PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?date WHERE {
  wd:Q211386 wdt:P585 ?date .
}

The San Francisco earthquake occurred on April 18, 1906.

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

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

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

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

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

[User] when did the san francisco earthquake occur?

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

[Tool]
Example 1:
Question:
when did the christchurch quake happened?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q151850 wdt:P585 ?x .
}

Using entities:
February 2011 Christchurch earthquake, also known as Christchurch 2011 earthquake, Christchurch earthquake (wd:Q151850): February 2011 earthquake in New Zealand

Using properties:
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 2:
Question:
what year was the great san francisco fire?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q211386 wdt:P585 ?x .
}

Using entities:
1906 San Francisco earthquake, also known as Great San Francisco Earthquake and Fire, San Francisco earthquake of 1906, San Francisco earthquake and fire, 1906, Great 1906 San Francisco Earthquake, earthquake of 1906 (wd:Q211386): major earthquake that struck San Francisco and the coast of Northern California

Using properties:
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 3:
Question:
where did the loma prieta earthquake occur?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1053476 wdt:P131 ?x .
}

Using entities:
1989 Loma Prieta earthquake, also known as 1989 San Francisco earthquake, Loma Prieta earthquake (wd:Q1053476): major earthquake in northern California

Using properties:
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 wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

[Assistant]
From the examples, the 1906 San Francisco earthquake is identified as wd:Q211386 and the property for the date or time of the event is wdt:P585 (point in time).

I will now write a SPARQL query to get the date when the San Francisco earthquake (1906) occurred using the property wdt:P585.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 1906-04-18T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the point in time (P585) for the entity representing the 1906 San Francisco earthquake (Q211386). The query returns the precise date April 18, 1906, which matches the known historical date of the earthquake. The answer is accurate, complete, and the query follows the rules by returning the actual date value. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?placeOfBirth ?placeOfBirthLabel ?countryOfCitizenship ?countryOfCitizenshipLabel WHERE {
  wd:Q13603754 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    wd:Q13603754 wdt:P27 ?countryOfCitizenship
  }
  OPTIONAL {
    ?placeOfBirth rdfs:label ?placeOfBirthLabel FILTER ( LANG( ?placeOfBirthLabel ) = "en" )
  }
  OPTIONAL {
    ?countryOfCitizenship rdfs:label ?countryOfCitizenshipLabel FILTER ( LANG( ?countryOfCitizenshipLabel ) = "en" )
  }
}
LIMIT 10

Got 1 row and 4 columns:
| placeOfBirth           | placeOfBirthLabel | countryOfCitizenship     | countryOfCitizenshipLabel |
| ---------------------- | ----------------- | ------------------------ | ------------------------- |
| Salisbury (wd:Q160642) | Salisbury (en)    | United Kingdom (wd:Q145) | United Kingdom (en)       |

Robert Irvine, the British celebrity chef, was born in Salisbury and has United Kingdom as his country of citizenship.

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

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

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

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

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

[User] where does robert irvine come from?

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

[Tool]
Example 1:
Question:
where did henry hudson come from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q179167 wdt:P19 ?x .
}

Using entities:
Henry Hudson, also known as Hendrik Hudson (wd:Q179167): English explorer b1565, explorer, seafarer

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 2:
Question:
where did robert boyle study?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q43393 wdt:P69 ?x .
}

Using entities:
Robert Boyle, also known as The Hon. Robert Boyle, Sir Robert Boyle, Hon. Robert Boyle (wd:Q43393): Anglo-Irish natural philosopher, chemist, physicist, and inventor, philosopher, physicist, chemist

Using properties:
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

Example 3:
Question:
what country did james watt come from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q9041 wdt:P27 ?x .
}

Using entities:
James Watt, also known as James von Breda Watt, James, Sr. Watt (wd:Q9041): British engineer (1736–1819), mathematician, entrepreneur, technician, physicist, engineer, inventor, chemist

Using properties:
country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt): the object is a country that recognizes the subject as its citizen, country

[Assistant]
From the examples, we see that "where does [person] come from?" can be interpreted as either place of birth (wdt:P19) or country of citizenship (wdt:P27). To answer the question about Robert Irvine's origin, we should first identify Robert Irvine in Wikidata and then query for his place of birth and country of citizenship.

