PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q28865 wdt:P31 / wdt:P279 * wd:Q9143
}

Yes, Python is a kind of programming language.

Is Python a kind of Programming languages?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian 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 Python a kind of Programming languages?

[Assistant] To answer the question "Is Python a kind of Programming languages?" I need to check if Python is an instance or subclass of the class "Programming language" in Wikidata. I will first search for the entity "Python" and the entity "Programming language" to get their identifiers. Then I will check if Python is an instance or subclass of Programming language.

[Tool]
Top 10 entity alternatives:
1. Python, also known as Python language, Python.org, Python (programming), Python programming language, Python (Programming Language) (wd:Q28865): general-purpose programming language, instance of object-based language
2. Python, also known as Piton (wd:Q271218): instance of taxon, genus of reptiles
3. Python (wd:Q15721): instance of mythological Greek character, instance of mythological serpent, earth-dragon of Delphi
4. CPython, also known as Python (wd:Q599384): instance of implementation of a programming language, instance of free and open-source software, instance of reference implementation, Python reference implementation, instance of free software, instance of interpreter
5. Python, also known as Python missile, RAFAEL Python, Derby (wd:Q15728)
6. Peithon, also known as Python (wd:Q1249144): Ancient Macedonian general, instance of human, military leader, satrap
7. Python, also known as Python II (wd:Q4363952): ancient peastan Greek vase-painter of red-figure style, red-figure vase painter, Paestan vase-painter, instance of human, painter
8. Python (wd:Q3411448): 2000 film directed by Richard Clabaugh, instance of television film
9. Python, also known as Peithon (wd:Q18691580): ancient Attic-Greek potter, instance of human, Attic potter, ceramicist
10. Python of Catana, also known as Python (wd:Q7263938): poet of the time of Alexander the Great, instance of human, tragedy writer, poet

[Tool]
Top 10 entity alternatives:
1. programming language, also known as computer programming, computer programming language, computer language, coding language (wd:Q9143)
2. language-oriented programming language (wd:Q28923009)
3. programmed in, also known as language, programming language, written in (wd:P277): the programming language(s) in which the software is developed, instance of Wikidata property related to software
4. programming language (wd:L315901-S1)
5. programming language (wd:L254536-S2)
6. programming language (wd:L551138-S1)
7. programming language (wd:L546252-S2)
8. programming language (wd:L546252-S1)
9. programming language (wd:L501100-S3)
10. programming language (wd:L475355-S2)

[Assistant] The entity for Python as a programming language is wd:Q28865. The entity for programming language is wd:Q9143. Now I will check if Python (wd:Q28865) is an instance of or subclass of programming language (wd:Q9143).

[Tool] True

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?numberOfSpeakers WHERE {
  wd:Q5287 wdt:P1098 ?numberOfSpeakers .
}

Yes, more than 100,000,000 people speak Japanese. The number of speakers is approximately 128,000,000.

Do more than 100000000 people speak Japanese?

[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] Do more than 100000000 people speak Japanese?

[Assistant]
To answer the question "Do more than 100,000,000 people speak Japanese?", I need to find the number of speakers of the Japanese language.

Step 1: Identify the entity for the Japanese language.
Step 2: Find the property that indicates the number of speakers of a language.
Step 3: Retrieve the number of speakers for Japanese and compare it to 100,000,000.

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

[Tool]
Top 10 entity alternatives:
1. Japanese, also known as Japanese language, Japanesic (wd:Q5287)
2. Japanese language (wd:Q10855582): instance of school subject, school subject
3. Japanese Language (wd:Q110262521): Stack Exchange site for students, teachers, and linguists wanting to discuss the finer points of the Japanese language, instance of Stack Exchange forum site
4. Japanese (language) (wd:L612945-S1)
5. Japanese Wikipedia, also known as ja.wikipedia.org, Ja.wikipedia.org, jawp, JAWP, Jp.wikipedia.org (wd:Q177837): instance of Wikipedia language edition, Japanese-language edition of Wikipedia
6. Japanese Sign Language, also known as JSL (wd:Q35601)
7. Classical Japanese, also known as Classical Japanese language (wd:Q1332057)
8. Japanese language education, also known as teaching Japanese as a foreign language (wd:Q17214937)
9. dictionary of the Japanese language, also known as dictionary of Japanese, Japanese dictionary, Japanese language dictionary (wd:Q6158568)
10. Japanese language department (wd:Q11086620): instance of academic department

[Assistant] The entity "Japanese" with the identifier wd:Q5287 corresponds to the Japanese language. Next, I will search for properties related to the number of speakers of a language to find the relevant property for the number of speakers of Japanese.

