PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?placeOfBirth ?residence WHERE {
  wd:Q207 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    wd:Q207 wdt:P551 ?residence .
  }
}

George W. Bush was born in New Haven, Connecticut, and he likely spent a significant part of his childhood in Dallas, Texas.

[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 did george w bush live as a child?

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

[Tool]
Example 1:
Question:
where did laura bush grow up?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q152019 wdt:P19 ?x .
}

Using entities:
Laura Bush, also known as Laura Welch, Laura Welch Bush, Laura Lane Welch, Laura Lane Welch Bush (wd:Q152019): First Lady of the United States from 2001 to 2009, HIV/AIDS activist, children's writer, autobiographer, politician, president, librarian, teacher, writer

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

Example 2:
Question:
where is george bush from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q207 wdt:P19 ?x .
}

Using entities:
George W. Bush, also known as GWB, POTUS 43, George Bush, President Bush, Bush Jr. (wd:Q207): President of the United States from 2001 to 2009, motivational speaker, rugby union player, military officer, autobiographer, businessperson, statesperson, politician, financier, painter

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

Example 3:
Question:
where does jeb bush live?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q221997 wdt:P551 ?x .
}

Using entities:
Jeb Bush, also known as John Ellis Bush, Governor Jeb Bush, Gov. Jeb Bush, John Ellis "Jeb" Bush, Gov. Bush (wd:Q221997): Governor of Florida from 1999 to 2007, real estate developer, businessperson, bookbinder, politician, governor, writer, banker

Using properties:
residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt): the place where the person is or has been, resident, location

[Assistant] From the examples, we can see that the place of birth (P19) and residence (P551) properties might be relevant for finding out where George W. Bush lived as a child. However, these properties typically refer to the current or most significant place of residence, not necessarily where someone lived during their childhood. We'll start by checking George W. Bush's place of birth and then look for more specific information about his childhood residences.

[Tool]
Top 10 entity alternatives:
1. George W. Bush, also known as GWB, POTUS 43, George Bush, President Bush, Bush Jr. (wd:Q207): President of the United States from 2001 to 2009, motivational speaker, rugby union player, military officer, autobiographer, businessperson, statesperson, politician, financier, painter
2. George Washington Bush, also known as George W. Bush (wd:Q5545944): American pioneer, husbandry worker, employee, pioneer, trapper, farmer
3. George W. Bush (wd:Q104844437): 2020 television documentary film
4. George W. Bush (wd:Q111279895): episode of Dokument utifrån
5. George W. Bush (wd:Q47513370): painting by Robert A. Anderson
6. George W. Bush, also known as George Washington Bush (wd:Q94496272): operator of George W. Bush & Son, a shipping business in Delaware, United States (1824-1900), consignor, director, merchant
7. George H. W. Bush, also known as G H W Bush, George W. Bush Senior, GHWB, GHW Bush, G.H.W. Bush (wd:Q23505): President of the United States from 1989 to 1993, baseball player, aircraft pilot, autobiographer, naval officer, statesperson, entrepreneur, politician, diplomat
8. George W. Bush Presidential Center, also known as GWB Library, GWB Presidential Library, George W. Bush Presidential Library, George W. Bush Library, Bush Presidential Library (wd:Q1508430): Presidential library and museum for U.S. President George W. Bush, located in Dallas, Texas
9. George W. Bush Administration cabinet, also known as Bush Cabinet, George W. Bush Cabinet, Cabinet of George W. Bush, Cabinet George W. Bush, Cabinet Bush (wd:Q28464532): most senior appointed officers of the executive branch of the fe...ent of the United States under the George W. Bush Administration
10. USS George W. Bush (wd:Q131753450): planned Gerald R. Ford-class aircraft carrier

[Assistant] The correct entity for George W. Bush is `wd:Q207`. Now, let's check his place of birth using the `P19` property.

[Tool]
Got 1 row and 1 column:
| placeOfBirth          |
| --------------------- |
| New Haven (wd:Q49145) |

[Assistant] George W. Bush was born in New Haven, Connecticut. However, this only tells us his place of birth, not necessarily where he lived as a child. To find out more about his childhood residences, we need to look for more specific information. Let's search for properties related to childhood or residence that might provide more details.

[Tool]
Top 10 property alternatives:
1. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt/p): the place where the person is or has been, resident, location
2. family, also known as royal house, noble family, member of family, house, dynasty (wd:P53 as wdt/p): family, including dynasty and nobility houses. Not family name (use P734 for family name)., member of
3. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
4. child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt/p): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
5. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as wdt/p): location where persons or organisations were actively participating in employment, business or other work, significant place, location
6. mother, also known as mom, is child of, mommy, mum, mummy (wd:P25 as wdt/p): female parent of the subject. For stepmother, use "stepparent" (P3448), parent
7. father, also known as son of, child of, parent, is son of, is daughter of (wd:P22 as wdt/p): male parent of the subject. For stepfather, use "stepparent" (P3448), parent
8. number of children, also known as children, children (number), how many children, issue, kids (wd:P1971 as wdt/p): number of children of the person, animal, or character
9. sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as wdt/p): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative
10. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country

No other items found

[Assistant] The property `residence` (P551) seems to be the most relevant for finding out where George W. Bush lived as a child. Let's query for his residences and see if we can find any information about his childhood homes.