Step 1: Search for the entity "Robert Irvine" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Bobby Irvine, also known as Robert Irvine, Robert James Irvine (wd:Q4935169): Northern Irish association footballer (born 1942), association football player
2. Robert Irvine (wd:Q29561444): association football player
3. Robert Irvine, also known as Robert Paul Irvine (wd:Q13603754): British celebrity chef, chef
4. Irvine, Robert (wd:Q94829234): entry in Alumni Oxonienses: the Members of the University of Oxford, 1715-1886
5. Robert Irvine (wd:Q7345808): New Zealand rugby league footballer, rugby league player
6. Robert Brian Irvine, also known as Robert Irvine (wd:Q75949893): Peerage person ID=423923
7. Robert Irvine (wd:Q93942944)
8. Robert Irvine (wd:Q76346164): Peerage person ID=703910
9. Robert Irvine (wd:Q76170766): Peerage person ID=579126
10. Robert Irvine (wd:Q76037914): Peerage person ID=485894

[Assistant] There are multiple entities named Robert Irvine. The one most likely relevant to the common public figure "Robert Irvine" is the British celebrity chef (wd:Q13603754). I will now check the place of birth (wdt:P19) and country of citizenship (wdt:P27) for this entity.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant] The query failed due to the use of the SERVICE wikibase:label, which is not supported by the endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional clauses to get the labels for place of birth and country of citizenship.

[Tool]
Got 1 row and 4 columns:
| placeOfBirth           | placeOfBirthLabel | countryOfCitizenship     | countryOfCitizenshipLabel |
| ---------------------- | ----------------- | ------------------------ | ------------------------- |
| Salisbury (wd:Q160642) | Salisbury (en)    | United Kingdom (wd:Q145) | United Kingdom (en)       |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves Robert Irvine's place of birth and country of citizenship from Wikidata. The answer is accurate and complete, stating that Robert Irvine was born in Salisbury and has United Kingdom as his country of citizenship. The query uses appropriate properties (P19 for place of birth and P27 for country of citizenship) and includes labels for readability. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?headquarters ?headquartersLabel WHERE {
  wd:Q505922 wdt:P159 ?headquarters .
  OPTIONAL {
    ?headquarters rdfs:label ?headquartersLabel FILTER ( LANG( ?headquartersLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| headquarters         | headquartersLabel |
| -------------------- | ----------------- |
| Stockholm (wd:Q1754) | Stockholm (en)    |

Electrolux is based in Stockholm.

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

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

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

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

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

[User] where is electrolux based?

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

[Tool]
Example 1:
Question:
where is amazon based out of?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q10846439 wdt:P159 ?x .
}

Using entities:
Amazon.co.jp, also known as Amazon Japan (wd:Q10846439)

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place

Example 2:
Question:
where is sony ericsson based?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q376692 wdt:P159 ?x .
}

Using entities:
Sony Mobile Communications, also known as Sony Ericsson (wd:Q376692): Japanese telecommunications company owned by Sony

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place

Example 3:
Question:
where is eu headquarters located?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q458 wdt:P159 ?x .
}

Using entities:
European Union, also known as E.U., Europe, eu (wd:Q458): political and economic union of 27 European states

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place

[Assistant] From the examples, it is clear that the property "headquarters location" (wdt:P159) is used to find where a company is based. Now, I need to find the entity for Electrolux to use it in the query. I will search for the entity "Electrolux" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Electrolux, also known as AB Electrolux (wd:Q505922): Swedish multinational manufacturer and marketer of home appliances
2. Electrolux (wd:Q2923843): genus of fishes
3. ELECTROLUX, also known as ELECTROLUX, s.r.o. (wd:Q58218446): Czech company
4. Electrolux addisoni (wd:Q2387882): species of fish
5. Electrolux Assistent, also known as Electrolux Ankarsrum Assistent (wd:Q17496378): stand mixer introduced by Electrolux in 1940
6. Electrolux Trilobite (wd:Q5358060): first commercially available robotic vacuum cleaner
7. Electrolux (Italy), also known as Electrolux Italia S.p.A., Electrolux Rex (wd:Q106045223)
8. Electrolux F.C. (wd:Q96377107): football club
9. Electrolux Professional (wd:Q109974149): Swedish professional appliance manufacturer
10. Lux Norge AS, also known as Electrolux-Euroclean AS, Electrolux AS, Electrolux Rengjøringsmaskiner AS (wd:Q15729564)

[Assistant] The entity for Electrolux as a Swedish multinational manufacturer and marketer of home appliances is wd:Q505922. Now I will write a SPARQL query to find the headquarters location (property wdt:P159) of Electrolux (wd:Q505922).

