PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?birthDate WHERE {
  wd:Q4612 wdt:P569 ?birthDate .
}

Marlene Dietrich was born on December 27, 1901.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 was Marlene Dietrich born?

[Tool]
Top 10 entity alternatives:
1. Marlene Dietrich, also known as Dietrich, Marie Magdalene Dietrich (wd:Q4612): German and American actress and singer (1901–1992), resistance fighter, instance of human, television actor, autobiographer, stage actor, entertainer, film actor, violinist, singer, actor
2. Marlene Dietrich (wd:Q85965315): instance of dramatico-musical work, dramatico-musical work
3. Marlene Dietrich (wd:Q123596863): mural in Nordhausen, Germany, instance of mural
4. Marlene Dietrich Overseas (wd:Q24077922): album by Marlene Dietrich, instance of album
5. Marlene Dietrichstraat (wd:Q19337082): street in Almere, the Netherlands, instance of street
6. Rue Marlène Dietrich - Marlène Dietrichstraat, also known as Marlène Dietrichstraat, Rue Marlène Dietrich (wd:Q84699006): street in Jette, Belgium, instance of street
7. rue Marlène Dietrich (wd:Q110865691): street in Brest, France, instance of street
8. Marlene Dietrich au music-hall (wd:Q23816458): instance of human
9. Incontri con Marlene Dietrich (wd:Q96240628): instance of news article, article
10. Marlene Dietrich: Ein Blicke (wd:Q131576163): online exhibition from Historisches Centrum Hagen / Historical Center Hagen, instance of online exhibition

[Tool]
Got 1 row and 1 column:
| birthDate                           |
| ----------------------------------- |
| 1901-12-27T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?entity ?entityLabel ?startTime ?endTime WHERE {
  wd:Q42053 p:P1376 ?statement .
  ?statement ps:P1376 ?entity .
  OPTIONAL {
    ?statement pq:P580 ?startTime .
  }
  OPTIONAL {
    ?statement pq:P582 ?endTime .
  }
  FILTER ( ( ! BOUND( ?startTime ) || ?startTime <= "0934-12-31T23:59:59Z" ^^ xsd:dateTime ) && ( ! BOUND( ?endTime ) || ?endTime >= "0934-01-01T00:00:00Z" ^^ xsd:dateTime ) ) ?entity rdfs:label ?entityLabel FILTER ( LANG( ?entityLabel ) = "en" )
}

Yes, Isfahan was a capital in the year 934. It was the capital of the Ziyarid dynasty from 931 to 935, which includes the year 934.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Was Isfahan a capital in the year 934?

[Assistant] Step 1: Identify the entity "Isfahan" in Wikidata to get its identifier. Then, identify the property that indicates if a place was a capital and the time qualifier for the year 934. I will first search for the entity "Isfahan" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Isfahan, also known as Ispahān, Isfahān, Ispahân, Ispahan, Spahan (wd:Q42053): city in Isfahan Province, Iran, instance of city of Iran, instance of largest city, instance of global city, instance of metropolis, instance of big city
2. Isfahan (wd:Q21998562): instance of musical work/composition, 1967 jazz song by Duke Ellington
3. Isfahan (wd:Q103936201): instance of encyclopedia article, encyclopedia article
4. Isfahan Province, also known as Esfahan Province (wd:Q1367759): instance of province of Iran, province of Iran
5. Isfahan County (wd:Q1282082): county in Isfahan Province, Iran, instance of county of Iran
6. Isfahan International Airport, also known as OIFM, Shahid Beheshti Airport, Shahid Beheshti International Airport, Isfahan Airport, IFN (wd:Q1151889): instance of commercial traffic aerodrome, International airport in Isfahan, Iran, instance of international airport, instance of airport
7. Baharestan, also known as Baharestan, Isfahan (wd:Q430773): city in Isfahan Province, Iran, instance of city of Iran
8. Isfahan Metro (wd:Q5963929): rapid transit system in Isfahan, Iran, instance of rapid transit
9. Dowlatabad, also known as Shapurabad, Dowlatabad, Isfahan, Dowlatabad, Borkhar, Dowlatābād, Daulatābād (wd:Q2616779): city in Isfahan Province, Iran, instance of city of Iran
10. Isfahan Grand Bazaar, also known as Isfahan Bazaar, Grand Bazaar of Isfahan, Grand Bazaar, Isfahan, Bazaar of Isfahan (wd:Q2754230): bazaar in Isfahan, Iran, instance of bazaar

[Assistant] Isfahan is identified as wd:Q42053 (city in Isfahan Province, Iran). Next, I need to find the property that indicates if a place was a capital and the qualifier for the time period (year 934). I will search for properties related to "capital" for the entity Isfahan.

