niryuu/sni-each-converted huggingface dataset:
most_eq { all_rows ; start source ; bbc sport } = true
Output: for the start source records of all rows , most of them fuzzily match to bbc sport .
and { only { filter_less { all_rows ; population ( 2006 ) ; 1000 } } ; eq { hop { filter_less { all_rows ; population ( 2006 ) ; 1000 } ; name } ; saint - andré } } = true
Output: select the rows whose population  2006  record is less than 1000 . there is only one such row in the table . the name record of this unqiue row is saint  andré .
eq { count { filter_eq { all_rows ; team ; honda } } ; 9 } = true
Output: select the rows whose team record fuzzily matches to honda . the number of such rows is 9 .
greater { hop { filter_eq { all_rows ; name ; greg merson } ; wsop cashes } ; hop { filter_eq { all_rows ; name ; michael esposito } ; wsop cashes } } = true
Output: select the rows whose name record fuzzily matches to greg merson . take the wsop cashes record of this row . select the rows whose name record fuzzily matches to michael esposito . take the wsop cashes record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; general classification ; nick nuyens } } ; 5 } = true
Output: select the rows whose general classification record fuzzily matches to nick nuyens . the number of such rows is 5 .
and { only { filter_eq { all_rows ; 2008 club ; army } } ; eq { hop { filter_eq { all_rows ; 2008 club ; army } ; name } ; guo peng } } = true
Output: select the rows whose 2008 club record fuzzily matches to army . there is only one such row in the table . the name record of this unqiue row is guo peng .
eq { hop { argmax { all_rows ; stages } ; year } ; 1974 } = true
Output: select the row whose stages record of all rows is maximum . the year record of this row is 1974 .
eq { count { filter_eq { all_rows ; opponent ; los angeles rams } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to los angeles rams . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; soviet union } = true
Output: select the row whose total record of all rows is 2nd maximum . the nation record of this row is soviet union .
greater { hop { filter_eq { all_rows ; club ; real murcia } ; wins } ; hop { filter_eq { all_rows ; club ; jerez cd } ; wins } } = true
Output: select the rows whose club record fuzzily matches to real murcia . take the wins record of this row . select the rows whose club record fuzzily matches to jerez cd . take the wins record of this row . the first record is greater than the second record .
eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -6 } } ; 2 } = 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 6 . the number of such rows is 2 .
eq { hop { argmax { filter_eq { all_rows ; season ; 2009 } ; points } ; club } ; barcelona } = true
Output: select the rows whose season record fuzzily matches to 2009 . select the row whose points record of these rows is maximum . the club record of this row is barcelona .
round_eq { sum { all_rows ; goals for } ; 812 } = true
Output: the sum of the goals for record of all rows is 812 .
round_eq { sum { all_rows ; enrollment } ; 81233 } = true
Output: the sum of the enrollment record of all rows is 81233 .
less { hop { filter_eq { all_rows ; year ; 1963 } ; finish } ; hop { filter_eq { all_rows ; year ; 1971 } ; finish } } = true
Output: select the rows whose year record fuzzily matches to 1963 . take the finish record of this row . select the rows whose year record fuzzily matches to 1971 . take the finish record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; teams ; giants } } ; eq { hop { filter_eq { all_rows ; teams ; giants } ; quarterback } ; kerry collins } } = true
Output: select the rows whose teams record fuzzily matches to giants . there is only one such row in the table . the quarterback record of this unqiue row is kerry collins .
less { hop { filter_eq { all_rows ; model ; aptus - ii 10 } ; seconds / frame } ; hop { filter_eq { all_rows ; model ; aptus - ii 12 } ; seconds / frame } } = true
Output: select the rows whose model record fuzzily matches to aptus  ii 10 . take the seconds  frame record of this row . select the rows whose model record fuzzily matches to aptus  ii 12 . take the seconds  frame record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; name ; robert childress } ; round } ; hop { filter_eq { all_rows ; name ; ivey armstrong } ; round } } = true
Output: select the rows whose name record fuzzily matches to robert childress . take the round record of this row . select the rows whose name record fuzzily matches to ivey armstrong . take the round record of this row . the first record is greater than the second record .
most_eq { all_rows ; address ; gilbert , az } = true
Output: for the address records of all rows , most of them fuzzily match to gilbert , az .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; buffalo bills } = true
Output: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is buffalo bills .
round_eq { avg { all_rows ; won } ; 8.8 } = true
Output: the average of the won record of all rows is 8.8 .
round_eq { avg { all_rows ; attendance } ; 55680.4 } = true
Output: the average of the attendance record of all rows is 55680.4 .
most_eq { all_rows ; major league lacrosse ; none } = true
Output: for the major league lacrosse records of all rows , most of them fuzzily match to none .
and { only { filter_eq { all_rows ; manager ; bob didier } } ; eq { hop { filter_eq { all_rows ; manager ; bob didier } ; year } ; 1977 } } = true
Output: select the rows whose manager record fuzzily matches to bob didier . there is only one such row in the table . the year record of this unqiue row is 1977 .
eq { count { filter_not_eq { all_rows ; result ; won } } ; 2 } = true
Output: select the rows whose result record does not match to won . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; rider ; marino lejarreta ( 3 ) } ; final position - giro } ; hop { filter_eq { all_rows ; rider ; marino lejarreta ( 2 ) } ; final position - giro } } = true
Output: select the rows whose rider record fuzzily matches to marino lejarreta  3  . take the final position  giro record of this row . select the rows whose rider record fuzzily matches to marino lejarreta  2  . take the final position  giro record of this row . the first record is less than the second record .
