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 ?genre ?genreLabel WHERE {
  wd:Q68061 wdt:P136 ?genre .
  ?genre rdfs:label ?genreLabel .
  FILTER ( LANG( ?genreLabel ) = 'en' )
}
ORDER BY strlen( ?genreLabel )
LIMIT 1

Using entities:
- Cage the Elephant (wd:Q68061): is instance of musical group / American rock band from Bowling Green, Kentucky

Using properties:
- genre (wd:P136 as 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

Answer: blues

blues

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 ?genre ?genreLabel WHERE {
  wd:Q68061 wdt:P136 ?genre .
  ?genre rdfs:label ?genreLabel .
  FILTER ( LANG( ?genreLabel ) = 'en' )
}
ORDER BY strlen( ?genreLabel )
LIMIT 1

Using entities:
- Cage the Elephant (wd:Q68061): is instance of musical group / American rock band from Bowling Green, Kentucky

Using properties:
- genre (wd:P136 as 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

Execution result:
Got 1 row and 2 columns:
| genre            | genreLabel      |
| ---------------- | --------------- |
| blues (wd:Q9759) | blues (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] Which genre that Cage the Elephant belongs to has the fewest english characters?

[Assistant]
To answer the question, "Which genre that Cage the Elephant belongs to has the fewest English characters?", I need to:

1. Identify the band "Cage the Elephant" in Wikidata.
2. Find the musical genres associated with this band.
3. Extract the English labels of these genres.
4. Determine which genre label has the fewest characters.

### Step 1: Search for the entity "Cage the Elephant"
I'll start by searching for the band "Cage the Elephant" in Wikidata.

Question: Cage the Elephant

Top 10 entity alternatives:
1. Cage the Elephant (wd:Q68061): American rock band from Bowling Green, Kentucky / is instance of musical group
2. Cage the Elephant (wd:Q1950243): debut album by the American rock band Cage the Elephant / is instance of album
3. The Elephant Man (wd:Q272860): 1980 film by David Lynch / is instance of film
4. Jumbo (wd:Q831999, matched via Jumbo the Elephant), also known as Jumbo the Circus Elephant, Jumbo the Elephant: is instance of individual animal / is instance of circus elephant / historic elephant 1860-1885 / is instance of zoo animals
5. Babar the Elephant (wd:Q132152), also known as Babar: is instance of anthropomorphic elephant / is instance of children's book series / has occupation environmentalist / has occupation Environmentalist / is instance of literary work / fictional character
6. The Elephant Whisperers (wd:Q115673833): 2022 Tamil film directed by Kartiki Gonsalves / is instance of film
7. The Fifth Elephant (wd:Q1198641): 1999 novel by Terry Pratchett / is instance of literary work
8. The Magician's Elephant (wd:Q104880985): is instance of animated film / 2023 film by Wendy Rogers
9. The Green Elephant (wd:Q2083023), also known as Green Elephant: 1999 film by Svetlana Baskova / is instance of film
10. The Cage (wd:Q20909), also known as Cage, Cage, The, Pilot: The Cage: episode of Star Trek: The Original Series directed by Robert Butler / is instance of Star Trek episode / is instance of Television film / is instance of television film

[Assistant]
The correct entity for the band "Cage the Elephant" is `wd:Q68061`, which is an instance of a musical group.

### Step 2: Find the genres associated with Cage the Elephant
Musical genres for bands are typically stored using the property `wdt:P136` (genre). I'll query for the genres of Cage the Elephant using this property.

I'll construct a SPARQL query to retrieve the genre items associated with `wd:Q68061` and fetch their English labels.

execute

SELECT ?genre ?genreLabel WHERE { wd:Q68061 wdt:P136 ?genre . 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 the query used `SERVICE wikibase:label`, which is not supported by the QLever SPARQL endpoint. I need to replace it with `rdfs:label` and filter for English labels manually.

I'll rewrite the query to use `rdfs:label` with `FILTER(LANG(?genreLabel) = 'en')` to get the English labels of the genres.

