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 ?party ?partyLabel WHERE {
  wd:Q153965 wdt:P102 ?party .
  OPTIONAL {
    ?party rdfs:label ?partyLabel FILTER ( LANG( ?partyLabel ) = "en" )
  }
}

Joseph Beuys was a member of the political parties The Greens and Aktionsgemeinschaft Unabhängiger Deutscher.

Of which political party was Joseph Beuys a member?

[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] Of which political party was Joseph Beuys a member?

[Assistant] To answer the question about the political party membership of Joseph Beuys, I will first search for the entity Joseph Beuys in Wikidata to get his identifier. Then, I will look for properties related to political party membership for this entity.

[Tool]
Top 10 entity alternatives:
1. Joseph Beuys, also known as Joseph Heinrich Beuys, Joseph Beyus, Jozef Bojs, Iosef Boĭs, J. Beuys (wd:Q153965): German artist, teacher, and theorist of art (1921–1986), university teacher, performance artist, instance of human, conceptual artist, choreographer, draftsperson, photographer, illustrator, printmaker, sculptor, painter
2. Joseph Beuys (wd:Q1706617): series of portraits by Andy Warhol, instance of painting
3. Beuys, Joseph (wd:Q103967412): encyclopedia article in Benezit Dictionary of Artists (2017-12-16), instance of encyclopedia article
4. Beuys, Joseph (wd:Q103893006): encyclopedia article (2017-12-12), instance of encyclopedia article
5. Joseph Beuys (wd:Q77010447): print in the National Gallery of Art (NGA 174896), instance of print
6. Joseph Beuys (wd:Q64145826): photograph in the National Gallery of Art (NGA 134808), instance of photograph
7. Joseph Beuys (wd:Q58517008): instance of academic journal article
8. Joseph Beuys (wd:Q56229019): instance of temporary exhibition, exhibition at MHK, 1977
9. Joseph Beuys (wd:Q50315692): painting by Andy Warhol, instance of painting
10. A Joseph Beuys (wd:Q99455889): instance of sculpture

[Assistant] The entity for Joseph Beuys as a German artist is wd:Q153965. Next, I will search for properties related to political party membership for this entity. I will look for properties that might indicate political party membership.

[Tool]
Top 10 property alternatives:
1. member of political party, also known as member of party, political party member, political party, party membership, party (wd:P102 as wdt/p): the political party of which a person is or has been a member or otherwise affiliated, subproperty of member of
2. represented by, also known as manager, talent agency, representative, rep, managed by (wd:P1875 as wdt/p): person or agency that represents or manages the subject
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, subproperty of country
4. Met Constituent ID, also known as Met constituent identifier, Metropolitan Museum of Art constituent ID, Metropolitan Museum of Art constituent identifier (wd:P10882 as wdt/p): identifier from the Metropolitan Museum of Art that describes pe... institutions, or entities attributed for the creation of a work
5. 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)
6. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as wdt/p): event in which a person or organization was/is a participant; inverse of P710 or P1923, subproperty of significant event, subproperty of part of
7. place of birth, also known as location born, POB, location of birth, born in, born at (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, subproperty of location
8. date of birth, also known as year of birth, DOB, born on, birthyear, born (wd:P569 as wdt/p): date on which the subject was born, subproperty of inception
9. educated at, also known as graduate of, place of education, school attended, schooled at, college attended (wd:P69 as wdt/p): educational institution attended by subject, subproperty of affiliation
10. 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

No other items found

[Assistant] The property "member of political party" (wdt:P102) is the relevant property to find the political party membership of Joseph Beuys. I will now query the political party or parties that Joseph Beuys was a member of using this property.

[Tool]
Got 2 rows and 2 columns:
| party                                                   | partyLabel                                      |
| ------------------------------------------------------- | ----------------------------------------------- |
| The Greens (wd:Q18761823)                               | The Greens (en)                                 |
| Aktionsgemeinschaft Unabhängiger Deutscher (wd:Q422605) | Aktionsgemeinschaft Unabhängiger Deutscher (en) |

[Tool] Stopped generation process


The data for Turkish verbs with lemma and part of speech information is not well represented in Wikidata. Attempts to find lexemes with language Turkish and part of speech verb, or items that are instances or subclasses of verb with Turkish labels ending with 'uş' returned no results. Therefore, it is not possible to provide a SPARQL query that reliably returns Turkish verbs ending with 'uş' along with their lemma from Wikidata.

Find all Turkish verbs ending with “uş” with their lemma.

[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] Find all Turkish verbs ending with “uş” with their lemma.

[Assistant]
Step 1: Identify the relevant entities and properties for the query.
- We want to find Turkish verbs.
- We want verbs that end with the suffix "uş".
- We want to retrieve their lemma.

Step 2: Search for the entity representing "Turkish language" to filter verbs in Turkish.
Step 3: Search for the property representing "part of speech" to filter verbs.
Step 4: Search for the property representing "lemma" to retrieve the lemma of the verbs.
Step 5: Use a filter to find verbs whose lemma ends with "uş".

I will start by searching for the entity representing "Turkish language".

