PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?language ?languageLabel WHERE {
  wd:Q148 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| language                        | languageLabel           |
| ------------------------------- | ----------------------- |
| Standard Chinese (wd:Q727694)   | Standard Chinese (en)   |
| Chinese (wd:Q7850)              | Chinese (en)            |
| languages of China (wd:Q835866) | languages of China (en) |

The main language used in China is Standard Chinese. Other related languages include Chinese in general and languages of China.

[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-sebastian

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 main language used in china?

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

[Tool]
Example 1:
Question:
what is the official language in china?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?x .
}

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East 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 type of language does china speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?x .
}

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East 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 kind of language do china speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?x .
}

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia

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

[Assistant]
From the examples, it is clear that the main language used in China can be found using the property "official language" (wdt:P37) for the entity "China" (wd:Q148).

I will now write a SPARQL query to get the main language used in China using this property and entity.

[Tool]
Got 3 rows and 2 columns:
| language                        | languageLabel           |
| ------------------------------- | ----------------------- |
| Standard Chinese (wd:Q727694)   | Standard Chinese (en)   |
| Chinese (wd:Q7850)              | Chinese (en)            |
| languages of China (wd:Q835866) | languages of China (en) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?venue ?venueLabel WHERE {
  wd:Q221878 wdt:P115 ?venue .
  OPTIONAL {
    ?venue rdfs:label ?venueLabel FILTER ( LANG( ?venueLabel ) = "en" )
  }
}

The Seattle Seahawks play at Lumen Field.

[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-sebastian

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 do the seattle seahawks play?

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

[Tool]
Example 1:
Question:
where do baltimore ravens play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q276539 wdt:P115 ?x .
}

Using entities:
Baltimore Ravens (wd:Q276539): National Football League franchise in Baltimore, Maryland

Using properties:
home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt): home stadium or venue of a sports team or applicable performing arts organization, location

Example 2:
Question:
where do the broncos play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q223507 wdt:P115 ?x .
}

Using entities:
Denver Broncos, also known as Broncos (wd:Q223507): National Football League franchise in Denver, Colorado

Using properties:
home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt): home stadium or venue of a sports team or applicable performing arts organization, location

Example 3:
Question:
where do the kansas city chiefs play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q223522 wdt:P159 ?x .
}

Using entities:
Kansas City Chiefs, also known as Chiefs, Dallas Texans, KC Chiefs (wd:Q223522): National Football League franchise in Kansas City, Missouri

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 similar examples, the property wdt:P115 (home venue) is used to find where sports teams play. The Seattle Seahawks is a National Football League franchise, so it is likely that the same property can be used to find their home venue.

Next, I will search for the entity "Seattle Seahawks" in Wikidata to get its identifier.

[Tool]
Top 10 entity alternatives:
1. Seattle Seahawks (wd:Q221878): American football team
2. Seattle Seahawks (wd:Q1431833): ice hockey team
3. history of the Seattle Seahawks, also known as Seattle Seahawks history, the Seattle Seahawks history (wd:Q3973903): aspect of history
4. 2021 Seattle Seahawks season (wd:Q104869618): 46th season in franchise history
5. 2022 Seattle Seahawks season (wd:Q110445415): 47th season in franchise history
6. 2023 Seattle Seahawks season (wd:Q116170066): 48th season in franchise history
7. 2020 Seattle Seahawks season (wd:Q81002024): 45th season in franchise history
8. 2018 Seattle Seahawks season (wd:Q47191924): 43rd season in franchise history; final one under ownership of Paul Allen
9. 2015 Seattle Seahawks season (wd:Q19255692): 40th season in franchise history
10. 2024 Seattle Seahawks season (wd:Q124167235): 49th season in franchise history

[Assistant] The entity "Seattle Seahawks" as an American football team is wd:Q221878. Based on the similar examples, the property wdt:P115 (home venue) is used to find where sports teams play. I will now query the home venue of the Seattle Seahawks.

[Tool]
Got 1 row and 2 columns:
| venue                    | venueLabel       |
| ------------------------ | ---------------- |
| Lumen Field (wd:Q612736) | Lumen Field (en) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?timezone ?timezoneLabel WHERE {
  wd:Q16568 wdt:P421 ?timezone .
  OPTIONAL {
    ?timezone rdfs:label ?timezoneLabel FILTER ( LANG( ?timezoneLabel ) = "en" )
  }
}

The timezone of Jacksonville, FL is Eastern Daylight Time.

[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-sebastian

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 jacksonville fl timezone?

