PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?character WHERE {
  wd:Q696071 wdt:P674 ?character .
  ?character wdt:P31 wd:Q5 .
  ?character wdt:P21 wd:Q6581072
}

Using entities:
- human (wd:Q5), also known as human being, humans, individual Homo sapiens, individual human, modern humans, etc.: is instance of class of organisms known by one particular common name / is instance of organisms known by a particular common name / is subclass of mammal / is subclass of natural person / is subclass of omnivore / is subclass of person / is subclass of individual animal / any single member of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo, from embryo to adult
- female (wd:Q6581072), also known as f, fairer sex, fem, female character, female gender, etc.: is instance of sex of humans / is instance of gender / is subclass of gendered / to be used in "sex or gender" (P21) to indicate that the human subject is a female / to be used in "sex or gender" (P21) to indicate that the ... (P10339) to indicate that a word refers to a female person
- Hunter × Hunter (wd:Q696071), also known as HUNTER × HUNTER, Hunter x Hunter, HXH: is instance of manga series / Japanese manga series

Using properties:
- sex or gender (wd:P21 as wdt), also known as biological sex, gender, gender expression, gender identity, gender or sex, etc.: sex or gender identity: male (Q6581097), female (Q6581072), ... (Q43445). Groups of same gender use subclass of (P279) / sex or gender identity of human or animal. For human: male, ... female organism. Groups of same gender use subclass of (P279) / sexual identity of subject: male (Q6581097), female ... (Q43445). Groups of same gender use "subclass of" (P279)
- instance of (wd:P31 as wdt), also known as example of, has type, is a, is an, is of type, etc.: that class of which this subject is a particular example and ... a subclass of mountain (and an instance of volcanic landform) / type to which this subject corresponds/belongs. Different ... instance of mountain; volcanoes form a subclass of mountains / type to which this subject corresponds/belongs. Different ... is an instance of mountain; volcano is a subclass of mountain
- characters (wd:P674 as wdt), also known as appears in this work, P674: characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games) / characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games) / characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games)

Answer: No, according to the structured data in Wikidata, there are no female main characters listed for Hunter x Hunter. However, this may reflect incomplete data, as Biscuit Krueger is a major female character in the series but is not currently documented in Wikidata with a link to the work.

No, according to the structured data in Wikidata, there are no female main characters listed for Hunter x Hunter. However, this may reflect incomplete data, as Biscuit Krueger is a major female character in the series but is not currently documented in Wikidata with a link to the work.

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?character WHERE {
  wd:Q696071 wdt:P674 ?character .
  ?character wdt:P31 wd:Q5 .
  ?character wdt:P21 wd:Q6581072
}

Using entities:
- human (wd:Q5), also known as human being, humans, individual Homo sapiens, individual human, modern humans, etc.: is instance of class of organisms known by one particular common name / is instance of organisms known by a particular common name / is subclass of mammal / is subclass of natural person / is subclass of omnivore / is subclass of person / is subclass of individual animal / any single member of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo, from embryo to adult
- female (wd:Q6581072), also known as f, fairer sex, fem, female character, female gender, etc.: is instance of sex of humans / is instance of gender / is subclass of gendered / to be used in "sex or gender" (P21) to indicate that the human subject is a female / to be used in "sex or gender" (P21) to indicate that the ... (P10339) to indicate that a word refers to a female person
- Hunter × Hunter (wd:Q696071), also known as HUNTER × HUNTER, Hunter x Hunter, HXH: is instance of manga series / Japanese manga series

Using properties:
- sex or gender (wd:P21 as wdt), also known as biological sex, gender, gender expression, gender identity, gender or sex, etc.: sex or gender identity: male (Q6581097), female (Q6581072), ... (Q43445). Groups of same gender use subclass of (P279) / sex or gender identity of human or animal. For human: male, ... female organism. Groups of same gender use subclass of (P279) / sexual identity of subject: male (Q6581097), female ... (Q43445). Groups of same gender use "subclass of" (P279)
- instance of (wd:P31 as wdt), also known as example of, has type, is a, is an, is of type, etc.: that class of which this subject is a particular example and ... a subclass of mountain (and an instance of volcanic landform) / type to which this subject corresponds/belongs. Different ... instance of mountain; volcanoes form a subclass of mountains / type to which this subject corresponds/belongs. Different ... is an instance of mountain; volcano is a subclass of mountain
- characters (wd:P674 as wdt), also known as appears in this work, P674: characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games) / characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games) / characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games)

Execution result:
Got no rows and 1 columns

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

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.

Available knowledge graphs:
- wikidata at https://qlever.dev/api/wikidata with prefix index for entities and similarity index for properties, and notes:
  - P106 = occupation. Use wdt:P106 to get occupations and fetch English labels with rdfs:label + FILTER(LANG(?label)='en'). When returning human occupations confirm the subject is human (wdt:P31 wd:Q5) to avoid non-human items. For 'main business' prefer more specific occupation items (e.g., real estate entrepreneur Q7301138) over broad labels like business magnate.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for direct values; for time-series use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect qualifiers (e.g., 'applies to part') and references (pr:P854) to detect scope. When computing density with area (wdt:P2046) confirm area unit/datatype (usually km²), ensure population and area present and >0, and BIND(?population/?area AS ?density).
  - Spouse nationality: do not rely only on P27. Check the spouse's place of birth (P19) and then the birthplace's country (P17) or admin parents (wdt:P131*). Many pre-unification German spouses appear under historical states—map those to modern countries when needed.
  - For languages on countries: prefer wdt:P37 (official language) and wdt:P2936 (language used). wdt:P1412 (languages spoken/written/signed) is more often used for peoples/individuals and may be empty for countries. Try P37 and P2936 first, then fall back to P1412/P407. Fetch English labels with rdfs:label + FILTER(LANG(?l)='en').
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Inception (P571) often uses Jan-01 placeholders for year-only dates; inspect p:P571 -> ps:P571 and check qualifiers/references for precision. For independence dates, also search for related event statements/items (e.g., 'declaration of independence') and prefer qualifiers/references over assuming day/month from year-only values.
  - To retrieve qualifiers (e.g., award year) use the statement/qualifier pattern: p:Pxxx / ps:Pxxx / pq:P585. Use wdt:Pxxx for normalized/direct values when you don't need qualifiers. Example: p:P166 + pq:P585 gives award year.
  - P166 = award received. Use wdt:P166 for normalized/direct values when you only need award items. To get award years or qualifiers use p:P166 / ps:P166 / pq:P585. Many statements lack pq:P585 — use OPTIONAL { ?stmt pq:P585 ?date } and ORDER BY DESC(?date). When listing award winners, filter ?person wdt:P31 wd:Q5 to ensure humans.
  - Population time-series: use p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . To get the most recent value prefer a subquery to compute MAX(?date) per subject and join back to the statement to retrieve the matching population. Avoid HAVING(MAX(?date)=?date) — QLever can error. Inspect qualifiers/references for scope (city-proper vs metro).
  - wdt:P131* can be expensive and may be incomplete. First try direct wdt:P131 for immediate administrative parent(s). If missing, check wdt:P17 (country) and wdt:P625 (coordinates) or the place item's own statements. Prefer bounded traversal (1-2 hops) or explicit parent patterns and use DISTINCT/ORDER/LIMIT to de-duplicate rather than broad P131*.
  - Use search_property_of_entity to find candidate properties but always verify the property's semantics (domain/range) and whether it links to the office, office-holder, or related entity. Test with a small query before relying on results. For creative works, after locating candidate properties, filter results by instance-of (e.g., films wd:Q11424; board games wd:Q131436) to avoid non-work items.
  - Use search_property_of_entity to find relevant properties. Political parties are modelled inconsistently (P1142, P1313, P463, P39/P106). Fallback: inspect party pages/lists and MEP group memberships; document assumptions. For country government type prefer P122 (basic form of government); P1288 is inconsistent. To exclude historical states filter NOT EXISTS { ?item wdt:P576 ?d }.
  - Inspect statement references and qualifiers: use prov:wasDerivedFrom on p: statements to find sources, and check qualifiers (e.g., 'applies to part' or other scope qualifiers) to disambiguate city vs metro vs admin area. Reference labels can be empty; inspect reference node properties for URLs or publication dates.
  - Place of burial (P119): use wdt:P119 to get the direct burial place. If you need the city, follow P119 / wdt:P131 or check the burial place's wdt:P17 (country) or wdt:P625 (coordinates) if P131 is missing. Use p:P119 / ps:P119 / pq: qualifiers when you need burial-date or scope details and inspect references for source/context.
  - P118 (league/competition) is used inconsistently: sometimes on clubs, sometimes directly on players. When finding clubs use ?club wdt:P118 wd:Q... and verify ?club wdt:P31 wd:Q483394. If P118 returns players, filter by ?item wdt:P31 wd:Q5 to detect humans and avoid false positives. To get current players prefer club→p:P54 statements: ?player p:P54 ?stmt; ?stmt ps:P54 ?club. OPTIONAL{ ?stmt pq:P582 ?end } FILTER(!BOUND(?end)). Verify qualifiers/references and DOBs for plausibility.
  - Querying holders of offices (P39): prefer p:P39 / ps:P39 patterns to access qualifiers. To find current holder use: ?person p:P39 ?stmt. ?stmt ps:P39 wd:Q... . OPTIONAL{ ?stmt pq:P582 ?end } FILTER(!BOUND(?end)). Watch for duplicate statements (re-elections)  use DISTINCT or ORDER BY DESC(?start) LIMIT 1 on pq:P580. Avoid HAVING(MAX(...)=...) on QLever; prefer subqueries with ORDER/LIMIT.
  - Headquarters: use wdt:P159 for org HQs. If P159 gives a building follow that building's wdt:P131* (bounded hops). If P159 points to a place, verify its wdt:P31. Don't rely only on P17 for org-country—also check P159 and P276. Use UNION/nested patterns to cover ?org wdt:P17 wd:Q... OR ?org wdt:P159|wdt:P276 ?place . ?place wdt:P17 wd:Q... or ?place wdt:P131* wd:Q...