### Step 3: Revised SPARQL query to get genre labels in English
I'll now execute the corrected SPARQL query:

SELECT ?genre ?genreLabel WHERE { wd:Q68061 wdt:P136 ?genre . ?genre rdfs:label ?genreLabel . FILTER(LANG(?genreLabel) = 'en') }

Got 6 rows and 2 columns:
| genre                         | genreLabel                 |
| ----------------------------- | -------------------------- |
| alternative rock (wd:Q11366)  | alternative rock (lang:en) |
| indie rock (wd:Q183504)       | indie rock (lang:en)       |
| psychedelic rock (wd:Q206159) | psychedelic rock (lang:en) |
| garage rock (wd:Q211573)      | garage rock (lang:en)      |
| punk rock (wd:Q3071)          | punk rock (lang:en)        |
| blues (wd:Q9759)              | blues (lang:en)            |

[Assistant]
The genres associated with Cage the Elephant and their English labels are:

1. **alternative rock** (15 characters)
2. **indie rock** (10 characters)
3. **psychedelic rock** (16 characters)
4. **garage rock** (11 characters)
5. **punk rock** (9 characters)
6. **blues** (5 characters)

### Step 4: Determine the genre with the fewest English characters
Among these, **"blues"** has the fewest characters (5 characters).

### Final Answer
The genre that Cage the Elephant belongs to with the fewest English characters is **blues**.

I'll now provide the final SPARQL query and answer.

answer

SELECT ?genre ?genreLabel WHERE { wd:Q68061 wdt:P136 ?genre . ?genre rdfs:label ?genreLabel . FILTER(LANG(?genreLabel) = 'en') } ORDER BY strlen(?genreLabel) LIMIT 1

Answer: blues


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 ?taxonLabel ?start WHERE {
  wd:Q21446362 rdfs:label ?taxonLabel .
  FILTER ( LANG( ?taxonLabel ) = 'en' ) .
  wd:Q21446362 wdt:P580 ?start
}

Using entities:
- Physeteroidea (wd:Q21446362), also known as Physiteroidea: is instance of taxon / superfamily of whales (Cetacea)

Using properties:
- start time (wd:P580 as wdt), also known as began, beginning, building date, effective, effectivity, etc.: is subproperty of point in time / 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

Answer: The first sperm whales, belonging to the superfamily Physeteroidea, existed approximately 25 million years ago.

The first sperm whales, belonging to the superfamily Physeteroidea, existed approximately 25 million years ago.

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 ?taxonLabel ?start WHERE {
  wd:Q21446362 rdfs:label ?taxonLabel .
  FILTER ( LANG( ?taxonLabel ) = 'en' ) .
  wd:Q21446362 wdt:P580 ?start
}

Using entities:
- Physeteroidea (wd:Q21446362), also known as Physiteroidea: is instance of taxon / superfamily of whales (Cetacea)

Using properties:
- start time (wd:P580 as wdt), also known as began, beginning, building date, effective, effectivity, etc.: is subproperty of point in time / 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

Execution result:
Got 1 row and 2 columns:
| taxonLabel              | start                                   |
| ----------------------- | --------------------------------------- |
| Physeteroidea (lang:en) | -25000000-01-01T00:00:00 (xsd:dateTime) |

[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] When did the first sperm whales exist?

[Assistant]
To determine when the first sperm whales existed, I need to find information about the earliest known appearance or origin of sperm whales in the fossil record or evolutionary timeline.

Step 1: Identify the entity for "sperm whale"
I will first search for the Wikidata entity corresponding to "sperm whale".

