows ; surface ; carpet ( i ) } } ; eq { hop { filter_eq { all_rows ; surface ; carpet ( i ) } ; tournament } ; wolfsburg , germany } } = true
Output: select the rows whose surface record fuzzily matches to carpet  i  . there is only one such row in the table . the tournament record of this unqiue row is wolfsburg , germany .
greater { hop { filter_eq { all_rows ; interview subject ; tobey maguire } ; date } ; hop { filter_eq { all_rows ; interview subject ; mike piazza } ; date } } = true
Output: select the rows whose interview subject record fuzzily matches to tobey maguire . take the date record of this row . select the rows whose interview subject record fuzzily matches to mike piazza . take the date record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; opponent ; new york islanders } } ; 3 } = true
Output: select the rows whose opponent record fuzzily matches to new york islanders . the number of such rows is 3 .
and { eq { nth_max { all_rows ; position ; 2 } ; 10th } ; eq { hop { nth_argmax { all_rows ; position ; 2 } ; competition } ; olympic games } } = true
Output: the 2nd maximum position record of all rows is 10th . the competition record of the row with 2nd maximum position record is olympic games .
eq { hop { nth_argmax { all_rows ; first elected ; 2 } ; incumbent } ; lane evans } = true
Output: select the row whose first elected record of all rows is 2nd maximum . the incumbent record of this row is lane evans .
most_eq { filter_eq { all_rows ; series finale ; present } ; weekly schedule ; monday to friday } = true
Output: select the rows whose series finale record fuzzily matches to present . for the weekly schedule records of these rows , most of them fuzzily match to monday to friday .
eq { count { filter_eq { filter_eq { all_rows ; date successor seated ; vacant until the next congress } ; vacator ; ( d ) } } ; 3 } = true
Output: select the rows whose date successor seated record fuzzily matches to vacant until the next congress . among these rows , select the rows whose vacator record fuzzily matches to  d  . the number of such rows is 3 .
round_eq { sum { all_rows ; us viewers ( million ) } ; 7.9 } = true
Output: the sum of the us viewers  million  record of all rows is 7.9 .
eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; city of license } ; tulia , texas } = true
Output: select the row whose frequency mhz record of all rows is 2nd maximum . the city of license record of this row is tulia , texas .
round_eq { avg { all_rows ; aspect } ; 4:3 } = true
Output: the average of the aspect record of all rows is 4:3 .
all_eq { all_rows ; result ; re-elected } = true
Output: for the result records of all rows , all of them fuzzily match to reelected .
less { hop { filter_eq { all_rows ; player ; eddie keher } ; rank } ; hop { filter_eq { all_rows ; player ; philly grimes } ; rank } } = true
Output: select the rows whose player record fuzzily matches to eddie keher . take the rank record of this row . select the rows whose player record fuzzily matches to philly grimes . take the rank record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; name ; eclair } ; launched } ; hop { filter_eq { all_rows ; name ; derwent } ; launched } } = true
Output: select the rows whose name record fuzzily matches to eclair . take the launched record of this row . select the rows whose name record fuzzily matches to derwent . take the launched record of this row . the first record is greater than the second record .
most_eq { all_rows ; station type ; relay } = true
Output: for the station type records of all rows , most of them fuzzily match to relay .
and { only { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school } ; benedictine university } } = true
Output: select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 . there is only one such row in the table . the school record of this unqiue row is benedictine university .
and { only { filter_eq { all_rows ; score ; l } } ; eq { hop { filter_eq { all_rows ; score ; l } ; team } ; la lakers } } = true
Output: select the rows whose score record fuzzily matches to l . there is only one such row in the table . the team record of this unqiue row is la lakers .
and { only { filter_eq { all_rows ; directed by ; james quinn } } ; eq { hop { filter_eq { all_rows ; directed by ; james quinn } ; title } ; indifference } } = true
Output: select the rows whose directed by record fuzzily matches to james quinn . there is only one such row in the table . the title record of this unqiue row is indifference .
