PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
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 piano, guitar, 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 http://amur:7001

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 break down the question:

1. The user is asking about "Scott Joplin" and what he "played". This likely refers to musical instruments or roles as a performer.
2. I need to identify the entity for Scott Joplin in Wikidata.
3. Then, I need to find properties related to what he played—this could be "instrument", "occupation", or similar.

First, I'll search for the entity "Scott Joplin".

[Tool]
Top 10 entity alternatives:
1. Scott Joplin, also known as The king of Ragtime (wd:Q191499): American composer, musician, and pianist, jazz musician, banjoist, composer, pianist
2. Scott Joplin (wd:Q12127336): 1977 film by Jeremy Kagan
3. Scott Joplin (wd:Q28730081): painting by Ellen Banks
4. Scott Joplinlaan (wd:Q19513256): street in Eindhoven, the Netherlands
5. Scott Joplinrode (wd:Q19513257): street in Zoetermeer, the Netherlands
6. Scott Joplin: Piano Rags (wd:Q7436590): album by Joshua Rifkin
7. Scott Joplin's foot (wd:Q30538526): scientific article published on July 1, 1998
8. Scott Joplin: Questions Remain (wd:Q113214829): scientific article published on 18 April 2006
9. Scott Joplin: Complete Piano Works, also known as The Collected Works of Scott Joplin (wd:Q28156183): book by Scott Joplin
10. Scott Joplin’s New Rag (wd:Q29003610): ragtime composition for piano by Scott Joplin

[Assistant]
The most relevant entity is Scott Joplin (wd:Q191499), the American composer, musician, and pianist.

Next, I need to find properties related to what he "played". The most likely property is "instrument", but it could also be "occupation" if the question is interpreted more broadly. I'll 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): image of a person's signature, image
6. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): 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 p/wdt): 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 p/wdt): main Wikimedia category
9. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): 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 p/wdt): notable scientific, artistic or literary work, or other work of significance among subject's works

No other items found

[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) |

[Tool]
Got 4 rows and 1 column:
| instrument          |
| ------------------- |
| cornet (wd:Q202027) |
| piano (wd:Q5994)    |
| guitar (wd:Q6607)   |
| violin (wd:Q8355)   |

[Tool]
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)     |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the instruments played by Scott Joplin by querying the 'plays instrument' property (wdt:P1303) for the entity Scott Joplin (wd:Q191499). The query returns the IRIs of the instruments and optionally their English labels, which is appropriate. The answer provided matches the query result. No further refinement is needed.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?occupation WHERE {
  wd:Q81960 wdt:P106 ?occupation
}

Robert Burns had the following jobs (occupations): 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 http://amur:7001

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 break down the question:

1. The entity is "Robert Burns".
2. The property of interest is "jobs" (occupation).

First, I will search for the entity "Robert Burns" in Wikidata to get its identifier.

[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 poet is wd:Q81960.

Next, I will search for the property corresponding to "jobs" or "occupation" to find the correct property to use in the query.

[Tool]
Top 10 property alternatives:
1. occupation, also known as job, work, vocation, profession, employment (wd:P106 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
2. field of this occupation, also known as practice, works in, profession's field, practices, field of this profession (wd:P425 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): field corresponding to this occupation or profession (use only f...fessions - for people use Property:P101, for companies use P452)
3. members have occupation, also known as profession of members, organization of, organization for, organisation of, organisation for (wd:P3989 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): some or all members of this group share the occupation
4. practiced by, also known as proponent, studied by, practitioners, practioner, occupation of this field (wd:P3095 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): type of agents that study this subject or work in this field
5. position held, also known as function, held position, holds position, job, office held (wd:P39 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): subject currently or formerly holds the object position or public office
6. employer, also known as working at, working for, working place, works at, works for (wd:P108 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): person or organization for which the subject works or worked, affiliation
7. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): the object is a country that recognizes the subject as its citizen, country
8. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): person or organization occupying property, used by
9. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): specialization of a person or organization; see P106 for the occupation, interested in
10. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): location where persons or organisations were actively participating in employment, business or other work, significant place, location

[Assistant]
The property "occupation" (wd:P106) is the correct one to use for "jobs".

Now, I will construct a SPARQL query to find all occupations (jobs) of Robert Burns (wd:Q81960) using the property occupation (wd:P106). I will also try to include optional labels for human readability.

[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 all occupations (jobs) associated with Robert Burns by querying the occupation property (wdt:P106) for the entity wd:Q81960 (Robert Burns). The answer accurately lists the jobs found in the query result. The query also returns the IRIs of the occupations, which is in line with the requirements. No further refinement is necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?zip WHERE {
  {
    wd:Q5083 wdt:P281 ?zip
  } UNION {
    ?place wdt:P131 wd:Q5083 ; wdt:P281 ?zip
  }
  FILTER ( STRSTARTS( STR( ?zip ) , "981" ) ) FILTER ( REGEX( STR( ?zip ) , "^[0-9]{5}$" ) )
}
ORDER BY ?zip

Seattle, Washington has multiple zip codes, all starting with '981'. Some of the standard 5-digit zip codes for Seattle include: 98101, 98102, 98103, 98104, 98105, 98106, 98107, 98108, 98109, 98112, 98115, 98116, 98117, 98118, 98119, 98121, 98122, 98125, 98133, 98134, 98144, 98164, 98174, 98195, and 98199. These are the primary Seattle zip codes according to Wikidata.