Index types used for entities and properties:
- Prefix index: Retrieves items by overlap between query keywords and item words. The query keywords can match item words exactly or as prefixes.
- Similarity index: Retrieves items by cosine similarity between their embeddings and the query embedding.

General notes across knowledge graphs:
- Use rdfs:label + FILTER(LANG(?label)='en') for labels; avoid SERVICE wikibase:label. If a query using SERVICE fails (e.g., HTTP 301 redirect), switch to rdfs:label + FILTER(LANG(...)) and/or schema:name. Language items (e.g., results from P2936) often lack rdfs:label@en; expect many hits without English labels—prefer P37 for official languages or map major languages by known QIDs.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct values; p:Pxxx/ps:Pxxx/pq:Pxxx for qualifiers. For roles: films use P161 (actor), directors P57; space missions use P1029 (crew). Query reverse and filter by instance-of film (wd:Q11424) or human (wd:Q5). For 'city' classification prefer ?x wdt:P31/wdt:P279* wd:Q515; use wdt:P2046 for area but check units/duplicates.
- When selecting latest qualifier-dated values (e.g., pq:P585 for award year or population), prefer a subquery to compute MAX(date) per subject and join back to get the matching statement/value. Avoid HAVING(MAX(?date)=?date) patterns on QLever (can cause engine errors). Use OPTIONAL for qualifiers when dates may be missing and GROUP_CONCAT for multi-values.
- Use search_entity to resolve names and verify instance-of (e.g., wdt:P31=wd:Q5). Note: search_entity can return class/designation items or noisy matches; constrain with instance-of, or search the related entity (e.g., country) and query its members. Use search_property_of_entity/search_autocomplete as fallback and always confirm P31 programmatically before trusting the top match.
- Use search_property to confirm property identifiers (e.g., "country" -> P17) before embedding them in queries. This prevents mistakes from assuming an ID and helps pick the best property if multiple choices exist.
- QLever endpoints do not support SERVICE wikibase:label — use rdfs:label + FILTER(LANG(...))='en'. Common SPARQL pitfalls: avoid '||' outside patterns (use UNION), put AND '&&' only inside FILTER, prefer VALUES over IN, and use rdfs:label filters to constrain language. When testing existence prefer FILTER EXISTS { ... } or OPTIONAL { ?s wdt:Pxx ?v . FILTER(?v = wd:Q...) } — writing OPTIONAL { ?s wdt:Pxx wd:Q... } creates no ?v for BOUND(?v). When execute() fails, simplify the query and test with list(...).
- Use list(...) to inspect all triples on an entity for exploration, but expect large results for notable items. Prefer wdt: properties or targeted p:/ps:/pq: patterns in SPARQL when you need normalized values or qualifiers, respectively.
- Before embedding property or item IDs in SPARQL, use search_property, search_property_of_entity, and search_object_of_property to discover the appropriate P/PID or object QIDs. This reduces errors from guessing IDs and surfaces better property alternatives and qualifiers. Also verify ambiguous item QIDs (e.g., currencies or template items) by checking rdfs:label and instance-of with list(...) before use.
- search_constrained and search_autocomplete can return no results for constrained queries or when the index is large. If they fail, fallback to search_entity + verify P31 (or use search_property_of_entity). Constrain with instance-of (wdt:P31) or other properties in the SPARQL to improve matches.
- When selecting awards by year, prefer xsd:dateTime range filters (e.g., >= "2001-01-01T00:00:00Z"^^xsd:dateTime && < "2002-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for portability and precision.
- When selecting award winners via wdt:P166, filter ?person wdt:P31 wd:Q5 to ensure humans — fictional characters or non-humans may have award claims. If you need award years use p:P166 / ps:P166 / pq:P585 and OPTIONAL { ?stmt pq:P585 ?year } to retrieve qualifier years.
- Use wdt:P625 (coordinates) together with wdt:P131* and wdt:P17 checks to validate a place's modern country or to filter by geography. When country labels mismatch historically, inspect admin parents and coordinates to resolve whether the item refers to historical/colonial scope or current boundaries.
- Prefer wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (e.g., pq:P585 for award year). Always fetch labels with rdfs:label + FILTER(LANG(?label) = "en"). When computing derived metrics (density/rates) confirm unit consistency (area units) and datatypes and do calculations inside SPARQL.
- If search_autocomplete returns too many candidates or errors, retry with constraints (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }) or narrower query text. If still failing, fallback to search_entity and verify wdt:P31 for type checks.
- For award-year queries use p:P166 / ps:P166 / pq:P585 to access award statements and year qualifiers. Filter by xsd:dateTime ranges (e.g., >= "2020-01-01T00:00:00Z"^^xsd:dateTime && < "2021-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for robustness.
- Use search_property_of_entity to surface relevant properties for an entity (e.g., 'birth' -> P569/P19; 'cause of death' -> P509; 'manner of death' -> P1196). list(...) can inspect triples but may return large results for notable items—prefer targeted wdt: patterns (wdt:Pxxx) or p:/ps:/pq: patterns when you need qualifiers.

SPARQL prefixes for use in function calls:
- bd: <http://www.bigdata.com/rdf#
- cc: <http://creativecommons.org/ns#
- data: <https://www.wikidata.org/wiki/Special:EntityData/
- dc: <http://purl.org/dc/elements/1.1/
- dct: <http://purl.org/dc/terms/
- foaf: <http://xmlns.com/foaf/0.1/
- geo: <http://www.opengis.net/ont/geosparql#
- geof: <http://www.opengis.net/def/function/geosparql/
- gn: <http://www.geonames.org/ontology#
- hint: <http://www.bigdata.com/queryHints#
- 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/
- qb: <http://purl.org/linked-data/cube#
- 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/
- void: <http://rdfs.org/ns/void#
- 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#

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not ask the user for clarifications without first attempting to complete the task. If the task input is incomplete or ambiguous, try to make reasonable assumptions and document them.
- 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.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- 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.
- 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.
- 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.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] is there female main character in hunter x hunter?

[Assistant]
To determine whether there is a female main character in *Hunter x Hunter*, I need to:

1. Identify the entity for *Hunter x Hunter* (a manga/anime series).
2. Find its main characters, particularly those identified as female.
3. Confirm whether any of them are considered "main characters" (i.e., central to the plot, not just supporting roles).

### Step 1: Search for the *Hunter x Hunter* entity
I'll start by searching for the entity representing *Hunter x Hunter* in Wikidata.

