code/validator_data/few_shot_prompt_order.txt

You are SQL Tutor that validates the student query. Given a database schema, a question, and SQL query generated by student and its response in database. Check each part of the query and point out if it's correct or not.
Examples:
database schema :
table schools , columns = [ schools.phone ( text | values : (510) 887-0152 , (510) 596-8901 ) , schools.district ( text ) , schools.edopscode ( text | comment : education option code | values : TRAD , JUV ) , schools.cdscode ( text | primary key | values : 01100170000000 , 01100170109835 ) , schools.school ( text | values : FAME Public Charter ) , schools.gsserved ( text | comment : grade span served. | values : K-12 , 9-12 ) , schools.eilcode ( text | comment : educational instruction level code | values : ELEMHIGH , HS ) , schools.edopsname ( text | comment : educational option name | values : Traditional , Juvenile Court School ) , schools.eilname ( text | comment : educational instruction level name | values : High School ) , schools.gsoffered ( text | comment : grade span offered | values : K-12 , 9-12 ) ]
table satscores , columns = [ satscores.avgscrread ( integer | comment : average scores in reading | values : 418 , 503 ) , satscores.dname ( text | comment : district name | values : Alameda Unified ) , satscores.sname ( text | comment : school name | values : FAME Public Charter ) , satscores.cds ( text | primary key | values : 10101080000000 , 10101080109991 ) , satscores.avgscrmath ( integer | comment : average scores in math | values : 418 , 546 ) , satscores.avgscrwrite ( integer | comment : average scores in writing | values : 417 , 505 ) , satscores.numtsttakr ( integer | comment : number of test takers | values : 88 , 17 ) , satscores.cname ( text | comment : county name | values : Alameda , Amador ) , satscores.numge1500 ( integer | comment : number of test takers whose total sat scores are greater or equal to 1500 | values : 14 , 9 ) , satscores.enroll12 ( integer | comment : enrollment (1st-12nd grade) | values : 398 , 62 ) ]
table frpm , columns = [ frpm.`district name` ( text ) , frpm.`school name` ( text | values : FAME Public Charter ) , frpm.`district code` ( integer | values : 10017 , 31609 ) , frpm.cdscode ( text | primary key | values : 01100170109835 , 01100170112607 ) , frpm.`school code` ( text | values : 0109835 , 0112607 ) , frpm.`county name` ( text | values : Alameda , Alpine ) , frpm.`district type` ( text | values : State Special Schools ) , frpm.`school type` ( text | values : K-12 Schools (Public) , High Schools (Public) ) , frpm.`enrollment (k-12)` ( real | values : 1087.0 , 395.0 ) , frpm.`county code` ( text | values : 01 , 02 ) ]
foreign keys :
frpm.cdscode = schools.cdscode
satscores.cds = schools.cdscode

Matched contents are written in this format table.column (some values can be found in that column)
matched contents :
schools.district ( Fresno Unified )
satscores.dname ( Fresno Unified )
satscores.cname ( Fresno )
frpm.`district name` ( Fresno Unified )
frpm.`county name` ( Fresno )

Question: Fresno Unified is a name of district; What is the telephone number for the school with the lowest average score in reading in Fresno Unified?

SQL query: SELECT schools.phone FROM schools INNER JOIN satscores ON schools.cdscode = satscores.cds WHERE satscores.dname = 'Fresno Unified' ORDER BY satscores.avgscrread ASC LIMIT 1

Execution response [written in pandas format]:
                0
0  (559) 490-4290

Feedback:
ORDER BY.
- The SQL query uses ```ORDER BY satscores.avgscrread ASC LIMIT 1```.
- However, the column ```satscores.avgscrread``` has None values, so the SQL query need to add condition ```satscores.avgscrread IS NOT NULL``` to filter out None values.
- Conclude: incorrect.
=========
database schema :
table cards , columns = [ cards.manacost ( text | values : {5}{W}{W} , {4}{W} ) , cards.id ( integer | primary key | comment : unique id number identifying the cards | values : 41138 , 1349 ) , cards.convertedmanacost ( real | values : 7.0 , 5.0 ) , cards.name ( text | values : Ancestor's Chosen , Angel of Mercy ) , cards.dueldeck ( text | values : a , b ) , cards.faceconvertedmanacost ( real | values : 4.0 , 5.0 ) , cards.setcode ( text | values : 10E , 2ED ) , cards.facename ( text | values : Dusk , Dawn ) , cards.number ( text | values : 1 , 2 ) , cards.asciiname ( text | values : El-Hajjaj , Junun Efreet ) ]
table sets , columns = [ sets.id ( integer | primary key | values : 1 , 2 ) , sets.name ( text | values : Tenth Edition , Unlimited Edition ) , sets.basesetsize ( integer | values : 383 , 302 ) , sets.totalsetsize ( integer | values : 508 , 302 ) , sets.code ( text | values : 10E , 2ED ) , sets.type ( text | values : core , masters ) , sets.mcmname ( text | comment : magic card market name | values : Tenth Edition , Double Masters ) , sets.parentcode ( text | values : JMP , MH1 ) , sets.block ( text | values : Core Set , Mirrodin ) , sets.mcmid ( integer | comment : magic card market id | values : 74 , 3204 ) ]
table set_translations , columns = [ set_translations.id ( integer | primary key | values : 1 , 2 ) , set_translations.setcode ( text | values : 10E , 4ED ) , set_translations.translation ( text | values : 核心系列第十版 , Dixième édition ) , set_translations.language ( text | values : Chinese Simplified , Chinese Traditional ) ]
table rulings , columns = [ rulings.id ( integer | primary key | values : 1 , 2 ) , rulings.text ( text ) , rulings.date ( date | values : 2007-07-15 , 2007-02-01 ) , rulings.uuid ( text ) ]
table legalities , columns = [ legalities.id ( integer | primary key | values : 1 , 2 ) , legalities.format ( text | values : commander , duel ) , legalities.status ( text | values : Legal , Banned ) , legalities.uuid ( text ) ]
table foreign_data , columns = [ foreign_data.name ( text | values : Ausgewählter der Ahnfrau , Elegido de la Antepasada ) , foreign_data.id ( integer | primary key | values : 1 , 2 ) , foreign_data.type ( text | values : Criatura — Clérigo humano ) , foreign_data.text ( text ) , foreign_data.language ( text | values : German , Spanish ) , foreign_data.multiverseid ( integer | values : 148411 , 150317 ) , foreign_data.uuid ( text ) , foreign_data.flavortext ( text ) ]
foreign keys :
foreign_data.uuid = cards.uuid
legalities.uuid = cards.uuid
set_translations.setcode = sets.code
rulings.uuid = cards.uuid

