niryuu/sni-each-converted huggingface dataset:
the 1st minimum week record of all rows is 1 . the date record of the row with 1st minimum week record is september 4 , 1994 .
Output: and { eq { nth_min { all_rows ; week ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; week ; 1 } ; date } ; september 4 , 1994 } }
the average of the population record of all rows is 65841 .
Output: round_eq { avg { all_rows ; population } ; 65841 }
select the row whose attendance record of all rows is 1st maximum . the home record of this row is calgary .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; home } ; calgary }
select the rows whose visitor record fuzzily matches to dallas . take the date record of this row . select the rows whose visitor record fuzzily matches to montreal . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; visitor ; dallas } ; date } ; hop { filter_eq { all_rows ; visitor ; montreal } ; date } }
select the rows whose ranking record fuzzily matches to 33rd . there is only one such row in the table . the season record of this unqiue row is 1 .
Output: and { only { filter_eq { all_rows ; ranking ; 33rd } } ; eq { hop { filter_eq { all_rows ; ranking ; 33rd } ; season } ; 1 } }
select the rows whose tournament record fuzzily matches to baton rouge , us . take the date record of this row . select the rows whose tournament record fuzzily matches to granby , canada . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tournament ; baton rouge , us } ; date } ; hop { filter_eq { all_rows ; tournament ; granby , canada } ; date } }
the sum of the ties record of all rows is 64 .
Output: round_eq { sum { all_rows ; ties } ; 64 }
select the row whose wins record of all rows is maximum . the year record of this row is 1988 .
Output: eq { hop { argmax { all_rows ; wins } ; year } ; 1988 }
for the podiums records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; podiums ; 0 }
for the district records of all rows , most of them fuzzily match to belize .
Output: most_eq { all_rows ; district ; belize }
the average of the td 's record of all rows is 8.27 .
Output: round_eq { avg { all_rows ; td 's } ; 8.27 }
select the row whose votes record of all rows is maximum . the election record of this row is 1990 .
Output: eq { hop { argmax { all_rows ; votes } ; election } ; 1990 }
select the rows whose three darts challenge record fuzzily matches to jermaine jackson . take the episode number record of this row . select the rows whose three darts challenge record fuzzily matches to meat loaf . take the episode number record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; three darts challenge ; jermaine jackson } ; episode number } ; hop { filter_eq { all_rows ; three darts challenge ; meat loaf } ; episode number } }
select the rows whose home team score record is greater than 15 . among these rows , select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; home team score ; 15 } ; crowd ; 10000 } } ; 2 }
select the rows whose result record fuzzily matches to retire . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; result ; retire } } ; 1 }
the maximum away team score record of all rows is 15.9 ( 99 ) .
Output: eq { max { all_rows ; away team score } ; 15.9 ( 99 ) }
select the row whose viewers record of all rows is maximum . the no record of this row is 1 .
Output: eq { hop { argmax { all_rows ; viewers } ; no } ; 1 }
select the rows whose circuit record is arbitrary . the number of such rows is 10 .
Output: eq { count { filter_all { all_rows ; circuit } } ; 10 }
select the row whose attendance record of all rows is 1st maximum . the season record of this row is 1993 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; season } ; 1993 }
select the row whose attendance record of all rows is maximum . the opponent record of this row is new orleans saints .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; new orleans saints }
select the row whose points record of all rows is maximum . the club record of this row is hércules cf .
Output: eq { hop { argmax { all_rows ; points } ; club } ; hércules cf }
select the rows whose version record fuzzily matches to maxi remix . take the length record of this row . select the rows whose version record fuzzily matches to single version . take the length record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; version ; maxi remix } ; length } ; hop { filter_eq { all_rows ; version ; single version } ; length } }
select the rows whose population metro area in millions record fuzzily matches to 5.3 . take the population metro area in millions record of this row . select the rows whose population metro area in millions record fuzzily matches to 3.9 . take the population metro area in millions record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; population metro area in millions ; 5.3 } ; population metro area in millions } ; hop { filter_eq { all_rows ; population metro area in millions ; 3.9 } ; population metro area in millions } }
the sum of the points record of all rows is 0 .
Output: round_eq { sum { all_rows ; points } ; 0 }
the average of the floors record of all rows is 24.6 .
Output: round_eq { avg { all_rows ; floors } ; 24.6 }
select the rows whose country record fuzzily matches to united states . for the to par records of these rows , most of them are greater than -8 .
Output: most_greater { filter_eq { all_rows ; country ; united states } ; to par ; -8 }
select the row whose became queen record of all rows is minimum . the name record of this row is maria louisa of spain .
Output: eq { hop { argmin { all_rows ; became queen } ; name } ; maria louisa of spain }
the sum of the no built record of all rows is 84 .
Output: round_eq { sum { all_rows ; no built } ; 84 }
for the pixel aspect ratio records of all rows , most of them fuzzily match to 1:1 .
