PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?partner ?partnerLabel WHERE {
  wd:Q2492923 wdt:P451 ?partner .
  ?partner rdfs:label ?partnerLabel
  FILTER ( LANG( ?partnerLabel ) = 'en' )
}

Using entities:
- Geralt of Rivia (wd:Q2492923), also known as Butcher of Blaviken, Geralt Roger Eric du Haute-Bellegarde, Gwynbleidd, Ravix of Fourhorn, White Wolf: has occupation swordfighter / has occupation witcher / has occupation monster hunter / is instance of video game character / is instance of animated character / is instance of television character / is instance of film character / is instance of witcher / is instance of literary character / fictional character from the Witcher series

Using properties:
- unmarried partner (wd:P451 as wdt), also known as boyfriend, cohabitant, domestic partner, enbyfriend, girlfriend, etc.: is subproperty of significant person / someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples / someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples

Got 4 rows and 2 columns:
| partner                              | partnerLabel                     |
| ------------------------------------ | -------------------------------- |
| Shani (wd:Q108678159)                | Shani (lang:en)                  |
| Fringilla Vigo (wd:Q11698917)        | Fringilla Vigo (lang:en)         |
| Yennefer of Vengerberg (wd:Q4205826) | Yennefer of Vengerberg (lang:en) |
| Triss Merigold (wd:Q4463198)         | Triss Merigold (lang:en)         |

Answer: Geralt of Rivia is the character who has two unmarried partners, Yennefer of Vengerberg and Triss Merigold.

Geralt of Rivia is the character who has two unmarried partners, Yennefer of Vengerberg and Triss Merigold.

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?partner ?partnerLabel WHERE {
  wd:Q2492923 wdt:P451 ?partner .
  ?partner rdfs:label ?partnerLabel
  FILTER ( LANG( ?partnerLabel ) = 'en' )
}

Using entities:
- Geralt of Rivia (wd:Q2492923), also known as Butcher of Blaviken, Geralt Roger Eric du Haute-Bellegarde, Gwynbleidd, Ravix of Fourhorn, White Wolf: has occupation swordfighter / has occupation witcher / has occupation monster hunter / is instance of video game character / is instance of animated character / is instance of television character / is instance of film character / is instance of witcher / is instance of literary character / fictional character from the Witcher series

Using properties:
- unmarried partner (wd:P451 as wdt), also known as boyfriend, cohabitant, domestic partner, enbyfriend, girlfriend, etc.: is subproperty of significant person / someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples / someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples

Execution result:
Got 4 rows and 2 columns:
| partner                              | partnerLabel                     |
| ------------------------------------ | -------------------------------- |
| Shani (wd:Q108678159)                | Shani (lang:en)                  |
| Fringilla Vigo (wd:Q11698917)        | Fringilla Vigo (lang:en)         |
| Yennefer of Vengerberg (wd:Q4205826) | Yennefer of Vengerberg (lang:en) |
| Triss Merigold (wd:Q4463198)         | Triss Merigold (lang:en)         |

[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] among the characters in the witcher, who has two unmarried partners, Yennefer of Vengerberg and Triss Merigold?

[Assistant]
To answer this question, we need to identify the character from *The Witcher* series who has two unmarried partners: Yennefer of Vengerberg and Triss Merigold. This character is Geralt of Rivia.

### Step 1: Confirm the character's identity
We know from the lore of *The Witcher* that Geralt of Rivia is the protagonist and is romantically involved with both Yennefer and Triss. We need to verify this in Wikidata.

First, let's search for Geralt of Rivia in Wikidata.