[Tool]
Top 10 entity alternatives:
1. Turkish, also known as tur, Türkçe, Turkish language, Turkish of Turkey, Anatolian Turkish (wd:Q256)
2. Turkish language (wd:L615174-S1)
3. Ottoman Turkish, also known as Ottoman language, Ottoman Turkish language, Ottoman, ota (wd:Q36730)
4. Turkish Language Association, also known as Türk Dil Kurumu (wd:Q1569712): official regulatory body of the Turkish language, instance of language regulator
5. Turkish Sign Language, also known as tsm, TİD, TID (wd:Q36885): instance of modern language, instance of sign language, sign language
6. Turkish bird language, also known as kuş dili (wd:Q64210939): whistled language in Turkey’s Eastern Black Sea Region – inscrib...st of Intangible Heritage in Need of Urgent Safeguarding in 2017, instance of whistled language
7. Turkish Language Olympiads (wd:Q1060833): instance of competition, student competition
8. Turkish language reform (wd:Q3458382): 1932–1982 government language campaign, instance of language reform
9. Turkish phonology, also known as Turkish language phonology (wd:Q3242271): phonology of the Turkish language
10. Thesaurus:penis (Turkish language), also known as Thesaurus:erkeklik organı (wd:Q30715151): instance of Wiktionary thesaurus page

[Assistant]
The entity for the Turkish language is wd:Q256.

Next, I will search for the property representing "part of speech" to filter verbs.

[Tool]
Top 10 property alternatives:
1. official language, also known as language official, language spoken, speaking, spoken in (wd:P37 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language designated as official by this item, subproperty of language used
2. usage example, also known as example sentence, sentence, use in a sentence (wd:P5831 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): example of how a lexeme (sense) might be used
3. combines lexemes, also known as contain (lexeme), prefix, contains (lexeme), has part (lexeme), have part (lexeme) (wd:P5238 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subproperty of derived from lexeme, lexemes combined in this lexeme
4. applies to part, also known as applies to aspect, applied to, when viewed as, valid for part, section (wd:P518 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): part, aspect, or form of the item to which the claim applies
5. subject lexeme, also known as lexeme, subject is about lexeme (wd:P6254 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): lexeme described or discussed in this item
6. proportion, also known as %, part, percent, ratio, share (wd:P1107 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): to be used as a qualifier, value must be between 0 and 1
7. language used, also known as working languages, working language, used language, uses language, medium of instruction (wd:P2936 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language widely used (spoken or written) in this place or at this event or organisation
8. languages spoken, written or signed, also known as language written, wrote language, writes language, uses language, used language (wd:P1412 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language(s) that a person or a people speaks, writes or signs, including the native language(s)
9. speaker, also known as speech by, spoken by (wd:P823 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person who is speaker for this event, ceremony, keynote, presentation or in a literary work, subproperty of participant, subproperty of spoken by
10. indigenous to, also known as native to, originating from, spoken by (wd:P2341 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): place or ethnic group where a language, art genre, cultural trad...biological species or variety is found (or was originally found)

[Assistant] The property "part of speech" is not directly listed in the top results. I will try searching for "part of speech" specifically as a property in Wikidata to find the exact property identifier.

[Tool]
Top 10 property alternatives:
1. official language, also known as language official, language spoken, speaking, spoken in (wd:P37 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language designated as official by this item, subproperty of language used
2. usage example, also known as example sentence, sentence, use in a sentence (wd:P5831 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): example of how a lexeme (sense) might be used
3. combines lexemes, also known as contain (lexeme), prefix, contains (lexeme), has part (lexeme), have part (lexeme) (wd:P5238 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subproperty of derived from lexeme, lexemes combined in this lexeme
4. applies to part, also known as applies to aspect, applied to, when viewed as, valid for part, section (wd:P518 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): part, aspect, or form of the item to which the claim applies
5. subject lexeme, also known as lexeme, subject is about lexeme (wd:P6254 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): lexeme described or discussed in this item
6. proportion, also known as %, part, percent, ratio, share (wd:P1107 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): to be used as a qualifier, value must be between 0 and 1
7. language used, also known as working languages, working language, used language, uses language, medium of instruction (wd:P2936 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language widely used (spoken or written) in this place or at this event or organisation
8. languages spoken, written or signed, also known as language written, wrote language, writes language, uses language, used language (wd:P1412 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language(s) that a person or a people speaks, writes or signs, including the native language(s)
9. speaker, also known as speech by, spoken by (wd:P823 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person who is speaker for this event, ceremony, keynote, presentation or in a literary work, subproperty of participant, subproperty of spoken by
10. indigenous to, also known as native to, originating from, spoken by (wd:P2341 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): place or ethnic group where a language, art genre, cultural trad...biological species or variety is found (or was originally found)

[Assistant]
I did not find a direct property named "part of speech" in the search results. I will try to find the property for "lexeme" or "grammatical category" which might be used to specify the part of speech for lexemes (words).

I will search for "lexeme" and "grammatical category" properties.

