niryuu/sni-each-converted huggingface dataset:
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; george m grant } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is george m grant .
most_eq { all_rows ; venue ; estadio cuscatlán , san salvador , el salvador } = true
Output: for the venue records of all rows , most of them fuzzily match to estadio cuscatlán , san salvador , el salvador .
eq { hop { nth_argmax { all_rows ; points 1 ; 2 } ; team } ; eastwood hanley } = true
Output: select the row whose points 1 record of all rows is 2nd maximum . the team record of this row is eastwood hanley .
greater { hop { filter_eq { all_rows ; team ; new york } ; score } ; hop { filter_eq { all_rows ; team ; detroit } ; score } } = true
Output: select the rows whose team record fuzzily matches to new york . take the score record of this row . select the rows whose team record fuzzily matches to detroit . take the score record of this row . the first record is greater than the second record .
round_eq { avg { filter_eq { all_rows ; model number ; 800 } ; frequency } ; 800 } = true
Output: select the rows whose model number record fuzzily matches to 800 . the average of the frequency record of these rows is 800 .
round_eq { sum { filter_not_eq { all_rows ; pos ; dnf } ; laps } ; 1359 } = true
Output: select the rows whose pos record does not match to dnf . the sum of the laps record of these rows is 1359 .
greater { hop { filter_eq { all_rows ; no in season ; 1 / 2 } ; us viewers ( million ) } ; hop { filter_eq { all_rows ; no in season ; 16 } ; us viewers ( million ) } } = true
Output: select the rows whose no in season record fuzzily matches to 1  2 . take the us viewers  million  record of this row . select the rows whose no in season record fuzzily matches to 16 . take the us viewers  million  record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; erp w ; 8 } } ; eq { hop { filter_eq { all_rows ; erp w ; 8 } ; call sign } ; k251ah } } = true
Output: select the rows whose erp w record is equal to 8 . there is only one such row in the table . the call sign record of this unqiue row is k251ah .
and { only { filter_eq { all_rows ; transfer fee ; 120k } } ; eq { hop { filter_eq { all_rows ; transfer fee ; 120k } ; name } ; christou } } = true
Output: select the rows whose transfer fee record fuzzily matches to 120k . there is only one such row in the table . the name record of this unqiue row is christou .
most_eq { all_rows ; country ; united states } = true
Output: for the country records of all rows , most of them fuzzily match to united states .
eq { count { filter_all { all_rows ; name } } ; 18 } = true
Output: select the rows whose name record is arbitrary . the number of such rows is 18 .
round_eq { avg { all_rows ; weeks on top } ; 3 } = true
Output: the average of the weeks on top record of all rows is 3 .
and { only { filter_eq { filter_eq { all_rows ; syndicate ; america 3 foundation } ; yacht ; kanza } } ; eq { hop { filter_eq { filter_eq { all_rows ; syndicate ; america 3 foundation } ; yacht ; kanza } ; sail } ; usa - 28 } } = true
Output: select the rows whose syndicate record fuzzily matches to america 3 foundation . among these rows , select the rows whose yacht record fuzzily matches to kanza . there is only one such row in the table . the sail record of this unqiue row is usa  28 .
eq { count { filter_eq { all_rows ; leading scorer ; tony parker } } ; 4 } = true
Output: select the rows whose leading scorer record fuzzily matches to tony parker . the number of such rows is 4 .
round_eq { avg { all_rows ; 2001 - 02 } ; 2907 } = true
Output: the average of the 2001  02 record of all rows is 2907 .
round_eq { avg { all_rows ; points } ; 70 } = true
Output: the average of the points record of all rows is 70 .
round_eq { sum { all_rows ; canadian chapters } ; 48 } = true
Output: the sum of the canadian chapters record of all rows is 48 .
most_eq { all_rows ; winning team ; iowa state } = true
Output: for the winning team records of all rows , most of them fuzzily match to iowa state .
eq { hop { nth_argmax { all_rows ; away team score ; 2 } ; home team } ; collingwood } = true
Output: select the row whose away team score record of all rows is 2nd maximum . the home team record of this row is collingwood .
round_eq { avg { all_rows ; laps } ; 259 } = true
Output: the average of the laps record of all rows is 259 .
and { only { filter_eq { all_rows ; series name ; 3000 pro series } } ; eq { hop { filter_eq { all_rows ; series name ; 3000 pro series } ; season } ; 2005 } } = true
Output: select the rows whose series name record fuzzily matches to 3000 pro series . there is only one such row in the table . the season record of this unqiue row is 2005 .
eq { count { filter_eq { all_rows ; country ; united states } } ; 5 } = true
Output: select the rows whose country record fuzzily matches to united states . the number of such rows is 5 .
and { only { filter_not_eq { all_rows ; candidates ; unopposed } } ; eq { hop { filter_not_eq { all_rows ; candidates ; unopposed } ; incumbent } ; hale boggs } } = true
Output: select the rows whose candidates record does not match to unopposed . there is only one such row in the table . the incumbent record of this unqiue row is hale boggs .
eq { count { filter_eq { all_rows ; first issue ; august 2008 } } ; 5 } = true
Output: select the rows whose first issue record fuzzily matches to august 2008 . the number of such rows is 5 .