Question: Hunter x Hunter

Top 10 entity alternatives:
1. Hunter × Hunter (wd:Q696071, matched via Hunter x Hunter), also known as HUNTER × HUNTER, Hunter x Hunter, HXH: is instance of manga series / Japanese manga series
2. Hunter × Hunter (wd:Q70209136, matched via Hunter x Hunter), also known as H×H, Hunter x Hunter, HxH: is instance of anime television series / 2011 Japanese anime television series
3. Hunter × Hunter (wd:Q21232458, matched via Hunter x Hunter), also known as Hunter x Hunter, HxH
4. Hunter × Hunter: Phantom Rouge (wd:Q11398237, matched via Hunter X Hunter: Phantom Rouge), also known as Hunter X Hunter: Phantom Rouge: is instance of аніме-фільм / is instance of anime film / 2013 film by Yūzō Satō
5. Hunter x Hunter: Wonder Adventure (wd:Q63225112), also known as HunterxHunter: Wonder Adventure: is instance of video game / 2012 video game
6. Hunter × Hunter: The Last Mission (wd:Q15205223, matched via Hunter x Hunter: The Last Mission), also known as Hunter x Hunter: The Last Mission: 2013 film by Keiichiro Kawaguchi / is instance of аніме-фільм / is instance of anime film
7. Hunter × Hunter: Nen × Impact (wd:Q124154515, matched via Hunter x Hunter: Nen x Impact), also known as Hunter Hunter Nen Impact, Hunter x Hunter: Nen x Impact, HunterxHunter: NenxImpact: is instance of video game / 2025 video game
8. Hunter × Hunter: Altar of Dragon Vein (wd:Q121774495, matched via Hunter x Hunter: Ryumyaku no Saidan), also known as Hunter x Hunter: Ryumyaku no Saidan: is instance of video game / 2001 video game
9. Monster Hunter Generations (wd:Q20856979, matched via Monster Hunter X), also known as Monster Hunter X: is instance of video game / 2015 Japanese video game
10. Hunter Hunter (wd:Q104398462): 2020 film directed by Shawn Linden / is instance of film

[Assistant]
The main entity for *Hunter x Hunter* is **wd:Q696071** (manga series) and **wd:Q70209136** (2011 anime series). Since the anime is an adaptation of the manga, and both share the same main characters, I'll focus on the manga series entity (wd:Q696071) to find its characters.

### Step 2: Find properties related to characters in a manga/anime series
I need to find which property links a manga/anime series to its characters. Common properties for this are:
- `wdt:P161` (cast member) — typically used for films/TV shows
- `wdt:P144` (based on) — not relevant here
- `wdt:P170` (creator) — not relevant
- Possibly `wdt:P1441` (character) — this is a known property for characters in works

I'll search for properties of the *Hunter x Hunter* entity to see what's used for characters.

Question: character

Top 10 property alternatives:
1. characters (wd:P674 as p/wdt), also known as appears in this work, P674: characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games) / characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games) / characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games)
2. title (wd:P1476 as p/wdt, matched via name), also known as article title, full title, headline, known as, name, etc.: published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work / published title of a work, such as a newspaper article, a literary work, a website, or a performance work / published title of a work, such as a newspaper article, a literary work, a website, or a performance work / is subproperty of name
3. topic's main category (wd:P910 as p/wdt, matched via category), also known as article's main category, category, category for this topic, catmain, has category, etc.: main category of this topic / main Wikimedia category
4. start time (wd:P580 as p/wdt, matched via from), also known as began, beginning, building date, effective, effectivity, etc.: indicates the time an item begins to exist or a statement starts being valid / indicates the time an item begins to exist or a statement starts being valid / time an entity begins to exist or a statement starts being valid / is subproperty of point in time
5. instance of (wd:P31 as p/wdt, matched via type), also known as example of, has type, is a, is an, is of type, etc.: that class of which this subject is a particular example and ... a subclass of mountain (and an instance of volcanic landform) / type to which this subject corresponds/belongs. Different ... instance of mountain; volcanoes form a subclass of mountains / type to which this subject corresponds/belongs. Different ... is an instance of mountain; volcano is a subclass of mountain
6. derivative work (wd:P4969 as p/wdt, matched via incarnation), also known as adaptation, adapted as, cover, derived work, has adaptation, etc.: work created from major part of this artwork; work created from major part of this software / new work of art (film, book, software, etc.) derived from major part of this work
7. author (wd:P50 as p/wdt, matched via maker), also known as co-author, creator, maker, playwright, poet, etc.: main creator(s) of a written work (use on works, not humans); ... name string) when Wikidata item is unknown or does not exist / main creator(s) of a written work (use on works, not humans) / main creator(s) of a written work (use on works, not humans) / is subproperty of creator
8. genre (wd:P136 as p/wdt), also known as artistic genre, art style, film genre, genre of literature, genre of music, etc.: creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic / genre of a creative work or genre in which an artist works
9. published in (wd:P1433 as p/wdt, matched via chapter of), also known as album, article of, chapter of, essay of, music album, etc.: larger work that a given work was published in, like a journal, a website, a collection, a book or a music album / larger work that a given work was published in / is subproperty of part of
10. different from (wd:P1889 as p/wdt, matched via not), also known as confused with, differs from, disambiguated from, distinct from, distinguished from, etc.: item that is different from another item, with which it is often confused / item that is different from another item, with which it may be confused

No other items found

[Assistant]
The property **wdt:P674** ("characters") is the correct one to use for finding characters in a work like *Hunter x Hunter*. Now I'll retrieve the characters of *Hunter x Hunter* (wd:Q696071) using this property.

### Step 3: Query for characters in *Hunter x Hunter*
I'll execute a query to get all characters linked via `wdt:P674` to the manga series.

execute

SELECT ?character WHERE { wd:Q696071 wdt:P674 ?character }

[Assistant]
I see the main characters of *Hunter x Hunter* are listed, but none of them are obviously female. Let me check the gender of each character by looking up their `wdt:P21` (sex or gender) property.

### Step 4: Get gender information for each character
I'll modify the query to include the gender of each character.

SELECT ?character ?genderLabel WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P21 ?gender . SERVICE wikibase:label { bd:serviceParam wikibase:language "en" } }

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: '
<title>301 Moved Permanently</title>

301 Moved Permanently'

[Assistant]
I need to avoid using `SERVICE wikibase:label` since it's not supported on QLever. Instead, I'll fetch the labels manually using `rdfs:label` with a language filter.

### Step 5: Retrieve character names and genders without SERVICE wikibase:label
I'll get the character IRIs and their gender values, then manually check the gender labels.

SELECT ?character ?gender WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P21 ?gender }

[Assistant]
All six main characters listed are marked as male (wd:Q6581097). However, this may not be exhaustive — *Hunter x Hunter* has other significant female characters such as **Meruem** (the Chimera Ant King, who is male), **Kite** (male), **Palm Siberia** (female, but not a main character), and **Pip** (male). But the most prominent female character often considered a main character is **Kurapika** — wait, Kurapika is male.

Actually, **Kurapika** is male, despite appearances. Let me check if there are any other female characters in the main cast that might be missing.

I'll search for female characters associated with *Hunter x Hunter* directly by searching for female entities linked to it.

### Step 6: Search for female characters in *Hunter x Hunter*
I'll look for characters that are female (wd:Q6581072) and linked to *Hunter x Hunter*.

SELECT ?character WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P21 wd:Q6581072 }

[Assistant]
No results — this confirms that **none of the main characters** listed in Wikidata as being part of *Hunter x Hunter* are female.

However, I know from external knowledge that **Palm Siberia** and **Biscuit Krueger** are important female characters in the series. Let me check if they are in Wikidata and linked to *Hunter x Hunter*.

### Step 7: Search for known female characters: Biscuit Krueger and Palm Siberia
I'll search for "Biscuit Krueger" and "Palm Siberia" in Wikidata.

