PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?team ?teamLabel WHERE {
  wd:Q119562 wdt:P54 ?team .
  ?team rdfs:label ?teamLabel .
  FILTER ( LANG( ?teamLabel ) = "en" )
}

Using entities:
- Sergio Agüero (wd:Q119562), also known as Kun Agüero, Sergio Leonel Agüero del Castillo: has occupation businessperson / has occupation online streamer / has occupation association football player / has occupation soccer player / is instance of human / Argentine association football player / Argentine footballer / Argentine footballer

Using properties:
- member of sports team (wd:P54 as wdt), also known as club played for, member of team, of team, part of team, played for, etc.: is subproperty of member of / sports teams or clubs that the subject currently represents or formerly represented / sports teams or clubs that the subject currently represents or formerly represented / sports teams or clubs that the subject represents or represented

Got 8 rows and 2 columns:
| team                                                           | teamLabel                                                    |
| -------------------------------------------------------------- | ------------------------------------------------------------ |
| Argentina national under-20 football team (wd:Q1187790)        | Argentina national under-20 football team (lang:en)          |
| Club Atlético Independiente (wd:Q214978)                       | Club Atlético Independiente (lang:en)                        |
| Argentina national under-23 football team (wd:Q2462207)        | Argentina national under-23 football team (lang:en)          |
| Argentina national under-17 football team (wd:Q2902690)        | Argentina national under-17 football team (lang:en)          |
| Manchester City F.C. (wd:Q50602)                               | Manchester City F.C. (lang:en)                               |
| FC Barcelona (wd:Q7156)                                        | FC Barcelona (lang:en)                                       |
| Argentina men's national association football team (wd:Q79800) | Argentina men's national association football team (lang:en) |
| Atlético de Madrid (wd:Q8701)                                  | Atlético de Madrid (lang:en)                                 |

Answer: Sergio Agüero played for the following teams: Club Atlético Independiente, Manchester City F.C., FC Barcelona, Atlético de Madrid, Argentina national under-17 football team, Argentina national under-20 football team, Argentina national under-23 football team, and Argentina men's national association football team.

Sergio Agüero played for the following teams: Club Atlético Independiente, Manchester City F.C., FC Barcelona, Atlético de Madrid, Argentina national under-17 football team, Argentina national under-20 football team, Argentina national under-23 football team, and Argentina men's national association football team.

SPARQL query over wikidata:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?team ?teamLabel WHERE {
  wd:Q119562 wdt:P54 ?team .
  ?team rdfs:label ?teamLabel .
  FILTER ( LANG( ?teamLabel ) = "en" )
}

Using entities:
- Sergio Agüero (wd:Q119562), also known as Kun Agüero, Sergio Leonel Agüero del Castillo: has occupation businessperson / has occupation online streamer / has occupation association football player / has occupation soccer player / is instance of human / Argentine association football player / Argentine footballer / Argentine footballer

Using properties:
- member of sports team (wd:P54 as wdt), also known as club played for, member of team, of team, part of team, played for, etc.: is subproperty of member of / sports teams or clubs that the subject currently represents or formerly represented / sports teams or clubs that the subject currently represents or formerly represented / sports teams or clubs that the subject represents or represented

Execution result:
Got 8 rows and 2 columns:
| team                                                           | teamLabel                                                    |
| -------------------------------------------------------------- | ------------------------------------------------------------ |
| Argentina national under-20 football team (wd:Q1187790)        | Argentina national under-20 football team (lang:en)          |
| Club Atlético Independiente (wd:Q214978)                       | Club Atlético Independiente (lang:en)                        |
| Argentina national under-23 football team (wd:Q2462207)        | Argentina national under-23 football team (lang:en)          |
| Argentina national under-17 football team (wd:Q2902690)        | Argentina national under-17 football team (lang:en)          |
| Manchester City F.C. (wd:Q50602)                               | Manchester City F.C. (lang:en)                               |
| FC Barcelona (wd:Q7156)                                        | FC Barcelona (lang:en)                                       |
| Argentina men's national association football team (wd:Q79800) | Argentina men's national association football team (lang:en) |
| Atlético de Madrid (wd:Q8701)                                  | Atlético de Madrid (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 http://localhost:7001 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.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for quick/direct values; for time-series/point-in-time use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect statement qualifiers (e.g., 'applies to part') and prov:wasDerivedFrom references (pr:P854 etc.) to detect city-proper vs metro/prefecture scopes before using values.
  - Common person properties: P31 (instance of human Q5), P106 (occupation), P569 (date of birth), P19 (place of birth), P27 (country of citizenship), P21 (sex/gender), P166 (award received). Use wdt: for direct values when possible.
  - P17 (country) can be incomplete/misleading for places (historical/overseas tags). To get modern cities in a country combine: instance-of city (P31=Q515) + wdt:P17 OR use wdt:P131* to find an admin parent whose wdt:P17 = the country. Also cross-check coordinates (P625) or admin hierarchy to spot outliers.
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Be aware many items have year-only or imprecise dates and coverage is biased toward notable/public figures (sportspeople, politicians, artists).
  - 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.
  - To get a person's occupations: use wdt:P106 for direct/normalized values and rdfs:label for human-readable names (FILTER(LANG(...))). Example: wd:Q... wdt:P106 ?occ . ?occ rdfs:label ?occLabel . FILTER(LANG(?occLabel)="en").
  - 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 pattern: wd:Q... p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . ORDER BY DESC(?date). pq:P585 is the point-in-time qualifier. IMPORTANT: time-series may mix scopes (city proper vs metro) — e.g., Tokyo has a 2016 entry ~38,140,000 likely metro vs smaller city figures. Inspect qualifiers/references for scope before use.
  - Use wdt:P131* to traverse the admin hierarchy and compare wdt:P17 on the place vs an admin parent to detect historical/colonial country assignments. When mismatches appear, inspect the admin parent's instance-of (P31), labels, qualifiers and references to interpret the historical scope.
  - wdt:P106 often yields multiple occupations for a person. Use GROUP_CONCAT(DISTINCT ?occLabel; separator=", ") or return multiple rows. Always fetch labels with rdfs:label + FILTER(LANG(?label)="en") to get readable occupation names.
  - When a search_entity returns multiple hits (person vs sculpture vs memorial), confirm you selected the human/statements by checking wdt:P31 (instance of) and relevant wdt: properties (e.g., wdt:P106). This avoids using a non-human QID by mistake.
  - search_autocomplete can be constrained with a SPARQL pattern containing ?search (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }). Use constraints to focus results (e.g., humans) and avoid ambiguous fallbacks; if it returns no results, fallback to search_entity and verify wdt:P31.
  - Use search_property_of_entity to find relevant properties for an entity (e.g., 'award' -> P166) and search_object_of_property to discover candidate objects (e.g., 'physicist' for P106). This reduces guessing P/Q ids and surfaces qualifiers.
  - 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.