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

[Tool]
Example 1:
Question:
what is time zone in new orleans?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34404 wdt:P421 ?x .
}

Using entities:
New Orleans, also known as Crescent City, The Big Easy (wd:Q34404): largest city of the state of Louisiana, United States

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 2:
Question:
what is the time zone in oklahoma city?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34863 wdt:P421 ?x .
}

Using entities:
Oklahoma City, also known as Oklahoma City, Oklahoma, Oklahoma City, OK, Okla. City, OKC (wd:Q34863): city and state capital of Oklahoma, United States

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 3:
Question:
what time zone is new york under?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1384 wdt:P421 ?x .
}

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

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

[Assistant] From the examples, the property used to find the timezone of a place is wdt:P421 (located in time zone). We need to find the entity for Jacksonville, FL and then query its timezone using this property. I will first search for the entity "Jacksonville FL" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Jacksonville, also known as Jacksonville, FL, Jacksonville, Florida (wd:Q16568): city in Duval County, Florida, United States
2. Jacksonville urban area, also known as Jacksonville, FL, Jacksonville, FL Urban Area (wd:Q125804929): urban area in Florida
3. Jacksonville Beach, also known as Jacksonville Beach, FL, Jacksonville Beach, Florida (wd:Q988928): city in Duval County, Florida, United States
4. flag of Jacksonville, also known as Jacksonville flag, Bold New City of the South, flag of Jacksonville, Florida (wd:Q5456759): municipal flag of Jacksonville, Florida, United States
5. Jacksonville Heights, also known as Jacksonville Heights, FL, Jacksonville Heights, Florida (wd:Q122594337): hamlet in Duval County, Florida, United States
6. West Jacksonville, also known as West Jacksonville, FL, West Jacksonville, Florida (wd:Q122543229): hamlet in Duval County, Florida, United States
7. Jacksonville metropolitan area, also known as Greater Jacksonville, Jacksonville, FL Metro Area, Jacksonville, FL Metropolitan Statistical Area, Jacksonville metropolitan area, Florida, Metro Jacksonville (wd:Q6118061): metropolitan area in Florida, United States
8. Ortega, also known as Ortega, Jacksonville, Florida (wd:Q7104308): neighborhood of Jacksonville, Florida, US
9. San Marco, also known as San Marco, Florida, South Jacksonville, Florida (wd:Q14686391): former city of South Jacksonville, Florida, United States; neighborhood of Jacksonville
10. Bayard, also known as Bayard, Jacksonville, Florida (wd:Q4874329): human settlement in Jacksonville, Florida, United States of America

[Assistant] The entity for Jacksonville, FL is wd:Q16568. Now I will query the timezone of Jacksonville using the property wdt:P421.

[Tool]
Got 1 row and 2 columns:
| timezone                             | timezoneLabel              |
| ------------------------------------ | -------------------------- |
| Eastern Daylight Time (wd:Q15080196) | Eastern Daylight Time (en) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?formOfGovernment ?formOfGovernmentLabel WHERE {
  wd:Q145 wdt:P122 ?formOfGovernment .
  OPTIONAL {
    ?formOfGovernment rdfs:label ?formOfGovernmentLabel FILTER ( LANG( ?formOfGovernmentLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| formOfGovernment                    | formOfGovernmentLabel        |
| ----------------------------------- | ---------------------------- |
| constitutional monarchy (wd:Q41614) | constitutional monarchy (en) |

The form of government of the United Kingdom is constitutional monarchy.

[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-sebastian

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 form of government is the uk?

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

[Tool]
Example 1:
Question:
what is the political system in england?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q21 wdt:P122 ?x .
}

Using entities:
England, also known as England, United Kingdom, England, UK, ENG (wd:Q21): country in north-west Europe, part of the United Kingdom

Using properties:
basic form of government, also known as form of government, government, type of government (wd:P122 as wdt): subject's government

Example 2:
Question:
what type of voting system does the uk have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q145 wdt:P122 ?x .
}

Using entities:
United Kingdom, also known as U.K., UK, U. K., U K, United Kingdom of Great Britain and Northern Ireland (wd:Q145): country in north-west Europe

Using properties:
basic form of government, also known as form of government, government, type of government (wd:P122 as wdt): subject's government

Example 3:
Question:
which countries are part of the united kingdom?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q145 wdt:P150 ?x .
}

Using entities:
United Kingdom, also known as U.K., UK, U. K., U K, United Kingdom of Great Britain and Northern Ireland (wd:Q145): country in north-west Europe

Using properties:
contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as wdt): (list of) direct subdivisions of an administrative territorial entity, has part(s)

[Assistant] From the similar examples, the entity for the United Kingdom is wd:Q145 and the property for form of government is wdt:P122. I will now write a SPARQL query to get the form of government of the United Kingdom.