[Tool]
Top 10 property alternatives:
1. grammatical gender, also known as gender (wd:P5185 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical gender of the word
2. has grammatical gender, also known as grammatical genders, has genders (wd:P5109 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): specific form of noun class system of a language. Use qualifier ... subject of" (P805) to link to language specific item if present
3. grammatical person (wd:P11053 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical person category (first, second, third, etc.) of pronouns
4. has grammatical mood, also known as mood, mode, has grammatical mode, grammatical mood, grammatical mode (wd:P3161 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language has this grammatical mood/mode for signaling modality, subproperty of has part(s)
5. grammatical number (wd:P11054 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical number category (singular, plural, etc.) of pronouns and certain types of nouns
6. grammatical aspect, also known as aspect (wd:P7486 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical category that expresses how an action, event or stat...ow of time: perfective, progressive, prospective, habitual, etc.
7. subclass of, also known as subcategory of, subgenre of, sort of, subtype of, type of (wd:P279 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
8. has tense, also known as grammatical tense, has grammatical tense (wd:P3103 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical category expressing time reference of the language. ...eded "He sees". If an activity afterwards is needed: "He reads"., subproperty of has part(s)
9. has grammatical person, also known as uses grammatical person (wd:P5110 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical feature used in this language
10. topic's main category, also known as category for this topic, Wikipedia category, Wikimedia category, Wiki category, subject category (wd:P910 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): main Wikimedia category

[Assistant] I found properties related to grammatical categories but not directly the "part of speech" property. I will try searching for "lexeme" as an entity to see how verbs are represented in Wikidata lexemes and how their part of speech is encoded.

[Tool]
Top 10 entity alternatives:
1. lexeme, also known as lexical word (wd:Q111352)
2. Wikibase lexeme, also known as wikibase:WikibaseLexeme, Wikidata lexeme, use on lexemes, lexeme (wd:Q51885771)
3. lexeme (wd:L1328309-S1)
4. lexeme sense, also known as lexeme, sense (wd:P7018): (qualifier) lexeme version of monolingual text (linked to specific sense), instance of Wikidata qualifier
5. lexeme (wd:L989119-S1)
6. lexeme (wd:L677592-S1)
7. lexeme (wd:L730470-S1)
8. lexeme (wd:L744464-S2)
9. lexeme (wd:L1328472-S1)
10. lexeme (wd:L1329641-S1)

[Assistant]
The entity for lexeme is wd:Q111352.

I will now search for the property that indicates the part of speech of a lexeme. I will try searching for properties of the lexeme entity wd:Q111352.

[Tool]
Top 10 property alternatives:
1. subject lexeme, also known as lexeme, subject is about lexeme (wd:P6254 as wdt/p): lexeme described or discussed in this item
2. has part(s), also known as has ingredient, has ingredients, had part, has member, has part (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
3. part of, also known as meronym of, contained within, of, part of-property, section of (wd:P361 as wdt/p): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
4. facet of, also known as subject in, topic of, subtopic of, subitem of, section in (wd:P1269 as wdt/p): topic of which this item is an aspect; item that offers a broader perspective on the same topic
5. said to be the same as, also known as may be, the same as, synonymous with, same as, same (wd:P460 as wdt/p): this item is said to be the same as that item, though this may be uncertain or disputed
6. name, also known as be referred as, referred to as, label, known as, be known as (wd:P2561 as wdt/p): name the subject is known by; preferably use a more specific subproperty if available
7. contains, also known as encloses, wraps, surrounds, stores, holds (wd:P4330 as wdt/p): item or substance located within this item but not part of it
8. 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
9. different from, also known as not to be confused with, to be distinguished from, similar to, see also, rejected match (wd:P1889 as wdt/p): item that is different from another item, with which it may be confused
10. used by, also known as armiger, of, played by, user (wd:P1535 as wdt/p): item or concept that makes use of the subject (use sub-properties when appropriate)

No other items found

[Assistant]
I did not find a direct property for "part of speech" for lexemes in the search results. I will try a different approach by searching for Turkish verbs as lexemes and see how they are represented.

I will search for lexemes that are verbs in the Turkish language. I will try to find the property for "grammatical category" or "part of speech" for lexemes.

[Tool]
Top 10 property alternatives:
1. grammatical gender, also known as gender (wd:P5185 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical gender of the word
2. has grammatical gender, also known as grammatical genders, has genders (wd:P5109 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): specific form of noun class system of a language. Use qualifier ... subject of" (P805) to link to language specific item if present
3. grammatical person (wd:P11053 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical person category (first, second, third, etc.) of pronouns
4. has grammatical mood, also known as mood, mode, has grammatical mode, grammatical mood, grammatical mode (wd:P3161 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language has this grammatical mood/mode for signaling modality, subproperty of has part(s)
5. grammatical number (wd:P11054 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical number category (singular, plural, etc.) of pronouns and certain types of nouns
6. grammatical aspect, also known as aspect (wd:P7486 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical category that expresses how an action, event or stat...ow of time: perfective, progressive, prospective, habitual, etc.
7. subclass of, also known as subcategory of, subgenre of, sort of, subtype of, type of (wd:P279 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
8. has tense, also known as grammatical tense, has grammatical tense (wd:P3103 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical category expressing time reference of the language. ...eded "He sees". If an activity afterwards is needed: "He reads"., subproperty of has part(s)
9. has grammatical person, also known as uses grammatical person (wd:P5110 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): grammatical feature used in this language
10. topic's main category, also known as category for this topic, Wikipedia category, Wikimedia category, Wiki category, subject category (wd:P910 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): main Wikimedia category

[Assistant]
I found properties related to grammatical gender, person, mood, number, aspect, tense, but not a direct "grammatical category" or "part of speech" property.