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. Use wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access statement qualifiers (e.g., award year via pq:P585). When dates matter, prefer xsd:dateTime and watch for year-only precision.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct/normalized values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (dates via pq:P585). For dates use xsd:dateTime and expect year-only precision. For places: combine P17 with P131 or coordinate (P625) filters to avoid historical/overseas mismatches.
- Use OPTIONAL for pq: qualifiers (e.g., pq:P585), sort by qualifier (ORDER BY pq:P585 DESC) to prefer recent values. Use GROUP_CONCAT to combine multiple labels/values in one row. Watch for typed literals (xsd:dateTime, xsd:decimal) when filtering or sorting.
- Before querying, use search_entity to resolve human-friendly names to wd:Q identifiers and verify instance-of (e.g., check wdt:P31 = wd:Q5 for humans) to avoid ambiguous matches. Prefer the top match but programmatically confirm P31 before using its data.
- 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.
- search_autocomplete can fall back to an unconstrained search or error when there are too many candidates. If this happens (e.g., "more than the maximum supported number of subject candidates"), retry with narrower constraints such as instance-of (wdt:P31), additional type filters, or short prefixes to reduce the candidate set; otherwise fallback to search_entity and verify wdt:P31.
- 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.
- 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").
- 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). 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] what team did aguero play for?

[Assistant]
To determine which team Sergio Agüero played for, I will follow these steps:

1. Identify the entity for Sergio Agüero in Wikidata.
2. Find the relevant property that indicates the teams he played for (likely related to "member of sports team" or similar).
3. Construct a SPARQL query to retrieve the teams he played for, along with their labels in English.
4. Execute the query and verify the results.

### Step 1: Search for Sergio Agüero in Wikidata
I'll start by searching for the entity corresponding to Sergio Agüero.

Question: sergio aguero

Top 10 entity alternatives:
1. Sergio Agüero (wd:Q119562), also known as Kun Agüero, Sergio Leonel Agüero del Castillo: has occupation association football player / Argentine association football player / has occupation online streamer / has occupation businessperson / has occupation soccer player / is instance of human / Argentine footballer / Argentine footballer
2. Sergio Aguero (wd:Q72271728): has occupation Film producer / has occupation film producer / is instance of human / film producer
3. Sergio Fabian Ezequiel Aguero (wd:Q28122489, matched via Sergio Ezequiel Agüero), also known as Aguero Sergio Fabian Ezequiel, Sergio Ezequiel Agüero: has occupation association football player / Argentinian association football player / Argentinian association football player / Argentinian association football player / has occupation soccer player / is instance of human
4. Sergio Agüeros (wd:Q13148028): is instance of human
5. Sergio Mattarella (wd:Q3956186): has occupation university teacher / President of Italy since 2015 / has occupation politician / has occupation lawyer / has occupation judge / is instance of human
6. Sergio Ramos (wd:Q483309): has occupation association football player / Spanish association football player / has occupation soccer player / is instance of human
7. Sergio Leone (wd:Q164562), also known as Bob Robertson: Italian filmmaker (1929–1989) / has occupation film producer / has occupation Film producer / has occupation film director / has occupation manufacturer / has occupation screenwriter / has occupation film actor / has occupation overseer / has occupation director / has occupation composer / has occupation producer / has occupation actor / is instance of human
8. Sergio Busquets (wd:Q49704), also known as Sergio Busquets Burgos: has occupation association football player / Spanish association football player / has occupation soccer player / is instance of human
9. Sergio Romero (wd:Q215463), also known as DESDE EL 5/10 GIGANTE ROMERO, Sergio Germán Romero: has occupation association football player / Argentine association football player / has occupation soccer player / is instance of human / Argentine footballer / Argentine footballer
10. Sergio Pérez (wd:Q82805), also known as "Checo" Pérez, el viejo sabroso, Sergio "Checo" Pérez, Sergio Perez: has occupation racing automobile driver / has occupation Formula One driver / Mexican Formula 1 racing driver / Mexican Formula 1 racing driver / Mexican racecar driver / Mexican racing driver / is instance of human