[Tool]
Got 1 row and 2 columns:
| formOfGovernment                    | formOfGovernmentLabel        |
| ----------------------------------- | ---------------------------- |
| constitutional monarchy (wd:Q41614) | constitutional monarchy (en) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?language ?languageLabel WHERE {
  wd:Q213 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| language         | languageLabel |
| ---------------- | ------------- |
| Czech (wd:Q9056) | Czech (en)    |

The language spoken in the Czech Republic is Czech.

[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-sebastian

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 spoken in czech republic?

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

[Tool]
Example 1:
Question:
what do people in the czech republic speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q213 wdt:P37 ?x .
}

Using entities:
Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe

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 region is czech republic in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q213 wdt:P361 ?x .
  ?x wdt:P31 wd:Q82794 .
}

Using entities:
Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe
geographic region, also known as geographic area, region, geographic zone, geographical zone, geographical region (wd:Q82794): 2D or 3D defined space on something, mainly in terrestrial and astrophysics sciences

Using properties:
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)
part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))

Example 3:
Question:
what form of government does czech republic have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q213 wdt:P122 ?x .
}

Using entities:
Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe

Using properties:
basic form of government, also known as form of government, government, type of government (wd:P122 as wdt): subject's government

[Assistant] From the similar example, the entity for Czech Republic is wd:Q213 and the property for language spoken or official language is wdt:P37. I will now write a SPARQL query to find what is spoken in the Czech Republic using these.

[Tool]
Got 1 row and 2 columns:
| language         | languageLabel |
| ---------------- | ------------- |
| Czech (wd:Q9056) | Czech (en)    |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?year WHERE {
  ?superbowl wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1346 wd:Q276539 ; wdt:P585 ?year .
}
ORDER BY ?year

The Baltimore Ravens won the Super Bowl in the years 2001 and 2013.

[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-sebastian

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 year did the baltimore ravens win superbowl?

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

[Tool]
Example 1:
Question:
what year did baltimore ravens win the superbowl?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1346 wd:Q276539 ; wdt:P585 ?x .
}

Using entities:
Baltimore Ravens (wd:Q276539): National Football League franchise in Baltimore, Maryland
Super Bowl, also known as AFL–NFL World Championship Game (wd:Q32096): National Football League championship game

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
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)
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 years have the ravens won the super bowl?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1346 wd:Q276539 ; wdt:P585 ?x .
}

Using entities:
Baltimore Ravens (wd:Q276539): National Football League franchise in Baltimore, Maryland
Super Bowl, also known as AFL–NFL World Championship Game (wd:Q32096): National Football League championship game

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
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)
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:
when did the raiders win the superbowl?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P31 / wdt:P279 * wd:Q32096 ; wdt:P1346 wd:Q324523 ; wdt:P585 ?x .
}

Using entities:
Super Bowl, also known as AFL–NFL World Championship Game (wd:Q32096): National Football League championship game
Las Vegas Raiders, also known as Los Angeles Raiders, Oakland Raiders, Raiders (wd:Q324523): National Football League franchise in Las Vegas, Nevada

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
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)
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)

[Assistant]
From the examples, I see that the Baltimore Ravens are identified as wd:Q276539 and the Super Bowl as wd:Q32096. The property for winner is wdt:P1346 and the property for the point in time (year) is wdt:P585. The query pattern is to find instances of the Super Bowl that have the Baltimore Ravens as the winner and then get the year of that event.

I will now execute a query to find the years when the Baltimore Ravens won the Super Bowl.

[Tool]
Got 2 rows and 1 column:
| year                                |
| ----------------------------------- |
| 2001-01-28T00:00:00Z (xsd:dateTime) |
| 2013-02-03T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?airport ?airportLabel WHERE {
  ?airport wdt:P31 / wdt:P279 * wd:Q1248784 .
  ?airport wdt:P131 ?location .
  ?location ( wdt:P131 ) * wd:Q60 .
  OPTIONAL {
    ?airport rdfs:label ?airportLabel FILTER ( LANG( ?airportLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| airport                                          | airportLabel                               |
| ------------------------------------------------ | ------------------------------------------ |
| Floyd Bennett Field (wd:Q1430350)                | Floyd Bennett Field (en)                   |
| LaGuardia Airport (wd:Q319654)                   | LaGuardia Airport (en)                     |
| John F. Kennedy International Airport (wd:Q8685) | John F. Kennedy International Airport (en) |

The airports located in New York City are Floyd Bennett Field, LaGuardia Airport, and John F. Kennedy International Airport.

[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-sebastian

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 name of airport in new york?