[Tool]
Top 10 property alternatives:
1. capital of, also known as national capital of, parish seat of, provincial capital of, seat of, state capital (wd:P1376 as wdt/p): country, state, department, canton or other administrative division of which the municipality is the governmental seat, subproperty of located in the administrative territorial entity
2. located in the administrative territorial entity, also known as is in the shire of, is in the voivodeship of, is in the state of, is in the territory of, is in the town of (wd:P131 as wdt/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., subproperty of location, subproperty of part of
3. country, also known as state, sovereign state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), subproperty of located in the administrative territorial entity
4. head of government, also known as head of national government, executive power headed by, mayor, premier, president (wd:P6 as wdt/p): head of the executive power of this town, city, municipality, state, country, or other governmental body, subproperty of director / manager, subproperty of chairperson
5. legislative body, also known as aboriginal council, Zila, Tehsil, rural council, representative body (wd:P194 as wdt/p): legislative body governing this entity; political institution wi...ted representatives, such as a parliament/legislature or council
6. described by source, also known as mentioned in, mentioned in news article, reviewed in, source of item, subject in (wd:P1343 as wdt/p): work where this item is described, subproperty of attested in
7. twinned administrative body, also known as partner city, partner town, sister city, sister town, twin cities (wd:P190 as wdt/p): twin towns, sister cities, twinned municipalities and other loca..., either legally or informally acknowledged by their governments
8. coordinate location, also known as gps co-ordinates, wgs 84, gps coordinates, gps location, latitude (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
9. page banner, also known as article banner, banner, Wikivoyage banner, WIR banner, Women in Red banner (wd:P948 as wdt/p): image at top of an article about the topic, mainly used by Wikivoyage and Women in Red, subproperty of image
10. official name, also known as long name, long title, name, official title, registered name (wd:P1448 as wdt/p): official name of the subject in its official language(s), subproperty of name

No other items found

[Assistant] The property "capital of" (wdt:P1376) is used to indicate the entity of which the place is the capital. To check if Isfahan was a capital in the year 934, I need to find if there is a statement with property "capital of" and a qualifier for the time period including the year 934. I will now query for statements where Isfahan (wd:Q42053) is the capital of some entity with a time qualifier that includes the year 934.

[Tool]
Got 3 rows and 4 columns:
| entity                         | entityLabel           | startTime                           | endTime                             |
| ------------------------------ | --------------------- | ----------------------------------- | ----------------------------------- |
| Isfahan County (wd:Q1282082)   | Isfahan County (en)   |                                     |                                     |
| Isfahan Province (wd:Q1367759) | Isfahan Province (en) |                                     |                                     |
| Ziyarid dynasty (wd:Q207044)   | Ziyarid dynasty (en)  | 0931-01-01T00:00:00Z (xsd:dateTime) | 0935-01-01T00:00:00Z (xsd:dateTime) |

[Assistant] The query results show that Isfahan was the capital of Isfahan County and Isfahan Province without specific time qualifiers. Additionally, Isfahan was the capital of the Ziyarid dynasty from 931 to 935, which includes the year 934. This indicates that Isfahan was indeed a capital in the year 934. I will now finalize the answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?occupation ?occupationLabel WHERE {
  wd:Q47493506 wdt:P106 ?occupation .
  OPTIONAL {
    ?occupation rdfs:label ?occupationLabel FILTER ( LANG( ?occupationLabel ) = "en" )
  }
}

Got 4 rows and 2 columns:
| occupation                    | occupationLabel      |
| ----------------------------- | -------------------- |
| writer (wd:Q36180)            | writer (en)          |
| novelist (wd:Q6625963)        | novelist (en)        |
| Bibliotherapist (wd:Q9170932) | Bibliotherapist (en) |
| bookseller (wd:Q998550)       | bookseller (en)      |

Kazuo Ishiguro's daughter, Naomi Ishiguro, has the occupations: writer, novelist, bookseller, and bibliotherapist.

What are the occupations of Kazuo Ishiguro’s daughter?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What are the occupations of Kazuo Ishiguro’s daughter?

[Assistant]
Step 1: Identify the entity "Kazuo Ishiguro" in Wikidata to get his identifier. Then find the property that links Kazuo Ishiguro to his daughter. After that, find the property that indicates the occupations of the daughter.

