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 ?school ?schoolLabel WHERE {
  wd:Q6711 wdt:P69 ?school .
  OPTIONAL {
    ?school rdfs:label ?schoolLabel FILTER ( LANG( ?schoolLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| school                                   | schoolLabel                     |
| ---------------------------------------- | ------------------------------- |
| Eastside High School (wd:Q14705485)      | Eastside High School (en)       |
| wd:Q49088                                | Columbia University (en)        |
| Montclair State University (wd:Q5018694) | Montclair State University (en) |

Allen Ginsberg attended Eastside High School.

Which High School did Allen Ginsberg attend?

[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] Which High School did Allen Ginsberg attend?

[Assistant]
Step 1: Identify the entity "Allen Ginsberg" in Wikidata to get his unique identifier. Then, find the property related to the high school or secondary school he attended.

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

[Tool]
Top 10 entity alternatives:
1. Allen Ginsberg, also known as Irwin Allen Ginsberg (wd:Q6711): American poet and writer (1926–1997), instance of human, autobiographer, photographer, screenwriter, playwright, musician, diarist, teacher, writer, poet
2. Allen Ginsberg (wd:Q102249265): Ph.D. Rutgers University, New Brunswick 1986, instance of human
3. Allen Ginsberg Library (wd:Q117833470): this entity is a library in the state of colorado. it was identified and verified by the chr task force, instance of library
4. Untitled (Allen Ginsberg) (wd:Q76558858): print in the National Gallery of Art (NGA 155538), instance of work of art
5. Allen Ginsberg no number (wd:Q64163104): photograph in the National Gallery of Art (NGA 88333), instance of photograph
6. Allen Ginsberg no number (wd:Q64163625): photograph in the National Gallery of Art (NGA 88331), instance of photograph
7. Allen Ginsberg no number (wd:Q64163102): photograph in the National Gallery of Art (NGA 88336), instance of photograph
8. Allen Ginsberg no number (wd:Q64163079): photograph in the National Gallery of Art (NGA 88334), instance of photograph
9. Allen Ginsberg no number (wd:Q64162725): photograph in the National Gallery of Art (NGA 88330), instance of photograph
10. Allen Ginsberg no number (wd:Q64162264): photograph in the National Gallery of Art (NGA 88332), instance of photograph

[Assistant] The entity for Allen Ginsberg is wd:Q6711. Now I will search for properties related to the high school or secondary school he attended. Common properties for educational institutions attended are "educated at" (P69) or "educated at" related properties. I will check the properties of Allen Ginsberg to find the relevant one.

[Tool]
Top 10 property alternatives:
1. movement, also known as artistic movement, trend, subculture, scientific movement, school (wd:P135 as wdt/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
2. educated at, also known as graduate of, place of education, school attended, schooled at, college attended (wd:P69 as wdt/p): educational institution attended by subject, subproperty of affiliation
3. occupation, also known as avocation, career, craft, employ, employment (wd:P106 as wdt/p): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
4. notable work, also known as representative of artwork type, representative work, significance, significant works, they created (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
5. official website, also known as site (official), site official, URL, URL (official), URL official (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, subproperty of URL
6. official shop URL, also known as website, web shop URL, URL shop, official store website, store URL (wd:P10225 as wdt/p): official URL where the agent sells products/merchandise, subproperty of official website
7. field of work, also known as scientific discipline, purpose, scientific area, responsible for, research on (wd:P101 as wdt/p): specialization of a person or organization; see P106 for the occupation, subproperty of interested in
8. languages spoken, written or signed, also known as language written, wrote language, writes language, uses language, used language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
9. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, subproperty of country
10. Bandcamp profile ID, also known as Bandcamp label ID, Bandcamp person ID, bc, Bandcamp group ID, Bandcamp ID (wd:P3283 as wdt/p): identifier for an artist (person or group) or record label, in the Bandcamp database

No other items found

[Assistant] The property "educated at" (wdt:P69) is the relevant property for the educational institution attended by Allen Ginsberg. I will now query for the high school or secondary school Allen Ginsberg attended using this property.

[Tool]
Got 3 rows and 2 columns:
| school                                   | schoolLabel                     |
| ---------------------------------------- | ------------------------------- |
| Eastside High School (wd:Q14705485)      | Eastside High School (en)       |
| wd:Q49088                                | Columbia University (en)        |
| Montclair State University (wd:Q5018694) | Montclair State University (en) |

[Assistant]
Allen Ginsberg attended Eastside High School. He also attended Columbia University and Montclair State University, but the high school he attended is Eastside High School.

I will now provide the final 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 ?opposite ?oppositeLabel WHERE {
  wd:Q204 wdt:P461 ?opposite .
  OPTIONAL {
    ?opposite rdfs:label ?oppositeLabel FILTER ( LANG( ?oppositeLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| opposite           | oppositeLabel |
| ------------------ | ------------- |
| zero (wd:Q204)     | zero (en)     |
| infinity (wd:Q205) | infinity (en) |

The opposites of zero (wd:Q204) according to Wikidata are zero itself and infinity (wd:Q205).

[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 opposites of zero?