Output: most_eq { all_rows ; pixel aspect ratio ; 1:1 }
select the row whose year record of all rows is minimum . the lifetime india distributor share record of this row is 99 , 02 , 00000 .
Output: eq { hop { argmin { all_rows ; year } ; lifetime india distributor share } ; 99 , 02 , 00000 }
select the rows whose internet plan record fuzzily matches to internet 100 . take the upstream record of this row . select the rows whose internet plan record fuzzily matches to internet 30 . take the upstream record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; internet plan ; internet 100 } ; upstream } ; hop { filter_eq { all_rows ; internet plan ; internet 30 } ; upstream } }
the average of the power record of all rows is 1900 rpm .
Output: round_eq { avg { all_rows ; power } ; 1900 rpm }
select the row whose assists record of all rows is maximum . the name record of this row is yotam halperin .
Output: eq { hop { argmax { all_rows ; assists } ; name } ; yotam halperin }
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 .
Output: eq { count { filter_greater { filter_eq { all_rows ; extra ; 110 m hurdles } ; result ; 3 } } ; 2 }
select the rows whose player record fuzzily matches to tom watson . take the total record of this row . select the rows whose player record fuzzily matches to tom weiskopf . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; tom watson } ; total } ; hop { filter_eq { all_rows ; player ; tom weiskopf } ; total } }
for the competition records of all rows , most of them fuzzily match to friendly .
Output: most_eq { all_rows ; competition ; friendly }
select the row whose losses record of all rows is 1st maximum . the team record of this row is new york liberty .
Output: eq { hop { nth_argmax { all_rows ; losses ; 1 } ; team } ; new york liberty }
select the row whose total record of all rows is maximum . the name record of this row is kevin kyle .
Output: eq { hop { argmax { all_rows ; total } ; name } ; kevin kyle }
select the row whose oilers first downs record of all rows is maximum . the opponent record of this row is new england patriots .
Output: eq { hop { argmax { all_rows ; oilers first downs } ; opponent } ; new england patriots }
select the rows whose winning driver record fuzzily matches to jack brabham . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; winning driver ; jack brabham } } ; 5 }
for the games records of all rows , most of them are equal to 5 .
Output: most_eq { all_rows ; games ; 5 }
select the rows whose result record fuzzily matches to - 0 . there is only one such row in the table . the opponent record of this unqiue row is san diego chargers .
Output: and { only { filter_eq { all_rows ; result ; - 0 } } ; eq { hop { filter_eq { all_rows ; result ; - 0 } ; opponent } ; san diego chargers } }
select the row whose ratings ( millions ) record of all rows is maximum . the episode record of this row is episode 1 .
Output: eq { hop { argmax { all_rows ; ratings ( millions ) } ; episode } ; episode 1 }
the average of the score in the final record of all rows is 5 .
Output: round_eq { avg { all_rows ; score in the final } ; 5 }
select the rows whose pos record fuzzily matches to cf . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; pos ; cf } } ; 2 }
select the rows whose location record fuzzily matches to california , united states . there is only one such row in the table . the opponent record of this unqiue row is muhsin corbbrey .
Output: and { only { filter_eq { all_rows ; location ; california , united states } } ; eq { hop { filter_eq { all_rows ; location ; california , united states } ; opponent } ; muhsin corbbrey } }
select the rows whose season record fuzzily matches to 2009 - 10 . take the goals record of this row . select the rows whose season record fuzzily matches to 2012 - 13 . take the goals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2009 - 10 } ; goals } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; goals } }
select the rows whose paul mccartney record fuzzily matches to acoustic guitar . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; paul mccartney ; acoustic guitar } } ; 2 }
select the rows whose head coach record fuzzily matches to jonas kazlauskas . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; head coach ; jonas kazlauskas } } ; 2 }
select the rows whose tournament record fuzzily matches to nice . take the date record of this row . select the rows whose tournament record fuzzily matches to estoril . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tournament ; nice } ; date } ; hop { filter_eq { all_rows ; tournament ; estoril } ; date } }
select the rows whose constructor record fuzzily matches to minardi - cosworth . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; constructor ; minardi - cosworth } } ; 2 }
select the rows whose us viewers ( million ) record is equal to 5.5 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; us viewers ( million ) ; 5.5 } } ; 2 }
select the rows whose result record fuzzily matches to selected . there is only one such row in the table . the week record of this unqiue row is wild card .
Output: and { only { filter_eq { all_rows ; result ; selected } } ; eq { hop { filter_eq { all_rows ; result ; selected } ; week } ; wild card } }
select the rows whose entrant record fuzzily matches to north american racing team . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; entrant ; north american racing team } } ; 3 }
for the pos records of all rows , most of them fuzzily match to dnf .
Output: most_eq { all_rows ; pos ; dnf }
for the type records of all rows , most of them fuzzily match to mountain .
Output: most_eq { all_rows ; type ; mountain }
select the rows whose attendance record fuzzily matches to canceled . there is only one such row in the table . the venue record of this unqiue row is blackcomb mountain .