I will start by searching for the entity "Kazuo Ishiguro" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Kazuo Ishiguro, also known as Sir Kazuo Ishiguro, Ishiguro Kazuo (wd:Q272855): British novelist (b. 1954), science fiction writer, instance of human, screenwriter, songwriter, lyricist, novelist, author, writer
2. Kazuo Ishiguro (wd:Q41741258): instance of television series episode, Mark Lawson Talks to... episode
3. Kazuo Ishiguro (wd:Q41741136): instance of television series episode, Talking Books episode
4. Kazuo Ishiguro (wd:Q28052306): painting by Peter Edwards, instance of painting
5. Kazuo Ishiguro and Memory (wd:Q58217552): instance of scholarly article
6. Kazuo Ishiguro's uncanny science (wd:Q47765134): instance of scholarly article, scientific article
7. Kazuo Ishiguro as a World Writer (wd:Q129596462): scholarly article published in February 2018, instance of scholarly article
8. Kazuo Ishiguro’s writing of historical trauma (wd:Q120745049): scholarly article in Journal of Central South University (Social Science), no. 1, 2016, instance of scholarly article
9. Kazuo (wd:Q1737568): instance of undifferentiated Japanese kana given name, Japanese male given name (かずお), instance of male given name
10. Ishiguro, also known as 石黒, いしぐろ (wd:Q21508024): Japanese family name (石黒), instance of family name

[Assistant] I found the entity for Kazuo Ishiguro as wd:Q272855. Now I will look for properties linking Kazuo Ishiguro to his daughter. Typically, the property for child is P40. I will check if Kazuo Ishiguro has any children listed.

[Tool]
Got 1 row and 3 columns:
| subject                     | property        | object                        |
| --------------------------- | --------------- | ----------------------------- |
| Kazuo Ishiguro (wd:Q272855) | child (wdt:P40) | Naomi Ishiguro (wd:Q47493506) |

[Assistant] Kazuo Ishiguro has one child listed: Naomi Ishiguro (wd:Q47493506). Next, I will find the occupations of Naomi Ishiguro. The property for occupation is P106. I will query the occupations of Naomi Ishiguro.

[Tool]
Got 4 rows and 3 columns:
| subject                       | property              | object                        |
| ----------------------------- | --------------------- | ----------------------------- |
| Naomi Ishiguro (wd:Q47493506) | occupation (wdt:P106) | writer (wd:Q36180)            |
| Naomi Ishiguro (wd:Q47493506) | occupation (wdt:P106) | novelist (wd:Q6625963)        |
| Naomi Ishiguro (wd:Q47493506) | occupation (wdt:P106) | bookseller (wd:Q998550)       |
| Naomi Ishiguro (wd:Q47493506) | occupation (wdt:P106) | Bibliotherapist (wd:Q9170932) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?final ?finalLabel WHERE {
  ?final wdt:P31 wd:Q842375 .
  ?final wdt:P1346 wd:Q128109 .
  OPTIONAL {
    ?final rdfs:label ?finalLabel FILTER ( LANG( ?finalLabel ) = "en" )
  }
}

Yes, the Chicago Bulls have won the NBA championship at least two seasons. They won the NBA Finals 6 times: in 1991, 1992, 1993, 1996, 1997, and 1998.

Did the Chicago Bulls win at least two seasons of the NBA championship?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Did the Chicago Bulls win at least two seasons of the NBA championship?

[Assistant] To answer this question, I need to find the entity for the Chicago Bulls and then check how many times they have won the NBA championship. The NBA championship wins are usually represented by the property "NBA championship titles" or similar in Wikidata. I will first search for the Chicago Bulls entity.

[Tool]
Top 10 entity alternatives:
1. Chicago Bulls, also known as Bulls, The Bulls (wd:Q128109): American professional basketball team, instance of basketball team
2. Chicago Bulls (wd:Q5095520): sports team in the American Football League, instance of American football team
3. 2018–19 Chicago Bulls season (wd:Q54866891): NBA professional basketball team season, instance of basketball team season
4. 1997–98 Chicago Bulls season, also known as Unforgettabulls, Unforget-a-Bulls, The Last Dance (wd:Q3667625): Thirty-second NBA season, and sixth championship season, for the Chicago Bulls, instance of basketball team season
5. 2021–22 Chicago Bulls season (wd:Q107739911): NBA professional basketball team season, instance of basketball team season
6. 2022–23 Chicago Bulls season (wd:Q112725795): NBA professional basketball team season, instance of basketball team season
7. 1996–97 Chicago Bulls season, also known as Unbeliev-a-Bulls (wd:Q3465487): NBA basketball team season (won championship), instance of basketball team season
8. 1995–96 Chicago Bulls season, also known as Unstop-a-Bulls (wd:Q3667623): NBA basketball team season (won championship), instance of basketball team season
9. 2012–13 Chicago Bulls season (wd:Q3468206): NBA professional basketball team season, instance of basketball team season
10. 1968–69 Chicago Bulls season (wd:Q48846): NBA professional basketball team season, instance of basketball team season