[Tool]
Got 1 row and 1 column:
| residence          |
| ------------------ |
| Dallas (wd:Q16557) |

[Assistant]
The query returned Dallas, Texas as one of George W. Bush's residences. However, this does not specify whether it was his childhood residence. To get more specific information about his childhood, we need to look for statements that include qualifiers indicating the time period.

Let's refine the query to include qualifiers that might specify the time period of his residence.

[Tool]
Got 1 row and 2 columns:
| residence          | timePeriod |
| ------------------ | ---------- |
| Dallas (wd:Q16557) |            |

[Assistant]
The query returned Dallas, Texas as one of George W. Bush's residences, but it did not provide any time periods. This suggests that the specific time periods for his residences are not explicitly stated in the data.

Given this, we can infer that while New Haven, Connecticut is his place of birth, he likely spent a significant part of his childhood in Dallas, Texas, as it is listed as one of his residences. However, without explicit time periods, we cannot definitively state that he lived in Dallas as a child.

To provide a more complete answer, we can include both his place of birth and the known residence:

**Answer:**
George W. Bush was born in New Haven, Connecticut, and he likely spent a significant part of his childhood in Dallas, Texas.

Let's finalize the SPARQL query and answer.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?government ?governmentLabel WHERE {
  wd:Q408 wdt:P122 ?government .
  OPTIONAL {
    ?government rdfs:label ?governmentLabel .
    FILTER ( LANG( ?governmentLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| government                            | governmentLabel               |
| ------------------------------------- | ----------------------------- |
| representative democracy (wd:Q188759) | representative democracy (en) |
| constitutional monarchy (wd:Q41614)   | constitutional monarchy (en)  |
| federation (wd:Q43702)                | federation (en)               |

Australia has a representative democracy, a constitutional monarchy, and a federation.

[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 government does australia have?

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

[Tool]
Example 1:
Question:
what type of government does australia currently have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q408 wdt:P122 ?x .
}

Using entities:
Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania

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 kind government does canada have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q16 wdt:P122 ?x .
}

Using entities:
Canada, also known as CA, CAN, Dominion of Canada, ISO 3166-1:CA (wd:Q16): country in North America

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:
what sort of government does brazil have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q155 wdt:P122 ?x .
}

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

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

[Assistant] Based on the examples, it seems that the property `wdt:P122` (basic form of government) is used to determine the type of government a country has. Let's confirm this by searching for the property and then constructing our SPARQL query.

[Tool]
Top 10 property alternatives:
1. basic form of government, also known as form of government, government, type of government (wd:P122 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): subject's government
2. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): seat of government of a country, province, state or other type of administrative territorial entity
3. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
4. office held by head of government, also known as title of head of government, position of head of government, office of head of government, head of government's office, position held by head of government (wd:P1313 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): political office that is fulfilled by the head of the government of this item
5. authority, also known as executive authority, governed by, governing authority, governing body, has authority (wd:P797 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): entity having executive power on given entity
6. voting system, also known as electoral system, voting system, type of election (wd:P8196 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): system used for this election or vote
7. Classification of the Functions of Government, also known as COFOG ID, COFOG number (wd:P9798 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): UN classification to compare government agencies across countries
8. office held by head of state, also known as head of state, position held by head of state, state headed by (wd:P1906 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): political office that is fulfilled by the head of state of this item
9. country, also known as sovereign state, state, land, host country (wd:P17 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
10. foundational text, also known as main text, treaty, organic law, founding document, establishing document (wd:P457 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): text through which an institution or object has been created or established

[Assistant]
The top result confirms that `wdt:P122` (basic form of government) is indeed the correct property to use. Now, let's identify the entity for Australia and construct the SPARQL query.

First, I'll search for the entity representing Australia.

[Tool]
Top 10 entity alternatives:
1. Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania
2. Australian continent, also known as New Holland, Sahul, Nova Hollandia, Meganesia, Australinea (wd:Q3960): continent in the Earth's Southern Hemisphere
3. Australia (wd:Q275180): 2008 film by Baz Luhrmann
4. Australia (wd:Q205546): 1989 film by Jean-Jacques Andrien
5. Australia (wd:Q4823546): 1996 single by Manic Street Preachers
6. Australia (wd:Q781244): board game
7. Australia (wd:Q16835533): Racehorse
8. Australia, also known as Australia (KA–5) (wd:Q4823541): yacht that raced in the Americas Cup
9. Australia (wd:Q4823543): 2000 album by Howie Day
10. Australia (wd:Q25907685): albedo feature on the planet Mercury

[Assistant]
The top result confirms that `wd:Q408` is the correct entity for Australia. Now, we can construct the SPARQL query to find the type of government in Australia.