all_eq { all_rows ; date ; july 8 , 2008 } = true
Output: for the date records of all rows , all of them fuzzily match to july 8 , 2008 .
greater { hop { filter_eq { all_rows ; opponent ; liverpool } ; result } ; hop { filter_eq { all_rows ; opponent ; chelsea } ; result } } = true
Output: select the rows whose opponent record fuzzily matches to liverpool . take the result record of this row . select the rows whose opponent record fuzzily matches to chelsea . take the result record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; days with frost ; 0 } } ; eq { hop { filter_eq { all_rows ; days with frost ; 0 } ; city / town } ; a coruña } } = true
Output: select the rows whose days with frost record is equal to 0 . there is only one such row in the table . the city  town record of this unqiue row is a coruña .
and { only { filter_eq { all_rows ; team ( league ) ; london knights } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; london knights } ; player } ; jim sandlak } } = true
Output: select the rows whose team  league  record fuzzily matches to london knights . there is only one such row in the table . the player record of this unqiue row is jim sandlak .
and { eq { min { all_rows ; place } ; 1st } ; eq { hop { argmin { all_rows ; place } ; song } ; what 's another year } } = true
Output: the minimum place record of all rows is 1st . the song record of the row with superlative place record is what s another year .
and { greater { hop { filter_eq { all_rows ; name ; jelena skerovic } ; assists } ; hop { filter_eq { all_rows ; name ; kathy wambe } ; assists } } ; and { eq { hop { filter_eq { all_rows ; name ; jelena skerovic } ; assists } ; 54 } ; eq { hop { filter_eq { all_rows ; name ; kathy wambe } ; assists } ; 48 } } } = true
Output: select the rows whose name record fuzzily matches to jelena skerovic . take the assists record of this row . select the rows whose name record fuzzily matches to kathy wambe . take the assists record of this row . the first record is greater than the second record . the assists record of the first row is 54 . the assists record of the second row is 48 .
greater { hop { filter_eq { all_rows ; series ; british formula three } ; points } ; hop { filter_eq { all_rows ; series ; formula bmw americas } ; points } } = true
Output: select the rows whose series record fuzzily matches to british formula three . take the points record of this row . select the rows whose series record fuzzily matches to formula bmw americas . take the points record of this row . the first record is greater than the second record .
eq { nth_max { all_rows ; frequency ; 2 } ; 933 mhz } = true
Output: the 2nd maximum frequency record of all rows is 933 mhz .
eq { count { filter_eq { all_rows ; average ; 32 } } ; 3 } = true
Output: select the rows whose average record is equal to 32 . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; enrollment ; 2 } ; institution } ; north georgia technical college } = true
Output: select the row whose enrollment record of all rows is 2nd minimum . the institution record of this row is north georgia technical college .
eq { hop { nth_argmax { all_rows ; total votes ; 2 } ; artist } ; virginia } = true
Output: select the row whose total votes record of all rows is 2nd maximum . the artist record of this row is virginia .
eq { hop { nth_argmax { all_rows ; lost ; 2 } ; club } ; fleur de lys rfc } = true
Output: select the row whose lost record of all rows is 2nd maximum . the club record of this row is fleur de lys rfc .
eq { hop { argmin { all_rows ; time } ; name } ; pieter van den hoogenband } = true
Output: select the row whose time record of all rows is minimum . the name record of this row is pieter van den hoogenband .
less { hop { filter_eq { all_rows ; english title ; paris , the strange attractive capital } ; year } ; hop { filter_eq { all_rows ; english title ; visitor 's fog } ; year } } = true
Output: select the rows whose english title record fuzzily matches to paris , the strange attractive capital . take the year record of this row . select the rows whose english title record fuzzily matches to visitor s fog . take the year record of this row . the first record is less than the second record .
all_eq { all_rows ; ihsaa class ; aaaa } = true
Output: for the ihsaa class records of all rows , all of them fuzzily match to aaaa .
eq { count { filter_eq { filter_eq { all_rows ; date ; september } ; date ; september } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to september . among these rows , select the rows whose date record fuzzily matches to september . the number of such rows is 2 .
not_eq { hop { filter_eq { all_rows ; date ; april 2 } ; high points } ; hop { filter_eq { all_rows ; date ; april 15 } ; high points } } = true
Output: select the rows whose date record fuzzily matches to april 2 . take the high points record of this row . select the rows whose date record fuzzily matches to april 15 . take the high points record of this row . the first record does not match to the second record .
eq { hop { argmin { all_rows ; years for rockets } ; player } ; perry , curtis curtis perry } = true
Output: select the row whose years for rockets record of all rows is minimum . the player record of this row is perry , curtis curtis perry .
and { only { filter_eq { all_rows ; time ; 6:21.11 } } ; eq { hop { filter_eq { all_rows ; time ; 6:21.11 } ; country } ; estonia } } = true
Output: select the rows whose time record fuzzily matches to 6:21.11 . there is only one such row in the table . the country record of this unqiue row is estonia .
most_less { all_rows ; % ( 2000 ) ; 6 } = true
Output: for the   2000  records of all rows , most of them are less than 6 .
