niryuu/sni-each-converted huggingface dataset:
and { only { filter_greater { all_rows ; total asian population ; 130000 } } ; eq { hop { filter_greater { all_rows ; total asian population ; 130000 } ; london borough } ; newham } } = true
Output: select the rows whose total asian population record is greater than 130000 . there is only one such row in the table . the london borough record of this unqiue row is newham .
greater { hop { filter_eq { all_rows ; date ; january 29 } ; high points } ; hop { filter_eq { all_rows ; date ; january 1 } ; high points } } = true
Output: select the rows whose date record fuzzily matches to january 29 . take the high points record of this row . select the rows whose date record fuzzily matches to january 1 . take the high points record of this row . the first record is greater than the second record .
and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } } = true
Output: select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 .
eq { hop { argmax { all_rows ; original air date } ; title } ; the enforcers } = true
Output: select the row whose original air date record of all rows is maximum . the title record of this row is the enforcers .
round_eq { avg { all_rows ; score } ; 2.375 } = true
Output: the average of the score record of all rows is 2.375 .
eq { count { filter_eq { all_rows ; location ( city , state ) ; wisconsin } } ; 4 } = true
Output: select the rows whose location  city , state  record fuzzily matches to wisconsin . the number of such rows is 4 .
all_eq { all_rows ; date ; 30 august 1975 } = true
Output: for the date records of all rows , all of them fuzzily match to 30 august 1975 .
eq { count { filter_eq { filter_eq { all_rows ; event ; ufc } ; res ; win } } ; 8 } = true
Output: select the rows whose event record fuzzily matches to ufc . among these rows , select the rows whose res record fuzzily matches to win . the number of such rows is 8 .
round_eq { avg { all_rows ; international tourist arrivals ( 2012 ) } ; 13.4 million } = true
Output: the average of the international tourist arrivals  2012  record of all rows is 13.4 million .
most_eq { all_rows ; date of vacancy ; 2010 } = true
Output: for the date of vacancy records of all rows , most of them fuzzily match to 2010 .
less { hop { filter_eq { all_rows ; athlete ; mayra gonzález } ; time } ; hop { filter_eq { all_rows ; athlete ; camila vargas } ; time } } = true
Output: select the rows whose athlete record fuzzily matches to mayra gonzález . take the time record of this row . select the rows whose athlete record fuzzily matches to camila vargas . take the time record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; crowd } ; 13333 } = true
Output: the average of the crowd record of all rows is 13333 .
eq { count { filter_eq { all_rows ; location ; tokyo , japan } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to tokyo , japan . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; written by ; david wolkove } } ; 2 } = true
Output: select the rows whose written by record fuzzily matches to david wolkove . the number of such rows is 2 .
and { only { filter_less { all_rows ; us viewers ( in millions ) ; 8 } } ; eq { hop { filter_less { all_rows ; us viewers ( in millions ) ; 8 } ; no by series } ; 6 } } = true
Output: select the rows whose us viewers  in millions  record is less than 8 . there is only one such row in the table . the no by series record of this unqiue row is 6 .
eq { hop { nth_argmax { filter_less { all_rows ; average annual rainfall ( mm ) ; 100 } ; average annual runoff ( mm ) ; 1 } ; administrative region } ; i - tarapacá } = true
Output: select the rows whose average annual rainfall  mm  record is less than 100 . select the row whose average annual runoff  mm  record of these rows is 1st maximum . the administrative region record of this row is i  tarapacá .
eq { hop { nth_argmin { all_rows ; planned release ; 2 } ; name } ; moon dog } = true
Output: select the row whose planned release record of all rows is 2nd minimum . the name record of this row is moon dog .
eq { hop { argmax { all_rows ; height } ; name } ; rodrigo santana } = true
Output: select the row whose height record of all rows is maximum . the name record of this row is rodrigo santana .
eq { hop { argmax { all_rows ; score } ; date } ; 15 june 1958 } = true
Output: select the row whose score record of all rows is maximum . the date record of this row is 15 june 1958 .
most_greater { all_rows ; specific impulse ( s ) ; 1000 } = true
Output: for the specific impulse  s  records of all rows , most of them are greater than 1000 .
and { only { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } } ; eq { hop { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } ; date } ; 23 march 2013 } } = true
Output: select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification . there is only one such row in the table . the date record of this unqiue row is 23 march 2013 .
eq { count { filter_eq { all_rows ; nation ; spain } } ; 3 } = true
Output: select the rows whose nation record fuzzily matches to spain . the number of such rows is 3 .
all_eq { filter_eq { all_rows ; surface ; clay } ; outcome ; runner-up } = true
Output: select the rows whose surface record fuzzily matches to clay . for the outcome records of these rows , all of them fuzzily match to runnerup .
most_eq { all_rows ; company ; bbc audio } = true
Output: for the company records of all rows , most of them fuzzily match to bbc audio .
