PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?country WHERE {
  ?country wdt:P37 wd:Q188 ; wdt:P31 / wdt:P279 * wd:Q6256 .
}

Got 12 rows and 1 column, showing the first 5 and last 5 rows below:
| country                                 |
| --------------------------------------- |
| Brandenburg-Prussia (wd:Q157367)        |
| Germany (wd:Q183)                       |
| Asgardia (wd:Q27304761)                 |
| Belgium (wd:Q31)                        |
| Luxembourg (wd:Q32)                     |
| ...                                     |
| Austria (wd:Q40)                        |
| German Empire (wd:Q43287)               |
| Grand Duchy of Westarctica (wd:Q551375) |
| Denmark–Norway (wd:Q62651)              |
| Kugelmugel (wd:Q877579)                 |

The countries that speak German officially are those entities that are instances or subclasses of countries and have German (wd:Q188) as an official language (wdt:P37). The query returns these countries.

[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] which countries speak german officially?

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

[Tool]
Example 1:
Question:
what countries in the world speak german?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P37 wd:Q188 ; wdt:P31 / wdt:P279 * wd:Q6256 .
}

Using entities:
German, also known as de, German language (wd:Q188): West Germanic language spoken mainly in Central Europe
country, also known as countries (wd:Q6256): distinct territorial body or political entity

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

Example 2:
Question:
what countries speak german as a first language?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 wd:Q6256 ; wdt:P37 wd:Q188 .
}

Using entities:
German, also known as de, German language (wd:Q188): West Germanic language spoken mainly in Central Europe
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
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:
where is german an official language?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P37 wd:Q188 ; wdt:P31 / wdt:P279 * wd:Q6256 .
}

Using entities:
German, also known as de, German language (wd:Q188): West Germanic language spoken mainly in Central Europe
country, also known as countries (wd:Q6256): distinct territorial body or political entity

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

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?dateOfBirth WHERE {
  wd:Q352 wdt:P569 ?dateOfBirth .
}

Adolf Hitler was born in the year 1889.

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

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

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

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

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

[User] what year was hitler was born?

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

[Tool]
Example 1:
Question:
what political party was adolf hitler from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q352 wdt:P102 ?x .
}

Using entities:
Adolf Hitler, also known as Bohemian Corporal (wd:Q352): dictator of Germany from 1933 to 1945, politician

Using properties:
member of political party, also known as political party member, political party, party membership, party, member of party (wd:P102 as wdt): the political party of which a person is or has been a member or otherwise affiliated, member of

Example 2:
Question:
what were adolf hitler's parents names?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q352 wdt:P22 | wdt:P25 ?x .
}

Using entities:
Adolf Hitler, also known as Bohemian Corporal (wd:Q352): dictator of Germany from 1933 to 1945, politician

Using properties:
father, also known as son of, child of, parent, is son of, is daughter of (wd:P22 as wdt): male parent of the subject. For stepfather, use "stepparent" (P3448), parent
mother, also known as mom, is child of, mommy, mum, mummy (wd:P25 as wdt): female parent of the subject. For stepmother, use "stepparent" (P3448), parent

Example 3:
Question:
what nation did hitler lead?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q352 wdt:P39 ?y .
  ?y wdt:P1001 ?x .
}

Using entities:
Adolf Hitler, also known as Bohemian Corporal (wd:Q352): dictator of Germany from 1933 to 1945, politician

Using properties:
applies to jurisdiction, also known as belongs to jurisdiction, applies to territorial jurisdiction, applies to region, applies to place, applies to geographic place (wd:P1001 as wdt): the item (institution, law, public office, public register...) o...a territorial jurisdiction: a country, state, municipality, ...), significant place, characteristic of, operating area
position held, also known as function, held position, holds position, job, office held (wd:P39 as wdt): subject currently or formerly holds the object position or public office

[Assistant] From the similar examples, I found that Adolf Hitler is represented by the entity wd:Q352 in Wikidata. To find the year he was born, I need to find the property related to the date of birth. Usually, the property for date of birth is wdt:P569 in Wikidata. I will now write a SPARQL query to get the year of birth of Adolf Hitler.

[Tool]
Got 1 row and 1 column:
| dateOfBirth                         |
| ----------------------------------- |
| 1889-04-20T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?song ?songLabel WHERE {
  ?song wdt:P31 wd:Q7366 .
  {
    ?song wdt:P86 wd:Q34086 .
  } UNION {
    ?song wdt:P50 wd:Q34086 .
  }
  OPTIONAL {
    ?song rdfs:label ?songLabel FILTER ( LANG( ?songLabel ) = "en" )
  }
}
ORDER BY ?songLabel

Justin Bieber wrote or composed the following songs according to Wikidata. The list includes songs where he is listed as a composer or writer.

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

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

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

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

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

[User] what are all the songs justin bieber wrote?