I will try to find how verbs are represented as lexemes in Wikidata by searching for Turkish verbs lexemes. I will search for lexemes with language Turkish and part of speech verb. I will try to find the property for language of lexeme and part of speech of lexeme.

[Tool]
Top 10 property alternatives:
1. Sandbox-Lexeme, also known as dup of, lexeme (wd:P5188 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): Sandbox property, use this to experiment linking to Lexeme entities
2. subject lexeme, also known as lexeme, subject is about lexeme (wd:P6254 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): lexeme described or discussed in this item
3. Wikidata language code, also known as Wikidata language identifier, Wikidata language ID, Wikidata language codes, term language code, termbox language code (wd:P9753 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a language or variant at Wikidata (use P424 (Wikimedia language code) to identify the language of a Wikimedia wiki), subproperty of Wikimedia language code
4. attested in, also known as form of lexeme found in, lexeme form found in (wd:P5323 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): document or corpus where the lemma or form of a Lexeme entity, word or name is used, but not described
5. creates lexeme type, also known as forms lexeme type, lexeme type created, lexeme type formed (wd:P5923 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): morpheme creates this type of lexeme
6. model lexeme, also known as example lexeme (wd:P11464 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): model lexeme entity for a paradigm, conjugation class, etc.
7. appears after lexeme form, also known as follows lexeme form, preceded by lexeme form (wd:P11953 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): this lexeme form appears only when it comes after this other specific lexeme form
8. paradigm class, also known as declension class, form pattern, inflection class, lexeme paradigm (wd:P5911 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): paradigm this lexeme belongs to, pattern of forms of the lexeme
9. programmed in, also known as language, programming language, written in (wd:P277 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the programming language(s) in which the software is developed
10. languages spoken, written or signed, also known as language written, wrote language, writes language, uses language, used language (wd:P1412 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language(s) that a person or a people speaks, writes or signs, including the native language(s)

[Assistant]
I found the property "Wikidata language code" (wdt:P9753) which is an identifier for a language or variant at Wikidata. This might be used to specify the language of a lexeme.

I will also search for "part of speech" property for lexemes again to confirm.

[Tool]
Top 10 property alternatives:
1. official language, also known as language official, language spoken, speaking, spoken in (wd:P37 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language designated as official by this item, subproperty of language used
2. usage example, also known as example sentence, sentence, use in a sentence (wd:P5831 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): example of how a lexeme (sense) might be used
3. combines lexemes, also known as contain (lexeme), prefix, contains (lexeme), has part (lexeme), have part (lexeme) (wd:P5238 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subproperty of derived from lexeme, lexemes combined in this lexeme
4. applies to part, also known as applies to aspect, applied to, when viewed as, valid for part, section (wd:P518 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): part, aspect, or form of the item to which the claim applies
5. subject lexeme, also known as lexeme, subject is about lexeme (wd:P6254 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): lexeme described or discussed in this item
6. proportion, also known as %, part, percent, ratio, share (wd:P1107 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): to be used as a qualifier, value must be between 0 and 1
7. language used, also known as working languages, working language, used language, uses language, medium of instruction (wd:P2936 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language widely used (spoken or written) in this place or at this event or organisation
8. languages spoken, written or signed, also known as language written, wrote language, writes language, uses language, used language (wd:P1412 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): language(s) that a person or a people speaks, writes or signs, including the native language(s)
9. speaker, also known as speech by, spoken by (wd:P823 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): person who is speaker for this event, ceremony, keynote, presentation or in a literary work, subproperty of participant, subproperty of spoken by
10. indigenous to, also known as native to, originating from, spoken by (wd:P2341 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): place or ethnic group where a language, art genre, cultural trad...biological species or variety is found (or was originally found)

[Assistant]
I did not find a direct property for "part of speech" for lexemes in the search results again.

I will try a different approach: I will search for Turkish verbs lexemes and check their properties to identify how the part of speech and language are encoded.

I will search for lexemes that are verbs in Turkish by querying lexemes with language Turkish (wd:Q256) and part of speech verb (wd:Q24905). I will try to find the property for lemma as well.

[Tool] Got no rows and 2 columns

[Assistant]
The query returned no results. It seems the properties or classes used for lexemes might be different or the data might be incomplete.

I will try to find Turkish verbs by searching for verbs (as items) that have the Turkish language as their language and check their labels or lemmas.

I will try a simpler approach: find items that are instances of verb (wd:Q24905) and have a Turkish label or description, then filter by lemma ending with "uş".

[Tool] Got no rows and 2 columns

[Assistant]
No verbs with Turkish labels ending with "uş" were found using the instance of verb (wd:Q24905) approach.

I will try to find Turkish verbs by searching for items that are instances of "verb" or subclass of "verb" and have a Turkish label ending with "uş".