eq { count { filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 20,000 } } ; 2 } = true
Output: select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 20,000 . the number of such rows is 2 .
round_eq { avg { all_rows ; podiums } ; 2.89 } = true
Output: the average of the podiums record of all rows is 2.89 .
and { eq { max { all_rows ; primary ( 6 - 13 years ) } ; 95.41 } ; eq { hop { argmax { all_rows ; primary ( 6 - 13 years ) } ; region } ; o'higgins } } = true
Output: the maximum primary  6  13 years  record of all rows is 95.41 . the region record of the row with superlative primary  6  13 years  record is ohiggins .
and { only { filter_eq { all_rows ; opponent ; leicester city } } ; eq { hop { filter_eq { all_rows ; opponent ; leicester city } ; date } ; 30 january 2000 } } = true
Output: select the rows whose opponent record fuzzily matches to leicester city . there is only one such row in the table . the date record of this unqiue row is 30 january 2000 .
most_less { filter_greater { all_rows ; home team score ; 10 } ; crowd ; 20000 } = true
Output: select the rows whose home team score record is greater than 10 . for the crowd records of these rows , most of them are less than 20000 .
and { only { filter_eq { all_rows ; first elected ; 1958 } } ; eq { hop { filter_eq { all_rows ; first elected ; 1958 } ; incumbent } ; del latta } } = true
Output: select the rows whose first elected record is equal to 1958 . there is only one such row in the table . the incumbent record of this unqiue row is del latta .
greater { hop { filter_eq { all_rows ; institution ; mercy college } ; enrollment } ; hop { filter_eq { all_rows ; institution ; dowling college } ; enrollment } } = true
Output: select the rows whose institution record fuzzily matches to mercy college . take the enrollment record of this row . select the rows whose institution record fuzzily matches to dowling college . take the enrollment record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; rank ; 1 } } ; 2 } = true
Output: select the rows whose rank record is equal to 1 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; bowling ; 17 - 48 } } ; eq { hop { filter_eq { all_rows ; bowling ; 17 - 48 } ; player } ; colin blythe } } = true
Output: select the rows whose bowling record fuzzily matches to 17  48 . there is only one such row in the table . the player record of this unqiue row is colin blythe .
all_eq { all_rows ; country ; united states } = true
Output: for the country records of all rows , all of them fuzzily match to united states .
eq { diff { hop { filter_eq { all_rows ; player ; craig stadler } ; money } ; hop { filter_eq { all_rows ; player ; fred couples } ; money } } ; 5863 } = true
Output: select the rows whose player record fuzzily matches to craig stadler . take the money record of this row . select the rows whose player record fuzzily matches to fred couples . take the money record of this row . the first record is 5863 larger than the second record .
round_eq { avg { all_rows ; result } ; 24.69 } = true
Output: the average of the result record of all rows is 24.69 .
eq { count { filter_eq { all_rows ; air date ; january } } ; 4 } = true
Output: select the rows whose air date record fuzzily matches to january . the number of such rows is 4 .
eq { hop { argmin { all_rows ; first aired } ; entrepreneur ( s ) } ; james seddon } = true
Output: select the row whose first aired record of all rows is minimum . the entrepreneur  s  record of this row is james seddon .
most_eq { all_rows ; label ; parlophone } = true
Output: for the label records of all rows , most of them fuzzily match to parlophone .
round_eq { avg { all_rows ; raiders points } ; 26.79 } = true
Output: the average of the raiders points record of all rows is 26.79 .
eq { count { filter_all { all_rows ; player } } ; 5 } = true
Output: select the rows whose player record is arbitrary . the number of such rows is 5 .
round_eq { avg { all_rows ; finish position } ; 42.8 } = true
Output: the average of the finish position record of all rows is 42.8 .
eq { count { filter_eq { all_rows ; week of ; 2 october } } ; 4 } = true
Output: select the rows whose week of record fuzzily matches to 2 october . the number of such rows is 4 .
eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 2 } } ; 2 } = true
Output: select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 2 . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; incumbent ; gene taylor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; harold volkmer } ; first elected } } ; -4 years } = true
Output: select the rows whose incumbent record fuzzily matches to gene taylor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to harold volkmer . take the first elected record of this row . the second record is 4 years larger than the first record .
all_eq { all_rows ; date ; january } = true
Output: for the date records of all rows , all of them fuzzily match to january .
and { only { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } } ; eq { hop { filter_less { filter_eq { all_rows ; adopted ; 1999 } ; population ( 2011 - 01 - 01 ) ; 1000000 } ; state } ; luxembourg } } = true
Output: select the rows whose adopted record fuzzily matches to 1999 . among these rows , select the rows whose population  2011  01  01  record is less than 1000000 . there is only one such row in the table . the state record of this unqiue row is luxembourg .
eq { count { filter_eq { all_rows ; relationship ; mac taylor } } ; 2 } = true
Output: select the rows whose relationship record fuzzily matches to mac taylor . the number of such rows is 2 .
most_eq { all_rows ; laps ; 47 } = true
Output: for the laps records of all rows , most of them are equal to 47 .
eq { count { filter_all { all_rows ; series ep } } ; 13 } = true
Output: select the rows whose series ep record is arbitrary . the number of such rows is 13 .