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

[Tool]
Example 1:
Question:
what are the airports in chicago?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q1248784 ; wdt:P131 wd:Q1297 .
}

Using entities:
airport, also known as aerodrome, air port, airports (wd:Q1248784): location where aircraft take off and land with extended support facilities, mostly for commercial air transport
Chicago, also known as The Second City, The Queen of the West, The Windy City, City of Broad Shoulders, City by the Lake (wd:Q1297): city and county seat of Cook County, Illinois, United States of America

Using properties:
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 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 3:
Question:
where is new york in the map?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1384 wdt:P131 ?x .
}

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

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, I see that airports are instances of the class "airport" (wd:Q1248784) and locations can be specified using the property "located in the administrative territorial entity" (wdt:P131). New York City is wd:Q60 in Wikidata.

I will now write a query to find airports located in New York City (wd:Q60) and retrieve their names.

[Tool] Got no rows and 2 columns

[Assistant]
The query returned no results, which might be because airports in New York City are not directly linked with the property "located in the administrative territorial entity" (wdt:P131) to New York City (wd:Q60). Airports might be linked to other administrative entities such as boroughs or counties.

I will try to find airports located in the state of New York (wd:Q1384) instead of New York City.

[Tool]
Got 147 rows and 2 columns, showing the first 5 and last 5 rows below:
| airport                                         | airportLabel                           |
| ----------------------------------------------- | -------------------------------------- |
| Marcy Field (wd:Q1031234)                       | Marcy Field (en)                       |
| Sky Park Airport (wd:Q10845078)                 | Sky Park Airport (en)                  |
| Potsdam Municipal Airport (wd:Q10851768)        | Potsdam Municipal Airport (en)         |
| Luther Airport (wd:Q10853210)                   | Luther Airport (en)                    |
| Malone-Dufort Airport (wd:Q10853400)            | Malone-Dufort Airport (en)             |
| ...                                             | ...                                    |
| Ticonderoga Municipal Airport (wd:Q7800749)     | Ticonderoga Municipal Airport (en)     |
| Warwick Municipal Airport (wd:Q7971163)         | Warwick Municipal Airport (en)         |
| Wheeler-Sack Army Airfield (wd:Q7992189)        | Wheeler-Sack Army Airfield (en)        |
| Williamson–Sodus Airport (wd:Q8021256)          | Williamson–Sodus Airport (en)          |
| Wurtsboro-Sullivan County Airport (wd:Q8039315) | Wurtsboro-Sullivan County Airport (en) |

[Assistant] There are many airports located in the state of New York (wd:Q1384). The user asked for the name of the airport in New York, which could mean the major airports in New York City. I will now refine the query to find airports located in New York City (wd:Q60) or its boroughs. I will first find the boroughs of New York City.

[Tool] Got no rows and 2 columns

[Assistant] I tried to find boroughs of New York City using the class "borough" (wd:Q747074) but got no results. I will try a different approach to find boroughs or administrative divisions within New York City (wd:Q60). I will look for entities that are located in New York City (wdt:P131 wd:Q60) and are subclasses or instances of administrative territorial entities.

[Tool]
Got 10 rows and 2 columns:
| division                                                   | divisionLabel                                   |
| ---------------------------------------------------------- | ----------------------------------------------- |
| New York City's 5th City Council district (wd:Q107377557)  | New York City's 5th City Council district (en)  |
| New York City's 7th City Council district (wd:Q107389527)  | New York City's 7th City Council district (en)  |
| New York City's 8th City Council district (wd:Q107395146)  | New York City's 8th City Council district (en)  |
| New York City's 9th City Council district (wd:Q107395188)  | New York City's 9th City Council district (en)  |
| New York City's 10th City Council district (wd:Q107404915) | New York City's 10th City Council district (en) |
| New York City's 11th City Council district (wd:Q107404918) | New York City's 11th City Council district (en) |
| New York City's 12th City Council district (wd:Q107407077) | New York City's 12th City Council district (en) |
| New York City's 13th City Council district (wd:Q107407078) | New York City's 13th City Council district (en) |
| New York City's 14th City Council district (wd:Q107407079) | New York City's 14th City Council district (en) |
| New York City's 15th City Council district (wd:Q107407080) | New York City's 15th City Council district (en) |

[Assistant] The query returned New York City's City Council districts, which are administrative divisions but not boroughs. I will try to find the boroughs of New York City by searching for entities that are part of New York City (wdt:P131 wd:Q60) and are instances of "borough" or "borough of New York City". I will search for entities with label "borough" to find the correct class.

