niryuu/sni-each-converted huggingface dataset:
eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; bahrain national stadium , manama } = true
Output: select the row whose date record of all rows is 2nd minimum . the venue record of this row is bahrain national stadium , manama .
eq { count { filter_all { all_rows ; race title } } ; 5 } = true
Output: select the rows whose race title record is arbitrary . the number of such rows is 5 .
round_eq { avg { filter_eq { all_rows ; us mission ; ranger } ; mass ( kg ) } ; 340 } = true
Output: select the rows whose us mission record fuzzily matches to ranger . the average of the mass  kg  record of these rows is 340 .
eq { hop { nth_argmax { all_rows ; nicky ; 2 } ; couple } ; melinda & fred } = true
Output: select the row whose nicky record of all rows is 2nd maximum . the couple record of this row is melinda  fred .
eq { hop { nth_argmax { all_rows ; notes ; 3 } ; year } ; 2004 } = true
Output: select the row whose notes record of all rows is 3rd maximum . the year record of this row is 2004 .
and { only { filter_eq { all_rows ; result ; 4-0 } } ; eq { hop { filter_eq { all_rows ; result ; 4-0 } ; year } ; 2006 } } = true
Output: select the rows whose result record fuzzily matches to 40 . there is only one such row in the table . the year record of this unqiue row is 2006 .
less { hop { filter_eq { all_rows ; title ; christmas time again } ; billboard peak } ; hop { filter_eq { all_rows ; title ; edge of forever } ; billboard peak } } = true
Output: select the rows whose title record fuzzily matches to christmas time again . take the billboard peak record of this row . select the rows whose title record fuzzily matches to edge of forever . take the billboard peak record of this row . the first record is less than the second record .
round_eq { sum { filter_less { all_rows ; decile ; 8 } ; roll } ; 770 } = true
Output: select the rows whose decile record is less than 8 . the sum of the roll record of these rows is 770 .
eq { count { filter_eq { all_rows ; position ; centre - left } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to centre  left . the number of such rows is 2 .
and { only { filter_eq { all_rows ; original air date ; january } } ; eq { hop { filter_eq { all_rows ; original air date ; january } ; title } ; checking out } } = true
Output: select the rows whose original air date record fuzzily matches to january . there is only one such row in the table . the title record of this unqiue row is checking out .
and { only { filter_eq { all_rows ; commissioned ; 1931 } } ; eq { hop { filter_eq { all_rows ; commissioned ; 1931 } ; name } ; persée } } = true
Output: select the rows whose commissioned record fuzzily matches to 1931 . there is only one such row in the table . the name record of this unqiue row is persée .
eq { count { filter_eq { all_rows ; result ; won } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to won . the number of such rows is 2 .
round_eq { avg { all_rows ; scored } ; 1.17 } = true
Output: the average of the scored record of all rows is 1.17 .
round_eq { avg { all_rows ; apparent magnitude } ; 12.24 } = true
Output: the average of the apparent magnitude record of all rows is 12.24 .
round_eq { avg { all_rows ; goals against } ; 24 } = true
Output: the average of the goals against record of all rows is 24 .
round_eq { sum { filter_eq { all_rows ; high assists ; damon stoudamire } ; high assists } ; 95 } = true
Output: select the rows whose high assists record fuzzily matches to damon stoudamire . the sum of the high assists record of these rows is 95 .
eq { count { filter_eq { filter_eq { all_rows ; game site ; qualcomm stadium } ; time ; 5:15 pm } } ; 3 } = true
Output: select the rows whose game site record fuzzily matches to qualcomm stadium . among these rows , select the rows whose time record fuzzily matches to 5:15 pm . the number of such rows is 3 .
eq { nth_min { filter_eq { all_rows ; team ; new jersey } ; date ; 1 } ; october 5 } = true
Output: select the rows whose team record fuzzily matches to new jersey . the 1st minimum date record of these rows is october 5 .
eq { count { filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 20,000 } } ; 2 } = true
Output: select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 20,000 . the number of such rows is 2 .
most_eq { all_rows ; winning team ; mobilecast impul } = true
Output: for the winning team records of all rows , most of them fuzzily match to mobilecast impul .
most_less { all_rows ; no of installments ; 100 } = true
Output: for the no of installments records of all rows , most of them are less than 100 .
round_eq { avg { all_rows ; races } ; 12 } = true
Output: the average of the races record of all rows is 12 .
eq { hop { nth_argmax { filter_less { all_rows ; away team score ; 10 } ; crowd ; 2 } ; venue } ; victoria park } = true
Output: select the rows whose away team score record is less than 10 . select the row whose crowd record of these rows is 2nd maximum . the venue record of this row is victoria park .
most_eq { all_rows ; political party ; democratic party } = true
Output: for the political party records of all rows , most of them fuzzily match to democratic party .
eq { hop { nth_argmin { all_rows ; from ; 1 } ; player } ; jim pollard } = true
Output: select the row whose from record of all rows is 1st minimum . the player record of this row is jim pollard .
eq { count { filter_eq { all_rows ; goals against ; 39 } } ; 3 } = true
Output: select the rows whose goals against record is equal to 39 . the number of such rows is 3 .
and { only { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } } ; eq { hop { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } ; engine type } ; nk - 33 rocket engine } } = true
Output: select the rows whose sfc in lb   lbf h  record is greater than 10 . there is only one such row in the table . the engine type record of this unqiue row is nk  33 rocket engine .