[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 http://amur:7001

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 break down the question:

1. The user is asking for the zip code for Seattle, Washington.
2. I need to identify the entity for Seattle, Washington in Wikidata.
3. Then, I need to find the property in Wikidata that represents "zip code" or "postal code".

First, I'll search for the entity "Seattle, Washington" in Wikidata.

[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 most relevant entity for Seattle, Washington is wd:Q5083 (Seattle).

Next, I need to find the property in Wikidata that represents "zip code" or "postal code". I'll search for the property "zip code".

[Tool]
Top 10 property alternatives:
1. postal code, also known as pincode, PIN Code, postal index number, post code, postcode (wd:P281 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): identifier assigned by postal authorities for the subject area or building
2. local dialing code, also known as area code, dialling code, local calling code, local dialling code, STD code (wd:P473 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): identifier dedicated to subject city by the area communication network
3. country calling code, also known as calling code, country code, country dialing code, dialing code, phone code (wd:P474 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): identifier for a country - dialed on phone after the international dialing prefix (precede value by +)
4. location code (wd:P13262 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): an assigned code for the location (physical or virtual) within a given system (please use more specific property if available), catalog code
5. CDDA designationTypeCode, also known as designated area code, CDDA type ID, CDDA type, CDDA, CDDA code (wd:P9800 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): code for the national designation type used in the Common Database on Designated Areas inventory (CDDA)
6. Dictionary of Occupational Titles Code (fourth edition, revised), also known as DOT code (wd:P8679 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): occupation code from the Dictionary of Occupational Titles, fourth edition, revised
7. post office box, also known as PO Box, Post Office Box, P.O. Box, P. O. Box, P O Box (wd:P2918 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): post office box number, as part of an address
8. quantity, also known as amount, total number, total, qty, occurrences (wd:P1114 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): number of instances of this subject in the universe of the subje...e actual number of instances in Wikidata may be lower or higher)
9. series ordinal, also known as ordinal, unit number, sort order, sorting order, series number (wd:P1545 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): position of an item in its parent series (most frequently a 1-ba...d from "ranking" defined as a property for evaluating a quality)
10. issue, also known as number, no., issue number (wd:P433 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): issue of a newspaper, a scientific journal or magazine for reference purpose

[Assistant]
The most relevant property for "zip code" is "postal code" (wd:P281).

Now, I'll check if Seattle (wd:Q5083) has a value for the property "postal code" (wd:P281).

[Tool]
Got 1 row and 3 columns:
| subject            | property               | object |
| ------------------ | ---------------------- | ------ |
| Seattle (wd:Q5083) | postal code (wdt:P281) | 98101  |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The answer is technically correct for the main downtown Seattle zip code (98101), and the SPARQL query is syntactically correct and returns the IRI as required. However, Seattle has many zip codes, not just one. The query only retrieves one postal code (likely the main or central one), but does not account for the fact that cities often have multiple zip codes. To improve, the query should retrieve all postal codes associated with Seattle. For example, ensure that all values for wdt:P281 are returned, not just one. The answer should also clarify that 98101 is one of several zip codes for Seattle.

[Assistant]
Thank you for the feedback. You are correct: Seattle has multiple zip codes, and the previous query only retrieved one. To improve, I will write a query that retrieves all postal codes (wdt:P281) associated with Seattle (wd:Q5083). I will also clarify in the answer that these are all the zip codes listed for Seattle in Wikidata.

First, I will check if there are multiple values for wdt:P281 for Seattle by listing all such triples.

[Tool]
Got 1 row and 3 columns:
| subject            | property               | object |
| ------------------ | ---------------------- | ------ |
| Seattle (wd:Q5083) | postal code (wdt:P281) | 98101  |

[Assistant]
It appears that Wikidata only lists one postal code (98101) directly for the entity Seattle (wd:Q5083). However, cities are often linked to multiple zip codes via neighborhoods, districts, or other administrative divisions. To be thorough, I should check if there are other entities (such as neighborhoods or districts within Seattle) that have postal codes and are part of Seattle.

To do this, I will:
1. Search for properties that link entities to Seattle (such as "located in the administrative territorial entity" or "part of").
2. Find all entities that are part of Seattle and have a postal code.
3. Retrieve all unique postal codes for these entities.

First, I will search for properties related to "part of" or "located in" to find how neighborhoods or districts might be linked to Seattle.