eq { count { filter_eq { all_rows ; starring ; warren mitchell as alf garnett } } ; 3 } = true
Output: select the rows whose starring record fuzzily matches to warren mitchell as alf garnett . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; december 9 , 1984 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is december 9 , 1984 .
round_eq { avg { all_rows ; length } ; 5:04 } = true
Output: the average of the length record of all rows is 5:04 .
less { hop { filter_eq { all_rows ; week ; top 24 ( 12 women ) } ; order } ; hop { filter_eq { all_rows ; week ; top 20 ( 10 women ) } ; order } } = true
Output: select the rows whose week record fuzzily matches to top 24  12 women  . take the order record of this row . select the rows whose week record fuzzily matches to top 20  10 women  . take the order record of this row . the first record is less than the second record .
most_eq { all_rows ; frequency ; 00 9 } = true
Output: for the frequency records of all rows , most of them fuzzily match to 00 9 .
round_eq { avg { all_rows ; gold } ; 3.7 } = true
Output: the average of the gold record of all rows is 3.7 .
and { only { filter_greater { all_rows ; time ; 6:00 } } ; eq { hop { filter_greater { all_rows ; time ; 6:00 } ; opponent } ; masaaki satake } } = true
Output: select the rows whose time record is greater than 6:00 . there is only one such row in the table . the opponent record of this unqiue row is masaaki satake .
eq { count { filter_eq { filter_less { all_rows ; laps ; 25 } ; time / retired ; ignition } } ; 2 } = true
Output: select the rows whose laps record is less than 25 . among these rows , select the rows whose time  retired record fuzzily matches to ignition . the number of such rows is 2 .
eq { hop { argmin { all_rows ; date } ; location attendance } ; td banknorth garden 18624 } = true
Output: select the row whose date record of all rows is minimum . the location attendance record of this row is td banknorth garden 18624 .
greater { hop { filter_eq { all_rows ; internet plan ; internet 100 } ; upstream } ; hop { filter_eq { all_rows ; internet plan ; internet 30 } ; upstream } } = true
Output: 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 .
and { only { filter_eq { all_rows ; points ; 47 } } ; eq { hop { filter_eq { all_rows ; points ; 47 } ; game } ; 32 } } = true
Output: select the rows whose points record is equal to 47 . there is only one such row in the table . the game record of this unqiue row is 32 .
eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; william j driver } = true
Output: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is william j driver .
eq { count { filter_eq { all_rows ; location ; dodger } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to dodger . the number of such rows is 3 .
most_eq { all_rows ; date ; june } = true
Output: for the date records of all rows , most of them fuzzily match to june .
eq { max { filter_eq { all_rows ; nationality ; soviet union } ; pick } ; 185 } = true
Output: select the rows whose nationality record fuzzily matches to soviet union . the maximum pick record of these rows is 185 .
only { filter_eq { all_rows ; technology ; wifi } } = true
Output: select the rows whose technology record fuzzily matches to wifi . there is only one such row in the table .
eq { nth_min { filter_eq { all_rows ; result ; w } ; date ; 2 } ; november 28 , 1999 } = true
Output: select the rows whose result record fuzzily matches to w . the 2nd minimum date record of these rows is november 28 , 1999 .
round_eq { avg { all_rows ; enrollment } ; 1128.5 } = true
Output: the average of the enrollment record of all rows is 1128.5 .
most_eq { filter_eq { all_rows ; high rebounds ; catchings } ; high points ; catchings } = true
Output: select the rows whose high rebounds record fuzzily matches to catchings . for the high points records of these rows , most of them fuzzily match to catchings .
less { hop { filter_eq { all_rows ; player ; bernie fryer } ; years for jazz } ; hop { filter_eq { all_rows ; player ; derek fisher } ; years for jazz } } = true
Output: select the rows whose player record fuzzily matches to bernie fryer . take the years for jazz record of this row . select the rows whose player record fuzzily matches to derek fisher . take the years for jazz record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; player ; jared borgetti } ; period } ; hop { filter_eq { all_rows ; player ; rafael figueroa } ; period } } = true
Output: select the rows whose player record fuzzily matches to jared borgetti . take the period record of this row . select the rows whose player record fuzzily matches to rafael figueroa . take the period record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; poles } ; 20 } = true
Output: the sum of the poles record of all rows is 20 .
most_eq { all_rows ; current status ; active } = true
Output: for the current status records of all rows , most of them fuzzily match to active .
less { hop { filter_eq { all_rows ; city of license ; ellijay , georgia } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; morrow , georgia } ; frequency mhz } } = true
Output: select the rows whose city of license record fuzzily matches to ellijay , georgia . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to morrow , georgia . take the frequency mhz record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; party ; conservative } ; votes } ; hop { filter_eq { all_rows ; party ; labour } ; votes } } = true
Output: select the rows whose party record fuzzily matches to conservative . take the votes record of this row . select the rows whose party record fuzzily matches to labour . take the votes record of this row . the first record is greater than the second record .