[Assistant]
The top result is Sergio Agüero (wd:Q119562), an Argentine association football player. This is the correct entity we are looking for. Now, I will investigate the properties associated with this entity to find the one that indicates the teams he played for.

### Step 2: Search for properties related to "team" or "sports team" for Sergio Agüero
I'll use `search_property_of_entity` to find properties related to teams or sports teams for Sergio Agüero.

Question: team

Top 10 property alternatives:
1. member of sports team (wd:P54 as p/wdt, matched via team), also known as club played for, member of team, of team, part of team, played for, etc.: sports teams or clubs that the subject currently represents or formerly represented / sports teams or clubs that the subject currently represents or formerly represented / sports teams or clubs that the subject represents or represented / is subproperty of member of
2. league or competition (wd:P118 as p/wdt, matched via competition), also known as competition, cup, division, sports league, tournament, etc.: league or competition in which team or player has played, or in which an event occurs / is subproperty of participant in / league in which the team plays / league in which the team plays
3. participant (wd:P710 as p/wdt, matched via player), also known as accused, agent, attendee, belligerents, between, etc.: person, group of people or organization (object) that ... has role" (P3831). Use P1923 for participants that are teams. / person, group of people or organization that actively takes/took part in the event / is subproperty of significant person
4. sport (wd:P641 as p/wdt), also known as play, plays, sport played, sports, P641: sport that the subject participates or participated in or is associated with / sport or discipline in which the entity participates or belongs to
5. spouse (wd:P26 as p/wdt, matched via partner), also known as common-law husband, common-law spouse, common-law wife, husband, husbands, etc.: the subject has the object as their spouse (husband, wife, ... Use "unmarried partner" (P451) for non-married companions / the subject has the object as their spouse (husband, wife, ... Use "unmarried partner" (P451) for non-married companions / the subject has the object as their spouse (husband, wife, ... Use "unmarried partner" (P451) for non-married companions / is subproperty of significant person / is subproperty of relative
6. competition won (wd:P2522 as p/wdt, matched via won), also known as victory, winner of, won, won event, P2522: competition or sports event won by the subject / is subproperty of participant in
7. award received (wd:P166 as p/wdt, matched via win), also known as award, awarded, awards, awards received, award won, etc.: award or recognition received by a person, organisation or creative work / award or recognition received by a person, organization or creative work / award or recognition received by a person, organization or creative work / awards received by a person, organisation or creative work
8. Soccerway player ID (wd:P2369 as p/wdt, matched via int.soccerway.com/), also known as https://int.soccerway.com/, https://int.soccerway.com/players/, int.soccerway.com/, soccerway.com/, Soccerway person ID, etc.: person identifier on the Soccerway website / player identifier on the Soccerway website / player id on the Soccerway website / player id on the Soccerway website
9. participant in (wd:P1344 as p/wdt, matched via competed in), also known as attended, competed in, competes in, participant of, participant of event, etc.: event in which a person or organisation was/is a participant; inverse of P710 or P1923 / event in which a person or organization was/is a participant; inverse of P710 or P1923 / is subproperty of significant event / is subproperty of part of
10. position played on team / speciality (wd:P413 as p/wdt), also known as fielding position, player position, position, position (on team), specialism, etc.: position the player plays. Samples: defenceman, pitcher, goalie / position the player plays. Samples: defenceman, pitcher, goalie / position or specialism of a player on a team

No other items found

[Assistant]
The most relevant property is **member of sports team (wd:P54)**, which indicates the teams or clubs a person has played for. This is exactly what we need.

### Step 3: Construct a SPARQL query to retrieve the teams Sergio Agüero played for
I will now write a SPARQL query to:
- Use `wd:Q119562` (Sergio Agüero) as the subject.
- Use `wdt:P54` (member of sports team) to get the teams.
- Retrieve the English labels of the teams using `rdfs:label` and `FILTER(LANG(?teamLabel) = "en")`.