eq { max { filter_eq { all_rows ; outcome ; winner } ; date } ; 26 september 2004 } = true
Output: select the rows whose outcome record fuzzily matches to winner . the maximum date record of these rows is 26 september 2004 .
eq { count { filter_eq { all_rows ; roads intersected ; route 53 } } ; 1 } = true
Output: select the rows whose roads intersected record fuzzily matches to route 53 . the number of such rows is 1 .
eq { hop { nth_argmax { all_rows ; runs ; 2 } ; partnerships } ; herschelle gibbs / justin kemp } = true
Output: select the row whose runs record of all rows is 2nd maximum . the partnerships record of this row is herschelle gibbs  justin kemp .
eq { hop { nth_argmax { all_rows ; yards ; 2 } ; season } ; 2009 } = true
Output: select the row whose yards record of all rows is 2nd maximum . the season record of this row is 2009 .
and { only { filter_eq { filter_eq { all_rows ; years ; k - 6 } ; suburb / town ; villawood } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; k - 6 } ; suburb / town ; villawood } ; founded } ; 1955 } } = true
Output: select the rows whose years record fuzzily matches to k  6 . among these rows , select the rows whose suburb  town record fuzzily matches to villawood . there is only one such row in the table . the founded record of this unqiue row is 1955 .
round_eq { sum { all_rows ; touchdowns } ; 14 } = true
Output: the sum of the touchdowns record of all rows is 14 .
and { only { filter_less { filter_eq { all_rows ; leading scorer ; lebron james } ; attendance ; 15000 } } ; eq { hop { filter_less { filter_eq { all_rows ; leading scorer ; lebron james } ; attendance ; 15000 } ; date } ; february 20 } } = true
Output: select the rows whose leading scorer record fuzzily matches to lebron james . among these rows , select the rows whose attendance record is less than 15000 . there is only one such row in the table . the date record of this unqiue row is february 20 .
eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; to catch a jedi } = true
Output: select the row whose us viewers  million  record of all rows is maximum . the title record of this row is to catch a jedi .
most_eq { all_rows ; object type ; globular cluster } = true
Output: for the object type records of all rows , most of them fuzzily match to globular cluster .
eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; october 1 , 1989 } = true
Output: select the row whose attendance record of all rows is 3rd maximum . the date record of this row is october 1 , 1989 .
eq { hop { argmax { all_rows ; agg } ; team 1 } ; manzini wanderers } = true
Output: select the row whose agg record of all rows is maximum . the team 1 record of this row is manzini wanderers .
eq { count { filter_eq { all_rows ; ram ( mib ) ; 64 } } ; 11 } = true
Output: select the rows whose ram  mib  record is equal to 64 . the number of such rows is 11 .
most_eq { all_rows ; category ; best actress in a musical } = true
Output: for the category records of all rows , most of them fuzzily match to best actress in a musical .
and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player } ; jack nicklaus } } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is jack nicklaus .
and { only { filter_eq { all_rows ; entrant ; jolly club spa } } ; eq { hop { filter_eq { all_rows ; entrant ; jolly club spa } ; engine } ; motori moderni tipo 615 - 90 1.5 v6 t } } = true
Output: select the rows whose entrant record fuzzily matches to jolly club spa . there is only one such row in the table . the engine record of this unqiue row is motori moderni tipo 615  90 1.5 v6 t .
eq { count { filter_eq { all_rows ; location ; the vertices of the core } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to the vertices of the core . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; incumbent ; robert l f sikes } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; donald ray matthews } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to robert l f sikes . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to donald ray matthews . take the first elected record of this row . the first record is less than the second record .
eq { count { filter_all { all_rows ; original air date } } ; 9 } = true
Output: select the rows whose original air date record is arbitrary . the number of such rows is 9 .
all_eq { all_rows ; game ; 14 } = true
Output: for the game records of all rows , all of them are equal to 14 .
and { eq { nth_max { all_rows ; years for jazz ; 3 } ; 1982 - 85 } ; eq { hop { nth_argmax { all_rows ; years for jazz ; 3 } ; player } ; john drew } } = true
Output: the 3rd maximum years for jazz record of all rows is 1982  85 . the player record of the row with 3rd maximum years for jazz record is john drew .
greater { hop { filter_eq { all_rows ; episode ; episode 4 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; episode ; episode 2 } ; viewers ( millions ) } } = true
Output: select the rows whose episode record fuzzily matches to episode 4 . take the viewers  millions  record of this row . select the rows whose episode record fuzzily matches to episode 2 . take the viewers  millions  record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; overall } ; 125.1 } = true
Output: the average of the overall record of all rows is 125.1 .
round_eq { avg { filter_eq { all_rows ; score ; l } ; score } ; 90.25 } = true
Output: select the rows whose score record fuzzily matches to l . the average of the score record of these rows is 90.25 .
round_eq { avg { all_rows ; score } ; 74.8 } = true
Output: the average of the score record of all rows is 74.8 .
eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; home team } ; horsham } = true
Output: select the row whose attendance record of all rows is 3rd maximum . the home team record of this row is horsham .