I will also check if the property "lemma" is available for verbs or lexemes.

[Tool]
Top 10 property alternatives:
1. homograph lexeme, also known as identical lemma, different from (lexeme) (wd:P5402 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): lexeme in the same language with the same spelling as this one
2. Adelung lemma ID (wd:P9384 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for this lexeme in the Grammatisch-Kritisches Wörterbuch der Hochdeutschen Mundart at woerterbuchnetz.de
3. LiLa Linking Latin URI, also known as Latin lemma on LiLa, Linking Latin ID, Linking Latin URI (wd:P11033 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the URI of this lexeme in LiLa Lemma Collection (Q117021953)
4. DWB lemma ID (wd:P9385 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for this lexeme in the German dictionary by Jacob and Wilhelm Grimm at woerterbuchnetz.de
5. Dicionário de Gentílicos e Topónimos lemma ID (wd:P11274 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for a lemma in the toponym and demonym dictionary published by O Instituto de Linguística Teórica e Computacional
6. GWB lemma ID (wd:P9387 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for this lexeme in the Goethe dictionary by Jacob and Wilhelm Grimm at woerterbuchnetz.de
7. MWB lemma ID (wd:P9358 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for this lexeme in the Middle High German dictionary at mhdwb-online.de
8. LexerN lemma ID (wd:P9380 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for this lexeme in the supplement to the Middle High German dictionary by Matthias Lexer at woerterbuchnetz.de
9. Meyers lemma ID (wd:P9388 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for this lexeme in the Meyers Großes Konversationslexikon at woerterbuchnetz.de
10. COR lemma ID, level 1 (wd:P10831 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): identifier for lemma (lexeme) at level 1 in The Danish Central Word Register (Det centrale ordregister)

[Assistant]
I did not find a direct property for lemma in general, only lemma IDs for specific dictionaries.

Given the difficulties in finding lexemes with part of speech and language properties, I will try a different approach:

I will search for Turkish verbs by looking for items that are instances of verb (wd:Q24905) or subclass of verb, and have a Turkish label ending with "uş". I will retrieve their labels as lemma.

I will try a query to find items that are instances or subclasses of verb and have Turkish labels ending with "uş".

[Tool] Got no rows and 2 columns

[Assistant]
No results found for items that are instances or subclasses of verb with Turkish labels ending with "uş".

Given the challenges and the lack of direct lexeme data for Turkish verbs with lemma and part of speech, it seems the data for Turkish verbs with lemma and suffix filtering is not well represented in Wikidata.

I will report this limitation.

[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:P31 wd:Q11424 ; wdt:P161 wd:Q37175 .
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 81 (xsd:int) |

Johnny Depp was cast in 81 movies according to Wikidata.

In how many movies was Johnny Depp casted in?

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

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

No general notes for all knowledge graphs available

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

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

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

[User] In how many movies was Johnny Depp casted in?

[Assistant]
To answer the question "In how many movies was Johnny Depp casted in?", I need to:

1. Identify the entity for Johnny Depp in Wikidata.
2. Find the property that links an actor to movies they were cast in.
3. Count the number of movies associated with Johnny Depp.

First, I will search for the entity of Johnny Depp in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Johnny Depp, also known as John Christopher Depp II, John Christopher "Johnny" Depp II, John Depp, John C. Depp II, John Christopher Depp (wd:Q37175): American actor (born 1963), instance of human, television actor, nightclub owner, film producer, film director, screenwriter, voice actor, film actor, guitarist, musician, actor
2. Depp, Johnny, also known as Lentapedia / Depp, Johnny (wd:Q17316271): instance of encyclopedia article, Lentapedia article
3. Gay for Johnny Depp (wd:Q1095965): instance of musical group, band
4. Johnny Depp cabinet video (wd:Q122931969): video of Johnny Depp taken by Amber Heard, the copyright ownersh...hich was later transferred to TMZ, an American celebrity tabloid, instance of video
5. Johnny Depp: Becoming Whitey Bulger (wd:Q26898929): 2016 documentary film directed by Constantine Nasr, instance of short film
6. Discontinued western production with Johnny Depp (wd:Q17787325): instance of Wikinews article, Wikinews article
7. Johnny Depp in January in Kustendorf (wd:Q17733641): instance of Wikinews article, Wikinews article
8. Johnny Depp: I like Justin Bieber (wd:Q17739091): instance of Wikinews article, Wikinews article
9. Johnny Depp in Ricky Gervais' comedy (wd:Q17739240): instance of Wikinews article, Wikinews article
10. Johnny Depp in Snow White film (wd:Q17739219): instance of Wikinews article, Wikinews article

[Assistant] The entity for Johnny Depp in Wikidata is wd:Q37175. Next, I need to find the property that links an actor to movies they were cast in. Typically, this is the property "cast member" (wdt:P161) used in the reverse direction, i.e., movies have cast members. So I will count the number of movies (instances of film) where Johnny Depp is a cast member. I will write a SPARQL query to count the number of movies where Johnny Depp (wd:Q37175) is a cast member.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 81 (xsd:int) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?nomination ) AS ?count ) WHERE {
  {
    wd:Q242643 wdt:P1411 ?nomination .
    ?nomination wdt:P31 wd:Q188914 .
  } UNION {
    ?work wdt:P50 wd:Q242643 .
    ?work wdt:P1411 ?nomination .
    ?nomination wdt:P31 wd:Q188914 .
  }
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

Based on the available data in Wikidata, there are no explicit records of Naomi Novik being nominated for the Hugo Award. Therefore, the number of times Naomi Novik was nominated for a Hugo Award cannot be determined from the current data.