Question: Biscuit Krueger

Top 10 entity alternatives:
1. biscuit (wd:Q13270), also known as cookie: is subclass of cookies, biscuits, crackers / small sweet baked product / is subclass of snack / sweet baked product
2. cookie (wd:Q13266, matched via biscuit), also known as biscuit, Cookies, cracker: is subclass of cookies, biscuits, crackers / is subclass of shelf-stable food / is subclass of confection / is subclass of Dessert / is subclass of dessert / is subclass of Pastry / is subclass of pastry / is subclass of snack / is subclass of dish / is subclass of bánh / baked treat / baked treat
3. Freddy Krueger (wd:Q329466), also known as es para todos los, Fred Krueger: the main antagonist of the A Nightmare on Elm Street film series / the main antagonist of the A Nightmare on Elm Street film series / the main antagonist of the A Nightmare on Elm Street film series / is instance of supernatural being in a work of fiction / is instance of fictional human / is instance of film character / has occupation serial killer
4. hardtack (wd:Q836891, matched via biscuit), also known as biscuit, brewis, cabin bread, hard biscuit, hard bread, etc.: simple, spoil-resistant cracker / is subclass of dry biscuits / is subclass of cracker / is subclass of Cracker / is subclass of dish
5. Clypeasteroida (wd:Q1071960, matched via snapper biscuit), also known as cake urchin, pansy shell, sand cake, sand dollar, sea cookie, etc.: order of irregular sea urchins / is instance of taxon
6. Marie biscuit (wd:Q3232603): vanilla-flavoured round biscuit / is subclass of Biscuit / is subclass of biscuit
7. Walter Krueger (wd:Q1362101): American soldier and general officer (1881-1967) / has occupation Military personnel / has occupation military personnel / has occupation military officer / has occupation army officer / has occupation officer / has occupation Soldier / has occupation soldier / is instance of human
8. Alan B. Krueger (wd:Q931129, matched via Alan Krueger), also known as Alan Bennett Krueger, Alan Krueger: has occupation university teacher / American economist (1960–2019) / has occupation Economist / has occupation economist / is instance of human
9. sandwich cookie (wd:Q1243043, matched via sandwich biscuit), also known as cookie sandwich, mouse-poultry, round sandwich biscuit, sandwich biscuit: cookies kept by two thin cookies or biscuits with filling in between / is subclass of biscuit / is subclass of cookie
10. digestive biscuit (wd:Q1247120), also known as sweet-meal biscuit: Scottish semi-sweet biscuit / is subclass of Biscuit / is subclass of biscuit / is subclass of food

[Assistant]
I see "Biscuit Krueger" is not directly found. Let me try searching for "Palm Siberia".

Question: Palm Siberia

Top 10 entity alternatives:
1. Siberia, Siberia (wd:Q4418346): is instance of literary work / non-fiction book
2. Siberia (wd:Q5428): is instance of geographical region / is instance of geographic region / is instance of region / northern Russia / region of Asia
3. Cocos nucifera (wd:Q13187, matched via coconut palm), also known as cocoanut, coconut, coconut palm, coconut tree: is subclass of useful plant / is instance of taxon / species of plant / species of plant / species of plant
4. Phoenix dactylifera (wd:Q25292, matched via date palm), also known as date palm: palm tree cultivated for its edible sweet fruit / is instance of taxon
5. palms (wd:Q14080, matched via palm family), also known as Arecaceae, palm family, palm tree family, palm trees: is instance of taxon / family of plants
6. Palm Sunday (wd:Q42236), also known as Palmarum, Passion, Sunday of the: commemoration of the triumphal entry of Jesus into Jerusalem / is instance of fasting Sunday
7. Siberian Federal District (wd:Q41979, matched via Central Siberia), also known as Central Russia, Central Siberia, Siberian Federal Okrug: non-administrative subdivision, grouping several federal subjects of Russia to the north of Central Asia / is instance of federal district of Russia
8. Palm Springs (wd:Q488004), also known as Palm City, Palm Springs, CA, Palm Springs, California: resort city in Riverside County, southern California, United States / resort city in Riverside County, southern California, United States / city in Riverside County, California, United States / is instance of charter city / is instance of Gay village / is instance of gay village / is instance of Resort town / is instance of resort town / is instance of resort / is instance of city
9. Areca catechu (wd:Q156969, matched via Areca-nut palm), also known as Areca-nut palm, Areca palm, Betel nut palm, Betel Palm: is subclass of useful plant / is instance of taxon / species of plant
10. palm oil (wd:Q231458), also known as dende oil, dendê oil, palm-oil: edible and biocombustible vegetable oil from fruit of oil palms / is instance of cooking and baking ingredient / is instance of food ingredient / is subclass of biocombustible / is subclass of cooking oil / is instance of mixture

[Assistant]
Neither "Biscuit Krueger" nor "Palm Siberia" appear as direct entities in Wikidata. This suggests that while these characters are well-known in the *Hunter x Hunter* fandom, they may not be formally documented in Wikidata with structured data linking them to the work.

### Step 8: Check if any female characters are linked via other means
I'll search for any female human characters (wd:Q5) with a connection to *Hunter x Hunter*.

SELECT ?character WHERE { ?character wdt:P1708 wd:Q696071 . ?character wdt:P31 wd:Q5 . ?character wdt:P21 wd:Q6581072 }

[Assistant]
No results. Let me try with `wdt:P674` again, but this time explicitly looking for female humans:

SELECT ?character WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P31 wd:Q5 . ?character wdt:P21 wd:Q6581072 }

[Assistant]
Still no results. This strongly suggests that **Wikidata does not currently list any female characters as main characters** in *Hunter x Hunter*, even though in the actual series, **Biscuit Krueger** is a major female character and **Palm Siberia** is a significant supporting character.

Given the data available in Wikidata, and based on the structured information present, **there are no female main characters listed** in the *Hunter x Hunter* entity's character relations.

However, this is likely due to **incomplete data in Wikidata**, not because such characters don't exist in the source material.

### Final Assessment:
- The main characters listed in Wikidata are all male.
- Known female main characters like **Biscuit Krueger** are not present in Wikidata’s structured data for *Hunter x Hunter*.
- Therefore, based on **Wikidata’s current knowledge**, the answer is **no** — but this is a limitation of the knowledge base, not the source material.

Since the question asks whether there is a female main character in *Hunter x Hunter*, and we must answer based on the knowledge graph, the answer is:

**No**, based on the structured data in Wikidata, there is no female main character listed for *Hunter x Hunter*. However, this may reflect incomplete data rather than the actual content of the series.

I will now provide the final SPARQL query and answer.

answer

SELECT ?character WHERE { wd:Q696071 wdt:P674 ?character . ?character wdt:P31 wd:Q5 . ?character wdt:P21 wd:Q6581072 }

Answer: No, according to the structured data in Wikidata, there are no female main characters listed for Hunter x Hunter. However, this may reflect incomplete data, as Biscuit Krueger is a major female character in the series but is not currently documented in Wikidata with a link to the work.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?season ) AS ?count ) WHERE {
  wd:Q30603704 wdt:P527 ?season
}

Using entities:
- Pose (wd:Q30603704): is instance of serial / is instance of television series / 2018 American drama television series

Using properties:
- has part(s) (wd:P527 as wdt), also known as amalgamation of, assembled from, assembled out of, composed of, comprised of, etc.: part of this subject, also valuable subclass or exemplar. Inverse property of "part of". / part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670). / part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

Got 1 row and 1 column:
| count       |
| ----------- |
| 3 (xsd:int) |

Answer: Pose has 3 seasons.

Pose has 3 seasons.

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?season ) AS ?count ) WHERE {
  wd:Q30603704 wdt:P527 ?season
}

Using entities:
- Pose (wd:Q30603704): is instance of serial / is instance of television series / 2018 American drama television series

Using properties:
- has part(s) (wd:P527 as wdt), also known as amalgamation of, assembled from, assembled out of, composed of, comprised of, etc.: part of this subject, also valuable subclass or exemplar. Inverse property of "part of". / part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670). / part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

Execution result:
Got 1 row and 1 column:
| count       |
| ----------- |
| 3 (xsd:int) |

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

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.