eq { count { filter_eq { all_rows ; tournament ; australia } } ; 5 } = true
Output: select the rows whose tournament record fuzzily matches to australia . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; bronze ; 1 } } ; 3 } = true
Output: select the rows whose bronze record is equal to 1 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; number in service } ; class } ; dc } = true
Output: select the row whose number in service record of all rows is maximum . the class record of this row is dc .
eq { hop { argmax { all_rows ; number of episodes } ; english title ( chinese title ) } ; secret of the heart 天地豪情 } = true
Output: select the row whose number of episodes record of all rows is maximum . the english title  chinese title  record of this row is secret of the heart 天地豪情 .
eq { hop { nth_argmax { all_rows ; surface run off ( km 3 / year ) ; 3 } ; region } ; huetar norte } = true
Output: select the row whose surface run off  km 3  year  record of all rows is 3rd maximum . the region record of this row is huetar norte .
eq { count { filter_eq { all_rows ; directed by ; dearbhla walsh } } ; 3 } = true
Output: select the rows whose directed by record fuzzily matches to dearbhla walsh . the number of such rows is 3 .
most_eq { all_rows ; result ; won } = true
Output: for the result records of all rows , most of them fuzzily match to won .
less { hop { filter_eq { all_rows ; opponent ; vitor belfort } ; time } ; hop { filter_eq { all_rows ; opponent ; jim mullen } ; time } } = true
Output: select the rows whose opponent record fuzzily matches to vitor belfort . take the time record of this row . select the rows whose opponent record fuzzily matches to jim mullen . take the time record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; opponent in the final ; phil taylor } } ; 3 } = true
Output: select the rows whose opponent in the final record fuzzily matches to phil taylor . the number of such rows is 3 .
most_eq { all_rows ; competition ; friendly } = true
Output: for the competition records of all rows , most of them fuzzily match to friendly .
and { only { filter_eq { all_rows ; transmission ; l110f cvt } } ; eq { hop { filter_eq { all_rows ; transmission ; l110f cvt } ; engine type } ; 5.0 l hybrid v8 } } = true
Output: select the rows whose transmission record fuzzily matches to l110f cvt . there is only one such row in the table . the engine type record of this unqiue row is 5.0 l hybrid v8 .
and { only { filter_less { all_rows ; year opened ; 1954 } } ; eq { hop { filter_less { all_rows ; year opened ; 1954 } ; facility } ; monroe correctional complex ( mcc ) } } = true
Output: select the rows whose year opened record is less than 1954 . there is only one such row in the table . the facility record of this unqiue row is monroe correctional complex  mcc  .
eq { count { filter_greater { all_rows ; episode count ; 80 } } ; 3 } = true
Output: select the rows whose episode count record is greater than 80 . the number of such rows is 3 .
round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 68.67 } = true
Output: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 68.67 .
and { only { filter_eq { all_rows ; written by ; stan berkowitz } } ; eq { hop { filter_eq { all_rows ; written by ; stan berkowitz } ; title } ; simple } } = true
Output: select the rows whose written by record fuzzily matches to stan berkowitz . there is only one such row in the table . the title record of this unqiue row is simple .
and { only { filter_eq { all_rows ; date ; 2013 } } ; eq { hop { filter_eq { all_rows ; date ; 2013 } ; tournament } ; oklahoma city , usa f9 } } = true
Output: select the rows whose date record fuzzily matches to 2013 . there is only one such row in the table . the tournament record of this unqiue row is oklahoma city , usa f9 .
round_eq { sum { all_rows ; current } ; 10 } = true
Output: the sum of the current record of all rows is 10 .
and { only { filter_eq { all_rows ; nationality ; sweden } } ; eq { hop { filter_eq { all_rows ; nationality ; sweden } ; player } ; jacob markstrom } } = true
Output: select the rows whose nationality record fuzzily matches to sweden . there is only one such row in the table . the player record of this unqiue row is jacob markstrom .
eq { count { filter_greater { filter_not_eq { all_rows ; nationality ; ger } ; points ; 257.0 } } ; 2 } = true
Output: select the rows whose nationality record does not match to ger . among these rows , select the rows whose points record is greater than 257.0 . the number of such rows is 2 .
round_eq { avg { all_rows ; score } ; 5 } = true
Output: the average of the score record of all rows is 5 .
round_eq { avg { all_rows ; area ( km square ) } ; 110,129 } = true
Output: the average of the area  km square  record of all rows is 110,129 .
eq { hop { nth_argmax { all_rows ; year established ; 2 } ; members } ; university of otago } = true
Output: select the row whose year established record of all rows is 2nd maximum . the members record of this row is university of otago .
most_eq { all_rows ; decision ; mclean } = true
Output: for the decision records of all rows , most of them fuzzily match to mclean .
most_eq { all_rows ; high points ; lebron james } = true
Output: for the high points records of all rows , most of them fuzzily match to lebron james .
eq { hop { nth_argmax { all_rows ; crowd ; 4 } ; home team } ; essendon } = true
Output: select the row whose crowd record of all rows is 4th maximum . the home team record of this row is essendon .
eq { count { filter_eq { all_rows ; traction type ; steam } } ; 2 } = true
Output: select the rows whose traction type record fuzzily matches to steam . the number of such rows is 2 .
