niryuu/sni-each-converted huggingface dataset:
select the row whose floors record of all rows is 3rd maximum . the name record of this row is hôtel loews le concorde .
Output: eq { hop { nth_argmax { all_rows ; floors ; 3 } ; name } ; hôtel loews le concorde }
for the high assists records of all rows , most of them fuzzily match to ambrose .
Output: most_eq { all_rows ; high assists ; ambrose }
select the rows whose played record is less than 114 . among these rows , select the rows whose average record is greater than 1.0 . there is only one such row in the table . the team record of this unqiue row is huracán .
Output: and { only { filter_greater { filter_less { all_rows ; played ; 114 } ; average ; 1.0 } } ; eq { hop { filter_greater { filter_less { all_rows ; played ; 114 } ; average ; 1.0 } ; team } ; huracán } }
the average of the score record of all rows is 102 .
Output: round_eq { avg { all_rows ; score } ; 102 }
select the row whose opened record of all rows is 2nd minimum . the name record of this row is the shock wave .
Output: eq { hop { nth_argmin { all_rows ; opened ; 2 } ; name } ; the shock wave }
the average of the score record of all rows is 70 .
Output: round_eq { avg { all_rows ; score } ; 70 }
select the rows whose founded record is greater than or equal to 1990 . the number of such rows is 2 .
Output: eq { count { filter_greater_eq { all_rows ; founded ; 1990 } } ; 2 }
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is adelaide 36ers .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; adelaide 36ers }
select the rows whose opponent record fuzzily matches to dallas cowboys . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 54714 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; dallas cowboys } ; date ; 2 } ; attendance } ; 54714 }
for the signed records of all rows , most of them fuzzily match to yes .
Output: most_eq { all_rows ; signed ; yes }
the sum of the us viewers ( millions ) record of all rows is 78.52 .
Output: round_eq { sum { all_rows ; us viewers ( millions ) } ; 78.52 }
select the rows whose name record fuzzily matches to dante taylor . take the weight ( lb ) record of this row . select the rows whose name record fuzzily matches to nasir robinson . take the weight ( lb ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; dante taylor } ; weight ( lb ) } ; hop { filter_eq { all_rows ; name ; nasir robinson } ; weight ( lb ) } }
select the row whose score record of all rows is maximum . the opponent record of this row is boston bruins .
Output: eq { hop { argmax { all_rows ; score } ; opponent } ; boston bruins }
select the rows whose losses record is equal to 8 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; losses ; 8 } } ; 2 }
select the rows whose date record fuzzily matches to december 3 , 1967 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 17 , 1967 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; december 3 , 1967 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 17 , 1967 } ; attendance } }
for the entrant records of all rows , most of them fuzzily match to osella squadra corse .
Output: most_eq { all_rows ; entrant ; osella squadra corse }
select the rows whose team 2 record fuzzily matches to stade quimpérois ( d2 ) . take the score record of this row . select the rows whose team 2 record fuzzily matches to rc lens ( d1 ) . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team 2 ; stade quimpérois ( d2 ) } ; score } ; hop { filter_eq { all_rows ; team 2 ; rc lens ( d1 ) } ; score } }
select the rows whose others record is greater than 6000 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; others ; 6000 } } ; 2 }
select the rows whose united states original airdate record fuzzily matches to july . among these rows , select the rows whose directed by record fuzzily matches to greg sullivan . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; united states original airdate ; july } ; directed by ; greg sullivan } } ; 3 }
select the row whose power record of all rows is maximum . the name record of this row is 9 adr .
Output: eq { hop { argmax { all_rows ; power } ; name } ; 9 adr }
select the rows whose college record fuzzily matches to wake forest . there is only one such row in the table . the player record of this unqiue row is mark moroz .
Output: and { only { filter_eq { all_rows ; college ; wake forest } } ; eq { hop { filter_eq { all_rows ; college ; wake forest } ; player } ; mark moroz } }
for the area records of all rows , most of them fuzzily match to papakura .
Output: most_eq { all_rows ; area ; papakura }
select the rows whose player record fuzzily matches to juan theron . take the overs record of this row . select the rows whose player record fuzzily matches to makhaya ntini . take the overs record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; juan theron } ; overs } ; hop { filter_eq { all_rows ; player ; makhaya ntini } ; overs } }
the sum of the against record of all rows is 49 .
Output: round_eq { sum { all_rows ; against } ; 49 }
select the rows whose round record is greater than 6 . there is only one such row in the table .
Output: only { filter_greater { all_rows ; round ; 6 } }
select the rows whose points record is equal to 4 . there is only one such row in the table . the year record of this unqiue row is 1980 .
Output: and { only { filter_eq { all_rows ; points ; 4 } } ; eq { hop { filter_eq { all_rows ; points ; 4 } ; year } ; 1980 } }
the sum of the ends won record of all rows is 516 .