greater { hop { filter_eq { all_rows ; player ; adam voges } ; runs } ; hop { filter_eq { all_rows ; player ; callum ferguson } ; runs } } = true
Output: select the rows whose player record fuzzily matches to adam voges . take the runs record of this row . select the rows whose player record fuzzily matches to callum ferguson . take the runs record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; original air date ; december } } ; 2 } = true
Output: select the rows whose original air date record fuzzily matches to december . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; date ; april 2 } ; location attendance } ; hop { filter_eq { all_rows ; date ; april 13 } ; location attendance } } = true
Output: select the rows whose date record fuzzily matches to april 2 . take the location attendance record of this row . select the rows whose date record fuzzily matches to april 13 . take the location attendance record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; time } ; 8:17.94 } = true
Output: the average of the time record of all rows is 8:17.94 .
eq { count { filter_eq { all_rows ; format ; oldies } } ; 4 } = true
Output: select the rows whose format record fuzzily matches to oldies . the number of such rows is 4 .
eq { hop { argmax { all_rows ; years for jazz } ; player } ; kirk snyder } = true
Output: select the row whose years for jazz record of all rows is maximum . the player record of this row is kirk snyder .
greater { hop { filter_eq { all_rows ; pitcher ; toad ramsey } ; strikeouts } ; hop { filter_eq { all_rows ; pitcher ; old hoss radbourn } ; strikeouts } } = true
Output: select the rows whose pitcher record fuzzily matches to toad ramsey . take the strikeouts record of this row . select the rows whose pitcher record fuzzily matches to old hoss radbourn . take the strikeouts record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; winner 's share } ; 355169.4 } = true
Output: the average of the winner s share record of all rows is 355169.4 .
round_eq { avg { all_rows ; points } ; 21.2 } = true
Output: the average of the points record of all rows is 21.2 .
all_eq { all_rows ; conditions of access ; visa - free } = true
Output: for the conditions of access records of all rows , all of them fuzzily match to visa  free .
all_eq { all_rows ; date ; 8 july 1933 } = true
Output: for the date records of all rows , all of them fuzzily match to 8 july 1933 .
eq { hop { argmax { all_rows ; production num } ; title } ; bosko 's fox hunt } = true
Output: select the row whose production num record of all rows is maximum . the title record of this row is bosko s fox hunt .
eq { count { filter_eq { all_rows ; position in 2001 ; first league } } ; 2 } = true
Output: select the rows whose position in 2001 record fuzzily matches to first league . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; date ; november } } ; 16 } = true
Output: select the rows whose date record fuzzily matches to november . the number of such rows is 16 .
eq { count { filter_eq { all_rows ; college ; miami ( fla ) } } ; 2 } = true
Output: select the rows whose college record fuzzily matches to miami  fla  . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; week } ; 8 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the week record of this row is 8 .
round_eq { sum { filter_eq { all_rows ; result ; final } ; jury votes } ; 29 } = true
Output: select the rows whose result record fuzzily matches to final . the sum of the jury votes record of these rows is 29 .
less { hop { filter_eq { all_rows ; season ; 2009 } ; giant slalom } ; hop { filter_eq { all_rows ; season ; 2011 } ; giant slalom } } = true
Output: select the rows whose season record fuzzily matches to 2009 . take the giant slalom record of this row . select the rows whose season record fuzzily matches to 2011 . take the giant slalom record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; games } ; 26.8 } = true
Output: the average of the games record of all rows is 26.8 .
eq { hop { argmin { all_rows ; laid down } ; name } ; chanticleer } = true
Output: select the row whose laid down record of all rows is minimum . the name record of this row is chanticleer .
eq { count { filter_eq { all_rows ; score ; w } } ; 6 } = true
Output: select the rows whose score record fuzzily matches to w . the number of such rows is 6 .
eq { count { filter_eq { all_rows ; country ; england } } ; 4 } = true
Output: select the rows whose country record fuzzily matches to england . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; points against ; 2 } ; club } ; maesteg rfc } = true
Output: select the row whose points against record of all rows is 2nd maximum . the club record of this row is maesteg rfc .
eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent in the final } ; víctor pecci } = true
Output: select the row whose date record of all rows is 2nd maximum . the opponent in the final record of this row is víctor pecci .
eq { count { filter_eq { all_rows ; constellation ; centaurus } } ; 4 } = true
Output: select the rows whose constellation record fuzzily matches to centaurus . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; location ; usa } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to usa . the number of such rows is 2 .
only { filter_eq { all_rows ; team 2 ; d3 } } = true
Output: select the rows whose team 2 record fuzzily matches to d3 . there is only one such row in the table .
and { only { filter_eq { all_rows ; score ; 0 - 0 } } ; and { eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; home team } ; port vale } ; eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; away team } ; watford } } } = true
Output: select the rows whose score record fuzzily matches to 0  0 . there is only one such row in the table . the home team record of this unqiue row is port vale . the away team record of this unqiue row is watford .
eq { hop { argmax { filter_eq { all_rows ; state ; illinois } ; enrollment } ; founded } ; 1887 } = true
Output: select the rows whose state record fuzzily matches to illinois . select the row whose enrollment record of these rows is maximum . the founded record of this row is 1887 .
and { less { hop { filter_eq { all_rows ; title ; big time babysitting } ; original us air date } ; hop { filter_eq { all_rows ; title ; big time gold } ; original us air date } } ; and { eq { hop { filter_eq { all_rows ; title ; big time babysitting } ; original us air date } ; october 13 , 2012 } ; eq { hop { filter_eq { all_rows ; title ; big time gold } ; original us air date } ; october 20 , 2012 } } } = true
Output: select the rows whose title record fuzzily matches to big time babysitting . take the original us air date record of this row . select the rows whose title record fuzzily matches to big time gold . take the original us air date record of this row . the first record is less than the second record . the original us air date record of the first row is october 13 , 2012 . the original us air date record of the second row is october 20 , 2012 .
greater { hop { filter_eq { all_rows ; incumbent ; curt weldon } ; opponent } ; hop { filter_eq { all_rows ; incumbent ; john murtha } ; opponent } } = true
Output: select the rows whose incumbent record fuzzily matches to curt weldon . take the opponent record of this row . select the rows whose incumbent record fuzzily matches to john murtha . take the opponent record of this row . the first record is greater than the second record .
eq { count { filter_eq { filter_eq { all_rows ; venue ; queen 's park oval , port of spain } ; date ; 2006 } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to queen s park oval , port of spain . among these rows , select the rows whose date record fuzzily matches to 2006 . the number of such rows is 2 .
round_eq { sum { all_rows ; attendance } ; 764271 } = true
Output: the sum of the attendance record of all rows is 764271 .
eq { count { filter_eq { all_rows ; opponent ; toronto maple leafs } } ; 3 } = true
Output: select the rows whose opponent record fuzzily matches to toronto maple leafs . the number of such rows is 3 .
only { filter_eq { all_rows ; incumbent ; vacant } } = true
Output: select the rows whose incumbent record fuzzily matches to vacant . there is only one such row in the table .
and { only { filter_greater { all_rows ; revenues ; 10000 koku } } ; eq { hop { filter_greater { all_rows ; revenues ; 10000 koku } ; name } ; ōoka tadatomo ( 大岡忠與 ) } } = true
Output: select the rows whose revenues record is greater than 10000 koku . there is only one such row in the table . the name record of this unqiue row is ōoka tadatomo  大岡忠與  .
eq { count { filter_eq { all_rows ; partner ; margaret court } } ; 6 } = true
Output: select the rows whose partner record fuzzily matches to margaret court . the number of such rows is 6 .
and { only { filter_eq { all_rows ; date ; 1872 } } ; eq { hop { filter_eq { all_rows ; date ; 1872 } ; number } ; 11 } } = true
Output: select the rows whose date record is equal to 1872 . there is only one such row in the table . the number record of this unqiue row is 11 .
eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; march 13 } = true
Output: select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is march 13 .