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

[Tool]
Example 1:
Question:
what instrument does justin bieber?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34086 wdt:P1303 ?x .
}

Using entities:
Justin Bieber, also known as Justin Drew Bieber (wd:Q34086): Canadian singer (born 1994), musician

Using properties:
instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as wdt): musical instrument that a person plays or teaches or used in a music occupation, uses

Example 2:
Question:
what are all songs by taylor swift?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P175 wd:Q26876 .
}

Using entities:
Taylor Swift, also known as Taylor Alison Swift (wd:Q26876): American singer-songwriter (born 1989), music video director, singer-songwriter, record producer, philanthropist, film director, lyricist, composer, singer, actor

Using properties:
performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as wdt): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject

Example 3:
Question:
what is the name of justin bieber brother?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34086 wdt:P3373 ?x .
  ?x wdt:P21 wd:Q6581097 .
}

Using entities:
Justin Bieber, also known as Justin Drew Bieber (wd:Q34086): Canadian singer (born 1994), musician
male, also known as guy, m, male character, male gender, male human (wd:Q6581097): to be used in "sex or gender" (P21) to indicate that the human s...gender" (P10339) to indicate that a word refers to a male person

Using properties:
sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as wdt): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as wdt): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative

[Assistant]
From the examples, I see that Justin Bieber is represented by wd:Q34086 in Wikidata. To find songs he wrote, I need to identify the property that relates a song to its writer or composer. Common properties for this are "writer" (wdt:P50) or "composer" (wdt:P86).

I will first check if there are songs where Justin Bieber is listed as the writer (wdt:P50). I will write a query to find all songs where Justin Bieber is the writer.

[Tool] Got no rows and 2 columns

[Assistant] There are no songs listed in Wikidata where Justin Bieber is explicitly stated as the writer (wdt:P50). I will now check if there are songs where he is listed as the composer (wdt:P86).

[Tool]
Got 2 rows and 2 columns:
| song                   | songLabel    |
| ---------------------- | ------------ |
| Friends (wd:Q37210766) | Friends (en) |
| Get Me (wd:Q83975347)  | Get Me (en)  |

[Assistant] There are two songs where Justin Bieber is listed as the composer: "Friends" and "Get Me." However, these results seem limited. I will now check if there are other properties related to songwriting or writing for songs that might include Justin Bieber. I will search for properties related to "writer" or "songwriter" for the entity Justin Bieber.

[Tool]
Top 10 property alternatives:
1. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as p/wdt): language(s) that a person or a people speaks, writes or signs, including the native language(s)
2. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
4. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
5. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
6. signature, also known as signature image, image of signature, facsimiles, autograph (wd:P109 as p/wdt): image of a person's signature, image
7. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as p/wdt): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
8. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
9. ethnic group, also known as ethnic or cultural origin, tribal affiliation, race, people, ethnicity (wd:P172 as p/wdt): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
10. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as p/wdt): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of

No other items found

[Assistant] There is no direct property labeled as "writer" or "songwriter" for Justin Bieber. I will try a different approach: I will search for songs where Justin Bieber is listed as a contributor or creator in any capacity. I will search for properties related to "contributor" or "creator" for songs.

[Tool]
Top 10 property alternatives:
1. contributed to creative work, also known as contributor to, contributor, contributed to, collaborator on, contributed to work (wd:P3919 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): person is cited as contributing to some creative or published work or series (qualify with "subject has role", P2868)
2. contributor to the creative work or subject, also known as assistant, collaborator, contributor, contributor(s) to the subject, creative contributor (wd:P767 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): person or organization that contributed to a subject: co-creator of a creative work or subject, participant
3. Encyclopædia Britannica contributor ID, also known as Britannica contributor ID, Britannica contributor identifier, Britannica ID, EB contributor, Encyclopædia Britannica contributor identifier (wd:P2450 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): identifier for a contributor to the 'Encyclopædia Britannica'
4. partner in business or sport, also known as professional partner, sport partner, sports partner, team mate, team-mate (wd:P1327 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): professional collaborator, significant person
5. New York Times contributor ID, also known as NYT contributor ID, NY Times contributor ID, NYTimes contributor ID, The New York Times contributor ID (wd:P8297 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): identifier of an author on the New York Times website
6. author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): 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
7. SOUNZ contributor ID, also known as SOUNZ composer ID, SOUNZ musician ID, SOUNZ organisation ID, SOUNZ organization ID, SOUNZ person ID (wd:P9136 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): numerical identifier for persons or organisations listed in the SOUNZ database of New Zealand Music
8. number of registered users/contributors, also known as number of contributors, number of registered users, number of users, users (wd:P1833 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): number of registered users on a website
9. talk show guest, also known as show guest, podcast guest, participant in talk show, participant in podcast interview, interviewed person (wd:P5030 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): person invited in a talk show, recorded participant
10. theorized by, also known as author, supported by, suggested by, proposer, proposed by (wd:P8403 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): subject who suggested or proposed this concept or immaterial invention

[Assistant]
There are two relevant properties that could indicate Justin Bieber's involvement in writing songs:
- contributed to creative work (wdt:P3919)
- contributor to the creative work or subject (wdt:P767)

I will now query for songs where Justin Bieber is a contributor using these properties.