### Step 4: Execute the query
Let me execute the SPARQL query.

SELECT ?team ?teamLabel WHERE { wd:Q119562 wdt:P54 ?team . ?team rdfs:label ?teamLabel . FILTER(LANG(?teamLabel) = "en") }

Got 8 rows and 2 columns:
| team                                                           | teamLabel                                                    |
| -------------------------------------------------------------- | ------------------------------------------------------------ |
| Argentina national under-20 football team (wd:Q1187790)        | Argentina national under-20 football team (lang:en)          |
| Club Atlético Independiente (wd:Q214978)                       | Club Atlético Independiente (lang:en)                        |
| Argentina national under-23 football team (wd:Q2462207)        | Argentina national under-23 football team (lang:en)          |
| Argentina national under-17 football team (wd:Q2902690)        | Argentina national under-17 football team (lang:en)          |
| Manchester City F.C. (wd:Q50602)                               | Manchester City F.C. (lang:en)                               |
| FC Barcelona (wd:Q7156)                                        | FC Barcelona (lang:en)                                       |
| Argentina men's national association football team (wd:Q79800) | Argentina men's national association football team (lang:en) |
| Atlético de Madrid (wd:Q8701)                                  | Atlético de Madrid (lang:en)                                 |

[Assistant]
The query successfully retrieved the teams Sergio Agüero played for. The results include both club teams and national teams. Here is the list of teams:

1. **Club Atlético Independiente**
2. **Manchester City F.C.**
3. **FC Barcelona**
4. **Atlético de Madrid**
5. **Argentina national under-17 football team**
6. **Argentina national under-20 football team**
7. **Argentina national under-23 football team**
8. **Argentina men's national association football team**

These represent the clubs and national teams he played for during his career.

### Final Answer
Sergio Agüero played for the following teams:
- Club Atlético Independiente
- Manchester City F.C.
- FC Barcelona
- Atlético de Madrid
- Argentina national under-17 football team
- Argentina national under-20 football team
- Argentina national under-23 football team
- Argentina men's national association football team

I will now provide the final SPARQL query and answer.

answer

SELECT ?team ?teamLabel WHERE { wd:Q119562 wdt:P54 ?team . ?team rdfs:label ?teamLabel . FILTER(LANG(?teamLabel) = "en") }

Answer: Sergio Agüero played for the following teams: Club Atlético Independiente, Manchester City F.C., FC Barcelona, Atlético de Madrid, Argentina national under-17 football team, Argentina national under-20 football team, Argentina national under-23 football team, and Argentina men's national association football team.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?language WHERE {
  wd:Q155 wdt:P37 ?language
}

Using entities:
- Brazil (wd:Q155), also known as br, Brasil, Federative Republic of Brazil: is instance of sovereign state / is instance of federal republic / is instance of Federal republic / is instance of country / is instance of rule of law / is instance of secular state / is instance of Secular state / country in South America / country in South America / country in South America / country in South America

Using properties:
- official language (wd:P37 as wdt), also known as language official, language spoken, speaking, spoken in, P37: is subproperty of language used / language designated as official by this item / language designated as official by this item / language designated as official by this item / language designated as official by this item

Answer: The official language of Brazil is Portuguese.

The official language of Brazil is Portuguese.

SPARQL query over wikidata:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?language WHERE {
  wd:Q155 wdt:P37 ?language
}

Using entities:
- Brazil (wd:Q155), also known as br, Brasil, Federative Republic of Brazil: is instance of sovereign state / is instance of federal republic / is instance of Federal republic / is instance of country / is instance of rule of law / is instance of secular state / is instance of Secular state / country in South America / country in South America / country in South America / country in South America

Using properties:
- official language (wd:P37 as wdt), also known as language official, language spoken, speaking, spoken in, P37: is subproperty of language used / language designated as official by this item / language designated as official by this item / language designated as official by this item / language designated as official by this item

Execution result:
Got 1 row and 1 column:
| language              |
| --------------------- |
| Portuguese (wd:Q5146) |

[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 http://localhost:7001 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.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for quick/direct values; for time-series/point-in-time use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect statement qualifiers (e.g., 'applies to part') and prov:wasDerivedFrom references (pr:P854 etc.) to detect city-proper vs metro/prefecture scopes before using values.
  - Common person properties: P31 (instance of human Q5), P106 (occupation), P569 (date of birth), P19 (place of birth), P27 (country of citizenship), P21 (sex/gender), P166 (award received). Use wdt: for direct values when possible.
  - P17 (country) can be incomplete/misleading for places (historical/overseas tags). To get modern cities in a country combine: instance-of city (P31=Q515) + wdt:P17 OR use wdt:P131* to find an admin parent whose wdt:P17 = the country. Also cross-check coordinates (P625) or admin hierarchy to spot outliers.
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Be aware many items have year-only or imprecise dates and coverage is biased toward notable/public figures (sportspeople, politicians, artists).
  - 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.
  - To get a person's occupations: use wdt:P106 for direct/normalized values and rdfs:label for human-readable names (FILTER(LANG(...))). Example: wd:Q... wdt:P106 ?occ . ?occ rdfs:label ?occLabel . FILTER(LANG(?occLabel)="en").
  - 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 pattern: wd:Q... p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . ORDER BY DESC(?date). pq:P585 is the point-in-time qualifier. IMPORTANT: time-series may mix scopes (city proper vs metro) — e.g., Tokyo has a 2016 entry ~38,140,000 likely metro vs smaller city figures. Inspect qualifiers/references for scope before use.
  - Use wdt:P131* to traverse the admin hierarchy and compare wdt:P17 on the place vs an admin parent to detect historical/colonial country assignments. When mismatches appear, inspect the admin parent's instance-of (P31), labels, qualifiers and references to interpret the historical scope.
  - wdt:P106 often yields multiple occupations for a person. Use GROUP_CONCAT(DISTINCT ?occLabel; separator=", ") or return multiple rows. Always fetch labels with rdfs:label + FILTER(LANG(?label)="en") to get readable occupation names.
  - When a search_entity returns multiple hits (person vs sculpture vs memorial), confirm you selected the human/statements by checking wdt:P31 (instance of) and relevant wdt: properties (e.g., wdt:P106). This avoids using a non-human QID by mistake.
  - search_autocomplete can be constrained with a SPARQL pattern containing ?search (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }). Use constraints to focus results (e.g., humans) and avoid ambiguous fallbacks; if it returns no results, fallback to search_entity and verify wdt:P31.
  - Use search_property_of_entity to find relevant properties for an entity (e.g., 'award' -> P166) and search_object_of_property to discover candidate objects (e.g., 'physicist' for P106). This reduces guessing P/Q ids and surfaces qualifiers.
  - 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.