[Tool]
Top 10 property alternatives:
1. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
3. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): person or organization occupying property, used by
4. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
5. adjacent building, also known as next to, neighboring building, located next to, adjoining building (wd:P3032 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): building adjacent to the item, connects with
6. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
7. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): environment (geographic feature, building type, etc.) the narrative work is set in, significant place, depicts
8. narrative location, also known as set in location, set in, setting, setting location, setting of work (wd:P840 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): the narrative of the work is set in this location, significant place, depicts
9. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
10. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

[Assistant]
The property "located in the administrative territorial entity" (wd:P131) is commonly used to link neighborhoods or districts to a city like Seattle.

Now, I will write a query to find all entities that are located in Seattle (wd:Q5083) via wd:P131 and have a postal code (wdt:P281), and collect all unique postal codes. I will also include the postal code directly attached to Seattle itself.

Let's construct the SPARQL query accordingly.

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

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is generally correct in its approach, as it retrieves postal codes (P281) directly associated with Seattle (wd:Q5083) and with places located within Seattle (using P131). However, the result includes some zip codes that are not strictly within Seattle proper (e.g., 98004, 98006, which are for Bellevue), and also includes some extended or formatted zip codes (e.g., 'WA 98195-4360') that may not be standard 5-digit zip codes.

To refine the query, consider filtering results to only include zip codes that start with '981', which are the standard Seattle zip codes, and exclude those that contain non-numeric characters or are associated with other cities. Additionally, you could use a FILTER to ensure only 5-digit zip codes are returned, unless the user explicitly asks for extended codes. This will make the answer more precise and relevant to the user's question about Seattle, Washington.

[Assistant]
Thank you for the detailed feedback. To refine the query:

1. I will filter the results to only include zip codes that start with '981', which are the standard Seattle zip codes.
2. I will exclude any zip codes that contain non-numeric characters or are not standard 5-digit codes, using a FILTER to ensure only 5-digit zip codes are returned.

Here's how I will adjust the SPARQL query:
- Use FILTER(STRSTARTS(..., "981")) to only include zip codes starting with '981'.
- Use FILTER(REGEX(..., "^[0-9]{5}$")) to only include standard 5-digit codes.

Let's construct and execute the refined query.

[Tool]
Got 24 rows and 1 column, showing the first 5 and last 5 rows below:
| zip   |
| ----- |
| 98101 |
| 98102 |
| 98103 |
| 98104 |
| 98105 |
| ...   |
| 98144 |
| 98164 |
| 98174 |
| 98195 |
| 98199 |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?boyfriend ?boyfriendLabel WHERE {
  wd:Q575689 wdt:P451 ?boyfriend .
  OPTIONAL {
    ?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 (romantic partners) listed in Wikidata are: Scott Disick (wd:Q7883337) and Younes Bendjima (wd:Q55200042). Their names are included in the results for clarity.

[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 http://amur:7001

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 break down the question:

1. The user is asking for the names of Kourtney Kardashian's boyfriends.
2. First, I need to identify the Wikidata entity for Kourtney Kardashian.
3. Then, I need to find the property that links a person to their romantic partners or boyfriends.
4. Finally, I will construct a SPARQL query to retrieve the identifiers (and optionally, labels) of Kourtney Kardashian's boyfriends.

Let's start by searching for the entity "Kourtney Kardashian" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. 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
2. Kardashian (wd:Q56538884): family name
3. Kourtney (wd:Q101065644): unisex given name
4. Kanye West, also known as Ye, Kanye Omari West, Yeezy, Kanye Kardashian, Kanye (wd:Q15935): American rapper, record producer, singer and fashion designer (born 1977), fashion designer, record producer, businessperson, entrepreneur, songwriter, director, singer, rapper
5. Kim Kardashian, also known as Kim Kardashian West, Kim K, Kim K., Kimberly Noel Kardashian-West, Kim Kardashian-West (wd:Q186304): American media personality, businesswoman, model and actress (born 1980), erotic photography model, television producer, fashion designer, entrepreneur, voice actor, celebrity, socialite, blogger, actor, model
6. Khloé Kardashian, also known as Khloé Kardashian Odom, Khloé Kardashian-Odom, Khloe Kardashian Odom, Khloe Kardashian, Khloe Odom (wd:Q231270): American media personality,influencer, businesswoman (born 1984), radio personality, television actor, fashion designer, film producer, manufacturer, entrepreneur, film actor, socialite, celebrity, novelist, writer, actor, model
7. Kris Jenner, also known as Kristen Mary Jenner, Kristen Jenner, Kristen Mary Houghton, Kristen Mary "Kris" Jenner, Chris Jenner (wd:Q1138235): American media personality, socialite, and businesswoman, television presenter, television producer, flight attendant, celebrity, socialite, author, actor
8. Robert Kardashian, also known as Robert George Kardashian, Rob Kardashian, Robert G. Kardashian (wd:Q1816737): American attorney and businessman (1944–2003), entrepreneur, jurist, lawyer
9. Rob Kardashian, also known as Robert Arthur Kardashian (wd:Q26318): American television personality and businessman, television actor, businessperson, socialite, model
10. Kardashian-Jenner family, also known as Kardashian family, Kardashians (wd:Q95747857): American family prominent in entertainment, business, and fashion

[Assistant]
The correct entity for Kourtney Kardashian is wd:Q575689.