Question: sperm whale

Top 10 entity alternatives:
1. sperm whale (wd:Q81214), also known as cachalot, Physeter macrocephalus: largest species of toothed whale / is instance of taxon / is subclass of whale / is subclass of Whale
2. Sperm Whale (wd:Q20111251), also known as Nahang-e-Anbar: 2015 film directed by Saman Moghadam / is instance of film
3. Sperm whale (wd:Q115479916): water colour painting of a sperm whale / is instance of painting
4. sperm whale (wd:Q113680749), also known as cachalot: fictional monster from Dungeons & Dragons / is subclass of monster / is subclass of whale
5. pygmy sperm whale (wd:Q244300), also known as Kogia breviceps: is instance of taxon / species of mammal
6. dwarf sperm whale (wd:Q42669), also known as Kogia sima: is instance of taxon / species of mammal
7. Sperm Whale Head (wd:Q21903224): is instance of point / point in Australia / point in Australia / point in Australia
8. macroraptorial sperm whale (wd:Q56274847), also known as raptorial sperm whale: highly predatory whales of the sperm whale superfamily (Physeteroidea) of the Miocene epoch / 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 Cetacea
9. sperm whale brain (wd:Q25685060): is subclass of encephalon
10. Sperm Whales (wd:Q58942960): scientific article published in Nature / is instance of scholarly article / is instance of academic article

[Assistant]
The primary entity for the sperm whale is wd:Q81214, which is the taxon for the modern sperm whale (Physeter macrocephalus). However, the question asks about the first sperm whales, which likely refers to the earliest ancestors or members of the sperm whale lineage (Physeteroidea), not just the modern species.

Step 2: Look for properties related to the origin or first appearance of sperm whales
I will search for properties of the sperm whale entity (wd:Q81214) that might indicate its evolutionary origin or first appearance.

Question: origin

Top 10 property alternatives:
1. 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
2. described by source (wd:P1343 as p/wdt, matched via found in), also known as defined in, described by biography, described by encyclopedia, described by obituary, described by reference work, etc.: dictionary, encyclopaedia, etc. where this item is described / work where this item is described / is subproperty of attested in
3. image (wd:P18 as p/wdt, matched via still), also known as camera picture, Commons image, drawing, File:, graph, etc.: image of relevant illustration of the subject; if available, ... only images which exist on Wikimedia Commons are acceptable / image of relevant illustration of the subject; if available, ... only images which exist on Wikimedia Commons are acceptable / image of relevant illustration of the subject; if available, ... map, flag image, signature image, logo image, collage image) / image of relevant illustration of the subject; if available, ... map, flag image, signature image, logo image, collage image)
4. subclass of (wd:P279 as p/wdt, matched via form of), also known as all members of this class also belong to, category of, form of, has superclass, is a class of, etc.: this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; ALL instances ... is a subclass of mountain; Everest is an instance of mountain
5. short name (wd:P1813 as p/wdt, matched via initialism), also known as abbrev, abbreviated name, abbreviation, acronym, brief name, etc.: short name of a place, organisation, person, journal, Wikidata property, etc. Used by some Wikipedia templates. / short name of a place, organisation, person etc. / short name of a place, organization, person etc. / is subproperty of name
6. taxon range (wd:P9714 as p/wdt, matched via range), also known as geographic range, geographic range of taxon, range, range of taxon, taxon geographic range, etc.: geographic area(s) where a taxon is found
7. instance of (wd:P31 as p/wdt, matched via is an), 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
8. 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
9. taxon rank (wd:P105 as p/wdt, matched via rank), also known as rank, taxonomic rank, type of taxon, P105: level in a taxonomic hierarchy / level in a taxonomic hierarchy / level in a taxonomic hierarchy / is subproperty of instance of
10. named after (wd:P138 as p/wdt, matched via named for), also known as eponym, etymology, name after, named for, named in honor of, etc.: entity or event that inspired the subject's name, or namesake ... "applies to name" (P5168) can be used to indicate which one / person or other entity that inspired this item's name / person or other entity that inspired this item's name

No other items found

[Assistant]
The property "start time" (P580) is relevant as it indicates when an item begins to exist. However, for evolutionary taxa, the first appearance is often recorded via fossil records or geological time, which may be stored as a qualifier on a taxon statement or on a related item representing the earliest known species.