Available knowledge graphs:
- wikidata at https://qlever.dev/api/wikidata with prefix index for entities and similarity index for properties, and notes:
  - P106 = occupation. Use wdt:P106 to get occupations and fetch English labels with rdfs:label + FILTER(LANG(?label)='en'). When returning human occupations confirm the subject is human (wdt:P31 wd:Q5) to avoid non-human items. For 'main business' prefer more specific occupation items (e.g., real estate entrepreneur Q7301138) over broad labels like business magnate.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for direct values; for time-series use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect qualifiers (e.g., 'applies to part') and references (pr:P854) to detect scope. When computing density with area (wdt:P2046) confirm area unit/datatype (usually km²), ensure population and area present and >0, and BIND(?population/?area AS ?density).
  - Spouse nationality: do not rely only on P27. Check the spouse's place of birth (P19) and then the birthplace's country (P17) or admin parents (wdt:P131*). Many pre-unification German spouses appear under historical states—map those to modern countries when needed.
  - For languages on countries: prefer wdt:P37 (official language) and wdt:P2936 (language used). wdt:P1412 (languages spoken/written/signed) is more often used for peoples/individuals and may be empty for countries. Try P37 and P2936 first, then fall back to P1412/P407. Fetch English labels with rdfs:label + FILTER(LANG(?l)='en').
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Inception (P571) often uses Jan-01 placeholders for year-only dates; inspect p:P571 -> ps:P571 and check qualifiers/references for precision. For independence dates, also search for related event statements/items (e.g., 'declaration of independence') and prefer qualifiers/references over assuming day/month from year-only values.
  - To retrieve qualifiers (e.g., award year) use the statement/qualifier pattern: p:Pxxx / ps:Pxxx / pq:P585. Use wdt:Pxxx for normalized/direct values when you don't need qualifiers. Example: p:P166 + pq:P585 gives award year.
  - P166 = award received. Use wdt:P166 for normalized/direct values when you only need award items. To get award years or qualifiers use p:P166 / ps:P166 / pq:P585. Many statements lack pq:P585 — use OPTIONAL { ?stmt pq:P585 ?date } and ORDER BY DESC(?date). When listing award winners, filter ?person wdt:P31 wd:Q5 to ensure humans.
  - Population time-series: use p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . To get the most recent value prefer a subquery to compute MAX(?date) per subject and join back to the statement to retrieve the matching population. Avoid HAVING(MAX(?date)=?date) — QLever can error. Inspect qualifiers/references for scope (city-proper vs metro).
  - wdt:P131* can be expensive and may be incomplete. First try direct wdt:P131 for immediate administrative parent(s). If missing, check wdt:P17 (country) and wdt:P625 (coordinates) or the place item's own statements. Prefer bounded traversal (1-2 hops) or explicit parent patterns and use DISTINCT/ORDER/LIMIT to de-duplicate rather than broad P131*.
  - Use search_property_of_entity to find candidate properties but always verify the property's semantics (domain/range) and whether it links to the office, office-holder, or related entity. Test with a small query before relying on results. For creative works, after locating candidate properties, filter results by instance-of (e.g., films wd:Q11424; board games wd:Q131436) to avoid non-work items.
  - Use search_property_of_entity to find relevant properties. Political parties are modelled inconsistently (P1142, P1313, P463, P39/P106). Fallback: inspect party pages/lists and MEP group memberships; document assumptions. For country government type prefer P122 (basic form of government); P1288 is inconsistent. To exclude historical states filter NOT EXISTS { ?item wdt:P576 ?d }.
  - Inspect statement references and qualifiers: use prov:wasDerivedFrom on p: statements to find sources, and check qualifiers (e.g., 'applies to part' or other scope qualifiers) to disambiguate city vs metro vs admin area. Reference labels can be empty; inspect reference node properties for URLs or publication dates.
  - Place of burial (P119): use wdt:P119 to get the direct burial place. If you need the city, follow P119 / wdt:P131 or check the burial place's wdt:P17 (country) or wdt:P625 (coordinates) if P131 is missing. Use p:P119 / ps:P119 / pq: qualifiers when you need burial-date or scope details and inspect references for source/context.
  - P118 (league/competition) is used inconsistently: sometimes on clubs, sometimes directly on players. When finding clubs use ?club wdt:P118 wd:Q... and verify ?club wdt:P31 wd:Q483394. If P118 returns players, filter by ?item wdt:P31 wd:Q5 to detect humans and avoid false positives. To get current players prefer club→p:P54 statements: ?player p:P54 ?stmt; ?stmt ps:P54 ?club. OPTIONAL{ ?stmt pq:P582 ?end } FILTER(!BOUND(?end)). Verify qualifiers/references and DOBs for plausibility.
  - Querying holders of offices (P39): prefer p:P39 / ps:P39 patterns to access qualifiers. To find current holder use: ?person p:P39 ?stmt. ?stmt ps:P39 wd:Q... . OPTIONAL{ ?stmt pq:P582 ?end } FILTER(!BOUND(?end)). Watch for duplicate statements (re-elections)  use DISTINCT or ORDER BY DESC(?start) LIMIT 1 on pq:P580. Avoid HAVING(MAX(...)=...) on QLever; prefer subqueries with ORDER/LIMIT.
  - Headquarters: use wdt:P159 for org HQs. If P159 gives a building follow that building's wdt:P131* (bounded hops). If P159 points to a place, verify its wdt:P31. Don't rely only on P17 for org-country—also check P159 and P276. Use UNION/nested patterns to cover ?org wdt:P17 wd:Q... OR ?org wdt:P159|wdt:P276 ?place . ?place wdt:P17 wd:Q... or ?place wdt:P131* wd:Q...

Index types used for entities and properties:
- Prefix index: Retrieves items by overlap between query keywords and item words. The query keywords can match item words exactly or as prefixes.
- Similarity index: Retrieves items by cosine similarity between their embeddings and the query embedding.

General notes across knowledge graphs:
- Use rdfs:label + FILTER(LANG(?label)='en') for labels; avoid SERVICE wikibase:label. If a query using SERVICE fails (e.g., HTTP 301 redirect), switch to rdfs:label + FILTER(LANG(...)) and/or schema:name. Language items (e.g., results from P2936) often lack rdfs:label@en; expect many hits without English labels—prefer P37 for official languages or map major languages by known QIDs.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct values; p:Pxxx/ps:Pxxx/pq:Pxxx for qualifiers. For roles: films use P161 (actor), directors P57; space missions use P1029 (crew). Query reverse and filter by instance-of film (wd:Q11424) or human (wd:Q5). For 'city' classification prefer ?x wdt:P31/wdt:P279* wd:Q515; use wdt:P2046 for area but check units/duplicates.
- When selecting latest qualifier-dated values (e.g., pq:P585 for award year or population), prefer a subquery to compute MAX(date) per subject and join back to get the matching statement/value. Avoid HAVING(MAX(?date)=?date) patterns on QLever (can cause engine errors). Use OPTIONAL for qualifiers when dates may be missing and GROUP_CONCAT for multi-values.
- Use search_entity to resolve names and verify instance-of (e.g., wdt:P31=wd:Q5). Note: search_entity can return class/designation items or noisy matches; constrain with instance-of, or search the related entity (e.g., country) and query its members. Use search_property_of_entity/search_autocomplete as fallback and always confirm P31 programmatically before trusting the top match.
- Use search_property to confirm property identifiers (e.g., "country" -> P17) before embedding them in queries. This prevents mistakes from assuming an ID and helps pick the best property if multiple choices exist.
- QLever endpoints do not support SERVICE wikibase:label — use rdfs:label + FILTER(LANG(...))='en'. Common SPARQL pitfalls: avoid '||' outside patterns (use UNION), put AND '&&' only inside FILTER, prefer VALUES over IN, and use rdfs:label filters to constrain language. When testing existence prefer FILTER EXISTS { ... } or OPTIONAL { ?s wdt:Pxx ?v . FILTER(?v = wd:Q...) } — writing OPTIONAL { ?s wdt:Pxx wd:Q... } creates no ?v for BOUND(?v). When execute() fails, simplify the query and test with list(...).
- Use list(...) to inspect all triples on an entity for exploration, but expect large results for notable items. Prefer wdt: properties or targeted p:/ps:/pq: patterns in SPARQL when you need normalized values or qualifiers, respectively.
- Before embedding property or item IDs in SPARQL, use search_property, search_property_of_entity, and search_object_of_property to discover the appropriate P/PID or object QIDs. This reduces errors from guessing IDs and surfaces better property alternatives and qualifiers. Also verify ambiguous item QIDs (e.g., currencies or template items) by checking rdfs:label and instance-of with list(...) before use.
- search_constrained and search_autocomplete can return no results for constrained queries or when the index is large. If they fail, fallback to search_entity + verify P31 (or use search_property_of_entity). Constrain with instance-of (wdt:P31) or other properties in the SPARQL to improve matches.
- When selecting awards by year, prefer xsd:dateTime range filters (e.g., >= "2001-01-01T00:00:00Z"^^xsd:dateTime && < "2002-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for portability and precision.
- When selecting award winners via wdt:P166, filter ?person wdt:P31 wd:Q5 to ensure humans — fictional characters or non-humans may have award claims. If you need award years use p:P166 / ps:P166 / pq:P585 and OPTIONAL { ?stmt pq:P585 ?year } to retrieve qualifier years.
- Use wdt:P625 (coordinates) together with wdt:P131* and wdt:P17 checks to validate a place's modern country or to filter by geography. When country labels mismatch historically, inspect admin parents and coordinates to resolve whether the item refers to historical/colonial scope or current boundaries.
- Prefer wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (e.g., pq:P585 for award year). Always fetch labels with rdfs:label + FILTER(LANG(?label) = "en"). When computing derived metrics (density/rates) confirm unit consistency (area units) and datatypes and do calculations inside SPARQL.
- If search_autocomplete returns too many candidates or errors, retry with constraints (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }) or narrower query text. If still failing, fallback to search_entity and verify wdt:P31 for type checks.
- For award-year queries use p:P166 / ps:P166 / pq:P585 to access award statements and year qualifiers. Filter by xsd:dateTime ranges (e.g., >= "2020-01-01T00:00:00Z"^^xsd:dateTime && < "2021-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for robustness.
- Use search_property_of_entity to surface relevant properties for an entity (e.g., 'birth' -> P569/P19; 'cause of death' -> P509; 'manner of death' -> P1196). list(...) can inspect triples but may return large results for notable items—prefer targeted wdt: patterns (wdt:Pxxx) or p:/ps:/pq: patterns when you need qualifiers.