Next, I need to find the property that links a person to their boyfriends or romantic partners. This is likely to be "partner" or "significant other." I will search for properties related to "boyfriend" or "partner."

[Tool]
Top 10 property alternatives:
1. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
2. Gazetteer for Scotland person ID, also known as GfS person (wd:P7349 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): identifier of a Scottish personality on www.scottish-places.info
3. number of points/goals/set scored, also known as goals scored, score for, points scored, number of points, number of goals (wd:P1351 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): goals / points scored in a match or an event used as qualifier to the participant. Use P1358 for league points.
4. connects with, also known as contacts, couples with, intersects at grade, links to, mates with (wd:P2789 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): item with which the item is physically connected, physically interacts with
5. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
6. kinship to subject, also known as type of relationship, type of kinship, relation to, relationship (kinship), nature of kinship (wd:P1039 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): qualifier of "relative" (P1038) to indicate less usual family re...; indicate how the qualificator item is related to the main item, object of statement has role
7. Historic Environment Scotland ID, also known as HB number, HES ID, HESID, HES number, HS ID (wd:P709 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): identifier for a building in the Historic Environment Scotland database
8. age estimated by a dating method, also known as stellar age, geological age, dating (wd:P7584 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): estimation of the age of a star, geological feature, fossil or object
9. relative, also known as relatives, grandparent, relation, non-binary parent, niece (wd:P1038 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), significant person
10. partner in business or sport, also known as professional partner, sport partner, sports partner, team mate, team-mate (wd:P1327 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): professional collaborator, significant person

[Assistant]
The most relevant property for "boyfriend" is "unmarried partner" (wd:P451), which is also known as "romantic partner" or "partner of."

Now, I will construct a SPARQL query to find all entities that are listed as Kourtney Kardashian's unmarried partners (boyfriends) using property wdt:P451. I will also optionally retrieve their labels for clarity.

[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=refine):
The SPARQL query correctly retrieves the IRIs of Kourtney Kardashian's boyfriends using the appropriate property (wdt:P451). However, the query only returns the IRIs and not any human-readable information (such as names), which would be helpful for user comprehension. While labels are optional unless explicitly requested, in this context, including them in an OPTIONAL clause would improve the answer's clarity. Suggest refining the query to include OPTIONAL { ?boyfriend rdfs:label ?boyfriendLabel. FILTER (lang(?boyfriendLabel) = 'en') }.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the IRIs of Kourtney Kardashian's boyfriends (romantic partners) using the appropriate property (wdt:P451) and includes their labels in an optional clause. The answer is accurate, complete, and follows all the required rules. No further refinement is needed.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?country WHERE {
  wd:Q192955 wdt:P27 ?country
}

Got 2 rows and 1 column:
| country                  |
| ------------------------ |
| United Kingdom (wd:Q145) |
| Canada (wd:Q16)          |

Steve Nash has the nationalities of Canada (wd:Q16) and United Kingdom (wd:Q145).

[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 http://amur:7001

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 break down the question:

1. The entity is "Steve Nash".
2. The property we are interested in is "nationality".

First, I will search for the entity "Steve Nash" in Wikidata to get its identifier.

[Tool]
Top 10 entity alternatives:
1. Steve Nash, also known as Stephen John Nash (wd:Q192955): Canadian basketball player and coach, basketball player, basketball coach, businessperson, film director
2. Steve Nash (wd:Q16137022): English rugby league footballer and coach, rugby league player
3. Steve Nash (wd:Q48851147): disc jockey
4. Steve Nash (wd:Q42316961): actor
5. Steven Jackson, also known as Steven Nash Jackson (wd:Q3499240): American baseball player, baseball player
6. Steven G. Nash (wd:Q98756087): American politician, businessperson, politician
7. Steven Roy Nash, also known as Dr. Steven Roy Nash (wd:Q76187877): Peerage person ID=590989
8. Edward Mungo Steven Nash, also known as Edward Nash (wd:Q76187880): (born 1990)
9. Nash, also known as Nash, Texas, Nash, TX (wd:Q978847): city in Bowie County, Texas, United States
10. Nash, also known as Nash, OK, Nash, Oklahoma (wd:Q2321969): town in Grant County, Oklahoma, United States

[Assistant]
The most relevant entity for "Steve Nash" the basketball player is wd:Q192955.

Next, I will search for the property "nationality" to find its corresponding property identifier in Wikidata.