How often was Naomi Novik nominated for a Hugo Award?

[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 often was Naomi Novik nominated for a Hugo Award?

[Assistant]
To answer the question "How often was Naomi Novik nominated for a Hugo Award?" I need to:

1. Identify the entity for Naomi Novik in Wikidata.
2. Identify the property or relation that links Naomi Novik to Hugo Award nominations.
3. Count the number of times Naomi Novik was nominated for a Hugo Award.

I will start by searching for the entity Naomi Novik in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Naomi Novik (wd:Q242643): science fiction writer, instance of human, American writer, fantasy author, programmer, novelist, writer
2. Naomi (wd:Q2596433): Ruth's mother-in-law in the Book of Ruth, instance of human biblical figure
3. Naomi, also known as Trinity, Trinity Fatu, Trinity Starr, Nalgomi, Naomi Knight (wd:Q3119295): American professional wrestler, professional wrestler, instance of human, celebrity, dancer, model
4. 6139 Naomi, also known as Naomi (wd:Q553669): instance of asteroid, asteroid
5. Naomi, also known as Naomi, PA, Naomi, Pennsylvania (wd:Q6964608): instance of unincorporated community in the United States, instance of census-designated place in the United States, settlement in Pennsylvania, US
6. Naomi (wd:Q108444706): instance of television series, American television series
7. Novik (wd:Q1547078): 1911 Russian destroyer, instance of destroyer
8. Naomi, also known as Naomi (given name), Naomi (first name) (wd:Q18508449): instance of female given name, female given name
9. Naomi, also known as Florin Moldovan (wd:Q22957923): Romanian recording artist, songwriter and actress, instance of human, songwriter, singer, actor
10. Naomi (wd:Q6964620): novel by Junichiro Tanizaki, instance of literary work, instance of hardcover

[Assistant] I found the entity for Naomi Novik as wd:Q242643. Next, I need to find the property or relation that links Naomi Novik to Hugo Award nominations. Typically, awards and nominations are represented by properties like "award received" or "nominated for." I will check the properties related to awards or nominations for Naomi Novik.

[Tool]
Top 10 property alternatives:
1. award received, also known as winner of, win, recognition title, received award, award won (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
2. nominated for, also known as nominee for award, shortlisted for, selected for award, selected for, nominee for (wd:P1411 as wdt/p): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, subproperty of country
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. 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
6. given name, also known as proper name, first name, personal name, name, middle name (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, subproperty of name
7. 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
8. place of birth, also known as location born, POB, location of birth, born in, born at (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, subproperty of location
9. date of birth, also known as year of birth, DOB, born on, birthyear, born (wd:P569 as wdt/p): date on which the subject was born, subproperty of inception
10. educated at, also known as graduate of, place of education, school attended, schooled at, college attended (wd:P69 as wdt/p): educational institution attended by subject, subproperty of affiliation

No other items found

[Assistant] Naomi Novik has the property "nominated for" (wdt:P1411) which can be used to find awards for which she was nominated. I will use this property to find how many times she was nominated for the Hugo Award. The Hugo Award entity needs to be identified first. I will search for the Hugo Award entity in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Hugo Award, also known as Hugo Awards (wd:Q188914): set of awards given annually for the best science fiction or fantasy works and achievements of the previous year (1953-), instance of science fiction award, instance of group of awards
2. Hugo Bergroth award (wd:Q55467606): instance of award
3. Gold Hugo, also known as Golden Hugo Award (wd:Q16025043): prize awarded for the best feature film at the Chicago International Film Festival, instance of film award
4. Hugo Alfvén Award (wd:Q10526919): instance of art prize
5. Hugo Awards, also known as Hugo Awards for Musical Theatre (wd:Q2882757): Argentinian musical theatre awards, instance of group of awards, instance of award
6. Hugo Anthologies, also known as Hugo Award Anthologies (wd:Q81199719): collection of most of the Hugo award-winning short fiction up to the 1994; series initially edited by Isaac Asimov, instance of editorial collection
7. O. Hugo Schuck Best Paper Award, also known as Hugo Schuck Award, Hugo Schuck Best Paper Award (wd:Q124425753): award given by the American Automatic Control Council, instance of award
8. Retro Hugo Award, also known as Retro-Hugo, Retrospective Hugo Awards (wd:Q21163264)
9. Hugo von Montfort Award (wd:Q15816130): instance of award
10. Discontinued Hugo Awards (wd:Q5281652): Former awards for science fiction or fantasy, instance of award

[Assistant] The Hugo Award entity is wd:Q188914. Now I will count how many times Naomi Novik (wd:Q242643) was nominated for the Hugo Award (wd:Q188914) using the property "nominated for" (wdt:P1411).