Output: round_eq { sum { all_rows ; ends won } ; 516 }
select the rows whose m60a3 patton record fuzzily matches to 105 mm m68 rifled tank - gun . there is only one such row in the table .
Output: only { filter_eq { all_rows ; m60a3 patton ; 105 mm m68 rifled tank - gun } }
for the authority records of all rows , most of them fuzzily match to state .
Output: most_eq { all_rows ; authority ; state }
select the rows whose opponent record fuzzily matches to poland . take the results record of this row . select the rows whose opponent record fuzzily matches to italy . take the results record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; poland } ; results } ; hop { filter_eq { all_rows ; opponent ; italy } ; results } }
select the rows whose wind farm record is arbitrary . the number of such rows is 17 .
Output: eq { count { filter_all { all_rows ; wind farm } } ; 17 }
select the rows whose venue record fuzzily matches to atlanta . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; venue ; atlanta } } ; 5 }
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 .
Output: eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; total ; 150 } } ; 2 }
the maximum release price ( usd ) record of all rows is 97 .
Output: eq { max { all_rows ; release price ( usd ) } ; 97 }
select the rows whose mean record is equal to 3.0 . there is only one such row in the table . the drug record of this unqiue row is heroin .
Output: and { only { filter_eq { all_rows ; mean ; 3.0 } } ; eq { hop { filter_eq { all_rows ; mean ; 3.0 } ; drug } ; heroin } }
select the rows whose year record fuzzily matches to 1965 . take the overall record record of this row . select the rows whose year record fuzzily matches to 1959 . take the overall record record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1965 } ; overall record } ; hop { filter_eq { all_rows ; year ; 1959 } ; overall record } }
select the rows whose equipment record fuzzily matches to ktm - vmc . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; equipment ; ktm - vmc } } ; 2 }
select the rows whose base record fuzzily matches to united states . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; base ; united states } } ; 2 }
select the rows whose nation record fuzzily matches to kingfisher east bengal fc . take the winners record of this row . select the rows whose nation record fuzzily matches to pahang fa . take the winners record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; kingfisher east bengal fc } ; winners } ; hop { filter_eq { all_rows ; nation ; pahang fa } ; winners } }
select the rows whose tournament record fuzzily matches to san diego . take the week record of this row . select the rows whose tournament record fuzzily matches to toronto . take the week record of this row . the second record is 14 days larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; tournament ; san diego } ; week } ; hop { filter_eq { all_rows ; tournament ; toronto } ; week } } ; -14 days }
select the row whose published record of all rows is maximum . the title record of this row is the golden age .
Output: eq { hop { argmax { all_rows ; published } ; title } ; the golden age }
select the rows whose race record fuzzily matches to super g . there is only one such row in the table . the season record of this unqiue row is 2009 .
Output: and { only { filter_eq { all_rows ; race ; super g } } ; eq { hop { filter_eq { all_rows ; race ; super g } ; season } ; 2009 } }
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is sept 7 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; sept 7 }
for the points records of all rows , most of them are greater than 6 .
Output: most_greater { all_rows ; points ; 6 }
select the rows whose total goals record is greater than 250 . among these rows , select the rows whose total appearances record is less than 400 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_greater { all_rows ; total goals ; 250 } ; total appearances ; 400 } } ; 2 }
select the rows whose opponent record fuzzily matches to notre dame . the sum of the total offense record of these rows is 948 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; notre dame } ; total offense } ; 948 }
select the rows whose institution record fuzzily matches to webber international university . take the founded record of this row . select the rows whose institution record fuzzily matches to ave maria university . take the founded record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; institution ; webber international university } ; founded } ; hop { filter_eq { all_rows ; institution ; ave maria university } ; founded } }
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 .
Output: and { only { filter_greater { all_rows ; points ; 20 } } ; eq { hop { filter_greater { all_rows ; points ; 20 } ; grid } ; 1 } }
the sum of the score record of all rows is 4577 .
Output: round_eq { sum { all_rows ; score } ; 4577 }
the average of the us msrp record of all rows is 365 .
Output: round_eq { avg { all_rows ; us msrp } ; 365 }
select the rows whose total record is equal to 1 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; total ; 1 } } ; 7 }
select the rows whose stolen ends record is greater than 15 . there is only one such row in the table . the skip ( club ) record of this unqiue row is chris gardner ( renfrew ) .
Output: and { only { filter_greater { all_rows ; stolen ends ; 15 } } ; eq { hop { filter_greater { all_rows ; stolen ends ; 15 } ; skip ( club ) } ; chris gardner ( renfrew ) } }
for the wins records of all rows , most of them are less than 20 .
Output: most_less { all_rows ; wins ; 20 }
for the date records of all rows , most of them fuzzily match to september .
Output: most_eq { all_rows ; date ; september }
select the rows whose nation record is arbitrary . the number of such rows is 13 .
Output: eq { count { filter_all { all_rows ; nation } } ; 13 }
select the rows whose tv time record fuzzily matches to cbs 12:00 pm . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 8 .