[Assistant]
The question "What are the opposites of zero?" can be interpreted in different ways depending on the context. In mathematics, the opposite of a number is typically its additive inverse, which when added to the original number results in zero. The additive inverse of zero is zero itself. However, if the question is about numbers that are opposite in some other sense (e.g., positive and negative numbers opposite to zero), it might refer to numbers that are not zero, such as positive and negative numbers.

To clarify, I will first check the concept of "opposite" related to zero in Wikidata to see if there is a specific property or concept that defines opposites of zero. Then I will check if there are any entities or concepts listed as opposites of zero.

[Tool]
Top 10 entity alternatives:
1. zero, also known as oh, o, zip, null, nought (wd:Q204): integer number zero; neutral element for the addition, instance of non-positive integer, instance of non-negative integer, instance of automorphic number, instance of additive identity, instance of Fibonacci number, instance of natural number, instance of 0 number class, instance of even number, instance of integer
2. Goku, also known as Son Gokū, Kakarotto, Son Goku, Zero, Gokū (wd:Q2142): protagonist of the Dragon Ball franchise, instance of fictional humanoid, instance of manga character, instance of anime character, instance of film character, amateur wrestler, martial artist, warrior, farmer
3. Mitsubishi A6M Zero, also known as Navy Type 0 Carrier Fighter, Reisen, Type 0, Zeke, A6M Zero (wd:Q40156)
4. zero of a function, also known as zero, x-intercept, root of a function, root (wd:Q214604)
5. Fatal Frame, also known as Project Zero, Zero (wd:Q586400): instance of video game series, video game series
6. Fatal Frame, also known as Zero, Project Zero (wd:Q2323933): instance of video game, 2001 video game
7. Zero (wd:Q30327528): 2018 film by Aanand L. Rai, instance of film
8. Lelouch Lamperouge, also known as Zero, Lulu, Lelouch vi Britannia (wd:Q952328): fictional main character of the anime series Code Geass: Lelouch of the Rebellion, instance of fictional vigilante, instance of manga character, instance of anime character, instance of fictional human, revolutionary, mech pilot, politician, vigilante, terrorist
9. Zero (wd:Q3048189): instance of video game character, instance of fictional android, instance of fictional robot, fictional Mega Man robot, swordfighter
10. zero, also known as root (wd:Q588247)

[Tool]
Top 10 property alternatives:
1. opposite of, also known as is the opposite of, opposite to, opposite, is opposite of, inverse (wd:P461 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): item that is in some way the opposite of this item
2. complementary property, also known as opposite of (wd:P8882 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): property expressing opposing information (e.g. start date and en...ate each other, use P11317. For relations among items, use P461.
3. inverse property, also known as antonym, property opposite of, property inverse, opposite property, opposite of (wd:P1696 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): links a property to its inverse property (which relates the subject and object in reverse order)
4. antonym, also known as antonym of, opposite sense, is the antonym of, antonyms (wd:P5974 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): sense of a lexeme with the opposite meaning to this sense, in the same language, subproperty of opposite of
5. negates property, also known as opposite property, negated property (wd:P11317 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): this property negates the specified property, subproperty of complementary property
6. image of backside, also known as backside image, reverse, image reverse, image of reverse, image backside (wd:P7417 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): image of the reverse side of this "2D" object, subproperty of image
7. counterpart position, also known as opposite number, shadow minister for, shadow minister of, shadow position of, shadows (wd:P9547 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): ruling party position which this opposition political party position is the counterpart of or shadows
8. mathematical inverse, also known as reciprocal, opposite number, inverse element, negation, multiplicative inverse (wd:P8865 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): inverse element with respect to binary operation given as a qualifier using P2210, subproperty of opposite of
9. in opposition to, also known as against, opposed, opposes, opposition to, protested against (wd:P5004 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): object of a social action
10. statement disputed by, also known as controversy stated by, rejected by, refused by, opposed by, disputed by (wd:P1310 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): entity that disputes a given statement

[Tool]
Got 2 rows and 3 columns:
| subject        | property               | object             |
| -------------- | ---------------------- | ------------------ |
| zero (wd:Q204) | opposite of (wdt:P461) | zero (wd:Q204)     |
| zero (wd:Q204) | opposite of (wdt:P461) | infinity (wd:Q205) |

[Tool]
Got 2 rows and 2 columns:
| opposite           | oppositeLabel |
| ------------------ | ------------- |
| zero (wd:Q204)     | zero (en)     |
| infinity (wd:Q205) | infinity (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 ?event ?eventLabel WHERE {
  ?event wdt:P276 wd:Q155810 .
  ?event wdt:P31 wd:Q7998 .
  ?event wdt:P361 wd:Q69118 .
  OPTIONAL {
    ?event rdfs:label ?eventLabel FILTER ( LANG( ?eventLabel ) = "en" )
  }
}
LIMIT 1

Yes, there was at least one Formula 1 event on the AVUS. The 1959 German Grand Prix, which was part of the 1959 Formula One season, was held at AVUS.

Was there at least one Formula 1 event on the AVUS?

[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 there at least one Formula 1 event on the AVUS?

[Assistant]
To answer this question, I need to check if there was at least one Formula 1 event held at the AVUS race track.