[Assistant] The entity for the Chicago Bulls as an American professional basketball team is wd:Q128109. Now I will check how many times the Chicago Bulls have won the NBA championship. I will look for properties related to NBA championships or titles won by this team.

[Tool]
Top 10 property alternatives:
1. league or competition, also known as cup, tournament, sports league, division, competition (wd:P118 as wdt/p): league or competition in which team or player has played, or in which an event occurs, subproperty of participant in
2. home venue, also known as venue, stadium, home water, home ground, home field (wd:P115 as wdt/p): home stadium or venue of a sports team or applicable performing arts organization, subproperty of location
3. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
4. country, also known as state, sovereign state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), subproperty of located in the administrative territorial entity
5. owned by, also known as provenance, shareholder, stockholder, stockholders, shareholders (wd:P127 as wdt/p): owner of the subject
6. head coach, also known as senior coach, team manager, trainer, coached by, led by (wd:P286 as wdt/p): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person
7. Flashscore.com team ID, also known as flashscore.com/team/, FlashScore.com team ID, https://www.flashscore.com/team/, www.flashscore.com/team/ (wd:P7876 as wdt/p): identifier for a sports team in the FlashScore.com database
8. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
9. hashtag, also known as Flickr tag, Twitter hashtag, tag (wd:P2572 as wdt/p): reference keyword associated with this item for online communiti...lude the "#" symbol which is often used as a prefixed indicator)
10. headquarters location, also known as headquarters locality, headquarters town, HQ, HQ location, location of headquarters (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, subproperty of significant place

No other items found

[Assistant] There is no direct property for NBA championship wins for the Chicago Bulls entity. I will try to find the NBA championships won by the Chicago Bulls by looking for the NBA championship events and checking the winners. The NBA championship event is likely an instance of a sports competition or tournament. I will search for NBA championships and then filter for those won by the Chicago Bulls.

[Tool]
Top 10 entity alternatives:
1. NBA Finals, also known as Basketball Association of Americ...ld Championship Series [1947-49], NBA World Championship Series [until 1987], NBA title, National Basketball Association ...Championship Series [until 1987], BAA World Championship Series [1947-49] (wd:Q842375)
2. Bill Russell NBA Finals Most Valuable Player Award, also known as NBA Finals MVP Award, NBA Finals MVP, NBA Finals Most Valuable Player Award, NBA Finals Most Valuable Player, Bill Russell NBA Finals MVP Award (wd:Q739499): instance of most valuable player award, award
3. 2022 NBA Finals (wd:Q112083544): 2022 edition of the NBA Finals, instance of NBA Finals
4. 2019 NBA Finals (wd:Q63940565): 2019 edition of the NBA Finals, between the Golden State Warriors and the Toronto Raptors, instance of NBA Finals
5. NBA conference finals, also known as National Basketball Association conference finals (wd:Q2628047): Eastern and Western championship series of the NBA
6. 2008 NBA Finals (wd:Q2036161): 2008 basketball championship series, instance of NBA Finals
7. 2023 NBA Finals (wd:Q118126592): 2023 edition of the NBA Finals, instance of NBA Finals
8. 2021 NBA Finals (wd:Q107377340): 2021 edition of the NBA Finals between the Phoenix Suns and Milwaukee Bucks, instance of NBA Finals
9. 2011 NBA Finals (wd:Q1320332): 2011 basketball championship series, instance of NBA Finals
10. 2012 NBA Finals (wd:Q2628013): 2012 basketball championship series, instance of NBA Finals

[Assistant] The NBA Finals entity is wd:Q842375, which represents the NBA championship series. I will now look for instances of NBA Finals and check which teams won those finals, filtering for the Chicago Bulls as the winner.