[Tool]
Top 10 entity alternatives:
1. borough, also known as Borough, burgh (wd:Q5195043): administrative division in various countries
2. Southwark, also known as Borough, The Borough (wd:Q5273898): district of Central London, England
3. Conwy Borough F.C., also known as Conwy Borough, Borough, The Tangerines, Conwy Borough Football Club, CBFC (wd:Q2801245): association football club in Conwy, Wales
4. Conwy Borough F.C., also known as Conwy Borough Football Club, The Tangerines, Conwy Borough FC, Conwy Borough, Conwy (wd:Q2801245): association football club in Conwy, Wales
5. borough in the United States, also known as borough, boroughs of the United States, United States borough (wd:Q4946305): administrative division at the local government level in the United States
6. borough of New Jersey, also known as boro, Borough, borough in New Jersey (wd:Q2911266): type of subdivision of New Jersey in the USA
7. Harrow Borough F.C., also known as Borough, Harrow, Harrow Borough, Harrow Borough FC, Harrow Borough Football Club (wd:Q6138819): association football club in Harrow, England
8. alev, also known as borough, town (wd:Q50809253): type (size class) of settlement in Estonia
9. municipality section, also known as borough (wd:Q28081313): type of administrative division in Belgium and the Netherlands
10. large village, also known as borough (wd:Q26714626): local centre, usually hosting a market, smaller than a town

[Assistant] The entity "borough" as an administrative division in various countries is wd:Q5195043. I will now try to find boroughs of New York City by querying for entities that are instances of borough (wd:Q5195043) and are located in New York City (wd:Q60).

[Tool]
Got 1 row and 2 columns:
| borough                               | boroughLabel                  |
| ------------------------------------- | ----------------------------- |
| borough of New York City (wd:Q408804) | borough of New York City (en) |

[Assistant]
The query returned a single entity "borough of New York City" (wd:Q408804), which is a general class for boroughs in New York City, not the individual boroughs themselves.

I will now try a different approach: I will find airports that are located in New York City (wd:Q60) or located in any administrative territorial entity that is part of New York City. This way, I can capture airports located in boroughs or other subdivisions of New York City.

[Tool]
Got 3 rows and 2 columns:
| airport                                          | airportLabel                               |
| ------------------------------------------------ | ------------------------------------------ |
| Floyd Bennett Field (wd:Q1430350)                | Floyd Bennett Field (en)                   |
| LaGuardia Airport (wd:Q319654)                   | LaGuardia Airport (en)                     |
| John F. Kennedy International Airport (wd:Q8685) | John F. Kennedy International Airport (en) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?school ?schoolLabel WHERE {
  wd:Q40224 wdt:P69 ?school .
  OPTIONAL {
    ?school rdfs:label ?schoolLabel FILTER ( LANG( ?schoolLabel ) = "en" )
  }
}

Karl Benz went to the University of Karlsruhe.

[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-sebastian

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 school did karl benz go to?

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

[Tool]
Example 1:
Question:
what school did michael jordan go to?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q41421 wdt:P69 ?x .
}

Using entities:
Michael Jordan, also known as His Airness, Air Jordan (wd:Q41421): American basketball player and businessman (born 1963), basketball player, baseball player, autobiographer, spokesperson, entrepreneur

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 2:
Question:
what school did sergio romo go to?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q3041597 wdt:P69 ?x .
}

Using entities:
Sergio Romo, also known as Sergio Francisco Romo (wd:Q3041597): American baseball pitcher, baseball player

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:
where did matthias schleiden go to school?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q76747 wdt:P69 ?x .
}

Using entities:
Matthias Jacob Schleiden, also known as Schleiden, Schleid., Matthias Jakob Schleiden (wd:Q76747): German botanist and philosopher (1804-1881), university teacher, physiologist, philosopher, biologist, botanist

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

[Assistant] From the similar examples, the property "educated at" (wdt:P69) is used to find the school or educational institution a person attended. Now, I need to find the Wikidata entity for Karl Benz to use it in the query. I will search for the entity "Karl Benz" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Carl Benz, also known as Carl Friedrich Benz, Karl Friedrich Benz, Karl Benz, Karl Friedrich Michael Vaillant (wd:Q40224): automotive and engine designer and manufacturer (1844–1929), engineer, inventor
2. Carl Benz, also known as Karl Friedrich Michael Vaillant, Karl Friedrich Benz, Karl Benz, Carl Friedrich Benz (wd:Q40224): automotive and engine designer and manufacturer (1844–1929), inventor, engineer
3. Willibrord Benzler, also known as Karl Benzler, Karl Heinrich Johann Eugen Benzler (wd:Q91025): bishop of Metz (1853–1921), Catholic priest, Catholic bishop
4. Károly Benza, also known as Karl Benza (wd:Q20667635): Hungarian actor, singer and director, director, singer, actor
5. Karl Benz (Landrat) (wd:Q104163395): jurist
6. Karl Josef Benz (wd:Q51682510): liturgist
7. Karl Benziger (wd:Q1730468): Swiss politician and publisher (1799-1873), politician, publisher
8. Karl Benzon (wd:Q17370389): Swedish journalist, journalist, translator
9. Karl Benzino (wd:Q94858455): politician, jurist, judge
10. Karl Benzel (wd:Q60835293): (1778-1802), robber

[Assistant] The entity for Karl Benz is wd:Q40224 (Carl Benz). Now I will use the property "educated at" (wdt:P69) to find the school Karl Benz attended.