Step 1: Identify the entity for "AVUS" in Wikidata.
Step 2: Identify the entity for "Formula 1" or "Formula 1 event" in Wikidata.
Step 3: Check if there is any event that is an instance of a Formula 1 race and took place at AVUS.

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

[Tool]
Top 10 entity alternatives:
1. AVUS (wd:Q155810): highway part and former race track, instance of road
2. Havuş, also known as Havush, Avush, Avuş (wd:Q5010570): human settlement in Azerbaijan, instance of human settlement
3. Ávus (wd:Q6474922)
4. Avus (wd:Q103485740): instance of Martian canal, Martian canal
5. Avusy, also known as Avusy GE (wd:Q68174): municipality in the canton of Geneva, Switzerland, instance of municipality of Switzerland
6. Dusicyon avus (wd:Q5815268): instance of fossil taxon, species of mammal
7. Neolophonotus avus (wd:Q10797750): instance of taxon, species of insect
8. Stenopogon avus (wd:Q10821492): instance of taxon, species of insect
9. Cyatholipus avus (wd:Q2611157): species of arachnid, instance of taxon
10. Psyllaephagus avus (wd:Q14013363): instance of taxon, species of insect

[Assistant] The entity for AVUS as a highway part and former race track is wd:Q155810. Next, I will search for the entity representing Formula 1 or Formula 1 event in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Formula One, also known as The Formula One race team, Formula 1, FIA Formula One World Championship, F1 (wd:Q1968): motorsport championship held worldwide, instance of automobile racing series, instance of world championship, instance of organization, instance of sport
2. F1, also known as Formula 1, Formula One (wd:Q114246242): 2025 film directed by Joseph Kosinski, instance of film project
3. Formula 1, also known as Formula One (wd:Q2418753): racing video game by Bizarre, instance of video game
4. Formuła, also known as F1, Formula 1 (wd:Q25413302): steel launched roller coaster in Energylandia, Poland, instance of steel roller coaster, instance of amusement ride
5. F1, also known as Formula One, Formula 1, F1 (video game series) (wd:Q1437614): video game series by Codemasters, instance of video game series
6. Gekisō! Ruben Kaiser, also known as Gekisou! Ruben Kaiser, Racing! Ruben Kaiser, Gekisou! Rubenkaiser, Gekisō! Rubenkaiser, Formula 1 (wd:Q11566079): instance of anime television series, Japanese car racing anime series
7. Formula 1 (wd:Q23724840): instance of board game, board game
8. Formula 1 (wd:Q110769473): instance of competition class, championship of waterskiing
9. Formula One car, also known as F1 car, Formula 1 car (wd:Q1002954)
10. Formula 17, also known as 17 sui de tian kong (wd:Q705380): 2004 Taiwanese film by Yin-jung Chen, instance of film

[Assistant]
The entity for the Formula One motorsport championship is wd:Q1968. Now I will check if there is any Formula 1 event (race) that took place at AVUS (wd:Q155810).

I will look for events that are instances of Formula 1 races and have a location or venue property pointing to AVUS.

[Tool] Got no rows and 2 columns

[Assistant]
There are no events that are directly instances of the Formula One motorsport championship (wd:Q1968) and have AVUS (wd:Q155810) as their location or venue.

I will try a different approach: I will look for events that are instances of Formula 1 races (which might have a different class than the overall championship) and check if any of those took place at AVUS. I will search for subclasses or instances of Formula 1 race or event.

[Tool]
Top 10 entity alternatives:
1. 1956 BRSCC Formula 1 Race (wd:Q111941479): motor car race
2. 1954 BARC Formula 1 Race (wd:Q112124848): motor car race
3. Cornwall MRC Formula 1 Race (wd:Q5171999)
4. 1955 Cornwall MRC Formula 1 Race (wd:Q4568353): instance of Cornwall MRC Formula 1 Race, motor car race
5. 1954 I Cornwall MRC Formula 1 Race (wd:Q4568072): instance of Cornwall MRC Formula 1 Race, motor car race
6. 1954 II Cornwall MRC Formula 1 Race (wd:Q4568070): instance of Cornwall MRC Formula 1 Race, motor car race
7. 2020 Bahrain Formula 3 Race 1 (wd:Q87256299): instance of postponed sports event due to the coronavirus pandemic, instance of Formula 3 race
8. 2020 Belgian Formula 3 Race 1 (wd:Q87256317): instance of Formula 3 race
9. 2020 British Formula 3 Race 1 (wd:Q87256311): instance of Formula 3 race
10. 2020 Dutch Formula 3 Race 1 (wd:Q87256302): instance of postponed sports event due to the coronavirus pandemic, instance of Formula 3 race

[Assistant]
There are many entities for specific Formula 1 races, but no clear general class for "Formula 1 race" as a subclass or instance. I will try to find the property used for "location" or "venue" in a known Formula 1 race to check if AVUS was the venue for any of them.

I will look up properties of a known Formula 1 race, for example, the "1956 BRSCC Formula 1 Race" (wd:Q111941479).

[Tool]
No property items found

No other items found

[Tool]
No property items found

No other items found

[Assistant] The known Formula 1 race entity does not have a direct property for location or venue. I will try to find properties related to "place" or "location" for this entity to see if the location is stored differently.