[Tool]
Got 6 rows and 2 columns:
| final                         | finalLabel           |
| ----------------------------- | -------------------- |
| 1991 NBA Finals (wd:Q4586976) | 1991 NBA Finals (en) |
| 1992 NBA Finals (wd:Q4587787) | 1992 NBA Finals (en) |
| 1993 NBA Finals (wd:Q4588662) | 1993 NBA Finals (en) |
| 1996 NBA Finals (wd:Q4591549) | 1996 NBA Finals (en) |
| 1997 NBA Finals (wd:Q4592651) | 1997 NBA Finals (en) |
| 1998 NBA Finals (wd:Q4593827) | 1998 NBA Finals (en) |

[Assistant] The Chicago Bulls won the NBA Finals 6 times: in 1991, 1992, 1993, 1996, 1997, and 1998. Since they have won at least two seasons of the NBA championship, the answer to the question is "Yes." I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inception WHERE {
  wd:Q237 wdt:P571 ?inception .
}

Vatican City was created on February 11, 1929.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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 was the Vatican City created?

[Assistant] To answer the question about the creation date of Vatican City, I need to find the entity representing Vatican City in Wikidata and then look for properties related to its creation or establishment date. Common properties for such information include "inception" (P571) or "date of establishment." I will start by searching for the entity Vatican City.

[Tool]
Top 10 entity alternatives:
1. Vatican City, also known as The Vatican, vat, va, Status Civitatis Vaticanae, Vatican City State (wd:Q237): Holy See's independent state, an enclave within Rome, Italy, instance of institutional complex, instance of Mediterranean country, instance of tourist destination, instance of tourist attraction, instance of confessional state, instance of landlocked country, instance of religious complex, instance of sacerdotal state, instance of sovereign state, instance of city-state, instance of urban area, instance of enclave, instance of country
2. Vatican City (wd:Q32039279)
3. Vatican City (wd:Q11327751): instance of World Heritage Site
4. Vatican City (wd:Q57225134): scientific article published on 01 November 1984, instance of scholarly article
5. Vatican City (wd:L254104): instance of endocentric compound
6. Vatican City (wd:L254104-F1)
7. flag of Vatican City, also known as Vatican City flag, Vatican flag, flag of the Vatican, flag of the Holy See (wd:Q79198): national flag of the Vatican City State and flag representing the Holy See, instance of religious flag, instance of national flag
8. Pontifical Swiss Guard, also known as Vatican City. Cohors elvetic, Vatican City. Schweizergarde, Swiss Guard, Pontificia Cohors Helvetica (wd:Q171504)
9. History of Vatican City, also known as the Vatican City history, Vatican City history (wd:Q1444151): instance of history of a country or state, aspect of history
10. geography of the Vatican City, also known as geography of Vatican City, Vatican City geography, the Vatican City geography (wd:Q2303997)

[Assistant] The most relevant entity for Vatican City is wd:Q237. Now I will look for properties related to the creation or establishment date of this entity, such as "inception" (P571) or similar.

[Tool]
Got 1 row and 3 columns:
| subject                | property             | object                              |
| ---------------------- | -------------------- | ----------------------------------- |
| Vatican City (wd:Q237) | inception (wdt:P571) | 1929-02-11T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?movement ?movementLabel WHERE {
  wd:Q5588 wdt:P135 ?movement .
  OPTIONAL {
    ?movement rdfs:label ?movementLabel FILTER ( LANG( ?movementLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| movement                   | movementLabel      |
| -------------------------- | ------------------ |
| magic realism (wd:Q147516) | magic realism (en) |
| surrealism (wd:Q39427)     | surrealism (en)    |

Frida Kahlo is linked to the surrealism art movement (wd:Q39427) as well as magic realism. Therefore, Frida Kahlo was a surrealist.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] Was Frida Kahlo a surrealist?

[Assistant]
To answer the question "Was Frida Kahlo a surrealist?" I need to check if Frida Kahlo is associated with the surrealism art movement in Wikidata. This can be done by checking if Frida Kahlo (the entity) has a property linking her to the surrealism movement or if she is described as a surrealist artist.

Step 1: Identify the entity for Frida Kahlo in Wikidata.
Step 2: Identify the entity for surrealism or surrealist movement in Wikidata.
Step 3: Check if Frida Kahlo is linked to surrealism or described as a surrealist.

I will start by searching for the entity of Frida Kahlo.