[Tool] Got no rows and 2 columns

[Assistant]
There are no songs where Justin Bieber is listed as a contributor using the properties "contributed to creative work" (wdt:P3919) or "contributor to the creative work or subject" (wdt:P767).

I will now try a different approach: I will search for songs where Justin Bieber is the performer (wdt:P175) and check if these songs have a writer or composer property that links back to him. This might help identify songs he wrote among those he performed.

[Tool] Got no rows and 3 columns

[Assistant] There are no songs performed by Justin Bieber that have a writer property (wdt:P50) linking to any writer. I will now check for songs performed by Justin Bieber that have a composer property (wdt:P86) linking to any composer. This might also indicate songs he wrote.

[Tool]
Got 71 rows and 3 columns, showing the first 5 and last 5 rows below:
| song                    | songLabel    | composer                         |
| ----------------------- | ------------ | -------------------------------- |
| Monster (wd:Q102104614) | Monster (en) | Shawn Mendes (wd:Q17198340)      |
| Monster (wd:Q102104614) | Monster (en) | Justin Bieber (wd:Q34086)        |
| Anyone (wd:Q104612841)  | Anyone (en)  | Michael Pollack (wd:Q100597036)  |
| Anyone (wd:Q104612841)  | Anyone (en)  | Camila Cabello (wd:Q18810940)    |
| Anyone (wd:Q104612841)  | Anyone (en)  | Andrew Watt (wd:Q24005266)       |
| ...                     | ...          | ...                              |
| Holy (wd:Q99441275)     | Holy (en)    | Chance the Rapper (wd:Q12470060) |
| Holy (wd:Q99441275)     | Holy (en)    | Jon Bellion (wd:Q18044284)       |
| Holy (wd:Q99441275)     | Holy (en)    | Justin Bieber (wd:Q34086)        |
| Holy (wd:Q99441275)     | Holy (en)    | Anthony M. Jones (wd:Q4772978)   |
| Holy (wd:Q99441275)     | Holy (en)    | Tommy Brown (wd:Q7819261)        |

[Assistant] There are 71 songs performed by Justin Bieber where he is listed as one of the composers. I will now write a query to get all distinct songs where Justin Bieber is a composer (wdt:P86) or writer (wdt:P50) to cover both cases comprehensively. I will also include the song labels.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?placeOfBirth ?placeOfBirthLabel ?countryOfCitizenship ?countryOfCitizenshipLabel WHERE {
  wd:Q34479 wdt:P19 ?placeOfBirth .
  wd:Q34479 wdt:P27 ?countryOfCitizenship .
  OPTIONAL {
    ?placeOfBirth rdfs:label ?placeOfBirthLabel FILTER ( LANG( ?placeOfBirthLabel ) = "en" )
  }
  OPTIONAL {
    ?countryOfCitizenship rdfs:label ?countryOfCitizenshipLabel FILTER ( LANG( ?countryOfCitizenshipLabel ) = "en" )
  }
}

Got 1 row and 4 columns:
| placeOfBirth                  | placeOfBirthLabel   | countryOfCitizenship | countryOfCitizenshipLabel |
| ----------------------------- | ------------------- | -------------------- | ------------------------- |
| Akasaka Estate (wd:Q11635414) | Akasaka Estate (en) | Japan (wd:Q17)       | Japan (en)                |

Emperor Hirohito was born at Akasaka Estate in Japan. There is no direct information about his residence, but it is likely he lived at the Akasaka Estate or other official residences in Japan.

[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 emperor hirohito live?

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

[Tool]
Example 1:
Question:
where do most of the people live in japan?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 / wdt:P279 * wd:Q515 ; wdt:P17 wd:Q17 ; wdt:P1082 ?y .
}
ORDER BY DESC ( ?y )
LIMIT 1

Using entities:
Japan, also known as State of Japan, Nippon-koku, Nippon, Nihon-koku, Nihon (wd:Q17): island country in East Asia
city, also known as cities (wd:Q515): large human settlement

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
where did pope benedict xvi live?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q2494 wdt:P551 ?x .
}

Using entities:
Benedict XVI, also known as Pope Benedict XVI, Joseph Alois Ratzinger, Pope Benedictus XVI, Benedetto XVI, Pope Emeritus Benedict XVI (wd:Q2494): pope of the Catholic Church from 2005 to 2013 (1927–2022), Latin Catholic priest, Latin Catholic bishop, Catholic theologian, transitional deacon, university teacher, religious writer, philosopher, archbishop, theologian, pianist

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

Example 3:
Question:
what government did japan have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q17 wdt:P122 ?x .
}

Using entities:
Japan, also known as State of Japan, Nippon-koku, Nippon, Nihon-koku, Nihon (wd:Q17): island country in East Asia

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

[Assistant]
From the similar examples, I see that the property "residence" (wdt:P551) is used to indicate where a person lived. Also, Emperor Hirohito should be identified as an entity in Wikidata.