[Tool]
No property items found

No other items found

[Assistant] It seems that the specific Formula 1 race entity does not have location or venue properties directly. I will try a different approach: I will check if AVUS (wd:Q155810) has any related events or races linked to it. This might reveal if any Formula 1 events were held there.

[Tool]
Got 5 rows and 2 columns:
| event                                                                           | eventLabel                                                            |
| ------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| cycling at the 1936 Summer Olympics – men's team road race (wd:Q16541231)       | cycling at the 1936 Summer Olympics – men's team road race (en)       |
| cycling at the 1936 Summer Olympics – men's individual road race (wd:Q16541234) | cycling at the 1936 Summer Olympics – men's individual road race (en) |
| cycling at the 1936 Summer Olympics – men's individual road race (wd:Q16541234) | cycling at the 1936 Summer Olympics – men's individual road race (en) |
| 1959 German Grand Prix (wd:Q296461)                                             | 1959 German Grand Prix (en)                                           |
| cycling at the 1936 Summer Olympics (wd:Q682636)                                | cycling at the 1936 Summer Olympics (en)                              |

[Assistant] I found an event called "1959 German Grand Prix" (wd:Q296461) that took place at AVUS. The German Grand Prix is a Formula 1 event. To confirm, I will check if the 1959 German Grand Prix is an instance of a Formula 1 race.

[Tool] False

[Assistant]
The 1959 German Grand Prix (wd:Q296461) is not directly classified as an instance or subclass of the Formula One motorsport championship (wd:Q1968) in Wikidata. However, it is known historically as a Formula 1 event.

I will check if the 1959 German Grand Prix has a property indicating it is part of the Formula 1 World Championship or related to Formula 1.

[Tool]
Got 2 rows and 4 columns:
| property              | propertyLabel | value                               | valueLabel                   |
| --------------------- | ------------- | ----------------------------------- | ---------------------------- |
| part of (wdt:P361)    |               | 1959 Formula One season (wd:Q69118) | 1959 Formula One season (en) |
| instance of (wdt:P31) |               | German Grand Prix (wd:Q7998)        | German Grand Prix (en)       |

[Assistant]
The 1959 German Grand Prix (wd:Q296461) is part of the "1959 Formula One season" (wd:Q69118) and is an instance of the German Grand Prix (wd:Q7998). This confirms it was a Formula 1 event held at AVUS.

I will now provide the final 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 ?university ?universityLabel ?inception WHERE {
  VALUES ?university {
    wd:Q151510 wd:Q156725
  }
  ?university wdt:P571 ?inception .
  OPTIONAL {
    ?university rdfs:label ?universityLabel FILTER ( LANG( ?universityLabel ) = "en" )
  }
}
ORDER BY ?inception

Heidelberg University was founded earlier in 1386, while University of Hamburg was founded later in 1919.

Is heidelberg university or university hamburg founded earlier?

[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] Is heidelberg university or university hamburg founded earlier?

[Assistant]
To answer the question of which university was founded earlier, Heidelberg University or University of Hamburg, I need to:

1. Identify the Wikidata entities for Heidelberg University and University of Hamburg.
2. Retrieve the founding dates (inception) for both universities.
3. Compare the founding dates to determine which one was founded earlier.

I will start by searching for the Wikidata entities of both universities.

[Tool]
Top 10 entity alternatives:
1. Heidelberg University, also known as University of Heidelberg, Ruprecht-Karls-Universität Heidelberg (wd:Q151510): public research university in Heidelberg, Baden-Württemberg, Germany, instance of public research university, instance of comprehensive university, instance of University of Excellence, instance of open-access publisher, instance of public university, instance of university campus
2. Heidelberg University, also known as Heidelberg College, The 'Berg, Hiedelburg College, Hiedelberg University, Hiedelberg College (wd:Q5698693): instance of private not-for-profit educational institution, liberal arts college located in Tiffin, Ohio, USA, instance of university
3. University Hospital Heidelberg, also known as Heidelberg University Hospital (wd:Q324206): instance of university hospital, hospital in Germany
4. Heidelberg University Library, also known as UB Heidelberg, Universitätsbibliothek Heidelberg, Heidelberg UB (wd:Q880794): central scientific library providing literature and information for the whole University of Heidelberg, Germany, instance of open-access publisher, instance of scientific collection, instance of academic library, instance of building
5. University Square, also known as Universitätsplatz, Universitätsplatz, Heidelberg, University Square, Heidelberg (wd:Q1169041): square in Heidelberg, instance of square
6. SRH Hochschule Heidelberg, also known as SRH University Heidelberg (wd:Q2207707): higher education institution in Germany, instance of private university, instance of private Hochschule
7. Heidelberg Student Princes, also known as Heidelberg University athletics (wd:Q18171284): intercollegiate sports teams of Heidelberg University, instance of university and college sports club
8. Heidelberg University Archives, also known as Heidelberg University Archive (wd:Q28373153): Archives of the University of Heidelberg, Germany, instance of university archive
9. Heidelberg University Bookstore (wd:Q99434034): bookstore in Tiffin, OH, United States of America, instance of bookstore
10. Kompetenzzentrum Forschungsdaten, also known as Ruprecht-Karls-Universität Heidelberg, University of Heidelberg (wd:Q113805734): research data management, Heidelberg University, instance of organizational unit