Output: eq { count { filter_eq { filter_eq { all_rows ; tv time ; cbs 12:00 pm } ; result ; w } } ; 8 }
the average of the money record of all rows is 18630 .
Output: round_eq { avg { all_rows ; money } ; 18630 }
select the row whose money record of all rows is 2nd maximum . the player record of this row is tom watson .
Output: eq { hop { nth_argmax { all_rows ; money ; 2 } ; player } ; tom watson }
select the row whose micro ( 10ha ) record of all rows is maximum . the department record of this row is potosi .
Output: eq { hop { argmax { all_rows ; micro ( 10ha ) } ; department } ; potosi }
select the rows whose date introduced record fuzzily matches to june . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; date introduced ; june } } ; 5 }
select the rows whose player record fuzzily matches to mark eaton . take the nationality record of this row . select the rows whose player record fuzzily matches to jerry eaves . take the nationality record of this row . the first record fuzzily matches to the second record . the nationality record of the first row is united states . the nationality record of the second row is united states .
Output: and { eq { hop { filter_eq { all_rows ; player ; mark eaton } ; nationality } ; hop { filter_eq { all_rows ; player ; jerry eaves } ; nationality } } ; and { eq { hop { filter_eq { all_rows ; player ; mark eaton } ; nationality } ; united states } ; eq { hop { filter_eq { all_rows ; player ; jerry eaves } ; nationality } ; united states } } }
select the rows whose title record fuzzily matches to chicken run . take the year record of this row . select the rows whose title record fuzzily matches to creature comforts . take the year record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; chicken run } ; year } ; hop { filter_eq { all_rows ; title ; creature comforts } ; year } }
the average of the wins record of all rows is .44 .
Output: round_eq { avg { all_rows ; wins } ; .44 }
the average of the enrollment record of all rows is 1535 .
Output: round_eq { avg { all_rows ; enrollment } ; 1535 }
the average of the crowd record of all rows is 21221 .
Output: round_eq { avg { all_rows ; crowd } ; 21221 }
the average of the enrollment record of all rows is 538 .
Output: round_eq { avg { all_rows ; enrollment } ; 538 }
the average of the viewership record of all rows is 4.75 million .
Output: round_eq { avg { all_rows ; viewership } ; 4.75 million }
the average of the goals record of all rows is 113 .
Output: round_eq { avg { all_rows ; goals } ; 113 }
select the row whose date record of all rows is 2nd maximum . the opponent in the final record of this row is víctor pecci .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent in the final } ; víctor pecci }
select the row whose june 14 , 2009 record of all rows is 2nd maximum . the mexico record of this row is croatia .
Output: eq { hop { nth_argmax { all_rows ; june 14 , 2009 ; 2 } ; mexico } ; croatia }
select the rows whose total record is less than 13 . the sum of the silver record of these rows is 2 .
Output: round_eq { sum { filter_less { all_rows ; total ; 13 } ; silver } ; 2 }
select the rows whose club record fuzzily matches to orlando metro . there is only one such row in the table . the name record of this unqiue row is olivia courtney .
Output: and { only { filter_eq { all_rows ; club ; orlando metro } } ; eq { hop { filter_eq { all_rows ; club ; orlando metro } ; name } ; olivia courtney } }
select the rows whose rec record is greater than 50 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; rec ; 50 } } ; 4 }
select the rows whose date record fuzzily matches to 2 april . select the row whose crowd record of these rows is 1st maximum . the home team record of this row is collingwood .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; date ; 2 april } ; crowd ; 1 } ; home team } ; collingwood }
select the row whose crowd record of all rows is maximum . the venue record of this row is mcg .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; mcg }
select the rows whose first elected record is equal to 1958 . there is only one such row in the table . the incumbent record of this unqiue row is del latta .
Output: and { only { filter_eq { all_rows ; first elected ; 1958 } } ; eq { hop { filter_eq { all_rows ; first elected ; 1958 } ; incumbent } ; del latta } }
select the row whose date record of all rows is minimum . the location attendance record of this row is verizon center 20173 .
Output: eq { hop { argmin { all_rows ; date } ; location attendance } ; verizon center 20173 }
select the rows whose traction type record fuzzily matches to petrol . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; traction type ; petrol } } ; 2 }
select the row whose pick record of all rows is 2nd maximum . the player record of this row is igor larionov .
Output: eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; igor larionov }
select the rows whose result record fuzzily matches to re - elected . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 2 }
select the rows whose no of villages record is less than 5 . there is only one such row in the table . the district record of this unqiue row is boe .
Output: and { only { filter_less { all_rows ; no of villages ; 5 } } ; eq { hop { filter_less { all_rows ; no of villages ; 5 } ; district } ; boe } }
select the row whose against record of all rows is 2nd maximum . the opposing teams record of this row is scotland .