[Tool]
Got 1 row and 2 columns:
| headquarters         | headquartersLabel |
| -------------------- | ----------------- |
| Stockholm (wd:Q1754) | Stockholm (en)    |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the headquarters location of Electrolux using the property P159. The query returns the identifier and the English label of the headquarters, which is Stockholm. The answer provided is accurate and complete based on the query result. No further refinement is needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?zipCode WHERE {
  wd:Q793929 wdt:P281 ?zipCode .
}

Got 1 row and 1 column:
| zipCode |
| ------- |
| 76020   |

The zip code for Azle, Texas is 76020.

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

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

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

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

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

[User] what is the zip code for azle texas?

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

[Tool]
Example 1:
Question:
what is arlington texas zip code?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q17943 wdt:P281 ?x .
}

Using entities:
Arlington, also known as Arlington, Texas, Arlington, TX (wd:Q17943): suburban city in Texas, United States

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

Example 2:
Question:
what is the zip code for fort stockton texas?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q979901 wdt:P281 ?x .
}

Using entities:
Fort Stockton, also known as Fort Stockton, Texas, Fort Stockton, TX (wd:Q979901): city in and county seat of Pecos County, Texas, United States

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

Example 3:
Question:
what is the zip code for cordova tn?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5170040 wdt:P281 ?x .
}

Using entities:
Cordova, also known as Cordova, Tennessee (wd:Q5170040): unincorporated community in Shelby County, Tennessee

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

[Assistant] From the examples, we see that the postal code (zip code) is represented by the property wdt:P281 and the city entity is used as the subject. Now, I will search for the entity Azle, Texas in Wikidata to get its identifier.

[Tool]
Top 10 entity alternatives:
1. Azle, also known as Azle, Texas, Azle, TX (wd:Q793929): city in Texas, United States
2. Texas Health Resources Azle heliport (wd:Q61719753): heliport in Texas, United States
3. Causal factors for seismicity near Azle, Texas (wd:Q35541092): scientific article published on 21 April 2015
4. Texas, also known as Tex., US-TX, Lone Star State, Texas, United States, State of Texas (wd:Q1439): state of the United States of America
5. Texas, also known as Lone Star State, US-TX, TX, Texas, United States, Tex. (wd:Q1439): state of the United States of America
6. Texas (wd:Q916484): Scottish musical group; alternative rock band
7. Texas, also known as Texas, WI (wd:Q1887280): town in Wisconsin, United States
8. Texas (wd:Q7707414): 1941 Western film directed by George Marshall
9. Texas, also known as Texas, Queensland, Texas, Queensland, Australia (wd:Q144229): town in Queensland, Australia
10. Texas, also known as Texas, NY, Texas, New York (wd:Q3816828): town in Oswego County, New York

[Assistant] The entity for Azle, Texas is wd:Q793929. Now I will query the postal code (zip code) for this entity using the property wdt:P281.

[Tool]
Got 1 row and 1 column:
| zipCode |
| ------- |
| 76020   |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the postal code (P281) for Azle, Texas (Q793929) from Wikidata. The query is simple, precise, and returns the expected result 76020, which matches the answer provided. The query follows the rules by returning the identifier directly and does not include unnecessary elements. Therefore, the output is correct and complete.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?language ?languageLabel WHERE {
  wd:Q79 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| language           | languageLabel |
| ------------------ | ------------- |
| Arabic (wd:Q13955) | Arabic (en)   |

The language mainly spoken in Egypt is Arabic.

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

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

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

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

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

[User] what language is mainly spoken in egypt?

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

[Tool]
Example 1:
Question:
what languages do people speak in egypt?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q79 wdt:P37 ?x .
}

Using entities:
Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia

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

Example 2:
Question:
what language do u speak in egypt?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q79 wdt:P37 ?x .
}

Using entities:
Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia

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

Example 3:
Question:
what language do they speak in egyptian?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q79 wdt:P37 ?x .
}

Using entities:
Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia

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

[Assistant] From the similar examples, it is clear that the entity for Egypt is wd:Q79 and the property for the language mainly spoken or official language is wdt:P37. I will now write a SPARQL query to get the language mainly spoken in Egypt.