[Tool]
Top 10 entity alternatives:
1. University of Hamburg, also known as Hamburg University, Universität Hamburg (wd:Q156725): instance of comprehensive university, instance of University of Excellence, university in Hamburg, Germany, instance of public university
2. Hamburg University of Technology, also known as Technical University of Hamburg, TUHH, TU Hamburg (wd:Q1060): instance of institute of technology, instance of public university, university in Germany
3. University Medical Center Hamburg-Eppendorf, also known as University Hospital Hamburg-Eppendorf, University of Hamburg (wd:Q316207): instance of local Internet registry, instance of university hospital, hospital
4. Forschungsdatenreferat, also known as Technical University of Hamburg, TUHH, TU Hamburg (wd:Q113678997): research data management, Hamburg University of Technology, instance of organizational unit
5. Hamburg University of Applied Sciences, also known as Fachhochschule Hamburg, HAW Hamburg (wd:Q913987): instance of university of applied sciences, instance of public educational institution, university of applied sciences in Hamburg
6. University of Hamburg main building (wd:Q19310598): instance of building complex
7. Hochschule für Musik und Theater Hamburg, also known as Hamburg University of Music and Theater, Staatliche Hochschule für Musik, Public University of Music (wd:Q700824): university of music in Hamburg, instance of public university, instance of conservatory, instance of drama school
8. Helmut Schmidt University, also known as University of the german army Hamburg, Universität der Bundeswehr Hamburg, UniBw H, Helmut-Schmidt-Universität, HSU (wd:Q530991): University of the German Armed Forces in Hamburg for the academic study of officers, instance of higher federal authority, instance of public university, instance of military school
9. Hochschule für bildende Künste Hamburg, also known as Landeskunstschule Hamburg, University of Fine Arts in Hamburg, HFBK Hamburg, Hamburger Gewerbeschule (wd:Q1622237): fine Arts University in Germany, instance of public university, instance of art academy
10. Zoological Museum Hamburg, also known as ZMUH, Zoological Museum of the University of Hamburg, Zoologisches Museum Hamburg (wd:Q220267): instance of zoological museum, museum in Hamburg

[Assistant]
I found the Wikidata entities for both universities:
- Heidelberg University: wd:Q151510
- University of Hamburg: wd:Q156725

Next, I will retrieve the founding dates (inception) for both universities using the property "inception" (wdt:P571).

[Tool]
Got 2 rows and 3 columns:
| university                         | universityLabel            | inception                           |
| ---------------------------------- | -------------------------- | ----------------------------------- |
| Heidelberg University (wd:Q151510) | Heidelberg University (en) | 1386-01-01T00:00:00Z (xsd:dateTime) |
| University of Hamburg (wd:Q156725) | University of Hamburg (en) | 1919-01-01T00:00:00Z (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 ?language ?languageLabel WHERE {
  wd:Q8409 wdt:P1412 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| language                  | languageLabel      |
| ------------------------- | ------------------ |
| Ancient Greek (wd:Q35497) | Ancient Greek (en) |

The famous king who was a student of Aristotle, Alexander the Great, spoke Ancient Greek.

What language did the famous king, who was also a student of Aristotle's , speak?

[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 did the famous king, who was also a student of Aristotle's , speak?

[Assistant]
The question refers to a famous king who was a student of Aristotle. Historically, the most well-known king who was a student of Aristotle is Alexander the Great. Alexander the Great was tutored by Aristotle during his youth.

Step 1: Identify the entity "Alexander the Great" in Wikidata.
Step 2: Identify the language(s) spoken by Alexander the Great.

I will start by searching for "Alexander the Great" in Wikidata to get his entity ID.

[Tool]
Top 10 entity alternatives:
1. Alexander the Great, also known as Eskandar, Alexandros III, Alexandros the Great, Alexandros III of Macedonia, Alexandros III of Macedon (wd:Q8409): king of Macedonia and conqueror of Achaemenid Persia (356–323 BC), instance of human, military leader, politician, monarch
2. Alexander the Great (wd:Q619228): 1956 film by Robert Rossen, instance of film
3. Alexander Hernandez, also known as Alexander Xavier Hernandez, Alexander the Great (wd:Q51883206): American mixed martial arts fighter, mixed martial arts fighter, instance of human, amateur wrestler
4. Alexander the Great (wd:Q2834226): 1980 film by Theodoros Angelopoulos, instance of film
5. Pallas Athena, also known as Alexander the Great, Olimpiadi (wd:Q3610098): painting by Rembrandt, instance of painting
6. Alexander Solonik, also known as Alexander the Great, Superkiller, Boguslav the Macedonian, Alexander Viktorovich Solonik (wd:Q4428359): Russian mobster (1960–1997), instance of human, contract killer, guard
7. Alexander the Great (wd:Q3610808): instance of musical work/composition, song by Iron Maiden
8. Alexander the Great (wd:Q4720510): 2010 film directed by Murali Nagavalli, instance of film
9. Alexander the Great, also known as Ep. 1: The Path to Power, Ep. 2: Until the End of the World (wd:Q52161725): 2014 German two-part documentary, instance of miniseries
10. Alexander the Great, also known as Александар (wd:Q4720514): instance of board game, board game

[Assistant] The entity for Alexander the Great is wd:Q8409. Now I will find the language(s) spoken by Alexander the Great (wd:Q8409). Typically, the property for languages spoken or used is "languages spoken, written or signed" (P1412). I will check this property for Alexander the Great.

