PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?language ?languageLabel WHERE {
  wd:Q225 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| language            | languageLabel |
| ------------------- | ------------- |
| Croatian (wd:Q6654) | Croatian (en) |
| Serbian (wd:Q9299)  | Serbian (en)  |
| Bosnian (wd:Q9303)  | Bosnian (en)  |

The official languages spoken by people from Bosnia (Bosnia and Herzegovina) are Croatian, Serbian, and Bosnian.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what language do people from bosnia speak?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
where is bosnia?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q225 wdt:P30 ?x .
}

Using entities:
Bosnia and Herzegovina, also known as Bosnia-Herzegovina, Bosnia & Herzegovina, Bosnia and Hercegovina, Bosnia, BIH (wd:Q225): country in Southeast Europe

Using properties:
continent (wd:P30 as wdt): continent of which the subject is a part, located in/on physical feature

Example 2:
Question:
what language do people speak in turkey?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q43 wdt:P37 ?x .
}

Using entities:
Turkey, also known as Republic of Turkey, Republic of Turkiye, Republic of Türkiye, TR, TUR (wd:Q43): country straddling Southeast Europe and West Asia

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 3:
Question:
what do people from spain speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29 wdt:P37 ?x .
}

Using entities:
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

[Assistant]
From the examples, it is clear that the property for the language spoken in a country is wdt:P37 (official language). The entity for Bosnia is likely wd:Q225 (Bosnia and Herzegovina).

Next, I will verify the entity for Bosnia by searching for "Bosnia" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Bosnia and Herzegovina, also known as Bosnia-Herzegovina, Bosnia & Herzegovina, Bosnia and Hercegovina, Bosnia, BIH (wd:Q225): country in Southeast Europe
2. Bosnia (wd:Q188736): region in Bosnia and Herzegovina
3. Bosnia (wd:Q4947317): live album by Grand Funk Railroad
4. Bosna, also known as Bosnia (song), Bosnia (wd:Q25502969): Croatian song
5. Bosnia (wd:Q10433448): 1996 live album by Ulf Lundell
6. Bosnia (wd:Q29021052): song by Argentine musician Luis Alberto Spinetta in the álbum Spinetta y los Socios del Desierto
7. Bosnia (wd:Q103894070): encyclopedia article
8. Bosnia (wd:Q119441173): painting by James Potter (active 1998–2000), Royal Military Police Museum
9. Bosnia (wd:Q74241437): scientific article published on January 10, 1998
10. Bosnia (wd:Q80218280): photograph by Ron Haviv

[Assistant]
The entity for Bosnia and Herzegovina as a country is wd:Q225, which is the relevant entity for the question about the language spoken by people from Bosnia.

Now, I will construct a SPARQL query to find the official language(s) spoken in Bosnia and Herzegovina using the property wdt:P37.

[Tool]
Got 3 rows and 2 columns:
| language            | languageLabel |
| ------------------- | ------------- |
| Croatian (wd:Q6654) | Croatian (en) |
| Serbian (wd:Q9299)  | Serbian (en)  |
| Bosnian (wd:Q9303)  | Bosnian (en)  |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the official languages of Bosnia and Herzegovina using the property for official languages (wdt:P37). The query returns the identifiers and English labels for Croatian, Serbian, and Bosnian, which matches the known official languages of Bosnia and Herzegovina. The answer is accurate and complete based on the query results. No further refinement is necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
SELECT ?altLabel WHERE {
  wd:Q9299 skos:altLabel ?altLabel .
  FILTER ( LANG( ?altLabel ) = "en" )
}

Got 2 rows and 1 column:
| altLabel              |
| --------------------- |
| Serbian language (en) |
| Srpski (en)           |

The Serbian language is called 'Serbian language' or 'Srpski'.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what is serbian language called?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what is the language called in turkey?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q43 wdt:P37 ?x .
}

Using entities:
Turkey, also known as Republic of Turkey, Republic of Turkiye, Republic of Türkiye, TR, TUR (wd:Q43): country straddling Southeast Europe and West Asia

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 2:
Question:
what language does fiji?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q712 wdt:P37 ?x .
}

Using entities:
Fiji, also known as FIJ, Fijī, Fijī Gaṇarājya, fj, Matanitu Tugalala o Viti (wd:Q712): island sovereign state in Oceania

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 3:
Question:
what language do russian people speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q159 wdt:P37 ?x .
}