and { only { filter_eq { all_rows ; result ; retired jacksonian hold } } ; eq { hop { filter_eq { all_rows ; result ; retired jacksonian hold } ; incumbent } ; john floyd } } = true
Output: select the rows whose result record fuzzily matches to retired jacksonian hold . there is only one such row in the table . the incumbent record of this unqiue row is john floyd .
eq { hop { nth_argmax { all_rows ; transfer fee ; 2 } ; name } ; snodgrass } = true
Output: select the row whose transfer fee record of all rows is 2nd maximum . the name record of this row is snodgrass .
most_less_eq { all_rows ; pages ; 300 } = true
Output: for the pages records of all rows , most of them are less than or equal to 300 .
and { only { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } } ; eq { hop { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } ; home team } ; collingwood } } = true
Output: select the rows whose crowd record is greater than 20000 . among these rows , 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 .
and { eq { hop { argmin { all_rows ; place } ; artist } ; liam reilly } ; eq { hop { argmin { all_rows ; place } ; song } ; somewhere in europe } } = true
Output: select the row whose place record of all rows is minimum . the artist record of this row is liam reilly . the song record of this row is somewhere in europe .
only { filter_eq { all_rows ; womens singles ; zhou mi } } = true
Output: select the rows whose womens singles record fuzzily matches to zhou mi . there is only one such row in the table .
eq { count { filter_all { all_rows ; player } } ; 7 } = true
Output: select the rows whose player record is arbitrary . the number of such rows is 7 .
eq { hop { argmax { all_rows ; candidates } ; incumbent } ; joe moakley } = true
Output: select the row whose candidates record of all rows is maximum . the incumbent record of this row is joe moakley .
and { only { filter_eq { all_rows ; college ; ohio state } } ; eq { hop { filter_eq { all_rows ; college ; ohio state } ; player } ; kirk barton } } = true
Output: select the rows whose college record fuzzily matches to ohio state . there is only one such row in the table . the player record of this unqiue row is kirk barton .
eq { hop { nth_argmax { all_rows ; length ; 2 } ; line name } ; north auckland line } = true
Output: select the row whose length record of all rows is 2nd maximum . the line name record of this row is north auckland line .
eq { count { filter_eq { all_rows ; platform ; wii } } ; 5 } = true
Output: select the rows whose platform record fuzzily matches to wii . the number of such rows is 5 .
eq { diff { hop { filter_eq { all_rows ; player ; scott garlick } ; games } ; hop { filter_eq { all_rows ; player ; dj countess } ; games } } ; 4 } = true
Output: select the rows whose player record fuzzily matches to scott garlick . take the games record of this row . select the rows whose player record fuzzily matches to dj countess . take the games record of this row . the first record is 4 larger than the second record .
eq { hop { argmin { all_rows ; score } ; player } ; tze - chung chen } = true
Output: select the row whose score record of all rows is minimum . the player record of this row is tze  chung chen .
eq { hop { argmax { all_rows ; crowd } ; home team } ; south dragons } = true
Output: select the row whose crowd record of all rows is maximum . the home team record of this row is south dragons .
most_eq { all_rows ; high points ; tim duncan } = true
Output: for the high points records of all rows , most of them fuzzily match to tim duncan .
and { only { filter_less { all_rows ; points ; 260 } } ; eq { hop { filter_less { all_rows ; points ; 260 } ; name } ; andreas kofler } } = true
Output: select the rows whose points record is less than 260 . there is only one such row in the table . the name record of this unqiue row is andreas kofler .
most_eq { all_rows ; joined ; 2010 } = true
Output: for the joined records of all rows , most of them are equal to 2010 .
and { only { filter_eq { all_rows ; final - rank ; dnq } } ; eq { hop { filter_eq { all_rows ; final - rank ; dnq } ; event } ; uneven bars } } = true
Output: select the rows whose final  rank record fuzzily matches to dnq . there is only one such row in the table . the event record of this unqiue row is uneven bars .
round_eq { avg { all_rows ; barrel length } ; 16.5 inches } = true
Output: the average of the barrel length record of all rows is 16.5 inches .