round_eq { avg { all_rows ; julian clary } ; 8 } = true
Output: the average of the julian clary record of all rows is 8 .
and { eq { nth_min { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; april 23 , 2010 } ; eq { hop { nth_argmin { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; title } ; the day that everything changed } } = true
Output: select the rows whose directed by record fuzzily matches to sam montes . the 1st minimum original airdate record of these rows is april 23 , 2010 . the title record of the row with 1st minimum original airdate record is the day that everything changed .
eq { count { filter_eq { all_rows ; chassis ; lola t100 } } ; 2 } = true
Output: select the rows whose chassis record fuzzily matches to lola t100 . the number of such rows is 2 .
round_eq { avg { all_rows ; vuelta } ; 1.67 } = true
Output: the average of the vuelta record of all rows is 1.67 .
and { only { filter_eq { all_rows ; country ; united states } } ; eq { hop { filter_eq { all_rows ; country ; united states } ; label } ; astralwerks } } = true
Output: select the rows whose country record fuzzily matches to united states . there is only one such row in the table . the label record of this unqiue row is astralwerks .
round_eq { sum { all_rows ; crowd } ; 174 , 580 } = true
Output: the sum of the crowd record of all rows is 174 , 580 .
and { only { filter_eq { all_rows ; engine ; chevrolet } } ; eq { hop { filter_eq { all_rows ; engine ; chevrolet } ; year } ; 1992 } } = true
Output: select the rows whose engine record fuzzily matches to chevrolet . there is only one such row in the table . the year record of this unqiue row is 1992 .
most_greater_eq { all_rows ; points 1 ; 30 } = true
Output: for the points 1 records of all rows , most of them are greater than or equal to 30 .
greater { hop { filter_eq { all_rows ; place ; pietersburg } ; population } ; hop { filter_eq { all_rows ; place ; alldays } ; population } } = true
Output: select the rows whose place record fuzzily matches to pietersburg . take the population record of this row . select the rows whose place record fuzzily matches to alldays . take the population record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; position ; 1st } } ; 6 } = true
Output: select the rows whose position record fuzzily matches to 1st . the number of such rows is 6 .
and { only { filter_greater { all_rows ; innings ; 27 } } ; eq { hop { filter_greater { all_rows ; innings ; 27 } ; player } ; marcus trescothick } } = true
Output: select the rows whose innings record is greater than 27 . there is only one such row in the table . the player record of this unqiue row is marcus trescothick .
and { eq { max { all_rows ; money } ; 2400 } ; eq { hop { argmax { all_rows ; money } ; player } ; jimmy demaret } } = true
Output: the maximum money record of all rows is 2400 . the player record of the row with superlative money record is jimmy demaret .
and { only { filter_eq { all_rows ; original artist ; the beatles } } ; eq { hop { filter_eq { all_rows ; original artist ; the beatles } ; song choice } ; you ca n't do that } } = true
Output: select the rows whose original artist record fuzzily matches to the beatles . there is only one such row in the table . the song choice record of this unqiue row is you ca nt do that .
round_eq { avg { all_rows ; goals } ; 63.78 } = true
Output: the average of the goals record of all rows is 63.78 .
eq { hop { argmin { all_rows ; attendance } ; opponent } ; buffalo bills } = true
Output: select the row whose attendance record of all rows is minimum . the opponent record of this row is buffalo bills .
eq { count { filter_eq { all_rows ; first elected ; 1882 } } ; 3 } = true
Output: select the rows whose first elected record is equal to 1882 . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; date ; august 10 } ; attendance } ; hop { filter_eq { all_rows ; date ; august 4 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to august 10 . take the attendance record of this row . select the rows whose date record fuzzily matches to august 4 . take the attendance record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; nation ; united states } } ; 5 } = true
Output: select the rows whose nation record fuzzily matches to united states . the number of such rows is 5 .
greater { hop { filter_eq { all_rows ; candidate ( party ) ; joseph estrada ( pmp ) } ; spent per voter ( php ) } ; hop { filter_eq { all_rows ; candidate ( party ) ; jejomar binay ( pdp - laban ) } ; spent per voter ( php ) } } = true
Output: select the rows whose candidate  party  record fuzzily matches to joseph estrada  pmp  . take the spent per voter  php  record of this row . select the rows whose candidate  party  record fuzzily matches to jejomar binay  pdp  laban  . take the spent per voter  php  record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; points jury } ; 235 } = true
Output: the sum of the points jury record of all rows is 235 .
most_eq { filter_eq { all_rows ; points ; 6 } ; lost ; 6 } = true
Output: select the rows whose points record is equal to 6 . for the lost records of these rows , most of them are equal to 6 .
eq { hop { nth_argmax { all_rows ; year ; 2 } ; womens singles } ; wang yihan } = true
Output: select the row whose year record of all rows is 2nd maximum . the womens singles record of this row is wang yihan .
and { only { filter_eq { all_rows ; manner of departure ; signed by blackburn rovers ( mutual consent ) } } ; eq { hop { filter_eq { all_rows ; manner of departure ; signed by blackburn rovers ( mutual consent ) } ; outgoing manager } ; paul ince } } = true
Output: select the rows whose manner of departure record fuzzily matches to signed by blackburn rovers  mutual consent  . there is only one such row in the table . the outgoing manager record of this unqiue row is paul ince .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; home } ; pistons } = true
Output: select the row whose attendance record of all rows is 1st maximum . the home record of this row is pistons .