most_greater_eq { all_rows ; episodes ; 40 } = true
Output: for the episodes records of all rows , most of them are greater than or equal to 40 .
eq { count { filter_eq { filter_eq { all_rows ; type ; transferred } ; transfer fee ; undisclosed } } ; 3 } = true
Output: select the rows whose type record fuzzily matches to transferred . among these rows , select the rows whose transfer fee record fuzzily matches to undisclosed . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; tournament ; buenos aires } ; date } ; hop { filter_eq { all_rows ; tournament ; panama city } ; date } } = true
Output: select the rows whose tournament record fuzzily matches to buenos aires . take the date record of this row . select the rows whose tournament record fuzzily matches to panama city . take the date record of this row . the first record is less than the second record .
eq { hop { argmin { filter_eq { all_rows ; class ; p 2 } ; quantity } ; year ( s ) built } ; 1886 } = true
Output: select the rows whose class record fuzzily matches to p 2 . select the row whose quantity record of these rows is minimum . the year  s  built record of this row is 1886 .
most_eq { all_rows ; high assists ; g payton } = true
Output: for the high assists records of all rows , most of them fuzzily match to g payton .
less { hop { filter_eq { all_rows ; name ; willie adams } ; pick } ; hop { filter_eq { all_rows ; name ; bob reed } ; pick } } = true
Output: select the rows whose name record fuzzily matches to willie adams . take the pick record of this row . select the rows whose name record fuzzily matches to bob reed . take the pick record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; rank ; 4th } } ; eq { hop { filter_eq { all_rows ; rank ; 4th } ; year } ; 2003 } } = true
Output: select the rows whose rank record fuzzily matches to 4th . there is only one such row in the table . the year record of this unqiue row is 2003 .
eq { count { filter_eq { all_rows ; result ; won } } ; 3 } = true
Output: select the rows whose result record fuzzily matches to won . the number of such rows is 3 .
and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } ; friendly } } } = true
Output: select the rows whose date record fuzzily matches to 16 august 2000 . take the competition record of this row . select the rows whose date record fuzzily matches to 22 august 2007 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; barney frank } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is barney frank .
eq { diff { hop { filter_eq { all_rows ; theme ; great blue heron } ; issue price } ; hop { filter_eq { all_rows ; theme ; common eider } ; issue price } } ; 2 } = true
Output: select the rows whose theme record fuzzily matches to great blue heron . take the issue price record of this row . select the rows whose theme record fuzzily matches to common eider . take the issue price record of this row . the first record is 2 larger than the second record .
eq { count { filter_less { all_rows ; frequency mhz ; 100 } } ; 3 } = true
Output: select the rows whose frequency mhz record is less than 100 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; points } ; team } ; fh } = true
Output: select the row whose points record of all rows is maximum . the team record of this row is fh .
most_greater { all_rows ; jersey number ( s ) ; 10 } = true
Output: for the jersey number  s  records of all rows , most of them are greater than 10 .
less { hop { filter_eq { all_rows ; opponent ; iowa } ; date } ; hop { filter_eq { all_rows ; opponent ; 4 michigan } ; date } } = true
Output: select the rows whose opponent record fuzzily matches to iowa . take the date record of this row . select the rows whose opponent record fuzzily matches to 4 michigan . take the date record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; races } ; 164 } = true
Output: the sum of the races record of all rows is 164 .
and { eq { nth_min { filter_eq { all_rows ; position ; defense } ; round ; 4 } ; 4 } ; eq { hop { nth_argmin { filter_eq { all_rows ; position ; defense } ; round ; 4 } ; player } ; dave hynek } } = true
Output: select the rows whose position record fuzzily matches to defense . the 4th minimum round record of these rows is 4 . the player record of the row with 4th minimum round record is dave hynek .
less { hop { filter_eq { all_rows ; opponent ; daniela hantuchová } ; edition } ; hop { filter_eq { all_rows ; opponent ; kirsten flipkens } ; edition } } = true
Output: 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 .
round_eq { sum { all_rows ; ties } ; 64 } = true
Output: the sum of the ties record of all rows is 64 .
eq { count { filter_eq { all_rows ; 1st leg ; 0 - 0 } } ; 2 } = true
Output: select the rows whose 1st leg record fuzzily matches to 0  0 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } } = true
Output: select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; home team ; townsville crocodiles } ; score } ; hop { filter_eq { all_rows ; home team ; cairns taipans } ; score } } = true
Output: select the rows whose home team record fuzzily matches to townsville crocodiles . take the score record of this row . select the rows whose home team record fuzzily matches to cairns taipans . take the score record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; venue ; randwick } } ; 5 } = true
Output: select the rows whose venue record fuzzily matches to randwick . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; winning driver ; raphael matos } } ; 1 } = true
Output: select the rows whose winning driver record fuzzily matches to raphael matos . the number of such rows is 1 .