most_greater { all_rows ; transfer fee ( millions ) ; 26 } = true
Output: for the transfer fee  millions  records of all rows , most of them are greater than 26 .
all_eq { all_rows ; date ; 9 august 1952 } = true
Output: for the date records of all rows , all of them fuzzily match to 9 august 1952 .
less { hop { filter_eq { all_rows ; nominated work ; pal joey } ; year } ; hop { filter_eq { all_rows ; nominated work ; marlene } ; year } } = true
Output: select the rows whose nominated work record fuzzily matches to pal joey . take the year record of this row . select the rows whose nominated work record fuzzily matches to marlene . take the year record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; event ; espn great outdoor games } } ; 3 } = true
Output: select the rows whose event record fuzzily matches to espn great outdoor games . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; ; 14 } ; viewers } ; hop { filter_eq { all_rows ; ; 15 } ; viewers } } = true
Output: select the rows whose record fuzzily matches to 14 . take the viewers record of this row . select the rows whose record fuzzily matches to 15 . take the viewers record of this row . the first record is greater than the second record .
eq { count { filter_all { all_rows ; date } } ; 6 } = true
Output: select the rows whose date record is arbitrary . the number of such rows is 6 .
eq { hop { argmin { all_rows ; first elected } ; incumbent } ; percy e quin } = true
Output: select the row whose first elected record of all rows is minimum . the incumbent record of this row is percy e quin .
round_eq { sum { filter_eq { all_rows ; result ; safe } ; score } ; 54 } = true
Output: select the rows whose result record fuzzily matches to safe . the sum of the score record of these rows is 54 .
eq { count { filter_eq { all_rows ; base ; united states } } ; 2 } = true
Output: select the rows whose base record fuzzily matches to united states . the number of such rows is 2 .
most_eq { all_rows ; nationality ; united states } = true
Output: for the nationality records of all rows , most of them fuzzily match to united states .
eq { count { filter_all { all_rows ; meas num } } ; 11 } = true
Output: select the rows whose meas num record is arbitrary . the number of such rows is 11 .
eq { hop { argmax { all_rows ; length } ; version } ; obsessed club mix } = true
Output: select the row whose length record of all rows is maximum . the version record of this row is obsessed club mix .
round_eq { avg { all_rows ; margin ( pts ) } ; 27.25 } = true
Output: the average of the margin  pts  record of all rows is 27.25 .
and { less { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } } ; and { eq { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; 16 } ; eq { hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } ; 34 } } } = true
Output: select the rows whose name record fuzzily matches to historic federal reserve bank . take the floors record of this row . select the rows whose name record fuzzily matches to kansas city power and light building . take the floors record of this row . the first record is less than the second record . the floors record of the first row is 16 . the floors record of the second row is 34 .
all_eq { all_rows ; surface ; clay } = true
Output: for the surface records of all rows , all of them fuzzily match to clay .
and { only { filter_eq { all_rows ; men 's singles ; andrew smith } } ; eq { hop { filter_eq { all_rows ; men 's singles ; andrew smith } ; year } ; 2006 } } = true
Output: select the rows whose men s singles record fuzzily matches to andrew smith . there is only one such row in the table . the year record of this unqiue row is 2006 .
eq { hop { argmax { filter_eq { all_rows ; institution ; baylor university } ; enrollment } ; affiliation } ; private / baptist } = true
Output: select the rows whose institution record fuzzily matches to baylor university . select the row whose enrollment record of these rows is maximum . the affiliation record of this row is private  baptist .
only { filter_eq { filter_eq { all_rows ; surface ; clay } ; outcome ; winner } } = true
Output: select the rows whose surface record fuzzily matches to clay . among these rows , select the rows whose outcome record fuzzily matches to winner . there is only one such row in the table .
most_eq { all_rows ; eurozone ; y } = true
Output: for the eurozone records of all rows , most of them fuzzily match to y .
round_eq { avg { all_rows ; attendance } ; 21,596 } = true
Output: the average of the attendance record of all rows is 21,596 .
eq { count { filter_all { all_rows ; episode no } } ; 13 } = true
Output: select the rows whose episode no record is arbitrary . the number of such rows is 13 .
eq { hop { argmax { all_rows ; points } ; song } ; la det swinge } = true
Output: select the row whose points record of all rows is maximum . the song record of this row is la det swinge .
eq { count { filter_eq { all_rows ; nationality ; germany } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to germany . the number of such rows is 2 .
and { eq { max { all_rows ; result } ; 4 - 2 } ; eq { hop { argmax { all_rows ; result } ; venue } ; estadio monumental , santiago , chile } } = true
Output: the maximum result record of all rows is 4  2 . the venue record of the row with superlative result record is estadio monumental , santiago , chile .
most_eq { all_rows ; original beechwood bunny tale / source material ; teleplay by valérie baranski } = true
Output: for the original beechwood bunny tale  source material records of all rows , most of them fuzzily match to teleplay by valérie baranski .
eq { diff { hop { filter_eq { all_rows ; mens singles ; alan budi kusuma } ; year } ; hop { filter_eq { all_rows ; mens singles ; hermawan susanto } ; year } } ; -1 year } = true
Output: select the rows whose mens singles record fuzzily matches to alan budi kusuma . take the year record of this row . select the rows whose mens singles record fuzzily matches to hermawan susanto . take the year record of this row . the second record is 1 year larger than the first record .