Here is the SPARQL query:

```sparql
SELECT DISTINCT ?government ?governmentLabel
WHERE {
  wd:Q408 wdt:P122 ?government .
  OPTIONAL { ?government rdfs:label ?governmentLabel . FILTER(LANG(?governmentLabel) = "en") }
}
```

Let's execute this query to get the result.

[Tool]
Got 3 rows and 2 columns:
| government                            | governmentLabel               |
| ------------------------------------- | ----------------------------- |
| representative democracy (wd:Q188759) | representative democracy (en) |
| constitutional monarchy (wd:Q41614)   | constitutional monarchy (en)  |
| federation (wd:Q43702)                | federation (en)               |

[Assistant]
The SPARQL query has returned the types of government in Australia. According to the results, Australia has a representative democracy, a constitutional monarchy, and a federation.

Therefore, the answer to the question "What type of government does Australia have?" is:

Australia has a representative democracy, a constitutional monarchy, and a federation.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?government ?governmentLabel
WHERE {
  wd:Q408 wdt:P122 ?government .
  OPTIONAL { ?government rdfs:label ?governmentLabel . FILTER(LANG(?governmentLabel) = "en") }
}
```

Answer: Australia has a representative democracy, a constitutional monarchy, and a federation.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?team WHERE {
  wd:Q611891 wdt:P54 ?team .
  ?team wdt:P118 wd:Q1215884 .
}

Got 3 rows and 1 column:
| team                            |
| ------------------------------- |
| New Orleans Saints (wd:Q172435) |
| Minnesota Vikings (wd:Q221150)  |
| Tennessee Titans (wd:Q320484)   |

Archie Manning played for the New Orleans Saints, Minnesota Vikings, and Tennessee Titans.

[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 team did peyton manning's dad play for?

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

[Tool]
Example 1:
Question:
what year was peyton manning's rookie year?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q7539 p:P54 ?p .
  ?p ps:P54 ?y .
  ?y wdt:P118 wd:Q1215884 .
  ?p pq:P580 ?x .
}
ORDER BY ?x
LIMIT 1

Using entities:
National Football League, also known as American Professional Football Association, American Professional Football Conference, APFA, APFC, NFL (wd:Q1215884): professional American football league
Peyton Manning, also known as The Sheriff, Peyton Williams Manning (wd:Q7539): American football player (born 1976), American football player, restaurateur, voice actor, actor

Using properties:
league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as wdt): league or competition in which team or player has played, or in which an event occurs, participant in
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as ps/p): sports teams or clubs that the subject represents or represented, member of
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time

Example 2:
Question:
what college did peyton manning?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q7539 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
higher education institution, also known as HEI, Higher Education school, institution of higher education, Superior Institute (wd:Q38723): institution that provides higher education (e.g., universities, ...es, liberal arts colleges, institutes of technology, and others)
Peyton Manning, also known as The Sheriff, Peyton Williams Manning (wd:Q7539): American football player (born 1976), American football player, restaurateur, voice actor, actor

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
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:
who has tom brady played for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q313381 wdt:P54 ?x .
}

Using entities:
Tom Brady, also known as Thomas Edward Patrick Brady, Jr., Thomas Edward Brady, Jr., Thomas Edward Patrick Brady Jr., Thomas Edward Patrick Brady, Thomas Brady (wd:Q313381): American football player (born 1977), American football player

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

[Assistant] Based on these examples, we need to identify the entity for Peyton Manning's father and then find out which team he played for. Let's first search for Peyton Manning's father.

[Tool]
Top 10 entity alternatives:
1. Peyton Manning's Monologue (wd:Q110057291): sketch from Saturday Night Live
2. Peyton Rous: father of the tumor virus (wd:Q24646294): scientific article
3. father, also known as fathers, pop, papa, pa, daddy (wd:Q7565): male parent
4. God the Father, also known as Eternal Father, Father, The Father (wd:Q186350): in Christianity, the first of the three persons of the Trinity, who begets the Son and from whom the Holy Spirit proceeds
5. Peyton, also known as Peyton, CO, Peyton, Colorado (wd:Q7179619): census-designated place in El Paso County, Colorado, United States
6. Father (wd:Q773110): 1966 film by István Szabó
7. Father, also known as Víctor Campuzano Bonilla (wd:Q29263290): association football player, My father
8. Geppetto, also known as Father, Polendina, Mister Geppetto, Mastro Geppetto (wd:Q1428120): fictional character who appear in Carlo Collodi's book The Adventures of Pinocchio, wood carver
9. Father, also known as padre (wd:Q435940): honorific prefix used by Catholic priests
10. Father, also known as Otac (wd:Q85989911): 2020 film by Srđan Golubović

[Assistant] None of the top 10 results seem to be Peyton Manning's father. Let me try a more specific search.

[Tool]
Top 10 entity alternatives:
1. Archie Manning, also known as Elisha Archibald Manning III (wd:Q611891): American football player (born 1949), American football player
2. Manning, also known as Manning, IA, Manning, Iowa (wd:Q2233616): city in Iowa, USA
3. Archie, also known as Archie, Missouri, Archie, MO (wd:Q965391): city in Cass County, Missouri, United States
4. Manning, also known as Manning, SC, Manning, South Carolina (wd:Q2025061): city in South Carolina, USA
5. Manning, also known as Manning, ND, Manning, North Dakota (wd:Q3286067): census-designated place in Dunn County, North Dakota, United States
6. Archie (wd:Q633739): FTP search engine
7. Manning (wd:Q955573): town in Alberta
8. Manning, also known as Manning, OR (wd:Q1354455): unincorporated community in Washington County, Oregon, United States
9. Manning, also known as Manning (surname) (wd:Q14629485): family name
10. Raymond Brendan Manning, also known as Manning, Raymond Manning, Raymond B. Manning, Raymond B Manning, Ray Manning (wd:Q3420704): American zoologist specializing in crustaceans (1934-2000), carcinologist, zoologist

[Assistant] The correct entity for Archie Manning, Peyton Manning's father, is `wd:Q611891`. Now let's find out which team he played for. We'll use the property `P54` (member of sports team) to find the team.

[Tool]
Got 4 rows and 1 column:
| team                                   |
| -------------------------------------- |
| New Orleans Saints (wd:Q172435)        |
| Minnesota Vikings (wd:Q221150)         |
| Tennessee Titans (wd:Q320484)          |
| Ole Miss Rebels football (wd:Q7085897) |

[Assistant]
Archie Manning played for multiple teams. The results include both his professional and college teams. To provide a more precise answer, let's filter out the college team and focus on the professional teams.

We can use the property `P106` (occupation) to ensure we are looking at his professional career.

Let's refine the query to only include professional teams.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the initial approach to filter by the type of team didn't work. Let's try another approach by checking the context of the teams he played for. We can look at the teams he played for during his professional career by checking the league he played in, which should be the National Football League (NFL).

Let's use the property `P118` (league or competition) to filter the teams to those in the NFL.

[Tool]
Got 3 rows and 1 column:
| team                            |
| ------------------------------- |
| New Orleans Saints (wd:Q172435) |
| Minnesota Vikings (wd:Q221150)  |
| Tennessee Titans (wd:Q320484)   |

[Assistant]
Archie Manning played for the New Orleans Saints, Minnesota Vikings, and Tennessee Titans during his professional career in the National Football League (NFL).

To provide a concise answer, we can list the teams he played for.

Here is the final SPARQL query and answer:

```sparql
SELECT ?team WHERE {
  wd:Q611891 wdt:P54 ?team .
  ?team wdt:P118 wd:Q1215884 .
}
```

Answer: Archie Manning played for the New Orleans Saints, Minnesota Vikings, and Tennessee Titans.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?lyricist ?lyricistLabel WHERE {
  wd:Q83099 wdt:P676 ?lyricist .
  OPTIONAL {
    ?lyricist rdfs:label ?lyricistLabel .
    FILTER ( LANG( ?lyricistLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| lyricist                       | lyricistLabel            |
| ------------------------------ | ------------------------ |
| Rabindranath Tagore (wd:Q7241) | Rabindranath Tagore (en) |

The query returned multiple labels for Rabindranath Tagore in different languages. To simplify the answer, we can filter the results to only include the English label. Here is the refined SPARQL query:

```sparql
SELECT ?lyricist ?lyricistLabel WHERE {
  wd:Q83099 wdt:P676 ?lyricist .
  OPTIONAL { ?lyricist rdfs:label ?lyricistLabel . FILTER(LANG(?lyricistLabel) = "en") }
}
```

Let's execute this final query.

<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT ?lyricist ?lyricistLabel WHERE {\n  wd:Q83099 wdt:P676 ?lyricist .\n  OPTIONAL { ?lyricist rdfs:label ?lyricistLabel . FILTER(LANG(?lyricistLabel) = \\"en\\") }\n}"}}
</tool_call>

[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] who wrote the jana gana mana?

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

[Tool]
Example 1:
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] None of the examples found are directly relevant to our question about who wrote "Jana Gana Mana". However, we can infer that we need to look for the author or composer of the song. Let's first search for the entity "Jana Gana Mana".

[Tool]
Top 10 entity alternatives:
1. Jana Gana Mana (wd:Q83099): national anthem of India
2. Jana Gana Mana (wd:Q112072761): 2022 film directed by Dijo Jose Antony
3. Jana Gana Mana (wd:Q80196923): musical score approved for Indian national anthem
4. Jana Gana Mana (wd:Q6150500): 2012 film
5. Jana Gana Mana (wd:Q112274393): Telegu film
6. Jana Gana Mana (wd:Q121757500): 1942 radio performance
7. Jana Gana Mana Video (wd:Q6150505): national anthem
8. Jana Gana Mana Adhinayaka Jaya He (wd:Q121756199): vocal track by Amala Dutt, Nandita Devi, Sudhin Dutt & Santi Ghosh; 1934 studio recording
9. Jana Gana Mana Adhinayaka Jaya He (wd:Q121763513)
10. Manas Jana (wd:Q93070394): researcher (ORCID 0000-0003-3178-0647), researcher

[Assistant] The top result, "Jana Gana Mana" (wd:Q83099), is the national anthem of India. This is likely the correct entity we are looking for. Next, we need to find out who wrote it. We can search for properties related to the author or composer of this entity.

[Tool]
Top 10 property alternatives:
1. lyricist, also known as text poet, writer (of song lyrics), words by, text writer, text by (wd:P676 as wdt/p): author of song lyrics, author
2. composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as wdt/p): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator
3. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt/p): date or point in time when a work was first published or released, point in time
4. title, also known as name, titled, page name, original title, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
5. tonality, also known as in key, key, key of musical composition, key used, musical key (wd:P826 as wdt/p): key of a musical composition
6. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
7. based on, also known as modelled after, inspired by, iteration of, modeled after, fork of (wd:P144 as wdt/p): the work(s) or inputs used as the basis for subject item; for fictional analog use P1074, references work, tradition or theory, inspired by
8. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as wdt/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
9. permanent duplicated item, also known as same concept as, same as, duplicate of, permanently duplicated item, duplicate of item (wd:P2959 as wdt/p): this item duplicates another item and the two can't be merged, a... cdowiki, gomwiki, nanwiki).  Use P31=Q17362920 for other wikis.
10. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

No other items found

[Assistant]
The top result for the property search is "lyricist" (wd:P676), which seems appropriate since "Jana Gana Mana" is a song. We can use this property to find the author of the lyrics. Let's construct a SPARQL query to retrieve the lyricist of "Jana Gana Mana" (wd:Q83099).

```sparql
SELECT ?lyricist WHERE {
  wd:Q83099 wdt:P676 ?lyricist .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| lyricist                       |
| ------------------------------ |
| Rabindranath Tagore (wd:Q7241) |

[Assistant]
The lyricist of "Jana Gana Mana" is Rabindranath Tagore (wd:Q7241).

To provide a complete and clear answer, let's include the label of the lyricist in the final SPARQL query.

```sparql
SELECT ?lyricist ?lyricistLabel WHERE {
  wd:Q83099 wdt:P676 ?lyricist .
  OPTIONAL { ?lyricist rdfs:label ?lyricistLabel . }
}
```

Let's execute this final query.

[Tool]
Got 184 rows and 2 columns, showing the first 5 and last 5 rows below:
| lyricist                       | lyricistLabel                          |
| ------------------------------ | -------------------------------------- |
| Rabindranath Tagore (wd:Q7241) | Daiqgwzwj (za)                         |
| Rabindranath Tagore (wd:Q7241) | Lá-bĭng-dáik-lá-nà-sṳ̆ Tái-gŏ̤-ī (cdo) |
| Rabindranath Tagore (wd:Q7241) | Rabėndranats Taguorė (sgs)             |
| Rabindranath Tagore (wd:Q7241) | Rabindranatas Tagorė (lt)              |
| Rabindranath Tagore (wd:Q7241) | Rabindranath Tagore (af)               |
| ...                            | ...                                    |
| Rabindranath Tagore (wd:Q7241) | 罗宾德拉纳特·泰戈尔 (wuu)                       |
| Rabindranath Tagore (wd:Q7241) | 罗宾德拉纳特·泰戈尔 (zh)                        |
| Rabindranath Tagore (wd:Q7241) | 罗宾德拉纳特·泰戈尔 (zh-cn)                     |
| Rabindranath Tagore (wd:Q7241) | 罗宾德拉纳特·泰戈尔 (zh-hans)                   |
| Rabindranath Tagore (wd:Q7241) | 羅賓德拉納特·泰戈爾 (zh-hant)                   |

[Assistant]
The query returned multiple labels for Rabindranath Tagore in different languages. To simplify the answer, we can filter the results to only include the English label. Here is the refined SPARQL query:

```sparql
SELECT ?lyricist ?lyricistLabel WHERE {
  wd:Q83099 wdt:P676 ?lyricist .
  OPTIONAL { ?lyricist rdfs:label ?lyricistLabel . FILTER(LANG(?lyricistLabel) = "en") }
}
```

Let's execute this final query.