[Tool]
Got 1 row and 2 columns:
| school                                 | schoolLabel                  |
| -------------------------------------- | ---------------------------- |
| University of Karlsruhe (wd:Q29426438) | University of Karlsruhe (en) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?genre ?genreLabel WHERE {
  wd:Q4700 wdt:P136 ?genre .
  OPTIONAL {
    ?genre rdfs:label ?genreLabel FILTER ( LANG( ?genreLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| genre                               | genreLabel               |
| ----------------------------------- | ------------------------ |
| impressionist music (wd:Q105697765) | impressionist music (en) |
| opera (wd:Q1344)                    | opera (en)               |
| classical music (wd:Q9730)          | classical music (en)     |

Claude Debussy played impressionist music, opera, and classical music.

[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-sebastian

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 type of music did claude debussy play?

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

[Tool]
Example 1:
Question:
what type of music did richard wagner play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1511 wdt:P136 ?x .
}

Using entities:
Richard Wagner, also known as Wilhelm Richard Wagner, Wagner (wd:Q1511): German composer (1813–1883), theatrical director, autobiographer, music critic, librettist, conductor, essayist, composer, diarist, pianist, writer, poet

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

Example 2:
Question:
what type of music is tchaikovsky?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q7315 wdt:P136 ?x .
}

Using entities:
Pyotr Ilyich Tchaikovsky, also known as Pyotr Il'yich Tchaikovsky, Pyotr Tchaikovsky, Tchaikovsky, Tschaikowsky, Pyotr Il'ich Chaikovskiy (wd:Q7315): Russian composer (1840–1893), university teacher, music educator, autobiographer, choreographer, music critic, translator, librettist, conductor, composer, pianist, diarist

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 type of music did vivaldi write?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1340 wdt:P136 ?x .
}

Using entities:
Antonio Vivaldi, also known as Antonio Luciano Vivaldi, Vivaldi, Prete rosso, A. Vivaldis, Antonio Vivarudi (wd:Q1340): Italian composer and violinist (1678-1741), composer

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

[Assistant] From the similar examples, it is clear that the property to find the type of music or genre associated with a composer is wdt:P136 (genre). The entity for Claude Debussy needs to be identified first, then we can query the genre property for him. I will now search for the entity Claude Debussy in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Claude Debussy, also known as C. Debussy, Achille-Claude, Claude Achille Debussy, Claude-Achille Debussy, Debussy (wd:Q4700): French classical composer (1862–1918), music critic, composer, pianist
2. Claude Debussy (wd:Q17491085): painting by Marcel Baschet
3. Musée Claude-Debussy, also known as Claude Debussy Museum, Maison Claude Debussy (wd:Q19387976): museum in France
4. Claude Debussylaan (wd:Q18980854): street in Amsterdam, the Netherlands
5. allée Claude Debussy (wd:Q121600379): alley of Châtillon, in Hauts-de-Seine
6. allée Claude Debussy (wd:Q116242003): alley in Châtenay-Malabry, France
7. allée Claude Debussy (wd:Q113455135): alley in Clichy-sous-Bois, France
8. allée Claude Debussy (wd:Q113413920): alley in Villetaneuse, en Seine-Saint-Denis
9. allée Claude Debussy (wd:Q112064008): alley in L'Haÿ-les-Roses, France
10. allée Claude Debussy (wd:Q111916504): alley in Valenton, France

[Assistant] The entity for Claude Debussy as a French classical composer is wd:Q4700. Now I will query the genre (wdt:P136) property for this entity to find out what type of music he played.

[Tool]
Got 3 rows and 2 columns:
| genre                               | genreLabel               |
| ----------------------------------- | ------------------------ |
| impressionist music (wd:Q105697765) | impressionist music (en) |
| opera (wd:Q1344)                    | opera (en)               |
| classical music (wd:Q9730)          | classical music (en)     |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?case ?caseLabel WHERE {
  ?case wdt:P31 wd:Q19692072 .
  ?case wdt:P4884 wd:Q11201 .
  OPTIONAL {
    ?case rdfs:label ?caseLabel FILTER ( LANG( ?caseLabel ) = "en" )
  }
}
LIMIT 20