[Tool]
Got 1 row and 3 columns:
| subject                        | property                                        | object                    |
| ------------------------------ | ----------------------------------------------- | ------------------------- |
| Alexander the Great (wd:Q8409) | languages spoken, written or signed (wdt:P1412) | Ancient Greek (wd:Q35497) |

[Assistant]
Alexander the Great spoke Ancient Greek (wd:Q35497).

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 ?actor ?actorLabel WHERE {
  wd:Q2518973 wdt:P175 ?actor .
  OPTIONAL {
    ?actor rdfs:label ?actorLabel FILTER ( LANG( ?actorLabel ) = "en" )
  }
}

The original actor who played Pinhead is Doug Bradley.

Who was the original actor who played Pinhead ?

[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 was the original actor who played Pinhead ?

[Assistant]
To answer the question "Who was the original actor who played Pinhead?", I need to identify the character "Pinhead" and find the actor who originally portrayed this character.

Step 1: Search for the entity "Pinhead" in Wikidata to confirm the character and get its identifier.
Step 2: Find the property that links the character to the actor who played the role.
Step 3: Determine the original actor, which usually means the first actor who played the role.

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

[Tool]
Top 10 entity alternatives:
1. Pinhead (wd:Q2518973): fictional character from the Hellraiser series, instance of fictional human, instance of film character, instance of Cenobite, priest
2. pnhd, also known as im:7138239, pinhead, YF-398 (wd:Q29771926): protein-coding gene in the species Danio rerio, instance of gene
3. Pinhead (wd:Q114487428): instance of television series episode, episode of Romeo! (S2 E4)
4. Pinhead, also known as pnhd, uncharacterized protein LOC497366, im:7138239 (wd:Q29832145): protein found in Danio rerio, instance of protein
5. pinhead (wd:L325493-F1)
6. pinhead (wd:L325493)
7. Pinhead Gunpowder (wd:Q1073468): instance of musical group, American punk rock band
8. Sorocephalus teretifolius, also known as Pinhead clusterhead (wd:Q18082625): instance of taxon, species of plant
9. Pinhead Records (wd:Q2095714): instance of record label, Argentinian music label
10. steel-cut oats, also known as coarse oatmeal, pinhead oats, Irish oatmeal (wd:Q7605615)

[Assistant]
The entity "Pinhead" as a fictional character from the Hellraiser series is identified as wd:Q2518973. Now, I will look for the property that links this character to the actor who played the role. Typically, the property "portrayed by" (P725) is used for this purpose.

I will query the actors who portrayed Pinhead (wd:Q2518973) and check the earliest actor to identify the original one.

[Tool] Got no rows and 5 columns

[Assistant]
The query to find actors who portrayed Pinhead using property P725 did not return any results. This suggests that the property "portrayed by" (P725) might not be used for this character or the data might be stored differently.

I will try another approach: I will look for films where Pinhead (wd:Q2518973) is a character and then find the actors who played roles in those films, filtering for the character Pinhead.

I will first check the properties of the entity Pinhead to see if there is any property linking it to actors or films.

[Tool]
Got 95 rows and 3 columns, showing the first 10 rows below:
| subject               | property                    | object                                                                                |
| --------------------- | --------------------------- | ------------------------------------------------------------------------------------------- |
| Pinhead (wd:Q2518973) | instance of (wdt:P31)       | fictional human (wd:Q15632617)                                                              |
| Pinhead (wd:Q2518973) | occupation (wdt:P106)       | priest (wd:Q42603)                                                                          |
| Pinhead (wd:Q2518973) | sex or gender (wdt:P21)     | agender (wd:Q505371)                                                                        |
| Pinhead (wd:Q2518973) | Commons category (wdt:P373) | Pinhead (Hellraiser)                                                                        |
| Pinhead (wd:Q2518973) | image (wdt:P18)             | <http://commons.wikimedia.org/wiki/Special:FilePath/Comicon%20Virginia%20%28cropped%29.jpg> |
| Pinhead (wd:Q2518973) | official website (wdt:P856) | <http://www.dougbradley.com>                                                                |
| Pinhead (wd:Q2518973) | creator (wdt:P170)          | Clive Barker (wd:Q298761)                                                                   |
| Pinhead (wd:Q2518973) | performer (wdt:P175)        | Doug Bradley (wd:Q705531)                                                                   |
| Pinhead (wd:Q2518973) | conflict (wdt:P607)         | World War I (wd:Q361)                                                                       |
| Pinhead (wd:Q2518973) | present in work (wdt:P1441) | Hellraiser (wd:Q1162728)                                                                    |
| ...                   | ...                         | ...                                                                                |

[Assistant]
The property "performer" (wdt:P175) is used for Pinhead (wd:Q2518973) and it points to Doug Bradley (wd:Q705531). This likely indicates the actor who performed as Pinhead.