[Tool]
Top 10 entity alternatives:
1. Frida Kahlo, also known as Magdalena Carmen Frida Kahlo y Calderon, Frida Rivera-Kahlo, Magdalena Carmen Frida Kahlo Calderón, Magdalena Carmen Frida Kahlo Calderon, Mrs. Diego Rivera (wd:Q5588): Mexican painter (1907–1954), instance of human, visual artist, painter
2. Frida Kahlo (wd:Q3753166): 1982 documentary film, instance of film
3. Frida Kahlo (wd:Q3753167): instance of short film, 1971 short film
4. Frida Kahlo (wd:Q114939448): instance of version, edition or translation, 2022 Czech book edition
5. Frida Kahlo (wd:Q93654089): scientific article published in July 2005, instance of scholarly article
6. Frida Kahlo (wd:Q87280641): instance of radio series episode, episode of In Our Time
7. Frida Kahlo (wd:Q64157542): photograph in the National Gallery of Art (NGA 196156), instance of photograph
8. Frida Kahlo (wd:Q47513638): painting by Magda Pach, instance of painting
9. Frida Kahlo (wd:Q45838834): instance of scholarly article, scientific article
10. Frida Kahlo (wd:Q125964181): 2019 edition of written work by Joana Bonet Camprubí, instance of version, edition or translation

[Assistant] The entity for Frida Kahlo as a Mexican painter and visual artist is wd:Q5588. Next, I will search for the entity representing the surrealism art movement or surrealist to check if Frida Kahlo is linked to it.

[Tool]
Top 10 entity alternatives:
1. surrealism (wd:Q39427): international cultural movement that began in the early 1920s, instance of cultural movement, instance of art movement, instance of art style
2. Surrealism? (wd:Q27627635): instance of travelling exhibition
3. surrealism (wd:Q115125070): instance of literary movement, surrealism in literature
4. Surrealism (wd:Q87347219): instance of radio series episode, episode of In Our Time
5. Surrealism (wd:Q50385277): painting by Eugene Carchesio, instance of painting
6. Surrealism (wd:Q103969076): instance of encyclopedia article, encyclopedia article
7. surrealism (wd:L1314230-S1)
8. surrealism (wd:L1314231-S1)
9. surrealism (wd:L328863)
10. surrealism (wd:L328863-F1)

[Assistant] The entity for the surrealism art movement is wd:Q39427. Now, I will check if Frida Kahlo (wd:Q5588) is linked to the surrealism movement (wd:Q39427) or if she is described as a surrealist artist. I will look for statements about her movement or style.

[Tool]
Got 2 rows and 3 columns:
| subject                | property            | object                     |
| ---------------------- | ------------------- | -------------------------- |
| Frida Kahlo (wd:Q5588) | movement (wdt:P135) | magic realism (wd:Q147516) |
| Frida Kahlo (wd:Q5588) | movement (wdt:P135) | surrealism (wd:Q39427)     |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?manager ?managerLabel WHERE {
  wd:Q536656 wdt:P1037 ?manager .
  OPTIONAL {
    ?manager rdfs:label ?managerLabel FILTER ( LANG( ?managerLabel ) = "en" )
  }
}

The manager of the operator of Neumayer III (Alfred Wegener Institute for Polar and Marine Research) is Antje Boetius.

Who is the manager of the operator of Neumayer III?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Who is the manager of the operator of Neumayer III?

[Assistant]
Step 1: Identify the entity "Neumayer III" in Wikidata to get its identifier. Then find the operator of Neumayer III. After that, find the manager of that operator.

I will start by searching for the entity "Neumayer III" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Neumayer-Station III, also known as Neumayer Station III, Georg von Neumayer Station (wd:Q7933): instance of Antarctic research station, German Antarctic research station
2. Neumayer Station III Magnetic Observatory, also known as Neumayer Station Ⅲ Magnetic Observatory (wd:Q59508593): instance of magnetic observatory, magnetic observatory
3. 3, also known as M2, III, Ⅲ, 3 (number), number 3 (wd:Q201): instance of non-negative integer, instance of near-square prime, instance of triangular number, instance of Fibonacci number, instance of factorial prime, instance of primorial prime, instance of Fibonacci prime, instance of Mersenne number, instance of power of three, instance of harshad number, instance of natural number, instance of Mersenne prime, instance of Cantor number, instance of Euclid number, instance of Fermat number, instance of prime number, instance of odd number, natural number
4. 3, also known as III (wd:Q19636): instance of year, year
5. Abyan Governorate, also known as Abyan, Third Governorate, Muḩāfaz̧at Abyan, III, Governorate Number Three (wd:Q241774): instance of governorate of Yemen, governorate of Yemen
6. 9351 Neumayer, also known as Neumayer (wd:Q530314): instance of asteroid, asteroid
7. double-stranded RNA virus, also known as 3, dsRNA virus, III, virus group III (wd:Q3307900)
8. Gulfstream III, also known as Grumman G-1159A Gulfstream III, III, Gulfstream Aerospace Gulfstream III, G-III, G-1159A (wd:Q1554307)
9. Georg von Neumayer, also known as George Balthazar von Neumayer, Neumayer, Georg Balthazar von Neumayer (wd:Q67471): German polar explorer and scientist (1826-1909), instance of human, meteorologist, geophysicist, physicist, explorer, botanist
10. Neumayer (wd:Q1341221): instance of impact crater, lunar impact crater