[Tool]
Top 10 property alternatives:
1. number of speakers, writers, or signers, also known as signers of a language, speakers of language, writers of a language (wd:P1098 as wdt/p): number of people who use a language
2. indigenous to, also known as native to, originating from, spoken by (wd:P2341 as wdt/p): place or ethnic group where a language, art genre, cultural trad...biological species or variety is found (or was originally found)
3. audio, also known as audio file, audio recording, audio sample, recording, song (wd:P51 as wdt/p): relevant sound. If available, use a more specific property. Samples: "spoken text audio" (P989), "pronunciation audio" (P443)
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as wdt/p): regulatory body of a language
5. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as wdt/p): language status identifier by Ethnologue.com using EGIDS scale
6. described by source, also known as mentioned in, mentioned in news article, reviewed in, source of item, subject in (wd:P1343 as wdt/p): work where this item is described, subproperty of attested in
7. subclass of, also known as subcategory of, subgenre of, sort of, subtype of, type of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
8. ABS ASCL 2011 code, also known as 1267.0 Australian Standard Class... Languages, Second Edition, 2011, ASCL 2011 code, Australian Standard Classificati... Languages, Second Edition, 2011 (wd:P1251 as wdt/p): Numerical language identifier in the Australian Standard Classification of Languages regulated by Australian Bureau of Statistics
9. FAST ID, also known as Faceted Application of Subject Terminology ID, FAST subject heading, fst, worldcat.org/fast (wd:P2163 as wdt/p/wdtn): authority control identifier in WorldCat's “FAST Linked Data” authority file
10. native label, also known as official name, original title, official title, original label, original language label (wd:P1705 as wdt/p): label for the items in their official language  or their original language, subproperty of name

No other items found

[Assistant] The property "number of speakers, writers, or signers" (wdt:P1098) is the relevant property for the number of people who use a language. I will now write a SPARQL query to retrieve the number of speakers of the Japanese language (wd:Q5287) and check if it is more than 100,000,000.

[Tool]
Got 1 row and 1 column:
| numberOfSpeakers        |
| ----------------------- |
| 128000000 (xsd:decimal) |

[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 ?castMember ?castMemberLabel ?birthDate WHERE {
  wd:Q48817408 wdt:P161 ?castMember .
  ?castMember wdt:P569 ?birthDate .
  OPTIONAL {
    ?castMember rdfs:label ?castMemberLabel FILTER ( LANG( ?castMemberLabel ) = "en" )
  }
}
ORDER BY ?birthDate
LIMIT 1

The oldest cast member of the Netflix show "Queer Eye" is Karamo Brown, born on November 2, 1980.

Who is the oldest cast member of the Netflix show “Queer Eye” ?

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

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

No general notes for all knowledge graphs available

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

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

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

[User] Who is the oldest cast member of the Netflix show “Queer Eye” ?

[Assistant]
To answer the question "Who is the oldest cast member of the Netflix show 'Queer Eye'?", I will follow these steps:

1. Identify the entity for the Netflix show "Queer Eye" in Wikidata.
2. Find the cast members of this show.
3. Retrieve the birth dates of these cast members.
4. Determine the oldest cast member based on the birth dates.

First, I will search for the entity of the Netflix show "Queer Eye" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Queer Eye, also known as Queer Eye for the Straight Guy (wd:Q1418248): 2003 American reality television series, instance of television series
2. Queer Eye (wd:Q48817408): 2018 American television series, instance of television series
3. Queer Eye: Germany, also known as Queer Eye Germany (wd:Q111914878): German reality television series, instance of television series
4. Queer Eye, season 3 (wd:Q65590894): instance of television series season, season of television series
5. Queer Eye, season 4 (wd:Q67649081): instance of television series season, season of television series
6. Queer Eye, season 2 (wd:Q57313933): instance of television series season, season of television series
7. Queer Eye, season 1 (wd:Q57312338): instance of television series season, season of television series
8. Queer Eye, season 5 (wd:Q97455984): instance of television series season, 5th season of Netflix series
9. Queer Eye, season 6 (wd:Q110791710): instance of television series season, 6th season of Netflix series
10. Queer Eyes, Full Hearts (wd:Q18636591): instance of television series episode, episode of Modern Family

[Assistant] The Netflix show "Queer Eye" corresponds to the entity wd:Q48817408 in Wikidata. Next, I will find the cast members of this show. Typically, cast members are linked with the property "cast member" (wdt:P161). I will query for the cast members of this show.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant] The query failed due to the use of the SERVICE wikibase:label which is not supported by the QLever SPARQL endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label to get the cast member labels.