eq { count { filter_eq { filter_eq { all_rows ; nationality ; usa } ; position ; rw } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to usa . among these rows , select the rows whose position record fuzzily matches to rw . the number of such rows is 2 .
round_eq { avg { all_rows ; gold } ; .667 } = true
Output: the average of the gold record of all rows is .667 .
eq { hop { argmax { all_rows ; of seats won } ; election } ; 1984 } = true
Output: select the row whose of seats won record of all rows is maximum . the election record of this row is 1984 .
only { filter_eq { all_rows ; engine ; maserati straight - 6 } } = true
Output: select the rows whose engine record fuzzily matches to maserati straight  6 . there is only one such row in the table .
and { only { filter_eq { all_rows ; gold ; minna kauppi } } ; eq { hop { filter_eq { all_rows ; gold ; minna kauppi } ; year } ; 2006 } } = true
Output: select the rows whose gold record fuzzily matches to minna kauppi . there is only one such row in the table . the year record of this unqiue row is 2006 .
and { only { filter_eq { all_rows ; women 's singles ; sara persson } } ; eq { hop { filter_eq { all_rows ; women 's singles ; sara persson } ; year } ; 2006 } } = true
Output: select the rows whose women s singles record fuzzily matches to sara persson . there is only one such row in the table . the year record of this unqiue row is 2006 .
eq { min { all_rows ; date } ; september 4 , 1994 } = true
Output: the minimum date record of all rows is september 4 , 1994 .
eq { hop { argmax { all_rows ; area km 2 } ; official name } ; salisbury } = true
Output: select the row whose area km 2 record of all rows is maximum . the official name record of this row is salisbury .
eq { hop { argmax { all_rows ; points } ; team } ; olympiacos cfp } = true
Output: select the row whose points record of all rows is maximum . the team record of this row is olympiacos cfp .
and { eq { nth_min { all_rows ; year ; 1 } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; year } ; 1996 } ; and { eq { hop { nth_argmin { all_rows ; year ; 1 } ; category } ; 60 minute category } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; result } ; nominated } } } } = true
Output: the 1st minimum year record of all rows is 1996 . the year record of the row with 1st minimum year record is 1996 . the category record of the row with 1st minimum year record is 60 minute category . the result record of the row with 1st minimum year record is nominated .
only { filter_eq { all_rows ; tournament ; china } } = true
Output: select the rows whose tournament record fuzzily matches to china . there is only one such row in the table .
round_eq { avg { filter_less_eq { all_rows ; week ; 5 } ; attendance } ; 74,287 } = true
Output: select the rows whose week record is less than or equal to 5 . the average of the attendance record of these rows is 74,287 .
eq { hop { nth_argmin { all_rows ; home team score ; 2 } ; home team } ; fitzroy } = true
Output: select the row whose home team score record of all rows is 2nd minimum . the home team record of this row is fitzroy .
eq { count { filter_less { filter_greater { all_rows ; parishes ; 60 } ; high schools ; 5 } } ; 2 } = true
Output: select the rows whose parishes record is greater than 60 . among these rows , select the rows whose high schools record is less than 5 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 7 } } = true
Output: select the rows whose date record is equal to  . there is only one such row in the table . the week record of this unqiue row is 7 .
and { only { filter_greater { all_rows ; total ; 100 } } ; eq { hop { filter_greater { all_rows ; total ; 100 } ; nation } ; united states } } = true
Output: select the rows whose total record is greater than 100 . there is only one such row in the table . the nation record of this unqiue row is united states .
eq { count { filter_less { all_rows ; difference ; 0 } } ; 4 } = true
Output: select the rows whose difference record is less than 0 . the number of such rows is 4 .
eq { hop { nth_argmin { all_rows ; pick ; 1 } ; player } ; toni stãhl } = true
Output: select the row whose pick record of all rows is 1st minimum . the player record of this row is toni stãhl .
most_eq { all_rows ; laps led ; 0 } = true
Output: for the laps led records of all rows , most of them are equal to 0 .
eq { hop { nth_argmax { all_rows ; position ; 2 } ; year } ; 1987 } = true
Output: select the row whose position record of all rows is 2nd maximum . the year record of this row is 1987 .
most_greater { all_rows ; single day net gross ; 25 , 00 , 00000 } = true
Output: for the single day net gross records of all rows , most of them are greater than 25 , 00 , 00000 .
most_not_eq { all_rows ; lkf cup ; champion } = true
Output: for the lkf cup records of all rows , most of them do not match to champion .
and { only { filter_eq { all_rows ; 2011 ; qf } } ; eq { hop { filter_eq { all_rows ; 2011 ; qf } ; tournament } ; french open } } = true
Output: select the rows whose 2011 record fuzzily matches to qf . there is only one such row in the table . the tournament record of this unqiue row is french open .
eq { hop { nth_argmin { all_rows ; week ; 1 } ; opponent } ; detroit lions } = true
Output: select the row whose week record of all rows is 1st minimum . the opponent record of this row is detroit lions .