Question: Geralt of Rivia

Top 10 entity alternatives:
1. Geralt of Rivia (wd:Q2492923), also known as Butcher of Blaviken, Geralt Roger Eric du Haute-Bellegarde, Gwynbleidd, Ravix of Fourhorn, White Wolf: fictional character from the Witcher series / is instance of video game character / is instance of television character / is instance of literary character / is instance of animated character / has occupation monster hunter / is instance of film character / has occupation swordfighter / has occupation witcher / is instance of witcher
2. Geralt of Rivia (wd:Q117744310): is instance of mural / mural in Lodz
3. United States (wd:Q30, matched via US of A), also known as America, Merica, Murica, the States, the United States, etc.: country located primarily in North America / is instance of constitutional republic / is instance of democratic republic / is instance of federal republic / is instance of Federal republic / is instance of sovereign state / is instance of superpower / country in North America / country in North America / is instance of country
4. Turkey (wd:Q43, matched via Republic of Turkey), also known as Republic of Turkey, Republic of Türkiye, TR, TUR, Turkiye, etc.: transcontinental country straddling Western Asia and Southeastern Europe / country spanning Southeastern Europe and Western Asia / country in West Asia and Southeast Europe / is instance of transcontinental country / is instance of Mediterranean country / country spanning in Europe and Asia / is instance of sovereign state / is instance of successor state / is instance of regional power / is instance of secular state / is instance of Secular state / is instance of social state / is instance of country
5. Russia (wd:Q159, matched via Federation of Russia), also known as Federation of Russia, ISO 3166-1:RU, RF, ru, RU, etc.: country in Eastern Europe and Northern Asia / country in Eastern Europe and Northern Asia / is instance of transcontinental country / country in both Europe and Asia / country in both Europe and Asia / is instance of sovereign state / is instance of successor state / is instance of Secular state / is instance of secular state / is instance of federation / is instance of country
6. Japan (wd:Q17, matched via State of Japan), also known as Iapan, Iapon, Ja, JA, Jap, etc.: is instance of sovereign state / is instance of island country / is instance of island nation / island country in East Asia / island nation in East Asia / is instance of country
7. Italy (wd:Q38, matched via Republic of Italy), also known as 🇮🇹, IT, ITA, Italia, Italian Republic, etc.: is instance of sovereign state / republic in Southern Europe / country in Southern Europe / is instance of country / country in Europe
8. Finland (wd:Q33, matched via Republic of Finland), also known as 🇫🇮, fi, FIN, Finnia, Land of Thousand Lakes, etc.: is instance of country bordering the Baltic Sea / is instance of sovereign state / is instance of unitary state / country in Northern Europe / country in Northern Europe / country in Northern Europe / is instance of country
9. Mexico (wd:Q96, matched via US of M), also known as MEX, Mexican Republic, Mexican United States, mx, MX, etc.: is instance of sovereign state / country in North America / country in North America / country in North America / country in North America / is instance of country
10. India (wd:Q668, matched via Republic of India), also known as Bharat, Bharata, Bharat Ganarajya, Bharatvarsh, Hindoostan, etc.: is instance of constitutional republic / is instance of democratic republic / is instance of Federal republic / is instance of federal republic / is instance of sovereign state / is instance of Republic / is instance of republic / is instance of country / country in South Asia

[Assistant]
The correct entity for Geralt of Rivia is `wd:Q2492923`. Now, we need to verify his romantic relationships with Yennefer of Vengerberg and Triss Merigold.

### Step 2: Search for Yennefer of Vengerberg and Triss Merigold
Let’s search for Yennefer of Vengerberg and Triss Merigold in Wikidata to get their QIDs.

Question: Yennefer of Vengerberg

Top 10 entity alternatives:
1. Yennefer of Vengerberg (wd:Q4205826), also known as Yen, Yenna: is instance of half-elf in a work of fiction / fictional sorceress from the Witcher series / is instance of television character / is instance of video game character / is instance of fictional humanoid / is instance of literary character / is instance of fictional human / is instance of film character / has occupation conspirator / has occupation magician / has occupation adviser
2. United States (wd:Q30, matched via US of A), also known as America, Merica, Murica, the States, the United States, etc.: country located primarily in North America / is instance of constitutional republic / is instance of democratic republic / is instance of federal republic / is instance of Federal republic / is instance of sovereign state / is instance of superpower / country in North America / country in North America / is instance of country
3. Turkey (wd:Q43, matched via Republic of Turkey), also known as Republic of Turkey, Republic of Türkiye, TR, TUR, Turkiye, etc.: transcontinental country straddling Western Asia and Southeastern Europe / country spanning Southeastern Europe and Western Asia / country in West Asia and Southeast Europe / is instance of transcontinental country / is instance of Mediterranean country / country spanning in Europe and Asia / is instance of successor state / is instance of sovereign state / is instance of regional power / is instance of secular state / is instance of Secular state / is instance of social state / is instance of country
4. Russia (wd:Q159, matched via Federation of Russia), also known as Federation of Russia, ISO 3166-1:RU, RF, ru, RU, etc.: country in Eastern Europe and Northern Asia / country in Eastern Europe and Northern Asia / is instance of transcontinental country / country in both Europe and Asia / country in both Europe and Asia / is instance of sovereign state / is instance of successor state / is instance of secular state / is instance of Secular state / is instance of federation / is instance of country
5. Japan (wd:Q17, matched via State of Japan), also known as Iapan, Iapon, Ja, JA, Jap, etc.: is instance of sovereign state / is instance of island country / is instance of island nation / island country in East Asia / island nation in East Asia / is instance of country
6. Italy (wd:Q38, matched via Republic of Italy), also known as 🇮🇹, IT, ITA, Italia, Italian Republic, etc.: is instance of sovereign state / republic in Southern Europe / country in Southern Europe / is instance of country / country in Europe
7. Finland (wd:Q33, matched via Republic of Finland), also known as 🇫🇮, fi, FIN, Finnia, Land of Thousand Lakes, etc.: is instance of country bordering the Baltic Sea / is instance of sovereign state / is instance of unitary state / country in Northern Europe / country in Northern Europe / country in Northern Europe / is instance of country
8. Mexico (wd:Q96, matched via US of M), also known as MEX, Mexican Republic, Mexican United States, mx, MX, etc.: is instance of sovereign state / country in North America / country in North America / country in North America / country in North America / is instance of country
9. India (wd:Q668, matched via Republic of India), also known as Bharat, Bharata, Bharat Ganarajya, Bharatvarsh, Hindoostan, etc.: is instance of constitutional republic / is instance of democratic republic / is instance of Federal republic / is instance of federal republic / is instance of sovereign state / is instance of republic / is instance of Republic / is instance of country / country in South Asia
10. Spain (wd:Q29, matched via Kingdom of Spain), also known as Kingdom of Spain: country in southwestern Europe with territories in Africa / constitutional monarchy in Southwest Europe / constitutional monarchy in Southwest Europe / is instance of Mediterranean country / is instance of sovereign state / is instance of nation state / is instance of country / is instance of realm