[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 psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): 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 psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): country a person or a team represents when playing a sport, country
3. ethnic group, also known as ethnic or cultural origin, tribal affiliation, race, people, ethnicity (wd:P172 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
4. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): most specific known birth location of a person, animal or fictional character, location
5. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): date on which the subject was born, inception
6. category for people born here, also known as category for place of birth, category of people born here (wd:P1464 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): category item that groups people born in this place, related category
7. occupation, also known as job, work, vocation, profession, employment (wd:P106 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
8. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): language or languages a person has learned from early childhood, languages spoken, written or signed
9. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): full name of a person at birth, if different from their current, generally used name, name
10. birthday, also known as b-day, bday, birth day (wd:P3150 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): item for day and month on which the subject was born. Used when full "date of birth" (P569) isn't known., date of birth

[Assistant]
The most relevant property for "nationality" is "country of citizenship" (wd:P27).

Now, I will construct a SPARQL query to find the country of citizenship (nationality) of Steve Nash (wd:Q192955). I will also try to include the label of the country for readability, but the main result will be the IRI.

[Tool]
Got 2 rows and 2 columns:
| country                  | countryLabel        |
| ------------------------ | ------------------- |
| 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 IRIs of the countries for Steve Nash's nationality using the appropriate property (wdt:P27). The answer lists both Canada and the United Kingdom, which matches the query result. No further refinement is necessary.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?country WHERE {
  wd:Q181574 wdt:P527 ?country .
}

Got 3 rows and 1 column:
| country                |
| ---------------------- |
| Canada (wd:Q16)        |
| United States (wd:Q30) |
| Mexico (wd:Q96)        |

The countries involved in NAFTA are Canada (wd:Q16), United States (wd:Q30), and Mexico (wd:Q96).

[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 http://amur:7001

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 break down the question:

1. The user is asking for the countries involved in NAFTA.
2. "NAFTA" stands for North American Free Trade Agreement, so I need to find the entity for NAFTA in Wikidata.
3. Then, I need to find the property that links NAFTA to its member countries.

First, I will search for the entity "NAFTA" in Wikidata.

[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 correct entity for the original NAFTA agreement is "North American Free Trade Agreement" (wd:Q181574).

Next, I need to find the property that links this entity to its member or participant countries. I will search for properties related to "country" 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 p/wdt): 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 p/wdt): 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 p/wdt): main Wikimedia category
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon

No other items found

[Tool]
Got 516 rows and 3 columns, showing the first 10 rows below:
| subject                                          | property                                       | object                                                  |
| ------------------------------------------------ | ---------------------------------------------- | ------------------------------------------------------- |
| North American Free Trade Agreement (wd:Q181574) | instance of (wdt:P31)                          | trade bloc (wd:Q1129645)                                |
| North American Free Trade Agreement (wd:Q181574) | Commons category (wdt:P373)                    | North American Free Trade Agreement                     |
| North American Free Trade Agreement (wd:Q181574) | inception (wdt:P571)                           | 1994-01-01T00:00:00Z (xsd:dateTime)                     |
| North American Free Trade Agreement (wd:Q181574) | short name (wdt:P1813)                         | ALENA (fr)                                              |
| North American Free Trade Agreement (wd:Q181574) | has part(s) (wdt:P527)                         | United States (wd:Q30)                                  |
| North American Free Trade Agreement (wd:Q181574) | official website (wdt:P856)                    | <http://www.nafta-sec-alena.org/>                       |
| North American Free Trade Agreement (wd:Q181574) | follows (wdt:P155)                             | Canada–United States Free Trade Agreement (wd:Q2665587) |
| North American Free Trade Agreement (wd:Q181574) | followed by (wdt:P156)                         | United States–Mexico–Canada Agreement (wd:Q56839716)    |
| North American Free Trade Agreement (wd:Q181574) | on focus list of Wikimedia project (wdt:P5008) | wd:Q6173448                                             |
| North American Free Trade Agreement (wd:Q181574) | applies to jurisdiction (wdt:P1001)            | United States (wd:Q30)                                  |
| ...                                              | ...                                            | ...                                                     |

[Assistant]
From the results, I see that the property "has part(s)" (wdt:P527) links NAFTA to "United States" (wd:Q30), and likely to other member countries as well.

Additionally, "applies to jurisdiction" (wdt:P1001) also links to "United States", but "has part(s)" is more likely to list all member countries directly.

I will now write a SPARQL query to retrieve all entities that are "has part(s)" (wdt:P527) of NAFTA (wd:Q181574), and optionally their labels.