Output: and { only { filter_eq { all_rows ; attendance ; canceled } } ; eq { hop { filter_eq { all_rows ; attendance ; canceled } ; venue } ; blackcomb mountain } }
select the rows whose region record fuzzily matches to hoseo . among these rows , select the rows whose korean dialect record fuzzily matches to chungcheong dialect . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; region ; hoseo } ; korean dialect ; chungcheong dialect } } ; 1 }
select the row whose total record of all rows is 1st maximum . the nation record of this row is china .
Output: eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; china }
select the rows whose nationality record fuzzily matches to usa . there is only one such row in the table . the pick record of this unqiue row is 12 .
Output: and { only { filter_eq { all_rows ; nationality ; usa } } ; eq { hop { filter_eq { all_rows ; nationality ; usa } ; pick } ; 12 } }
select the rows whose club record fuzzily matches to sporting de gijón . take the points record of this row . select the rows whose club record fuzzily matches to cádiz cf . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; club ; sporting de gijón } ; points } ; hop { filter_eq { all_rows ; club ; cádiz cf } ; points } }
select the rows whose original canadian air date record fuzzily matches to august . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; original canadian air date ; august } } ; 3 }
select the rows whose record fuzzily matches to 2 . take the viewers record of this row . select the rows whose record fuzzily matches to 1 . take the viewers record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ; 2 } ; viewers } ; hop { filter_eq { all_rows ; ; 1 } ; viewers } }
the average of the total record of all rows is 152.8 .
Output: round_eq { avg { all_rows ; total } ; 152.8 }
select the rows whose 2004 record is greater than 1000 . there is only one such row in the table . the tournament record of this unqiue row is year end ranking .
Output: and { only { filter_greater { all_rows ; 2004 ; 1000 } } ; eq { hop { filter_greater { all_rows ; 2004 ; 1000 } ; tournament } ; year end ranking } }
select the rows whose first elected record is greater than or equal to 2000 . the number of such rows is 5 .
Output: eq { count { filter_greater_eq { all_rows ; first elected ; 2000 } } ; 5 }
the maximum crowd record of all rows is 29000 .
Output: eq { max { all_rows ; crowd } ; 29000 }
select the rows whose yards record is greater than 1400 . there is only one such row in the table . the player record of this unqiue row is derrick lewis .
Output: and { only { filter_greater { all_rows ; yards ; 1400 } } ; eq { hop { filter_greater { all_rows ; yards ; 1400 } ; player } ; derrick lewis } }
select the rows whose nationality record fuzzily matches to germany . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nationality ; germany } } ; 2 }
the average of the time record of all rows is 1:00.26 .
Output: round_eq { avg { all_rows ; time } ; 1:00.26 }
the average of the bronze record of all rows is 1.67 .
Output: round_eq { avg { all_rows ; bronze } ; 1.67 }
for the country records of all rows , most of them fuzzily match to united states .
Output: most_eq { all_rows ; country ; united states }
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 .
Output: and { only { filter_eq { all_rows ; commissioned ; 1931 } } ; eq { hop { filter_eq { all_rows ; commissioned ; 1931 } ; name } ; persée } }
the average of the score record of all rows is 55 .
Output: round_eq { avg { all_rows ; score } ; 55 }
the average of the number of borough councilors record of all rows is 3.8 .
Output: round_eq { avg { all_rows ; number of borough councilors } ; 3.8 }
select the rows whose date record is less than or equal to october 24 , 2004 . for the tv time records of these rows , most of them fuzzily match to cbs 1:00 pm .
Output: most_eq { filter_less_eq { all_rows ; date ; october 24 , 2004 } ; tv time ; cbs 1:00 pm }
for the authority records of all rows , all of them fuzzily match to state .
Output: all_eq { all_rows ; authority ; state }
select the rows whose leader at the summit record fuzzily matches to gonzalo aja . there is only one such row in the table . the year record of this unqiue row is 1974 .
Output: and { only { filter_eq { all_rows ; leader at the summit ; gonzalo aja } } ; eq { hop { filter_eq { all_rows ; leader at the summit ; gonzalo aja } ; year } ; 1974 } }
select the row whose gold record of all rows is maximum . the gold record of this row is 36 .
Output: eq { hop { argmax { all_rows ; gold } ; gold } ; 36 }
select the rows whose manufacturer record fuzzily matches to suzuki . the average of the laps record of these rows is 21.5 .
Output: round_eq { avg { filter_eq { all_rows ; manufacturer ; suzuki } ; laps } ; 21.5 }
the maximum score record of all rows is 118 - 104 .
Output: eq { max { all_rows ; score } ; 118 - 104 }
for the tournaments records of all rows , most of them are equal to 12 .
Output: most_eq { all_rows ; tournaments ; 12 }
for the away team records of all rows , most of them fuzzily match to melbourne .
