PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?company ?companyLabel ?employees WHERE {
  VALUES ?company {
    wd:Q20718 wd:Q312
  }
  ?company wdt:P1128 ?employees .
  OPTIONAL {
    ?company rdfs:label ?companyLabel FILTER ( LANG( ?companyLabel ) = "en" )
  }
}

Apple has more employees (164,000) than Samsung Electronics (109,541).

Does Samsung Electronics or Apple have more employees?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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

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.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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.

[User] Does Samsung Electronics or Apple have more employees?

[Assistant]
To answer this question, I need to find the entities for "Samsung Electronics" and "Apple" in Wikidata and then find the property that indicates the number of employees for these companies. After that, I will compare the number of employees for both companies.

First, I will search for the entities of "Samsung Electronics" and "Apple" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Samsung Electronics, also known as Samsung Electronics Co., Ltd., Samsung Electronics Co., Ltd. (Suwon-si), Samsung Electronics Ltd, SΛMSUNG, Samsung Electric Industries (wd:Q20718): South Korean multinational electronics corporation, instance of public company, instance of organization, instance of enterprise, instance of business
2. Samsung Electronics Sales (wd:Q16799998): instance of company
3. Samsung Electronics Service, also known as SAMSUNG ELECTRONICS SERVICE CO., LTD (wd:Q16978650): Samsung Electronics Service co., instance of enterprise
4. Samsung Electronics Logitech (wd:Q17346115): instance of enterprise, instance of business
5. Samsung Electronics Romania, also known as SAMSUNG ELECTRONICS ROMANIA SRL (wd:Q108169274): instance of enterprise, instance of business, company
6. Samsung Electronics Slovakia (wd:Q102544902): instance of enterprise, instance of business, Slovak company
7. Samsung Electronics Polska, also known as SAMSUNG ELECTRONICS POLSKA SP. Z O.O. (wd:Q108169146): instance of enterprise, instance of business, company
8. Samsung Electronics Magyar, also known as SAMSUNG ELECTRONICS MAGYAR ZRT. (wd:Q108169069): instance of enterprise, instance of business, company
9. Samsung Electronics Limited, also known as Samsung (SEUK), SAMSUNG ELECTRONICS (UK) LIMITED, Samsung Electronics UK, Samsung Electronics (UK) (wd:Q21121070): UK subsidiairy of Samsung Electronics, instance of business
10. Samsung Electronics Holding, also known as Samsung Electronics Holding (SEHG), Samsung Electronics (SEHG) (wd:Q21121072): instance of business

[Tool]
Top 10 entity alternatives:
1. apple, also known as apple fruit (wd:Q89)
2. Apple, also known as Apple, Inc, Apple Computer Inc., Apple Incorporated, Apple Computer Inc (wd:Q312): American multinational technology company based in Cupertino, California, instance of computer manufacturer, instance of technology company, instance of public company, instance of organization, instance of corporation, instance of enterprise, instance of business, instance of brand
3. Malus, also known as crabapple, apples, apple (wd:Q104819)
4. Apple Records, also known as Apple (label), Apple (wd:Q213710): instance of record label, instance of enterprise, instance of trademark, UK record label
5. Muggsy Bogues, also known as Muggs, Tyrone Curtis Bogues, Tyrone Bogues, Billy, Apple (wd:Q150502): American basketball player-coach, instance of human, basketball player, basketball coach
6. Malus pumila, also known as domestic apple, apple (wd:Q158657): species of apple tree, instance of taxon
7. The Apple, also known as Apple, Sib (wd:Q595660): 1998 film by Samira Makhmalbaf, instance of film
8. Apple (wd:Q1754545): 1990 debut studio album by Mother Love Bone, instance of album
9. Ariane Passenger Payload Experiment, also known as APPLE (wd:Q2248280): instance of communications satellite, communication satellite
10. Apple (wd:Q128117967): instance of musical work/composition, 2024 song by Charli XCX

[Assistant] I found the entities for Samsung Electronics (wd:Q20718) and Apple Inc. (wd:Q312). Next, I will look for the property that indicates the number of employees for these companies. I will check the properties of Samsung Electronics first to find the relevant property for the number of employees.