Step 3: Search for related items that might represent early sperm whales
I notice there is an item for "macroraptorial sperm whale" (wd:Q56274847), which are predatory whales from the Miocene epoch. This may be a candidate for early sperm whale ancestors. I will investigate this item to see if it has a start time or first appearance date.

list

wd:Q56274847

Got 60 rows and 3 columns, showing the first 10 rows below:
| subject                                   | property                               | object                                                                                |
| ----------------------------------------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
| macroraptorial sperm whale (wd:Q56274847) | instance of (wdt:P31)                  | organisms known by a particular common name (wd:Q55983715)                                                  |
| macroraptorial sperm whale (wd:Q56274847) | Google Knowledge Graph ID (wdtn:P2671) | <http://g.co/kg/g/11g1qh1d9n>                                                                               |
| macroraptorial sperm whale (wd:Q56274847) | image (wdt:P18)                        | <http://commons.wikimedia.org/wiki/Special:FilePath/Livyatan%20melvillei.jpg>                               |
| macroraptorial sperm whale (wd:Q56274847) | subclass of (wdt:P279)                 | Cetacea (wd:Q160)                                                                                |
| macroraptorial sperm whale (wd:Q56274847) | time period (wdt:P2348)                | Miocene (wd:Q76267)                                                                                |
| macroraptorial sperm whale (wd:Q56274847) | schema:description                     | baleias altamente predadoras da superfamília dos cachalotes (Physeteroidea) da época do Miocénico (lang:pt) |
| macroraptorial sperm whale (wd:Q56274847) | schema:name                            | cachalote macroraptorial (lang:es)                                                                          |
| macroraptorial sperm whale (wd:Q56274847) | rdf:type                               | wikibase:Item                                                                                |
| macroraptorial sperm whale (wd:Q56274847) | rdfs:label                             | cachalote macroraptorial (lang:es)                                                                          |
| macroraptorial sperm whale (wd:Q56274847) | skos:altLabel                          | raptorial sperm whale (lang:en)                                                                             |
| ...                                       | ...                                    | ...                                                                                |

[Assistant]
The item wd:Q56274847 (macroraptorial sperm whale) is a subclass of Cetacea and lived during the Miocene epoch (wd:Q76267). The Miocene epoch spans from about 23 to 5.3 million years ago. However, this is not the very first sperm whale — it's a later, specialized predatory form.

Step 4: Search for the earliest known sperm whale or the origin of the Physeteroidea superfamily
I will now search for properties related to the origin of the sperm whale lineage by looking at the modern sperm whale (wd:Q81214) and its taxonomic hierarchy to see if there's a "first appearance" or "temporal range" property.

I will check if the modern sperm whale (wd:Q81214) has a "temporal range" or "first appearance" property.

Question: temporal range

Top 10 property alternatives:
1. taxon range (wd:P9714 as p/wdt, matched via range), also known as geographic range, geographic range of taxon, range, range of taxon, taxon geographic range, etc.: geographic area(s) where a taxon is found
2. taxon range map image (wd:P181 as p/wdt, matched via range map), also known as distribution map (taxa), occurrence data map, range map, spread map, P181: is subproperty of image / range map of a taxon / range map of a taxon
3. 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
4. subclass of (wd:P279 as p/wdt, matched via subcategory of), also known as all members of this class also belong to, category of, form of, has superclass, is a class of, etc.: this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; ALL instances ... is a subclass of mountain; Everest is an instance of mountain
5. described by source (wd:P1343 as p/wdt, matched via topic in), also known as defined in, described by biography, described by encyclopedia, described by obituary, described by reference work, etc.: dictionary, encyclopaedia, etc. where this item is described / work where this item is described / is subproperty of attested in
6. gestation period (wd:P3063 as p/wdt), also known as P3063: amount of time needed for gestation for this animal taxon / amount of time needed for gestation for this animal taxon / is subproperty of duration
7. main food source (wd:P1034 as p/wdt, matched via ration), also known as diet, eats, ecology (Fossilworks field  name), food plant, food source, etc.: species, genus or family that an organism depends on for nutrition
8. taxon rank (wd:P105 as p/wdt, matched via rank), also known as rank, taxonomic rank, type of taxon, P105: level in a taxonomic hierarchy / level in a taxonomic hierarchy / level in a taxonomic hierarchy / is subproperty of instance of
9. 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
10. topic's main category (wd:P910 as p/wdt, matched via subject 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

No other items found

[Assistant]
The property "start time" (P580) is the most relevant for determining when the sperm whale lineage began. However, this property is typically not directly attached to the modern species (wd:Q81214) but rather to the taxon at a higher level (e.g., the superfamily Physeteroidea).