[Assistant]
Yennefer of Vengerberg is `wd:Q4205826`. Now let's search for Triss Merigold.

Question: Triss Merigold

Top 10 entity alternatives:
1. Triss Merigold (wd:Q4463198), also known as Fourteenth of the Hill, Merigold the Fearless: fictional sorceress from the Witcher series / is instance of video game character / is instance of television character / is instance of literary character / is instance of fictional human / is instance of film character / has occupation magician / has occupation adviser / has occupation healer
2. Merigold (wd:Q867528), also known as Merigold (Mississippi), Merigold, Mississippi, Merigold, MS: town in Bolivar County, Mississippi, United States / is instance of town in the United States
3. Triss (wd:Q3204428): is instance of literary work / 2002 novel by Brian Jacques
4. Merigold Brake (wd:Q34740101): swamp in Bolivar County, Mississippi, United States of America / is instance of swamp
5. Triss (wd:Q15113667): has occupation Military personnel / has occupation military personnel / is instance of literary character / character in the Shannara / is instance of character / is instance of elf
6. Merigold Cemetery (wd:Q117690282): cemetery in Bolivar County, Mississippi, United States / is instance of cemetery
7. Merigold (wd:Q37520933): is instance of family name / is instance of surname / family name / family name / surname
8. Trissino (wd:Q47238): is instance of commune of Italy / Italian comune / Italian comune / Italian comune
9. Gian Giorgio Trissino (wd:Q436738, matched via Giangiorgio Trissino), also known as Giangiorgio, Count Trissino, Giangiorgio Trissino, Giovan Giorgio Trissino, Giovanni Giorgio Trissino: Italian Renaissance humanist / has occupation Philosopher / has occupation philosopher / has occupation playwright / has occupation writer / is instance of human / has occupation poet
10. Trissolcus (wd:Q14495753): is instance of taxon / genus of insects

[Assistant]
Triss Merigold is `wd:Q4463198`. Now we need to find the character who has romantic relationships with both Yennefer (`wd:Q4205826`) and Triss (`wd:Q4463198`).

### Step 3: Check relationships of Geralt of Rivia
We'll examine the relationships of Geralt of Rivia (`wd:Q2492923`) to see if he is linked to Yennefer and Triss. The property for romantic relationships or partner is often `wdt:P451` (partner) or `wdt:P26` (spouse). Since they are unmarried partners, `wdt:P451` is more appropriate.

Let’s check if Geralt has these two as partners.

execute

SELECT ?partner ?partnerLabel WHERE { wd:Q2492923 wdt:P451 ?partner. 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]
The error occurred because we used `SERVICE wikibase:label`, which is not supported in QLever. We need to use `rdfs:label` with a language filter instead.

Let’s rewrite the query to fetch the partner labels using `rdfs:label` and `FILTER(LANG(?partnerLabel) = 'en')`.

SELECT ?partner ?partnerLabel WHERE { wd:Q2492923 wdt:P451 ?partner. ?partner rdfs:label ?partnerLabel FILTER(LANG(?partnerLabel) = 'en') }

Got 4 rows and 2 columns:
| partner                              | partnerLabel                     |
| ------------------------------------ | -------------------------------- |
| Shani (wd:Q108678159)                | Shani (lang:en)                  |
| Fringilla Vigo (wd:Q11698917)        | Fringilla Vigo (lang:en)         |
| Yennefer of Vengerberg (wd:Q4205826) | Yennefer of Vengerberg (lang:en) |
| Triss Merigold (wd:Q4463198)         | Triss Merigold (lang:en)         |

[Assistant]
Perfect! The query confirms that Geralt of Rivia (`wd:Q2492923`) has four partners listed in Wikidata: Shani, Fringilla Vigo, Yennefer of Vengerberg, and Triss Merigold. Among these, the two mentioned in the question — Yennefer of Vengerberg and Triss Merigold — are indeed his partners.

Thus, the character who has two unmarried partners, Yennefer of Vengerberg and Triss Merigold, is **Geralt of Rivia**.

### Final Answer:
The character is Geralt of Rivia. The SPARQL query to confirm this is:

```sparql
SELECT ?partner ?partnerLabel WHERE {
  wd:Q2492923 wdt:P451 ?partner.
  ?partner rdfs:label ?partnerLabel FILTER(LANG(?partnerLabel) = 'en')
}
```

This query returns Yennefer of Vengerberg and Triss Merigold as two of Geralt's partners.