<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT ?lyricist ?lyricistLabel WHERE {\n  wd:Q83099 wdt:P676 ?lyricist .\n  OPTIONAL { ?lyricist rdfs:label ?lyricistLabel . FILTER(LANG(?lyricistLabel) = \\"en\\") }\n}"}}
</tool_call>


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?team WHERE {
  wd:Q25369 wdt:P647 ?team .
}

The Charlotte Hornets drafted Kobe Bryant.

[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] who was kobe bryant drafted by?

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

[Tool]
Example 1:
Question:
who's kobe bryant?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q25369 wdt:P106 ?x .
}

Using entities:
Kobe Bryant, also known as Kobe Bean Bryant, Kobe B. Bryant, Kobe, KB24, Black Mamba (wd:Q25369): American basketball player (1978–2020), professional athlete, basketball player, screenwriter

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

Example 2:
Question:
who drafted kevin garnett?

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

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

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

Example 3:
Question:
where did kobe bryant attend high school?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q25369 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q9826 .
}

Using entities:
Kobe Bryant, also known as Kobe Bean Bryant, Kobe B. Bryant, Kobe, KB24, Black Mamba (wd:Q25369): American basketball player (1978–2020), professional athlete, basketball player, screenwriter
high school, also known as H.S., upper middle school, senior high school, HS (wd:Q9826): institution which provides all or part of secondary education

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
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] Based on the examples, it seems like the property "drafted by" (P647) is what we need to find out who drafted Kobe Bryant. Let's confirm this by searching for the property and then executing a SPARQL query.

[Tool]
Top 10 property alternatives:
1. drafted by (wd:P647 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): which team the player was drafted by
2. draft, also known as draught (wd:P2262 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): depth below the water line to the bottom of a vessel's hull; summer draft is implied unless stated otherwise.
3. study or design for, also known as design for, draft for, first version of, preparatory work for, study for (wd:P6606 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): finished work of art for which the subject is a preliminary study, design, or conceptual drawing
4. draft pick number, also known as draft number (wd:P1836 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): overall pick number with which a player is selected in a sports draft, series ordinal
5. The Draft Review ID, also known as Draft Review identifier, The Draft Review identifier, Draft Review ID (wd:P9514 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): identifier for a basketball player in the database of The Draft Review
6. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
7. participant, also known as suspect, player, party, participants, has participant (wd:P710 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person
8. instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): musical instrument that a person plays or teaches or used in a music occupation, uses
9. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): sports teams or clubs that the subject represents or represented, member of
10. DraftExpress ID, also known as draftexpress.com ID (wd:P3533 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): identifier for a basketball player at draftexpress.com

[Assistant]
The top result is "drafted by" (P647), which matches our expectation. Now let's execute a SPARQL query to find out who drafted Kobe Bryant.

I will use the entity for Kobe Bryant (Q25369) and the property "drafted by" (P647).

Let's run the query:

```sparql
SELECT DISTINCT ?team WHERE {
  wd:Q25369 wdt:P647 ?team .
}
```

This should give us the team that drafted Kobe Bryant.

[Tool]
Got 1 row and 1 column:
| team                           |
| ------------------------------ |
| Charlotte Hornets (wd:Q163480) |

[Assistant]
The SPARQL query returned that the Charlotte Hornets (wd:Q163480) drafted Kobe Bryant.