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. Use wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access statement qualifiers (e.g., award year via pq:P585). When dates matter, prefer xsd:dateTime and watch for year-only precision.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct/normalized values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (dates via pq:P585). For dates use xsd:dateTime and expect year-only precision. For places: combine P17 with P131 or coordinate (P625) filters to avoid historical/overseas mismatches.
- Use OPTIONAL for pq: qualifiers (e.g., pq:P585), sort by qualifier (ORDER BY pq:P585 DESC) to prefer recent values. Use GROUP_CONCAT to combine multiple labels/values in one row. Watch for typed literals (xsd:dateTime, xsd:decimal) when filtering or sorting.
- Before querying, use search_entity to resolve human-friendly names to wd:Q identifiers and verify instance-of (e.g., check wdt:P31 = wd:Q5 for humans) to avoid ambiguous matches. Prefer the top match but programmatically confirm P31 before using its data.
- 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.
- search_autocomplete can fall back to an unconstrained search or error when there are too many candidates. If this happens (e.g., "more than the maximum supported number of subject candidates"), retry with narrower constraints such as instance-of (wdt:P31), additional type filters, or short prefixes to reduce the candidate set; otherwise fallback to search_entity and verify wdt:P31.
- 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.
- 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").
- 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). 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] what is the official language of brazil now?

[Assistant]
To determine the official language of Brazil, I need to find the property that links Brazil to its official language in the knowledge graph.

Step 1: Identify the entity for Brazil.
I will search for the entity corresponding to Brazil.

Question: brazil

Top 10 entity alternatives:
1. Brazil (wd:Q155), also known as br, Brasil, Federative Republic of Brazil: is instance of Federal republic / is instance of federal republic / is instance of sovereign state / is instance of secular state / is instance of Secular state / is instance of rule of law / country in South America / country in South America / country in South America / country in South America / is instance of country
2. Brazil (wd:Q25057): 1985 dystopian film by Terry Gilliam / is instance of film
3. Brazil (wd:Q1998364), also known as Brazil, IN, Brazil, Indiana: city in Clay County, Indiana, United States / is instance of city in the United States / is instance of city of the United States
4. State of Brazil (wd:Q11876909, matched via Brazil), also known as Brazil, Colonial Brazil, Portuguese Brazil: is instance of historical country / state of the Portuguese Empire
5. Brazil (wd:Q4958616), also known as Stars and Guitars: 1944 film by Joseph Santley / is instance of film
6. Brazil (wd:Q17511873), also known as Brazil, IA: is instance of unincorporated community in the United States / human settlement in Appanoose County, Iowa, United States
7. Viceroyalty of Brazil (wd:Q2920081, matched via Brazil), also known as Brasil, Brazil: Portuguese viceroyalty in Brazil 1763-1815 / is instance of viceroyalty / is instance of Viceroyalty
8. Brazil (wd:Q13581616): is instance of musical work/composition / 1991 song by Bebi Dol
9. Brazil (wd:Q4095706): is instance of written work / 1994 novel by John Updike
10. Brésil (balloon) (wd:Q115682559, matched via Brazil), also known as Brazil: is instance of balloon / Spherical balloon

[Assistant]
The primary entity for Brazil is wd:Q155, which represents the sovereign state of Brazil. I will now search for properties related to the official language of Brazil.

Step 2: Search for properties related to "official language" for Brazil.