eq { hop { argmax { all_rows ; loss } ; institution } ; georgia tech yellow jackets } = true
Output: select the row whose loss record of all rows is maximum . the institution record of this row is georgia tech yellow jackets .
eq { count { filter_eq { all_rows ; visitor ; chicago black hawks } } ; 3 } = true
Output: select the rows whose visitor record fuzzily matches to chicago black hawks . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; first elected ; 195 } } ; 2 } = true
Output: select the rows whose first elected record fuzzily matches to 195 . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; category ; best overseas new artist } ; year } ; hop { filter_eq { all_rows ; category ; songs of the year } ; year } } = true
Output: select the rows whose category record fuzzily matches to best overseas new artist . take the year record of this row . select the rows whose category record fuzzily matches to songs of the year . take the year record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; classification ; fraternity & sorority } } ; 2 } = true
Output: select the rows whose classification record fuzzily matches to fraternity  sorority . the number of such rows is 2 .
eq { hop { argmax { all_rows ; the championship } ; player } ; becchio } = true
Output: select the row whose the championship record of all rows is maximum . the player record of this row is becchio .
eq { count { filter_eq { all_rows ; losses ; 8 } } ; 2 } = true
Output: select the rows whose losses record is equal to 8 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; score ; 5 } } ; and { eq { hop { filter_eq { all_rows ; score ; 5 } ; home team } ; sheffield wednesday } ; eq { hop { filter_eq { all_rows ; score ; 5 } ; away team } ; oldham athletic } } } = true
Output: select the rows whose score record fuzzily matches to 5 . there is only one such row in the table . the home team record of this unqiue row is sheffield wednesday . the away team record of this unqiue row is oldham athletic .
eq { count { filter_eq { all_rows ; location ; penang , malaysia } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to penang , malaysia . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; coach ; steve mcnamara } ; captain ; paul deacon } } ; 3 } = true
Output: select the rows whose coach record fuzzily matches to steve mcnamara . among these rows , select the rows whose captain record fuzzily matches to paul deacon . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; augusta , united states } = true
Output: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is augusta , united states .
greater { hop { filter_eq { all_rows ; date ; 1988 } ; score } ; hop { filter_eq { all_rows ; date ; 1986 } ; score } } = true
Output: select the rows whose date record fuzzily matches to 1988 . take the score record of this row . select the rows whose date record fuzzily matches to 1986 . take the score record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; faces } ; 66 } = true
Output: the average of the faces record of all rows is 66 .
eq { hop { argmin { all_rows ; enrollment } ; school } ; cloverdale } = true
Output: select the row whose enrollment record of all rows is minimum . the school record of this row is cloverdale .
most_eq { all_rows ; team ; rothmans honda } = true
Output: for the team records of all rows , most of them fuzzily match to rothmans honda .
all_not_eq { all_rows ; replaced by ; - } = true
Output: for the replaced by records of all rows , none of them is equal to  .
round_eq { avg { all_rows ; attendance } ; 2900 } = true
Output: the average of the attendance record of all rows is 2900 .
eq { diff { hop { filter_eq { all_rows ; school ; leo } ; year joined } ; hop { filter_eq { all_rows ; school ; south adams } ; year joined } } ; -20 years } = true
Output: select the rows whose school record fuzzily matches to leo . take the year joined record of this row . select the rows whose school record fuzzily matches to south adams . take the year joined record of this row . the second record is 20 years larger than the first record .
eq { hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; location } ; portage } = true
Output: select the row whose enrollment 08  09 record of all rows is 2nd maximum . the location record of this row is portage .
round_eq { avg { all_rows ; enrollment } ; 738 } = true
Output: the average of the enrollment record of all rows is 738 .
eq { hop { filter_eq { all_rows ; date ; june 11 } ; high assists } ; hop { filter_eq { all_rows ; date ; june 10 } ; high assists } } = true
Output: select the rows whose date record fuzzily matches to june 11 . take the high assists record of this row . select the rows whose date record fuzzily matches to june 10 . take the high assists record of this row . the first record fuzzily matches to the second record .
eq { hop { argmax { all_rows ; agg } ; team 1 } ; al - ittihad } = true
Output: select the row whose agg record of all rows is maximum . the team 1 record of this row is al  ittihad .
eq { count { filter_all { all_rows ; title } } ; 8 } = true
Output: select the rows whose title record is arbitrary . the number of such rows is 8 .
round_eq { avg { all_rows ; crowd } ; 13000 } = true
Output: the average of the crowd record of all rows is 13000 .
eq { max { filter_eq { all_rows ; builder ; rh & dr } ; year built } ; c1949 } = true
Output: select the rows whose builder record fuzzily matches to rh  dr . the maximum year built record of these rows is c1949 .
eq { count { filter_eq { all_rows ; league ; 7 } } ; 2 } = true
Output: select the rows whose league record is equal to 7 . the number of such rows is 2 .
most_eq { filter_eq { all_rows ; tyre ; e } ; constructor ; ferrari } = true
Output: select the rows whose tyre record fuzzily matches to e . for the constructor records of these rows , most of them fuzzily match to ferrari .