Next, I will search for the entity "Emperor Hirohito" in Wikidata to get his identifier.

[Tool]
Top 10 entity alternatives:
1. Hirohito, also known as Prince Michi, Emperor Shōwa, Showa, Shōwa, Emperor Hirohito (wd:Q34479): Emperor of Japan from 1926 to 1989, military personnel, marine biologist, aristocrat, zoologist, diplomat, monarch
2. Annals of Emperor Showa, also known as Annals of Emperor Shōwa, Annals of Emperor Hirohito (wd:Q86339815)
3. Portrait of Emperor Hirohito (wd:Q120122666): photograph by Vincenzo Laviosa
4. Emperor Hirohito and Empress Nagako (wd:Q63195683)
5. 250 Shillings (Emperor Hirohito) [Num29821] (wd:Q132003665): Non-circulating coins - Milled - Somalia
6. 25 Shillings (Emperor Hirohito) [Num84930] (wd:Q132003361): Non-circulating coins - Milled - Somalia
7. emperor, also known as empress, imperator (wd:Q39018): type of monarch
8. monarch, also known as emperor, empress regnant, king, queen regnant (wd:Q116): person at the head of a monarchy
9. Emperor dragonfly, also known as Anax imperator, Blue Emperor, Emperor, Emperor Dragonfly (wd:Q527496): species of insect
10. Emperor of China, also known as Huang-ti, Huang Ti, Son of Heaven, thearch, Ti (wd:Q268218): sovereign of Imperial China

[Assistant]
The entity for Emperor Hirohito is wd:Q34479. From the similar example, the property for residence is wdt:P551.

I will now write a SPARQL query to find where Emperor Hirohito lived using the residence property.

[Tool] Got no rows and 2 columns

[Assistant]
The query returned no results for the property wdt:P551 (residence) for Emperor Hirohito. It is possible that the property used for where he lived is different or that the information is stored differently.

I will now search for properties of Emperor Hirohito related to living or residence to find the correct property.

[Tool]
Top 10 property alternatives:
1. family, also known as royal house, noble family, member of family, house, dynasty (wd:P53 as p/wdt): family, including dynasty and nobility houses. Not family name (use P734 for family name)., member of
2. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country
4. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
5. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
6. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
7. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
8. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as p/wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
9. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
10. signature, also known as signature image, image of signature, facsimiles, autograph (wd:P109 as p/wdt): image of a person's signature, image

No other items found

[Assistant]
There is no direct property related to residence or living place for Emperor Hirohito in the property search results. The property "residence" (wdt:P551) did not yield results either.

I will try to find the place of residence by looking for statements about places related to Emperor Hirohito, such as "place of residence" or "residence" in a broader sense. I will list all properties used for Emperor Hirohito to see if any property might indicate where he lived.

[Tool]
Got 1,150 rows and 3 columns, showing the first 10 rows below:
| subject              | property                         | object                                                                                |
| -------------------- | -------------------------------- | ---------------------------------------------------------------------------------------- |
| Hirohito (wd:Q34479) | instance of (wdt:P31)            | human (wd:Q5)                                                                            |
| Hirohito (wd:Q34479) | occupation (wdt:P106)            | monarch (wd:Q116)                                                                        |
| Hirohito (wd:Q34479) | sex or gender (wdt:P21)          | male (wd:Q6581097)                                                                       |
| Hirohito (wd:Q34479) | given name (wdt:P735)            | Hirohito (wd:Q11709840)                                                                  |
| Hirohito (wd:Q34479) | date of birth (wdt:P569)         | 1901-04-29T00:00:00Z (xsd:dateTime)                                                      |
| Hirohito (wd:Q34479) | Commons category (wdt:P373)      | Emperor Shōwa                                                                            |
| Hirohito (wd:Q34479) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Hirohito%20in%20dress%20uniform.jpg> |
| Hirohito (wd:Q34479) | country of citizenship (wdt:P27) | Japan (wd:Q17)                                                                           |
| Hirohito (wd:Q34479) | family name (p:P734)             | s:Q34479-A01CFA1A-BFB2-4663-A476-EE1DF0376733                                            |
| Hirohito (wd:Q34479) | part of (wdt:P361)               | Emperor Hirohito and Empress Nagako (wd:Q63195683)                                       |
| ...                  | ...                              | ...                                                                                |

[Assistant]
The list of properties for Emperor Hirohito does not show an obvious property for residence or place where he lived.

I will try a different approach and search for places associated with Emperor Hirohito using the property "place of residence" or similar properties that might be linked to him indirectly.

I will search for places where Emperor Hirohito is mentioned as a resident or similar.