eq { hop { argmax { all_rows ; height } ; player } ; pape badiane } = true
Output: select the row whose height record of all rows is maximum . the player record of this row is pape badiane .
round_eq { avg { all_rows ; attendance } ; 43583 } = true
Output: the average of the attendance record of all rows is 43583 .
eq { hop { nth_argmax { all_rows ; round ; 1 } ; opponent } ; antoine jaoude } = true
Output: select the row whose round record of all rows is 1st maximum . the opponent record of this row is antoine jaoude .
eq { hop { argmax { all_rows ; original air date } ; title } ; road to the north pole } = true
Output: select the row whose original air date record of all rows is maximum . the title record of this row is road to the north pole .
eq { hop { argmax { all_rows ; agg } ; team 1 } ; pyunik } = true
Output: select the row whose agg record of all rows is maximum . the team 1 record of this row is pyunik .
most_greater { all_rows ; launch ; 2000 } = true
Output: for the launch records of all rows , most of them are greater than 2000 .
eq { count { filter_eq { all_rows ; stage ( winner ) ; mark cavendish } } ; 3 } = true
Output: select the rows whose stage  winner  record fuzzily matches to mark cavendish . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; league ; aa } } ; 3 } = true
Output: select the rows whose league record fuzzily matches to aa . the number of such rows is 3 .
most_eq { all_rows ; standard ; umts hspa } = true
Output: for the standard records of all rows , most of them fuzzily match to umts hspa .
eq { count { filter_eq { all_rows ; 2nd evening ; n / a } } ; 3 } = true
Output: select the rows whose 2nd evening record fuzzily matches to n  a . the number of such rows is 3 .
eq { hop { argmax { all_rows ; against } ; opposing team } ; buenos aires selection } = true
Output: select the row whose against record of all rows is maximum . the opposing team record of this row is buenos aires selection .
all_eq { all_rows ; bandwidth included ; unlimited mb } = true
Output: for the bandwidth included records of all rows , all of them fuzzily match to unlimited mb .
eq { hop { nth_argmax { all_rows ; att ; 3 } ; date } ; october 16 } = true
Output: select the row whose att record of all rows is 3rd maximum . the date record of this row is october 16 .
most_greater { all_rows ; founded ; 1950 } = true
Output: for the founded records of all rows , most of them are greater than 1950 .
eq { count { filter_eq { all_rows ; game site ; kingdome } } ; 8 } = true
Output: select the rows whose game site record fuzzily matches to kingdome . the number of such rows is 8 .
and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; hop { filter_eq { all_rows ; lost ; 15 } ; lost } } ; and { eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } ; eq { hop { filter_eq { all_rows ; lost ; 15 } ; lost } ; 15 } } } = true
Output: select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . select the rows whose lost record fuzzily matches to 15 . take the lost record of this row . the first record is equal to the second record . the lost record of the first row is 15 . the lost record of the second row is 15 .
eq { count { filter_eq { all_rows ; location ; rio de janeiro } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to rio de janeiro . the number of such rows is 2 .
and { only { filter_eq { all_rows ; top - 5 ; 1 } } ; eq { hop { filter_eq { all_rows ; top - 5 ; 1 } ; tournament } ; the open championship } } = true
Output: select the rows whose top  5 record is equal to 1 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
most_eq { all_rows ; location ; saitama , japan } = true
Output: for the location records of all rows , most of them fuzzily match to saitama , japan .
round_eq { sum { all_rows ; against } ; 121 } = true
Output: the sum of the against record of all rows is 121 .
all_less { all_rows ; enrollment ( 2010 ) ; 1000 } = true
Output: for the enrollment  2010  records of all rows , all of them are less than 1000 .
eq { count { filter_eq { all_rows ; theme ; judge 's choice } } ; 3 } = true
Output: select the rows whose theme record fuzzily matches to judge s choice . the number of such rows is 3 .
round_eq { sum { all_rows ; population census 2009 } ; 4383743 } = true
Output: the sum of the population census 2009 record of all rows is 4383743 .
eq { hop { argmax { all_rows ; points } ; club } ; wigan warriors } = true
Output: select the row whose points record of all rows is maximum . the club record of this row is wigan warriors .
eq { hop { argmax { all_rows ; elevation ( m ) } ; peak } ; mount popomanaseu } = true
Output: select the row whose elevation  m  record of all rows is maximum . the peak record of this row is mount popomanaseu .
eq { count { filter_eq { all_rows ; time ; 5:00 } } ; 8 } = true
Output: select the rows whose time record fuzzily matches to 5:00 . the number of such rows is 8 .
eq { count { filter_eq { all_rows ; under - 13 ; emily park } } ; 2 } = true
Output: select the rows whose under  13 record fuzzily matches to emily park . the number of such rows is 2 .
eq { max { all_rows ; total } ; 45:40 } = true
Output: the maximum total record of all rows is 45:40 .
less { hop { filter_eq { all_rows ; local government area ; anangu pitjantjatjara yankunytjatjara } ; pop 2006 } ; hop { filter_eq { all_rows ; local government area ; outback areas community development trust } ; pop 2006 } } = true
Output: select the rows whose local government area record fuzzily matches to anangu pitjantjatjara yankunytjatjara . take the pop 2006 record of this row . select the rows whose local government area record fuzzily matches to outback areas community development trust . take the pop 2006 record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; colour ; brown } } ; eq { hop { filter_eq { all_rows ; colour ; brown } ; name } ; ted } } = true
Output: select the rows whose colour record fuzzily matches to brown . there is only one such row in the table . the name record of this unqiue row is ted .