[Tool]
Got 1 row and 2 columns:
| language           | languageLabel |
| ------------------ | ------------- |
| Arabic (wd:Q13955) | Arabic (en)   |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the main language spoken in Egypt by using the property for official language (wdt:P37) on the entity for Egypt (wd:Q79). The query also optionally retrieves the English label for the language. The result correctly identifies Arabic as the main language spoken in Egypt. The answer is accurate and complete, and the query follows the rules for returning identifiers and labels. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?language ?languageLabel WHERE {
  wd:Q45 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| language              | languageLabel   |
| --------------------- | --------------- |
| Mirandese (wd:Q13330) | Mirandese (en)  |
| Portuguese (wd:Q5146) | Portuguese (en) |

Portuguese people speak Portuguese and Mirandese as official languages.

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

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

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

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

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

[User] what do portuguese people speak?

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

[Tool]
Example 1:
Question:
what countries do people speak portuguese?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P37 wd:Q5146 ; wdt:P31 / wdt:P279 * wd:Q6256 .
}

Using entities:
Portuguese, also known as Portuguese language, pt (wd:Q5146): Western Romance language of the Indo-European language family
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 2:
Question:
where is portuguese language spoken?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P37 wd:Q5146 ; wdt:P31 / wdt:P279 * wd:Q6256 .
}

Using entities:
Portuguese, also known as Portuguese language, pt (wd:Q5146): Western Romance language of the Indo-European language family
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 3:
Question:
what main language do they speak in brazil?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q155 wdt:P37 ?x .
}

Using entities:
Brazil, also known as br, Brasil, Federative Republic of Brazil (wd:Q155): country in South America

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

[Assistant]
From the examples, it is clear that the property "official language" (wdt:P37) is used to find the language spoken by people of a country. Portuguese people are from Portugal, so we need to find the official language of Portugal (wd:Q45).

I will now write a SPARQL query to find the official language spoken by Portuguese people by querying the official language of Portugal.

[Tool]
Got 2 rows and 2 columns:
| language              | languageLabel   |
| --------------------- | --------------- |
| Mirandese (wd:Q13330) | Mirandese (en)  |
| Portuguese (wd:Q5146) | Portuguese (en) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the official languages spoken by Portuguese people by querying the official languages of Portugal (wd:Q45) using the property wdt:P37. The result includes both Portuguese and Mirandese, which matches the known official languages of Portugal. The query also optionally retrieves English labels for the languages, which is helpful for readability. The answer provided is accurate and complete based on the query results. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?player ?playerLabel WHERE {
  ?player wdt:P647 wd:Q162990 .
  OPTIONAL {
    ?player rdfs:label ?playerLabel FILTER ( LANG( ?playerLabel ) = "en" )
  }
}
LIMIT 100

Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| player                        | playerLabel           |
| ----------------------------- | --------------------- |
| Larry Sanders (wd:Q1034012)   | Larry Sanders (en)    |
| LeBron James (wd:Q108870659)  | LeBron James (en)     |
| Eddie Jordan (wd:Q1134403)    | Eddie Jordan (en)     |
| Reggie Geary (wd:Q115509)     | Reggie Geary (en)     |
| DeSagana Diop (wd:Q1179612)   | DeSagana Diop (en)    |
| ...                           | ...                   |
| Charles Oakley (wd:Q608514)   | Charles Oakley (en)   |
| Joe Jakubick (wd:Q6210519)    | Joe Jakubick (en)     |
| Dylan Windler (wd:Q63117693)  | Dylan Windler (en)    |
| Vitaly Potapenko (wd:Q645386) | Vitaly Potapenko (en) |
| Dion Waiters (wd:Q65039)      | Dion Waiters (en)     |

The Cleveland Cavaliers drafted players including Larry Sanders, LeBron James, Eddie Jordan, Reggie Geary, DeSagana Diop, Charles Oakley, Joe Jakubick, Dylan Windler, Vitaly Potapenko, and Dion Waiters among others.

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

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

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

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

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

[User] who did the cleveland cavaliers draft?