Output: most_eq { all_rows ; away team ; melbourne }
select the rows whose opposing team record fuzzily matches to queensland . take the against record of this row . select the rows whose opposing team record fuzzily matches to new south wales . take the against record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opposing team ; queensland } ; against } ; hop { filter_eq { all_rows ; opposing team ; new south wales } ; against } }
for the speed records of all rows , most of them are greater than or equal to 80 .
Output: most_greater_eq { all_rows ; speed ; 80 }
select the rows whose year record is equal to 1987 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; year ; 1987 } } ; 5 }
select the rows whose player record fuzzily matches to dave viti . take the round record of this row . select the rows whose player record fuzzily matches to billy neighbors . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; dave viti } ; round } ; hop { filter_eq { all_rows ; player ; billy neighbors } ; round } }
select the rows whose driver record fuzzily matches to alex ribeiro . there is only one such row in the table . the year record of this unqiue row is 1979 .
Output: and { only { filter_eq { all_rows ; driver ; alex ribeiro } } ; eq { hop { filter_eq { all_rows ; driver ; alex ribeiro } ; year } ; 1979 } }
select the row whose against record of all rows is maximum . the opposing team record of this row is buenos aires selection .
Output: eq { hop { argmax { all_rows ; against } ; opposing team } ; buenos aires selection }
the sum of the attendance record of all rows is 764271 .
Output: round_eq { sum { all_rows ; attendance } ; 764271 }
the average of the points record of all rows is 3 .
Output: round_eq { avg { all_rows ; points } ; 3 }
select the rows whose position record fuzzily matches to 4th . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; 4th } } ; 2 }
select the rows whose position record fuzzily matches to running back . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; running back } } ; 2 }
for the result records of all rows , most of them fuzzily match to win .
Output: most_eq { all_rows ; result ; win }
select the row whose points record of all rows is maximum . the player record of this row is paul magoffin .
Output: eq { hop { argmax { all_rows ; points } ; player } ; paul magoffin }
select the rows whose place record fuzzily matches to t2 . the maximum score record of these rows is 73 + 69 = 142 .
Output: eq { max { filter_eq { all_rows ; place ; t2 } ; score } ; 73 + 69
the sum of the list votes record of all rows is 66617 .
Output: round_eq { sum { all_rows ; list votes } ; 66617 }
for the laps records of all rows , most of them are equal to 27 .
Output: most_eq { all_rows ; laps ; 27 }
select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 6 }
for the laps records of all rows , most of them are equal to 30 .
Output: most_eq { all_rows ; laps ; 30 }
for the object type records of all rows , most of them fuzzily match to open cluster .
Output: most_eq { all_rows ; object type ; open cluster }
select the row whose winnings record of all rows is maximum . the driver record of this row is mark martin .
Output: eq { hop { argmax { all_rows ; winnings } ; driver } ; mark martin }
select the rows whose wins record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is montreal victorias .
Output: and { only { filter_eq { all_rows ; wins ; 6 } } ; eq { hop { filter_eq { all_rows ; wins ; 6 } ; team } ; montreal victorias } }
select the rows whose written by record fuzzily matches to lucy watkins . there is only one such row in the table . the title record of this unqiue row is sweet dreams .
Output: and { only { filter_eq { all_rows ; written by ; lucy watkins } } ; eq { hop { filter_eq { all_rows ; written by ; lucy watkins } ; title } ; sweet dreams } }
select the rows whose opposing teams record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing teams record fuzzily matches to france . take the against record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opposing teams ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing teams ; france } ; against } }
for the format records of all rows , most of them fuzzily match to vinyl .
Output: most_eq { all_rows ; format ; vinyl }
for the year records of all rows , most of them are greater than 2000 .
Output: most_greater { all_rows ; year ; 2000 }
select the rows whose wins record is equal to 14 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; wins ; 14 } } ; 2 }
select the rows whose competition record fuzzily matches to friendly . the sum of the result record of these rows is 4 .
Output: round_eq { sum { filter_eq { all_rows ; competition ; friendly } ; result } ; 4 }
select the rows whose points record is less than 10 . for the difference records of these rows , most of them fuzzily match to - .
Output: most_eq { filter_less { all_rows ; points ; 10 } ; difference ; - }
select the rows whose date record fuzzily matches to november . the maximum final score record of these rows is 24 - 38 .
Output: eq { max { filter_eq { all_rows ; date ; november } ; final score } ; 24 - 38 }
the sum of the total record of all rows is 111 .
Output: round_eq { sum { all_rows ; total } ; 111 }
select the row whose points record of all rows is 2nd maximum . the performer record of this row is honor heffernan .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; performer } ; honor heffernan }
for the headquarters records of all rows , most of them fuzzily match to united states .
Output: most_eq { all_rows ; headquarters ; united states }
select the rows whose rank record is arbitrary . the number of such rows is 19 .
Output: eq { count { filter_all { all_rows ; rank } } ; 19 }
for the type records of all rows , most of them fuzzily match to end of contract .