and { only { filter_eq { all_rows ; country ; ethiopia } } ; eq { hop { filter_eq { all_rows ; country ; ethiopia } ; player } ; yordanos abay } } = true
Output: select the rows whose country record fuzzily matches to ethiopia . there is only one such row in the table . the player record of this unqiue row is yordanos abay .
eq { hop { argmax { all_rows ; wins } ; constructor } ; citroën total world rally team } = true
Output: select the row whose wins record of all rows is maximum . the constructor record of this row is citroën total world rally team .
and { only { filter_eq { all_rows ; year ; 2011 } } ; eq { hop { filter_eq { all_rows ; year ; 2011 } ; title of movie } ; already famous 《 一泡而紅 》 } } = true
Output: select the rows whose year record is equal to 2011 . there is only one such row in the table . the title of movie record of this unqiue row is already famous 《 一泡而紅 》 .
round_eq { avg { all_rows ; crowd } ; 19900 } = true
Output: the average of the crowd record of all rows is 19900 .
eq { count { filter_eq { all_rows ; competition ; friendly match } } ; 5 } = true
Output: select the rows whose competition record fuzzily matches to friendly match . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; home ; detroit } } ; 3 } = true
Output: select the rows whose home record fuzzily matches to detroit . the number of such rows is 3 .
most_greater { filter_eq { all_rows ; date ; october } ; attendance ; 50000 } = true
Output: select the rows whose date record fuzzily matches to october . for the attendance records of these rows , most of them are greater than 50000 .
eq { hop { nth_argmin { all_rows ; original air date ; 2 } ; family / families } ; the peterfreund family } = true
Output: select the row whose original air date record of all rows is 2nd minimum . the family  families record of this row is the peterfreund family .
less { hop { filter_eq { all_rows ; particle ; lambda } ; rest mass mev / c 2 } ; hop { filter_eq { all_rows ; particle ; omega } ; rest mass mev / c 2 } } = true
Output: select the rows whose particle record fuzzily matches to lambda . take the rest mass mev  c 2 record of this row . select the rows whose particle record fuzzily matches to omega . take the rest mass mev  c 2 record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; candidates } ; 70.31 } = true
Output: the average of the candidates record of all rows is 70.31 .
and { eq { min { all_rows ; place } ; 1 } ; eq { hop { argmin { all_rows ; place } ; dancers } ; katja koukkula & jussi väänänen } } = true
Output: the minimum place record of all rows is 1 . the dancers record of the row with superlative place record is katja koukkula  jussi väänänen .
eq { count { filter_eq { all_rows ; to par ; + 2 } } ; 5 } = true
Output: select the rows whose to par record fuzzily matches to + 2 . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; total wins ; 1 } } ; 3 } = true
Output: select the rows whose total wins record is equal to 1 . the number of such rows is 3 .
eq { hop { nth_argmin { filter_eq { all_rows ; tournament ; united kingdom } ; date ; 2 } ; surface } ; hard } = true
Output: select the rows whose tournament record fuzzily matches to united kingdom . select the row whose date record of these rows is 2nd minimum . the surface record of this row is hard .
eq { count { filter_eq { all_rows ; borough or census area ; fairbanks north star } } ; 4 } = true
Output: select the rows whose borough or census area record fuzzily matches to fairbanks north star . the number of such rows is 4 .
all_eq { all_rows ; notes ; high jump } = true
Output: for the notes records of all rows , all of them fuzzily match to high jump .
and { only { filter_eq { all_rows ; entrant ; spirit racing } } ; eq { hop { filter_eq { all_rows ; entrant ; spirit racing } ; year } ; 1983 } } = true
Output: select the rows whose entrant record fuzzily matches to spirit racing . there is only one such row in the table . the year record of this unqiue row is 1983 .
eq { count { filter_eq { filter_eq { all_rows ; edition ; 2005 } ; outcome ; loss } } ; 2 } = true
Output: select the rows whose edition record is equal to 2005 . among these rows , select the rows whose outcome record fuzzily matches to loss . the number of such rows is 2 .
most_greater_eq { all_rows ; frequency mhz ; 90 } = true
Output: for the frequency mhz records of all rows , most of them are greater than or equal to 90 .
eq { count { filter_eq { all_rows ; date ; 2002 } } ; 4 } = true
Output: select the rows whose date record fuzzily matches to 2002 . the number of such rows is 4 .
most_eq { all_rows ; type ; village } = true
Output: for the type records of all rows , most of them fuzzily match to village .
less { hop { filter_eq { all_rows ; peak ; pietrosul rodnei } ; elevation ( m ) } ; hop { filter_eq { all_rows ; peak ; moldoveanu peak } ; elevation ( m ) } } = true
Output: select the rows whose peak record fuzzily matches to pietrosul rodnei . take the elevation  m  record of this row . select the rows whose peak record fuzzily matches to moldoveanu peak . take the elevation  m  record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } } ; eq { hop { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } ; make and model } ; new flyer de35lf } } = true
Output: select the rows whose fuel propulsion record fuzzily matches to diesel  electric hybrid . there is only one such row in the table . the make and model record of this unqiue row is new flyer de35lf .