Using entities:
Russia, also known as Russian Federation, RUS, RU, RF, ISO 3166-1:RU (wd:Q159): country in Eastern Europe and Northern Asia

Using properties:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

[Assistant] From the examples, the property wdt:P37 (official language) is used to find the language spoken in a country. To find what the Serbian language is called, we can look for the entity representing the Serbian language itself. Let's search for the entity "Serbian language" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Serbian, also known as Srpski, Serbian language (wd:Q9299): standardized variety of Serbo-Croatian language used by Serbs
2. Sorbian, also known as Serbian language, Wendic language, Wendish (wd:Q25442): Lechitic language spoken by the Sorbs
3. Serbian language (wd:L616516-S1)
4. Serbian (language) (wd:L613158-S1)
5. History of Serbian, also known as Serbian language history (wd:Q31183797)
6. Serbian Sign Language (wd:Q113203025)
7. Serbian language in Croatia, also known as use of the Serbian language in Croatia (wd:Q20974148): overview about the use of the Serbian language in Croatia
8. Institute for Serbian Language (wd:Q16082649)
9. Serbian language in Montenegro (wd:Q65201055)
10. romanization of Serbian, also known as romanisation of Serbian, romanization of the Serbian language (wd:Q7362708): overview about the romanization of the Serbian language

[Assistant]
The entity "Serbian" (wd:Q9299) is described as the standardized variety of Serbo-Croatian language used by Serbs and is also known as "Serbian language" or "Srpski". This seems to be the correct entity for the Serbian language.

Now, I will write a SPARQL query to get the labels or alternative names for this entity to find out what the Serbian language is called.

[Tool]
Got 1 row and 1 column:
| label        |
| ------------ |
| Serbian (en) |

[Tool]
Got 2 rows and 1 column:
| altLabel              |
| --------------------- |
| Serbian language (en) |
| Srpski (en)           |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the English alternative labels for the Serbian language entity (wd:Q9299), which are 'Serbian language' and 'Srpski'. The answer provided is accurate and complete, directly addressing the user's question about what the Serbian language is called. The query follows the rules by returning identifiers and labels without unnecessary complexity. No further refinement is needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?instrument ?instrumentLabel WHERE {
  wd:Q191499 wdt:P1303 ?instrument .
  OPTIONAL {
    ?instrument rdfs:label ?instrumentLabel FILTER ( LANG( ?instrumentLabel ) = "en" )
  }
}

Got 4 rows and 2 columns:
| instrument          | instrumentLabel |
| ------------------- | --------------- |
| cornet (wd:Q202027) | cornet (en)     |
| piano (wd:Q5994)    | piano (en)      |
| guitar (wd:Q6607)   | guitar (en)     |
| violin (wd:Q8355)   | violin (en)     |

Scott Joplin played the guitar, piano, violin, and cornet.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what did scott joplin play?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what type of music is scott joplin?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q191499 wdt:P136 ?x .
}

Using entities:
Scott Joplin, also known as The king of Ragtime (wd:Q191499): American composer, musician, and pianist, jazz musician, banjoist, composer, pianist

Using properties:
genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic

Example 2:
Question:
what type of music was scott joplin noted for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q191499 wdt:P136 ?x .
}

Using entities:
Scott Joplin, also known as The king of Ragtime (wd:Q191499): American composer, musician, and pianist, jazz musician, banjoist, composer, pianist

Using properties:
genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic

Example 3:
Question:
what guitar did clapton play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P1535 wd:Q48187 ; wdt:P31 / wdt:P279 * wd:Q6607 .
}

Using entities:
Eric Clapton, also known as Slowhand, Eric Patrick Clapton (wd:Q48187): singer-songwriter, British musician, recording artist, jazz musician, guitarist, composer, singer
guitar (wd:Q6607): fretted string instrument

Using properties:
used by, also known as armiger, user, played by, of (wd:P1535 as wdt): item or concept that makes use of the subject (use sub-properties when appropriate)
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant]
The similar examples suggest that Scott Joplin is an entity in Wikidata (wd:Q191499) and the property related to what he played could be related to the instrument or type of music. The property "genre" (wdt:P136) is used for the type of music he played or was noted for. For the instrument he played, we might need to look for properties like "instrument played" or "instrument" or "occupation" related to musician or pianist.

I will first check what instrument or occupation Scott Joplin is associated with in Wikidata.