[Assistant] The entity "Neumayer III" corresponds to "Neumayer-Station III" with the Wikidata ID wd:Q7933. Next, I will find the operator of Neumayer-Station III (wd:Q7933).

[Tool]
Got 1 row and 2 columns:
| operator                                                            | operatorLabel                                               |
| ------------------------------------------------------------------- | ----------------------------------------------------------- |
| Alfred Wegener Institute for Polar and Marine Research (wd:Q536656) | Alfred Wegener Institute for Polar and Marine Research (en) |

[Assistant] The operator of Neumayer-Station III (Neumayer III) is the Alfred Wegener Institute for Polar and Marine Research (wd:Q536656). Now, I will find the manager of this operator (wd:Q536656).

[Tool]
Got 1 row and 2 columns:
| manager                    | managerLabel       |
| -------------------------- | ------------------ |
| Antje Boetius (wd:Q101405) | Antje Boetius (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?language ?languageLabel WHERE {
  wd:Q36 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| language         | languageLabel |
| ---------------- | ------------- |
| Polish (wd:Q809) | Polish (en)   |

The language spoken in Poland is Polish.

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What language do they speak in Poland ?

[Assistant]
To answer the question "What language do they speak in Poland?" I need to find the entity for Poland and then find the property that indicates the language(s) spoken there. Typically, the property for official language or languages spoken in a country is used.

Step 1: Search for the entity "Poland" in Wikidata.
Step 2: Identify the property for languages spoken or official language(s) of Poland.
Step 3: Query the language(s) spoken in Poland.

I will start by searching for the entity "Poland" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Poland, also known as Republic of Poland (wd:Q36): instance of country bordering the Baltic Sea, instance of sovereign state, instance of successor state, country in Central Europe, instance of country
2. Polish–Lithuanian Commonwealth, also known as Poland-Lithuania, Republic of Both Nations, Polish-Lithuanian Commonwealth, Poland–Lithuania, Poland (wd:Q172107): historical multinational monarchist state in Central Europe (Crown of the Kingdom of Poland and the Grand Duchy of Lithuania), instance of historical country
3. Crown of the Kingdom of Poland, also known as Kingdom of Poland, Poland (wd:Q171348): 1385–1795 territorial possessions of the King of Poland, instance of historical country, instance of Crown land, instance of realm
4. Poland, also known as Poland, OH, Poland, Ohio (wd:Q2300093): village in Ohio, United States, instance of village of Ohio
5. Poland, also known as Poland, Maine, Poland, ME (wd:Q1572109): town in Androscoggin County, Maine, United States, instance of town in the United States
6. Polish, also known as Poland (wd:Q385059)
7. Poland, also known as Boran (wd:Q5763870): instance of village, village in Kiribati
8. Poland, also known as Poland, NY, Poland, Chautauqua County, New York, Poland, New York (wd:Q7208883): town in Chautauqua County, New York, United States, instance of town in the United States, instance of town of New York
9. Poland, also known as Poland, NY, Poland, Herkimer County, New York (wd:Q7208887): village in Herkimer County, New York, United States, instance of village in the United States, instance of village of New York
10. Poland - The Warsaw Concert, also known as Poland (wd:Q599300): album by Tangerine Dream, instance of album

[Assistant]
The entity for Poland as a country is wd:Q36. Next, I need to find the property that indicates the language(s) spoken or official language(s) of Poland. Common properties for languages spoken in a country include "official language" (wdt:P37) or "language spoken" (wdt:P1412).

I will check the official languages of Poland using the property wdt:P37.

[Tool]
Got 1 row and 2 columns:
| language         | languageLabel |
| ---------------- | ------------- |
| Polish (wd:Q809) | Polish (en)   |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?author ?authorLabel WHERE {
  wd:Q565911 wdt:P50 ?author .
  OPTIONAL {
    ?author rdfs:label ?authorLabel FILTER ( LANG( ?authorLabel ) = "en" )
  }
}

The author of Anne of Green Gables (wd:Q565911) is Lucy Maud Montgomery (wd:Q273034).