[Tool]
Top 10 property alternatives:
1. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): the place where the person is or has been, resident, location
2. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): person or organization occupying property, used by
3. official residence, also known as resides at, lives in, palace, presidential mansion, presidential palace (wd:P263 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): the residence at which heads of government and other senior figures officially reside, residence
4. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): location where persons or organisations were actively participating in employment, business or other work, significant place, location
5. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): most specific known birth location of a person, animal or fictional character, location
6. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): seat of government of a country, province, state or other type of administrative territorial entity
7. capital of, also known as national capital of, parish seat of, provincial capital of, seat of, state capital (wd:P1376 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): country, state, department, canton or other administrative division of which the municipality is the governmental seat, located in the administrative territorial entity
8. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): the object is a country that recognizes the subject as its citizen, country
9. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): subject's ordinal position as qualitatively evaluated relative to other members of a group
10. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object

[Assistant]
The property "official residence" (wdt:P263) might be relevant for Emperor Hirohito's place of living, especially as an emperor.

I will now query for the official residence of Emperor Hirohito using this property.

[Tool] Got no rows and 2 columns

[Assistant]
The query for the official residence (wdt:P263) of Emperor Hirohito returned no results.

I will now try the property "location" (wdt:P276) which is a more general property for the location of an object or person. This might include places where he lived.

[Tool] Got no rows and 2 columns

[Assistant]
The property "location" (wdt:P276) also returned no results for Emperor Hirohito.

I will try one more property "place of residence" (wdt:P551) again but this time I will also check for any statements with qualifiers or references that might indicate residence. I will query all statements with property P551 for Emperor Hirohito.

[Tool] Got no rows and 3 columns

[Assistant]
There are no statements with the property "residence" (wdt:P551) for Emperor Hirohito in Wikidata.

Since direct properties for residence or living place are not available, I will try to find the place of birth and country of citizenship as indirect indicators of where he lived.

I will query for the place of birth (wdt:P19) and country of citizenship (wdt:P27) for Emperor Hirohito.

[Tool]
Got 1 row and 4 columns:
| placeOfBirth                  | placeOfBirthLabel   | countryOfCitizenship | countryOfCitizenshipLabel |
| ----------------------------- | ------------------- | -------------------- | ------------------------- |
| Akasaka Estate (wd:Q11635414) | Akasaka Estate (en) | Japan (wd:Q17)       | Japan (en)                |

[Assistant]
Emperor Hirohito was born at Akasaka Estate and his country of citizenship is Japan. Since there is no direct information about his residence, it is likely that he lived at the Akasaka Estate or other official residences in Japan.

I will now provide the answer with this information.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?coordinate WHERE {
  wd:Q3572 wdt:P625 ?coordinate .
}

Tunis is located at the coordinates POINT(10.180000 36.800833) on the map.

[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 is tunis on a map?

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

[Tool]
Example 1:
Question:
where is burundi located on a map?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q967 wdt:P625 ?x .
}

Using entities:
Burundi, also known as bi, Gouvernement du Burundi, Republic of Burundi, Republika y'Uburundi (wd:Q967): sovereign state in Africa

Using properties:
coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

Example 2:
Question:
what is the currency used in tunisia?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q948 wdt:P38 ?x .
}

Using entities:
Tunisia, also known as Republic of Tunisia, tn, TUN, Tunisian Republic (wd:Q948): country in North Africa

Using properties:
currency, also known as base currency, used money (wd:P38 as wdt): currency used by item, uses

Example 3:
Question:
who is ruling tunisia now?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q948 wdt:P6 ?x .
}

Using entities:
Tunisia, also known as Republic of Tunisia, tn, TUN, Tunisian Republic (wd:Q948): country in North Africa

Using properties:
head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson

[Assistant]
The similar example shows that to find the location of a place on a map, we can query the coordinate location (property P625) of the entity representing that place.

Now, I will search for the entity "Tunis" to confirm its identifier in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Tunis (wd:Q3572): capital of Tunisia
2. French protectorate of Tunisia, also known as Protectorat français de Tunisie, Tunis, Tunisia (wd:Q2017684): 1881-1956 monarchy in Northern Africa
3. Tunis, also known as American Tunis (wd:Q7853119): sheep breed
4. Tunis (wd:Q14222206): village in the Egyptian depression of el-Faiyum
5. Tunis (wd:Q12203526): village in Sohag Governorate, Egypt
6. Tunis (wd:Q96411338): census Designated Place in Texas, United States
7. TUNIS (wd:Q7672034): UNIX-like operating system developed at the University of Toronto
8. Tunis (wd:Q26199309): human settlement in Oman
9. Tunis (wd:Q47457038): given name
10. Tunis (wd:Q16902089): human settlement in Unorganized North Cochrane District, Ontario, Canada

[Assistant]
The entity "Tunis" as the capital of Tunisia is identified as wd:Q3572 in Wikidata.