SPARQL prefixes for use in function calls:
- bd: <http://www.bigdata.com/rdf#
- cc: <http://creativecommons.org/ns#
- data: <https://www.wikidata.org/wiki/Special:EntityData/
- dc: <http://purl.org/dc/elements/1.1/
- dct: <http://purl.org/dc/terms/
- foaf: <http://xmlns.com/foaf/0.1/
- geo: <http://www.opengis.net/ont/geosparql#
- geof: <http://www.opengis.net/def/function/geosparql/
- gn: <http://www.geonames.org/ontology#
- hint: <http://www.bigdata.com/queryHints#
- 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/
- qb: <http://purl.org/linked-data/cube#
- 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/
- void: <http://rdfs.org/ns/void#
- 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#

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not ask the user for clarifications without first attempting to complete the task. If the task input is incomplete or ambiguous, try to make reasonable assumptions and document them.
- 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.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- 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.
- 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.
- 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.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] How many seasons does Pose have?

[Assistant]
To determine how many seasons the TV series "Pose" has, I need to find information about the show in the knowledge graph. The key steps are:

1. Search for the entity "Pose" to identify its Wikidata item.
2. Once the entity is found, check for properties related to the number of seasons (e.g., "number of seasons" or similar).
3. If the direct property is not available, look for statements about episodes or air dates to infer the number of seasons.

Let me start by searching for the entity "Pose".

Question: Pose

Top 10 entity alternatives:
1. Pose (wd:Q30603704): 2018 American drama television series / is instance of television series / is instance of serial
2. posture (wd:Q8514257, matched via pose), also known as attitude, human position, pose: physical configuration that a human or animal body can take / is subclass of spatial arrangement
3. Pose (wd:Q29966944): is instance of single / 2017 song by Rihanna
4. Pose (wd:Q432543): is instance of single / single
5. pose (wd:Q1671968): position and orientation of an object in an image / is instance of technical term
6. Pose (wd:Q108250580): 2021 song by Red Velvet / 2021 song by Red Velvet / is instance of song
7. pose (wd:Q2105738), also known as posing: sustained posture or positioning of the torso and limbs / is subclass of posture
8. Pose (wd:Q85794108): 2021 single by Yo Gotti featuring Lil Uzi Vert / is instance of musical work/composition
9. Pose (wd:Q10352082): Album by Engenheiros do Hawaii / is instance of album
10. Pose (wd:Q25213080): has occupation artist / is instance of human / American artist

[Assistant]
The correct entity for the TV series "Pose" is `wd:Q30603704`. Now, I need to find information about the number of seasons for this TV series.

I will check the properties of this entity to see if there is a direct property for "number of seasons" (e.g., `wdt:P1112`). If not, I will look for other relevant properties such as episodes or air dates to infer the number of seasons.

Let me list the triples for `wd:Q30603704` to explore its properties.

list

wd:Q30603704

[Assistant]
From the triples, I can see that the TV series "Pose" has parts listed under `wdt:P527` (has part(s)), and one of the parts is "Pose, season 3" (`wd:Q106401384`). This suggests that there are multiple seasons, and season 3 is explicitly mentioned.

To determine the total number of seasons, I can look for all the season items that are parts of "Pose". The season items are likely to be instances of "television season" (`wd:Q21191275`). I will construct a query to find all the seasons of "Pose" by looking for items that are parts of `wd:Q30603704` and are instances of "television season".

Let me write a SPARQL query to count the number of seasons.

execute

SELECT (COUNT(?season) AS ?count) WHERE { wd:Q30603704 wdt:P527 ?season . ?season wdt:P31 wd:Q21191275 }

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

[Assistant]
The query returned 0, which suggests that the seasons are not directly linked via `wdt:P527` with the type `wd:Q21191275` (television season). This might mean that the season items are not explicitly typed as "television season" in Wikidata, or the property `wdt:P527` is not used consistently.

Let me check the structure of the season items listed under `wdt:P527`. I will retrieve the season items and inspect their properties to understand their structure better.

SELECT ?season WHERE { wd:Q30603704 wdt:P527 ?season }

[Assistant]
I can see that there are three season items for "Pose": season 1, season 2, and season 3. This indicates that the TV series has 3 seasons.

Even though the season items are not explicitly typed as "television season" (`wd:Q21191275`), the fact that there are three distinct season items linked via `wdt:P527` confirms that "Pose" has 3 seasons.

Thus, the answer is 3 seasons.

I will now provide the final SPARQL query and answer.

answer

SELECT (COUNT(?season) AS ?count) WHERE { wd:Q30603704 wdt:P527 ?season }

Answer: Pose has 3 seasons.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT ( COUNT( * ) AS ?count ) WHERE {
  wd:Q312674 p:P166 ?stmt .
  ?stmt ps:P166 wd:Q1422140
}

Using entities:
- Golden Globe Award for Best Original Score (wd:Q1422140): is instance of Golden Globe Awards / is instance of class of award / award
- Giorgio Moroder (wd:Q312674), also known as Giovanni Giorgio Moroder, Hansjörg Moroder: has occupation film producer / has occupation songwriter / has occupation composer / has occupation Film producer / has occupation disc jockey / has occupation record producer / has occupation film score composer / has occupation DJ / is instance of human / Italian record producer and composer

Using properties:
- award received (wd:P166 as p/ps), also known as award, awarded, awards, awards received, award won, etc.: award or recognition received by a person, organisation or creative work / award or recognition received by a person, organization or creative work / award or recognition received by a person, organization or creative work / awards received by a person, organisation or creative work

Got 1 row and 1 column:
| count       |
| ----------- |
| 2 (xsd:int) |

Answer: Giorgio Moroder has won the Golden Globe Award 2 times.

Giorgio Moroder has won the Golden Globe Award 2 times.

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT ( COUNT( * ) AS ?count ) WHERE {
  wd:Q312674 p:P166 ?stmt .
  ?stmt ps:P166 wd:Q1422140
}

Using entities:
- Golden Globe Award for Best Original Score (wd:Q1422140): is instance of Golden Globe Awards / is instance of class of award / award
- Giorgio Moroder (wd:Q312674), also known as Giovanni Giorgio Moroder, Hansjörg Moroder: has occupation film producer / has occupation songwriter / has occupation composer / has occupation Film producer / has occupation disc jockey / has occupation record producer / has occupation film score composer / has occupation DJ / is instance of human / Italian record producer and composer

Using properties:
- award received (wd:P166 as p/ps), also known as award, awarded, awards, awards received, award won, etc.: award or recognition received by a person, organisation or creative work / award or recognition received by a person, organization or creative work / award or recognition received by a person, organization or creative work / awards received by a person, organisation or creative work

Execution result:
Got 1 row and 1 column:
| count       |
| ----------- |
| 2 (xsd:int) |

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

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.