all_eq { all_rows ; city of license ; virginia } = true
Output: for the city of license records of all rows , all of them fuzzily match to virginia .
and { eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; week ; 1 } ; opponent } ; new york jets } ; eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; week ; 1 } ; game site } ; qualcomm stadium } } = true
Output: select the rows whose result record fuzzily matches to w . select the row whose week record of these rows is 1st minimum . the opponent record of this row is new york jets . the game site record of this row is qualcomm stadium .
round_eq { sum { all_rows ; population } ; 9617 } = true
Output: the sum of the population record of all rows is 9617 .
eq { hop { argmin { all_rows ; round } ; name } ; sean weatherspoon } = true
Output: select the row whose round record of all rows is minimum . the name record of this row is sean weatherspoon .
eq { count { filter_eq { all_rows ; first episode ; golden parachute } } ; 6 } = true
Output: select the rows whose first episode record fuzzily matches to golden parachute . the number of such rows is 6 .
eq { count { filter_greater { filter_eq { all_rows ; extra ; 110 m hurdles } ; result ; 3 } } ; 2 } = true
Output: select the rows whose extra record fuzzily matches to 110 m hurdles . among these rows , select the rows whose result record is greater than 3 . the number of such rows is 2 .
round_eq { sum { all_rows ; winnings } ; 2076417 } = true
Output: the sum of the winnings record of all rows is 2076417 .
and { only { filter_greater { all_rows ; us viewers ( million ) ; 9.0 } } ; eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 9.0 } ; title } ; pilot } } = true
Output: select the rows whose us viewers  million  record is greater than 9.0 . there is only one such row in the table . the title record of this unqiue row is pilot .
eq { hop { argmax { all_rows ; votes } ; year } ; 1962 } = true
Output: select the row whose votes record of all rows is maximum . the year record of this row is 1962 .
eq { hop { nth_argmax { all_rows ; age on mission ; 1 } ; name } ; jim lovell } = true
Output: select the row whose age on mission record of all rows is 1st maximum . the name record of this row is jim lovell .
most_eq { all_rows ; home ; philadelphia } = true
Output: for the home records of all rows , most of them fuzzily match to philadelphia .
and { only { filter_eq { all_rows ; cores ; 1 } } ; eq { hop { filter_eq { all_rows ; cores ; 1 } ; model number } ; itanium 2 9010 } } = true
Output: select the rows whose cores record is equal to 1 . there is only one such row in the table . the model number record of this unqiue row is itanium 2 9010 .
round_eq { sum { filter_eq { all_rows ; language ; english } ; points } ; 43 } = true
Output: select the rows whose language record fuzzily matches to english . the sum of the points record of these rows is 43 .
and { only { filter_eq { all_rows ; shirt sponsor ; n / a } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; n / a } ; team } ; dubai } } = true
Output: select the rows whose shirt sponsor record fuzzily matches to n  a . there is only one such row in the table . the team record of this unqiue row is dubai .
less { hop { filter_eq { all_rows ; player ; hubert green } ; to par } ; hop { filter_eq { all_rows ; player ; dave stockton } ; to par } } = true
Output: select the rows whose player record fuzzily matches to hubert green . take the to par record of this row . select the rows whose player record fuzzily matches to dave stockton . take the to par record of this row . the first record is less than the second record .
and { only { filter_greater { all_rows ; points ; 20 } } ; eq { hop { filter_greater { all_rows ; points ; 20 } ; grid } ; 1 } } = true
Output: select the rows whose points record is greater than 20 . there is only one such row in the table . the grid record of this unqiue row is 1 .
less { hop { filter_eq { all_rows ; rank ; 1 } ; bronze } ; hop { filter_eq { all_rows ; rank ; 4 } ; bronze } } = true
Output: select the rows whose rank record fuzzily matches to 1 . take the bronze record of this row . select the rows whose rank record fuzzily matches to 4 . take the bronze record of this row . the first record is less than the second record .
most_eq { all_rows ; type ; private } = true
Output: for the type records of all rows , most of them fuzzily match to private .
round_eq { avg { all_rows ; lwat } ; 26 } = true
Output: the average of the lwat record of all rows is 26 .
eq { hop { nth_argmin { all_rows ; round ; 5 } ; player } ; nicholas tremblay } = true
Output: select the row whose round record of all rows is 5th minimum . the player record of this row is nicholas tremblay .
eq { hop { nth_argmin { all_rows ; location ; 2 } ; mountain peak } ; west spanish peak } = true
Output: select the row whose location record of all rows is 2nd minimum . the mountain peak record of this row is west spanish peak .
and { only { filter_less { all_rows ; rushing yards ; 1000 } } ; eq { hop { filter_less { all_rows ; rushing yards ; 1000 } ; year } ; 1989 } } = true
Output: select the rows whose rushing yards record is less than 1000 . there is only one such row in the table . the year record of this unqiue row is 1989 .
eq { count { filter_eq { all_rows ; team ; pescarolo sport } } ; 2 } = true
Output: select the rows whose team record fuzzily matches to pescarolo sport . the number of such rows is 2 .