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

[Tool]
Example 1:
Question:
who did the cleveland browns draft?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P647 wd:Q223527 .
}

Using entities:
Cleveland Browns, also known as Browns (wd:Q223527): National Football League franchise in Cleveland, Ohio

Using properties:
drafted by (wd:P647 as wdt): which team the player was drafted by

Example 2:
Question:
who drafted kevin garnett?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q189240 wdt:P647 ?x .
}

Using entities:
Kevin Garnett, also known as The Kid, The Big Ticket, KG, Kevin Maurice Garnett (wd:Q189240): American retired professional basketball player and actor, basketball player, actor

Using properties:
drafted by (wd:P647 as wdt): which team the player was drafted by

Example 3:
Question:
what year was kevin durant drafted?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29545 p:P647 ?y .
  ?y pq:P585 ?x .
}

Using entities:
Kevin Durant, also known as KD, Kevin Wayne Durant, K. Durant (wd:Q29545): American basketball player, basketball player

Using properties:
point in time, also known as at time, as of, year, when, time of event (wd:P585 as pq): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
drafted by (wd:P647 as p): which team the player was drafted by

[Assistant]
From the examples, I see that the property "drafted by" (wdt:P647) is used to indicate which team drafted a player. To find who the Cleveland Cavaliers drafted, I need to find players where the property "drafted by" points to the Cleveland Cavaliers entity.

Next, I will search for the entity representing the Cleveland Cavaliers in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Cleveland Cavaliers, also known as Cavaliers, Cleveland Cavs, Cavs (wd:Q162990): National Basketball Association team in Cleveland, Ohio
2. history of the Cleveland Cavaliers, also known as Cleveland Cavaliers history, the Cleveland Cavaliers history (wd:Q20710370): aspect of history
3. 2022–23 Cleveland Cavaliers season (wd:Q112133040): NBA professional basketball team season
4. 2023–24 Cleveland Cavaliers season (wd:Q118908694): 2023–24 NBA season by team
5. 2015–16 Cleveland Cavaliers season (wd:Q20020792): NBA professional basketball team season (won championship)
6. 2018–19 Cleveland Cavaliers season (wd:Q54958273): season of National Basketball Association team the Cleveland Cavaliers
7. 2020–21 Cleveland Cavaliers season (wd:Q98553687): NBA professional basketball team season
8. 2021–22 Cleveland Cavaliers season (wd:Q107570641): NBA season for the Cleveland Cavaliers (2021-2022)
9. 2017–18 Cleveland Cavaliers season (wd:Q30641924): NBA professional basketball team season
10. 2019–20 Cleveland Cavaliers season (wd:Q63859185): NBA professional basketball team season

[Assistant] The entity for the Cleveland Cavaliers is wd:Q162990. Now I will write a SPARQL query to find players who were drafted by the Cleveland Cavaliers using the property "drafted by" (wdt:P647).

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| player                        | playerLabel           |
| ----------------------------- | --------------------- |
| Larry Sanders (wd:Q1034012)   | Larry Sanders (en)    |
| LeBron James (wd:Q108870659)  | LeBron James (en)     |
| Eddie Jordan (wd:Q1134403)    | Eddie Jordan (en)     |
| Reggie Geary (wd:Q115509)     | Reggie Geary (en)     |
| DeSagana Diop (wd:Q1179612)   | DeSagana Diop (en)    |
| ...                           | ...                   |
| Charles Oakley (wd:Q608514)   | Charles Oakley (en)   |
| Joe Jakubick (wd:Q6210519)    | Joe Jakubick (en)     |
| Dylan Windler (wd:Q63117693)  | Dylan Windler (en)    |
| Vitaly Potapenko (wd:Q645386) | Vitaly Potapenko (en) |
| Dion Waiters (wd:Q65039)      | Dion Waiters (en)     |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves players who were drafted by the Cleveland Cavaliers using the property 'drafted by' (wdt:P647) with the team entity (wd:Q162990). The query includes player labels in English as optional, which is helpful for readability. The result includes well-known players such as LeBron James and others, matching the user's question. The query is well-formed, returns relevant results, and adheres to the rules. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?artisticStyle ?artisticStyleLabel WHERE {
  wd:Q156386 wdt:P135 ?artisticStyle .
  OPTIONAL {
    ?artisticStyle rdfs:label ?artisticStyleLabel FILTER ( LANG( ?artisticStyleLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| artisticStyle                   | artisticStyleLabel      |
| ------------------------------- | ----------------------- |
| post-impressionism (wd:Q166713) | post-impressionism (en) |
| naïve art (wd:Q281108)          | naïve art (en)          |
| primitivism (wd:Q848365)        | primitivism (en)        |

Henri Rousseau worked in the artistic styles of post-impressionism, naïve art, and primitivism.

