PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?creator ?creatorLabel WHERE {
  wd:Q19798744 wdt:P170 ?creator .
  wd:Q19798744 p:P161 ?stmt .
  ?stmt ps:P161 ?creator .
  ?creator rdfs:label ?creatorLabel
  FILTER ( LANG( ?creatorLabel ) = 'en' )
}

Using entities:
- The OA (wd:Q19798744): is instance of web series / is instance of serial / is instance of television series / 2016 American mystery drama television series

Using properties:
- cast member (wd:P161 as p/ps), also known as actor, actress, contestant of a play, feat., film starring, etc.: is subproperty of performer / is subproperty of significant person / actor in the subject production [use "character role" (P453) ... as qualifiers] [use "voice actor" (P725) for voice-only role] / actor in the subject production [use "character role" (P453) ... "recorded participant" (P11108) for non-fiction productions] / actor performing live for a camera or audience [use ... as qualifier] [use "voice actor" (P725) for voice-only role]
- creator (wd:P170 as wdt), also known as art by, artist (non-musical), art work by, artwork by, by, etc.: maker of a creative work or other object (where no more specific property exists) / maker of this creative work or other object (where no more specific property exists) / maker of this creative work or other object (where no more specific property exists)

Answer: Brit Marling is the creator of The OA who also acted in it.

Brit Marling is the creator of The OA who also acted in it.

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?creator ?creatorLabel WHERE {
  wd:Q19798744 wdt:P170 ?creator .
  wd:Q19798744 p:P161 ?stmt .
  ?stmt ps:P161 ?creator .
  ?creator rdfs:label ?creatorLabel
  FILTER ( LANG( ?creatorLabel ) = 'en' )
}

Using entities:
- The OA (wd:Q19798744): is instance of web series / is instance of serial / is instance of television series / 2016 American mystery drama television series

Using properties:
- cast member (wd:P161 as p/ps), also known as actor, actress, contestant of a play, feat., film starring, etc.: is subproperty of performer / is subproperty of significant person / actor in the subject production [use "character role" (P453) ... as qualifiers] [use "voice actor" (P725) for voice-only role] / actor in the subject production [use "character role" (P453) ... "recorded participant" (P11108) for non-fiction productions] / actor performing live for a camera or audience [use ... as qualifier] [use "voice actor" (P725) for voice-only role]
- creator (wd:P170 as wdt), also known as art by, artist (non-musical), art work by, artwork by, by, etc.: maker of a creative work or other object (where no more specific property exists) / maker of this creative work or other object (where no more specific property exists) / maker of this creative work or other object (where no more specific property exists)

Execution result:
Got 1 row and 2 columns:
| creator                   | creatorLabel           |
| ------------------------- | ---------------------- |
| Brit Marling (wd:Q289380) | Brit Marling (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 creator of The OA also acted in it?

[Assistant]
To find the creator of *The OA* who also acted in it, I'll follow these steps:

1. Identify the entity for *The OA* (the TV series).
2. Find the creator(s) of *The OA* using the appropriate property (likely P170 = creator).
3. Check if any of those creators also acted in the series (using P161 = actor).
4. Ensure the results are humans (P31 = Q5) to avoid non-human entities.

Let me start by searching for the entity for *The OA*.

Question: The OA

Top 10 entity alternatives:
1. The OA (wd:Q19798744): 2016 American mystery drama television series / is instance of television series / is instance of web series / is instance of serial
2. The Oa (wd:Q1325866), also known as The Oa RSPB Nature Reserve: peninsula in the southwest of Islay, Argyll and Bute, Scotland, UK / is instance of nature reserve / is instance of Nature reserve / is instance of peninsula
3. The Oa (wd:Q126204501): Special Protection Area in Scotland, UK / is instance of Special Protection Area
4. The Oath (wd:Q27834781), also known as Eiðurinn, Oath: 2016 film by Baltasar Kormákur / is instance of film
5. The Oak (wd:Q2328850), also known as Oak: is instance of film / 1992 film
6. Il giuramento (wd:Q1469058, matched via The Oath), also known as The Oath: is instance of dramatico-musical work / opera by Saverio Mercadante
7. Di Shvue (wd:Q5270055, matched via The Oath), also known as Di Bundishe Shvue, The Oath: is instance of musical work/composition / Jewish song, anthem of the Bund / is instance of Yiddish song
8. Epsom Oaks (wd:Q3347856, matched via The Oaks), also known as Oaks, The Oaks, The Oaks Stakes: British Group 1 horse race tor 3-year-old filles over 1 mile, 4 furlongs and 10 yards (2,423 metres) / is subclass of horse race
9. The Oatmeal (wd:Q2015830), also known as theoatmeal.com: is instance of pseudonym / is instance of website / humor website
10. The Oaks (wd:Q20972573): is instance of human settlement / place in Limpopo, South Africa

[Assistant]
The correct entity for *The OA* is wd:Q19798744, which is the 2016 American mystery drama television series.