I will verify if Doug Bradley was the original actor by checking if he was the first to portray Pinhead in the Hellraiser film series. I will check the earliest film in which Pinhead appears and see if Doug Bradley is the actor in that film.

[Tool]
Got 1 row and 3 columns:
| film                     | filmLabel | date                                |
| ------------------------ | --------- | ----------------------------------- |
| Hellraiser (wd:Q1162728) |           | 1987-01-01T00:00:00Z (xsd:dateTime) |

[Assistant] The earliest film in which Pinhead appears is "Hellraiser" (wd:Q1162728) released in 1987. Now I will check the cast of this film to confirm if Doug Bradley (wd:Q705531) played Pinhead in this original film.

[Tool] Got no rows and 4 columns

[Assistant]
The query to find the cast of the film "Hellraiser" and check if Doug Bradley played Pinhead did not return results as expected. I will try a different approach to find the cast of the film "Hellraiser" and the characters they played.

I will query the cast (P161) of the film "Hellraiser" (wd:Q1162728) and the characters they portrayed (P4532 or P453) to find who played Pinhead.

[Tool] Got no rows and 4 columns

[Assistant]
The query to find the characters played by actors in the film "Hellraiser" did not return results. It seems the character played by actors in films might not be consistently modeled in Wikidata.

Since the property "performer" (wdt:P175) for Pinhead points to Doug Bradley, and Doug Bradley is known as the actor who originally played Pinhead, I will conclude that Doug Bradley was the original actor who played Pinhead.

I will now provide the final 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 ?location ?locationLabel WHERE {
  wd:Q54555985 wdt:P740 ?location .
  OPTIONAL {
    ?location rdfs:label ?locationLabel FILTER ( LANG( ?locationLabel ) = "en" )
  }
}

The psychedelic band Khruangbin formed in Burton.

Where did the psychedelic band “Khruangbin” form?

[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] Where did the psychedelic band “Khruangbin” form?

[Assistant]
To answer the question "Where did the psychedelic band 'Khruangbin' form?", I need to find the entity for the band "Khruangbin" in Wikidata and then look for properties related to the place or location of formation. Common properties for the formation place of a band include "place of origin" (P495), "location" (P276), or "place of formation" (P740).

Step 1: Search for the entity "Khruangbin" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Khruangbin (wd:Q54555985): American psychedelia band from Texas, instance of musical group
2. LateNightTales: Khruangbin, also known as Late Night Tales: Khruangbin (wd:Q104867603): 2020 compilation mix album by Khruangbin, instance of album
3. Khruangbin concert during Montreux Jazz Festival (wd:Q99182816): Khruangbin concert during 2019 Montreux Jazz Festival, instance of concert

[Assistant] The entity "Khruangbin" as an American psychedelia band from Texas is wd:Q54555985. Now I will look for properties related to the place or location of formation for this entity. I will check the properties of this entity to find relevant information about its formation place.

[Tool]
Top 10 property alternatives:
1. location of formation, also known as originates from, place of incorporation, place of establishment, place of formation, place of foundation (wd:P740 as wdt/p): location where a group or organization was formed, subproperty of location of creation
2. inception, also known as inception date, founding date, founded in, founded on date, founded on (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, subproperty of start time
3. has part(s), also known as has ingredient, has ingredients, had part, has member, has part (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
4. country of origin, also known as comes from, place of origin, origin country, originates from, country (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), subproperty of significant place, subproperty of country
5. work period (start), also known as start date of activity, start time for work, start working, years active, fl. (start) (wd:P2031 as wdt/p): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, subproperty of start time, subproperty of floruit

No other items found

[Assistant] The property "location of formation" (wdt:P740) is the most relevant for the place where the band was formed. I will now query the location of formation for the band Khruangbin (wd:Q54555985).

[Tool]
Got 1 row and 2 columns:
| location             | locationLabel |
| -------------------- | ------------- |
| Burton (wd:Q2222379) | Burton (en)   |

[Assistant] The psychedelic band "Khruangbin" formed in Burton. I will now provide this as the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?person ) AS ?count ) WHERE {
  ?person wdt:P166 wd:Q37922 .
  ?person wdt:P31 wd:Q5 .
}

Since its inception, 121 distinct people have won the Nobel Prize in Literature.

How many people have won the Nobel Prize in Literature since its inception?

[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] How many people have won the Nobel Prize in Literature since its inception?

[Assistant]
To answer the question "How many people have won the Nobel Prize in Literature since its inception?", I need to:

1. Identify the entity for the Nobel Prize in Literature.
2. Find all people who have won this prize.
3. Count the number of distinct people who have won it.