[Tool]
Got 4 rows and 3 columns:
| subject                   | property              | object                       |
| ------------------------- | --------------------- | ---------------------------- |
| Scott Joplin (wd:Q191499) | occupation (wdt:P106) | composer (wd:Q36834)         |
| Scott Joplin (wd:Q191499) | occupation (wdt:P106) | pianist (wd:Q486748)         |
| Scott Joplin (wd:Q191499) | occupation (wdt:P106) | jazz musician (wd:Q15981151) |
| Scott Joplin (wd:Q191499) | occupation (wdt:P106) | banjoist (wd:Q9648008)       |

[Assistant] Scott Joplin's occupations include composer, pianist, jazz musician, and banjoist. This indicates the instruments he played include piano and banjo. To confirm, I will check if there is a property specifically indicating the instrument he played. I will search for properties related to "instrument" for Scott Joplin.

[Tool]
Top 10 property alternatives:
1. instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as wdt/p): musical instrument that a person plays or teaches or used in a music occupation, uses
2. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
3. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
4. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country
5. signature, also known as signature image, image of signature, facsimiles, autograph (wd:P109 as wdt/p): image of a person's signature, image
6. occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt/p): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
7. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
8. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category
9. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
10. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works

No other items found

[Assistant] There is a property "instrument" (wdt:P1303) which is described as the musical instrument that a person plays or teaches or used in a music occupation. I will now check which instruments are linked to Scott Joplin using this property.

[Tool]
Got 4 rows and 3 columns:
| subject                   | property               | object              |
| ------------------------- | ---------------------- | ------------------- |
| Scott Joplin (wd:Q191499) | instrument (wdt:P1303) | guitar (wd:Q6607)   |
| Scott Joplin (wd:Q191499) | instrument (wdt:P1303) | piano (wd:Q5994)    |
| Scott Joplin (wd:Q191499) | instrument (wdt:P1303) | violin (wd:Q8355)   |
| Scott Joplin (wd:Q191499) | instrument (wdt:P1303) | cornet (wd:Q202027) |

[Assistant] Scott Joplin is linked to the instruments guitar, piano, violin, and cornet with the property "instrument" (wdt:P1303). Since the question is "what did Scott Joplin play?", I will create a SPARQL query to return these instruments he played. I will also include their labels for better readability.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the musical instruments that Scott Joplin played using the property 'instrument' (wdt:P1303). The query returns the instrument identifiers and their English labels, which is appropriate and follows the guidelines. The answer lists the instruments found in the query result: guitar, piano, violin, and cornet. This is a complete and accurate response to the user's question. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?occupation ?occupationLabel WHERE {
  wd:Q81960 wdt:P106 ?occupation .
  OPTIONAL {
    ?occupation rdfs:label ?occupationLabel FILTER ( LANG( ?occupationLabel ) = "en" )
  }
}

Got 5 rows and 2 columns:
| occupation                  | occupationLabel   |
| --------------------------- | ----------------- |
| farmer (wd:Q131512)         | farmer (en)       |
| musicologist (wd:Q14915627) | musicologist (en) |
| writer (wd:Q36180)          | writer (en)       |
| poet (wd:Q49757)            | poet (en)         |
| songwriter (wd:Q753110)     | songwriter (en)   |

Robert Burns had the following jobs: farmer, musicologist, writer, poet, and songwriter.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what jobs did robert burns have?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what books did robert burns wrote?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q47461344 ; wdt:P50 wd:Q81960 .
}

Using entities:
written work, also known as creative written work, written delivery (wd:Q47461344): any work expressed in writing, such as inscriptions, manuscripts, documents or maps
Robert Burns, also known as Robbie Burns, Robden of Solway Firth, Scotland's favorite son, The Bard, Rabbie Burns (wd:Q81960): Scottish poet and lyricist (1759–1796), musicologist, songwriter, farmer, writer, poet

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

Example 2:
Question:
what jobs did ben franklin do?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34969 wdt:P106 ?x .
}

Using entities:
Benjamin Franklin, also known as The First American, Silence Dogood, Poor Richard, Ben Franklin, Franklin (wd:Q34969): American polymath and statesman (1706–1790), politician, polymath, diplomat, writer

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)

Example 3:
Question:
what other jobs did benjamin franklin have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34969 wdt:P106 ?x .
}

Using entities:
Benjamin Franklin, also known as The First American, Silence Dogood, Poor Richard, Ben Franklin, Franklin (wd:Q34969): American polymath and statesman (1706–1790), politician, polymath, diplomat, writer

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)

[Assistant] From the similar examples, it is clear that the property "occupation" (wdt:P106) is used to find the jobs or occupations of a person. Next, I will search for the entity "Robert Burns" in Wikidata to get his identifier. Then I will query his occupations using the property wdt:P106.