[Tool]
Got 3 rows and 1 column:
| country                |
| ---------------------- |
| Canada (wd:Q16)        |
| United States (wd:Q30) |
| Mexico (wd:Q96)        |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The answer is correct and complete. The SPARQL query correctly retrieves the countries involved in NAFTA by querying the 'has part(s)' property of the NAFTA entity. The result includes the IRIs of the countries as required. No further refinement is needed.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?place ?placeLabel WHERE {
  wd:Q1744 wdt:P19 ?place .
  OPTIONAL {
    ?place rdfs:label ?placeLabel .
    FILTER ( LANG( ?placeLabel ) = "en" )
  }
}

According to Wikidata, Madonna's place of birth is Bay City (wd:Q746820). There is no information about her childhood residences or where she specifically grew up beyond her birthplace.

[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 http://amur:7001

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] where did madonna grew up?

[Assistant]
Let's break down the question:

1. The user is asking about the place where Madonna grew up.
2. This likely refers to Madonna (the singer), and the property is probably "place where grew up" or something similar.

First, I will search for the entity "Madonna" in Wikidata to get her identifier.

[Tool]
Top 10 entity alternatives:
1. Madonna, also known as Madonna Ciccone, Madonna Louise Veronica Ciccone, Madonna Louise Ciccone, Madonna L. Ciccone (wd:Q1744): American singer, songwriter, and actress (born 1958), songwriter, singer, actor
2. Mary, also known as Mother of God, Mother of Jesus, Notre Dame, Our Lady, Queen of the Universe (wd:Q345): mother of Jesus
3. Madonna, also known as Madonna: The First Album (wd:Q177943): 1983 self-titled debut studio album by Madonna
4. Madonna (wd:Q1989780): painting composition by Edvard Munch, in different versions
5. Madonna, also known as Madonna in art, The Madonna in art, Virgin Mary in art (wd:Q926743): artistic representation of Mary, either alone or with her child Jesus
6. Madonna (wd:Q19794647): 2015 South Korean film directed by Shin Su-won
7. Madonna (wd:Q1147459): album by …And You Will Know Us by the Trail of Dead
8. Madonna (wd:Q486604): 2010 EP by Secret
9. Madonna, also known as Madonna, MD, Madonna, Maryland (wd:Q6728358): unincorporated community in Harford County, Maryland
10. Virgin and Child with an Angel (Madonna del Latte), also known as Madonna, Madonna with child and John the Baptist (wd:Q3842618): painting by Antonio da Correggio from 1520

[Assistant]
The most relevant entity for "Madonna" the singer is wd:Q1744.

Next, I need to find the property that corresponds to "place where grew up" or similar. I will search for properties related to "grew up" for Madonna.

[Tool]
Top 10 property alternatives:
1. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
2. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
3. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as p/wdt): full name of a person at birth, if different from their current, generally used name, name
4. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as p/wdt): educational institution attended by subject, affiliation
5. child, also known as has son, kids, mother of, kid, issue (wd:P40 as p/wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
6. studied in, also known as is studied by, studied by, science, that studies, researched in, researched by (wd:P2579 as p/wdt): subject is studied by this science or domain, field of work
7. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as p/wdt): notable scientific, artistic or literary work, or other work of significance among subject's works
8. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as p/wdt): significant or notable events associated with the subject
9. work period (start), also known as start working, flourished (start), start time for work, start date of activity, fl. (start) (wd:P2031 as p/wdt): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, start time, floruit
10. mother, also known as mom, is child of, mommy, mum, mummy (wd:P25 as p/wdt): female parent of the subject. For stepmother, use "stepparent" (P3448), parent

No other items found

[Tool]
Top 10 property alternatives:
1. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
2. name in native language, also known as native name (wd:P1559 as p/wdt): name of a person in their native language, name
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country
4. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as p/wdt): educational institution attended by subject, affiliation
5. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
6. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as p/wdt): language or languages a person has learned from early childhood, languages spoken, written or signed
7. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
8. studied in, also known as is studied by, studied by, science, that studies, researched in, researched by (wd:P2579 as p/wdt): subject is studied by this science or domain, field of work
9. named after, also known as toponym, namesake, named in honor of, named for, etymology (wd:P138 as p/wdt): entity or event that inspired the subject's name, or namesake (i...fier "applies to name" (P5168) can be used to indicate which one
10. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as p/wdt): full name of a person at birth, if different from their current, generally used name, name

No other items found

[Tool]
Top 10 property alternatives:
1. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
2. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country
3. owner of, also known as owns, owns property, shareholder of (wd:P1830 as p/wdt): entities owned by the subject
4. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
5. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as p/wdt): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
6. net worth, also known as declared patrimony, estimated net worth, net wealth, net worth estimate, personal assets (wd:P2218 as p/wdt): totality of wealth possessed by a person
7. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
8. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
9. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as p/wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
10. Guardian topic ID, also known as Guardian, The Guardian topic, The Guardian topic ID (wd:P3106 as p/wdt): identifier for a topic at the Guardian newspaper website