Now, I'll find the creator(s) of *The OA* using the property P170 (creator).

list

wd:Q19798744

[Assistant]
The creators of *The OA* are Brit Marling (wd:Q289380) and Zal Batmanglij (wd:Q13499404).

Next, I'll check if either of them acted in *The OA* using the property P161 (actor).

wd:Q289380

[Assistant]
It seems Brit Marling (wd:Q289380) doesn't have any direct actor claims in Wikidata using P161. This might be because the actor information is stored using the statement pattern (p:P161) with qualifiers rather than direct wdt:P161. Let me check using the statement pattern to see if Brit Marling acted in *The OA*.

I'll also check Zal Batmanglij's actor claims.

wd:Q13499404

[Assistant]
Neither Brit Marling nor Zal Batmanglij have direct P161 (actor) claims in Wikidata. This suggests that the actor information might be stored using the statement pattern (p:P161) with qualifiers, or it might not be fully populated in Wikidata.

Let me try querying for actors in *The OA* using the statement pattern to see if Brit Marling is listed as an actor there.

wd:Q19798744

[Assistant]
The cast member statements for *The OA* are stored as statement items (s:...), and I need to retrieve the actual actor entities from these statements. I'll now query for the actors linked to these statements using the ps:P161 (statement property) to get the actor entities.

execute

SELECT ?actor WHERE { wd:Q19798744 p:P161 ?stmt . ?stmt ps:P161 ?actor }

Got 10 rows and 1 column:
| actor                         |
| ----------------------------- |
| Ian Alexander (wd:Q56062807)  |
| Brandon Perea (wd:Q62505213)  |
| Jason Isaacs (wd:Q214223)     |
| Scott Wilson (wd:Q718135)     |
| Brit Marling (wd:Q289380)     |
| Patrick Gibson (wd:Q28003087) |
| Brendan Meyer (wd:Q3209068)   |
| Emory Cohen (wd:Q13563393)    |
| Alice Krige (wd:Q236479)      |
| Phyllis Smith (wd:Q508280)    |

[Assistant]
Great! I can see that Brit Marling (wd:Q289380) is listed as an actor in *The OA* via the statement pattern. Now I can confirm that Brit Marling is both a creator (P170) and an actor (P161) of *The OA*.

Let me verify this by constructing a SPARQL query that finds creators of *The OA* who also acted in it.

SELECT ?creator ?creatorLabel WHERE { wd:Q19798744 wdt:P170 ?creator . wd:Q19798744 p:P161 ?stmt . ?stmt ps:P161 ?creator . SERVICE wikibase:label { bd:serviceParam wikibase:language "en" } }

SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '
<title>301 Moved Permanently</title>

301 Moved Permanently'

[Assistant]
I need to avoid using `SERVICE wikibase:label` since it's not supported by the QLever endpoint. Instead, I'll use `rdfs:label` with a language filter to get the English labels.

Let me rewrite the query without `SERVICE wikibase:label`.

SELECT ?creator ?creatorLabel WHERE { wd:Q19798744 wdt:P170 ?creator . wd:Q19798744 p:P161 ?stmt . ?stmt ps:P161 ?creator . ?creator rdfs:label ?creatorLabel FILTER(LANG(?creatorLabel) = 'en') }

[Assistant]
The query confirms that Brit Marling is both a creator of *The OA* (via P170) and an actor in it (via P161).

Now I can provide the final answer with the correct SPARQL query.

answer

SELECT ?creator ?creatorLabel WHERE { wd:Q19798744 wdt:P170 ?creator . wd:Q19798744 p:P161 ?stmt . ?stmt ps:P161 ?creator . ?creator rdfs:label ?creatorLabel FILTER(LANG(?creatorLabel) = 'en') }

Answer: Brit Marling is the creator of The OA who also acted in it.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?founder WHERE {
  wd:Q95 wdt:P112 ?founder .
  FILTER ( ?founder = wd:Q5284 )
}

Using entities:
- Bill Gates (wd:Q5284), also known as William Gates, William Henry "Bill" Gates, William Henry "Bill" Gates III, William Henry Gates, William Henry Gates III, etc.: has occupation entrepreneur / has occupation Entrepreneur / has occupation programmer / has occupation investor / has occupation computer scientist / is instance of human / American businessman, investor, and philanthropist (born 1955)
- Google (wd:Q95), also known as Big G, google.com, Google Inc., Google, Inc., Google LLC: is instance of technology company / is instance of online service / is instance of business / is instance of identity provider / is instance of public company / American multinational Internet and technology corporation / American multinational technology company, a subsidiary of Alphabet Inc. / American multinational technology company, a subsidiary of Alphabet Inc.