[Tool]
Got 6 rows and 3 columns:
| castMember                       | castMemberLabel        | birthDate                           |
| -------------------------------- | ---------------------- | ----------------------------------- |
| Jeremiah Brent (wd:Q29452671)    | Jeremiah Brent (en)    | 1984-11-24T00:00:00Z (xsd:dateTime) |
| Karamo Brown (wd:Q44870529)      | Karamo Brown (en)      | 1980-11-02T00:00:00Z (xsd:dateTime) |
| Bobby Berk (wd:Q49729241)        | Bobby Berk (en)        | 1982-08-25T00:00:00Z (xsd:dateTime) |
| Antoni Porowski (wd:Q50377216)   | Antoni Porowski (en)   | 1984-03-14T00:00:00Z (xsd:dateTime) |
| Jonathan Van Ness (wd:Q50379405) | Jonathan Van Ness (en) | 1987-03-28T00:00:00Z (xsd:dateTime) |
| Tan France (wd:Q50379407)        | Tan France (en)        | 1983-04-20T00:00:00Z (xsd:dateTime) |

[Assistant]
I retrieved the cast members of the Netflix show "Queer Eye" along with their birth dates. The cast members are:

- Jeremiah Brent, born 1984-11-24
- Karamo Brown, born 1980-11-02
- Bobby Berk, born 1982-08-25
- Antoni Porowski, born 1984-03-14
- Jonathan Van Ness, born 1987-03-28
- Tan France, born 1983-04-20

Next, I will determine the oldest cast member by finding the earliest birth date among them.

[Tool]
Got 1 row and 3 columns:
| castMember                  | castMemberLabel   | birthDate                           |
| --------------------------- | ----------------- | ----------------------------------- |
| Karamo Brown (wd:Q44870529) | Karamo Brown (en) | 1980-11-02T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?inception WHERE {
  wd:Q220015 wdt:P571 ?inception .
}

The magazine "Weekly Shōnen Jump" in which "Hunter x Hunter" is published was established in 1968.

when was the magazine in which hunter x hunter is published established?

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

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

No general notes for all knowledge graphs available

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

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

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

[User] when was the magazine in which hunter x hunter is published established?

[Assistant]
To answer this question, I need to find the magazine in which "Hunter x Hunter" is published and then find the establishment date of that magazine.

Step 1: Identify the entity for "Hunter x Hunter" in Wikidata.
Step 2: Find the magazine in which "Hunter x Hunter" is published.
Step 3: Find the establishment date of that magazine.

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

[Tool]
Top 10 entity alternatives:
1. Hunter × Hunter, also known as HXH, Hunter x Hunter, HUNTER × HUNTER (wd:Q696071): instance of manga series, Japanese manga series
2. Hunter × Hunter, also known as HxH, Hunter x Hunter, H×H (wd:Q70209136): 2011 Japanese anime television series, instance of anime television series
3. Hunter × Hunter, also known as Hunter x Hunter, HxH (wd:Q21232458): 1999 Japanese anime television series, instance of anime television series
4. Monster Hunter Generations, also known as Monster Hunter X (wd:Q20856979): 2015 Japanese video game, instance of video game
5. Hunter Xcite (wd:Q60767092)
6. Xavier Hunter (wd:Q100806788): basketball player, instance of human
7. Hunter × Hunter: Phantom Rouge, also known as Hunter X Hunter: Phantom Rouge (wd:Q11398237): instance of anime film, 2013 film by Yūzō Satō
8. City Hunter XYZ (wd:Q97170832): fan film by Filip Wong, instance of fan film
9. Monster Hunter Generations Ultimate, also known as Monster Hunter XX (wd:Q30302563): instance of video game, 2017 video game
10. XX: Beautiful Hunter (wd:Q8042573): 1994 film by Masaru Konuma, instance of film

[Assistant] The entity "Hunter × Hunter" as a manga series is identified as wd:Q696071. Next, I will find the magazine in which this manga series is published.

[Tool]
Got 1 row and 2 columns:
| magazine                        | magazineLabel           |
| ------------------------------- | ----------------------- |
| Weekly Shōnen Jump (wd:Q220015) | Weekly Shōnen Jump (en) |

[Assistant] The magazine in which "Hunter x Hunter" is published is "Weekly Shōnen Jump" (wd:Q220015). Now, I will find the establishment date of the magazine "Weekly Shōnen Jump."

[Tool]
Got 1 row and 1 column:
| inception                           |
| ----------------------------------- |
| 1968-01-01T00:00:00Z (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 ?genre ?genreLabel WHERE {
  wd:Q158895 wdt:P136 ?genre .
  OPTIONAL {
    ?genre rdfs:label ?genreLabel FILTER ( LANG( ?genreLabel ) = "en" )
  }
}

The music genre of Helloween is power metal, which is a subclass of rock music. Therefore, the music style of Helloween belongs to rock music.

Does the music style of Helloween belong to rock music?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian 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] Does the music style of Helloween belong to rock music?