Output: most_eq { all_rows ; type ; end of contract }
select the rows whose location record fuzzily matches to hartford civic center . there is only one such row in the table . the game record of this unqiue row is 45 .
Output: and { only { filter_eq { all_rows ; location ; hartford civic center } } ; eq { hop { filter_eq { all_rows ; location ; hartford civic center } ; game } ; 45 } }
for the high assists records of all rows , most of them fuzzily match to mike conley .
Output: most_eq { all_rows ; high assists ; mike conley }
select the row whose commercial operation record of all rows is minimum . the unit record of this row is hamaoka - 1 .
Output: eq { hop { argmin { all_rows ; commercial operation } ; unit } ; hamaoka - 1 }
select the rows whose laps record is less than 20 . there is only one such row in the table . the name record of this unqiue row is ryan dalziel .
Output: and { only { filter_less { all_rows ; laps ; 20 } } ; eq { hop { filter_less { all_rows ; laps ; 20 } ; name } ; ryan dalziel } }
select the rows whose song title record fuzzily matches to nee maatalo . take the year record of this row . select the rows whose song title record fuzzily matches to hey po . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; song title ; nee maatalo } ; year } ; hop { filter_eq { all_rows ; song title ; hey po } ; year } }
select the rows whose batsmen record fuzzily matches to kim barnett . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; batsmen ; kim barnett } } ; 2 }
for the location records of all rows , most of them fuzzily match to td waterhouse centre .
Output: most_eq { all_rows ; location ; td waterhouse centre }
select the rows whose tournament record fuzzily matches to china . there is only one such row in the table .
Output: only { filter_eq { all_rows ; tournament ; china } }
select the rows whose reg gp record is equal to 2 . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; reg gp ; 2 } } ; 1 }
select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon . there is only one such row in the table . the name record of this unqiue row is steve smith .
Output: and { only { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } } ; eq { hop { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } ; name } ; steve smith } }
select the rows whose location record fuzzily matches to south africa . there is only one such row in the table . the name record of this unqiue row is likhoelesaurus .
Output: and { only { filter_eq { all_rows ; location ; south africa } } ; eq { hop { filter_eq { all_rows ; location ; south africa } ; name } ; likhoelesaurus } }
select the rows whose centerfold model record fuzzily matches to tamara witmer . take the date record of this row . select the rows whose centerfold model record fuzzily matches to vanessa hoelsher . take the date record of this row . the second record is 1 month larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; centerfold model ; tamara witmer } ; date } ; hop { filter_eq { all_rows ; centerfold model ; vanessa hoelsher } ; date } } ; -1 month }
for the team records of all rows , most of them fuzzily match to rothmans honda .
Output: most_eq { all_rows ; team ; rothmans honda }
for the laps records of all rows , all of them are equal to 197 .
Output: all_eq { all_rows ; laps ; 197 }
select the row whose age record of all rows is maximum . the name record of this row is donte gamble .
Output: eq { hop { argmax { all_rows ; age } ; name } ; donte gamble }
select the rows whose team record is arbitrary . the number of such rows is 7 .
Output: eq { count { filter_all { all_rows ; team } } ; 7 }
select the rows whose home team record fuzzily matches to carlton . take the crowd record of this row . select the rows whose home team record fuzzily matches to collingwood . take the crowd record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; home team ; carlton } ; crowd } ; hop { filter_eq { all_rows ; home team ; collingwood } ; crowd } }
select the row whose gold record of all rows is maximum . the rank record of this row is 1 .
Output: eq { hop { argmax { all_rows ; gold } ; rank } ; 1 }
select the rows whose floors record is greater than 30 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; floors ; 30 } } ; 3 }
select the row whose total record of all rows is maximum . the nation record of this row is france .
Output: eq { hop { argmax { all_rows ; total } ; nation } ; france }
for the crowd records of all rows , most of them are greater than 10000 .
Output: most_greater { all_rows ; crowd ; 10000 }
select the rows whose location record fuzzily matches to groves stadium winston - salem , nc . among these rows , select the rows whose attendance record is greater than 20000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_eq { all_rows ; location ; groves stadium winston - salem , nc } ; attendance ; 20000 } } ; 3 }
for the competition records of all rows , most of them fuzzily match to 1908 olympics .
Output: most_eq { all_rows ; competition ; 1908 olympics }
the average of the championships in st louis record of all rows is .3125 .
Output: round_eq { avg { all_rows ; championships in st louis } ; .3125 }
select the rows whose drawn record is equal to 1 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; drawn ; 1 } } ; 2 }
select the rows whose format record fuzzily matches to vinyl record . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; format ; vinyl record } } ; 2 }
the maximum home team score record of all rows is 15.17 ( 107 ) . the home team record of the row with superlative home team score record is south melbourne . the date record of the row with superlative home team score record is 7 august 1926 .
Output: and { eq { max { all_rows ; home team score } ; 15.17 ( 107 ) } ; and { eq { hop { argmax { all_rows ; home team score } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; home team score } ; date } ; 7 august 1926 } } }
select the rows whose away team score record fuzzily matches to 15.2 . there is only one such row in the table . the venue record of this unqiue row is windy hill .