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

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

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

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

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

[User] what artistic style did henri rousseau work in?

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

[Tool]
Example 1:
Question:
what style of art did henri matisse use?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5589 wdt:P135 ?x .
}

Using entities:
Henri Matisse, also known as Matisse, Henri-Matisse, H. Matisse, h. matisse, Henri Emile Benoit Matisse (wd:Q5589): French artist (1869–1954), lithographer, draftsperson, ceramicist, printmaker, sculptor, painter, artist

Using properties:
movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.

Example 2:
Question:
what kind of artist is henri matisse?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5589 wdt:P136 ?x .
}

Using entities:
Henri Matisse, also known as Matisse, Henri-Matisse, H. Matisse, h. matisse, Henri Emile Benoit Matisse (wd:Q5589): French artist (1869–1954), lithographer, draftsperson, ceramicist, printmaker, sculptor, painter, artist

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

Example 3:
Question:
what style of art did vincent van gogh do?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5582 wdt:P135 ?x .
}

Using entities:
Vincent van Gogh (wd:Q5582): Dutch painter (1853–1890), draftsperson, printmaker, painter

Using properties:
movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.

[Assistant] From the similar examples, it is clear that the property wdt:P135 (movement) is used to indicate the artistic style or movement an artist worked in. Also, Henri Rousseau should be identified as an entity in Wikidata. Let's search for Henri Rousseau in Wikidata to get his entity ID.

[Tool]
Top 10 entity alternatives:
1. Henri Rousseau, also known as le Douanier Rousseau, Henri rousseau, Le Douanier, le Douanier, h. rousseau (wd:Q156386): French painter (1844–1910), printmaker, painter, artist
2. Henri Rousseau (wd:Q106847442): French illustrator, illustrator
3. Henri Rousseau (wd:Q110034910): Righteous Among the Nations, farmer
4. Henri Rousseau (wd:Q110890688): 1942 exhibition at Museum of Modern Art
5. Henri Rousseau (wd:Q110893138): 1985 exhibition at Museum of Modern Art
6. Henri Emilien Rousseau, also known as Henri Émilien Rousseau, Henri E. Rousseau (wd:Q3132420): French painter (1875-1933), painter
7. Henri-Paul Rousseau (wd:Q3130451): Canadian businessperson, university teacher, businessperson
8. Rousseau, Henri(-Julien-Félix) (wd:Q103964446): encyclopedia article
9. Portrait of Henri Rousseau (wd:Q52083876): underglass painting by Franz Marc
10. Charles Henri Joseph Rousseau (wd:Q125622073): artisan (1732-1820), artisan

[Assistant] The entity Henri Rousseau (wd:Q156386) corresponds to the French painter Henri Rousseau (1844–1910). Now, I will query the artistic style or movement (property wdt:P135) associated with Henri Rousseau (wd:Q156386).

[Tool]
Got 3 rows and 2 columns:
| artisticStyle                   | artisticStyleLabel      |
| ------------------------------- | ----------------------- |
| post-impressionism (wd:Q166713) | post-impressionism (en) |
| naïve art (wd:Q281108)          | naïve art (en)          |
| primitivism (wd:Q848365)        | primitivism (en)        |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the artistic styles associated with Henri Rousseau using the property P135 (movement). The query returns the expected artistic styles: post-impressionism, naïve art, and primitivism, which matches the answer provided. The query also includes optional labels in English, which is helpful for readability. The output is accurate and complete for the user's question.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?deity ?deityLabel WHERE {
  ?deity wdt:P1049 wd:Q432 .
  OPTIONAL {
    ?deity rdfs:label ?deityLabel FILTER ( LANG( ?deityLabel ) = "en" )
  }
}

Got 4 rows and 2 columns:
| deity                      | deityLabel        |
| -------------------------- | ----------------- |
| God in Islam (wd:Q2095353) | God in Islam (en) |
| Adam (wd:Q70899)           | Adam (en)         |
| Moses (wd:Q9077)           | Moses (en)        |
| Abraham (wd:Q9181)         | Abraham (en)      |

Islamic people primarily pray to God in Islam. Other important figures in Islam include Adam, Moses, and Abraham, but the main deity worshipped is God in Islam.