eq { hop { argmin { all_rows ; first elected } ; incumbent } ; philip r thompson } = true
Output: select the row whose first elected record of all rows is minimum . the incumbent record of this row is philip r thompson .
eq { hop { nth_argmax { all_rows ; founded ; 2 } ; institution } ; high point university } = true
Output: select the row whose founded record of all rows is 2nd maximum . the institution record of this row is high point university .
less { hop { filter_eq { all_rows ; designated home ; miami dolphins } ; year } ; hop { filter_eq { all_rows ; designated home ; new orleans saints } ; year } } = true
Output: select the rows whose designated home record fuzzily matches to miami dolphins . take the year record of this row . select the rows whose designated home record fuzzily matches to new orleans saints . take the year record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; candidates ; david bard } } ; 2 } = true
Output: select the rows whose candidates record fuzzily matches to david bard . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; tournament ; indianapolis } ; date } ; hop { filter_eq { all_rows ; tournament ; palm beach gardens } ; date } } = true
Output: select the rows whose tournament record fuzzily matches to indianapolis . take the date record of this row . select the rows whose tournament record fuzzily matches to palm beach gardens . take the date record of this row . the first record is less than the second record .
round_eq { avg { filter_eq { all_rows ; parent ; the cheviot } ; height ( m ) } ; 642 } = true
Output: select the rows whose parent record fuzzily matches to the cheviot . the average of the height  m  record of these rows is 642 .
eq { count { filter_eq { all_rows ; performer 4 ; tony slattery } } ; 7 } = true
Output: select the rows whose performer 4 record fuzzily matches to tony slattery . the number of such rows is 7 .
round_eq { avg { all_rows ; draws } ; 9.38 } = true
Output: the average of the draws record of all rows is 9.38 .
round_eq { sum { all_rows ; crowd } ; 105500 } = true
Output: the sum of the crowd record of all rows is 105500 .
and { only { filter_eq { all_rows ; opponent ; boris becker } } ; eq { hop { filter_eq { all_rows ; opponent ; boris becker } ; date } ; 1985 } } = true
Output: select the rows whose opponent record fuzzily matches to boris becker . there is only one such row in the table . the date record of this unqiue row is 1985 .
most_less { all_rows ; decile ; 6 } = true
Output: for the decile records of all rows , most of them are less than 6 .
and { only { filter_not_eq { all_rows ; quarterfinals ; did not advance } } ; eq { hop { filter_not_eq { all_rows ; quarterfinals ; did not advance } ; athlete } ; tariq alqallaf } } = true
Output: select the rows whose quarterfinals record does not match to did not advance . there is only one such row in the table . the athlete record of this unqiue row is tariq alqallaf .
and { only { filter_eq { all_rows ; event ; butterfly } } ; eq { hop { filter_eq { all_rows ; event ; butterfly } ; venue } ; rio de janeiro } } = true
Output: select the rows whose event record fuzzily matches to butterfly . there is only one such row in the table . the venue record of this unqiue row is rio de janeiro .
and { only { filter_eq { all_rows ; top - 25 ; 6 } } ; eq { hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament } ; masters tournament } } = true
Output: select the rows whose top  25 record is equal to 6 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .
and { only { filter_eq { all_rows ; platform ( s ) ; wii } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game } ; super mario galaxy 2 } } = true
Output: select the rows whose platform  s  record fuzzily matches to wii . there is only one such row in the table . the game record of this unqiue row is super mario galaxy 2 .
round_eq { avg { all_rows ; time } ; 1:09.00 } = true
Output: the average of the time record of all rows is 1:09.00 .
and { only { filter_eq { all_rows ; position ; forward-center } } ; eq { hop { filter_eq { all_rows ; position ; forward-center } ; player } ; keon clark } } = true
Output: select the rows whose position record fuzzily matches to forwardcenter . there is only one such row in the table . the player record of this unqiue row is keon clark .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; university of cincinnati } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is university of cincinnati .
and { only { filter_eq { all_rows ; chassis ; lola } } ; eq { hop { filter_eq { all_rows ; chassis ; lola } ; year } ; 1993 } } = true
Output: select the rows whose chassis record fuzzily matches to lola . there is only one such row in the table . the year record of this unqiue row is 1993 .
less { hop { filter_eq { all_rows ; category ; helicopter } ; date } ; hop { filter_eq { all_rows ; category ; glider ( sailplane ) } ; date } } = true
Output: select the rows whose category record fuzzily matches to helicopter . take the date record of this row . select the rows whose category record fuzzily matches to glider  sailplane  . take the date record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; position ; catcher } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to catcher . the number of such rows is 2 .
eq { hop { argmin { all_rows ; round } ; name } ; joseph addai } = true
Output: select the row whose round record of all rows is minimum . the name record of this row is joseph addai .
round_eq { sum { filter_eq { all_rows ; result ; win } ; black knights points } ; 85 } = true
Output: select the rows whose result record fuzzily matches to win . the sum of the black knights points record of these rows is 85 .