Output: and { only { filter_eq { all_rows ; away team score ; 15.2 } } ; eq { hop { filter_eq { all_rows ; away team score ; 15.2 } ; venue } ; windy hill } }
for the motor records of all rows , all of them fuzzily match to castor 4b .
Output: all_eq { all_rows ; motor ; castor 4b }
for the class records of all rows , all of them fuzzily match to d .
Output: all_eq { all_rows ; class ; d }
select the rows whose class record fuzzily matches to a . there is only one such row in the table .
Output: only { filter_eq { all_rows ; class ; a } }
select the rows whose type record fuzzily matches to public . among these rows , select the rows whose founded record is less than 1940 . there is only one such row in the table .
Output: only { filter_less { filter_eq { all_rows ; type ; public } ; founded ; 1940 } }
select the rows whose area km 2 record is greater than 500 . among these rows , select the rows whose population record is greater than 1000 . the number of such rows is 4 .
Output: eq { count { filter_greater { filter_greater { all_rows ; area km 2 ; 500 } ; population ; 1000 } } ; 4 }
select the rows whose decision record fuzzily matches to wall . there is only one such row in the table . the date record of this unqiue row is september 20 .
Output: and { only { filter_eq { all_rows ; decision ; wall } } ; eq { hop { filter_eq { all_rows ; decision ; wall } ; date } ; september 20 } }
select the rows whose year record fuzzily matches to 2004 . take the rank record of this row . select the rows whose year record fuzzily matches to 2006 . take the rank record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; year ; 2004 } ; rank } ; hop { filter_eq { all_rows ; year ; 2006 } ; rank } }
select the row whose seconds record of all rows is maximum . the name record of this row is kerstin jürgens - szymkowiak .
Output: eq { hop { argmax { all_rows ; seconds } ; name } ; kerstin jürgens - szymkowiak }
select the rows whose played record is equal to 17 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; played ; 17 } } ; 6 }
the average of the score record of all rows is 2-0 .
Output: round_eq { avg { all_rows ; score } ; 2-0 }
select the rows whose age at appointment record is equal to 50 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; age at appointment ; 50 } } ; 4 }
select the rows whose nation record fuzzily matches to united states . take the bronze record of this row . select the rows whose nation record fuzzily matches to poland . take the bronze record of this row . the first record is equal to the second record . the bronze record of the first row is 1 . the bronze record of the second row is 1 .
Output: and { eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; hop { filter_eq { all_rows ; nation ; poland } ; bronze } } ; and { eq { hop { filter_eq { all_rows ; nation ; united states } ; bronze } ; 1 } ; eq { hop { filter_eq { all_rows ; nation ; poland } ; bronze } ; 1 } } }
select the rows whose mountain peak record fuzzily matches to pikes peak . take the location record of this row . select the rows whose mountain peak record fuzzily matches to blanca peak . take the location record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; mountain peak ; pikes peak } ; location } ; hop { filter_eq { all_rows ; mountain peak ; blanca peak } ; location } }
select the row whose enrollment record of all rows is 1st maximum . the school record of this row is carson .
Output: eq { hop { nth_argmax { all_rows ; enrollment ; 1 } ; school } ; carson }
select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is exile on main st .
Output: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; exile on main st }
for the type records of all rows , all of them fuzzily match to sovereign .
Output: all_eq { all_rows ; type ; sovereign }
select the row whose population record of all rows is 3rd maximum . the city record of this row is beijing .
Output: eq { hop { nth_argmax { all_rows ; population ; 3 } ; city } ; beijing }
select the rows whose drawn record is greater than 5 . there is only one such row in the table . the team record of this unqiue row is portuguesa .
Output: and { only { filter_greater { all_rows ; drawn ; 5 } } ; eq { hop { filter_greater { all_rows ; drawn ; 5 } ; team } ; portuguesa } }
select the rows whose position record fuzzily matches to tackle . for the round records of these rows , all of them are less than 7 .
Output: all_less { filter_eq { all_rows ; position ; tackle } ; round ; 7 }
select the row whose 2nd run record of all rows is maximum . the name record of this row is artūrs matisons ( lat ) .
Output: eq { hop { argmax { all_rows ; 2nd run } ; name } ; artūrs matisons ( lat ) }
select the rows whose draw record is less than or equal to 3 . among these rows , select the rows whose votes record is greater than 5000 . the number of such rows is 1 .
Output: eq { count { filter_greater { filter_less_eq { all_rows ; draw ; 3 } ; votes ; 5000 } } ; 1 }
select the rows whose bleeding time record fuzzily matches to unaffected . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; bleeding time ; unaffected } } ; 6 }
for the result records of all rows , most of them fuzzily match to safe .
Output: most_eq { all_rows ; result ; safe }
select the row whose year ( ceremony ) record of all rows is 2nd minimum . the director record of this row is aigars grauba .