Step 5: Find the taxon for the sperm whale superfamily (Physeteroidea)
I will search for the taxon representing the superfamily Physeteroidea, which includes all sperm whales (modern and extinct).

Question: Physeteroidea

Top 10 entity alternatives:
1. Physeteroidea (wd:Q21446362), also known as Physiteroidea: superfamily of whales (Cetacea) / is instance of taxon
2. Macroraptorial sperm whales (Cetacea, Odontoceti, Physeteroidea) from the Miocene of Peru (wd:Q55868672): scholarly article by Olivier Lambert et al published September 2016 in Zoological Journal of the Linnean Society / is instance of scholarly article / is instance of academic article
3. ‘Aulophyseter’ rionegrensis (Cetacea: Odontoceti: Physeteroidea) from the Miocene of Patagonia (Argentina): a reappraisal (wd:Q110669611): scholarly article published in 2020 / is instance of scholarly article / is instance of academic article
4. A new stem-sperm whale (Cetacea, Odontoceti, Physeteroidea) from the Latest Miocene of Peru (wd:Q54086928): scholarly article by Olivier Lambert et al published August 2008 in Comptes Rendus Palevol / is instance of scholarly article / is instance of academic article
5. First record of a macroraptorial sperm whale (Cetacea, Physeteroidea) from the Miocene of Argentina (wd:Q128652853): is instance of scholarly article / is instance of academic article / scholarly article
6. A fossil sperm whale (Cetacea, Physeteroidea) from the Pleistocene of Nauru, equatorial southwest Pacific (wd:Q55983268): scholarly article by Erich M. G. Fitzgerald published July 2011 in Journal of Vertebrate Paleontology / is instance of scholarly article / is instance of academic article
7. Fossil Pygmy Sperm Whales (Odontoceti; Physeteroidea; Kogiidae) from the Late Miocene of Panama and Early Pliocene of Florida (wd:Q127715972): is instance of scholarly article / is instance of academic article / scholarly article
8. A Sperm Whale (Cetacea: Physeteroidea) from the Paraná ... Miocene) of Entre Rís, Argentina. Environment and Taphonomy (wd:Q63383487): is instance of scholarly article / is instance of academic article / article
9. Sperm whales (Physeteroidea) from the Pisco Formation, Peru, and their trophic role as fat sources for late Miocene sharks (wd:Q121177237): scientific article published in 2022 / is instance of scholarly article / is instance of academic article
10. A new longirostrine sperm whale (Cetacea, Physeteroidea) from the lower Miocene of the Pisco Basin (southern coast of Peru) (wd:Q106699541): scholarly article by Olivier Lambert et al published 21 September 2020 in Journal of Systematic Palaeontology / is instance of scholarly article / is instance of academic article

[Assistant]
The superfamily Physeteroidea is wd:Q21446362. This is the correct taxonomic level to investigate for the origin of sperm whales. I will now check its properties to find the start time (first appearance) of this group.