[Assistant]
Step 1: Identify the entities and properties involved in the question.
- Entity: Helloween (a band)
- Property: music style or genre
- Entity to check: Rock music (to see if Helloween's music style belongs to this)

Step 2: Search for the entity "Helloween" in Wikidata to get its identifier.
Step 3: Search for the property related to music style or genre for the entity Helloween.
Step 4: Check if the music style or genre of Helloween is a subclass or instance of rock music.

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

[Tool]
Top 10 entity alternatives:
1. Helloween (wd:Q158895): instance of musical group, German power metal band
2. Helloween (wd:Q1300430): 1985 self-titled debut EP by Helloween, instance of extended play
3. Helloween (wd:Q105918470): 2021 studio album by Helloween, instance of album
4. The Eastern Tribute to Helloween (wd:Q10381343): album by Helloween, instance of album
5. The Keepers of Jericho: A Tribute to Helloween (wd:Q6144686): 2000 tribute album, instance of album
6. The Keepers of Jericho: A Tribute to Helloween Part II (wd:Q10381513): instance of album, album

[Assistant]
The entity "Helloween" as a German power metal band is identified as wd:Q158895.

Next, I will search for the property related to the music style or genre of Helloween (wd:Q158895). I will look for properties that describe the music genre or style of this band.

[Tool]
Top 10 property alternatives:
1. 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
2. AllMusic artist ID, also known as AllMusic, AllMusic ID, amg artist (wd:P1728 as wdt/p): identifier for an artist, musical group, or record label in the AllMusic database (record labels are listed as "artists")
3. 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
4. record label, also known as label, labels (wd:P264 as wdt/p): brand and trademark associated with the marketing of subject music recordings and music videos
5. Apple Music artist ID (U.S. version), also known as Apple Books author ID, music.apple.com/artist/, iTunes author ID, iTunes artist ID (American version), https://music.apple.com/artist/ (wd:P2850 as wdt/p): identifier for a musical artist or author in the U.S. version of Apple Music
6. Rate Your Music artist ID, also known as RateYourMusic artist ID, Rate Your Music artist identifier, RYM artist ID, RYM artist identifier (wd:P5404 as wdt/p): identifier for an artist or group on the website Rate Your Music
7. MusicBrainz artist ID, also known as MBID artist, MB artist ID (wd:P434 as wdt/p/wdtn): identifier for an artist in the MusicBrainz open music encyclopedia
8. discography, also known as discography link, recording catalog (wd:P358 as wdt/p): item for list pages with discography of artist or band, subproperty of list of works
9. Last.fm ID, also known as Lastfm ID, Last FM ID, LastFM music ID (wd:P3192 as wdt/p): identifier for an artist, group or work, on Last.fm
10. Bandsintown artist ID (wd:P7195 as wdt/p): Identifier for an artist on Bandsintown site

No other items found

[Assistant]
The property "genre" (wdt:P136) is the relevant property for music genre or style of Helloween.

Next, I will query the music genres of Helloween (wd:Q158895) using the property genre (wdt:P136) and check if any of these genres belong to rock music or are subclasses of rock music.

[Tool]
Got 1 row and 2 columns:
| genre                   | genreLabel       |
| ----------------------- | ---------------- |
| power metal (wd:Q57143) | power metal (en) |

[Assistant]
Helloween's music genre is identified as "power metal" (wd:Q57143).

Next, I will check if "power metal" is a subclass or instance of rock music or if it belongs to the rock music category. I will query the subclass hierarchy of power metal to see if rock music appears in the chain.

[Tool]
Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| superclass                       | superclassLabel        |
| -------------------------------- | ---------------------- |
| mechanical wave (wd:Q1132631)    | mechanical wave (en)   |
| rock music (wd:Q11399)           | rock music (en)        |
| sound (wd:Q11461)                | sound (en)             |
| change (wd:Q1150070)             | change (en)            |
| music (wd:Q115484611)            | music (en)             |
| ...                              | ...                    |
| structure (wd:Q6671777)          | structure (en)         |
| occurrent (wd:Q67518978)         | occurrent (en)         |
| abstract entity (wd:Q7048977)    | abstract entity (en)   |
| property (wd:Q937228)            | property (en)          |
| collective entity (wd:Q99527517) | collective entity (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?country ) AS ?count ) WHERE {
  ?country wdt:P31 wd:Q6256 ; wdt:P30 wd:Q18 .
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 10 (xsd:int) |

There are 10 countries on the continent of South America.

How many countries are on the continent of South America ?

[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 countries are on the continent of South America ?

[Assistant]
Step 1: Identify the entity for the continent "South America" in Wikidata.
Step 2: Identify the property that relates countries to continents.
Step 3: Write a SPARQL query to count the number of countries located on the continent of South America.