Output: eq { hop { nth_argmin { all_rows ; year ( ceremony ) ; 2 } ; director } ; aigars grauba }
the average of the weight record of all rows is 188 .
Output: round_eq { avg { all_rows ; weight } ; 188 }
select the rows whose name record fuzzily matches to santa maria da feira . take the area ( km square ) record of this row . select the rows whose name record fuzzily matches to vale de cambra . take the area ( km square ) record of this row . the first record is greater than the second record . the area ( km square ) record of the first row is 215.1 . the area ( km square ) record of the second row is 146.5 .
Output: and { greater { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } } ; and { eq { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; 215.1 } ; eq { hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } ; 146.5 } } }
select the row whose balls record of all rows is maximum . the player record of this row is sanath jayasuriya .
Output: eq { hop { argmax { all_rows ; balls } ; player } ; sanath jayasuriya }
select the row whose released record of all rows is 2nd minimum . the title record of this row is mission to magnus .
Output: eq { hop { nth_argmin { all_rows ; released ; 2 } ; title } ; mission to magnus }
select the rows whose 50 m split record is less than 29.0 . among these rows , select the rows whose nationality record fuzzily matches to zimbabwe . there is only one such row in the table . the name record of this unqiue row is kirsty coventry .
Output: and { only { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } } ; eq { hop { filter_eq { filter_less { all_rows ; 50 m split ; 29.0 } ; nationality ; zimbabwe } ; name } ; kirsty coventry } }
select the row whose weeks record of all rows is 2nd maximum . the title record of this row is evolution .
Output: eq { hop { nth_argmax { all_rows ; weeks ; 2 } ; title } ; evolution }
the maximum score record of all rows is 5 - 1 .
Output: eq { max { all_rows ; score } ; 5 - 1 }
select the rows whose winnings record is greater than 300000 . there is only one such row in the table . the year record of this unqiue row is 1998 .
Output: and { only { filter_greater { all_rows ; winnings ; 300000 } } ; eq { hop { filter_greater { all_rows ; winnings ; 300000 } ; year } ; 1998 } }
select the row whose founded record of all rows is 1st minimum . the institution record of this row is guilford college .
Output: eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; guilford college }
select the rows whose total record is greater than 2 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; total ; 2 } } ; 3 }
for the winner records of all rows , all of them fuzzily match to ( ita ) .
Output: all_eq { all_rows ; winner ; ( ita ) }
for the gdp ( billion us ) records of all rows , most of them are greater than 100 .
Output: most_greater { all_rows ; gdp ( billion us ) ; 100 }
for the pick records of all rows , most of them are equal to 6 .
Output: most_eq { all_rows ; pick ; 6 }
for the first elected records of all rows , most of them are equal to 1948 .
Output: most_eq { all_rows ; first elected ; 1948 }
select the rows whose moving from record fuzzily matches to sedan . there is only one such row in the table . the name record of this unqiue row is abdou .
Output: and { only { filter_eq { all_rows ; moving from ; sedan } } ; eq { hop { filter_eq { all_rows ; moving from ; sedan } ; name } ; abdou } }
select the row whose matches record of all rows is maximum . the goalkeeper record of this row is chema .
Output: eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; chema }
select the rows whose performer 2 record fuzzily matches to nyima funk . there is only one such row in the table . the original airdate record of this unqiue row is september 3 , 2013 .
Output: and { only { filter_eq { all_rows ; performer 2 ; nyima funk } } ; eq { hop { filter_eq { all_rows ; performer 2 ; nyima funk } ; original airdate } ; september 3 , 2013 } }
for the date records of all rows , most of them fuzzily match to february .
Output: most_eq { all_rows ; date ; february }
for the sport records of all rows , most of them fuzzily match to football .
Output: most_eq { all_rows ; sport ; football }
for the city of license records of all rows , all of them fuzzily match to virginia .
Output: all_eq { all_rows ; city of license ; virginia }
select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; crowd ; 20000 } } ; 3 }
select the rows whose single record fuzzily matches to wild eyes . take the other details record of this row . select the rows whose single record fuzzily matches to tell the world . take the other details record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; single ; wild eyes } ; other details } ; hop { filter_eq { all_rows ; single ; tell the world } ; other details } }
select the rows whose junctions record fuzzily matches to sh 359 us 59 . select the row whose route name record of these rows is 1st maximum . the termini record of this row is aguilares , texas us 59 .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; junctions ; sh 359 us 59 } ; route name ; 1 } ; termini } ; aguilares , texas us 59 }
select the rows whose result record fuzzily matches to 28 - 17 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1962 .
Output: and { only { filter_eq { all_rows ; result ; 28 - 17 } } ; eq { hop { filter_eq { all_rows ; result ; 28 - 17 } ; date } ; september 9 , 1962 } }
the sum of the played record of all rows is 264 .