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The query result shows that there are no direct nominations of Naomi Novik for the Hugo Award entity wd:Q188914 using the property "nominated for" (wdt:P1411). It is possible that the nominations are recorded on the works (books, stories) authored by Naomi Novik rather than on her as a person. I will now check if any of Naomi Novik's works were nominated for the Hugo Award.

First, I will find works authored by Naomi Novik.

[Tool]
Got 10 rows and 1 column:
| work                                                      |
| --------------------------------------------------------- |
| A Deadly Education (wd:Q104190017)                        |
| Araminta, or, The Wreck of the Amphidrake (wd:Q105524069) |
| The Last Graduate (wd:Q108819577)                         |
| The Scholomance (wd:Q108819998)                           |
| The Golden Enclaves (wd:Q114345348)                       |
| Ve stínu Hvozdu (wd:Q115539417)                           |
| V zajetí zimy (wd:Q115575814)                             |
| Temeraire (wd:Q1208412)                                   |
| wd:Q122111179                                             |
| wd:Q122134330                                             |

[Assistant] I found several works authored by Naomi Novik. Now I will check if any of these works were nominated for the Hugo Award (wd:Q188914). I will check the nominations for the first few works.

[Tool] Got no rows and 2 columns

[Assistant] None of the checked works authored by Naomi Novik have direct nominations for the Hugo Award using the property "nominated for" (wdt:P1411). It is possible that the nominations are recorded differently or the data is incomplete. I will try to find nominations for Hugo Award related to Naomi Novik or her works using a broader approach by checking nominations that mention Naomi Novik or her works as the nominee.

[Tool] Got no rows and 2 columns

[Assistant] There are no direct nominations with labels containing "Naomi Novik" for the Hugo Award. I will try to find nominations for the Hugo Award where the nominee is linked to Naomi Novik either as the author or creator. This means checking nominations for works that have Naomi Novik as the author.

[Tool] Got no rows and 1 columns

[Assistant] There are no direct nominations for the Hugo Award where Naomi Novik is listed as the author (wdt:P50). Given the lack of direct nomination data, I will try to find if Naomi Novik has received the Hugo Award (wdt:P166) to check if there is any award data related to the Hugo Award for her. This might give an indication of nominations as well.

[Tool] Got no rows and 1 columns

[Assistant]
There are no records of Naomi Novik having received the Hugo Award either. Given the data available, it seems that the Wikidata knowledge graph does not have explicit information on how many times Naomi Novik was nominated for the Hugo Award.

I will now provide a final answer based on this finding.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?label WHERE {
  wd:Q4186 rdfs:label ?label .
  FILTER ( LANG( ?label ) = "zh" )
}

Got 1 row and 1 column:
| label      |
| ---------- |
| 哈爾茨山脈 (zh) |

The Harz (Harz Mountains) is called 哈爾茨山脈 in Mandarin Chinese.

How is the Harz called in Mandarin Chinese?

[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 is the Harz called in Mandarin Chinese?

[Assistant]
To answer the question "How is the Harz called in Mandarin Chinese?", I need to find the entity for "Harz" in Wikidata and then look for its label or name in Mandarin Chinese (Chinese language, Mandarin dialect).

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

[Tool]
Top 10 entity alternatives:
1. Harz, also known as Harz Mountains (wd:Q4186): mountain range in northern Germany, instance of mountain system
2. Harz, also known as Harz (wd:Q820677): unincorporated area in Göttingen district, Lower Saxony, Germany, instance of unincorporated area in Germany
3. Carl Otto Harz, also known as Harz (wd:Q104943): German mycologist (1842-1906), instance of human, mycologist, pharmacist, botanist
4. Harz, also known as Harz (wd:Q321389): unincorporated area in Goslar district, Lower Saxony, Germany, instance of unincorporated area in Germany
5. Harz, also known as constituency for the Bundestag election Harz (wd:Q1008262): instance of federal electoral district of Germany, federal electoral district of Germany
6. Harz (wd:Q100702269): street in Halle (Saale), Saxony-Anhalt, Germany, instance of street
7. Harz (wd:Q16866971): instance of family name, family name
8. Harz (wd:Q19189529): street in Helmond, the Netherlands, instance of street
9. Harz (wd:Q102010653): street in Sangerhausen, Saxony-Anhalt, Germany, instance of street
10. Harz (wd:Q19189487): street in Assendelft, the Netherlands, instance of street

[Assistant] The most relevant entity for "Harz" in the context of a geographical name is "Harz Mountains" (wd:Q4186), which is a mountain range in northern Germany. Now I will check the label of this entity in Mandarin Chinese.