Next, I will query the coordinate location (property P625) of Tunis (wd:Q3572) to find its location on a map.

[Tool]
Got 1 row and 1 column:
| coordinate                                  |
| ------------------------------------------- |
| POINT(10.180000 36.800833) (geo:wktLiteral) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?language ?languageLabel WHERE {
  wd:Q96 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| language                 | languageLabel     |
| ------------------------ | ----------------- |
| Spanish (wd:Q1321)       | Spanish (en)      |
| Nahuatl (wd:Q13300)      | Nahuatl (en)      |
| Yucatec Maya (wd:Q13354) | Yucatec Maya (en) |

The main language spoken in Mexico is Spanish. Additionally, Nahuatl and Yucatec Maya are also official languages.

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

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

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

[Tool]
Example 1:
Question:
what city has the largest population in mexico?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P31 wd:Q515 ; wdt:P17 wd:Q96 ; wdt:P1082 ?y .
}
ORDER BY DESC ( ?y )
LIMIT 1

Using entities:
city, also known as cities (wd:Q515): large human settlement
Mexico, also known as US of M, United Mexican States, MXN, MX, Mexican United States (wd:Q96): country in North America

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
what is the main language spoken in switzerland?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q39 wdt:P37 ?x .
}

Using entities:
Switzerland, also known as Confoederatio Helvetica, Swiss, Swiss Confederation (wd:Q39): country in Central Europe

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

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

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

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

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

[Assistant] From the examples, it is clear that the property "official language" (wdt:P37) is used to find the main language spoken in a country. Mexico is represented by wd:Q96. I will now write a SPARQL query to get the main language spoken in Mexico using this property.

[Tool]
Got 3 rows and 2 columns:
| language                 | languageLabel     |
| ------------------------ | ----------------- |
| Spanish (wd:Q1321)       | Spanish (en)      |
| Nahuatl (wd:Q13300)      | Nahuatl (en)      |
| Yucatec Maya (wd:Q13354) | Yucatec Maya (en) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?invention ?inventionLabel WHERE {
  ?invention wdt:P61 wd:Q762 .
  OPTIONAL {
    ?invention rdfs:label ?inventionLabel FILTER ( LANG( ?inventionLabel ) = "en" )
  }
}
LIMIT 10

Got 8 rows and 2 columns:
| invention                                 | inventionLabel                   |
| ----------------------------------------- | -------------------------------- |
| Leonardo's fighting vehicle (wd:Q1124226) | Leonardo's fighting vehicle (en) |
| Leonardo's robot (wd:Q148270)             | Leonardo's robot (en)            |
| sfumato (wd:Q210788)                      | sfumato (en)                     |
| wd:Q24941172                              |                                  |
| Clavi-viola (wd:Q3680332)                 | Clavi-viola (en)                 |
| Coulomb friction (wd:Q473991)             | Coulomb friction (en)            |
| Sasso Cavallo (wd:Q56103093)              | Sasso Cavallo (en)               |
| Leonardo's crossbow (wd:Q871725)          | Leonardo's crossbow (en)         |

Some of Leonardo da Vinci's most famous inventions include Leonardo's fighting vehicle, Leonardo's robot, sfumato, Clavi-viola, Coulomb friction, Sasso Cavallo, and Leonardo's crossbow.

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

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

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

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

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

[User] what are some of leonardo da vinci most famous inventions?

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

[Tool]
Example 1:
Question:
what inventions did da vinci made?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P61 wd:Q762 .
}

Using entities:
Leonardo da Vinci (wd:Q762): Italian Renaissance polymath (1452−1519), architectural draftsperson, civil engineer, visual artist, mathematician, caricaturist, physiologist, philosopher, astronomer, architect, scientist, zoologist, anatomist, physicist, polymath, botanist, engineer, designer, composer, sculptor, inventor, diplomat, chemist, painter, writer

Using properties:
discoverer or inventor, also known as inventor or discoverer, inventor, invented by, invented, introduced by (wd:P61 as wdt): subject who discovered, first described, invented, or developed this discovery or invention or scientific hypothesis or theory

Example 2:
Question:
what kind of art did leonardo da vinci produce?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q762 wdt:P101 ?x .
}

Using entities:
Leonardo da Vinci (wd:Q762): Italian Renaissance polymath (1452−1519), architectural draftsperson, civil engineer, visual artist, mathematician, caricaturist, physiologist, philosopher, astronomer, architect, scientist, zoologist, anatomist, physicist, polymath, botanist, engineer, designer, composer, sculptor, inventor, diplomat, chemist, painter, writer

Using properties:
field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as wdt): specialization of a person or organization; see P106 for the occupation, interested in

Example 3:
Question:
what city was leonardo da vinci from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q762 wdt:P19 ?x .
}

Using entities:
Leonardo da Vinci (wd:Q762): Italian Renaissance polymath (1452−1519), architectural draftsperson, civil engineer, visual artist, mathematician, caricaturist, physiologist, philosopher, astronomer, architect, scientist, zoologist, anatomist, physicist, polymath, botanist, engineer, designer, composer, sculptor, inventor, diplomat, chemist, painter, 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

[Assistant]
The first example shows a query that retrieves inventions associated with Leonardo da Vinci using the property "discoverer or inventor" (wdt:P61). This property seems relevant to our question about his famous inventions.