Question: official language

Top 10 property alternatives:
1. official language (wd:P37 as p/wdt), also known as language official, language spoken, speaking, spoken in, P37: language designated as official by this item / language designated as official by this item / language designated as official by this item / language designated as official by this item / is subproperty of language used
2. native label (wd:P1705 as p/wdt, matched via official language name), also known as autonym, label in official language, label in original language, name in official language, name in original language, etc.: label for the items in their official language (P37) or their original language (P364) / label for the items in their official language (P37) or their original language (P364) / label for the items in their official language (P37) or their original language (P364) / label for the items in their official language  or their original language / is subproperty of name
3. language used (wd:P2936 as p/wdt, matched via working language), also known as languages used, medium of instruction, used language, uses language, working language, etc.: language widely used (spoken or written) in this place or at this event or organisation
4. official name (wd:P1448 as p/wdt), also known as be named, complete title, full title, legal name, long name, etc.: official name of the subject in its official language(s) / official name of the subject in its official language(s) / official name of the subject in its official language(s) / is subproperty of name
5. official website (wd:P856 as p/wdt, matched via official site), also known as home page, homepage, home page (official), homepage (official), official homepage, etc.: URL of the official page of an item (current or former). ... see the "Hijacked or dead websites" section of the Talk page / is subproperty of URL
6. part of (wd:P361 as p/wdt, matched via of), also known as assembly of, branch of, chain, collateral branch of, component of, etc.: object of which the subject is a part (if this subject is ... "has part" (P527, see also "has parts of the class" (P2670)) / object of which the subject is a part (if this subject is ... "has part" (P527, see also "has parts of the class" (P2670)) / object of which the subject is a part (if this subject is ... "has part" (P527, see also "has parts of the class" (P2670)) / object of which the subject is a part (if this subject is ... "has part" (P527, see also "has parts of the class" (P2670))
7. located in/on physical feature (wd:P706 as p/wdt, matched via on), also known as geographical region, is in, is on, landform, located on island, etc.: located on the specified landform or body of water. Should ... (provinces, states, countries, etc.). / located on the specified (geo)physical feature. Should not be ... political/administrative (P131) or a mountain range (P4552). / is subproperty of location / is subproperty of part of
8. ethnic group (wd:P172 as p/wdt, matched via (cultural) nationality), also known as (cultural) nationality, culture, ethnicity, ethnic or cultural origin, people, etc.: subject's ethnicity (consensus is that a VERY high standard ... on by scholars, or 3) is fictional and portrayed as such) / subject's ethnicity (consensus is that a VERY high standard ... on by scholars, or 3) is fictional and portrayed as such)
9. NDL Authority ID (wd:P349 as p/wdt/wdtn, matched via ndl), also known as National Diet Library Auth ID, National Diet Library ID, National Diet Library of Japan authority control ID, National Diet Library of Japan ID, ndl, etc.: identifier for authority control per the National Diet Library of Japan
10. diplomatic relation (wd:P530 as p/wdt, matched via foreign relation), also known as ambassadorial relation, ambassadorial relations, diplomatic relations, foreign relation, foreign relations, etc.: diplomatic relations of the country

No other items found

[Assistant]
The property for "official language" is wd:P37. Now I will retrieve the official language of Brazil using this property.

Step 3: Construct and execute a SPARQL query to find the official language of Brazil using wd:P37.

execute

SELECT ?language WHERE { wd:Q155 wdt:P37 ?language }

[Assistant]
The official language of Brazil is Portuguese. I will now provide the final answer with the SPARQL query used.

answer

SELECT ?language WHERE { wd:Q155 wdt:P37 ?language }

Answer: The official language of Brazil is Portuguese.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?postalCode WHERE {
  wd:Q653636 wdt:P281 ?postalCode
}

Using entities:
- Morgantown (wd:Q653636), also known as Morgantown, West Virginia, Morgantown, WV: is instance of city in the United States / is instance of city of the United States / is instance of county seat / city in and county seat of Monongalia County, West Virginia, United States

Using properties:
- postal code (wd:P281 as wdt), also known as pincode, PIN Code, postal index number, post code, postcode, etc.: identifier assigned by postal authorities for the subject area / identifier assigned by postal authorities for the subject area / identifier assigned by postal authorities for the subject area or building

Answer: The ZIP codes for Morgantown, WV include 26501, 26504, 26506, and 26501–26508. The primary ZIP code is 26501.

The ZIP codes for Morgantown, WV include 26501, 26504, 26506, and 26501–26508. The primary ZIP code is 26501.

SPARQL query over wikidata:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?postalCode WHERE {
  wd:Q653636 wdt:P281 ?postalCode
}

Using entities:
- Morgantown (wd:Q653636), also known as Morgantown, West Virginia, Morgantown, WV: is instance of city in the United States / is instance of city of the United States / is instance of county seat / city in and county seat of Monongalia County, West Virginia, United States

Using properties:
- postal code (wd:P281 as wdt), also known as pincode, PIN Code, postal index number, post code, postcode, etc.: identifier assigned by postal authorities for the subject area / identifier assigned by postal authorities for the subject area / identifier assigned by postal authorities for the subject area or building

Execution result:
Got 4 rows and 1 column:
| postalCode  |
| ----------- |
| 26501       |
| 26501–26508 |
| 26504       |
| 26506       |