eq { count { filter_eq { all_rows ; club ; sussex } } ; 3 } = true
Output: select the rows whose club record fuzzily matches to sussex . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; class ; freshman } } ; 3 } = true
Output: select the rows whose class record fuzzily matches to freshman . the number of such rows is 3 .
eq { count { filter_eq { filter_eq { all_rows ; date ; october } ; result ; l } } ; 3 } = true
Output: select the rows whose date record fuzzily matches to october . among these rows , select the rows whose result record fuzzily matches to l . the number of such rows is 3 .
eq { count { filter_greater { all_rows ; height ; 80 } } ; 4 } = true
Output: select the rows whose height record is greater than 80 . the number of such rows is 4 .
and { only { filter_eq { all_rows ; time ; 2:18 } } ; eq { hop { filter_eq { all_rows ; time ; 2:18 } ; nationality } ; germany } } = true
Output: select the rows whose time record fuzzily matches to 2:18 . there is only one such row in the table . the nationality record of this unqiue row is germany .
eq { hop { nth_argmax { all_rows ; date ; 1 } ; opponent } ; frölunda hc gothenburg } = true
Output: select the row whose date record of all rows is 1st maximum . the opponent record of this row is frölunda hc gothenburg .
eq { count { filter_eq { all_rows ; citrate ; negative } } ; 3 } = true
Output: select the rows whose citrate record fuzzily matches to negative . the number of such rows is 3 .
eq { count { filter_greater { filter_eq { all_rows ; studio ; universal } ; gross ; 100000000 } } ; 2 } = true
Output: select the rows whose studio record fuzzily matches to universal . among these rows , select the rows whose gross record is greater than 100000000 . the number of such rows is 2 .
eq { count { filter_greater_eq { all_rows ; shot pct ; 80 % } } ; 3 } = true
Output: select the rows whose shot pct record is greater than or equal to 80  . the number of such rows is 3 .
eq { hop { nth_argmin { filter_eq { all_rows ; fate ; damaged } ; date ; 1 } ; ship } ; llangibby castle } = true
Output: select the rows whose fate record fuzzily matches to damaged . select the row whose date record of these rows is 1st minimum . the ship record of this row is llangibby castle .
and { only { filter_eq { all_rows ; mascot ; eagles } } ; eq { hop { filter_eq { all_rows ; mascot ; eagles } ; school } ; columbia city } } = true
Output: select the rows whose mascot record fuzzily matches to eagles . there is only one such row in the table . the school record of this unqiue row is columbia city .
eq { count { filter_eq { all_rows ; high points ; dorell wright } } ; 2 } = true
Output: select the rows whose high points record fuzzily matches to dorell wright . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; name ; aran } ; tournaments } ; hop { filter_eq { all_rows ; name ; itai } ; tournaments } } ; -1 } = true
Output: select the rows whose name record fuzzily matches to aran . take the tournaments record of this row . select the rows whose name record fuzzily matches to itai . take the tournaments record of this row . the second record is 1 larger than the first record .
eq { hop { argmax { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd } ; venue } ; punt road oval } = true
Output: select the rows whose date record fuzzily matches to 12 june 1937 . select the row whose crowd record of these rows is maximum . the venue record of this row is punt road oval .
and { only { filter_eq { filter_eq { all_rows ; score ; 2 } ; opponent ; minnesota north stars } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; 2 } ; opponent ; minnesota north stars } ; february } ; 1 } } = true
Output: select the rows whose score record fuzzily matches to 2 . among these rows , select the rows whose opponent record fuzzily matches to minnesota north stars . there is only one such row in the table . the february record of this unqiue row is 1 .
all_eq { all_rows ; status ; re-elected } = true
Output: for the status records of all rows , all of them fuzzily match to reelected .
eq { count { filter_eq { all_rows ; location ; minsk } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to minsk . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; production code ; 2 } ; episode } ; 809 } = true
Output: select the row whose production code record of all rows is 2nd maximum . the episode record of this row is 809 .
and { only { filter_eq { all_rows ; high points ; m williams } } ; eq { hop { filter_eq { all_rows ; high points ; m williams } ; date } ; may 2 } } = true
Output: select the rows whose high points record fuzzily matches to m williams . there is only one such row in the table . the date record of this unqiue row is may 2 .
eq { hop { argmax { all_rows ; points defending } ; player } ; martina hingis } = true
Output: select the row whose points defending record of all rows is maximum . the player record of this row is martina hingis .
eq { count { filter_all { all_rows ; location } } ; 7 } = true
Output: select the rows whose location record is arbitrary . the number of such rows is 7 .
eq { count { filter_eq { filter_less { all_rows ; year ; 1990 } ; 250 cc ; sito pons } } ; 3 } = true
Output: select the rows whose year record is less than 1990 . among these rows , select the rows whose 250 cc record fuzzily matches to sito pons . the number of such rows is 3 .
round_eq { avg { all_rows ; losses } ; 3.43 } = true
Output: the average of the losses record of all rows is 3.43 .
all_eq { all_rows ; date ; 10 july 1954 } = true
Output: for the date records of all rows , all of them fuzzily match to 10 july 1954 .