Got 20 rows and 2 columns, showing the first 5 and last 5 rows below:
| case                                                               | caseLabel                                               |
| ------------------------------------------------------------------ | ------------------------------------------------------- |
| Trump v. New York (wd:Q100496432)                                  | Trump v. New York (en)                                  |
| New Hampshire v. Maine (wd:Q101093598)                             | New Hampshire v. Maine (en)                             |
| Roman Catholic Diocese of Brooklyn v. Cuomo (wd:Q102532067)        | Roman Catholic Diocese of Brooklyn v. Cuomo (en)        |
| EEOC v. Wyoming (wd:Q104672335)                                    | EEOC v. Wyoming (en)                                    |
| Bogan v. Scott-Harris (wd:Q104821362)                              | Bogan v. Scott-Harris (en)                              |
| ...                                                                | ...                                                     |
| Edwards v. Vannoy (wd:Q104878313)                                  | Edwards v. Vannoy (en)                                  |
| Tanzin v. Tanvir (wd:Q104880243)                                   | Tanzin v. Tanvir (en)                                   |
| Barton v. Barr (wd:Q104880695)                                     | Barton v. Barr (en)                                     |
| National Collegiate Athletic Association v. Alston (wd:Q104881529) | National Collegiate Athletic Association v. Alston (en) |
| United States v. Craft (wd:Q104881625)                             | United States v. Craft (en)                             |

The supreme court cases (specifically United States Supreme Court decisions) include cases such as Trump v. New York, New Hampshire v. Maine, Roman Catholic Diocese of Brooklyn v. Cuomo, EEOC v. Wyoming, and Bogan v. Scott-Harris, among others. The query returns a list of such cases with their labels.

[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-sebastian

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 are the supreme court cases?

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

[Tool]
Example 1:
Question:
who are the judges of the supreme court?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P39 ?y .
  ?y wdt:P279 wd:Q20706330 .
}

Using entities:
United States Supreme Court justice, also known as Justice of the Supreme Court of the United States, Justices of the Supreme Court of the United States, Justices of the United States Supreme Court (wd:Q20706330): Chief Justice and associate justice of the Supreme Court of the United States

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
position held, also known as function, held position, holds position, job, office held (wd:P39 as wdt): subject currently or formerly holds the object position or public office

Example 2:
Question:
what is the jurisdiction of the us supreme court?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q11201 wdt:P1001 ?x .
}

Using entities:
Supreme Court of the United States, also known as US Supreme Court, U.S. Supreme Court, United States Supreme Court, Supreme Court of the U.S., SCOTUS (wd:Q11201): highest court of jurisdiction in the United States

Using properties:
applies to jurisdiction, also known as belongs to jurisdiction, applies to territorial jurisdiction, applies to region, applies to place, applies to geographic place (wd:P1001 as wdt): the item (institution, law, public office, public register...) o...a territorial jurisdiction: a country, state, municipality, ...), significant place, characteristic of, operating area

Example 3:
Question:
what is a judge in the supreme court called?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q11201 wdt:P2388 ?x .
}

Using entities:
Supreme Court of the United States, also known as US Supreme Court, U.S. Supreme Court, United States Supreme Court, Supreme Court of the U.S., SCOTUS (wd:Q11201): highest court of jurisdiction in the United States

Using properties:
position held by head of the organization, also known as position held by head of the organisation, secretary's position, president's position, position held by head of the organization, office held by head of the organisation (wd:P2388 as wdt): position of the head of this item

[Assistant]
The question "what are the supreme court cases?" is somewhat broad. It could mean a request for notable or landmark cases decided by a supreme court, or it could mean a list of cases decided by a specific supreme court (e.g., the United States Supreme Court).

From the similar examples, I see that the entity for the "Supreme Court of the United States" is wd:Q11201. However, there is no direct example for listing cases decided by the Supreme Court.

I will first search for the entity "supreme court case" to see if there is a specific class or entity representing supreme court cases. This will help me understand how cases are represented in Wikidata and if there is a way to link them to a supreme court.

[Tool]
Top 10 entity alternatives:
1. ELUFIOYE AND 9 OTHERS v HALILU AND 17 OTHERS, also known as Supreme Court case (wd:Q125556110): supreme court case between Elufloye and 9 others and Halilu and 17 others
2. FEDERAL REPUBLIC OF NIGERIA v T. A. DAIRO & Others, also known as Supreme court case (wd:Q125658363): Federal government in court with T.A. Dairo & others.
3. Salihu v Wasiu, also known as Supreme court case (wd:Q125403314): Supreme Court case between Salihu v Wasiu
4. Nsirim v Dr. Amadi ( SC.40/2005) [2016] NGSC 67 (21 January 2016), also known as Nigeria supreme court case (wd:Q125415178): a Nigeria  supreme Court cases
5. Nevada Supreme Court decision, also known as Nevada Supreme Court case (wd:Q121470056): decision rendered by the highest court of Nevada
6. United States Supreme Court decision, also known as Supreme Court decision, US Supreme Court cases, US Supreme Court case, United States Supreme Court case, United States Supreme Court cases (wd:Q19692072): decision rendered by the highest court of the United States
7. Supreme Court Case Selections Act, also known as Supreme Court Case Selections Act of 1988 (wd:Q7644854): 1988 U.S. law removing some appeals-by-right to the Supreme Court
8. Monkey case in Supreme Court (wd:Q59013757): scientific article published in Nature
9. Seed-patent case in Supreme Court (wd:Q74202143): journal article; published in Nature on 2013-02-19
10. Supreme court of Nigeria case (wd:Q125677284)