Using properties:
- founded by (wd:P112 as wdt), also known as begun by, co-founded by, cofounded by, co-founder, cofounder, etc.: is subproperty of creator / founder of this organisation, religion, or place / founder or co-founder of the organization or place / founder or co-founder of this organization, religion, place or entity

Answer: No, Google was not founded by Bill Gates. It was founded by Larry Page and Sergey Brin.

No, Google was not founded by Bill Gates. It was founded by Larry Page and Sergey Brin.

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?founder WHERE {
  wd:Q95 wdt:P112 ?founder .
  FILTER ( ?founder = wd:Q5284 )
}

Using entities:
- Bill Gates (wd:Q5284), also known as William Gates, William Henry "Bill" Gates, William Henry "Bill" Gates III, William Henry Gates, William Henry Gates III, etc.: has occupation entrepreneur / has occupation Entrepreneur / has occupation programmer / has occupation investor / has occupation computer scientist / is instance of human / American businessman, investor, and philanthropist (born 1955)
- Google (wd:Q95), also known as Big G, google.com, Google Inc., Google, Inc., Google LLC: is instance of technology company / is instance of online service / is instance of business / is instance of identity provider / is instance of public company / American multinational Internet and technology corporation / American multinational technology company, a subsidiary of Alphabet Inc. / American multinational technology company, a subsidiary of Alphabet Inc.

Using properties:
- founded by (wd:P112 as wdt), also known as begun by, co-founded by, cofounded by, co-founder, cofounder, etc.: is subproperty of creator / founder of this organisation, religion, or place / founder or co-founder of the organization or place / founder or co-founder of this organization, religion, place or entity

Execution result:
Got no rows and 1 columns

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

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

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

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

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

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

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

[User] Was Google founded by Bill Gates?

[Assistant]
To determine whether Google was founded by Bill Gates, I need to check the founders of Google and compare them with Bill Gates.

Step 1: Identify the entity for Google.
I will search for the entity "Google" in Wikidata.

Question: Google

Top 10 entity alternatives:
1. Google (wd:Q95), also known as Big G, google.com, Google Inc., Google, Inc., Google LLC: American multinational technology company, a subsidiary of Alphabet Inc. / American multinational technology company, a subsidiary of Alphabet Inc. / American multinational Internet and technology corporation / is instance of technology company / is instance of identity provider / is instance of public company / is instance of online service / is instance of business
2. Google Search (wd:Q9366, matched via Google), also known as BackRub, goo.gle, Google, google.com, Google homepage, etc.: is instance of search engine / search engine by Google LLC / search engine by Google LLC / search engine by Google LLC
3. Alphabet Inc. (wd:Q20800404, matched via Google), also known as abc.xyz, Alphabet, Alphabet company, Alphabet Inc, Alphabet, Inc, etc.: American holding company and conglomerate created by Google / American multinational technology conglomerate / is instance of holding company / is instance of public company / is instance of organization / is instance of organisation / is instance of business
4. Google+ (wd:Q356), also known as G+, Google Plus: is instance of application programming interface / is instance of social networking service / is instance of service on Internet / is instance of computing platform / is instance of online community / is instance of cloud computing / social networking service / social networking service / is instance of software / social network
5. .google (wd:Q20573262), also known as dot google: brand top-level domain used in the Domain Name System of the Internet / is instance of generic top-level domain / top-level Internet domain
6. google (wd:Q1156923), also known as googling, to google: transitive verb, meaning to search for something using the Google search engine / is instance of verb
7. Google (wd:Q125851407): is instance of mobile app / mobile app
8. Google+ (wd:Q125851441): is instance of social media app / defunct social media app
9. google (wd:Q107382801): is instance of Python package / Python library
10. Google (wd:Q110997421): is instance of male given name / male given name / male given name / given name

[Assistant]
The main entity for Google is Q95. Now, I will check the founders of Google by looking at the property for "founder" (P112) associated with Google.

list

wd:Q95

[Assistant]
Google was founded by Larry Page and Sergey Brin, not Bill Gates. Now, I will verify the entity for Bill Gates to ensure accuracy.