To finalize the answer, I will provide the SPARQL query and the answer.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?team WHERE {
  wd:Q25369 wdt:P647 ?team .
}
```

Answer: The Charlotte Hornets drafted Kobe Bryant.

Now, I will submit this as the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?author WHERE {
  wd:Q36766 wdt:P50 ?author .
}

John the Apostle (wd:Q44015)

[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] who wrote the gospel according to john?

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

[Tool]
Example 1:
Question:
who wrote the book of st. john?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q42040 wdt:P50 ?x .
}

Using entities:
Book of Revelation, also known as The Apocalypse, The Uncovering, Revelation to John, Revelation of John, Revelation (wd:Q42040): final book of the New Testament

Using properties:
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

Example 2:
Question:
what books did aristotle wrote?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q47461344 ; wdt:P50 wd:Q868 .
}

Using entities:
written work, also known as creative written work, written delivery (wd:Q47461344): any work expressed in writing, such as inscriptions, manuscripts, documents or maps
Aristotle, also known as the Stagirite (wd:Q868): 4th-century BCE Classical Greek philosopher and polymath, philosopher

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

Example 3:
Question:
where was jesus born and raised?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q302 wdt:P19 ?x .
}

Using entities:
Jesus, also known as The Messiah, Son of Man, The Saviour, Lord of Lords, Saviour of the world (wd:Q302): central figure of Christianity (6 or 4 BC – AD 30 or 33), religious leader, father of faith, thaumaturge, carpenter, preacher, Messiah, teacher, prophet, pastor, healer, tektōn, rabbi

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

[Assistant]
Based on the similar examples, we can see that the property `wdt:P50` is used to represent the author of a written work. We need to find the entity representing "Gospel According to John" and then use the `wdt:P50` property to find its author.

First, let's search for the entity "Gospel According to John".

[Tool]
Top 10 entity alternatives:
1. Gospel of John, also known as John (Bible), The Gospel according to John, Gospel according to John, Bible. John (wd:Q36766): book of the New Testament
2. The Gospel According to John (wd:Q7737359): book by D. A. Carson
3. The Gospel According to John (wd:Q117245690): academic book chapter
4. The Gospel According to John (wd:Q116053332): journal article
5. The gospel according to John (wd:Q105481691)
6. The Gospel according to St. John (wd:Q86714449): Gospel of John transl. into Haussa by Jam. Fred. Schön. (London : Brit and for Bible Society 1857.)
7. The Gospel according to St. John (wd:Q86598319): Gospel of John  transl. into the Ibo language by Jo. Chr. Taylor. (London : Brit and for Society 1865.)
8. The Law and Love in the Gospel According to John (wd:Q129638849): scholarly article
9. Gospel of Matthew, also known as Gospel according to Matthew, Matthew (wd:Q392302): book of the New Testament
10. Gospel of Mark, also known as Gospel according to Mark, Mark (wd:Q107388): Canonical Gospel of the New Testament

[Assistant]
The top result is the entity for the "Gospel of John" (wd:Q36766). Now, let's use this entity and the `wdt:P50` property to find the author of the "Gospel of John".

I'll construct a simple SPARQL query to retrieve the author.

[Tool]
Got 1 row and 1 column:
| author                       |
| ---------------------------- |
| John the Apostle (wd:Q44015) |

[Assistant]
The author of the "Gospel According to John" is John the Apostle (wd:Q44015).

Now, I will provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


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

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

The main language used in China is Standard Chinese (Standard Mandarin).

[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] Based on the similar examples, it seems like we need to find the official language of China. Let's confirm the entity for China and the property for official language.

[Tool]
Top 10 entity alternatives:
1. People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia
2. porcelain, also known as china, fine china (wd:Q130693): ceramic material
3. China, also known as All under Heaven, Zhonghua, Cathay, Zhongguo, Tianxia (wd:Q29520): cultural region, ancient civilization, and nation in East Asia, ...in political situation and rarely refer to the Republic of China
4. China, also known as Sinae, Zhonghua, Zhongguo, Tianxia, Middle Kingdom (wd:Q29520): cultural region, ancient civilization, and nation in East Asia, ...in political situation and rarely refer to the Republic of China
5. 1125 China, also known as (1125) China, China (wd:Q136211): asteroid
6. China, also known as China, TX, China, Texas (wd:Q288864): city in Jefferson County, Texas, United States
7. China, also known as China, Maine, China, ME (wd:Q3447500): town in Kennebec County, Maine, United States
8. China (wd:Q932423): town in Ōshima district, Kagoshima prefecture, Japan
9. Tianxia, also known as All under Heaven, Tien Hsia, T'ien Hsia, Tien-hsia, T'ien-hsia (wd:Q955223): Chinese cultural, philosophical, and political concept
10. China (wd:Q942154): municipality in the Nuevo León, Mexico