[Assistant]
There is an entity for "United States Supreme Court decision" (wd:Q19692072) which represents decisions rendered by the highest court of the United States. This seems to be the closest entity representing supreme court cases.

Next, I will check how these cases are linked to the Supreme Court of the United States (wd:Q11201) and try to find a property that connects these cases to the court. This will help me build a query to list supreme court cases.

[Tool]
Got 163 rows and 3 columns, showing the first 10 rows below:
| subject                                             | property                            | object                                                                                |
| --------------------------------------------------- | ----------------------------------- | ------------------------------------------------------------------------------------------------------- |
| United States Supreme Court decision (wd:Q19692072) | country (wdt:P17)                   | United States (wd:Q30)                                                                                |
| United States Supreme Court decision (wd:Q19692072) | image (wdt:P18)                     | <http://commons.wikimedia.org/wiki/Special:FilePath/Oblique%20facade%203%2C%20US%20Supreme%20Court.jpg> |
| United States Supreme Court decision (wd:Q19692072) | subclass of (wdt:P279)              | legal case (wd:Q2334719)                                                                                |
| United States Supreme Court decision (wd:Q19692072) | applies to jurisdiction (wdt:P1001) | United States (wd:Q30)                                                                                |
| United States Supreme Court decision (wd:Q19692072) | topic's main category (wdt:P910)    | wd:Q6996306                                                                                |
| United States Supreme Court decision (wd:Q19692072) | topic's main template (wdt:P1424)   | wd:Q14373911                                                                                |
| United States Supreme Court decision (wd:Q19692072) | has list (wdt:P2354)                | wd:Q6646863                                                                                |
| United States Supreme Court decision (wd:Q19692072) | court (wdt:P4884)                   | Supreme Court of the United States (wd:Q11201)                                                          |
| United States Supreme Court decision (wd:Q19692072) | MeSH tree code (wdtn:P672)          | <http://id.nlm.nih.gov/mesh/I01.880.604.583.858>                                                        |
| United States Supreme Court decision (wd:Q19692072) | MeSH descriptor ID (wdtn:P486)      | <http://id.nlm.nih.gov/mesh/D035521>                                                                    |
| ...                                                 | ...                                 | ...                                                                                |

[Assistant]
The property "court" (wdt:P4884) links the entity "United States Supreme Court decision" (wd:Q19692072) to the "Supreme Court of the United States" (wd:Q11201).

Now, I will build a SPARQL query to list instances of United States Supreme Court decisions (cases) that are linked to the Supreme Court of the United States by the property "court" (wdt:P4884). I will also include optional labels for readability.

[Tool]
Got 20 rows and 2 columns, showing the first 5 and last 5 rows below:
| case                                                               | caseLabel                                               |
| ------------------------------------------------------------------ | ------------------------------------------------------- |
| Trump v. New York (wd:Q100496432)                                  | Trump v. New York (en)                                  |
| New Hampshire v. Maine (wd:Q101093598)                             | New Hampshire v. Maine (en)                             |
| Roman Catholic Diocese of Brooklyn v. Cuomo (wd:Q102532067)        | Roman Catholic Diocese of Brooklyn v. Cuomo (en)        |
| EEOC v. Wyoming (wd:Q104672335)                                    | EEOC v. Wyoming (en)                                    |
| Bogan v. Scott-Harris (wd:Q104821362)                              | Bogan v. Scott-Harris (en)                              |
| ...                                                                | ...                                                     |
| Edwards v. Vannoy (wd:Q104878313)                                  | Edwards v. Vannoy (en)                                  |
| Tanzin v. Tanvir (wd:Q104880243)                                   | Tanzin v. Tanvir (en)                                   |
| Barton v. Barr (wd:Q104880695)                                     | Barton v. Barr (en)                                     |
| National Collegiate Athletic Association v. Alston (wd:Q104881529) | National Collegiate Athletic Association v. Alston (en) |
| United States v. Craft (wd:Q104881625)                             | United States v. Craft (en)                             |

[Tool] Stopped generation process