Now, I will provide the final answer.

answer

SELECT ?partner ?partnerLabel WHERE { wd:Q2492923 wdt:P451 ?partner. ?partner rdfs:label ?partnerLabel FILTER(LANG(?partnerLabel) = 'en') }

Answer: Geralt of Rivia is the character who has two unmarried partners, Yennefer of Vengerberg and Triss Merigold.


PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT ( COUNT( DISTINCT ?person ) AS ?count ) WHERE {
  ?person p:P39 ?stmt .
  ?stmt ps:P39 wd:Q1362118 .
  ?person wdt:P31 wd:Q5 .
  ?person wdt:P21 wd:Q6581072
}

Using entities:
- First Mayor of Hamburg (wd:Q1362118), also known as Erster Bürgermeister: is instance of position / is subclass of mayor of a place in Germany / is subclass of head of government / is subclass of Head of government / is subclass of Minister-President / head of government of the city and state of Hamburg, Germany
- 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

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
- position held (wd:P39 as p/ps), also known as function, held position, holds position, job, office held, etc.: subject currently or formerly holds the object position or public office / subject currently or formerly holds the object position or public office / subject currently or formerly holds the object position or public office / subject currently or formerly holds the object position or public office

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

Answer: Hamburg has had 0 different female mayors.

Hamburg has had 0 different female mayors.

SPARQL query over wikidata:
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT ( COUNT( DISTINCT ?person ) AS ?count ) WHERE {
  ?person p:P39 ?stmt .
  ?stmt ps:P39 wd:Q1362118 .
  ?person wdt:P31 wd:Q5 .
  ?person wdt:P21 wd:Q6581072
}

Using entities:
- First Mayor of Hamburg (wd:Q1362118), also known as Erster Bürgermeister: is instance of position / is subclass of mayor of a place in Germany / is subclass of head of government / is subclass of Head of government / is subclass of Minister-President / head of government of the city and state of Hamburg, Germany
- 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

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
- position held (wd:P39 as p/ps), also known as function, held position, holds position, job, office held, etc.: subject currently or formerly holds the object position or public office / subject currently or formerly holds the object position or public office / subject currently or formerly holds the object position or public office / subject currently or formerly holds the object position or public office

Execution result:
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (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 different female mayors did Hamburg have?

[Assistant]
To determine how many different female mayors Hamburg had, I need to:

1. Identify the entity for Hamburg.
2. Find the property that links mayors to Hamburg (likely P39: position held).
3. Identify female mayors by checking gender (P21: gender) and ensuring the value is female (wd:Q6581072).
4. Ensure the position held is specifically "mayor of Hamburg" (not just any mayor).
5. Count the distinct female mayors.

Let me start by searching for the entity for Hamburg.

Question: Hamburg

Top 10 entity alternatives:
1. Hamburg (wd:Q1055), also known as Free and Hanseatic City of Hamburg, Hamburg, Freie und Hansestadt, Hamburg, Germany: is instance of Unitary municipality in Germany / is instance of urban municipality in Germany / is instance of Lists of former Hansa cities / is instance of federated state of Germany / is instance of urban district of Hamburg / is instance of federal state of Germany / city and state in the North of Germany / is instance of Free imperial city / is instance of free imperial city / city and federal state in Germany / is instance of Hanseatic city / is instance of Metropolis / is instance of metropolis / is instance of city-state / is instance of port city / is instance of big city
2. Hamburger SV (wd:Q51974, matched via Hamburg), also known as Hamburg, Hamburger, Hamburger Sport-Verein, Hamburger S.V., HamburgerSV, etc.: German sports club based in Hamburg, with its largest branch being its football department / German professional sport club based in Hamburg, founded in 1887 / German professional sport club based in Hamburg, founded in 1887 / is instance of sports club / is instance of sport club
3. Hamburg (wd:Q1005682), also known as Hamburg, Minnesota, Hamburg, MN: city in Carver County, Minnesota, United States / is instance of city in the United States / is instance of city of the United States
4. Hamburg (wd:Q998718), also known as Hamburg, IA, Hamburg, Iowa: city in Fremont County, Iowa, United States / city in Fremont County, Iowa, United States / is instance of city of the United States / is instance of city in the United States
5. Hamburg (wd:Q79657), also known as Hamburg, AR, Hamburg, Arkansas: city in Ashley County, Arkansas, United States / is instance of city of the United States / is instance of city in the United States
6. Hamburg (wd:Q1082931), also known as Hamburg, New Jersey, Hamburg, NJ: borough in Sussex County, New Jersey, United States / is instance of borough of New Jersey
7. Hamburg (wd:Q742785), also known as Hamburg, New York, Hamburg, NY: town in Erie County, New York, United States / is instance of town in the United States / is instance of town of New York
8. Hamburg (wd:Q2672139), also known as Hamburg, IL: village in Calhoun County, Illinois, United States / is instance of village of Illinois
9. Hamburg (wd:Q1181264), also known as Hamburg, PA, Hamburg, Pennsylvania: borough in Berks County, Pennsylvania, United States / is instance of borough of Pennsylvania
10. Hamburg (wd:Q939795), also known as Hamburgh: breed of chicken developed in Germany and Holland prior to 1700 / is instance of chicken breed / is subclass of chicken / is subclass of Chicken

[Assistant]
The correct entity for Hamburg is `wd:Q1055` (Free and Hanseatic City of Hamburg). Now, I need to find the property that links mayors to Hamburg. The most relevant property is `wdt:P39` (position held). I will check what positions are associated with Hamburg.