Matched contents are written in this format table.column (some values can be found in that column)
matched contents :
sets.name ( Journey into Nyx )
sets.mcmname ( Journey into Nyx )

Question: set 'Journey into Nyx Hero's Path' refers to name = 'Journey into Nyx Hero''s Path'; Which card name in the set 'Journey into Nyx Hero's Path' has the highest converted mana cost.

SQL query: SELECT cards.name FROM cards INNER JOIN sets ON cards.setcode = sets.code WHERE sets.name = 'Journey into Nyx Hero''s Path' ORDER BY cards.convertedmanacost DESC LIMIT 1

Execution response [written in pandas format]:
                 0
0  Hall of Triumph

Feedback:
ORDER BY.
- The SQL query uses ```ORDER BY cards.convertedmanacost DESC LIMIT 1```.
- Based on the question, the query should use ```ORDER BY cards.convertedmanacost DESC LIMIT 1```.
- The query chooses correct column for ORDER BY.
- Conclude: correct.
=========
database schema :
table patient , columns = [ patient.sex ( text | values : F , M ) , patient.diagnosis ( text | values : RA susp. , PSS ) , patient.id ( integer | primary key | values : 2110 , 11408 ) , patient.`first date` ( date | values : 1993-02-10 , 1973-01-01 ) , patient.description ( date | values : 1994-02-14 , 1996-12-01 ) , patient.admission ( text | values : + , - ) , patient.birthday ( date | values : 1934-02-13 , 1937-05-02 ) ]
table examination , columns = [ examination.diagnosis ( text | values : MCTD, AMI , SLE ) , examination.id ( integer | values : 14872 , 48473 ) , examination.`examination date` ( date | values : 1997-05-27 , 1992-12-21 ) , examination.symptoms ( text | values : AMI , CNS lupus ) , examination.`acl iga` ( integer | comment : anti-cardiolipin antibody (iga) concentration | values : 0 , 3 ) , examination.`acl igg` ( real | comment : anti-cardiolipin antibody (igg) | values : 1.3 , 4.3 ) , examination.`ana pattern` ( text | comment : pattern observed in the sheet of ana examination | values : P , P,S ) , examination.`acl igm` ( real | comment : anti-cardiolipin antibody (igm) | values : 1.6 , 4.6 ) , examination.kct ( text | comment : measure of degree of coagulation | values : - , + ) , examination.ana ( integer | comment : anti-nucleus antibody | values : 256 , 0 ) ]
table laboratory , columns = [ laboratory.dna ( text | comment : anti-dna | values : 41.9 , 18 ) , laboratory.`dna-ii` ( integer | comment : anti-dna ) , laboratory.id ( integer | primary key | values : 27654 , 30609 ) , laboratory.centromea ( text | comment : anti-centromere | values : 0 , negative ) , laboratory.date ( date | primary key | values : 1991-09-11 , 1991-09-17 ) , laboratory.sm ( text | comment : anti-sm | values : 0 , 1 ) , laboratory.ssa ( text | comment : anti-ssa | values : 0 , negative ) , laboratory.ssb ( text | comment : anti-ssb | values : 0 , negative ) , laboratory.sc170 ( text | comment : anti-scl70 | values : 16 , 1 ) , laboratory.pic ( integer | values : 320 , 513 ) ]
foreign keys :
examination.id = patient.id
laboratory.id = patient.id

Matched contents are written in this format table.column (some values can be found in that column)
matched contents :
patient.diagnosis ( AORTITIS )
examination.diagnosis ( Aortitis )

Question: gender means sex; 'AORTITIS' refers to diagnosis; What was the gender of the first AORTITIS diagnosed patient?

SQL query: SELECT patient.sex FROM patient INNER JOIN examination ON patient.id = examination.id WHERE patient.diagnosis = 'AORTITIS' ORDER BY patient.`first date` ASC LIMIT 1

Execution response [written in pandas format]:
   0
0  M

Feedback:
ORDER BY.
- The SQL query uses ```ORDER BY patient.`first date` ASC LIMIT 1```.
- However, the column ```patient.`first date```` has None values, so the SQL query need to add condition ```patient.`first date` IS NOT NULL``` to filter out None values.
- Conclude: incorrect.