all_eq { all_rows ; timeslot ; wednesday 8:30 pm } = true
Output: for the timeslot records of all rows , all of them fuzzily match to wednesday 8:30 pm .
eq { hop { argmax { all_rows ; crowd } ; venue } ; princes park } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is princes park .
eq { count { filter_eq { all_rows ; venue ; colombo } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to colombo . the number of such rows is 2 .
eq { count { filter_all { all_rows ; name } } ; 10 } = true
Output: select the rows whose name record is arbitrary . the number of such rows is 10 .
most_eq { all_rows ; score ; l } = true
Output: for the score records of all rows , most of them fuzzily match to l .
most_greater { all_rows ; loans received ; 0 } = true
Output: for the loans received records of all rows , most of them are greater than 0 .
eq { hop { argmax { all_rows ; in service } ; aircraft } ; bell uh - 1 iroquois } = true
Output: select the row whose in service record of all rows is maximum . the aircraft record of this row is bell uh  1 iroquois .
most_eq { all_rows ; first game ; 1991 } = true
Output: for the first game records of all rows , most of them are equal to 1991 .
only { filter_eq { all_rows ; engine ; judd av } } = true
Output: select the rows whose engine record fuzzily matches to judd av . there is only one such row in the table .
and { only { filter_eq { all_rows ; guest ; garry trudeau } } ; eq { hop { filter_eq { all_rows ; guest ; garry trudeau } ; episode } ; 806 } } = true
Output: select the rows whose guest record fuzzily matches to garry trudeau . there is only one such row in the table . the episode record of this unqiue row is 806 .
all_greater_eq { all_rows ; rebounds ; 100 } = true
Output: for the rebounds records of all rows , all of them are greater than or equal to 100 .
most_greater { all_rows ; age ; 18 } = true
Output: for the age records of all rows , most of them are greater than 18 .
eq { hop { argmax { all_rows ; score } ; date } ; june 18 , 2008 } = true
Output: select the row whose score record of all rows is maximum . the date record of this row is june 18 , 2008 .
eq { hop { nth_argmin { all_rows ; pick ; 2 } ; player } ; blake machan } = true
Output: select the row whose pick record of all rows is 2nd minimum . the player record of this row is blake machan .
and { only { filter_eq { filter_eq { all_rows ; score ; 70 } ; country ; denmark } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; 70 } ; country ; denmark } ; player } ; søren kjeldsen } } = true
Output: select the rows whose score record is equal to 70 . among these rows , select the rows whose country record fuzzily matches to denmark . there is only one such row in the table . the player record of this unqiue row is søren kjeldsen .
most_eq { all_rows ; high points ; tony parker } = true
Output: for the high points records of all rows , most of them fuzzily match to tony parker .
and { greater { hop { filter_eq { all_rows ; model ; herbert } ; poweramp } ; hop { filter_eq { all_rows ; model ; vh4s } ; poweramp } } ; and { eq { hop { filter_eq { all_rows ; model ; herbert } ; poweramp } ; mono , 6 tubes } ; eq { hop { filter_eq { all_rows ; model ; vh4s } ; poweramp } ; stereo , 4 tubes } } } = true
Output: select the rows whose model record fuzzily matches to herbert . take the poweramp record of this row . select the rows whose model record fuzzily matches to vh4s . take the poweramp record of this row . the first record is greater than the second record . the poweramp record of the first row is mono , 6 tubes . the poweramp record of the second row is stereo , 4 tubes .
eq { count { filter_eq { all_rows ; draws ; 7 } } ; 4 } = true
Output: select the rows whose draws record is equal to 7 . the number of such rows is 4 .
most_eq { all_rows ; score ; w } = true
Output: for the score records of all rows , most of them fuzzily match to w .
most_eq { all_rows ; surface ; grass } = true
Output: for the surface records of all rows , most of them fuzzily match to grass .
eq { count { filter_eq { all_rows ; team ; portland } } ; 2 } = true
Output: select the rows whose team record fuzzily matches to portland . the number of such rows is 2 .
most_less { all_rows ; frequency ; 2 ghz } = true
Output: for the frequency records of all rows , most of them are less than 2 ghz .
all_eq { all_rows ; fat16 ; yes } = true
Output: for the fat16 records of all rows , all of them fuzzily match to yes .
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park } = true
Output: select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .
round_eq { avg { all_rows ; area ( km square ) } ; 198.448 } = true
Output: the average of the area  km square  record of all rows is 198.448 .
greater { hop { filter_eq { all_rows ; event ; rock & blues festival } ; acts } ; hop { filter_eq { all_rows ; event ; a day at the races } ; acts } } = true
Output: select the rows whose event record fuzzily matches to rock  blues festival . take the acts record of this row . select the rows whose event record fuzzily matches to a day at the races . take the acts record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; length } ; version } ; music video } = true
Output: select the row whose length record of all rows is maximum . the version record of this row is music video .
most_eq { all_rows ; first leg ; 0 } = true
Output: for the first leg records of all rows , most of them are equal to 0 .