I'll search for properties related to "mayor" for Hamburg.

Question: mayor

Top 10 property alternatives:
1. head of government (wd:P6 as p/wdt, matched via mayor), also known as chancellor, executive power headed by, first minister, government headed by, governor, etc.: head of the executive power of this town, city, municipality, state, country, or other governmental body / head of the executive power of a town, city, municipality, state, country, or other governmental body / head of the executive power of a town, city, municipality, state, country, or other governmental body / is subproperty of director / manager / is subproperty of manager/director / is subproperty of director/manager / is subproperty of chairperson
2. legislative body (wd:P194 as p/wdt, matched via municipal council), also known as aboriginal council, assembly, city council, council, diet, etc.: legislative body governing this entity; political institution ... representatives, such as a parliament/legislature or council / legislative body governing this entity; political institution ... representatives, such as a parliament/legislature or council / legislative body governing this entity; political institution ... representatives, such as a parliament/legislature or council
3. capital of (wd:P1376 as p/wdt, matched via national capital), also known as administrative seat of, capital, county seat for, county seat of, is capital of, etc.: country, state, department, canton or other administrative division of which the municipality is the governmental seat / country, state, department, canton or other administrative division of which the municipality is the governmental seat / country, state, department, canton or other administrative division of which the municipality is the governmental seat / is subproperty of located in the administrative territorial entity
4. office held by head of government (wd:P1313 as p/wdt, matched via position of head of government), also known as head of government's office, office of head of government, position held by head of government, position of head of government, title of head of government, etc.: political office that is fulfilled by the head of the government of this item / political office that is fulfilled by the head of the government of this item
5. located in the administrative territorial entity (wd:P131 as p/wdt, matched via town), also known as administrative territorial entity, administrative territory, city, happens in, in, etc.: the item is located on the territory of the following ... the item falls only partially into the administrative entity. / the item is located on the territory of the following ... the item falls only partially into the administrative entity. / the item is located on the territory of the following administrative entity / the item is located on the territory of the following administrative entity / is subproperty of location / is subproperty of part of
6. executive body (wd:P208 as p/wdt, matched via executive branch), also known as executive branch, P208: branch of government for the daily administration of the territorial entity / branch of government for the daily administration of the state
7. highest judicial authority (wd:P209 as p/wdt, matched via court), also known as court, has supreme court, highest court, judicial branch, judiciary, etc.: supreme judicial body within a country, administrative division, or other organization / supreme judicial body within a country, administrative division, or other organization
8. population (wd:P1082 as p/wdt), also known as human population, inhabitants, P1082: number of people inhabiting the place; number of people of subject / number of people inhabiting the place; number of people of subject / number of people inhabiting the place; number of people of subject
9. twinned administrative body (wd:P190 as p/wdt, matched via sister city), also known as partner city, partner town, sister city, sister town, twin cities, etc.: twin towns, sister cities, twinned municipalities and other ... legally or informally acknowledged by their governments / twin towns, sister cities, twinned municipalities and other localities / twin towns, sister cities, twinned municipalities and other localities
10. postal code (wd:P281 as p/wdt, matched via zipcode), also known as pincode, PIN Code, postal index number, post code, postcode, etc.: identifier assigned by postal authorities for the subject area or building for the purpose of sorting and routing mail / identifier assigned by postal authorities for the subject area / identifier assigned by postal authorities for the subject area

No other items found

[Assistant]
The property `wdt:P39` (position held) is the most relevant for identifying mayors of Hamburg. I will now check what specific positions are held by individuals in Hamburg using `wdt:P39`.

I'll query for all position holders of Hamburg and check if any of them are mayors.

execute

SELECT ?person ?position WHERE { wd:Q1055 wdt:P39 ?position . ?person wdt:P39 ?position . ?position rdfs:label ?label . FILTER(LANG(?label)='en') } LIMIT 10

[Assistant]
It seems the direct use of `wdt:P39` on Hamburg did not return results. This suggests that the mayors are linked via statements with qualifiers or through the `p:P39` pattern (statement-based). I'll try querying using the statement pattern `p:P39` to get the position held statements for Hamburg.

I'll also check if any of these positions are labeled as "mayor" or similar.