[Tool]
Top 10 entity alternatives:
1. Robert Burns, also known as Robbie Burns, Robden of Solway Firth, Scotland's favorite son, The Bard, Rabbie Burns (wd:Q81960): Scottish poet and lyricist (1759–1796), musicologist, songwriter, farmer, writer, poet
2. Robert Hunter, also known as Robert C. Christie Hunter, Robert Burns (wd:Q1375663): American lyricist, musician, singer-songwriter, translator, and poet, songwriter, translator, guitarist, lyricist, musician, writer, singer, poet
3. Bob Burns, also known as Robin Burn, Robin William Burn, Robert Burns, Robbie Burn (wd:Q887831): American musical comedian (1890–1956), radio personality, businessperson, film actor, comedian, musician
4. Robert Burns, also known as Robert Clayton Burns (wd:Q7342516): British artist (1869-1941), visual artist, art educator, designer, painter
5. Robert Burns (wd:Q27824730): Australian cyclist, sport cyclist
6. Robert Burns (wd:Q2156654): American politician (1792-1866), politician
7. Robert Burns, also known as William Robert Burns (wd:Q15997904): American attorney and politician in Oklahoma (1874-1950), politician, lawyer
8. Robert Burns (wd:Q3434685): Canadian politician (1936–2014), trade unionist, politician, lawyer, judge
9. Bob Burns III, also known as Bob Burns, Robert Burns, Kogar (wd:Q4931984): American film historian and actor, property designer, film historian, film actor
10. Robert H. Burns, also known as Robert Burns (wd:Q16066723): American politician in Wisconsin (1870-19__), politician

[Assistant] The most relevant entity for Robert Burns, the Scottish poet and lyricist, is wd:Q81960. Now I will query the occupations (jobs) of Robert Burns using the property wdt:P106.

[Tool]
Got 5 rows and 2 columns:
| occupation                  | occupationLabel   |
| --------------------------- | ----------------- |
| farmer (wd:Q131512)         | farmer (en)       |
| musicologist (wd:Q14915627) | musicologist (en) |
| writer (wd:Q36180)          | writer (en)       |
| poet (wd:Q49757)            | poet (en)         |
| songwriter (wd:Q753110)     | songwriter (en)   |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the occupations of Robert Burns using the property wdt:P106. The query returns the occupation identifiers and their English labels, which matches the user's question about his jobs. The answer lists all occupations found in the query result, which are farmer, musicologist, writer, poet, and songwriter. The query and answer are accurate and complete for the question asked.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?postalCode WHERE {
  ?place wdt:P281 ?postalCode .
  ?place wdt:P131 wd:Q5083 .
  FILTER ( REGEX( ?postalCode , "^[0-9]{5}(-[0-9]{4})?$" ) )
}

Seattle, Washington has multiple postal codes. These include standard 5-digit postal codes and extended 9-digit postal codes. Some of the postal codes associated with Seattle are 98004, 98006, 98101, 98102, 98103, 98195-7720, 98195-7735, 98195-7920, 98195-8280, 98199, among others.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what the zip code for seattle washington?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what is the zip code for minneapolis?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q36091 wdt:P281 ?x .
}

Using entities:
Minneapolis, also known as Minneapolis, Minnesota, Minneapolis, MN (wd:Q36091): city in and county seat of Hennepin County, Minnesota, United States

Using properties:
postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as wdt): identifier assigned by postal authorities for the subject area or building

Example 2:
Question:
what is zip code for chicago illinois?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1297 wdt:P281 ?x .
}

Using entities:
Chicago, also known as The Second City, The Queen of the West, The Windy City, City of Broad Shoulders, City by the Lake (wd:Q1297): city and county seat of Cook County, Illinois, United States of America

Using properties:
postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as wdt): identifier assigned by postal authorities for the subject area or building

Example 3:
Question:
what zip code is chicago il?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1297 wdt:P281 ?x .
}

Using entities:
Chicago, also known as The Second City, The Queen of the West, The Windy City, City of Broad Shoulders, City by the Lake (wd:Q1297): city and county seat of Cook County, Illinois, United States of America

Using properties:
postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as wdt): identifier assigned by postal authorities for the subject area or building

[Assistant] From the examples, the property for postal code is wdt:P281 and the entity for the city is needed. Now I will search for the entity Seattle Washington.