Output: eq { hop { nth_argmax { all_rows ; against ; 2 } ; opposing teams } ; scotland }
select the rows whose incumbent record fuzzily matches to wilmer mizell . take the candidates record of this row . select the rows whose incumbent record fuzzily matches to earl b ruth . take the candidates record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; incumbent ; wilmer mizell } ; candidates } ; hop { filter_eq { all_rows ; incumbent ; earl b ruth } ; candidates } }
select the row whose length record of all rows is minimum . the vessel record of this row is marianarray .
Output: eq { hop { argmin { all_rows ; length } ; vessel } ; marianarray }
select the rows whose team record fuzzily matches to minardi team usa . select the row whose laps record of these rows is maximum . the driver record of this row is robert doornbos .
Output: eq { hop { argmax { filter_eq { all_rows ; team ; minardi team usa } ; laps } ; driver } ; robert doornbos }
select the row whose number of episodes record of all rows is maximum . the english title ( chinese title ) record of this row is dark tales ii 聊齋 ( 貳 ) .
Output: eq { hop { argmax { all_rows ; number of episodes } ; english title ( chinese title ) } ; dark tales ii 聊齋 ( 貳 ) }
select the row whose average record of all rows is 2nd minimum . the goalkeeper record of this row is jacobo .
Output: eq { hop { nth_argmin { all_rows ; average ; 2 } ; goalkeeper } ; jacobo }
for the regional county municipality records of all rows , all of them fuzzily match to not part of a rcm .
Output: all_eq { all_rows ; regional county municipality ; not part of a rcm }
the sum of the round record of all rows is 8 .
Output: round_eq { sum { all_rows ; round } ; 8 }
select the rows whose voivodeship after 1569 record fuzzily matches to samogitian eldership . take the year established record of this row . select the rows whose voivodeship after 1569 record fuzzily matches to trakai voivodeship . take the year established record of this row . the second record is 2 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; voivodeship after 1569 ; samogitian eldership } ; year established } ; hop { filter_eq { all_rows ; voivodeship after 1569 ; trakai voivodeship } ; year established } } ; -2 years }
select the rows whose fa cup record is greater than 0 . select the row whose championship record of these rows is 2nd maximum . the name record of this row is stern john .
Output: eq { hop { nth_argmax { filter_greater { all_rows ; fa cup ; 0 } ; championship ; 2 } ; name } ; stern john }
select the rows whose winning percentage record is equal to 1 . among these rows , select the rows whose wins record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; winning percentage ; 1 } ; wins ; 2 } } ; 2 }
the average of the total freshwater withdrawal record of all rows is 60.34 .
Output: round_eq { avg { all_rows ; total freshwater withdrawal } ; 60.34 }
select the rows whose date record is greater than november 1 , 1955 . among these rows , select the rows whose result record fuzzily matches to l . there is only one such row in the table .
Output: only { filter_eq { filter_greater { all_rows ; date ; november 1 , 1955 } ; result ; l } }
select the rows whose college record fuzzily matches to notre dame . there is only one such row in the table . the player record of this unqiue row is rodney culver .
Output: and { only { filter_eq { all_rows ; college ; notre dame } } ; eq { hop { filter_eq { all_rows ; college ; notre dame } ; player } ; rodney culver } }
select the rows whose high assists record fuzzily matches to mo williams . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; high assists ; mo williams } } ; 2 }
for the date records of all rows , all of them fuzzily match to 10 may 1969 .
Output: all_eq { all_rows ; date ; 10 may 1969 }
select the rows whose ground record fuzzily matches to football park . among these rows , select the rows whose crowd record is less than 16000 . the number of such rows is 1 .
Output: eq { count { filter_less { filter_eq { all_rows ; ground ; football park } ; crowd ; 16000 } } ; 1 }
select the row whose kerry record of all rows is 2nd maximum . the county record of this row is fairfield .
Output: eq { hop { nth_argmax { all_rows ; kerry ; 2 } ; county } ; fairfield }
select the row whose time record of all rows is maximum . the game record of this row is 1 .
Output: eq { hop { argmax { all_rows ; time } ; game } ; 1 }
select the row whose winnings record of all rows is maximum . the year record of this row is 2004 .
Output: eq { hop { argmax { all_rows ; winnings } ; year } ; 2004 }
for the district records of all rows , all of them fuzzily match to pennsylvania .
Output: all_eq { all_rows ; district ; pennsylvania }
select the rows whose original channel record fuzzily matches to bbc one . there is only one such row in the table . the programme record of this unqiue row is superstars .
Output: and { only { filter_eq { all_rows ; original channel ; bbc one } } ; eq { hop { filter_eq { all_rows ; original channel ; bbc one } ; programme } ; superstars } }
select the rows whose player record fuzzily matches to leron mitchell . take the pick record of this row . select the rows whose player record fuzzily matches to aaron wagner . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; leron mitchell } ; pick } ; hop { filter_eq { all_rows ; player ; aaron wagner } ; pick } }
the sum of the number of seasons in superettan record of all rows is 76 .