Question: Bill Gates

Top 10 entity alternatives:
1. Bill Gates (wd:Q5284), also known as William Gates, William Henry "Bill" Gates, William Henry "Bill" Gates III, William Henry Gates, William Henry Gates III, etc.: American businessman, investor, and philanthropist (born 1955) / has occupation computer scientist / has occupation entrepreneur / has occupation Entrepreneur / has occupation programmer / has occupation investor / is instance of human
2. Bill Gates (wd:Q123411797), also known as William Lazenby Gates: has occupation association football player / English footballer (1944–2023) / has occupation soccer player / is instance of human
3. Bill Gates (wd:Q100794547): college basketball player (1959–1959) Boston University / has occupation basketball player / is instance of human
4. Bill Gates Sr. (wd:Q684014), also known as William Henry Gates II: American attorney and philanthropist (1925–2020) / has occupation businessperson / has occupation philanthropist / has occupation lawyer / is instance of human
5. Bill Gates' flower fly (wd:Q1953052), also known as Eristalis gatesi: is instance of taxon / flower fly species
6. Bill Gates's house (wd:Q371973), also known as Xanadu 2.0: is instance of earth shelter / is instance of mansion / building
7. Bill Gates Must Die (wd:Q22043547): is instance of musical work/composition / 2000 song performed by John Vanderslice
8. Email to Bill Gates (wd:Q23562744): is instance of television film / is instance of Television film / 2007 film by Josh Broecker
9. Aposentadoria de Bill Gates (wd:Q129415038): is instance of scholarly article / is instance of academic article / scholarly article
10. Bill Gates funds geoengineering (wd:Q115430184): scientific article published in 2010 / scientific article published in 2010 / scientific article published in 2010 / is instance of scholarly article / is instance of academic article

[Assistant]
Bill Gates is identified as Q5284. Since Google was founded by Larry Page and Sergey Brin, and not by Bill Gates, the answer to the question "Was Google founded by Bill Gates?" is false.

I will now construct a SPARQL query to confirm this conclusion by checking if Bill Gates is listed as a founder of Google.

execute

SELECT ?founder WHERE { wd:Q95 wdt:P112 ?founder . FILTER(?founder = wd:Q5284) }

[Assistant]
The SPARQL query returned no results, confirming that Bill Gates is not listed as a founder of Google. Therefore, the answer to the question "Was Google founded by Bill Gates?" is false.

I will now provide the final answer.

answer

SELECT ?founder WHERE { wd:Q95 wdt:P112 ?founder . FILTER(?founder = wd:Q5284) }

Answer: No, Google was not founded by Bill Gates. It was founded by Larry Page and Sergey Brin.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?founder WHERE {
  wd:Q848490 wdt:P112 ?founder
}

Using entities:
- Roland Corporation (wd:Q848490), also known as Roland, Roland Corp.: is instance of business / is instance of musical instrument manufacturing company / Japanese audiovisual equipment company

Using properties:
- founded by (wd:P112 as wdt), also known as begun by, co-founded by, cofounded by, co-founder, cofounder, etc.: is subproperty of creator / founder of this organisation, religion, or place / founder or co-founder of the organization or place / founder or co-founder of this organization, religion, place or entity

Answer:
The label confirms that the founder of the Roland Corporation is Ikutaro Kakehashi. I now have sufficient information to provide the final answer.

### Final SPARQL Query and Answer
The SPARQL query to find the founder of the Roland Corporation is:

```sparql
SELECT ?founder WHERE { wd:Q848490 wdt:P112 ?founder }
```

The result shows that the founder is Ikutaro Kakehashi.

answer(kg="wikidata", sparql="SELECT ?founder WHERE { wd:Q848490 wdt:P112 ?founder }", answer="Ikutaro Kakehashi")

The label confirms that the founder of the Roland Corporation is Ikutaro Kakehashi. I now have sufficient information to provide the final answer.

### Final SPARQL Query and Answer
The SPARQL query to find the founder of the Roland Corporation is:

```sparql
SELECT ?founder WHERE { wd:Q848490 wdt:P112 ?founder }
```

The result shows that the founder is Ikutaro Kakehashi.

answer(kg="wikidata", sparql="SELECT ?founder WHERE { wd:Q848490 wdt:P112 ?founder }", answer="Ikutaro Kakehashi")

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?founder WHERE {
  wd:Q848490 wdt:P112 ?founder
}

Using entities:
- Roland Corporation (wd:Q848490), also known as Roland, Roland Corp.: is instance of business / is instance of musical instrument manufacturing company / Japanese audiovisual equipment company

Using properties:
- founded by (wd:P112 as wdt), also known as begun by, co-founded by, cofounded by, co-founder, cofounder, etc.: is subproperty of creator / founder of this organisation, religion, or place / founder or co-founder of the organization or place / founder or co-founder of this organization, religion, place or entity

Execution result:
Got 1 row and 1 column:
| founder                        |
| ------------------------------ |
| Ikutaro Kakehashi (wd:Q322845) |

[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] Who founded the Roland Corporation?