[Tool]
Top 10 entity alternatives:
1. Seattle, also known as Seattle, WA, Seattle, Washington, Duwamps, New York Alki (wd:Q5083): city in and county seat of King County, State of Washington, United States
2. Seattle Washington Temple (wd:Q3844785): temple of the Church of Jesus Christ of Latter-day Saints in Bellevue, Washington
3. Washington Park, also known as Washington Park, Seattle (wd:Q7972093)
4. "Carole, Seattle, Washington" (wd:Q64144357): photograph in the National Gallery of Art (NGA 135163)
5. East Seattle, also known as East Seattle, WA, East Seattle, Washington (wd:Q122593560): populated place in King County, Washington, United States
6. Seattle, Washington Auditions (wd:Q114507485): episode of American Idol (S6 E2)
7. Seattleites, also known as Seattle, Washington, residents, Seattlites, Seattlers (wd:Q55779668): people from or residents of Seattle, Washington
8. Seattle Hill, also known as Seattle Hill, WA, Seattle Hill, Washington (wd:Q122540504): populated place in Snohomish County, Washington, United States
9. Seattle Heights, also known as Seattle Heights, WA, Seattle Heights, Washington (wd:Q122543838): neighborhood in Snohomish County, Washington, United States
10. University of Washington, also known as UW, washington.edu, UW-Seattle, U. of W., U of W (wd:Q219563): public research university in Seattle, Washington, United States

[Assistant] The entity for Seattle, Washington is wd:Q5083. From the examples, the postal code property is wdt:P281. I will now query the postal code for Seattle.

[Tool]
Got 1 row and 1 column:
| postalCode |
| ---------- |
| 98101      |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The answer provided is correct in that 98101 is a postal code for Seattle, Washington. However, Seattle has multiple postal codes, not just one. The SPARQL query only returns one postal code (98101), which might be misleading as it suggests Seattle has a single zip code. To improve, the query should be adjusted to return all postal codes associated with Seattle to provide a more complete answer.

[Assistant] Thank you for the feedback. I will adjust the SPARQL query to return all postal codes associated with Seattle, Washington, to provide a more complete answer.

[Tool]
Got 1 row and 1 column:
| postalCode |
| ---------- |
| 98101      |

[Assistant] The query still returns only one postal code. I will check if there are other postal codes related to Seattle using a broader query that looks for postal codes of places located in Seattle or postal codes that have Seattle as their location.

[Tool]
Got 102 rows and 1 column, showing the first 5 and last 5 rows below:
| postalCode    |
| ------------- |
| 98004         |
| 98006         |
| 98101         |
| 98102         |
| 98103         |
| ...           |
| 98195-7735    |
| 98195-7920    |
| 98195-8280    |
| 98199         |
| WA 98195-4360 |

[Assistant] I found multiple postal codes related to Seattle, Washington by looking for places located in Seattle with postal codes. I will now provide the list of these postal codes as the answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query retrieves postal codes for places located in the administrative territorial entity wd:Q5083, which is Seattle. However, the query returns some postal codes with additional suffixes (e.g., '98195-7735') and even one with a state prefix ('WA 98195-4360'), which are not standard postal codes. The answer is correct in stating that Seattle has multiple postal codes, but it would be better to refine the query to filter out postal codes that contain non-numeric characters or spaces to provide cleaner results. Additionally, the query could be improved by ensuring it only retrieves postal codes directly associated with Seattle itself, not its subdivisions or related entities, if that is the intent. Overall, the answer is sensible but could be improved for clarity and precision.

[Assistant] Thank you for the detailed feedback. To improve the query, I will filter the postal codes to include only those that consist purely of numeric characters (and possibly a hyphen for extended ZIP codes) and exclude those with spaces or state prefixes. I will also ensure the postal codes are directly associated with Seattle itself. I will refine the SPARQL query accordingly.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: Escaping the character d is not allowed in QLever's regex filters. (Regex was ^\d{5}(-\d{4})?$) Please note that there are two levels of escaping in place here: One for SPARQL and one for the regex engine

[Assistant] The regex pattern I used to filter postal codes caused an error due to escaping issues. I will adjust the regex to avoid escaping the character 'd' and use a simpler pattern to match 5-digit postal codes optionally followed by a hyphen and 4 digits. I will try the pattern "^[0-9]{5}(-[0-9]{4})?$" instead.