Output: round_eq { sum { all_rows ; number of seasons in superettan } ; 76 }
select the rows whose incumbent record fuzzily matches to edward everett eslick . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to gordon browning . take the first elected record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; incumbent ; edward everett eslick } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; gordon browning } ; first elected } }
select the rows whose rank record is greater than or equal to 4 . the sum of the total record of these rows is 5 .
Output: round_eq { sum { filter_greater_eq { all_rows ; rank ; 4 } ; total } ; 5 }
select the rows whose product record fuzzily matches to grain . take the 2001 record of this row . select the rows whose product record fuzzily matches to timber . take the 2001 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; product ; grain } ; 2001 } ; hop { filter_eq { all_rows ; product ; timber } ; 2001 } }
the 4th minimum episode record of all rows is 4 . the date record of the row with 4th minimum episode record is 23 january 1988 .
Output: and { eq { nth_min { all_rows ; episode ; 4 } ; 4 } ; eq { hop { nth_argmin { all_rows ; episode ; 4 } ; date } ; 23 january 1988 } }
select the row whose score record of all rows is maximum . the opposition record of this row is surrey .
Output: eq { hop { argmax { all_rows ; score } ; opposition } ; surrey }
select the rows whose silver record is greater than 0 . the number of such rows is 5 .
Output: eq { count { filter_greater { all_rows ; silver ; 0 } } ; 5 }
select the row whose population , in thousands , ( 1905 ) record of all rows is maximum . the governorate record of this row is warsaw governorate .
Output: eq { hop { argmax { all_rows ; population , in thousands , ( 1905 ) } ; governorate } ; warsaw governorate }
select the row whose date record of all rows is 1st minimum . the opponent record of this row is matea mezak .
Output: eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; matea mezak }
for the date records of all rows , all of them are greater than 2000 .
Output: all_greater { all_rows ; date ; 2000 }
select the row whose poll losses record of all rows is maximum . the advocate record of this row is andrew daly .
Output: eq { hop { argmax { all_rows ; poll losses } ; advocate } ; andrew daly }
select the row whose games record of all rows is 2nd maximum . the player record of this row is david gonzalvez .
Output: eq { hop { nth_argmax { all_rows ; games ; 2 } ; player } ; david gonzalvez }
select the rows whose televote record fuzzily matches to 2.33 % . there is only one such row in the table . the artist record of this unqiue row is simona sivanio .
Output: and { only { filter_eq { all_rows ; televote ; 2.33 % } } ; eq { hop { filter_eq { all_rows ; televote ; 2.33 % } ; artist } ; simona sivanio } }
the maximum population ( 2010 census ) record of all rows is 241528 . the district record of the row with superlative population ( 2010 census ) record is sampaloc .
Output: and { eq { max { all_rows ; population ( 2010 census ) } ; 241528 } ; eq { hop { argmax { all_rows ; population ( 2010 census ) } ; district } ; sampaloc } }
select the row whose dolphins points record of all rows is 2nd maximum . the date record of this row is sept 23 .
Output: eq { hop { nth_argmax { all_rows ; dolphins points ; 2 } ; date } ; sept 23 }
select the rows whose gold record is equal to 13 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; gold ; 13 } } ; 2 }
the average of the viewers record of all rows is 880000 .
Output: round_eq { avg { all_rows ; viewers } ; 880000 }
select the rows whose protocol record fuzzily matches to proprietary . among these rows , select the rows whose license record fuzzily matches to apache license . there is only one such row in the table . the project record of this unqiue row is jxta remote desktop .
Output: and { only { filter_eq { filter_eq { all_rows ; protocol ; proprietary } ; license ; apache license } } ; eq { hop { filter_eq { filter_eq { all_rows ; protocol ; proprietary } ; license ; apache license } ; project } ; jxta remote desktop } }
select the rows whose category record fuzzily matches to best overseas new artist . take the year record of this row . select the rows whose category record fuzzily matches to songs of the year . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; category ; best overseas new artist } ; year } ; hop { filter_eq { all_rows ; category ; songs of the year } ; year } }
select the rows whose recorded record fuzzily matches to 1980 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; recorded ; 1980 } }
select the row whose area record of all rows is maximum . the rr romaja record of this row is gyeongsangbuk .
Output: eq { hop { argmax { all_rows ; area } ; rr romaja } ; gyeongsangbuk }
select the rows whose wickets record fuzzily matches to none . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; wickets ; none } } ; 5 }
select the rows whose team 2 record fuzzily matches to d3 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; team 2 ; d3 } }
for the largest ethnic group ( year ) records of all rows , most of them fuzzily match to serbs .
Output: most_eq { all_rows ; largest ethnic group ( year ) ; serbs }
select the row whose year record of all rows is 2nd minimum . the director record of this row is john ford .
Output: eq { hop { nth_argmin { all_rows ; year ; 2 } ; director } ; john ford }
select the row whose points record of all rows is maximum . the team record of this row is sc internacional de são paulo .