Available knowledge graphs:
- wikidata at https://qlever.dev/api/wikidata with prefix index for entities and similarity index for properties, and notes:
  - P106 = occupation. Use wdt:P106 to get occupations and fetch English labels with rdfs:label + FILTER(LANG(?label)='en'). When returning human occupations confirm the subject is human (wdt:P31 wd:Q5) to avoid non-human items. For 'main business' prefer more specific occupation items (e.g., real estate entrepreneur Q7301138) over broad labels like business magnate.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for direct values; for time-series use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect qualifiers (e.g., 'applies to part') and references (pr:P854) to detect scope. When computing density with area (wdt:P2046) confirm area unit/datatype (usually km²), ensure population and area present and >0, and BIND(?population/?area AS ?density).
  - Spouse nationality: do not rely only on P27. Check the spouse's place of birth (P19) and then the birthplace's country (P17) or admin parents (wdt:P131*). Many pre-unification German spouses appear under historical states—map those to modern countries when needed.
  - For languages on countries: prefer wdt:P37 (official language) and wdt:P2936 (language used). wdt:P1412 (languages spoken/written/signed) is more often used for peoples/individuals and may be empty for countries. Try P37 and P2936 first, then fall back to P1412/P407. Fetch English labels with rdfs:label + FILTER(LANG(?l)='en').
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Inception (P571) often uses Jan-01 placeholders for year-only dates; inspect p:P571 -> ps:P571 and check qualifiers/references for precision. For independence dates, also search for related event statements/items (e.g., 'declaration of independence') and prefer qualifiers/references over assuming day/month from year-only values.
  - To retrieve qualifiers (e.g., award year) use the statement/qualifier pattern: p:Pxxx / ps:Pxxx / pq:P585. Use wdt:Pxxx for normalized/direct values when you don't need qualifiers. Example: p:P166 + pq:P585 gives award year.
  - P166 = award received. Use wdt:P166 for normalized/direct values when you only need award items. To get award years or qualifiers use p:P166 / ps:P166 / pq:P585. Many statements lack pq:P585 — use OPTIONAL { ?stmt pq:P585 ?date } and ORDER BY DESC(?date). When listing award winners, filter ?person wdt:P31 wd:Q5 to ensure humans.
  - Population time-series: use p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . To get the most recent value prefer a subquery to compute MAX(?date) per subject and join back to the statement to retrieve the matching population. Avoid HAVING(MAX(?date)=?date) — QLever can error. Inspect qualifiers/references for scope (city-proper vs metro).
  - wdt:P131* can be expensive and may be incomplete. First try direct wdt:P131 for immediate administrative parent(s). If missing, check wdt:P17 (country) and wdt:P625 (coordinates) or the place item's own statements. Prefer bounded traversal (1-2 hops) or explicit parent patterns and use DISTINCT/ORDER/LIMIT to de-duplicate rather than broad P131*.
  - Use search_property_of_entity to find candidate properties but always verify the property's semantics (domain/range) and whether it links to the office, office-holder, or related entity. Test with a small query before relying on results. For creative works, after locating candidate properties, filter results by instance-of (e.g., films wd:Q11424; board games wd:Q131436) to avoid non-work items.
  - Use search_property_of_entity to find relevant properties. Political parties are modelled inconsistently (P1142, P1313, P463, P39/P106). Fallback: inspect party pages/lists and MEP group memberships; document assumptions. For country government type prefer P122 (basic form of government); P1288 is inconsistent. To exclude historical states filter NOT EXISTS { ?item wdt:P576 ?d }.
  - Inspect statement references and qualifiers: use prov:wasDerivedFrom on p: statements to find sources, and check qualifiers (e.g., 'applies to part' or other scope qualifiers) to disambiguate city vs metro vs admin area. Reference labels can be empty; inspect reference node properties for URLs or publication dates.
  - Place of burial (P119): use wdt:P119 to get the direct burial place. If you need the city, follow P119 / wdt:P131 or check the burial place's wdt:P17 (country) or wdt:P625 (coordinates) if P131 is missing. Use p:P119 / ps:P119 / pq: qualifiers when you need burial-date or scope details and inspect references for source/context.
  - P118 (league/competition) is used inconsistently: sometimes on clubs, sometimes directly on players. When finding clubs use ?club wdt:P118 wd:Q... and verify ?club wdt:P31 wd:Q483394. If P118 returns players, filter by ?item wdt:P31 wd:Q5 to detect humans and avoid false positives. To get current players prefer club→p:P54 statements: ?player p:P54 ?stmt; ?stmt ps:P54 ?club. OPTIONAL{ ?stmt pq:P582 ?end } FILTER(!BOUND(?end)). Verify qualifiers/references and DOBs for plausibility.
  - Querying holders of offices (P39): prefer p:P39 / ps:P39 patterns to access qualifiers. To find current holder use: ?person p:P39 ?stmt. ?stmt ps:P39 wd:Q... . OPTIONAL{ ?stmt pq:P582 ?end } FILTER(!BOUND(?end)). Watch for duplicate statements (re-elections)  use DISTINCT or ORDER BY DESC(?start) LIMIT 1 on pq:P580. Avoid HAVING(MAX(...)=...) on QLever; prefer subqueries with ORDER/LIMIT.
  - Headquarters: use wdt:P159 for org HQs. If P159 gives a building follow that building's wdt:P131* (bounded hops). If P159 points to a place, verify its wdt:P31. Don't rely only on P17 for org-country—also check P159 and P276. Use UNION/nested patterns to cover ?org wdt:P17 wd:Q... OR ?org wdt:P159|wdt:P276 ?place . ?place wdt:P17 wd:Q... or ?place wdt:P131* wd:Q...

Index types used for entities and properties:
- Prefix index: Retrieves items by overlap between query keywords and item words. The query keywords can match item words exactly or as prefixes.
- Similarity index: Retrieves items by cosine similarity between their embeddings and the query embedding.

General notes across knowledge graphs:
- Use rdfs:label + FILTER(LANG(?label)='en') for labels; avoid SERVICE wikibase:label. If a query using SERVICE fails (e.g., HTTP 301 redirect), switch to rdfs:label + FILTER(LANG(...)) and/or schema:name. Language items (e.g., results from P2936) often lack rdfs:label@en; expect many hits without English labels—prefer P37 for official languages or map major languages by known QIDs.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct values; p:Pxxx/ps:Pxxx/pq:Pxxx for qualifiers. For roles: films use P161 (actor), directors P57; space missions use P1029 (crew). Query reverse and filter by instance-of film (wd:Q11424) or human (wd:Q5). For 'city' classification prefer ?x wdt:P31/wdt:P279* wd:Q515; use wdt:P2046 for area but check units/duplicates.
- When selecting latest qualifier-dated values (e.g., pq:P585 for award year or population), prefer a subquery to compute MAX(date) per subject and join back to get the matching statement/value. Avoid HAVING(MAX(?date)=?date) patterns on QLever (can cause engine errors). Use OPTIONAL for qualifiers when dates may be missing and GROUP_CONCAT for multi-values.
- Use search_entity to resolve names and verify instance-of (e.g., wdt:P31=wd:Q5). Note: search_entity can return class/designation items or noisy matches; constrain with instance-of, or search the related entity (e.g., country) and query its members. Use search_property_of_entity/search_autocomplete as fallback and always confirm P31 programmatically before trusting the top match.
- Use search_property to confirm property identifiers (e.g., "country" -> P17) before embedding them in queries. This prevents mistakes from assuming an ID and helps pick the best property if multiple choices exist.
- QLever endpoints do not support SERVICE wikibase:label — use rdfs:label + FILTER(LANG(...))='en'. Common SPARQL pitfalls: avoid '||' outside patterns (use UNION), put AND '&&' only inside FILTER, prefer VALUES over IN, and use rdfs:label filters to constrain language. When testing existence prefer FILTER EXISTS { ... } or OPTIONAL { ?s wdt:Pxx ?v . FILTER(?v = wd:Q...) } — writing OPTIONAL { ?s wdt:Pxx wd:Q... } creates no ?v for BOUND(?v). When execute() fails, simplify the query and test with list(...).
- Use list(...) to inspect all triples on an entity for exploration, but expect large results for notable items. Prefer wdt: properties or targeted p:/ps:/pq: patterns in SPARQL when you need normalized values or qualifiers, respectively.
- Before embedding property or item IDs in SPARQL, use search_property, search_property_of_entity, and search_object_of_property to discover the appropriate P/PID or object QIDs. This reduces errors from guessing IDs and surfaces better property alternatives and qualifiers. Also verify ambiguous item QIDs (e.g., currencies or template items) by checking rdfs:label and instance-of with list(...) before use.
- search_constrained and search_autocomplete can return no results for constrained queries or when the index is large. If they fail, fallback to search_entity + verify P31 (or use search_property_of_entity). Constrain with instance-of (wdt:P31) or other properties in the SPARQL to improve matches.
- When selecting awards by year, prefer xsd:dateTime range filters (e.g., >= "2001-01-01T00:00:00Z"^^xsd:dateTime && < "2002-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for portability and precision.
- When selecting award winners via wdt:P166, filter ?person wdt:P31 wd:Q5 to ensure humans — fictional characters or non-humans may have award claims. If you need award years use p:P166 / ps:P166 / pq:P585 and OPTIONAL { ?stmt pq:P585 ?year } to retrieve qualifier years.
- Use wdt:P625 (coordinates) together with wdt:P131* and wdt:P17 checks to validate a place's modern country or to filter by geography. When country labels mismatch historically, inspect admin parents and coordinates to resolve whether the item refers to historical/colonial scope or current boundaries.
- Prefer wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (e.g., pq:P585 for award year). Always fetch labels with rdfs:label + FILTER(LANG(?label) = "en"). When computing derived metrics (density/rates) confirm unit consistency (area units) and datatypes and do calculations inside SPARQL.
- If search_autocomplete returns too many candidates or errors, retry with constraints (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }) or narrower query text. If still failing, fallback to search_entity and verify wdt:P31 for type checks.
- For award-year queries use p:P166 / ps:P166 / pq:P585 to access award statements and year qualifiers. Filter by xsd:dateTime ranges (e.g., >= "2020-01-01T00:00:00Z"^^xsd:dateTime && < "2021-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for robustness.
- Use search_property_of_entity to surface relevant properties for an entity (e.g., 'birth' -> P569/P19; 'cause of death' -> P509; 'manner of death' -> P1196). list(...) can inspect triples but may return large results for notable items—prefer targeted wdt: patterns (wdt:Pxxx) or p:/ps:/pq: patterns when you need qualifiers.