wd:Q21446362

Got 207 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                                        | object                                                                      |
| ---------------------------- | ----------------------------------------------- | --------------------------------------------------------------------------- |
| Physeteroidea (wd:Q21446362) | instance of (wdt:P31)                           | taxon (wd:Q16521)                                                           |
| Physeteroidea (wd:Q21446362) | Commons category (wdt:P373)                     | Physeteroidea                                                               |
| Physeteroidea (wd:Q21446362) | image (wdt:P18)                                 | <http://commons.wikimedia.org/wiki/Special:FilePath/Sperm%20whale%2012.jpg> |
| Physeteroidea (wd:Q21446362) | Freebase ID (wdt:P646)                          | /m/01tk70                                                                   |
| Physeteroidea (wd:Q21446362) | parent taxon (wdt:P171)                         | Odontoceti (wd:Q144144)                                                     |
| Physeteroidea (wd:Q21446362) | taxon name (wdt:P225)                           | Physeteroidea                                                               |
| Physeteroidea (wd:Q21446362) | taxon rank (wdt:P105)                           | superfamily (wd:Q2136103)                                                   |
| Physeteroidea (wd:Q21446362) | start time (wdt:P580)                           | -25000000-01-01T00:00:00 (xsd:dateTime)                                     |
| Physeteroidea (wd:Q21446362) | topic's main category (wdt:P910)                | wd:Q9673529                                                                 |
| Physeteroidea (wd:Q21446362) | Bibliothèque nationale de France ID (wdtn:P268) | <http://data.bnf.fr/ark:/12148/cb11971058v#about>                           |
| ...                          | ...                                             | ...                                                                         |

[Assistant]
Great! I found that the superfamily Physeteroidea (wd:Q21446362), which includes all sperm whales, has a "start time" (P580) of -25000000-01-01T00:00:00Z, which corresponds to 25 million years ago (in the Oligocene epoch).

This is the earliest known origin of the sperm whale lineage. The modern sperm whale (Physeter macrocephalus) evolved much later, but the first sperm whales (members of Physeteroidea) appeared around 25 million years ago.

Now I will construct a SPARQL query to return this information clearly, including the label for the taxon and the start time in a human-readable format.

Note: The date is stored as an xsd:dateTime with a negative year, which represents years before the common era. I will format the answer to reflect "25 million years ago" for clarity.

Final SPARQL query:

execute

SELECT ?taxon ?taxonLabel ?start WHERE { wd:Q21446362 wdt:P580 ?start . 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` because it is not supported by the QLever endpoint. Instead, I will use `rdfs:label` with a language filter to get the English label.

Let me correct the query:

SELECT ?taxon ?start WHERE { wd:Q21446362 wdt:P580 ?start . ?taxon rdfs:label ?label . FILTER(LANG(?label) = 'en') . FILTER(?taxon = wd:Q21446362) }

[Assistant]
It seems the direct `rdfs:label` for wd:Q21446362 is not being returned. This can happen if the label is stored under a different property or if the item has no direct label. Let me try to get the label using a different approach — by querying the item's label explicitly with a filter on the subject.

I'll try again with a more direct approach:

SELECT ?label WHERE { wd:Q21446362 rdfs:label ?label . FILTER(LANG(?label) = 'en') }

Got 1 row and 1 column:
| label                   |
| ----------------------- |
| Physeteroidea (lang:en) |

[Assistant]
Perfect! The label for wd:Q21446362 is "Physeteroidea" in English.

Now I have all the information needed:
- The first sperm whales belong to the superfamily Physeteroidea (wd:Q21446362).
- Their first appearance (start time) is 25 million years ago (wdt:P580 = -25000000-01-01T00:00:00Z).

I will now construct the final SPARQL query that returns both the taxon label and the start time in a clean format.

Final SPARQL query:

SELECT ?taxonLabel ?start WHERE { wd:Q21446362 rdfs:label ?taxonLabel . FILTER(LANG(?taxonLabel) = 'en') . wd:Q21446362 wdt:P580 ?start }

[Assistant]
The query successfully returns that the first sperm whales (Physeteroidea) existed 25 million years ago.

Answer: The first sperm whales, belonging to the superfamily Physeteroidea, existed approximately 25 million years ago during the Oligocene epoch.

Final SPARQL query:

answer

SELECT ?taxonLabel ?start WHERE { wd:Q21446362 rdfs:label ?taxonLabel . FILTER(LANG(?taxonLabel) = 'en') . wd:Q21446362 wdt:P580 ?start }

Answer: The first sperm whales, belonging to the superfamily Physeteroidea, existed approximately 25 million years ago.