Output: eq { hop { argmax { all_rows ; points } ; team } ; sc internacional de são paulo }
select the rows whose player record fuzzily matches to brad lamb . take the round record of this row . select the rows whose player record fuzzily matches to mark maddox . take the round record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; brad lamb } ; round } ; hop { filter_eq { all_rows ; player ; mark maddox } ; round } } ; -1 }
select the rows whose manner of departure record fuzzily matches to resigned . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; manner of departure ; resigned } } ; 6 }
select the rows whose pole position record fuzzily matches to mattias ekström . take the date record of this row . select the rows whose pole position record fuzzily matches to oliver jarvis . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; pole position ; mattias ekström } ; date } ; hop { filter_eq { all_rows ; pole position ; oliver jarvis } ; date } }
select the row whose date record of all rows is minimum . the venue record of this row is texas stadium .
Output: eq { hop { argmin { all_rows ; date } ; venue } ; texas stadium }
the average of the pick record of all rows is 13.14 .
Output: round_eq { avg { all_rows ; pick } ; 13.14 }
select the rows whose top - 25 record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; top - 25 ; 0 } } ; 2 }
select the rows whose directed by record fuzzily matches to guy bee . there is only one such row in the table . the title record of this unqiue row is family matters .
Output: and { only { filter_eq { all_rows ; directed by ; guy bee } } ; eq { hop { filter_eq { all_rows ; directed by ; guy bee } ; title } ; family matters } }
select the rows whose winner 's share record is equal to 8400 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; winner 's share ; 8400 } } ; 3 }
select the row whose loses record of all rows is 3rd maximum . the club record of this row is nevėžis kėdainiai .
Output: eq { hop { nth_argmax { all_rows ; loses ; 3 } ; club } ; nevėžis kėdainiai }
select the row whose year record of all rows is 2nd maximum . the year record of this row is 2012 .
Output: eq { hop { nth_argmax { all_rows ; year ; 2 } ; year } ; 2012 }
select the row whose number of electorates ( 2003 ) record of all rows is maximum . the name record of this row is niwari .
Output: eq { hop { argmax { all_rows ; number of electorates ( 2003 ) } ; name } ; niwari }
select the row whose discontinued record of all rows is 2nd minimum . the model record of this row is lisa 2 .
Output: eq { hop { nth_argmin { all_rows ; discontinued ; 2 } ; model } ; lisa 2 }
select the rows whose directed by record fuzzily matches to tucker gates . there is only one such row in the table . the title record of this unqiue row is the road to damascus .
Output: and { only { filter_eq { all_rows ; directed by ; tucker gates } } ; eq { hop { filter_eq { all_rows ; directed by ; tucker gates } ; title } ; the road to damascus } }
select the rows whose nickname record fuzzily matches to saints . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; nickname ; saints } } ; 1 }
for the location records of all rows , most of them fuzzily match to japan .
Output: most_eq { all_rows ; location ; japan }
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 .
Output: most_eq { filter_eq { all_rows ; date ; 1990 - 09 } ; result ; l }
the 1st minimum time record of all rows is 2:25.46 . the rank record of the row with 1st minimum time record is 1 . the name record of the row with 1st minimum time record is kristy kowal .
Output: and { eq { nth_min { all_rows ; time ; 1 } ; 2:25.46 } ; and { eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 } ; eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; kristy kowal } } }
select the rows whose team record fuzzily matches to dinamo minsk . take the position in 1992 record of this row . select the rows whose team record fuzzily matches to dinamo brest . take the position in 1992 record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; team ; dinamo minsk } ; position in 1992 } ; hop { filter_eq { all_rows ; team ; dinamo brest } ; position in 1992 } }
select the rows whose performer 4 record fuzzily matches to tony slattery . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; performer 4 ; tony slattery } } ; 7 }
select the rows whose chassis record fuzzily matches to ford fiesta . the sum of the points record of these rows is 727 .
Output: round_eq { sum { filter_eq { all_rows ; chassis ; ford fiesta } ; points } ; 727 }
select the row whose length record of all rows is 3rd maximum . the model record of this row is flxible 35096 - 6 - 1 .
Output: eq { hop { nth_argmax { all_rows ; length ; 3 } ; model } ; flxible 35096 - 6 - 1 }
select the rows whose opponent record fuzzily matches to philadelphia eagles . take the result record of this row . select the rows whose opponent record fuzzily matches to chicago bears . take the result record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; result } ; hop { filter_eq { all_rows ; opponent ; chicago bears } ; result } }
for the date records of all rows , most of them fuzzily match to january .
Output: most_eq { all_rows ; date ; january }
select the row whose erp / power w record of all rows is 2nd maximum . the call sign record of this row is wrli - fm .
Output: eq { hop { nth_argmax { all_rows ; erp / power w ; 2 } ; call sign } ; wrli - fm }
select the rows whose leading scorer record fuzzily matches to tim duncan . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; leading scorer ; tim duncan } } ; 7 }
select the row whose date of successors formal installation record of all rows is minimum . the successor record of this row is john parker hale ( r ) .