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

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

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

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

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

[User] who do islamic people pray to?

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

[Tool]
Example 1:
Question:
who does the islamic worship?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P1049 wd:Q432 .
}

Using entities:
Islam, also known as al-’islām, Islamic religion, religion of the Muslims, Mohammedanism, Muslim religion (wd:Q432): Abrahamic religion founded by Muhammad

Using properties:
worshipped by, also known as deity of (wd:P1049 as wdt): religion or group/civilization that worships a given deity, part of

Example 2:
Question:
who does the islam worship?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P1049 wd:Q432 .
}

Using entities:
Islam, also known as al-’islām, Islamic religion, religion of the Muslims, Mohammedanism, Muslim religion (wd:Q432): Abrahamic religion founded by Muhammad

Using properties:
worshipped by, also known as deity of (wd:P1049 as wdt): religion or group/civilization that worships a given deity, part of

Example 3:
Question:
where do islamic people go to worship?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P1049 wd:Q432 .
}

Using entities:
Islam, also known as al-’islām, Islamic religion, religion of the Muslims, Mohammedanism, Muslim religion (wd:Q432): Abrahamic religion founded by Muhammad

Using properties:
worshipped by, also known as deity of (wd:P1049 as wdt): religion or group/civilization that worships a given deity, part of

[Assistant]
From the similar examples, it appears that the property "worshipped by" (wdt:P1049) can be used to find the deity or entity that is worshipped by a particular religion. The entity for Islam is wd:Q432. Using this, we can query for the entity that is worshipped by Islam.

I will now construct and execute a SPARQL query to find who Islamic people pray to using this property and entity.

[Tool]
Got 4 rows and 2 columns:
| deity                      | deityLabel        |
| -------------------------- | ----------------- |
| God in Islam (wd:Q2095353) | God in Islam (en) |
| Adam (wd:Q70899)           | Adam (en)         |
| Moses (wd:Q9077)           | Moses (en)        |
| Abraham (wd:Q9181)         | Abraham (en)      |

[Assistant]
The query results show multiple entities worshipped by Islam: "God in Islam," "Adam," "Moses," and "Abraham." The primary deity Islamic people pray to is "God in Islam." Adam, Moses, and Abraham are important prophetic figures in Islam but not the primary deity.

I will now provide the final answer with the SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the main deity worshipped by Islamic people, which is 'God in Islam', along with other important religious figures like Adam, Moses, and Abraham. The answer is accurate and complete based on the query results. The query uses the appropriate property 'worshipped by' (wdt:P1049) to find deities worshipped by Islam (wd:Q432). The optional label retrieval is correctly done without using unsupported SERVICE clauses. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?place ?placeLabel WHERE {
  ?place wdt:P31 / wdt:P279 * wd:Q570116 ; wdt:P131 wd:Q60 .
  OPTIONAL {
    ?place rdfs:label ?placeLabel FILTER ( LANG( ?placeLabel ) = "en" )
  }
}
LIMIT 20

Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| place                                                 | placeLabel                                   |
| ----------------------------------------------------- | -------------------------------------------- |
| Aphrodite cries (wd:Q107090856)                       | Aphrodite cries (en)                         |
| National September 11 Memorial & Museum (wd:Q1136137) | National September 11 Memorial & Museum (en) |
| Bryan Gallery of Christian Art (wd:Q115406903)        | Bryan Gallery of Christian Art (en)          |
| New Museum (wd:Q1156823)                              | New Museum (en)                              |
| The Sackler Wing (wd:Q116981455)                      | The Sackler Wing (en)                        |
| ...                                                   | ...                                          |
| B&B Carousell (wd:Q55887486)                          | B&B Carousell (en)                           |
| Bess Cutler Gallery (wd:Q69980110)                    | Bess Cutler Gallery (en)                     |
| Thread Waxing Space (wd:Q72814609)                    | Thread Waxing Space (en)                     |
| Steeplechase Park (wd:Q741612)                        | Steeplechase Park (en)                       |
| Statue of Liberty (wd:Q9202)                          | Statue of Liberty (en)                       |

In New York City, you can visit various tourist attractions such as the National September 11 Memorial & Museum, New Museum, Statue of Liberty, Steeplechase Park, and many art galleries like Bryan Gallery of Christian Art and Bess Cutler Gallery.