[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 http://localhost:7001 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.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for quick/direct values; for time-series/point-in-time use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect statement qualifiers (e.g., 'applies to part') and prov:wasDerivedFrom references (pr:P854 etc.) to detect city-proper vs metro/prefecture scopes before using values.
  - Common person properties: P31 (instance of human Q5), P106 (occupation), P569 (date of birth), P19 (place of birth), P27 (country of citizenship), P21 (sex/gender), P166 (award received). Use wdt: for direct values when possible.
  - P17 (country) can be incomplete/misleading for places (historical/overseas tags). To get modern cities in a country combine: instance-of city (P31=Q515) + wdt:P17 OR use wdt:P131* to find an admin parent whose wdt:P17 = the country. Also cross-check coordinates (P625) or admin hierarchy to spot outliers.
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Be aware many items have year-only or imprecise dates and coverage is biased toward notable/public figures (sportspeople, politicians, artists).
  - 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.
  - To get a person's occupations: use wdt:P106 for direct/normalized values and rdfs:label for human-readable names (FILTER(LANG(...))). Example: wd:Q... wdt:P106 ?occ . ?occ rdfs:label ?occLabel . FILTER(LANG(?occLabel)="en").
  - 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 pattern: wd:Q... p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . ORDER BY DESC(?date). pq:P585 is the point-in-time qualifier. IMPORTANT: time-series may mix scopes (city proper vs metro) — e.g., Tokyo has a 2016 entry ~38,140,000 likely metro vs smaller city figures. Inspect qualifiers/references for scope before use.
  - Use wdt:P131* to traverse the admin hierarchy and compare wdt:P17 on the place vs an admin parent to detect historical/colonial country assignments. When mismatches appear, inspect the admin parent's instance-of (P31), labels, qualifiers and references to interpret the historical scope.
  - wdt:P106 often yields multiple occupations for a person. Use GROUP_CONCAT(DISTINCT ?occLabel; separator=", ") or return multiple rows. Always fetch labels with rdfs:label + FILTER(LANG(?label)="en") to get readable occupation names.
  - When a search_entity returns multiple hits (person vs sculpture vs memorial), confirm you selected the human/statements by checking wdt:P31 (instance of) and relevant wdt: properties (e.g., wdt:P106). This avoids using a non-human QID by mistake.
  - search_autocomplete can be constrained with a SPARQL pattern containing ?search (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }). Use constraints to focus results (e.g., humans) and avoid ambiguous fallbacks; if it returns no results, fallback to search_entity and verify wdt:P31.
  - Use search_property_of_entity to find relevant properties for an entity (e.g., 'award' -> P166) and search_object_of_property to discover candidate objects (e.g., 'physicist' for P106). This reduces guessing P/Q ids and surfaces qualifiers.
  - 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.

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. Use wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access statement qualifiers (e.g., award year via pq:P585). When dates matter, prefer xsd:dateTime and watch for year-only precision.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct/normalized values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (dates via pq:P585). For dates use xsd:dateTime and expect year-only precision. For places: combine P17 with P131 or coordinate (P625) filters to avoid historical/overseas mismatches.
- Use OPTIONAL for pq: qualifiers (e.g., pq:P585), sort by qualifier (ORDER BY pq:P585 DESC) to prefer recent values. Use GROUP_CONCAT to combine multiple labels/values in one row. Watch for typed literals (xsd:dateTime, xsd:decimal) when filtering or sorting.
- Before querying, use search_entity to resolve human-friendly names to wd:Q identifiers and verify instance-of (e.g., check wdt:P31 = wd:Q5 for humans) to avoid ambiguous matches. Prefer the top match but programmatically confirm P31 before using its data.
- 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.
- search_autocomplete can fall back to an unconstrained search or error when there are too many candidates. If this happens (e.g., "more than the maximum supported number of subject candidates"), retry with narrower constraints such as instance-of (wdt:P31), additional type filters, or short prefixes to reduce the candidate set; otherwise fallback to search_entity and verify wdt:P31.
- 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.
- 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").
- 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). 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] what is the zip code for morgantown wv?

[Assistant]
To find the ZIP code for Morgantown, WV, I need to determine the correct entity for Morgantown, West Virginia, and then retrieve its ZIP code information.

### Step 1: Search for the entity for Morgantown, WV
I will first search for the entity representing Morgantown, West Virginia, in Wikidata.

Question: Morgantown WV

Top 10 entity alternatives:
1. Morgantown (wd:Q653636, matched via Morgantown, WV), also known as Morgantown, West Virginia, Morgantown, WV: city in and county seat of Monongalia County, West Virginia, United States / is instance of city in the United States / is instance of city of the United States / is instance of county seat
2. Morgantown urban area (wd:Q125805682, matched via Morgantown, WV), also known as Morgantown, WV, Morgantown, WV Urban Area: is instance of urban area of the United States / urban area in West Virginia
3. Morgantown metropolitan area (wd:Q17081833, matched via Morgantown, WV MSA), also known as Morgantown metro area, Morgantown metropolitan statistical area, Morgantown MSA, Morgantown, WV Metro Area, Morgantown, WV metropolitan area, etc.: metropolitan area in West Virginia, United States / is instance of metropolitan statistical area
4. West Virginia (wd:Q1371, matched via WV), also known as Almost Heaven, State of West Virginia, The Mountain State, US-WV, West Virginia, United States, etc.: is instance of state of the United States / state of the United States of America / state of the United States of America / state of the United States of America / state of the United States / is instance of U.S. state
5. Charleston (wd:Q44564, matched via Charleston, WV), also known as Charleston, West Virginia, Charleston, WV: is instance of state or insular area capital of the United States / capital city of West Virginia, United States / is instance of city of the United States / is instance of city in the United States / is instance of county seat
6. Huntington (wd:Q241808, matched via Huntington, WV), also known as Huntington, West Virginia, Huntington, WV: city in Cabell County, West Virginia, United States / is instance of city of the United States / is instance of city in the United States
7. Parkersburg (wd:Q329485, matched via Parkersburg, WV), also known as Parkersburg, West Virginia, Parkersburg, WV: city in and county seat of Wood County, West Virginia / is instance of city of the United States / is instance of city in the United States / is instance of county seat
8. Wheeling (wd:Q838112, matched via Wheeling, WV), also known as Wheeling, Virginia, Wheeling, West Virginia, Wheeling, WV: city and seat county of Ohio County, West Virginia, United States / is instance of city of the United States / is instance of city in the United States
9. Beckley (wd:Q813745, matched via Beckley, WV), also known as Beckley, West Virginia, Beckley, WV: city in and county seat of Raleigh County, West Virginia, United States / is instance of city of the United States / is instance of city in the United States / is instance of county seat
10. Fairmont (wd:Q2001693, matched via Fairmont, WV), also known as Fairmont, West Virginia, Fairmont, WV: county seat of Marion County, West Virginia, United States / is instance of city in the United States / is instance of city of the United States