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] Who wrote Anne of Green Gables ?

[Assistant]
To answer the question "Who wrote Anne of Green Gables?", I need to identify the entity for "Anne of Green Gables" and find the property that links it to its author. Typically, the property for author or writer in Wikidata is "author" (wdt:P50).

Step 1: Search for the entity "Anne of Green Gables" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Anne of Green Gables, also known as Anne of Green Gables book 1, Anne Shirley book 1 (wd:Q565911): novel by Lucy Maud Montgomery; first in a series, instance of literary work
2. Anne Shirley, also known as Carrots, Cordelia Fitzgerald, Anne of Green Gables (wd:Q771213): Fictional girl and woman, protagonist of the eponymous series, introduced 1908, instance of children's book series, instance of television character, instance of literary character, instance of fictional orphan, instance of fictional human, instance of film character, school teacher, writer
3. Anne of Green Gables, also known as Akage no Anne (wd:Q514043): instance of anime television series, Japanese anime television series
4. Anne of Green Gables (wd:Q264901): 1985 television miniseries directed by Kevin Sullivan, instance of television film broadcast in two parts, instance of miniseries
5. Anne of Green Gables (wd:Q565949): 1919 film by William Desmond Taylor, instance of film
6. Anne of Green Gables (wd:Q384524): 1934 film by George Nicholls, Jr., instance of film
7. Anne of Green Gables, also known as the Anne of Green Gables books, the Anne of Green Gables series, the Anne Shirley books series (wd:Q3209782): series of children's novels, instance of book series
8. Anne of Green Gables (wd:Q4768923): instance of miniseries, 1972 miniseries
9. Anne of Green Gables (wd:Q4768924): 1956 TV movie directed by Don Harron, instance of television film
10. Anne of Green Gables (wd:Q30900241): instance of manga series, Japanese manga series

[Assistant] The entity "Anne of Green Gables" as a novel is identified as wd:Q565911. Now, I will look for the author (property wdt:P50) of this entity.

[Tool]
Got 1 row and 2 columns:
| author                            | authorLabel               |
| --------------------------------- | ------------------------- |
| Lucy Maud Montgomery (wd:Q273034) | Lucy Maud Montgomery (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?country ?countryLabel WHERE {
  wd:Q130736 wdt:P17 ?country .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| country            | countryLabel  |
| ------------------ | ------------- |
| Pakistan (wd:Q843) | Pakistan (en) |

Nanga Parbat is located in the country Pakistan.

In which country is the Nanga Parbat located?

[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 with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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] In which country is the Nanga Parbat located?

[Assistant] To answer the question "In which country is the Nanga Parbat located?", I will first search for the entity "Nanga Parbat" in Wikidata to get its identifier. Then, I will query the country in which this mountain is located.

[Tool]
Top 10 entity alternatives:
1. Nanga Parbat, also known as Diamer, Diamir (wd:Q130736): mountain in Pakistan; 9th highest in the world, instance of mountain, instance of summit
2. Nanga Parbat (wd:Q329340): 2010 film by Joseph Vilsmaier, instance of film
3. Nanga Parbat (wd:Q1964581): album by Reinhard Mey, instance of album
4. Nanga Parbat, also known as IMO 8903129 (wd:Q115573644): LPG carrier built in 1991, instance of LPG carrier
5. Nanga Parbat Mountain (wd:Q6963447): mountain on border of Alberta and British Columbia in Canada, instance of mountain
6. Nanga Parbat 1953 (wd:Q21035708): 1953 film by Hans Ertl, instance of film
7. Berge Nanga Parbat, also known as IMO 9578622 (wd:Q117026914): bulk carrier built in 2011, instance of bulk carrier
8. Nanga Parbat, 1937-38 (wd:Q70317004): scientific article published on 01 January 1983, instance of scholarly article
9. Nanga Parbat Adventure (wd:Q60115620): scientific article published in Nature, instance of scholarly article
10. 2013 Nanga Parbat massacre (wd:Q13530561): terrorist attack in Pakistan on June 22, 2013, instance of terrorist attack

[Assistant] The entity "Nanga Parbat" as a mountain in Pakistan is identified as wd:Q130736. Now I will query the country in which this mountain is located.

[Tool]
Got 1 row and 2 columns:
| country            | countryLabel  |
| ------------------ | ------------- |
| Pakistan (wd:Q843) | Pakistan (en) |

[Tool] Stopped generation process