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

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

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

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

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

[User] what can do in new york?

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

[Tool]
Example 1:
Question:
what do do in new york?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q570116 ; wdt:P131 wd:Q60 .
}

Using entities:
tourist attraction, also known as attraction for tourists, POI for tourists, tourism attraction, touristic attraction, tourist place (wd:Q570116): place of interest where tourists visit
New York City, also known as Capital of the World, Big Apple, the five boroughs, The City So Nice They Named It Twice, NY, NY (wd:Q60): most populous city in the United States

Using properties:
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 wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
what are some places to visit in new york?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q570116 ; wdt:P131 wd:Q60 .
}

Using entities:
tourist attraction, also known as attraction for tourists, POI for tourists, tourism attraction, touristic attraction, tourist place (wd:Q570116): place of interest where tourists visit
New York City, also known as Capital of the World, Big Apple, the five boroughs, The City So Nice They Named It Twice, NY, NY (wd:Q60): most populous city in the United States

Using properties:
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 wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
what are historical places in new york?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q570116 ; wdt:P131 wd:Q1384 .
}

Using entities:
New York, also known as New York state, New York, United States, US-NY, The Empire State, Empire State (wd:Q1384): state of the United States of America
tourist attraction, also known as attraction for tourists, POI for tourists, tourism attraction, touristic attraction, tourist place (wd:Q570116): place of interest where tourists visit

Using properties:
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 wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant]
The examples suggest that to answer "what can do in New York?" we can look for tourist attractions or places of interest located in New York City (wd:Q60). The property "located in the administrative territorial entity" (wdt:P131) can be used to filter items located in New York City. The items should be instances or subclasses of tourist attractions (wd:Q570116).

I will now execute a query to get some tourist attractions or places of interest located in New York City.

[Tool]
Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| place                                                 | placeLabel                                   |
| ----------------------------------------------------- | -------------------------------------------- |
| Aphrodite cries (wd:Q107090856)                       | Aphrodite cries (en)                         |
| National September 11 Memorial & Museum (wd:Q1136137) | National September 11 Memorial & Museum (en) |
| Bryan Gallery of Christian Art (wd:Q115406903)        | Bryan Gallery of Christian Art (en)          |
| New Museum (wd:Q1156823)                              | New Museum (en)                              |
| The Sackler Wing (wd:Q116981455)                      | The Sackler Wing (en)                        |
| ...                                                   | ...                                          |
| B&B Carousell (wd:Q55887486)                          | B&B Carousell (en)                           |
| Bess Cutler Gallery (wd:Q69980110)                    | Bess Cutler Gallery (en)                     |
| Thread Waxing Space (wd:Q72814609)                    | Thread Waxing Space (en)                     |
| Steeplechase Park (wd:Q741612)                        | Steeplechase Park (en)                       |
| Statue of Liberty (wd:Q9202)                          | Statue of Liberty (en)                       |

[Assistant]
In New York City, you can visit various tourist attractions such as the National September 11 Memorial & Museum, New Museum, Statue of Liberty, Steeplechase Park, and many art galleries like Bryan Gallery of Christian Art and Bess Cutler Gallery.

Here is the SPARQL query I used to find these places:

SELECT DISTINCT ?place ?placeLabel WHERE {
  ?place wdt:P31/wdt:P279* wd:Q570116 ;
         wdt:P131 wd:Q60 .
  OPTIONAL { ?place rdfs:label ?placeLabel FILTER (LANG(?placeLabel) = "en") }
} LIMIT 20

Would you like me to provide more types of activities or specific categories of things to do in New York?

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves places of interest (tourist attractions) located in New York City, which directly answers the user's question about what can be done in New York. The answer provides a variety of notable attractions, including museums, memorials, parks, and galleries, which is relevant and informative. The query uses appropriate properties and classes (instance of tourist attraction and located in New York City) and returns both the identifiers and English labels of the places. The result set is diverse and representative of activities in New York City. No further refinement is necessary.