eq { hop { argmin { filter_greater { all_rows ; seats contested ; 40 } ; no of votes } ; party } ; bharatiya janata party } = true
Output: select the rows whose seats contested record is greater than 40 . select the row whose no of votes record of these rows is minimum . the party record of this row is bharatiya janata party .
eq { hop { argmin { all_rows ; date } ; venue } ; texas stadium } = true
Output: select the row whose date record of all rows is minimum . the venue record of this row is texas stadium .
eq { count { filter_eq { all_rows ; career win - loss ; 3 - 5 } } ; 2 } = true
Output: select the rows whose career win  loss record fuzzily matches to 3  5 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; vessel ; smeaton array } ; max speed } ; hop { filter_eq { all_rows ; vessel ; marianarray } ; max speed } } = true
Output: select the rows whose vessel record fuzzily matches to smeaton array . take the max speed record of this row . select the rows whose vessel record fuzzily matches to marianarray . take the max speed record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; time } ; 1:00.26 } = true
Output: the average of the time record of all rows is 1:00.26 .
greater { hop { filter_eq { all_rows ; class ; 6ai } ; quantity } ; hop { filter_eq { all_rows ; class ; 6d } ; quantity } } = true
Output: select the rows whose class record fuzzily matches to 6ai . take the quantity record of this row . select the rows whose class record fuzzily matches to 6d . take the quantity record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; race ; 28 } } ; 2 } = true
Output: select the rows whose race record is equal to 28 . the number of such rows is 2 .
eq { hop { nth_argmin { filter_eq { all_rows ; written by ; glenn leopold } ; no in series ; 1 } ; original air date } ; september 23 , 1995 } = true
Output: select the rows whose written by record fuzzily matches to glenn leopold . select the row whose no in series record of these rows is 1st minimum . the original air date record of this row is september 23 , 1995 .
most_eq { all_rows ; location ; three rivers stadium } = true
Output: for the location records of all rows , most of them fuzzily match to three rivers stadium .
eq { hop { nth_argmin { all_rows ; scoring average ; 3 } ; year } ; 2011 } = true
Output: select the row whose scoring average record of all rows is 3rd minimum . the year record of this row is 2011 .
most_greater_eq { filter_greater { all_rows ; date ; 9 march 1985 } ; score ; 1 } = true
Output: select the rows whose date record is greater than 9 march 1985 . for the score records of these rows , most of them are greater than or equal to 1 .
round_eq { avg { filter_eq { all_rows ; home team ; melbourne } ; crowd } ; 15750 } = true
Output: select the rows whose home team record fuzzily matches to melbourne . the average of the crowd record of these rows is 15750 .
eq { count { filter_eq { all_rows ; 1st ship delivery date ; may 1942 } } ; 3 } = true
Output: select the rows whose 1st ship delivery date record fuzzily matches to may 1942 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; venue ; johannesburg } } ; 6 } = true
Output: select the rows whose venue record fuzzily matches to johannesburg . the number of such rows is 6 .
eq { hop { argmax { all_rows ; weeks at 1 } ; artist } ; antonn , kareen } = true
Output: select the row whose weeks at 1 record of all rows is maximum . the artist record of this row is antonn , kareen .
eq { count { filter_eq { all_rows ; session ; qualifying } } ; 2 } = true
Output: select the rows whose session record fuzzily matches to qualifying . the number of such rows is 2 .
and { only { filter_eq { all_rows ; location attendance ; wachovia center } } ; eq { hop { filter_eq { all_rows ; location attendance ; wachovia center } ; game } ; 10 } } = true
Output: select the rows whose location attendance record fuzzily matches to wachovia center . there is only one such row in the table . the game record of this unqiue row is 10 .
greater { hop { filter_eq { all_rows ; opponent ; ryan thomas } ; time } ; hop { filter_eq { all_rows ; opponent ; stryder fann } ; time } } = true
Output: select the rows whose opponent record fuzzily matches to ryan thomas . take the time record of this row . select the rows whose opponent record fuzzily matches to stryder fann . take the time record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; profit ( mil ) } ; 121.66 } = true
Output: the average of the profit  mil  record of all rows is 121.66 .