[Tool]
Got 1 row and 1 column:
| label      |
| ---------- |
| 哈爾茨山脈 (zh) |

[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 ?film ?filmLabel ?director ?directorLabel WHERE {
  VALUES ?film {
    wd:Q696928 wd:Q951002
  }
  ?film wdt:P57 ?director .
  OPTIONAL {
    ?film rdfs:label ?filmLabel FILTER ( LANG( ?filmLabel ) = "en" )
  }
  OPTIONAL {
    ?director rdfs:label ?directorLabel FILTER ( LANG( ?directorLabel ) = "en" )
  }
}

Got 2 rows and 4 columns:
| film                          | filmLabel             | director                   | directorLabel       |
| ----------------------------- | --------------------- | -------------------------- | ------------------- |
| Funny Games (wd:Q696928)      | Funny Games (en)      | Michael Haneke (wd:Q84199) | Michael Haneke (en) |
| Funny Games U.S. (wd:Q951002) | Funny Games U.S. (en) | Michael Haneke (wd:Q84199) | Michael Haneke (en) |

Both the original Funny Games (1997) and its remake Funny Games U.S. (2007) were directed by the same person, Michael Haneke.

Was Funny Games and its remake directed by different persons?

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

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

No general notes for all knowledge graphs available

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

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

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

[User] Was Funny Games and its remake directed by different persons?

[Assistant]
Step 1: Identify the entities "Funny Games" and its "remake" and the property "director" to check if the original and the remake have different directors.

Step 2: Search for the entity "Funny Games" in Wikidata to get its identifier.

I will start by searching for "Funny Games" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Funny Games (wd:Q696928): 1997 film by Michael Haneke, instance of film
2. Funny Games U.S., also known as Funny Games (wd:Q951002): 2007 film by Michael Haneke, instance of film
3. Funny Games II (wd:Q20756417): short film directed by Dieggo Coello, instance of short film
4. Funnyish Games (wd:Q131098334): instance of television series episode, episode of American Dad!
5. Kinda Funny Gamescast: Video Games Podcast (wd:Q110573559): instance of podcast show, podcast
6. Kinda Funny Games Daily: Video Games News Podcast (wd:Q110568325): instance of podcast show, podcast
7. game, also known as games, recreational games, recreational game, plays and games (wd:Q11410)
8. Games (wd:Q2364140): 1967 film directed by Curtis Harrington, instance of film
9. Funny How Time Slips Away, also known as Funny, Funny (How Time Slips Away) (wd:Q3560987): original song written and composed by Willie Nelson; first recorded by Billy Walker, instance of musical work/composition
10. The Games, also known as Games (wd:Q3417793): 1970 film by Michael Winner, instance of film

[Assistant]
I found two relevant entities for "Funny Games":
- Funny Games (wd:Q696928): 1997 film by Michael Haneke
- Funny Games U.S. (wd:Q951002): 2007 film by Michael Haneke (remake)

Step 3: Check the directors of these two films to see if they were directed by different persons. I will query the directors of both films.

[Tool]
Got 2 rows and 4 columns:
| film                          | filmLabel             | director                   | directorLabel       |
| ----------------------------- | --------------------- | -------------------------- | ------------------- |
| Funny Games (wd:Q696928)      | Funny Games (en)      | Michael Haneke (wd:Q84199) | Michael Haneke (en) |
| Funny Games U.S. (wd:Q951002) | Funny Games U.S. (en) | Michael Haneke (wd:Q84199) | Michael Haneke (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?author ?authorLabel WHERE {
  wd:Q27863945 wdt:P50 ?author .
  OPTIONAL {
    ?author rdfs:label ?authorLabel FILTER ( LANG( ?authorLabel ) = "en" )
  }
}

The author of "Convenience Store Woman" is Sayaka Murata.

Who is the author of “Convenience Store Woman” ?

[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 author of “Convenience Store Woman” ?

[Assistant] To answer the question "Who is the author of 'Convenience Store Woman'?", I will first search for the entity "Convenience Store Woman" in Wikidata to find its identifier. Then, I will look for the property that indicates the author of this work.

[Tool]
Top 10 entity alternatives:
1. Convenience Store Woman (wd:Q27863945): 2016 novel by Sayaka Murata, instance of literary work
2. convenience store, also known as C-store, corner store, daily consumer goods store, food mart, convenience shop (wd:Q7361709)
3. Convenience Store, also known as Produkty 24 (wd:Q110792920): 2022 film directed by Mikhail Borodin, instance of film
4. 🏪, also known as U+1F3EA, CONVENIENCE STORE, convenience store emoji (wd:Q87577280): instance of Unicode character, Unicode character, instance of emoji
5. Ep 502 - Convenience Store Woman, by Sayaka Murata (wd:Q116783358): instance of podcast episode, Overdue podcast episode
6. Spatial Structures of City Narrative in Convenience Store Woman (wd:Q119984073): instance of scholarly article
7. convenience store (wd:L1418021-S3)
8. convenience store (wd:L643872)
9. convenience store (wd:L643872-F1)
10. Backstreet Rookie, also known as Convenience Store Saet-byul (wd:Q91091667): instance of television series, 2020 South Korean TV series

[Assistant] The entity "Convenience Store Woman" is identified as wd:Q27863945 in Wikidata, described as a 2016 novel by Sayaka Murata. Now, I will query the author of this literary work using the property for author.

[Tool]
Got 1 row and 2 columns:
| author                       | authorLabel        |
| ---------------------------- | ------------------ |
| Sayaka Murata (wd:Q11523472) | Sayaka Murata (en) |

[Tool] Stopped generation process