and { only { filter_eq { all_rows ; team name ; eng makers } } ; eq { hop { filter_eq { all_rows ; team name ; eng makers } ; season } ; 2006 } } = true
Output: select the rows whose team name record fuzzily matches to eng makers . there is only one such row in the table . the season record of this unqiue row is 2006 .
and { only { filter_eq { all_rows ; laps ; 39 } } ; eq { hop { filter_eq { all_rows ; laps ; 39 } ; year } ; 1949 } } = true
Output: select the rows whose laps record is equal to 39 . there is only one such row in the table . the year record of this unqiue row is 1949 .
eq { hop { argmin { all_rows ; year } ; nominee ( s ) } ; jenny bicks } = true
Output: select the row whose year record of all rows is minimum . the nominee  s  record of this row is jenny bicks .
greater { hop { filter_eq { all_rows ; award ; london critics circle film awards } ; year } ; hop { filter_eq { all_rows ; award ; satellite awards } ; year } } = true
Output: select the rows whose award record fuzzily matches to london critics circle film awards . take the year record of this row . select the rows whose award record fuzzily matches to satellite awards . take the year record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; ties ; 18 } } ; 2 } = true
Output: select the rows whose ties record is equal to 18 . the number of such rows is 2 .
most_greater { all_rows ; crowd ; 15000 } = true
Output: for the crowd records of all rows , most of them are greater than 15000 .
all_eq { all_rows ; class ; d } = true
Output: for the class records of all rows , all of them fuzzily match to d .
and { only { filter_eq { all_rows ; directed by ; turi meyer } } ; and { eq { hop { filter_eq { all_rows ; directed by ; turi meyer } ; - } ; 6 } ; eq { hop { filter_eq { all_rows ; directed by ; turi meyer } ; title } ; harvest } } } = true
Output: select the rows whose directed by record fuzzily matches to turi meyer . there is only one such row in the table . the  record of this unqiue row is 6 . the title record of this unqiue row is harvest .
eq { hop { argmax { all_rows ; pick } ; player } ; james campbell } = true
Output: select the row whose pick record of all rows is maximum . the player record of this row is james campbell .
most_eq { all_rows ; to par ; + } = true
Output: for the to par records of all rows , most of them fuzzily match to + .
eq { count { filter_eq { all_rows ; road ; i - 80 } } ; 2 } = true
Output: select the rows whose road record fuzzily matches to i  80 . the number of such rows is 2 .
only { filter_eq { all_rows ; points ; 1 } } = true
Output: select the rows whose points record is equal to 1 . there is only one such row in the table .
round_eq { avg { all_rows ; money } ; 18630 } = true
Output: the average of the money record of all rows is 18630 .
all_eq { all_rows ; date ; 17 june 1939 } = true
Output: for the date records of all rows , all of them fuzzily match to 17 june 1939 .
most_eq { all_rows ; to par ; 7 } = true
Output: for the to par records of all rows , most of them are equal to 7 .
and { eq { max { all_rows ; crowd } ; 26500 } ; eq { hop { argmax { all_rows ; crowd } ; date } ; 4 august 1951 } } = true
Output: the maximum crowd record of all rows is 26500 . the date record of the row with superlative crowd record is 4 august 1951 .
eq { hop { argmax { all_rows ; viewers ( millions ) } ; episode } ; 7 } = true
Output: select the row whose viewers  millions  record of all rows is maximum . the episode record of this row is 7 .
eq { count { filter_eq { all_rows ; result ; re - elected } } ; 6 } = true
Output: select the rows whose result record fuzzily matches to re  elected . the number of such rows is 6 .
most_eq { all_rows ; us viewers ( millions ) ; n / a } = true
Output: for the us viewers  millions  records of all rows , most of them fuzzily match to n  a .
eq { diff { hop { filter_eq { all_rows ; city of license ; norton , virginia } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; pound , virginia } ; frequency mhz } } ; 0.4 } = true
Output: select the rows whose city of license record fuzzily matches to norton , virginia . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to pound , virginia . take the frequency mhz record of this row . the first record is 0.4 larger than the second record .
eq { count { filter_eq { all_rows ; score ; w } } ; 4 } = true
Output: select the rows whose score record fuzzily matches to w . the number of such rows is 4 .
eq { hop { nth_argmin { all_rows ; rank ; 6 } ; name } ; emilio falla ( ecu ) } = true
Output: select the row whose rank record of all rows is 6th minimum . the name record of this row is emilio falla  ecu  .
most_eq { all_rows ; format ; cd } = true
Output: for the format records of all rows , most of them fuzzily match to cd .
eq { count { filter_eq { all_rows ; location ; las vegas , nevada , usa } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to las vegas , nevada , usa . the number of such rows is 3 .
eq { hop { argmax { all_rows ; starts } ; year } ; 2001 } = true
Output: select the row whose starts record of all rows is maximum . the year record of this row is 2001 .
eq { count { filter_greater { filter_greater { all_rows ; home team score ; 12 } ; crowd ; 20000 } } ; 2 } = true
Output: select the rows whose home team score record is greater than 12 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .
round_eq { sum { filter_eq { all_rows ; team ; forsythe racing } ; points } ; 8 } = true
Output: select the rows whose team record fuzzily matches to forsythe racing . the sum of the points record of these rows is 8 .
eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; total ; 150 } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than 150 . the number of such rows is 2 .