I will start by searching for the entity representing the Nobel Prize in Literature in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Nobel Prize in Literature, also known as Nobel Literature Prize (wd:Q37922)
2. 1901 Nobel Prize in Literature (wd:Q15918052): instance of award ceremony, instance of award
3. 2022 Nobel Prize in Literature (wd:Q112594135): literary award to French author Annie Ernaux, instance of award ceremony, instance of award
4. 2023 Nobel Prize in Literature (wd:Q115937544): instance of award ceremony, instance of award
5. 1916 Nobel Prize in Literature (wd:Q112829730): instance of award ceremony, instance of award
6. 1913 Nobel Prize in Literature (wd:Q113372309): instance of award ceremony, instance of award
7. 2012 Nobel Prize in Literature (wd:Q10845886): instance of Nobel Prize in Literature, instance of award ceremony, instance of award
8. 1908 Nobel Prize in Literature (wd:Q112731708): instance of award ceremony, instance of award
9. 1905 Nobel Prize in Literature (wd:Q112731128): instance of award ceremony, instance of award
10. 2003 Nobel Prize in Literature (wd:Q113212896): instance of award ceremony, instance of award

[Assistant] The entity for the Nobel Prize in Literature is wd:Q37922. Next, I will find all people who have won this prize by looking for entities that have received this award. Then, I will count the number of distinct people who have won it. I will write a SPARQL query to count the number of distinct laureates of the Nobel Prize in Literature.

[Tool]
Got 1 row and 1 column:
| count         |
| ------------- |
| 121 (xsd:int) |

[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:Q910199 wdt:P135 ?movement .
  OPTIONAL {
    ?movement rdfs:label ?movementLabel FILTER ( LANG( ?movementLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| movement                 | movementLabel    |
| ------------------------ | ---------------- |
| Cubism (wd:Q42934)       | Cubism (en)      |
| primitivism (wd:Q848365) | primitivism (en) |

The painting "Les Demoiselles d'Avignon" is assigned to the movements Cubism and primitivism.

Which movements is the painting “Les Demoiselles d'Avignon” assigned to?

[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] Which movements is the painting “Les Demoiselles d'Avignon” assigned to?

[Assistant] Step 1: Identify the entity for the painting "Les Demoiselles d'Avignon" in Wikidata. I will search for this painting to get its Wikidata entity ID.

[Tool]
Top 10 entity alternatives:
1. Les Demoiselles d'Avignon, also known as Les Demoiselles d’Avignon, The Young Ladies of Avignon (wd:Q910199): painting by Pablo Picasso, instance of painting
2. Picasso's Les Demoiselles d'Avignon (wd:Q38456102): scientific article published on June 2008, instance of scholarly article
3. Pastiche: Picasso's 'Les demoiselles d'Avignon' (wd:Q119665752): painting by Rob Butcher (active 2000s), The City of Liverpool College, instance of painting
4. Study for Les Demoiselles D'Avignon (wd:Q19883516): painting by Pablo Picasso, instance of painting
5. Bust (Study for "Les Demoiselles d'Avignon") (wd:Q133800138): painting by Pablo Picasso, instance of painting
6. Picasso's Les Demoiselles d'Avignon at 100 (wd:Q110893828): 2007 exhibition at Museum of Modern Art, instance of art exhibition, instance of event
7. Matisse, Picasso, and Les Demoiselles d'Avignon (wd:Q110893420): 1993 exhibition at Museum of Modern Art, instance of art exhibition, instance of event
8. LES DEMOISELLES D’AVIGNON:CORE OF PICASSO’S LABORATORY (wd:Q58549913): instance of scholarly article
9. Les Demoiselles d'Avignon: Conserving a Modern Masterpiece (wd:Q133011857): online exhibition from Museum of Modern Art (New York), instance of online exhibition
10. Les démoiselles d'Avignon and "e = m.c2" (wd:Q52373790): scientific article published in January 2002, instance of scholarly article

[Assistant]
The painting "Les Demoiselles d'Avignon" corresponds to the Wikidata entity wd:Q910199.

Step 2: Now I will find the movements (art movements) assigned to this painting. I will look for properties related to art movements for this entity.

[Tool]
Top 10 property alternatives:
1. movement, also known as artistic movement, trend, subculture, scientific movement, school (wd:P135 as wdt/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
2. location, also known as neighborhood, locale, neighbourhood, place, place held (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. depicts, also known as reproduces, portrays, represents, quotes, preserves (wd:P180 as wdt/p): entity visually depicted in an image, literarily described in a ...o an audiovisual or other medium; see also P921,  'main subject', subproperty of has part(s)
4. main subject, also known as main topic, primary subject, primary keyword, plot keyword, of (wd:P921 as wdt/p): primary topic of a work, subproperty of depicts
5. genre, also known as type of music, type of film, music genre, literary genre, kind of music (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
6. inspired by, also known as inspiration, source of inspiration (wd:P941 as wdt/p): work, human, place or event which inspired this creative work or fictional entity
7. 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
8. inception, also known as inception date, founding date, founded in, founded on date, founded on (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, subproperty of start time
9. width, also known as size, bus width, breadth (wd:P2049 as wdt/p): subproperty of length, width of an object
10. title, also known as page name, titled, original title, name, known as (wd:P1476 as wdt/p): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, subproperty of name

No other items found

[Assistant]
The property "movement" (wdt:P135) is the relevant property for the artistic movement of the painting.

Step 3: I will now query the movements assigned to the painting "Les Demoiselles d'Avignon" (wd:Q910199) using the property wdt:P135.

[Tool]
Got 2 rows and 2 columns:
| movement                 | movementLabel    |
| ------------------------ | ---------------- |
| Cubism (wd:Q42934)       | Cubism (en)      |
| primitivism (wd:Q848365) | primitivism (en) |

[Tool] Stopped generation process