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

[Tool]
Top 10 entity alternatives:
1. South America, also known as S. America, South American continent, S America (wd:Q18): instance of statistical territorial entity, instance of geographic region, instance of subcontinent, instance of continent, continent
2. South America (wd:Q23639894): artwork by Aimé Millet in Paris, France, instance of statue
3. America South. (wd:Q125532381): journal article from 'The Hispanic American Historical Review' published in 1938, instance of scholarly article
4. South America. (wd:Q125530894): journal article from 'The Hispanic American Historical Review' published in 1931, instance of scholarly article
5. South America. (wd:Q125530395): journal article from 'The Hispanic American Historical Review' published in 1919, instance of scholarly article
6. South America, also known as Star Trek South America (wd:Q99290067): continent of South America as depicted in Star Trek, instance of fictional continent, instance of Star Trek location
7. South America (wd:Q56762941): instance of scholarly article
8. South America (wd:Q46148222): scientific article published in April 1927, instance of scholarly article
9. South America (wd:Q20201199): painting by John Wesley, instance of painting
10. South America (wd:Q119412766): painting by María Freire (1917–2015), Essex Collection of Art from Latin America, instance of painting

[Assistant]
The entity for the continent "South America" is wd:Q18.

Next, I will identify the property that relates countries to continents. Typically, the property "continent" (wdt:P30) is used to indicate the continent a country is located in.

I will now write a SPARQL query to count the number of countries (entities that are instances of "country" wd:Q6256) located on the continent of South America (wd:Q18).

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 10 (xsd:int) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?film ) AS ?count ) WHERE {
  ?film wdt:P144 wd:Q92582 .
  ?film wdt:P31 / wdt:P279 * wd:Q11424 .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 5 (xsd:int) |

There are 5 unique films derived from the original Ghost in the Shell manga.

How many films are derived from the original Ghost in the Shell Manga?

[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 films are derived from the original Ghost in the Shell Manga?

[Assistant]
Step 1: Identify the entity for the original "Ghost in the Shell" manga.
Step 2: Identify the property that links films derived from this manga.
Step 3: Query the number of films derived from the original manga.

I will start by searching for the entity "Ghost in the Shell manga" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Ghost in the Shell (wd:Q1066948): 1995 film by Mamoru Oshii, instance of anime film
2. Ghost in the Shell, also known as Ghost in the Shell: An IMAX 3D Experience, Ghost in the Shell 3D (wd:Q22575835): 2017 film directed by Rupert Sanders, instance of film
3. Ghost in the Shell (wd:Q92580): instance of media franchise, Japanese media franchise
4. Ghost in the Shell, also known as Kōkaku Kidōtai, Koukaku Kidoutai (wd:Q92582): instance of manga series, manga by Masamune Shirow
5. Ghost in the Shell (wd:Q259804): instance of video game, 1997 video game
6. Ghost in the Shell (wd:Q11699759): 2005 mixtape by Styles P, instance of album
7. Ghost in the shell (wd:Q124841206): scientific article published in 2021, instance of scholarly article
8. M01 Chant I - Making of Cyborg, also known as Making of Cyborg, 謡I-Making of Cyborg, Theme From Ghost In The Shell, Ghost In The Shell, Ghost in the Shell Main theme (wd:Q126960525): instance of musical work/composition, Musical composition by Kenji Kawai
9. The Ghost in the Shell, also known as Ghost in the Shell, Kōkaku Kidōtai, Koukaku Kidoutai (wd:Q126095177): 2026 Japanese anime television series, instance of anime television series
10. Ghost in the Shell 2: Innocence, also known as Innocence (wd:Q1058469): 2004 film by Mamoru Oshii, instance of anime film

[Assistant] The original "Ghost in the Shell" manga is represented by the entity wd:Q92582. Now I will find films derived from this manga. Typically, films derived from a manga are linked by properties such as "based on" (P144) or "adaptation of" (P629). I will check for films that have a "based on" or "adaptation of" property pointing to this manga.

[Tool]
Top 10 property alternatives:
1. based on, also known as modelled after, inspired by, iteration of, modeled after, input (wd:P144 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the work(s) or inputs used as the basis for subject item; for fictional analog use P1074, subproperty of references work, tradition or theory, subproperty of inspired by
2. criterion used, also known as on the basis of, reason for distinction, respectively to, with reference to, with respect to (wd:P1013 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): property by which a distinction or classification is made
3. natural product of taxon, also known as comes from (taxon), derived from, fruit of (taxon), made from, produced by taxon (wd:P1582 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): links a natural product with its source (animal, plant, fungal, algal, etc.)
4. derived from lexeme, also known as word origin, etymology, origin of a word, history of a word, derived from (wd:P5191 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): most direct lexeme(s) this one has developed from
5. after a work by, also known as inspiring artist, artist who inspired this, inspirational artist, copy of a work by, based on a work by (wd:P1877 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): artist whose work strongly inspired / was copied in this item, subproperty of significant person
6. statement supported by, also known as according to, claimed by, per, recognized by, stated by (wd:P3680 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): entity that supports a given statement
7. determination method or standard, also known as methodology, standard, rationale, method of determination, measured by (wd:P459 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): how a value is determined, or the standard by which it is declared
8. derivative work, also known as is source work of, is the basis for, iteration, reboot, remake (wd:P4969 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): new work of art (film, book, software, etc.) derived from major part of this work
9. object sense, also known as derived from sense, sense derivation (wd:P5980 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): (qualifier) statement applies only to listed senses of statement's object
10. semantic derivation of, also known as derived from sense, sense derived from sense (wd:P12410 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): links a lexeme sense to a particular sense it is derived from