and { only { filter_greater { all_rows ; position ; 15th } } ; and { eq { hop { filter_greater { all_rows ; position ; 15th } ; year } ; 2008 } ; eq { hop { filter_greater { all_rows ; position ; 15th } ; competition } ; olympic games } } } = true
Output: select the rows whose position record is greater than 15th . there is only one such row in the table . the year record of this unqiue row is 2008 . the competition record of this unqiue row is olympic games .
eq { hop { argmax { all_rows ; national titles } ; school } ; york college } = true
Output: select the row whose national titles record of all rows is maximum . the school record of this row is york college .
and { only { filter_eq { all_rows ; college ; charleston southern } } ; eq { hop { filter_eq { all_rows ; college ; charleston southern } ; player } ; shawn gifford } } = true
Output: select the rows whose college record fuzzily matches to charleston southern . there is only one such row in the table . the player record of this unqiue row is shawn gifford .
and { only { filter_eq { all_rows ; 1989 ; 3r } } ; eq { hop { filter_eq { all_rows ; 1989 ; 3r } ; tournament } ; wimbledon } } = true
Output: select the rows whose 1989 record fuzzily matches to 3r . there is only one such row in the table . the tournament record of this unqiue row is wimbledon .
round_eq { avg { all_rows ; erp w } ; 37.5 watts } = true
Output: the average of the erp w record of all rows is 37.5 watts .
and { only { filter_eq { all_rows ; location ; las vegas , nevada , united states } } ; eq { hop { filter_eq { all_rows ; location ; las vegas , nevada , united states } ; opponent } ; rick roufus } } = true
Output: select the rows whose location record fuzzily matches to las vegas , nevada , united states . there is only one such row in the table . the opponent record of this unqiue row is rick roufus .
greater { hop { filter_eq { all_rows ; county ; clark } ; casinos } ; hop { filter_eq { all_rows ; county ; south lake tahoe } ; casinos } } = true
Output: select the rows whose county record fuzzily matches to clark . take the casinos record of this row . select the rows whose county record fuzzily matches to south lake tahoe . take the casinos record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; director ; clint eastwood } ; record set } ; hop { filter_eq { all_rows ; director ; john huston } ; record set } } = true
Output: select the rows whose director record fuzzily matches to clint eastwood . take the record set record of this row . select the rows whose director record fuzzily matches to john huston . take the record set record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; withdrawal } ; irish name } ; flóirín } = true
Output: select the row whose withdrawal record of all rows is maximum . the irish name record of this row is flóirín .
most_less { all_rows ; points 1 ; 40 } = true
Output: for the points 1 records of all rows , most of them are less than 40 .
greater { hop { filter_eq { all_rows ; team ; tauro } ; goals scored } ; hop { filter_eq { all_rows ; team ; alianza } ; goals scored } } = true
Output: select the rows whose team record fuzzily matches to tauro . take the goals scored record of this row . select the rows whose team record fuzzily matches to alianza . take the goals scored record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { filter_eq { all_rows ; locomotive type ; steam } ; withdrawn ; 1 } ; number } ; 1 } = true
Output: select the rows whose locomotive type record fuzzily matches to steam . select the row whose withdrawn record of these rows is 1st minimum . the number record of this row is 1 .
and { only { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; mcg } } ; eq { hop { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; mcg } ; home team score } ; 18.6 ( 114 ) } } = true
Output: select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose venue record fuzzily matches to mcg . there is only one such row in the table . the home team score record of this unqiue row is 18.6  114  .
and { only { filter_less { all_rows ; total votes ; 3000000 } } ; eq { hop { filter_less { all_rows ; total votes ; 3000000 } ; election } ; 2001 } } = true
Output: select the rows whose total votes record is less than 3000000 . there is only one such row in the table . the election record of this unqiue row is 2001 .
most_eq { all_rows ; pictorials ; girls } = true
Output: for the pictorials records of all rows , most of them fuzzily match to girls .
and { only { filter_greater { all_rows ; loss ; 9 } } ; eq { hop { filter_greater { all_rows ; loss ; 9 } ; team } ; college of saint benilde } } = true
Output: select the rows whose loss record is greater than 9 . there is only one such row in the table . the team record of this unqiue row is college of saint benilde .
most_eq { all_rows ; played ; 16 } = true
Output: for the played records of all rows , most of them are equal to 16 .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; november 13 } = true
Output: select the row whose attendance record of all rows is 1st maximum . the date record of this row is november 13 .
round_eq { sum { filter_less_eq { all_rows ; year ; 2006 } ; player of the tournament } ; 33 } = true
Output: select the rows whose year record is less than or equal to 2006 . the sum of the player of the tournament record of these rows is 33 .
and { only { filter_eq { all_rows ; monday to friday ; monday to saturday } } ; eq { hop { filter_eq { all_rows ; monday to friday ; monday to saturday } ; mexico } ; bosnia and herzegovina } } = true
Output: select the rows whose monday to friday record fuzzily matches to monday to saturday . there is only one such row in the table . the mexico record of this unqiue row is bosnia and herzegovina .
and { only { filter_less { all_rows ; channel ; 10 } } ; eq { hop { filter_less { all_rows ; channel ; 10 } ; callsign } ; wnbc } } = true
Output: select the rows whose channel record is less than 10 . there is only one such row in the table . the callsign record of this unqiue row is wnbc .