Output: eq { hop { argmin { all_rows ; date of successors formal installation } ; successor } ; john parker hale ( r ) }
select the row whose league record of all rows is 3rd maximum . the name record of this row is bill foulkes .
Output: eq { hop { nth_argmax { all_rows ; league ; 3 } ; name } ; bill foulkes }
select the row whose frequency mhz record of all rows is maximum . the call sign record of this row is w283au .
Output: eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w283au }
select the row whose pts record of all rows is maximum . the entrant record of this row is leyton house march racing team .
Output: eq { hop { argmax { all_rows ; pts } ; entrant } ; leyton house march racing team }
select the rows whose clean & jerk record is greater than 110 . there is only one such row in the table . the name record of this unqiue row is maryse turcotte ( can ) .
Output: and { only { filter_greater { all_rows ; clean & jerk ; 110 } } ; eq { hop { filter_greater { all_rows ; clean & jerk ; 110 } ; name } ; maryse turcotte ( can ) } }
select the rows whose surface record fuzzily matches to hard . take the score record of this row . select the rows whose surface record fuzzily matches to clay . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; surface ; hard } ; score } ; hop { filter_eq { all_rows ; surface ; clay } ; score } }
select the rows whose date record fuzzily matches to november . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; date ; november } } ; 4 }
for the score records of all rows , most of them are greater than or equal to 70 .
Output: most_greater_eq { all_rows ; score ; 70 }
select the rows whose opponent record fuzzily matches to daniela hantuchová . take the edition record of this row . select the rows whose opponent record fuzzily matches to kirsten flipkens . take the edition record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; daniela hantuchová } ; edition } ; hop { filter_eq { all_rows ; opponent ; kirsten flipkens } ; edition } }
select the rows whose directed by record fuzzily matches to paul holahan . for the us viewers ( millions ) records of these rows , most of them are greater than 4.5 .
Output: most_greater { filter_eq { all_rows ; directed by ; paul holahan } ; us viewers ( millions ) ; 4.5 }
select the rows whose years in assembly record fuzzily matches to 2008 . among these rows , select the rows whose residence record fuzzily matches to san francisco . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; years in assembly ; 2008 } ; residence ; san francisco } } ; 2 }
select the rows whose name record fuzzily matches to kim hyun - seok . take the total assists record of this row . select the rows whose name record fuzzily matches to lee dong - gook . take the total assists record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; kim hyun - seok } ; total assists } ; hop { filter_eq { all_rows ; name ; lee dong - gook } ; total assists } }
select the rows whose laps record is greater than or equal to 200 . select the row whose rank record of these rows is 2nd maximum . the year record of this row is 1953 .
Output: eq { hop { nth_argmax { filter_greater_eq { all_rows ; laps ; 200 } ; rank ; 2 } ; year } ; 1953 }
for the date of successors formal installation records of all rows , most of them fuzzily match to elected .
Output: most_eq { all_rows ; date of successors formal installation ; elected }
select the rows whose player record fuzzily matches to scott garlick . take the games record of this row . select the rows whose player record fuzzily matches to dj countess . take the games record of this row . the first record is 4 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; scott garlick } ; games } ; hop { filter_eq { all_rows ; player ; dj countess } ; games } } ; 4 }
for the high points records of all rows , most of them fuzzily match to lennox .
Output: most_eq { all_rows ; high points ; lennox }
select the row whose floors record of all rows is maximum . the name record of this row is commerzbank tower .
Output: eq { hop { argmax { all_rows ; floors } ; name } ; commerzbank tower }
for the event records of all rows , most of them do not match to team competitions .
Output: most_not_eq { all_rows ; event ; team competitions }
select the row whose age record of all rows is maximum . the candidate record of this row is kirsty parkhill .
Output: eq { hop { argmax { all_rows ; age } ; candidate } ; kirsty parkhill }
select the rows whose driver / passenger record fuzzily matches to jan hendrickx / tim smeuninx . take the points record of this row . select the rows whose driver / passenger record fuzzily matches to marko happich / meinrad schelbert . take the points record of this row . the first record is greater than the second record . the points record of the first row is 405 . the points record of the second row is 317 .
Output: and { greater { hop { filter_eq { all_rows ; driver / passenger ; jan hendrickx / tim smeuninx } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; marko happich / meinrad schelbert } ; points } } ; and { eq { hop { filter_eq { all_rows ; driver / passenger ; jan hendrickx / tim smeuninx } ; points } ; 405 } ; eq { hop { filter_eq { all_rows ; driver / passenger ; marko happich / meinrad schelbert } ; points } ; 317 } } }
select the rows whose opponent record fuzzily matches to ramazan ramazanov . take the round record of this row . select the rows whose opponent record fuzzily matches to eduardo maiorino . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; ramazan ramazanov } ; round } ; hop { filter_eq { all_rows ; opponent ; eduardo maiorino } ; round } }
select the rows whose written by record fuzzily matches to adam barr . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; written by ; adam barr } } ; 2 }
select the row whose originalairdate record of all rows is 2nd minimum . the title record of this row is seeds .