[Assistant] The property "based on" (wdt:P144) is the most relevant for linking films derived from the original manga. I will now query for films that have the property "based on" pointing to the original "Ghost in the Shell" manga (wd:Q92582).

[Tool]
Got 6 rows and 1 column:
| film                                                                       |
| -------------------------------------------------------------------------- |
| Ghost in the Shell: The New Movie (wd:Q20038326)                           |
| Ghost in the Shell (wd:Q22575835)                                          |
| Ghost in the Shell 2: Innocence (wd:Q1058469)                              |
| Ghost in the Shell (wd:Q1066948)                                           |
| Ghost in the Shell: Stand Alone Complex - Solid State Society (wd:Q764897) |
| Ghost in the Shell: Stand Alone Complex - Solid State Society (wd:Q764897) |

[Assistant] I found 6 films derived from the original "Ghost in the Shell" manga according to the "based on" property. One film appears twice in the results, so there are 5 unique films. I will now finalize the answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?king ) AS ?count ) WHERE {
  ?king wdt:P39 wd:Q18384454 ; wdt:P509 ?causeOfDeath .
  FILTER ( ?causeOfDeath NOT IN ( wd:Q12136 , wd:Q18123741 , wd:Q12156 , wd:Q12202 , wd:Q183134 ) )
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 12 (xsd:int) |

There are 12 French kings who didn't die of natural causes.

How many French kings didn't die of natural causes?

[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 French kings didn't die of natural causes?

[Assistant]
To answer the question "How many French kings didn't die of natural causes?" we need to:

1. Identify the entity for "French kings."
2. Identify the property for "cause of death."
3. Identify which causes of death are considered "not natural" (e.g., assassination, execution, accident, etc.).
4. Count the number of French kings whose cause of death is not natural.

Step 1: Search for the entity representing "French kings." This could be a class or a list of individuals with the title "King of France."

Step 2: Search for the property representing "cause of death."

Step 3: Identify the values for cause of death that are not natural.

Let's start by searching for the entity "King of France."

[Tool]
Top 10 entity alternatives:
1. king of France, also known as king of the French, His Most Christian Majesty, French king (wd:Q18384454)
2. King of France and Navarre, also known as King of France (wd:Q3439798)
3. King of France (wd:Q55029477): operatic character in the opera Lear by Aribert Reimann, instance of operatic character, instance of fictional human
4. Kingdom of France, also known as Kingdom of France and Navarre, French kingdom, França e Navarra (wd:Q70972): kingdom in Western Europe (987–1792; 1815–1848), instance of historical country
5. July Monarchy, also known as Kingdom of France, Monarchie de Juillet (wd:Q58202): kingdom governing France, 1830–1848, instance of historical country, instance of historical period, instance of regime
6. Bourbon Restoration in France, also known as France, Kingdom of France (wd:Q207162): period of French history, 1814–1830, instance of historical country, instance of regime
7. French constitutional monarchy, also known as Kingdom of France (wd:Q69323): Constitutional monarchy of France (1791–1792), instance of historical country
8. Louis XIII of France, also known as King of France Louis XIII, roi de France Louis XIII, Luis XIII el Justo, Louis XIII, Louis Xiii (wd:Q737533): King of France and Navarre (r. 1610–1643), instance of human, politician
9. Louis XI of France, also known as King Louis XI, Louis XI, Louis the Prudent, King of France Louis XI (wd:Q8058): king of France from 1461 to 1483, instance of human, politician
10. Philip IV of France, also known as Philipe le Bel, Philippe le Bel, Philip IV, Philippe le Roi de fer, Philip the Fair (wd:Q130969): King of France 1285-1314, instance of human, ruler

[Assistant]
The entity "king of France" is represented by wd:Q18384454. This seems to be the best candidate for the class or entity representing French kings.

Next, I will search for the property representing "cause of death."