round_eq { avg { all_rows ; crowd } ; 17128 } = true
Output: the average of the crowd record of all rows is 17128 .
eq { count { filter_eq { all_rows ; make ; toyota } } ; 4 } = true
Output: select the rows whose make record fuzzily matches to toyota . the number of such rows is 4 .
eq { max { all_rows ; score } ; w 132 - 93 ( ot ) } = true
Output: the maximum score record of all rows is w 132  93  ot  .
and { only { filter_eq { all_rows ; 2010 ; 12 } } ; eq { hop { filter_eq { all_rows ; 2010 ; 12 } ; tournament } ; win - loss } } = true
Output: select the rows whose 2010 record fuzzily matches to 12 . there is only one such row in the table . the tournament record of this unqiue row is win  loss .
eq { hop { nth_argmin { all_rows ; no in season ; 2 } ; written by } ; jeremy carver } = true
Output: select the row whose no in season record of all rows is 2nd minimum . the written by record of this row is jeremy carver .
eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; frank wolf } = true
Output: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is frank wolf .
most_eq { filter_eq { all_rows ; event ; billabong pro } ; winner ; usa } = true
Output: select the rows whose event record fuzzily matches to billabong pro . for the winner records of these rows , most of them fuzzily match to usa .
most_eq { filter_eq { all_rows ; date ; 1990 - 09 } ; result ; l } = true
Output: select the rows whose date record fuzzily matches to 1990  09 . for the result records of these rows , most of them fuzzily match to l .
only { filter_greater { all_rows ; tournaments played ; 20 } } = true
Output: select the rows whose tournaments played record is greater than 20 . there is only one such row in the table .
and { only { filter_eq { all_rows ; owner ( s ) ; private owner } } ; eq { hop { filter_eq { all_rows ; owner ( s ) ; private owner } ; number & name } ; no 1163 whitehead } } = true
Output: select the rows whose owner  s  record fuzzily matches to private owner . there is only one such row in the table . the number  name record of this unqiue row is no 1163 whitehead .
most_greater { all_rows ; attendance ; 15000 } = true
Output: for the attendance records of all rows , most of them are greater than 15000 .
round_eq { avg { filter_eq { all_rows ; result ; l } ; result } ; less than 15 points } = true
Output: select the rows whose result record fuzzily matches to l . the average of the result record of these rows is less than 15 points .
eq { count { filter_greater { all_rows ; viewers ( millions ) ; 9 million } } ; 3 } = true
Output: select the rows whose viewers  millions  record is greater than 9 million . the number of such rows is 3 .
and { greater { hop { filter_eq { all_rows ; name ; eninho } ; goals } ; hop { filter_eq { all_rows ; name ; ko jeong - woon } ; goals } } ; and { eq { hop { filter_eq { all_rows ; name ; eninho } ; goals } ; 51 } ; eq { hop { filter_eq { all_rows ; name ; ko jeong - woon } ; goals } ; 42 } } } = true
Output: select the rows whose name record fuzzily matches to eninho . take the goals record of this row . select the rows whose name record fuzzily matches to ko jeong  woon . take the goals record of this row . the first record is greater than the second record . the goals record of the first row is 51 . the goals record of the second row is 42 .
round_eq { sum { all_rows ; points } ; 2 } = true
Output: the sum of the points record of all rows is 2 .
eq { count { filter_eq { all_rows ; winners ; cliftonville fc } } ; 2 } = true
Output: select the rows whose winners record fuzzily matches to cliftonville fc . the number of such rows is 2 .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; college all - stars at chicago } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is college all  stars at chicago .
eq { count { filter_eq { all_rows ; college ; arizona state } } ; 2 } = true
Output: select the rows whose college record fuzzily matches to arizona state . the number of such rows is 2 .
and { only { filter_eq { all_rows ; nationality ; italy } } ; eq { hop { filter_eq { all_rows ; nationality ; italy } ; name } ; federica biscia } } = true
Output: select the rows whose nationality record fuzzily matches to italy . there is only one such row in the table . the name record of this unqiue row is federica biscia .
round_eq { avg { all_rows ; international tourism expenditure 2011 } ; 45-48 billion } = true
Output: the average of the international tourism expenditure 2011 record of all rows is 4548 billion .
eq { count { filter_eq { all_rows ; opponent ; boston bruins } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to boston bruins . the number of such rows is 2 .
and { only { filter_eq { all_rows ; apps ; 24 } } ; eq { hop { filter_eq { all_rows ; apps ; 24 } ; season } ; 2005 / 06 } } = true
Output: select the rows whose apps record is equal to 24 . there is only one such row in the table . the season record of this unqiue row is 2005  06 .
round_eq { sum { all_rows ; laps } ; 1098 } = true
Output: the sum of the laps record of all rows is 1098 .
less { hop { filter_eq { all_rows ; team ; portland trail blazers } ; date } ; hop { filter_eq { all_rows ; team ; detroit pistons } ; date } } = true
Output: select the rows whose team record fuzzily matches to portland trail blazers . take the date record of this row . select the rows whose team record fuzzily matches to detroit pistons . take the date record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; frequency } ; model number } ; x5 - 150 adw } = true
Output: select the row whose frequency record of all rows is maximum . the model number record of this row is x5  150 adw .