[Assistant]
The top result is `wd:Q653636`, which corresponds to Morgantown, West Virginia, a city in the United States. I will now investigate its properties to find the ZIP code.

### Step 2: Check for properties related to ZIP code
I will search for properties of Morgantown (`wd:Q653636`) that might represent ZIP code information.

Question: ZIP code

Top 10 property alternatives:
1. postal code (wd:P281 as p/wdt, matched via zip code), also known as pincode, PIN Code, postal index number, post code, postcode, etc.: identifier assigned by postal authorities for the subject area or building / identifier assigned by postal authorities for the subject area / identifier assigned by postal authorities for the subject area
2. local dialing code (wd:P473 as p/wdt, matched via area code), also known as area code, dialling code, local calling code, local dialling code, STD code, etc.: identifier dedicated to subject city by the area communication network / code to identify a local area in a telephone network
3. UN/LOCODE (wd:P1937 as p/wdt, matched via UN location code), also known as LOCCODE, LOCODE, UNECE location code, UNECE LOCODE, United Nations location code, etc.: geographic location code maintained by UNECE / geographic location code mantained by UNECE
4. 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
5. coordinate location (wd:P625 as p/wdt, matched via location), also known as co-ordinate location, co-ordinates, coordinates, co-ords, coords, etc.: geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment / coordinates of the subject / coordinates of the subject
6. GeoNames ID (wd:P1566 as p/wdt/wdtn, matched via GeoNames), also known as GeoNames, Geonames Id, P1566: identifier in the GeoNames geographical database / identifier in the GeoNames geographical database / identifier in the GeoNames geographical database
7. FIPS 55-3 (locations in the US) (wd:P774 as p/wdt), also known as Federal Information Processing Standard 55-3, FIPS 55-3, fips code for US locations, P774: identifier for places in the United States per former Federal Information Processing Standard FIPS 55-3 / identification code for geographic objects in the US / identification code for geographic objects in the US
8. capital of (wd:P1376 as p/wdt, matched via state 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
9. Library of Congress authority ID (wd:P244 as p/wdt/wdtn, matched via LOC ID), also known as Authority LCCN, LCAuth ID, LCAuth identifier, LCCN, LCCN (LCSH), etc.: Library of Congress name authority (persons, families, ... by 8-10 digits (see regex). For manifestations, use P1144] / Library of Congress ID for authority control for persons and organisations (for books use P1144) / Library of Congress control number for a name or subject authority record
10. GNIS Feature ID (wd:P590 as p/wdt/wdtn, matched via Geographic Names Information System ID), also known as Geographic Names Information System ID, GNIS ID, USGS GNIS ID, P590: identifier for geographic objects in the United States issued by the USGS. For Antarctica, use Property:P804 / identifier for geographic objects in the US issued by the USGS. For Antarctica, use Property:P804 / identifier for geographic objects in the US issued by the USGS. For Antarctica, use Property:P804

No other items found

[Assistant]
The property `wdt:P281` (postal code) is the most relevant for ZIP codes. I will now retrieve the postal code for Morgantown, WV (`wd:Q653636`) using this property.

### Step 3: Query for the postal code of Morgantown, WV
I will execute a SPARQL query to get the postal code (ZIP code) for Morgantown, WV.

execute

SELECT ?postalCode WHERE { wd:Q653636 wdt:P281 ?postalCode }

[Assistant]
The query returned multiple ZIP codes for Morgantown, WV: `26501`, `26504`, `26506`, and a range `26501–26508`. These are all valid ZIP codes for different areas within Morgantown.

Since the question asks for "the ZIP code" (singular), the most commonly referenced primary ZIP code for Morgantown is **26501**, which is the main postal code for the city center.

### Final Answer:
The primary ZIP code for Morgantown, WV is **26501**.

I will now provide the final SPARQL query and answer.

answer

SELECT ?postalCode WHERE { wd:Q653636 wdt:P281 ?postalCode }

Answer: The ZIP codes for Morgantown, WV include 26501, 26504, 26506, and 26501–26508. The primary ZIP code is 26501.