[Tool]
Top 10 property alternatives:
1. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
2. manner of death, also known as circumstance of death, death manner, death type, nature of death, type of death (wd:P1196 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): general circumstances of a person's death; e.g. natural causes, ...ysiological mechanism, e.g. heart attack, trauma, pneumonia etc.
3. place of death, also known as POD, location of death, killed in, died in, died (wd:P20 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, subproperty of location
4. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): total (cumulative) number of people who died since start as a direct result of an event or cause
5. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subproperty of dissolved, abolished or demolished date, date on which the subject died, subproperty of end time
6. case fatality rate, also known as case fatality, case fatality ratio, case fatality risk, CFR, fatality rate (wd:P3457 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): proportion of patients who die of a particular medical condition out of all who have this condition within a given time frame
7. death rate, also known as mortality rate (wd:P10091 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the total number of persons dead per 1,000 population per year
8. killed by, also known as slaughtered by, mortally wounded by, murdered by, murderer, shot dead by (wd:P157 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person or organization who killed the subject, subproperty of significant person
9. has effect, also known as results in, result, outcome, led to, leads to (wd:P1542 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subproperty of followed by, effect of this item
10. category for deaths in this time period (wd:P12687 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): category item for people who died in this time period, subproperty of point in time

[Assistant]
The property "cause of death" (wdt:P509) is the most relevant property for identifying the cause of death of individuals.

Next, I need to identify which causes of death are considered "not natural." I will look for values of the property "cause of death" that are not natural causes, such as assassination, execution, accident, murder, etc.

I will search for some common non-natural causes of death to use as filters. Let's start by searching for "assassination" and "execution" as possible causes of death.

[Tool]
Top 10 entity alternatives:
1. assassination, also known as political killing (wd:Q3882219)
2. Assassination (wd:Q2363323): 1987 film directed by Peter R. Hunt, instance of film
3. Assassination, also known as Amsal (wd:Q19801741): 2015 South Korean film directed by Choi Dong-hoon, instance of film
4. Assassination, also known as Ansatsu, Assassin, The Assassin, The Assassination (wd:Q4808490): 1964 film by Masahiro Shinoda, instance of film
5. AssassiNation (wd:Q3281471): instance of album, album by Krisiun
6. Assassination (wd:Q3625390): 1967 film by Emilio Miraglia, instance of film
7. Assassination (wd:Q14952600): 1927 German film directed by Richard Oswald, instance of film
8. Assassination, also known as 2 Days/1963 (wd:Q118398004): upcoming film by David Mamet, instance of film project
9. Assassination (wd:Q16125512): 1996 film directed by Nader Galal, instance of film
10. Assassination (wd:Q74048502): print in the National Gallery of Art (NGA 63635), instance of print

[Assistant]
The entity for "assassination" is wd:Q3882219.

Next, I will search for the entity "execution" to include it as another non-natural cause of death.

[Tool]
Top 10 entity alternatives:
1. capital punishment, also known as sentenced to death, execution, judicial homicide, state-sanctioned execution, death penalty (wd:Q8454)
2. implementation, also known as program implementation, realization, implementing, project implementation, carrying out (wd:Q245962)
3. execution (wd:Q3966286)
4. execution, also known as program execution, running code, running, executing code, code execution (wd:Q1077724)
5. writ of execution, also known as execution (wd:Q25593566)
6. Execution (wd:Q1232059): 1968 film by Domenico Paolella, instance of film
7. Execution (wd:Q2635645): 26th episode of the first season of The Twilight Zone, instance of television series episode
8. Execution (wd:Q3061886): Chinese art painting by Beijing artist Yue Minjun, instance of painting
9. Execution (wd:Q3284430): the first full-length album by Tribuzy., instance of album
10. execution (wd:Q24900172)

[Assistant]
The entity for "execution" as capital punishment is wd:Q8454.

Now, I will write a SPARQL query to count the number of French kings (instances or holders of the title "King of France") whose cause of death is either assassination (wd:Q3882219) or execution (wd:Q8454). I will also consider other possible non-natural causes of death by excluding natural causes if possible.

I will first check how the cause of death is recorded for French kings to understand if there are other common non-natural causes.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant]
The query failed because the SERVICE wikibase:label is not supported by the endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label for labels.

I will retry the query to get some French kings and their causes of death with labels.