SELECT ?person ?position WHERE { wd:Q1055 p:P39 ?stmt . ?stmt ps:P39 ?position . ?position rdfs:label ?label . FILTER(LANG(?label)='en') } LIMIT 10

[Assistant]
Still no results. This suggests that the mayors of Hamburg might not be directly linked via `P39` to Hamburg, or the position item might not have an English label. Let me check the possible position items that could represent "mayor of Hamburg" by searching for entities related to "mayor" and checking their labels.

I'll search for entities related to "mayor" and see if any match Hamburg's context.

Question: mayor of Hamburg

Top 10 entity alternatives:
1. First Mayor of Hamburg (wd:Q1362118), also known as Erster Bürgermeister: head of government of the city and state of Hamburg, Germany / is subclass of mayor of a place in Germany / is subclass of head of government / is subclass of Head of government / is subclass of Minister-President / is instance of position
2. mayor of Novo Hamburgo (wd:Q130279902): head of the Executive Power in the Brazilian municipality of Novo Hamburgo / is subclass of elective office / is subclass of public office / is instance of mayor
3. [Response to the mayor of Hamburg] (wd:Q69674806): scientific article published on 01 October 1986 / is instance of scholarly article / is instance of academic article
4. mayor of Mayorga (wd:Q131146491): is subclass of mayor of a place in the Philippines / is instance of position / Philippines
5. mayor of Mayorga (wd:Q26697017): is instance of public office / political position in Spain / is subclass of alcalde
6. mayor of Cumbres Mayores (wd:Q26692277): is instance of public office / political position in Spain / is subclass of alcalde
7. mayor of Zizur Mayor (wd:Q26690555), also known as mayor of Zizur Mayor-Zizur Nagusia: is instance of public office / political position in Spain / is subclass of alcalde
8. mayor of Isla Mayor (wd:Q26691847): is instance of public office / political position in Spain / is subclass of alcalde
9. mayor of Mansilla Mayor (wd:Q26691234): is instance of public office / political position in Spain / is subclass of alcalde
10. University of Hamburg (wd:Q156725), also known as Hamburg University, Universität Hamburg: is instance of University of Excellence / is instance of comprehensive university / is instance of public university / university in Hamburg, Germany

[Assistant]
I found the relevant entity: `wd:Q1362118` (First Mayor of Hamburg). This is the position held by the mayor of Hamburg. Now I need to find all individuals who have held this position and are female.

To do this:
1. Find all people (`wdt:P31 wd:Q5`) who have held the position `wd:Q1362118`.
2. Filter for those whose gender is female (`wdt:P21 wd:Q6581072`).
3. Count the distinct individuals.

I'll write a SPARQL query to achieve this.

SELECT DISTINCT ?person WHERE { ?person wdt:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 }

[Assistant]
No results returned. This could mean that the position `wd:Q1362118` is not directly linked via `wdt:P39` to the individuals, or the data is stored using the statement pattern (`p:P39`). Let me check using the statement pattern to see if the mayors are linked via qualifiers or statements.

I'll query using `p:P39` to get the statements for the position held by individuals, filtering for the position `wd:Q1362118` and gender female.

SELECT DISTINCT ?person WHERE { ?person p:P39 ?stmt . ?stmt ps:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 }

[Assistant]
Still no results. This suggests that either:

1. The position `wd:Q1362118` is not used directly in the data, or
2. The mayors are linked through a different property or position item.

Let me check what positions are held by people who are mayors of Hamburg by querying for any position held by individuals associated with Hamburg, and inspecting the labels.

I'll query for all position holders of Hamburg using `p:P39` and get the position labels.

SELECT DISTINCT ?person ?position ?label WHERE { wd:Q1055 p:P39 ?stmt . ?stmt ps:P39 ?position . ?position rdfs:label ?label . FILTER(LANG(?label)='en') } LIMIT 20

[Assistant]
Still no results. This is unusual. Let me check if there are any people who have held a mayoral position in Hamburg by searching for individuals who are mayors and whose place of work or office is Hamburg.

I'll search for people who are mayors (using occupation or position) and check if their place of work is Hamburg.