[Tool]
Got 1 row and 1 column:
| postalCode |
| ---------- |
| 98101      |

[Assistant] The refined query returned only one postal code (98101) directly associated with Seattle. To provide a more comprehensive list of postal codes for Seattle, I will now try to include postal codes of places that are part of Seattle (located in Seattle) but apply the same regex filter to exclude non-standard postal codes.

[Tool]
Got 101 rows and 1 column, showing the first 5 and last 5 rows below:
| postalCode |
| ---------- |
| 98004      |
| 98006      |
| 98101      |
| 98102      |
| 98103      |
| ...        |
| 98195-7720 |
| 98195-7735 |
| 98195-7920 |
| 98195-8280 |
| 98199      |

[Assistant] The query returned multiple postal codes for places located in Seattle, filtered to exclude non-standard codes with spaces or state prefixes. However, some postal codes still have the extended 9-digit format with a hyphen, which is a valid postal code format. I will now provide the list of these postal codes as the answer, mentioning that these include standard 5-digit and extended 9-digit postal codes.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?boyfriend ?boyfriendLabel WHERE {
  wd:Q575689 wdt:P451 ?boyfriend .
  ?boyfriend rdfs:label ?boyfriendLabel .
  FILTER ( LANG( ?boyfriendLabel ) = "en" )
}

Got 2 rows and 2 columns:
| boyfriend                      | boyfriendLabel       |
| ------------------------------ | -------------------- |
| Younes Bendjima (wd:Q55200042) | Younes Bendjima (en) |
| Scott Disick (wd:Q7883337)     | Scott Disick (en)    |

Kourtney Kardashian's boyfriends according to Wikidata are Younes Bendjima and Scott Disick.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what kourtney kardashian boyfriends name?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what kourtney kardashian baby girls name?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q575689 wdt:P40 ?x .
}

Using entities:
Kourtney Kardashian, also known as Kourtney Kardashian Barker, Kourtney Mary Kardashian, Kourtney (wd:Q575689): American media personality, model and businesswoman (born 1979), media personality, fashion designer, television actor, businessperson, entrepreneur, socialite, celebrity, actor, model

Using properties:
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

Example 2:
Question:
what kourtney kardashian name her daughter?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q575689 wdt:P40 ?x .
}

Using entities:
Kourtney Kardashian, also known as Kourtney Kardashian Barker, Kourtney Mary Kardashian, Kourtney (wd:Q575689): American media personality, model and businesswoman (born 1979), media personality, fashion designer, television actor, businessperson, entrepreneur, socialite, celebrity, actor, model

Using properties:
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

Example 3:
Question:
what did kourtney kardashian go to college for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q575689 p:P69 ?y .
  ?y pq:P812 ?x .
}

Using entities:
Kourtney Kardashian, also known as Kourtney Kardashian Barker, Kourtney Mary Kardashian, Kourtney (wd:Q575689): American media personality, model and businesswoman (born 1979), media personality, fashion designer, television actor, businessperson, entrepreneur, socialite, celebrity, actor, model

Using properties:
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as p): educational institution attended by subject, affiliation
academic major, also known as academic subject, college major, field of study, major, studied (wd:P812 as pq): major someone studied at college/university

[Assistant] The examples show that Kourtney Kardashian is identified as wd:Q575689 in Wikidata. To find the name of her boyfriend, I need to identify the relevant property for "boyfriend" or a similar relationship. I will search for properties related to "boyfriend" for the entity Kourtney Kardashian (wd:Q575689).

[Tool]
Top 10 property alternatives:
1. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as wdt/p): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
2. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt/p): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
3. relative, also known as relatives, grandparent, relation, non-binary parent, niece (wd:P1038 as wdt/p): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), significant person
4. father, also known as son of, child of, parent, is son of, is daughter of (wd:P22 as wdt/p): male parent of the subject. For stepfather, use "stepparent" (P3448), parent
5. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as wdt/p): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
6. sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as wdt/p): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative
7. Instagram username, also known as IG username, www.instagram.com, urinsta, ufinsta, Insta ID (wd:P2003 as wdt/p): item's username on Instagram, website username or ID
8. stepparent, also known as step-parent, step parent, stepmother, step-mother, step mother (wd:P3448 as wdt/p): subject has the object as their stepparent, relative
9. child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt/p): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant] The property "unmarried partner" (wdt:P451) is the most relevant property for "boyfriend" or romantic partner in Wikidata. I will now query the unmarried partner(s) of Kourtney Kardashian (wd:Q575689).