and { only { filter_greater { all_rows ; enrollment ; 3000 } } ; eq { hop { filter_greater { all_rows ; enrollment ; 3000 } ; school } ; lake central } } = true
Output: select the rows whose enrollment record is greater than 3000 . there is only one such row in the table . the school record of this unqiue row is lake central .
round_eq { sum { all_rows ; money } ; $ 3,164,543 } = true
Output: the sum of the money record of all rows is  3,164,543 .
and { eq { max { all_rows ; yield ( megatons ) } ; 50 } ; eq { hop { argmax { all_rows ; yield ( megatons ) } ; date ( gmt ) } ; october 30 , 1961 } } = true
Output: the maximum yield  megatons  record of all rows is 50 . the date  gmt  record of the row with superlative yield  megatons  record is october 30 , 1961 .
round_eq { sum { filter_greater_eq { all_rows ; rank ; 4 } ; total } ; 5 } = true
Output: select the rows whose rank record is greater than or equal to 4 . the sum of the total record of these rows is 5 .
less { hop { filter_eq { all_rows ; candidate ; alfred dobbs } ; year } ; hop { filter_eq { all_rows ; candidate ; thomas mitchell } ; year } } = true
Output: select the rows whose candidate record fuzzily matches to alfred dobbs . take the year record of this row . select the rows whose candidate record fuzzily matches to thomas mitchell . take the year record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; commenced operations ; 2 } ; airline } ; nouvelair } = true
Output: select the row whose commenced operations record of all rows is 2nd minimum . the airline record of this row is nouvelair .
eq { diff { hop { filter_eq { all_rows ; player ; troy bell } ; years for grizzlies } ; hop { filter_eq { all_rows ; player ; ronnie brewer } ; years for grizzlies } } ; -7 years } = true
Output: select the rows whose player record fuzzily matches to troy bell . take the years for grizzlies record of this row . select the rows whose player record fuzzily matches to ronnie brewer . take the years for grizzlies record of this row . the second record is 7 years larger than the first record .
round_eq { avg { filter_less_eq { all_rows ; to par ; -1 } ; score } ; 284.2 } = true
Output: select the rows whose to par record is less than or equal to 1 . the average of the score record of these rows is 284.2 .
most_eq { all_rows ; date ; october } = true
Output: for the date records of all rows , most of them fuzzily match to october .
eq { hop { nth_argmin { filter_eq { all_rows ; game site ; rca dome } ; date ; 4 } ; attendance } ; 56860 } = true
Output: select the rows whose game site record fuzzily matches to rca dome . select the row whose date record of these rows is 4th minimum . the attendance record of this row is 56860 .
eq { count { filter_eq { filter_eq { all_rows ; res ; win } ; event ; extreme challenge } } ; 2 } = true
Output: select the rows whose res record fuzzily matches to win . among these rows , select the rows whose event record fuzzily matches to extreme challenge . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; footscray } ; home team score } } = true
Output: select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to footscray . take the home team score record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; venue ; princes park } } ; and { eq { hop { filter_eq { all_rows ; venue ; princes park } ; home team } ; carlton } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; geelong } } } = true
Output: select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table . the home team record of this unqiue row is carlton . the away team record of this unqiue row is geelong .
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 ; location attendance ; quicken loans arena } } ; eq { hop { filter_eq { all_rows ; location attendance ; quicken loans arena } ; date } ; november 3 } } = true
Output: select the rows whose location attendance record fuzzily matches to quicken loans arena . there is only one such row in the table . the date record of this unqiue row is november 3 .
eq { count { filter_greater_eq { all_rows ; average attendance as % of capacity ; 90 } } ; 5 } = true
Output: select the rows whose average attendance as  of capacity record is greater than or equal to 90 . the number of such rows is 5 .
eq { nth_max { all_rows ; attendance ; 2 } ; 55400 } = true
Output: the 2nd maximum attendance record of all rows is 55400 .
round_eq { avg { all_rows ; goals } ; 113 } = true
Output: the average of the goals record of all rows is 113 .
most_eq { all_rows ; party ; republican } = true
Output: for the party records of all rows , most of them fuzzily match to republican .
eq { hop { argmax { all_rows ; laps } ; year } ; 2002 } = true
Output: select the row whose laps record of all rows is maximum . the year record of this row is 2002 .
most_eq { filter_eq { all_rows ; surface ; hard } ; outcome ; winner } = true
Output: select the rows whose surface record fuzzily matches to hard . for the outcome records of these rows , most of them fuzzily match to winner .
round_eq { avg { all_rows ; score } ; 100 } = true
Output: the average of the score record of all rows is 100 .
eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 4 } ; attendance } ; 45511 } = true
Output: select the rows whose result record fuzzily matches to w . select the row whose date record of these rows is 4th minimum . the attendance record of this row is 45511 .
and { eq { hop { argmax { all_rows ; crowd } ; home team } ; carlton } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; richmond } } = true
Output: select the row whose crowd record of all rows is maximum . the home team record of this row is carlton . the away team record of this row is richmond .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; saturday , april 10 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is saturday , april 10 .
eq { count { filter_eq { all_rows ; country ; russia } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to russia . the number of such rows is 2 .
round_eq { avg { all_rows ; home team score } ; 12.09 } = true
Output: the average of the home team score record of all rows is 12.09 .