[Tool]
Got 20 rows and 4 columns, showing the first 5 and last 5 rows below:
| king                              | kingLabel                 | causeOfDeath                      | causeOfDeathLabel       |
| --------------------------------- | ------------------------- | --------------------------------- | ----------------------- |
| Louis X of France (wd:Q8384)      | Louis X of France (en)    | disease (wd:Q12136)               | disease (en)            |
| Francis I of France (wd:Q129857)  | Francis I of France (en)  | disease (wd:Q12136)               | disease (en)            |
| Charles VI of France (wd:Q160349) | Charles VI of France (en) | malaria (wd:Q12156)               | malaria (en)            |
| Philip IV of France (wd:Q130969)  | Philip IV of France (en)  | stroke (wd:Q12202)                | stroke (en)             |
| Louis XI of France (wd:Q8058)     | Louis XI of France (en)   | stroke (wd:Q12202)                | stroke (en)             |
| ...                               | ...                       | ...                               | ...                     |
| Philip IV of France (wd:Q130969)  | Philip IV of France (en)  | hunting accident (wd:Q18012374)   | hunting accident (en)   |
| Charles V of France (wd:Q167782)  | Charles V of France (en)  | infectious disease (wd:Q18123741) | infectious disease (en) |
| Henry II of France (wd:Q132545)   | Henry II of France (en)   | sepsis (wd:Q183134)               | sepsis (en)             |
| Henry III of France (wd:Q53448)   | Henry III of France (en)  | stab wound (wd:Q4228595)          | stab wound (en)         |
| Henry IV of France (wd:Q936976)   | Henry IV of France (en)   | stab wound (wd:Q4228595)          | stab wound (en)         |

[Assistant]
From the results, we see that French kings have various causes of death including diseases (natural causes) and also non-natural causes like "hunting accident" and "stab wound."

To identify French kings who didn't die of natural causes, we should exclude natural causes such as diseases, infections, and strokes. Non-natural causes include accidents, wounds, assassinations, executions, etc.

I will now write a query to count French kings whose cause of death is not one of the natural causes. I will exclude common natural causes like disease, infectious disease, malaria, stroke, sepsis, etc., and count the rest.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 12 (xsd:int) |

[Assistant]
There are 12 French kings who didn't die of natural causes (excluding common natural causes like disease, infectious disease, malaria, stroke, and sepsis).

I will now provide the final answer with the SPARQL query used.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?twitterUsername WHERE {
  wd:Q163173 wdt:P2002 ?twitterUsername .
}

Got 1 row and 1 column:
| twitterUsername |
| --------------- |
| runningwildnet  |

The Twitter username of Running Wild (the heavy metal band) is 'runningwildnet'.

What is the twitter name of Running Wild?

[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 is the twitter name of Running Wild?

[Assistant] To answer the question about the Twitter name of "Running Wild," I first need to identify the entity "Running Wild" in Wikidata. "Running Wild" could refer to different things such as a band, a song, or other entities, so I will search for "Running Wild" to find the most relevant entity, likely the band. Then, I will look for the property related to the Twitter username or account for that entity. I will start by searching for the entity "Running Wild" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Running Wild, also known as Granite Heart, Rock'n Rolf (wd:Q163173): instance of heavy metal band, German heavy metal band
2. Running Wild (wd:Q20949957): 2016 film by Alex Ranarivelo, instance of film
3. Running Wild (wd:Q7379998): 1955 film by Abner Biberman, instance of film
4. Running Wild (wd:Q7379999): instance of film, 2006 film
5. Running Wild, also known as Born Wild (wd:Q4161477): 1992 film by Dee McLachlan, instance of film
6. Running Wild (wd:Q7380003): instance of literary work, novel by J. G. Ballard
7. Running Wild (wd:Q3015443): 1927 film by Gregory La Cava, instance of film
8. Running Wild (wd:Q3054134): 2009 novel by Michael Morpurgo, instance of literary work
9. Running Wild (wd:Q1469109): 1992 film by Egon Humer, instance of film
10. Running Wild (wd:Q131443100): instance of musical work/composition, 2024 single by Jin

[Assistant] The most relevant entity for "Running Wild" in the context of a Twitter name is likely the heavy metal band "Running Wild" (wd:Q163173). Next, I will look for the property that corresponds to the Twitter username or Twitter account for this entity. I will search for properties related to Twitter for the entity "Running Wild" (wd:Q163173).

[Tool]
Top 10 property alternatives:
1. X (Twitter) username, also known as TWT username, X username, X handle, x.com/, X account (wd:P2002 as wdt/p): username on X (formerly Twitter); do not include the '@' symbol, subproperty of website username or ID
2. Myspace ID, also known as Myspace, MySpace ID (wd:P3265 as wdt/p): identifier for a person or organisation, at Myspace
3. Facebook username, also known as Facebook user ID, www.facebook.com/, https://www.facebook.com/, https://m.facebook.com/, FB ID (wd:P2013 as wdt/p): identifier for an official (preferably) Facebook person, product...e (everything that follows URL part 'https://www.facebook.com/'), subproperty of website username or ID
4. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
5. 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
6. 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
7. 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
8. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
9. official name, also known as long name, long title, name, official title, registered name (wd:P1448 as wdt/p): official name of the subject in its official language(s), subproperty of name
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 for the Twitter username is "X (Twitter) username" (wdt:P2002). Now I will query the Twitter username for the entity "Running Wild" (wd:Q163173) using this property.

[Tool]
Got 1 row and 1 column:
| twitterUsername |
| --------------- |
| runningwildnet  |

[Tool] Stopped generation process