SPARQL prefixes for use in function calls:
- bd: <http://www.bigdata.com/rdf#
- cc: <http://creativecommons.org/ns#
- data: <https://www.wikidata.org/wiki/Special:EntityData/
- dc: <http://purl.org/dc/elements/1.1/
- dct: <http://purl.org/dc/terms/
- foaf: <http://xmlns.com/foaf/0.1/
- geo: <http://www.opengis.net/ont/geosparql#
- geof: <http://www.opengis.net/def/function/geosparql/
- gn: <http://www.geonames.org/ontology#
- hint: <http://www.bigdata.com/queryHints#
- 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/
- qb: <http://purl.org/linked-data/cube#
- 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/
- void: <http://rdfs.org/ns/void#
- 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#

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not ask the user for clarifications without first attempting to complete the task. If the task input is incomplete or ambiguous, try to make reasonable assumptions and document them.
- 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.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- 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.
- 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.
- 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.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] how many times has Giorgio Moroder won golden globe award?

[Assistant]
To determine how many times Giorgio Moroder has won a Golden Globe Award, I need to:

1. Identify Giorgio Moroder's entity in Wikidata.
2. Find the awards he has received, specifically the Golden Globe Award.
3. Count the number of times he has won the Golden Globe Award.

### Step 1: Search for Giorgio Moroder's entity
I'll start by searching for Giorgio Moroder in Wikidata.

Question: Giorgio Moroder

Top 10 entity alternatives:
1. Giorgio Moroder (wd:Q312674), also known as Giovanni Giorgio Moroder, Hansjörg Moroder: Italian record producer and composer / has occupation film score composer / has occupation record producer / has occupation Film producer / has occupation film producer / has occupation disc jockey / has occupation songwriter / has occupation composer / is instance of human / has occupation DJ
2. Giorgio by Moroder (wd:Q15720519): is instance of musical work/composition / 2013 song by Daft Punk
3. Metropolis (wd:Q76367460, matched via Giorgio Moroder Presents Metropolis), also known as Giorgio Moroder Presents Metropolis: 1984 version by Giorgio Moroder / is instance of film
4. Philip Oakey & Giorgio Moroder (wd:Q7184164): 1989 self-titled studio album by Philip Oakey & Giorgio Moroder / is instance of album
5. Giorgio Napolitano (wd:Q1220): President of Italy from 2006 to 2015 / has occupation journalist / has occupation politician / is instance of human
6. Giorgio Vasari (wd:Q128027), also known as Aretino, Arettino, Dzhordzho Vazari, Georges Vasari, George Vasari, etc.: Italian painter, architect, writer and historian (1511-1574) / has occupation architectural draftsperson / has occupation art historian / has occupation visual artist / has occupation art theorist / has occupation aesthetician / has occupation biographer / has occupation architect / has occupation Architect / has occupation painter / has occupation writer / is instance of human
7. Georges Braque (wd:Q153793, matched via Giorgio Braque), also known as Braque, G. Braque, geo braque, geo. braque, George B., etc.: 20th-century French painter and sculptor / 20th-century French painter and sculptor / French painter and sculptor (1882-1963) / has occupation jewellery designer / has occupation jewelry designer / has occupation scenographer / has occupation draftsperson / has occupation lithographer / has occupation printmaker / has occupation collagist / has occupation designer / has occupation sculptor / has occupation painter / has occupation artist / has occupation drawer / is instance of human
8. Giorgio Chiellini (wd:Q80306): Italian sports executive and former association football player / has occupation association football player / has occupation sports commentator / has occupation soccer player / has occupation lawyer / is instance of human
9. Giorgione (wd:Q8459, matched via Giorgio), also known as Barbarella Giorgio detto il Giorgione, Cavaglier Georgione, Dzhordzhone, G. Barbarelli, genannt Giorgione Georgius Parbarelli, etc.: Venetian Renaissance painter (1478–1510) / has occupation visual artist / has occupation painter / is instance of human

[Assistant]
Giorgio Moroder's entity is `wd:Q312674`. Now, I'll check his awards by looking for the property related to awards received (P166) and specifically identify Golden Globe Award wins.

### Step 2: Check the awards received by Giorgio Moroder
I'll query for the awards he has received using the property `wdt:P166` (award received).

execute

SELECT ?award WHERE { wd:Q312674 wdt:P166 ?award }

Got 11 rows and 1 column, showing the first 5 and last 5 rows below:
| award                                                                  |
| ---------------------------------------------------------------------- |
| Academy Award for Best Original Song (wd:Q112243)                      |
| David di Donatello Award for Lifetime Achievement (wd:Q13634272)       |
| Grammy Award for Best Instrumental Composition (wd:Q13635451)          |
| Golden Globe Award for Best Original Score (wd:Q1422140)               |
| Commander of the Order of Merit of the Italian Republic (wd:Q14539729) |
| ...                                                                    |
| Grammy Award for Best Score Soundtrack for Visual Media (wd:Q4376972)  |
| Academy Award for Best Original Score (wd:Q488651)                     |
| Bambi Award (wd:Q630018)                                               |
| Grammy Award for Best Dance/Electronic Recording (wd:Q740968)          |
| Grammy Award for Album of the Year (wd:Q904528)                        |

[Assistant]
I can see that Giorgio Moroder has won the **Golden Globe Award for Best Original Score** (wd:Q1422140). Now, I need to determine how many times he has won this award.

The property `wdt:P166` gives the award items, but to count the number of wins, I need to check if there are multiple statements for the same award (e.g., multiple Golden Globe wins). I'll check if there are multiple statements for the Golden Globe Award using the statement-based pattern (`p:P166 / ps:P166`) to see if there are multiple award instances.

### Step 3: Check for multiple Golden Globe Award statements
I'll query for all award statements for Giorgio Moroder and filter for the Golden Globe Award for Best Original Score to count how many times he received it.

SELECT COUNT(*) AS ?count WHERE { wd:Q312674 p:P166 ?stmt . ?stmt ps:P166 wd:Q1422140 }

Call to function execute returned an error:
failed to parse input: parse error at position 7 for token COUNT with content 'COUNT' (the input most likely does not follow the grammar)

[Assistant]
I made an error in the SPARQL syntax. The `COUNT(*)` should be used in a `SELECT` clause with proper aliasing. Let me correct the query to count the number of Golden Globe Award statements for Giorgio Moroder.

### Corrected Step 3: Count the number of Golden Globe Award statements
I'll use the correct SPARQL syntax to count the number of times Giorgio Moroder received the Golden Globe Award for Best Original Score.

SELECT (COUNT(*) AS ?count) WHERE { wd:Q312674 p:P166 ?stmt . ?stmt ps:P166 wd:Q1422140 }

Got 1 row and 1 column:
| count       |
| ----------- |
| 2 (xsd:int) |

[Assistant]
Giorgio Moroder has won the Golden Globe Award for Best Original Score **2 times**.

### Final Answer:
The SPARQL query correctly counts the number of Golden Globe Award statements for Giorgio Moroder, and the result shows he has won it twice.

I will now provide the final answer.

answer

SELECT (COUNT(*) AS ?count) WHERE { wd:Q312674 p:P166 ?stmt . ?stmt ps:P166 wd:Q1422140 }

Answer: Giorgio Moroder has won the Golden Globe Award 2 times.