round_eq { avg { all_rows ; caps } ; 14.83 } = true
Output: the average of the caps record of all rows is 14.83 .
less { hop { filter_eq { all_rows ; player ; alan farina } ; years played } ; hop { filter_eq { all_rows ; player ; kent matthes } ; years played } } = true
Output: select the rows whose player record fuzzily matches to alan farina . take the years played record of this row . select the rows whose player record fuzzily matches to kent matthes . take the years played record of this row . the first record is less than the second record .
most_eq { all_rows ; card ; main } = true
Output: for the card records of all rows , most of them fuzzily match to main .
eq { count { filter_eq { filter_eq { all_rows ; location ; hermanos rodriguez } ; constructor ; williams - renault } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to hermanos rodriguez . among these rows , select the rows whose constructor record fuzzily matches to williams  renault . the number of such rows is 2 .
eq { hop { argmax { all_rows ; date } ; cover model } ; lauren hill } = true
Output: select the row whose date record of all rows is maximum . the cover model record of this row is lauren hill .
and { only { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } } ; eq { hop { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } ; season } ; 10 } } = true
Output: select the rows whose timeslot  edt  record fuzzily matches to monday  friday 10:00 pm . there is only one such row in the table . the season record of this unqiue row is 10 .
eq { hop { argmax { all_rows ; no of barangays } ; city / municipality } ; binangonan } = true
Output: select the row whose no of barangays record of all rows is maximum . the city  municipality record of this row is binangonan .
eq { max { all_rows ; score } ; 5 - 0 } = true
Output: the maximum score record of all rows is 5  0 .
less { hop { filter_eq { all_rows ; opponent ; minnesota vikings } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } } = true
Output: select the rows whose opponent record fuzzily matches to minnesota vikings . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . the first record is less than the second record .
eq { hop { argmin { filter_eq { all_rows ; office ; former governor } ; popular vote } ; candidate } ; buddy roemer } = true
Output: select the rows whose office record fuzzily matches to former governor . select the row whose popular vote record of these rows is minimum . the candidate record of this row is buddy roemer .
eq { diff { hop { filter_eq { all_rows ; club ; terang } ; wins } ; hop { filter_eq { all_rows ; club ; terang mortlake } ; wins } } ; 501 } = true
Output: select the rows whose club record fuzzily matches to terang . take the wins record of this row . select the rows whose club record fuzzily matches to terang mortlake . take the wins record of this row . the first record is 501 larger than the second record .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; southern university at new orleans } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is southern university at new orleans .
eq { hop { argmin { all_rows ; to par } ; player } ; bill rogers } = true
Output: select the row whose to par record of all rows is minimum . the player record of this row is bill rogers .
eq { count { filter_eq { all_rows ; year of release ; 1979 } } ; 2 } = true
Output: select the rows whose year of release record is equal to 1979 . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; school ; jackson } ; join date } ; hop { filter_eq { all_rows ; school ; lake } ; join date } } ; -23 years } = true
Output: select the rows whose school record fuzzily matches to jackson . take the join date record of this row . select the rows whose school record fuzzily matches to lake . take the join date record of this row . the second record is 23 years larger than the first record .
eq { hop { nth_argmax { all_rows ; result ; 2 } ; result } ; mac collins ( r ) 61.11 % jim chafin ( d ) 38.89 % } = true
Output: select the row whose result record of all rows is 2nd maximum . the result record of this row is mac collins  r  61.11  jim chafin  d  38.89  .
round_eq { avg { all_rows ; attendance } ; 45585 } = true
Output: the average of the attendance record of all rows is 45585 .
and { only { filter_less { all_rows ; capacity ; 2000 } } ; eq { hop { filter_less { all_rows ; capacity ; 2000 } ; city } ; scafati } } = true
Output: select the rows whose capacity record is less than 2000 . there is only one such row in the table . the city record of this unqiue row is scafati .
most_greater { all_rows ; wins ; 10 } = true
Output: for the wins records of all rows , most of them are greater than 10 .
round_eq { sum { all_rows ; number of cars } ; 63 } = true
Output: the sum of the number of cars record of all rows is 63 .
eq { hop { nth_argmin { all_rows ; pick ; 9 } ; player } ; dick o'keefe } = true
Output: select the row whose pick record of all rows is 9th minimum . the player record of this row is dick okeefe .
only { filter_eq { all_rows ; surface ; carpet } } = true
Output: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table .
eq { hop { nth_argmax { all_rows ; points ; 1 } ; team 1 } ; san lorenzo } = true
Output: select the row whose points record of all rows is 1st maximum . the team 1 record of this row is san lorenzo .
eq { hop { argmax { all_rows ; ratings } ; translation of title } ; english showdown ! fight for stupid 6 } = true
Output: select the row whose ratings record of all rows is maximum . the translation of title record of this row is english showdown ! fight for stupid 6 .
eq { hop { argmax { filter_greater_eq { all_rows ; points ; 10 } ; drawn } ; team } ; botafogo } = true
Output: select the rows whose points record is greater than or equal to 10 . select the row whose drawn record of these rows is maximum . the team record of this row is botafogo .
round_eq { avg { all_rows ; crowd } ; 16,707 } = true
Output: the average of the crowd record of all rows is 16,707 .
only { filter_eq { all_rows ; current status ; operational } } = true
Output: select the rows whose current status record fuzzily matches to operational . there is only one such row in the table .