eq { count { filter_eq { all_rows ; type ; plain stage } } ; 10 } = true
Output: select the rows whose type record fuzzily matches to plain stage . the number of such rows is 10 .
greater { hop { filter_eq { all_rows ; opponent ; vancouver canucks } ; points } ; hop { filter_eq { all_rows ; opponent ; washington capitals } ; points } } = true
Output: select the rows whose opponent record fuzzily matches to vancouver canucks . take the points record of this row . select the rows whose opponent record fuzzily matches to washington capitals . take the points record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; rank ; 3 } } ; 3 } = true
Output: select the rows whose rank record fuzzily matches to 3 . the number of such rows is 3 .
eq { count { filter_all { filter_less_eq { all_rows ; episode ; 175 } ; segment a } } ; 6 } = true
Output: select the rows whose episode record is less than or equal to 175 . among these rows , select the rows whose segment a record is arbitrary . the number of such rows is 6 .
most_eq { all_rows ; tier ; tier ii } = true
Output: for the tier records of all rows , most of them fuzzily match to tier ii .
greater { hop { filter_eq { all_rows ; season ; 2005 } ; races } ; hop { filter_eq { all_rows ; season ; 2012 } ; races } } = true
Output: select the rows whose season record fuzzily matches to 2005 . take the races record of this row . select the rows whose season record fuzzily matches to 2012 . take the races record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; margin of victory } ; champion } ; jiyai shin } = true
Output: select the row whose margin of victory record of all rows is maximum . the champion record of this row is jiyai shin .
round_eq { avg { all_rows ; high assists } ; 4.5 } = true
Output: the average of the high assists record of all rows is 4.5 .
eq { count { filter_eq { all_rows ; country ; france } } ; 3 } = true
Output: select the rows whose country record fuzzily matches to france . the number of such rows is 3 .
most_greater { all_rows ; net capacity ; 300 mw } = true
Output: for the net capacity records of all rows , most of them are greater than 300 mw .
less { hop { filter_eq { all_rows ; name ; newport } ; year withdrawn } ; hop { filter_eq { all_rows ; name ; cowes } ; year withdrawn } } = true
Output: select the rows whose name record fuzzily matches to newport . take the year withdrawn record of this row . select the rows whose name record fuzzily matches to cowes . take the year withdrawn record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; capacity ( mw ) ; 1 } ; wind farm } ; codling } = true
Output: select the row whose capacity  mw  record of all rows is 1st maximum . the wind farm record of this row is codling .
less { hop { filter_eq { all_rows ; player ; john senst } ; pick } ; hop { filter_eq { all_rows ; player ; paul brown } ; pick } } = true
Output: select the rows whose player record fuzzily matches to john senst . take the pick record of this row . select the rows whose player record fuzzily matches to paul brown . take the pick record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; highest } ; team } ; st johnstone } = true
Output: select the row whose highest record of all rows is maximum . the team record of this row is st johnstone .
round_eq { avg { all_rows ; overall record } ; 5 } = true
Output: the average of the overall record record of all rows is 5 .
greater { hop { filter_eq { all_rows ; artist ; bun b } ; number of reviews } ; hop { filter_eq { all_rows ; artist ; black milk } ; number of reviews } } = true
Output: select the rows whose artist record fuzzily matches to bun b . take the number of reviews record of this row . select the rows whose artist record fuzzily matches to black milk . take the number of reviews record of this row . the first record is greater than the second record .
and { only { filter_eq { filter_eq { all_rows ; original air date ; 2004 } ; season ; 15 } } ; eq { hop { filter_eq { filter_eq { all_rows ; original air date ; 2004 } ; season ; 15 } ; episode title } ; aaron carter } } = true
Output: select the rows whose original air date record fuzzily matches to 2004 . among these rows , select the rows whose season record is equal to 15 . there is only one such row in the table . the episode title record of this unqiue row is aaron carter .
and { only { filter_eq { all_rows ; hometown ; tx } } ; eq { hop { filter_eq { all_rows ; hometown ; tx } ; player } ; kerry wood } } = true
Output: select the rows whose hometown record fuzzily matches to tx . there is only one such row in the table . the player record of this unqiue row is kerry wood .
greater { hop { filter_eq { all_rows ; class ; dx } ; number in service } ; hop { filter_eq { all_rows ; class ; dsc } ; number in service } } = true
Output: select the rows whose class record fuzzily matches to dx . take the number in service record of this row . select the rows whose class record fuzzily matches to dsc . take the number in service record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; west lancashire ; 4 } } ; 2 } = true
Output: select the rows whose west lancashire record is equal to 4 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; top 10s ; 0 } } ; eq { hop { filter_eq { all_rows ; top 10s ; 0 } ; year } ; 2007 } } = true
Output: select the rows whose top 10s record is equal to 0 . there is only one such row in the table . the year record of this unqiue row is 2007 .