No other items found

[Tool]
Top 10 property alternatives:
1. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): the place where the person is or has been, resident, location
2. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): most specific known birth location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): date on which the subject was born, inception
4. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): full name of a person at birth, if different from their current, generally used name, name
5. official residence, also known as resides at, lives in, palace, presidential mansion, presidential palace (wd:P263 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): the residence at which heads of government and other senior figures officially reside, residence
6. lifestyle, also known as diet, follows lifestyle, has lifestyle, life style (wd:P1576 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): typical way of life of an individual, group, or culture
7. wears, also known as wore, wearing, uniform, outfit, garment (wd:P3828 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): clothing or accessory worn on subject's body, item operated
8. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): educational institution attended by subject, affiliation
9. ASF KID Cave Tag Number, also known as Australian Speleological Federation KID, KID, Karst Index Database (wd:P2792 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): unique identifier for caves within the Karst Index Database of the Australian Speleological Federation
10. Kunstindeks Danmark Artist ID, also known as KID, KID artist ID, KID ID (wd:P1138 as psn/prn/pqv/psv/wdt/prv/pq/pr/pqn/wdtn/p/ps): unique artist identifier used by Kunstindeks Danmark

[Tool] Got no rows and 3 columns

[Tool]
Got 1 row and 3 columns:
| subject            | property                 | object                |
| ------------------ | ------------------------ | --------------------- |
| Madonna (wd:Q1744) | place of birth (wdt:P19) | Bay City (wd:Q746820) |

[Tool]
Got 2 rows and 3 columns:
| subject            | property              | object                                                                |
| ------------------ | --------------------- | --------------------------------------------------------------------- |
| Madonna (wd:Q1744) | educated at (wdt:P69) | Rochester Adams High School (wd:Q7353940)                             |
| Madonna (wd:Q1744) | educated at (wdt:P69) | University of Michigan School of Music, Theatre & Dance (wd:Q7895775) |

[Tool] Got no rows and 3 columns

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                           | property             | object                               |
| --------------------------------- | -------------------- | ------------------------------------ |
| Abhishek Bhatia (wd:Q100152292)   | residence (wdt:P551) | United States (wd:Q30)               |
| Omar Khashram (wd:Q104147083)     | residence (wdt:P551) | Turkey (wd:Q43)                      |
| Splinter (wd:Q100264004)          | residence (wdt:P551) | Japan (wd:Q17)                       |
| Enrico Bottoni (wd:Q105202488)    | residence (wdt:P551) | Italy (wd:Q38)                       |
| Thomas Gentille (wd:Q100157076)   | residence (wdt:P551) | Germany (wd:Q183)                    |
| Amir Escandari (wd:Q100353921)    | residence (wdt:P551) | Finland (wd:Q33)                     |
| E. Naudy (wd:Q100604482)          | residence (wdt:P551) | France (wd:Q142)                     |
| Caterina Camastra (wd:Q100987766) | residence (wdt:P551) | Mexico (wd:Q96)                      |
| Mahatma Gandhi (wd:Q1001)         | residence (wdt:P551) | India (wd:Q668)                      |
| Cindy Wang Brandt (wd:Q104696196) | residence (wdt:P551) | People's Republic of China (wd:Q148) |
| ...                               | ...                  | ...                                  |

[Tool]
Got 2,171 rows and 3 columns, showing the first 10 rows below:
| subject            | property                         | object                                                                                |
| ------------------ | -------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| Madonna (wd:Q1744) | instance of (wdt:P31)            | human (wd:Q5)                                                                                |
| Madonna (wd:Q1744) | occupation (wdt:P106)            | actor (wd:Q33999)                                                                                |
| Madonna (wd:Q1744) | sex or gender (wdt:P21)          | female (wd:Q6581072)                                                                                |
| Madonna (wd:Q1744) | given name (wdt:P735)            | Louise (wd:Q3215140)                                                                                |
| Madonna (wd:Q1744) | date of birth (wdt:P569)         | 1958-08-16T00:00:00Z (xsd:dateTime)                                                                                |
| Madonna (wd:Q1744) | Commons category (wdt:P373)      | Madonna (entertainer)                                                                                |
| Madonna (wd:Q1744) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Madonna%20Rebel%20Heart%20Tour%202015%20-%20Stockholm%20%2823051472299%29%20%28cropped%29.jpg> |
| Madonna (wd:Q1744) | country of citizenship (wdt:P27) | United States (wd:Q30)                                                                                |
| Madonna (wd:Q1744) | family name (wdt:P734)           | Ciccone (wd:Q20830931)                                                                                |
| Madonna (wd:Q1744) | place of birth (wdt:P19)         | Bay City (wd:Q746820)                                                                                |
| ...                | ...                              | ...                                                                                |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
Based on the available data in Wikidata, the only relevant location information for where Madonna grew up is her place of birth (Bay City) and her education (Rochester Adams High School, University of Michigan School of Music, Theatre & Dance). There is no direct property for "grew up" or "hometown" for Madonna in Wikidata.

I will construct a SPARQL query that returns her place of birth and the places where she was educated, as these are the closest available proxies for where she grew up.