[Assistant] The first result, People's Republic of China (wd:Q148), seems to be the correct entity for China. Now let's verify the property for official language.

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language designated as official by this item, language used
2. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): label for the items in their official language  or their original language, name
3. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language status identifier by Ethnologue.com using EGIDS scale
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): regulatory body of a language
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language or languages a person has learned from early childhood, languages spoken, written or signed
7. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language widely used (spoken or written) in this place or at this event or organisation
8. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
9. programmed in, also known as language, programming language, written in (wd:P277 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): the programming language(s) in which the software is developed
10. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): language in which the writer has written their work, languages spoken, written or signed

[Assistant]
The first result, official language (wd:P37), seems to be the correct property. We can now construct a simple SPARQL query to find the official language of China (wd:Q148).

Let's run the query and see what we get.

[Tool]
Got 3 rows and 1 column:
| language                        |
| ------------------------------- |
| Standard Chinese (wd:Q727694)   |
| Chinese (wd:Q7850)              |
| languages of China (wd:Q835866) |

[Assistant]
The query returned three results: Standard Chinese (wd:Q727694), Chinese (wd:Q7850), and languages of China (wd:Q835866). To provide a more precise answer, we should check the labels of these entities to understand what they represent.

Let's add an optional clause to fetch the labels of these entities.

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

[Assistant]
The results show that the official languages of China include:

- **Standard Chinese** (Standard Mandarin)
- **Chinese** (which is a broader term that includes various dialects and languages)
- **Languages of China** (which refers to the collection of languages spoken in China)

Given the context of the question, the most specific and commonly recognized official language is **Standard Chinese** (Standard Mandarin).

Therefore, the main language used in China is **Standard Chinese** (Standard Mandarin).

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?venue WHERE {
  wd:Q221878 wdt:P115 ?venue .
}

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]
Based on these examples, it seems like we need to find the Seattle Seahawks team and then look for their home venue using the property `P115` (home venue).

First, let's find the entity for the Seattle Seahawks.

[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 correct entity for the Seattle Seahawks is `wd:Q221878`. Now, let's use the property `P115` to find their home venue.

I'll construct a simple SPARQL query to get the home venue of the Seattle Seahawks.

[Tool]
Got 1 row and 1 column:
| venue                    |
| ------------------------ |
| Lumen Field (wd:Q612736) |

[Assistant]
The Seattle Seahawks play at Lumen Field.

Now, I will provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


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

The time zone for Jacksonville, Florida 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]
Based on these examples, it seems like we need to find the entity for Jacksonville, Florida and then use the property `located in time zone` (P421) to get the time zone information.
Let's first search for the entity representing Jacksonville, Florida.

[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 Beach, also known as Jacksonville Beach, FL, Jacksonville Beach, Florida (wd:Q988928): city in Duval County, Florida, United States
3. Ortega, also known as Ortega, Jacksonville, Florida (wd:Q7104308): neighborhood of Jacksonville, Florida, US
4. San Marco, also known as San Marco, Florida, South Jacksonville, Florida (wd:Q14686391): former city of South Jacksonville, Florida, United States; neighborhood of Jacksonville
5. Bayard, also known as Bayard, Jacksonville, Florida (wd:Q4874329): human settlement in Jacksonville, Florida, United States of America
6. history of Jacksonville, Florida, also known as Jacksonville, Florida history (wd:Q17022720): aspect of history
7. education in Jacksonville, Florida, also known as education of Jacksonville, Florida, Jacksonville, Florida education (wd:Q5341043): Overview of education in Jacksonville, Florida
8. Northside, also known as North Jacksonville, Florida, Northside (Jacksonville), Northside Jacksonville, Northside, Jacksonville, North Jacksonville (wd:Q85778622): region of Jacksonville, Florida, United States
9. Jacksonville Heights, also known as Jacksonville Heights, FL, Jacksonville Heights, Florida (wd:Q122594337): hamlet in Duval County, Florida, United States
10. Jacksonville, Florida Auditions (wd:Q114464461): episode of American Idol (S8 E5)

[Assistant]
The top result is the entity for Jacksonville, Florida (wd:Q16568). Now that we have the correct entity, let's construct a SPARQL query to find the time zone of Jacksonville, Florida using the property `located in time zone` (P421).

Here's the SPARQL query:

```sparql
SELECT DISTINCT ?timezone WHERE {
  wd:Q16568 wdt:P421 ?timezone .
}
```

I'll execute this query now.

[Tool]
Got 1 row and 1 column:
| timezone                             |
| ------------------------------------ |
| Eastern Daylight Time (wd:Q15080196) |

[Assistant]
The time zone for Jacksonville, Florida is Eastern Daylight Time (wd:Q15080196).

To provide a more complete answer, let's also include the label of the time zone in the query. We can do this by adding an optional clause to fetch the label.

Here's the updated SPARQL query:

```sparql
SELECT DISTINCT ?timezone ?timezoneLabel WHERE {
  wd:Q16568 wdt:P421 ?timezone .
  OPTIONAL { ?timezone rdfs:label ?timezoneLabel . FILTER(LANG(?timezoneLabel) = "en") }
}
```

I'll execute this query now.

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

[Assistant]
The time zone for Jacksonville, Florida is Eastern Daylight Time (wd:Q15080196).

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?timezone ?timezoneLabel WHERE {
  wd:Q16568 wdt:P421 ?timezone .
  OPTIONAL { ?timezone rdfs:label ?timezoneLabel . FILTER(LANG(?timezoneLabel) = "en") }
}
```

Answer: The time zone for Jacksonville, Florida is Eastern Daylight Time.

Now, I will provide the final answer using the `answer` function.

[Tool] Stopped generation process