Question: mayor

Top 10 entity alternatives:
1. mayor (wd:Q30185), also known as Mayor: head of municipal government such as a town or city / head of municipal government such as a town or city / head of municipal government such as a town or city / head of municipal government such as a town or city / is subclass of head of government / is subclass of Head of government / is subclass of local politician / is subclass of elective office / is subclass of governing body / is subclass of head of a city / is subclass of authority / is instance of position
2. burgomaster (wd:Q177529, matched via mayor), also known as burgemeester, burgermeister, burgmeister, mayor: archaic term for a mayor (to be used with P39) / is subclass of chief magistrate / is instance of position / is subclass of mayor
3. city head (wd:Q4145633, matched via mayor), also known as head of city, head of town, mayor, town head: is subclass of local politician / is instance of public office / is subclass of public office / is instance of occupation / is subclass of authority / is instance of position / is subclass of mayor / position
4. mayor (wd:Q17175157), also known as mayor (Germany), mayor of a place in Germany: is instance of position / mayor of a German town / is subclass of mayor
5. military staff (wd:Q6813432, matched via Plana Mayor), also known as Flat Major, General staff, Plana Mayor, Senior staff, staff (military): group of military officers that are responsible for the needs of a unit / is subclass of military organisation / is subclass of military organization / is subclass of Military organization / is subclass of military profession / is subclass of military unit / is subclass of command
6. Lord Mayor (wd:Q11902879): municipal executive for certain large cities in the Commonwealth / is instance of title of honour / is instance of title of honor / is instance of public office / is instance of occupation / is subclass of mayor
7. deputy mayor (wd:Q581817), also known as vice mayor: governance position - the second in command mayor / is subclass of local politician / is instance of elective office / is instance of public office / is instance of position / is subclass of deputy
8. Mayor of New York City (wd:Q785304, matched via NYC Mayor), also known as Mayor of New York City, New York, Mayor of NYC, Mayor of the City of New York, Mayor of the City of New York City, Mayor of the City of NYC, etc.: head of the executive branch of New York City's government / is subclass of mayor of a place in New York / is instance of public office
9. night mayor (wd:Q4494591), also known as night czar, nightmayor, Night Mayor: is subclass of civil service / is subclass of civil servant / is instance of position / is instance of title / unofficial title
10. caballerizo mayor del rey (wd:Q5015195, matched via Caballerizo mayor), also known as Caballerizo mayor, Equerry, Gentleman of the Horse, Great Equerry: the Officer of the Royal Household and Heritage of the Crown of Spain / is subclass of Count of the Stable / is instance of court appointment / is instance of nobility of Spain / is subclass of caballerizo / is instance of position

No other items found

No literal items found

[Assistant]
The position "mayor" is represented by `wd:Q30185`. Now, I'll check if any of the mayors of Hamburg are linked via `wdt:P106` (occupation) to `wd:Q30185`, and then check if they are female.

I'll query for people whose occupation is mayor and who are associated with Hamburg (via place of work or residence). But since Hamburg is a city-state, the mayor's position is directly tied to Hamburg, so I'll try to find mayors of Hamburg by checking if they are linked to Hamburg via `wdt:P131` (located in the administrative territorial entity) or `wdt:P17` (country).

Alternatively, I'll search for people who are mayors (`wdt:P106 wd:Q30185`) and are female (`wdt:P21 wd:Q6581072`), and then check if any of them are associated with Hamburg.

Let me try this:

SELECT DISTINCT ?person WHERE { ?person wdt:P106 wd:Q30185 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 } LIMIT 10

Got 10 rows and 1 column:
| person                                              |
| --------------------------------------------------- |
| Dárcy Vera (wd:Q10269224)                           |
| Marcia Rosa (wd:Q10325226)                          |
| Emmie Craddock (wd:Q104056876)                      |
| Reyna Rueda (wd:Q104216727)                         |
| Rohana Muthalib (wd:Q104422425)                     |
| Jan Barnes (wd:Q104591833)                          |
| Katharina Woess-Krall (wd:Q104975423)               |
| Abiba Dafia Ouassagari (wd:Q105085918)              |
| Rosette Ndongo Mengolo-Mboutchouang (wd:Q105971474) |
| Radka Vladyková (wd:Q106094715)                     |

[Assistant]
These are female mayors, but none are obviously linked to Hamburg. I need to find mayors specifically of Hamburg.

Let me try to find if any of these mayors are associated with Hamburg by checking their place of work or if they held the position of mayor of Hamburg.

I'll check if any of the mayors have `wdt:P131` (located in administrative territorial entity) pointing to Hamburg (`wd:Q1055`).

Let me query for female mayors and check if any are associated with Hamburg.

SELECT DISTINCT ?person WHERE { ?person wdt:P106 wd:Q30185 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 . ?person wdt:P131 wd:Q1055 }

[Assistant]
No results. This suggests that the mayors of Hamburg are not linked via `wdt:P106` (occupation) or `wdt:P131` (location) in a straightforward way. The most likely scenario is that the mayors are linked via `p:P39` (position held) with the position being `wd:Q1362118` (First Mayor of Hamburg), but the data might be incomplete or the property usage is different.

Let me check the item `wd:Q1362118` (First Mayor of Hamburg) to see if it has any statements about who held it.

list

wd:Q1362118

[Assistant]
No results. Let me try listing all properties of `wd:Q1362118` to understand its structure better.