[Tool]
Got 2 rows and 2 columns:
| boyfriend                      | boyfriendLabel       |
| ------------------------------ | -------------------- |
| Younes Bendjima (wd:Q55200042) | Younes Bendjima (en) |
| Scott Disick (wd:Q7883337)     | Scott Disick (en)    |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the names and identifiers of Kourtney Kardashian's boyfriends using the property for unmarried partners (wdt:P451). The answer lists the two boyfriends found in Wikidata, Younes Bendjima and Scott Disick, which matches the query result. The query includes labels in English and returns distinct results. The output is accurate, complete, and follows the guidelines for SPARQL queries. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?nationality ?nationalityLabel WHERE {
  wd:Q192955 wdt:P27 ?nationality .
  OPTIONAL {
    ?nationality rdfs:label ?nationalityLabel FILTER ( LANG( ?nationalityLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| nationality              | nationalityLabel    |
| ------------------------ | ------------------- |
| United Kingdom (wd:Q145) | United Kingdom (en) |
| Canada (wd:Q16)          | Canada (en)         |

Steve Nash has two nationalities: Canada and United Kingdom.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what is steve nash nationality?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
who all has steve nash played for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q192955 wdt:P54 ?x .
}

Using entities:
Steve Nash, also known as Stephen John Nash (wd:Q192955): Canadian basketball player and coach, basketball player, basketball coach, businessperson, film director

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

Example 2:
Question:
who is steve nash married to?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q192955 wdt:P26 ?x .
}

Using entities:
Steve Nash, also known as Stephen John Nash (wd:Q192955): Canadian basketball player and coach, basketball player, basketball coach, businessperson, film director

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

Example 3:
Question:
what year did steve nash play for the mavericks?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q192955 p:P54 ?p .
  ?p ps:P54 wd:Q132893 ; pq:P580 ?x ; pq:P582 ?y .
}

Using entities:
Dallas Mavericks, also known as Mavs (wd:Q132893): American professional basketball team, basketball player
Steve Nash, also known as Stephen John Nash (wd:Q192955): Canadian basketball player and coach, basketball player, basketball coach, businessperson, film director

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as ps/p): sports teams or clubs that the subject represents or represented, member of
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time
end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as pq): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time

[Assistant] The entity for Steve Nash is wd:Q192955. Now I need to find the property that represents nationality for this entity. I will search for properties related to nationality for Steve Nash.

[Tool]
Top 10 property alternatives:
1. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country
2. country for sport, also known as sports nationality, sports country, sport nationality, sporting nationality, sport country (wd:P1532 as wdt/p): country a person or a team represents when playing a sport, country
3. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
4. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt/p): date on which the subject was born, inception
5. occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt/p): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
6. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
7. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt/p): the place where the person is or has been, resident, location
8. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt/p): sports teams or clubs that the subject represents or represented, member of
9. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as wdt/p): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
10. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as wdt/p): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)

No other items found

[Assistant] The property "country of citizenship" (wdt:P27) corresponds to nationality. I will now write a SPARQL query to get the nationality of Steve Nash using this property.

[Tool]
Got 2 rows and 2 columns:
| nationality              | nationalityLabel    |
| ------------------------ | ------------------- |
| United Kingdom (wd:Q145) | United Kingdom (en) |
| Canada (wd:Q16)          | Canada (en)         |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the nationalities of Steve Nash using the property 'country of citizenship' (wdt:P27). The query returns both the identifiers and English labels for the nationalities, which are Canada and United Kingdom. The answer is accurate and complete based on the data from Wikidata. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?country ?countryLabel WHERE {
  wd:Q181574 wdt:P1001 ?country .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| country                | countryLabel       |
| ---------------------- | ------------------ |
| Canada (wd:Q16)        | Canada (en)        |
| United States (wd:Q30) | United States (en) |
| Mexico (wd:Q96)        | Mexico (en)        |

The countries involved in NAFTA are Canada, the United States, and Mexico.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] what countries are involved in the nafta?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what countries in north america continent?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q49 wdt:P527 ?x .
}

Using entities:
North America, also known as Turtle Island, Amérique du Nord, North and Central America, North America (region), North American Continent (wd:Q49): continent

Using properties:
has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

Example 2:
Question:
what 3 countries does mexico border?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q96 wdt:P47 ?x .
}
LIMIT 3

Using entities:
Mexico, also known as US of M, United Mexican States, MXN, MX, Mexican United States (wd:Q96): country in North America

Using properties:
shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as wdt): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.