Output: round_eq { sum { all_rows ; played } ; 264 }
select the row whose number of viewers record of all rows is 2nd maximum . the show record of this row is warehouse 13 .
Output: eq { hop { nth_argmax { all_rows ; number of viewers ; 2 } ; show } ; warehouse 13 }
select the rows whose directed by record fuzzily matches to tony goldwyn . there is only one such row in the table . the title record of this unqiue row is in which sam receives an unexpected visitor .
Output: and { only { filter_eq { all_rows ; directed by ; tony goldwyn } } ; eq { hop { filter_eq { all_rows ; directed by ; tony goldwyn } ; title } ; in which sam receives an unexpected visitor } }
select the rows whose round of 16 record does not match to did not advance . the number of such rows is 4 .
Output: eq { count { filter_not_eq { all_rows ; round of 16 ; did not advance } } ; 4 }
the average of the try bonus record of all rows is 5 .
Output: round_eq { avg { all_rows ; try bonus } ; 5 }
select the rows whose player record fuzzily matches to chris garner . take the years in toronto record of this row . select the rows whose player record fuzzily matches to dion glover . take the years in toronto record of this row . the second record is 7 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; chris garner } ; years in toronto } ; hop { filter_eq { all_rows ; player ; dion glover } ; years in toronto } } ; -7 years }
select the rows whose location record fuzzily matches to norfolk . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; location ; norfolk } } ; 4 }
select the row whose semifinalists record of all rows is 2nd maximum . the country / territory record of this row is south africa .
Output: eq { hop { nth_argmax { all_rows ; semifinalists ; 2 } ; country / territory } ; south africa }
for the result records of all rows , most of them fuzzily match to l .
Output: most_eq { all_rows ; result ; l }
select the rows whose result record fuzzily matches to re-elected . among these rows , select the rows whose first elected record fuzzily matches to 193 . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; result ; re-elected } ; first elected ; 193 } } ; 3 }
the average of the lost record of all rows is 7.125 .
Output: round_eq { avg { all_rows ; lost } ; 7.125 }
for the language records of all rows , most of them fuzzily match to hindi .
Output: most_eq { all_rows ; language ; hindi }
select the rows whose venue record fuzzily matches to victoria park . there is only one such row in the table . the home team record of this unqiue row is collingwood . the away team record of this unqiue row is north melbourne .
Output: and { only { filter_eq { all_rows ; venue ; victoria park } } ; and { eq { hop { filter_eq { all_rows ; venue ; victoria park } ; home team } ; collingwood } ; eq { hop { filter_eq { all_rows ; venue ; victoria park } ; away team } ; north melbourne } } }
for the season outcome records of all rows , most of them fuzzily match to failed to make playoffs .
Output: most_eq { all_rows ; season outcome ; failed to make playoffs }
select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . the first record is greater than the second record . the away team record of the first row is footscray . the away team record of the second row is st kilda .
Output: and { greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; geelong } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; fitzroy } ; away team } ; footscray } ; eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; st kilda } } }
select the rows whose college record fuzzily matches to north carolina . for the nba draft records of these rows , all of them fuzzily match to undrafted .
Output: all_eq { filter_eq { all_rows ; college ; north carolina } ; nba draft ; undrafted }
select the rows whose date record fuzzily matches to february 2 . take the attendance record of this row . select the rows whose date record fuzzily matches to february 29 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; february 2 } ; attendance } ; hop { filter_eq { all_rows ; date ; february 29 } ; attendance } }
select the rows whose area ( km square ) record is less than 100000 . the number of such rows is 1 .
Output: eq { count { filter_less { all_rows ; area ( km square ) ; 100000 } } ; 1 }
select the rows whose type record fuzzily matches to transfer . among these rows , select the rows whose moving to record fuzzily matches to milan . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; type ; transfer } ; moving to ; milan } } ; 2 }
select the rows whose entrant record fuzzily matches to team lotus . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; entrant ; team lotus } } ; 4 }
for the result records of all rows , most of them fuzzily match to aus .
Output: most_eq { all_rows ; result ; aus }
for the service records of all rows , most of them fuzzily match to freight .
Output: most_eq { all_rows ; service ; freight }
the average of the score record of all rows is 143 .
Output: round_eq { avg { all_rows ; score } ; 143 }
the 2nd maximum 187 kg record of all rows is 215 kg . the world record record of the row with 2nd maximum 187 kg record is olympic record .
Output: and { eq { nth_max { all_rows ; 187 kg ; 2 } ; 215 kg } ; eq { hop { nth_argmax { all_rows ; 187 kg ; 2 } ; world record } ; olympic record } }
select the rows whose lost record is equal to 0 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; lost ; 0 } } ; 5 }
select the rows whose venue record fuzzily matches to johannesburg . the maximum date record of these rows is 23 - 03 - 2003 .
Output: eq { max { filter_eq { all_rows ; venue ; johannesburg } ; date } ; 23 - 03 - 2003 }
select the rows whose time record fuzzily matches to accident . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; time ; accident } } ; 4 }