Output: eq { hop { nth_argmin { all_rows ; originalairdate ; 2 } ; title } ; seeds }
select the rows whose status record fuzzily matches to re - elected . the number of such rows is 10 .
Output: eq { count { filter_eq { all_rows ; status ; re - elected } } ; 10 }
select the rows whose clinical status record fuzzily matches to fda - approved . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; clinical status ; fda - approved } } ; 6 }
select the rows whose total record is greater than 13 . the average of the fighting spirit record of these rows is 5 .
Output: round_eq { avg { filter_greater { all_rows ; total ; 13 } ; fighting spirit } ; 5 }
select the rows whose opponent record fuzzily matches to jacksonville jaguars . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 68809 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; jacksonville jaguars } ; date ; 2 } ; attendance } ; 68809 }
select the row whose profits ( billion ) record of all rows is maximum . the company record of this row is exxonmobil .
Output: eq { hop { argmax { all_rows ; profits ( billion ) } ; company } ; exxonmobil }
select the rows whose points record is not equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_not_eq { all_rows ; points ; 0 } } ; 2 }
select the rows whose player record fuzzily matches to julien cayer . take the overall pick record of this row . select the rows whose player record fuzzily matches to gustav nyquist . take the overall pick record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; julien cayer } ; overall pick } ; hop { filter_eq { all_rows ; player ; gustav nyquist } ; overall pick } }
select the row whose first broadcast record of all rows is 2nd maximum . the episode record of this row is 3x07 .
Output: eq { hop { nth_argmax { all_rows ; first broadcast ; 2 } ; episode } ; 3x07 }
the average of the attendance record of all rows is 58246 .
Output: round_eq { avg { all_rows ; attendance } ; 58246 }
select the rows whose date record fuzzily matches to / 05 / . the 1st maximum against record of these rows is 22 .
Output: eq { nth_max { filter_eq { all_rows ; date ; / 05 / } ; against ; 1 } ; 22 }
select the rows whose team record fuzzily matches to san antonio . the maximum location attendance record of these rows is staples center 18997 .
Output: eq { max { filter_eq { all_rows ; team ; san antonio } ; location attendance } ; staples center 18997 }
for the qual 1 records of all rows , most of them are less than 1:18:000 .
Output: most_less { all_rows ; qual 1 ; 1:18:000 }
select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } }
select the rows whose player record is arbitrary . the number of such rows is 8 .
Output: eq { count { filter_all { all_rows ; player } } ; 8 }
select the row whose date record of all rows is 3rd minimum . the centerfold model record of this row is sandra hubby .
Output: eq { hop { nth_argmin { all_rows ; date ; 3 } ; centerfold model } ; sandra hubby }
select the rows whose style record fuzzily matches to west coast swing . there is only one such row in the table . the week record of this unqiue row is 4 .
Output: and { only { filter_eq { all_rows ; style ; west coast swing } } ; eq { hop { filter_eq { all_rows ; style ; west coast swing } ; week } ; 4 } }
select the rows whose artist record fuzzily matches to demos beke . take the points record of this row . select the rows whose artist record fuzzily matches to lucas christodolou . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; artist ; demos beke } ; points } ; hop { filter_eq { all_rows ; artist ; lucas christodolou } ; points } }
select the row whose established record of all rows is 2nd maximum . the league record of this row is ahl .
Output: eq { hop { nth_argmax { all_rows ; established ; 2 } ; league } ; ahl }
the average of the secondary ( 14 - 17 years ) record of all rows is 70.95 .
Output: round_eq { avg { all_rows ; secondary ( 14 - 17 years ) } ; 70.95 }
select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is keith ellison .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; keith ellison }
select the rows whose year record fuzzily matches to 1980 . take the top team in regular season ( points ) record of this row . select the rows whose year record fuzzily matches to 1982 . take the top team in regular season ( points ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1980 } ; top team in regular season ( points ) } ; hop { filter_eq { all_rows ; year ; 1982 } ; top team in regular season ( points ) } }
select the rows whose venue record fuzzily matches to anyang stadium , anyang . there is only one such row in the table . the date record of this unqiue row is 15 august 2012 .
Output: and { only { filter_eq { all_rows ; venue ; anyang stadium , anyang } } ; eq { hop { filter_eq { all_rows ; venue ; anyang stadium , anyang } ; date } ; 15 august 2012 } }
select the rows whose born / died record fuzzily matches to present . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; born / died ; present } } ; 2 }
for the crowd records of all rows , most of them are greater than 20000 .
Output: most_greater { all_rows ; crowd ; 20000 }
select the row whose points record of all rows is maximum . the year record of this row is 2010 .
Output: eq { hop { argmax { all_rows ; points } ; year } ; 2010 }