Example 3:
Question:
what three countries does mexico border?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q96 wdt:P47 ?x .
}

Using entities:
Mexico, also known as US of M, United Mexican States, MXN, MX, Mexican United States (wd:Q96): country in North America

Using properties:
shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as wdt): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.

[Assistant]
The examples found do not directly answer the question about the countries involved in NAFTA, but they show how to query for parts or members of an entity and how to query for countries related to another country.

Next, I will search for the entity "NAFTA" in Wikidata to find its identifier. Then I will check the properties related to it to find the countries involved.

[Tool]
Top 10 entity alternatives:
1. North American Free Trade Agreement, also known as NAFTA (wd:Q181574): trade bloc
2. Nafta, also known as Nefta (wd:Q1026298): Tunisian town
3. nafta (wd:Q1213853): chemical compound
4. Nafta (wd:Q11788993): 1961 film by Stanisław Lenartowicz
5. Nafta (wd:Q6958220): Soviet Union oil company operating abroad
6. Nafta, also known as IMO 8853946 (wd:Q52335786): bunkering tanker built in 1978
7. NAFTA, also known as NAFTA a.s. (wd:Q12772242): Slovak multinational company operating in the field of storage and extraction of hydrocarbons
8. Naftalan (wd:Q152825): city in Azerbaijan
9. Naphtali, also known as Naftali (wd:Q1771963): biblical figure and the sixth son of Jacob through his concubine Bilhah
10. United States–Mexico–Canada Agreement, also known as Canada–United States–Mexico Agreement, CUSMA, NAFTA 2.0, NAFTA Redux, New NAFTA (wd:Q56839716): North American trade bloc

[Assistant]
The entity "North American Free Trade Agreement" (NAFTA) is identified as wd:Q181574. There is also a related entity "United States–Mexico–Canada Agreement" (CUSMA, NAFTA 2.0) as wd:Q56839716, but the original NAFTA is wd:Q181574.

Next, I will look for properties of NAFTA (wd:Q181574) that might indicate the countries involved. I will search for properties related to "country" or "member" for this entity.

[Tool]
Top 10 property alternatives:
1. applies to jurisdiction, also known as belongs to jurisdiction, applies to territorial jurisdiction, applies to region, applies to place, applies to geographic place (wd:P1001 as wdt/p): the item (institution, law, public office, public register...) o...a territorial jurisdiction: a country, state, municipality, ...), significant place, characteristic of, operating area
2. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
3. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt/p): language designated as official by this item, language used
5. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
6. locator map image, also known as locator map, map image, map (wd:P242 as wdt/p): geographic map image which highlights the location of the subject within some larger entity, image
7. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
8. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as wdt/p): inherent or distinguishing quality or feature of the entity. Use a more specific property when possible
9. short name, also known as short title, shortname, abbreviation, shortened name, initialism (wd:P1813 as wdt/p): short name of a place, organisation, person, journal, wikidata property, etc. Used by some Wikipedia templates., name
10. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as wdt/p): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon

No other items found

[Assistant] The property "applies to jurisdiction" (wdt:P1001) seems relevant as it indicates the territorial jurisdiction (e.g., country) to which the entity applies. I will now list the values of this property for NAFTA (wd:Q181574) to find the countries involved.

[Tool]
Got 3 rows and 2 columns:
| country                | countryLabel       |
| ---------------------- | ------------------ |
| Canada (wd:Q16)        | Canada (en)        |
| United States (wd:Q30) | United States (en) |
| Mexico (wd:Q96)        | Mexico (en)        |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the countries involved in NAFTA by using the property 'applies to jurisdiction' (wdt:P1001) for the NAFTA entity (wd:Q181574). The query returns the three expected countries: Canada, the United States, and Mexico, along with their English labels. The answer is accurate and complete, and the query follows the rules by returning identifiers and labels optionally. No further refinement is necessary.