eq { count { filter_eq { all_rows ; points ; 0 } } ; 2 } = true
Output: select the rows whose points record is equal to 0 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; population ( 2011 ) } ; name } ; beaubassin east } = true
Output: select the row whose population  2011  record of all rows is maximum . the name record of this row is beaubassin east .
less { hop { filter_eq { all_rows ; championship ; santiago , chile } ; date } ; hop { filter_eq { all_rows ; championship ; forest hills , us } ; date } } = true
Output: select the rows whose championship record fuzzily matches to santiago , chile . take the date record of this row . select the rows whose championship record fuzzily matches to forest hills , us . take the date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; school / club team ; oklahoma state } } ; eq { hop { filter_eq { all_rows ; school / club team ; oklahoma state } ; player } ; tony allen } } = true
Output: select the rows whose school  club team record fuzzily matches to oklahoma state . there is only one such row in the table . the player record of this unqiue row is tony allen .
eq { count { filter_greater { filter_greater_eq { all_rows ; away team score ; 10 } ; crowd ; 15000 } } ; 3 } = true
Output: select the rows whose away team score record is greater than or equal to 10 . among these rows , select the rows whose crowd record is greater than 15000 . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; home team ; richmond } ; crowd } ; hop { filter_eq { all_rows ; home team ; footscray } ; crowd } } = true
Output: select the rows whose home team record fuzzily matches to richmond . take the crowd record of this row . select the rows whose home team record fuzzily matches to footscray . take the crowd record of this row . the first record is greater than the second record .
most_eq { all_rows ; time ; 2:15 . } = true
Output: for the time records of all rows , most of them fuzzily match to 2:15 . .
round_eq { avg { all_rows ; crowd } ; 16667 } = true
Output: the average of the crowd record of all rows is 16667 .
eq { count { filter_eq { all_rows ; ihsaa class ; aaa } } ; 4 } = true
Output: select the rows whose ihsaa class record fuzzily matches to aaa . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; toxic materials ; yes } } ; 4 } = true
Output: select the rows whose toxic materials record fuzzily matches to yes . the number of such rows is 4 .
less { hop { filter_eq { all_rows ; opponent ; larry reynolds } ; date } ; hop { filter_eq { all_rows ; opponent ; junior pitbull } ; date } } = true
Output: select the rows whose opponent record fuzzily matches to larry reynolds . take the date record of this row . select the rows whose opponent record fuzzily matches to junior pitbull . take the date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; written by ; chuck tatham } } ; eq { hop { filter_eq { all_rows ; written by ; chuck tatham } ; title } ; signed , sealed and intercepted } } = true
Output: select the rows whose written by record fuzzily matches to chuck tatham . there is only one such row in the table . the title record of this unqiue row is signed , sealed and intercepted .
eq { hop { nth_argmax { all_rows ; laps ; 1 } ; year } ; 2008 } = true
Output: select the row whose laps record of all rows is 1st maximum . the year record of this row is 2008 .
most_eq { filter_eq { all_rows ; 1st ship delivery date ; 1942 } ; location ( city , state ) ; california } = true
Output: select the rows whose 1st ship delivery date record fuzzily matches to 1942 . for the location  city , state  records of these rows , most of them fuzzily match to california .
eq { hop { argmin { all_rows ; start } ; year } ; 1954 } = true
Output: select the row whose start record of all rows is minimum . the year record of this row is 1954 .
and { only { filter_eq { all_rows ; origin ; france } } ; eq { hop { filter_eq { all_rows ; origin ; france } ; aircraft } ; aerospatiale as 365 dauphin } } = true
Output: select the rows whose origin record fuzzily matches to france . there is only one such row in the table . the aircraft record of this unqiue row is aerospatiale as 365 dauphin .
eq { count { filter_eq { all_rows ; city or town ; istanbul } } ; 4 } = true
Output: select the rows whose city or town record fuzzily matches to istanbul . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; lost ; 3 } } ; 2 } = true
Output: select the rows whose lost record is equal to 3 . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; year ; 2 } ; album title } ; realism } = true
Output: select the row whose year record of all rows is 2nd maximum . the album title record of this row is realism .
eq { count { filter_eq { all_rows ; born / died ; present } } ; 2 } = true
Output: select the rows whose born  died record fuzzily matches to present . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; placing ; 1 } } ; 5 } = true
Output: select the rows whose placing record is equal to 1 . the number of such rows is 5 .
eq { count { filter_greater { filter_eq { all_rows ; original album ; previously unreleased } ; time ; 4:00 } } ; 2 } = true
Output: select the rows whose original album record fuzzily matches to previously unreleased . among these rows , select the rows whose time record is greater than 4:00 . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; player ; scott simpson } ; to par } ; hop { filter_eq { all_rows ; player ; larry nelson } ; to par } } ; -3 } = true
Output: select the rows whose player record fuzzily matches to scott simpson . take the to par record of this row . select the rows whose player record fuzzily matches to larry nelson . take the to par record of this row . the second record is 3 larger than the first record .
most_eq { all_rows ; round ; final } = true
Output: for the round records of all rows , most of them fuzzily match to final .
round_eq { avg { all_rows ; number of hurricanes } ; 4.3 } = true
Output: the average of the number of hurricanes record of all rows is 4.3 .
eq { hop { nth_argmax { all_rows ; progressive conservative ; 3 } ; date of polling } ; february 12 - march 4 , 2008 } = true
Output: select the row whose progressive conservative record of all rows is 3rd maximum . the date of polling record of this row is february 12  march 4 , 2008 .