wd:Q1362118

Got 91 rows and 3 columns, showing the first 10 rows below:
| subject                              | property                            | object                                                |
| ------------------------------------ | ----------------------------------- | ----------------------------------------------------- |
| First Mayor of Hamburg (wd:Q1362118) | instance of (wdt:P31)               | position (wd:Q4164871)                                |
| First Mayor of Hamburg (wd:Q1362118) | country (wdt:P17)                   | Germany (wd:Q183)                                     |
| First Mayor of Hamburg (wd:Q1362118) | subclass of (wdt:P279)              | head of government (wd:Q2285706)                      |
| First Mayor of Hamburg (wd:Q1362118) | Freebase ID (wdt:P646)              | /m/0bdv7hk                                            |
| First Mayor of Hamburg (wd:Q1362118) | applies to jurisdiction (wdt:P1001) | Hamburg (wd:Q1055)                                    |
| First Mayor of Hamburg (wd:Q1362118) | appointed by (wdt:P748)             | Hamburg Parliament (wd:Q636377)                       |
| First Mayor of Hamburg (wd:Q1362118) | official residence (wdt:P263)       | Hamburg City Hall (wd:Q3987)                          |
| First Mayor of Hamburg (wd:Q1362118) | schema:description                  | Hanburgoko hiriko eta estatuko gobernuburua (lang:eu) |
| First Mayor of Hamburg (wd:Q1362118) | schema:name                         | Céad Mhéara Hamburg (lang:ga)                         |
| First Mayor of Hamburg (wd:Q1362118) | rdf:type                            | wikibase:Item                                         |
| ...                                  | ...                                 | ...                                                   |

[Assistant]
I see that `wd:Q1362118` has `wdt:P1001` (applies to jurisdiction) pointing to `wd:Q1055` (Hamburg). This means the position applies to Hamburg. Now, I need to find people who have held this position.

The position is held by individuals via `p:P39` (position held), where the position is `wd:Q1362118`. I'll query for people who have held this position and are female.

execute

SELECT DISTINCT ?person WHERE { ?person p:P39 ?stmt . ?stmt ps:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 }

[Assistant]
Still no results. This is puzzling. Let me check if there are any people who have held the position of mayor of Hamburg by looking at the reverse: who has `wdt:P39` pointing to `wd:Q1362118`?

I'll try a direct query using `wdt:P39` (not `p:P39`) to see if there are any direct links.

SELECT DISTINCT ?person WHERE { ?person wdt:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 }

[Assistant]
No results. This suggests that the data might not be populated in Wikidata for female mayors of Hamburg, or the position is not linked in the way we expect.

Let me check if there are any mayors of Hamburg at all, regardless of gender, using the statement pattern.

SELECT DISTINCT ?person WHERE { ?person p:P39 ?stmt . ?stmt ps:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 }

Got 17 rows and 1 column, showing the first 5 and last 5 rows below:
| person                                |
| ------------------------------------- |
| Johannes Versmann (wd:Q104237)        |
| Hans-Ulrich Klose (wd:Q447940)        |
| Ole von Beust (wd:Q60426)             |
| Olaf Scholz (wd:Q61053)               |
| Klaus von Dohnanyi (wd:Q62673)        |
| ...                                   |
| Peter Schulz (wd:Q85791)              |
| Paul Nevermann (wd:Q85910)            |
| Rudolf Petersen (wd:Q87670)           |
| Nicolaus Ferdinand Haller (wd:Q91493) |
| Peter Tschentscher (wd:Q971774)       |

[Assistant]
Great! There are 17 mayors of Hamburg listed. Now I need to find which of these are female. I'll check their gender.

I'll modify the query to include the gender information and filter for female.

SELECT DISTINCT ?person ?gender WHERE { ?person p:P39 ?stmt . ?stmt ps:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 ?gender }

Got 17 rows and 2 columns, showing the first 5 and last 5 rows below:
| person                                | gender             |
| ------------------------------------- | ------------------ |
| Johannes Versmann (wd:Q104237)        | male (wd:Q6581097) |
| Hans-Ulrich Klose (wd:Q447940)        | male (wd:Q6581097) |
| Ole von Beust (wd:Q60426)             | male (wd:Q6581097) |
| Olaf Scholz (wd:Q61053)               | male (wd:Q6581097) |
| Klaus von Dohnanyi (wd:Q62673)        | male (wd:Q6581097) |
| ...                                   | ...                |
| Peter Schulz (wd:Q85791)              | male (wd:Q6581097) |
| Paul Nevermann (wd:Q85910)            | male (wd:Q6581097) |
| Rudolf Petersen (wd:Q87670)           | male (wd:Q6581097) |
| Nicolaus Ferdinand Haller (wd:Q91493) | male (wd:Q6581097) |
| Peter Tschentscher (wd:Q971774)       | male (wd:Q6581097) |

[Assistant]
All 17 mayors of Hamburg listed are male. This suggests that, as of now, Hamburg has not had any female mayors.

To confirm this, I'll count the number of female mayors by filtering for female gender and see if any exist.

SELECT (COUNT(DISTINCT ?person) AS ?count) WHERE { ?person p:P39 ?stmt . ?stmt ps:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 }

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

[Assistant]
The query confirms that there are 0 female mayors of Hamburg in the Wikidata database.

Therefore, the answer is that Hamburg has had 0 different female mayors.

I will now provide the final answer.

answer

SELECT (COUNT(DISTINCT ?person) AS ?count) WHERE { ?person p:P39 ?stmt . ?stmt ps:P39 wd:Q1362118 . ?person wdt:P31 wd:Q5 . ?person wdt:P21 wd:Q6581072 }

Answer: Hamburg has had 0 different female mayors.