I will now construct a SPARQL query to get some of Leonardo da Vinci's inventions using this property. I will also try to get the labels of the inventions for better readability.

[Tool]
Got 8 rows and 2 columns:
| invention                                 | inventionLabel                   |
| ----------------------------------------- | -------------------------------- |
| Leonardo's fighting vehicle (wd:Q1124226) | Leonardo's fighting vehicle (en) |
| Leonardo's robot (wd:Q148270)             | Leonardo's robot (en)            |
| sfumato (wd:Q210788)                      | sfumato (en)                     |
| wd:Q24941172                              |                                  |
| Clavi-viola (wd:Q3680332)                 | Clavi-viola (en)                 |
| Coulomb friction (wd:Q473991)             | Coulomb friction (en)            |
| Sasso Cavallo (wd:Q56103093)              | Sasso Cavallo (en)               |
| Leonardo's crossbow (wd:Q871725)          | Leonardo's crossbow (en)         |

[Assistant] I found some of Leonardo da Vinci's inventions such as Leonardo's fighting vehicle, Leonardo's robot, sfumato, Clavi-viola, Coulomb friction, Sasso Cavallo, and Leonardo's crossbow. Some items do not have English labels. I will now finalize the answer with these results.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?year WHERE {
  ?season wdt:P3450 wd:Q265538 ; wdt:P1346 wd:Q461595 ; wdt:P585 ?year .
}
ORDER BY ?year

The Atlanta Braves won the World Series in the years 1957, 1995, and 2021.

[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] when the atlanta braves won the world series?

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

[Tool]
Example 1:
Question:
when did braves win the world series?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P3450 wd:Q265538 ; wdt:P1346 wd:Q461595 ; wdt:P585 ?x .
}

Using entities:
World Series, also known as Fall Classic (wd:Q265538): Championship of Major League Baseball
Atlanta Braves, also known as Beaneaters, Rustlers, Milwaukee Braves, Doves, Braves (wd:Q461595): Major League Baseball team in Cumberland, Georgia

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
sports season of league or competition, also known as sport season, seasons of, edition of annual athletics meeting, season of sports tournament, season of sports league (wd:P3450 as wdt): property that shows the competition of which the item is a season. Use P5138 for "season of club or team"., part of
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 2:
Question:
what years did the atlanta braves win the world series?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P3450 wd:Q265538 ; wdt:P1346 wd:Q461595 ; wdt:P585 ?x .
}

Using entities:
World Series, also known as Fall Classic (wd:Q265538): Championship of Major League Baseball
Atlanta Braves, also known as Beaneaters, Rustlers, Milwaukee Braves, Doves, Braves (wd:Q461595): Major League Baseball team in Cumberland, Georgia

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
sports season of league or competition, also known as sport season, seasons of, edition of annual athletics meeting, season of sports tournament, season of sports league (wd:P3450 as wdt): property that shows the competition of which the item is a season. Use P5138 for "season of club or team"., part of
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

Example 3:
Question:
what year did florida marlins win the world series?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?y wdt:P3450 wd:Q265538 ; wdt:P1346 wd:Q652879 ; wdt:P585 ?x .
}

Using entities:
World Series, also known as Fall Classic (wd:Q265538): Championship of Major League Baseball
Miami Marlins, also known as Florida Marlins, Marlins (wd:Q652879): baseball team and Major League Baseball franchise in Miami, Florida, United States

Using properties:
winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
sports season of league or competition, also known as sport season, seasons of, edition of annual athletics meeting, season of sports tournament, season of sports league (wd:P3450 as wdt): property that shows the competition of which the item is a season. Use P5138 for "season of club or team"., part of
point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

[Assistant]
From the similar examples, I see that the World Series is represented by wd:Q265538 and the Atlanta Braves by wd:Q461595. The property for the winner of a competition is wdt:P1346, the property for the sports season of a league or competition is wdt:P3450, and the property for the point in time is wdt:P585.

I will now write a SPARQL query to find the years when the Atlanta Braves won the World Series using these entities and properties.

[Tool]
Got 3 rows and 1 column:
| year                                |
| ----------------------------------- |
| 1957-01-01T00:00:00Z (xsd:dateTime) |
| 1995-01-01T00:00:00Z (xsd:dateTime) |
| 2021-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?subclass ?subclassLabel WHERE {
  wd:Q39285 wdt:P279 ?subclass .
  OPTIONAL {
    ?subclass rdfs:label ?subclassLabel FILTER ( LANG( ?subclassLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| subclass            | subclassLabel |
| ------------------- | ------------- |
| dog (wd:Q144)       | dog (en)      |
| terrier (wd:Q38984) | terrier (en)  |

Staffordshire Bull Terriers (wd:Q39285) are a subclass of dogs (wd:Q144) and terriers (wd:Q38984). So, Staffordshire Terriers are a type of dog, specifically a type of terrier breed.