and { only { filter_eq { filter_eq { all_rows ; position ; guard } ; height in ft ; 6 - 2 } } ; eq { hop { filter_eq { filter_eq { all_rows ; position ; guard } ; height in ft ; 6 - 2 } ; player } ; garland , winston winston garland } } = true
Output: select the rows whose position record fuzzily matches to guard . among these rows , select the rows whose height in ft record fuzzily matches to 6  2 . there is only one such row in the table . the player record of this unqiue row is garland , winston winston garland .
eq { count { filter_eq { all_rows ; date ; october } } ; 5 } = true
Output: select the rows whose date record fuzzily matches to october . the number of such rows is 5 .
round_eq { avg { all_rows ; wins } ; .12 } = true
Output: the average of the wins record of all rows is .12 .
eq { count { filter_eq { all_rows ; reason for change ; died } } ; 3 } = true
Output: select the rows whose reason for change record fuzzily matches to died . the number of such rows is 3 .
eq { count { filter_greater { all_rows ; speed ; 100 } } ; 2 } = true
Output: select the rows whose speed record is greater than 100 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; ethnic group ; tigrigna } ; christians } ; hop { filter_eq { all_rows ; ethnic group ; saho } ; christians } } = true
Output: select the rows whose ethnic group record fuzzily matches to tigrigna . take the christians record of this row . select the rows whose ethnic group record fuzzily matches to saho . take the christians record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; date ; 2004 } } ; 3 } = true
Output: select the rows whose date record fuzzily matches to 2004 . the number of such rows is 3 .
eq { hop { nth_argmax { filter_eq { all_rows ; position ; defensive back } ; pick ; 1 } ; player } ; glenn jones } = true
Output: select the rows whose position record fuzzily matches to defensive back . select the row whose pick record of these rows is 1st maximum . the player record of this row is glenn jones .
eq { count { filter_eq { all_rows ; result ; loss } } ; 3 } = true
Output: select the rows whose result record fuzzily matches to loss . the number of such rows is 3 .
most_less { all_rows ; designer , year ; 1950 } = true
Output: for the designer , year records of all rows , most of them are less than 1950 .
and { less { hop { filter_eq { all_rows ; home team ; geelong } ; crowd } ; hop { filter_eq { all_rows ; home team ; essendon } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; home team ; geelong } ; away team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; home team ; essendon } ; away team } ; richmond } } } = true
Output: select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . select the rows whose home team record fuzzily matches to essendon . take the crowd record of this row . the first record is less than the second record . the away team record of the first row is fitzroy . the away team record of the second row is richmond .
eq { count { filter_eq { all_rows ; location ; japan } } ; 8 } = true
Output: select the rows whose location record fuzzily matches to japan . the number of such rows is 8 .
eq { hop { argmax { all_rows ; total } ; nation } ; australia } = true
Output: select the row whose total record of all rows is maximum . the nation record of this row is australia .
eq { count { filter_eq { all_rows ; partner ; stephanie vogt } } ; 2 } = true
Output: select the rows whose partner record fuzzily matches to stephanie vogt . the number of such rows is 2 .
and { only { filter_eq { all_rows ; ihsaa class ; aaa } } ; eq { hop { filter_eq { all_rows ; ihsaa class ; aaa } ; school } ; indian creek } } = true
Output: select the rows whose ihsaa class record fuzzily matches to aaa . there is only one such row in the table . the school record of this unqiue row is indian creek .
round_eq { sum { all_rows ; literate female } ; 1084609 } = true
Output: the sum of the literate female record of all rows is 1084609 .
round_eq { avg { all_rows ; points } ; 2 } = true
Output: the average of the points record of all rows is 2 .
most_greater { filter_greater { all_rows ; new adherents per year ; 0 } ; growth rate ; 1.00 % } = true
Output: select the rows whose new adherents per year record is greater than 0 . for the growth rate records of these rows , most of them are greater than 1.00  .
and { only { filter_eq { all_rows ; area ; newmarket } } ; eq { hop { filter_eq { all_rows ; area ; newmarket } ; name } ; newmarket school } } = true
Output: select the rows whose area record fuzzily matches to newmarket . there is only one such row in the table . the name record of this unqiue row is newmarket school .
and { only { filter_eq { all_rows ; country ; bosnia and herzegovina } } ; eq { hop { filter_eq { all_rows ; country ; bosnia and herzegovina } ; film } ; prva plata } } = true
Output: select the rows whose country record fuzzily matches to bosnia and herzegovina . there is only one such row in the table . the film record of this unqiue row is prva plata .
all_eq { all_rows ; series ep ; 13 - } = true
Output: for the series ep records of all rows , all of them fuzzily match to 13  .
eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; nathaniel macon } = true
Output: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is nathaniel macon .