[Assistant]
To find who founded the Roland Corporation, I need to identify the founder(s) of the company. This typically involves looking for properties that link a person to the company as its founder. In Wikidata, the property for "founder" is `wdt:P112`.

### Step 1: Search for the Roland Corporation entity
First, I'll search for the Roland Corporation in Wikidata to get its entity ID.

Question: Roland Corporation

Top 10 entity alternatives:
1. Roland Corporation (wd:Q848490), also known as Roland, Roland Corp.: is instance of musical instrument manufacturing company / Japanese audiovisual equipment company / is instance of business
2. Roland DG (wd:Q2162714, matched via Roland DG Corporation), also known as Roland DG Corporation: is instance of enterprise / is instance of business
3. Roland Foods Corporation (wd:Q23461918): Food importer in New York
4. Roland DGA Corporation (wd:Q22812960): American arm of Roland DG Corporation / is instance of business
5. Roland, Roland (wd:Q106829994): Source: GB-Cu Ms Add 9285 (Cambridge 9285), 633–4 / is instance of musical work/composition
6. McDonald’s (wd:Q38076, matched via McDonald's Corporation), also known as Macca's, Maccas, McD, McDo, McDonald, etc.: is instance of fast food restaurant chain / American fast food restaurant chain / American fast food restaurant chain / is instance of public company / is instance of organization / is instance of organisation / is instance of trademark / is instance of business
7. Sony Group (wd:Q41187, matched via Sony Corporation), also known as Sony, Sony Corp., Sony Corporation, Tokyo Telecommunications Engineering Corporation, Tokyo Tsushin Kogyo K.K., etc.: Japanese multinational conglomerate corporation / Japanese multinational conglomerate corporation / Japanese multinational conglomerate corporation / Japanese multinational conglomerate corporation / is instance of multinational corporation / is instance of camera manufacturer / is instance of holding company / is instance of public company / is instance of conglomerate / is instance of brand
8. IBM (wd:Q37156, matched via IBM Corporation), also known as 18M, Big Blue, ibm.com, IBM Corp., IBM Corporation, etc.: American multinational technology and consulting corporation / American multinational technology corporation / is instance of professional services firm / is instance of multinational corporation / is instance of technology company / is instance of software company / is instance of public company / is instance of organization / is instance of organisation / is instance of enterprise / is instance of business
9. Nokia (wd:Q1418, matched via Nokia Corporation), also known as Euronext: NOKIA, Nokia Corporation, Nokia Oyj, Oy Nokia Ab: Finnish multinational telecommunications and information technology company / is instance of public company / is instance of conglomerate / is instance of business
10. KFC (wd:Q524757, matched via KFC Corporation), also known as Kentucky Fried Chicken, KFC Corporation, KFC Restaurant: American fast food restaurant chain specializing in fried chicken / American fast food restaurant chain specializing in fried chicken / is instance of fast food restaurant chain / American fast food restaurant chain / American fast food restaurant chain / is instance of public company / is instance of brand

[Assistant]
The Roland Corporation is identified as `wd:Q848490`. Now, I will check its properties to find the founder using the `wdt:P112` (founder) property.

### Step 2: Query for the founder of Roland Corporation
I will execute a SPARQL query to retrieve the founder(s) of Roland Corporation using the `wdt:P112` property.

execute

SELECT ?founder WHERE { wd:Q848490 wdt:P112 ?founder }

[Assistant]
The founder of the Roland Corporation is Ikutaro Kakehashi. I will now verify this by checking the label of the founder entity to ensure accuracy.

### Step 3: Get the label of the founder
I will retrieve the English label for `wd:Q322845` (Ikutaro Kakehashi) to confirm the identity.

SELECT ?label WHERE { wd:Q322845 rdfs:label ?label FILTER(LANG(?label) = 'en') }

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

[Assistant]
The label confirms that the founder of the Roland Corporation is Ikutaro Kakehashi. I now have sufficient information to provide the final answer.

### Final SPARQL Query and Answer
The SPARQL query to find the founder of the Roland Corporation is:

```sparql
SELECT ?founder WHERE { wd:Q848490 wdt:P112 ?founder }
```

The result shows that the founder is Ikutaro Kakehashi.

answer(kg="wikidata", sparql="SELECT ?founder WHERE { wd:Q848490 wdt:P112 ?founder }", answer="Ikutaro Kakehashi")