most_eq { all_rows ; quarterfinals ; did not advance } = true
Output: for the quarterfinals records of all rows , most of them fuzzily match to did not advance .
eq { count { filter_eq { all_rows ; 2010 ; sf } } ; 2 } = true
Output: select the rows whose 2010 record fuzzily matches to sf . the number of such rows is 2 .
eq { hop { argmin { all_rows ; time } ; rider } ; chas mortimer } = true
Output: select the row whose time record of all rows is minimum . the rider record of this row is chas mortimer .
greater { hop { filter_eq { all_rows ; team ; bourgoin } ; tries for } ; hop { filter_eq { all_rows ; team ; london irish } ; tries for } } = true
Output: select the rows whose team record fuzzily matches to bourgoin . take the tries for record of this row . select the rows whose team record fuzzily matches to london irish . take the tries for record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; nationality ; canada } } ; 4 } = true
Output: select the rows whose nationality record fuzzily matches to canada . the number of such rows is 4 .
all_eq { all_rows ; release date ; q1 , 2007 } = true
Output: for the release date records of all rows , all of them fuzzily match to q1 , 2007 .
eq { count { filter_all { all_rows ; name } } ; 13 } = true
Output: select the rows whose name record is arbitrary . the number of such rows is 13 .
most_eq { all_rows ; 1st ship delivery date ; may 1943 } = true
Output: for the 1st ship delivery date records of all rows , most of them fuzzily match to may 1943 .
round_eq { avg { all_rows ; enrollment } ; 651.8 } = true
Output: the average of the enrollment record of all rows is 651.8 .
round_eq { avg { all_rows ; starts } ; 7.67 } = true
Output: the average of the starts record of all rows is 7.67 .
round_eq { sum { all_rows ; attendance } ; 881037 } = true
Output: the sum of the attendance record of all rows is 881037 .
round_eq { avg { all_rows ; total receipts } ; 12444351 } = true
Output: the average of the total receipts record of all rows is 12444351 .
eq { count { filter_eq { all_rows ; country ; united states } } ; 10 } = true
Output: select the rows whose country record fuzzily matches to united states . the number of such rows is 10 .
eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; november 18 , 1962 } = true
Output: select the row whose attendance record of all rows is 2nd minimum . the date record of this row is november 18 , 1962 .
eq { hop { argmax { all_rows ; public vote } ; couple } ; coleen & stuart } = true
Output: select the row whose public vote record of all rows is maximum . the couple record of this row is coleen  stuart .
all_less { filter_eq { all_rows ; date ; december } ; attendance ; 75000 } = true
Output: select the rows whose date record fuzzily matches to december . for the attendance records of these rows , all of them are less than 75000 .
greater { hop { filter_eq { all_rows ; year ; 1975 } ; stages } ; hop { filter_eq { all_rows ; year ; 1976 } ; stages } } = true
Output: select the rows whose year record fuzzily matches to 1975 . take the stages record of this row . select the rows whose year record fuzzily matches to 1976 . take the stages record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; to par } ; -5.94 } = true
Output: the average of the to par record of all rows is 5.94 .
and { only { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } } ; eq { hop { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } ; duration } ; 2009 - incumbent } } = true
Output: select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam . among these rows , select the rows whose name of mp record fuzzily matches to t r baalu . there is only one such row in the table . the duration record of this unqiue row is 2009  incumbent .
greater { hop { filter_eq { all_rows ; 1973 democratic initial primary ; mario biaggi } ; brooklyn } ; hop { filter_eq { all_rows ; 1973 democratic initial primary ; albert h blumenthal } ; brooklyn } } = true
Output: select the rows whose 1973 democratic initial primary record fuzzily matches to mario biaggi . take the brooklyn record of this row . select the rows whose 1973 democratic initial primary record fuzzily matches to albert h blumenthal . take the brooklyn record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; us viewers ( millions ) } ; title } ; escape from the castle ! } = true
Output: select the row whose us viewers  millions  record of all rows is maximum . the title record of this row is escape from the castle ! .
and { only { filter_eq { all_rows ; club ; balmain tigers } } ; eq { hop { filter_eq { all_rows ; club ; balmain tigers } ; name } ; gavin woods } } = true
Output: select the rows whose club record fuzzily matches to balmain tigers . there is only one such row in the table . the name record of this unqiue row is gavin woods .
most_eq { all_rows ; loser ; kansas city chiefs } = true
Output: for the loser records of all rows , most of them fuzzily match to kansas city chiefs .
eq { count { filter_greater { filter_less { all_rows ; home team score ; 15 } ; crowd ; 15000 } } ; 3 } = true
Output: select the rows whose home team score record is less than 15 . among these rows , select the rows whose crowd record is greater than 15000 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; 2006 ; 1r } } ; eq { hop { filter_eq { all_rows ; 2006 ; 1r } ; tournament } ; australian open } } = true
Output: select the rows whose 2006 record fuzzily matches to 1r . there is only one such row in the table . the tournament record of this unqiue row is australian open .