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

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

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

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

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

[User] what are staffordshire terriers?

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

[Tool]
Example 1:
Question:
what are american staffordshire terriers?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q39285 wdt:P279 ?x .
}

Using entities:
Staffordshire Bull Terrier, also known as Staffbull, Staffie (wd:Q39285): dog breed

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

Example 2:
Question:
what are king charles spaniels?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q38132 wdt:P279 ?x .
}

Using entities:
King Charles Spaniel, also known as English Toy Spaniel (wd:Q38132): dog breed

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

[Assistant]
The examples suggest that "what are [dog breed]?" questions can be answered by finding the "subclass of" (wdt:P279) of the dog breed entity. So, to answer "what are staffordshire terriers?" I need to:

1. Identify the Wikidata entity for Staffordshire Terrier.
2. Query the "subclass of" property for that entity to find what kind of thing it is (e.g., dog breed).

I will first search for the entity "Staffordshire Terrier" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. American Staffordshire Terrier, also known as AmStaff, AST (wd:Q37688): dog breed
2. Staffordshire Bull Terrier, also known as Staffbull, Staffie (wd:Q39285): dog breed
3. Irish Staffordshire Bull Terrier, also known as Irish Bull Terrier (wd:Q21170233): dog breed
4. Myotonia in a Staffordshire terrier (wd:Q71829041): scientific article published on 01 July 1983
5. Cerebellar Degeneration in a Mature Staffordshire Terrier (wd:Q48184564): scientific article published on September 1, 2003
6. Juvenile-onset polyneuropathy in American Staffordshire Terriers (wd:Q57469076): article
7. L-2-Hydroxyglutaric aciduria in Staffordshire Bull Terriers (wd:Q48238065): scientific article
8. L-2-hydroxyglutaric aciduria in Staffordshire bull terriers. (wd:Q43538454): scientific article published in July 2003
9. Metabolic defect in Staffordshire bull terriers. (wd:Q48712167): scientific article published in October 2001
10. Staffordshire, also known as Staffs (wd:Q23105): ceremonial county in England

[Assistant]
There are two relevant entities related to Staffordshire Terriers:
- American Staffordshire Terrier (wd:Q37688)
- Staffordshire Bull Terrier (wd:Q39285)

Since the user asked about "Staffordshire Terriers" in general, it is more likely they mean the Staffordshire Bull Terrier (wd:Q39285), which is a well-known breed. I will now query the "subclass of" property (wdt:P279) for the Staffordshire Bull Terrier entity to find what kind of thing it is.

[Tool]
Got 2 rows and 2 columns:
| subclass            | subclassLabel |
| ------------------- | ------------- |
| dog (wd:Q144)       | dog (en)      |
| terrier (wd:Q38984) | terrier (en)  |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?college ?collegeLabel WHERE {
  wd:Q213346 wdt:P69 ?college .
  ?college wdt:P31 / wdt:P279 * wd:Q38723 .
  OPTIONAL {
    ?college rdfs:label ?collegeLabel FILTER ( LANG( ?collegeLabel ) = "en" )
  }
}

John Stockton went to Gonzaga University.

[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 college did john stockton go to?

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

[Tool]
Example 1:
Question:
what position did john stockton play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q213346 wdt:P413 ?x .
}

Using entities:
John Stockton, also known as Stock, John Houston Stockton (wd:Q213346): American basketball player, basketball player

Using properties:
position played on team / speciality, also known as position (on team), speciality, specialism, position, player position (wd:P413 as wdt): position or specialism of a player on a team

Example 2:
Question:
where did david duke go to college?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q163042 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
David Duke, also known as Dorothy Vanderbilt, James Konrad, David Ernest Duke (wd:Q163042): American white nationalist and politician, politician, journalist, historian, writer
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)

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:
what college did joakim noah attend?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q311735 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
Joakim Noah, also known as Joakim Simon Noah, Joakim simon Noah (wd:Q311735): American–French basketball player, basketball player
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)

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]
From the similar examples, we see that the property "educated at" (wdt:P69) is used to find the college or higher education institution a person attended. Also, John Stockton is identified as wd:Q213346 in Wikidata.

I will now write a SPARQL query to find the college John Stockton went to using the property "educated at" (wdt:P69) and filter the results to only include higher education institutions (wd:Q38723).

[Tool]
Got 1 row and 2 columns:
| college                          | collegeLabel            |
| -------------------------------- | ----------------------- |
| Gonzaga University (wd:Q1537303) | Gonzaga University (en) |

[Tool] Stopped generation process