Here is the SPARQL query:

```sparql
SELECT ?place ?placeLabel WHERE {
  VALUES ?person { wd:Q1744 }
  {
    ?person wdt:P19 ?place .
  }
  UNION
  {
    ?person wdt:P69 ?place .
  }
  OPTIONAL { ?place rdfs:label ?placeLabel . FILTER(LANG(?placeLabel) = "en") }
}
```

This will return the IRIs for the places and their English labels, if available.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query retrieves Madonna's place of birth and educational institutions, which are reasonable proxies for where she grew up, given the limitations of Wikidata's structure. However, the query could be improved by also considering the 'residence' property (wd:P551), which may provide more direct information about where she lived during her formative years. Additionally, the query should return the IRIs of the places, not just their labels, to comply with the guidelines. Consider refining the query to include possible residences and ensure IRIs are returned.

[Assistant]
Thank you for the feedback. You are correct: including the 'residence' property (wd:P551) could provide more direct information about where Madonna lived, which may better answer where she grew up. Also, I will ensure the query returns the IRIs of the places, not just their labels, and that labels are optional.

Here's my step-by-step plan:
1. Retrieve Madonna's place of birth (wd:P19).
2. Retrieve educational institutions (wd:P69).
3. Retrieve places of residence (wd:P551).
4. Return the IRIs for all places, with optional English labels.

Now, I will construct the refined SPARQL query.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: Variable ?place is selected but not aggregated. All non-aggregated variables must be part of the GROUP BY clause. Note: The GROUP BY in this query is implicit because an aggregate expression was used in the SELECT clause

[Assistant]
The error is due to using SAMPLE() without a GROUP BY. To fix this, I will remove the aggregation and simply return the IRIs and optional labels for all places, allowing for possible duplicates.

Here is the revised query:

```sparql
SELECT DISTINCT ?place ?placeLabel WHERE {
  VALUES ?person { wd:Q1744 }
  {
    ?person wdt:P19 ?place .
  }
  UNION
  {
    ?person wdt:P69 ?place .
  }
  UNION
  {
    ?person wdt:P551 ?place .
  }
  OPTIONAL { ?place rdfs:label ?placeLabel . FILTER(LANG(?placeLabel) = "en") }
}
```
I will now execute this query.

[Tool]
Got 3 rows and 2 columns:
| place                                                                 | placeLabel                                                   |
| --------------------------------------------------------------------- | ------------------------------------------------------------ |
| Rochester Adams High School (wd:Q7353940)                             | Rochester Adams High School (en)                             |
| Bay City (wd:Q746820)                                                 | Bay City (en)                                                |
| University of Michigan School of Music, Theatre & Dance (wd:Q7895775) | University of Michigan School of Music, Theatre & Dance (en) |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is a good start, as it retrieves Madonna's place of birth, places of education, and places of residence. However, the answer could be refined in two ways:

1. The query currently includes educational institutions ("educated at"), which may not directly correspond to where Madonna grew up, especially higher education institutions like the University of Michigan School of Music, Theatre & Dance. For a question about where someone "grew up," the focus should be on place of birth and places of residence during childhood, not necessarily places of higher education.

2. The answer should clarify that while Bay City is her birthplace, and Rochester Adams High School is a school she attended, the actual city or town where she spent her formative years (e.g., her childhood home or city) is not directly specified in Wikidata. If there is a more specific childhood residence (using P551 with a qualifier for time period), it should be included.

Suggestion: Refine the query to focus on place of birth and childhood residences (if available), and exclude higher education institutions from the answer unless specifically relevant to her upbringing.

[Assistant]
Thank you for the detailed feedback. Here’s my refined approach:

1. Focus on Madonna’s place of birth (P19) and places of residence (P551).
2. Try to filter residences to those relevant to her childhood, if possible. In Wikidata, qualifiers such as "start time" (P580), "end time" (P582), or "point in time" (P585) may be used to specify when someone lived at a particular residence. I will look for such qualifiers to identify childhood residences.
3. Exclude educational institutions, especially higher education, unless they are directly relevant to her upbringing.

Step 1: Check if any of Madonna’s residences (P551) have qualifiers indicating they correspond to her childhood.

[Tool] Got no rows and 3 columns

[Assistant]
There are no direct "residence" (P551) statements for Madonna in Wikidata, so we cannot retrieve any childhood residences or time-qualified residences.

Given this, the best available data are:
- Place of birth (Bay City, wd:Q746820).

Since there are no childhood residences or time-qualified residences, I will construct a query that only returns her place of birth. I will also include an optional label for clarity.

Here is the final SPARQL query:

```sparql
SELECT ?place ?placeLabel WHERE {
  wd:Q1744 wdt:P19 ?place .
  OPTIONAL { ?place rdfs:label ?placeLabel . FILTER(LANG(?placeLabel) = "en") }
}
```

This query directly answers the question with the best available data in Wikidata.

[Tool] Stopped generation process