eq { count { filter_eq { filter_greater { all_rows ; events ; 10 } ; top - 10 ; 2 } } ; 2 } = true
Output: select the rows whose events record is greater than 10 . among these rows , select the rows whose top  10 record is equal to 2 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; points } ; year } ; 2007 - 08 } = true
Output: select the row whose points record of all rows is maximum . the year record of this row is 2007  08 .
most_eq { all_rows ; class ; a } = true
Output: for the class records of all rows , most of them fuzzily match to a .
eq { hop { argmax { all_rows ; magnitude } ; epicenter } ; saravan , iran } = true
Output: select the row whose magnitude record of all rows is maximum . the epicenter record of this row is saravan , iran .
eq { hop { nth_argmax { all_rows ; points ; 1 } ; club } ; deportivo de la coruña } = true
Output: select the row whose points record of all rows is 1st maximum . the club record of this row is deportivo de la coruña .
all_eq { all_rows ; date ; 28 may 1927 } = true
Output: for the date records of all rows , all of them fuzzily match to 28 may 1927 .
round_eq { avg { all_rows ; score } ; 277 } = true
Output: the average of the score record of all rows is 277 .
eq { count { filter_eq { all_rows ; novelty ; gen et sp nov } } ; 4 } = true
Output: select the rows whose novelty record fuzzily matches to gen et sp nov . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; result ; draw } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to draw . the number of such rows is 2 .
all_eq { all_rows ; original us air date ; 2012 } = true
Output: for the original us air date records of all rows , all of them fuzzily match to 2012 .
eq { hop { argmax { all_rows ; population density ( people / km square ) } ; metropolitan area } ; mumbai ( bombay ) } = true
Output: select the row whose population density  people  km square  record of all rows is maximum . the metropolitan area record of this row is mumbai  bombay  .
only { filter_eq { all_rows ; title ; olympic } } = true
Output: select the rows whose title record fuzzily matches to olympic . there is only one such row in the table .
and { only { filter_eq { all_rows ; record ; 3 - 3 } } ; eq { hop { filter_eq { all_rows ; record ; 3 - 3 } ; high points } ; lebron james ( 25 ) } } = true
Output: select the rows whose record record fuzzily matches to 3  3 . there is only one such row in the table . the high points record of this unqiue row is lebron james  25  .
less { hop { filter_eq { all_rows ; song ; retour } ; place } ; hop { filter_eq { all_rows ; song ; waterman } ; place } } = true
Output: select the rows whose song record fuzzily matches to retour . take the place record of this row . select the rows whose song record fuzzily matches to waterman . take the place record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; name ; portland } ; date of commission } ; hop { filter_eq { all_rows ; name ; st albans } ; date of commission } } = true
Output: select the rows whose name record fuzzily matches to portland . take the date of commission record of this row . select the rows whose name record fuzzily matches to st albans . take the date of commission record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; join date ; 2 } ; school } ; lake } = true
Output: select the row whose join date record of all rows is 2nd maximum . the school record of this row is lake .
round_eq { sum { filter_greater_eq { all_rows ; year ; 2002 } ; laps } ; 699 } = true
Output: select the rows whose year record is greater than or equal to 2002 . the sum of the laps record of these rows is 699 .
most_greater { all_rows ; points ; 6 } = true
Output: for the points records of all rows , most of them are greater than 6 .
round_eq { avg { all_rows ; duration } ; 54 } = true
Output: the average of the duration record of all rows is 54 .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; attendance } ; 17621 } = true
Output: select the row whose date record of all rows is 2nd minimum . the attendance record of this row is 17621 .
round_eq { avg { all_rows ; to par } ; -5.9 } = true
Output: the average of the to par record of all rows is 5.9 .
eq { hop { argmax { all_rows ; capacity } ; venue } ; dinamo , minsk } = true
Output: select the row whose capacity record of all rows is maximum . the venue record of this row is dinamo , minsk .
eq { hop { argmax { all_rows ; gold } ; nation } ; united states } = true
Output: select the row whose gold record of all rows is maximum . the nation record of this row is united states .
eq { hop { argmax { all_rows ; viewers ( million ) } ; title } ; no excuses } = true
Output: select the row whose viewers  million  record of all rows is maximum . the title record of this row is no excuses .
eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high assists ; g payton } } ; 3 } = true
Output: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high assists record fuzzily matches to g payton . the number of such rows is 3 .
only { filter_eq { all_rows ; position ; fl } } = true
Output: select the rows whose position record fuzzily matches to fl . there is only one such row in the table .
and { only { filter_eq { all_rows ; party ; republican } } ; eq { hop { filter_eq { all_rows ; party ; republican } ; incumbent } ; e ross adair } } = true
Output: select the rows whose party record fuzzily matches to republican . there is only one such row in the table . the incumbent record of this unqiue row is e ross adair .
eq { count { filter_eq { all_rows ; designer , year ; alister mackenzie } } ; 2 } = true
Output: select the rows whose designer , year record fuzzily matches to alister mackenzie . the number of such rows is 2 .
and { only { filter_eq { all_rows ; title ; ruler } } ; eq { hop { filter_eq { all_rows ; title ; ruler } ; state } ; qin } } = true
Output: select the rows whose title record fuzzily matches to ruler . there is only one such row in the table . the state record of this unqiue row is qin .
round_eq { avg { all_rows ; gold } ; 0.86 } = true
Output: the average of the gold record of all rows is 0.86 .