eq { count { filter_eq { all_rows ; tournament ; brazil } } ; 3 } = true
Output: select the rows whose tournament record fuzzily matches to brazil . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; name ( english ) ; alliance '90 / the greens } ; votes ( 2011 ) } ; hop { filter_eq { all_rows ; name ( english ) ; free democratic party } ; votes ( 2011 ) } } = true
Output: select the rows whose name  english  record fuzzily matches to alliance 90  the greens . take the votes  2011  record of this row . select the rows whose name  english  record fuzzily matches to free democratic party . take the votes  2011  record of this row . the first record is greater than the second record .
most_greater { all_rows ; tropical lows ; 10 } = true
Output: for the tropical lows records of all rows , most of them are greater than 10 .
round_eq { avg { filter_less_eq { all_rows ; place ; 3 } ; score } ; 68 } = true
Output: select the rows whose place record is less than or equal to 3 . the average of the score record of these rows is 68 .
eq { diff { hop { filter_eq { all_rows ; date ( s ) conducted ; 27 october 2010 } ; green } ; hop { filter_eq { all_rows ; date ( s ) conducted ; 24 november 2010 } ; green } } ; -1 } = true
Output: select the rows whose date  s  conducted record fuzzily matches to 27 october 2010 . take the green record of this row . select the rows whose date  s  conducted record fuzzily matches to 24 november 2010 . take the green record of this row . the second record is 1 larger than the first record .
eq { count { filter_eq { all_rows ; political party ; democratic party of albania } } ; 2 } = true
Output: select the rows whose political party record fuzzily matches to democratic party of albania . the number of such rows is 2 .
eq { hop { argmax { all_rows ; attendance } ; date } ; 17 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is 17 .
and { only { filter_eq { all_rows ; entrant ; tyrrell racing organisation } } ; eq { hop { filter_eq { all_rows ; entrant ; tyrrell racing organisation } ; year } ; 1966 } } = true
Output: select the rows whose entrant record fuzzily matches to tyrrell racing organisation . there is only one such row in the table . the year record of this unqiue row is 1966 .
eq { count { filter_eq { all_rows ; country of origin ; united states } } ; 2 } = true
Output: select the rows whose country of origin record fuzzily matches to united states . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; party } ; conservative } = true
Output: select the row whose total record of all rows is 2nd maximum . the party record of this row is conservative .
eq { count { filter_eq { all_rows ; score ; 66 } } ; 4 } = true
Output: select the rows whose score record is equal to 66 . the number of such rows is 4 .
most_eq { filter_eq { all_rows ; date ; november } ; result ; l } = true
Output: select the rows whose date record fuzzily matches to november . for the result records of these rows , most of them fuzzily match to l .
eq { hop { argmax { all_rows ; attendance } ; date } ; sunday , may 29 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is sunday , may 29 .
greater { hop { filter_eq { all_rows ; opponent ; poland } ; results } ; hop { filter_eq { all_rows ; opponent ; italy } ; results } } = true
Output: 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 .
round_eq { avg { filter_eq { all_rows ; event ; 400 m h } ; notes } ; 54.54 seconds } = true
Output: select the rows whose event record fuzzily matches to 400 m h . the average of the notes record of these rows is 54.54 seconds .
eq { count { filter_greater_eq { all_rows ; points ; 20 } } ; 3 } = true
Output: select the rows whose points record is greater than or equal to 20 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; result ; runner - up } } ; 4 } = true
Output: select the rows whose result record fuzzily matches to runner  up . the number of such rows is 4 .
most_eq { all_rows ; tv time ; cbs } = true
Output: for the tv time records of all rows , most of them fuzzily match to cbs .
eq { count { filter_less_eq { all_rows ; rank ; 10 } } ; 8 } = true
Output: select the rows whose rank record is less than or equal to 10 . the number of such rows is 8 .
most_eq { all_rows ; engine ; ferrari v12 } = true
Output: for the engine records of all rows , most of them fuzzily match to ferrari v12 .
all_eq { all_rows ; date ; may } = true
Output: for the date records of all rows , all of them fuzzily match to may .
all_eq { all_rows ; distance ; 240.5 km } = true
Output: for the distance records of all rows , all of them fuzzily match to 240.5 km .
most_eq { all_rows ; laps ; 30 } = true
Output: for the laps records of all rows , most of them are equal to 30 .
eq { count { filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 } } ; 3 } = true
Output: select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 . the number of such rows is 3 .
round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 139.5 } = true
Output: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 139.5 .
eq { count { filter_eq { all_rows ; nationality ; nor } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to nor . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; position ; running back } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to running back . the number of such rows is 3 .
only { filter_eq { all_rows ; category ; iva } } = true
Output: select the rows whose category record fuzzily matches to iva . there is only one such row in the table .
less { hop { filter_eq { all_rows ; name ; over the deel } ; age } ; hop { filter_eq { all_rows ; name ; captain dibble } ; age } } = true
Output: select the rows whose name record fuzzily matches to over the deel . take the age record of this row . select the rows whose name record fuzzily matches to captain dibble . take the age record of this row . the first record is less than the second record .