greater { hop { filter_eq { all_rows ; driver ; erik darnell } ; car } ; hop { filter_eq { all_rows ; driver ; bobby east } ; car } } = true
Output: select the rows whose driver record fuzzily matches to erik darnell . take the car record of this row . select the rows whose driver record fuzzily matches to bobby east . take the car record of this row . the first record is greater than the second record .
only { filter_greater { all_rows ; high points ; ( 35 ) } } = true
Output: select the rows whose high points record is greater than  35  . there is only one such row in the table .
and { eq { hop { argmax { all_rows ; win % } ; played } ; 16 } ; eq { hop { argmax { all_rows ; win % } ; wins } ; 9 } } = true
Output: select the row whose win  record of all rows is maximum . the played record of this row is 16 . the wins record of this row is 9 .
round_eq { sum { all_rows ; gold } ; 3 } = true
Output: the sum of the gold record of all rows is 3 .
most_eq { all_rows ; linda mccartney ; keyboards } = true
Output: for the linda mccartney records of all rows , most of them fuzzily match to keyboards .
eq { hop { filter_eq { all_rows ; competition ; 1978 merdeka cup } ; score } ; hop { filter_eq { all_rows ; competition ; 1986 fifa world cup } ; score } } = true
Output: select the rows whose competition record fuzzily matches to 1978 merdeka cup . take the score record of this row . select the rows whose competition record fuzzily matches to 1986 fifa world cup . take the score record of this row . the first record fuzzily matches to the second record .
less { hop { filter_eq { all_rows ; name ; mario domínguez } ; qual 2 } ; hop { filter_eq { all_rows ; name ; alex sperafico } ; qual 2 } } = true
Output: select the rows whose name record fuzzily matches to mario domínguez . take the qual 2 record of this row . select the rows whose name record fuzzily matches to alex sperafico . take the qual 2 record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; league cup } ; 2 } = true
Output: select the row whose total record of all rows is 2nd maximum . the league cup record of this row is 2 .
only { filter_eq { all_rows ; championship ; memphis , us } } = true
Output: select the rows whose championship record fuzzily matches to memphis , us . there is only one such row in the table .
all_eq { all_rows ; open cup ; did not enter } = true
Output: for the open cup records of all rows , all of them fuzzily match to did not enter .
less { hop { filter_eq { all_rows ; director ; brian kelly } ; title } ; hop { filter_eq { all_rows ; director ; andrew lincoln } ; title } } = true
Output: select the rows whose director record fuzzily matches to brian kelly . take the title record of this row . select the rows whose director record fuzzily matches to andrew lincoln . take the title record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; year ; 1 } ; position } ; 2nd } = true
Output: select the row whose year record of all rows is 1st minimum . the position record of this row is 2nd .
eq { nth_max { filter_eq { all_rows ; type ; free transfer } ; age ; 2 } ; 31 } = true
Output: select the rows whose type record fuzzily matches to free transfer . the 2nd maximum age record of these rows is 31 .
round_eq { avg { all_rows ; location attendance } ; 18627 } = true
Output: the average of the location attendance record of all rows is 18627 .
eq { hop { argmax { all_rows ; margin of victory } ; date } ; 22 jun 2006 } = true
Output: select the row whose margin of victory record of all rows is maximum . the date record of this row is 22 jun 2006 .
round_eq { sum { all_rows ; televotes } ; 147968 } = true
Output: the sum of the televotes record of all rows is 147968 .
eq { count { filter_eq { filter_greater { all_rows ; round ; 2 } ; position ; defensive back } } ; 2 } = true
Output: select the rows whose round record is greater than 2 . among these rows , select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; final ; did not advance } } ; 3 } = true
Output: select the rows whose final record fuzzily matches to did not advance . the number of such rows is 3 .
most_eq { all_rows ; country ; japan } = true
Output: for the country records of all rows , most of them fuzzily match to japan .
round_eq { sum { filter_eq { all_rows ; attendance ; gund arena } ; attendance } ; 123371 } = true
Output: select the rows whose attendance record fuzzily matches to gund arena . the sum of the attendance record of these rows is 123371 .
and { eq { min { all_rows ; grid } ; 1 } ; eq { hop { argmin { all_rows ; grid } ; driver } ; nino vaccarella } } = true
Output: the minimum grid record of all rows is 1 . the driver record of the row with superlative grid record is nino vaccarella .
round_eq { sum { all_rows ; points } ; 96 } = true
Output: the sum of the points record of all rows is 96 .
most_greater { all_rows ; length ; 4:00 } = true
Output: for the length records of all rows , most of them are greater than 4:00 .
eq { hop { argmax { all_rows ; crowd } ; home team } ; collingwood } = true
Output: select the row whose crowd record of all rows is maximum . the home team record of this row is collingwood .
most_eq { all_rows ; series ; lt } = true
Output: for the series records of all rows , most of them fuzzily match to lt .
most_less { all_rows ; length ; 5:00 } = true
Output: for the length records of all rows , most of them are less than 5:00 .
eq { count { filter_greater_eq { filter_less_eq { all_rows ; rank ; 3 } ; loss ; 1 } } ; 2 } = true
Output: select the rows whose rank record is less than or equal to 3 . among these rows , select the rows whose loss record is greater than or equal to 1 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; vacator } ; ross bass ( d ) } } = true
Output: select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the vacator record of this unqiue row is ross bass  d  .