[Tool]
Top 10 property alternatives:
1. employees, also known as employee count, workers, staff, personnel, number of staff (wd:P1128 as wdt/p): total number of employees of a company at a given "point in time...not deprecated rank). Add data for recent years, don't overwrite
2. organizational divisions, also known as divisions, has business division, has division (wd:P199 as wdt/p): organizational divisions of this organization (which are not independent legal entities), subproperty of has subsidiary
3. headquarters location, also known as headquarters locality, headquarters town, HQ, HQ location, location of headquarters (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, subproperty of significant place
4. owned by, also known as provenance, shareholder, stockholder, stockholders, shareholders (wd:P127 as wdt/p): owner of the subject
5. affiliated worker organisation, also known as labor union, trades union, union, workers union (wd:P9239 as wdt/p): organization representing workers in a specific company or organisation
6. total revenue, also known as gross revenue, turnover, sales, revenue, net sales (wd:P2139 as wdt/p): income gained by an organization during a given time frame. Not to be confused with fiscal revenue
7. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, produces (wd:P1056 as wdt/p): material or product produced by an organization, industry, facility, or process, subproperty of immediate cause of
8. has subsidiary, also known as subordinate body, subordinate unit, subsidiary, subsidiary body, subsidiary company (wd:P355 as wdt/p): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., subproperty of has part(s), subproperty of owner of
9. social media followers, also known as number of followers, subscriber number, sub number, sub count, number of subscribers (wd:P8687 as wdt/p): number of subscribers on a particular social media website (use ... in time" and property for account. For Twitter, use numeric id.
10. member of, also known as of organization, membership, member of musical group, in musical group, band member of (wd:P463 as wdt/p): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), subproperty of part of

No other items found

[Assistant] The property "employees" (wdt:P1128) represents the total number of employees of a company. I will now query the number of employees for Samsung Electronics and Apple Inc. using this property.

[Tool]
Got 2 rows and 3 columns:
| company                         | companyLabel             | employees            |
| ------------------------------- | ------------------------ | -------------------- |
| Samsung Electronics (wd:Q20718) | Samsung Electronics (en) | 109541 (xsd:decimal) |
| Apple (wd:Q312)                 | Apple (en)               | 164000 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wikibase: <http://wikiba.se/ontology#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?dragon ?dragonLabel ?propertyLabel ?valueLabel WHERE {
  VALUES ?dragon {
    wd:Q37944796 wd:Q37944813 wd:Q37944783
  }
  ?dragon ?p ?value .
  ?property wikibase:directClaim ?p .
  FILTER ( CONTAINS( LCASE( STR( ?propertyLabel ) ) , "death" ) || CONTAINS( LCASE( STR( ?propertyLabel ) ) , "end" ) ) ?dragon rdfs:label ?dragonLabel FILTER ( LANG( ?dragonLabel ) = "en" ) OPTIONAL {
    ?property rdfs:label ?propertyLabel FILTER ( LANG( ?propertyLabel ) = "en" )
  }
  OPTIONAL {
    ?value rdfs:label ?valueLabel FILTER ( LANG( ?valueLabel ) = "en" )
  }
}

Got 9 rows and 4 columns:
| dragon                  | dragonLabel   | propertyLabel        | valueLabel           |
| ----------------------- | ------------- | -------------------- | -------------------- |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | place of death (en)  | Dragonstone (en)     |
| Viserion (wd:Q37944783) | Viserion (en) | sex or gender (en)   | male organism (en)   |
| Drogon (wd:Q37944796)   | Drogon (en)   | sex or gender (en)   | male organism (en)   |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | sex or gender (en)   | male organism (en)   |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | cause of death (en)  | stabbing (en)        |
| Viserion (wd:Q37944783) | Viserion (en) | cause of death (en)  | stabbing (en)        |
| Viserion (wd:Q37944783) | Viserion (en) | place of death (en)  | Beyond the Wall (en) |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | manner of death (en) | killing (en)         |
| Viserion (wd:Q37944783) | Viserion (en) | manner of death (en) | killing (en)         |

Among the dragons in Game of Thrones, Rhaegal and Viserion died. Both have cause of death listed as stabbing and manner of death as killing. Drogon does not have any death information, implying he survived.

Which of the dragons in Game of Thrones died?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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

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.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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.

[User] Which of the dragons in Game of Thrones died?

[Assistant]
To answer this question, I need to identify the dragons from the Game of Thrones series and then find out which of them died.

Step 1: Identify dragons related to Game of Thrones.
Step 2: Check the death status of these dragons.

I will start by searching for dragons related to Game of Thrones.

[Tool]
Top 10 entity alternatives:
1. Game of Thrones, also known as GoT, GOT (wd:Q23572): American fantasy drama television series, instance of television series
2. A Song of Ice and Fire, also known as Song of Ice and Fire, ASOIAF, Game of Thrones, A Song of Ice and Fire Series (wd:Q45875): series of epic fantasy novels by George R. R. Martin, instance of novel series
3. Game of Thrones, also known as Game of Thrones: A Telltale Games Series, Game of Thrones: Iron from Ice (wd:Q18150581): instance of video game, 2014 video game
4. Game of Thrones (wd:Q400488): 2012 action role-playing video game, instance of video game
5. Game of Game of Thrones (wd:Q110055480): instance of Saturday Night Live sketch, sketch from Saturday Night Live
6. Game of Thrones (wd:Q108370933): instance of video game series, video game series
7. A Game of Thrones, also known as A Game of Thrones (A Song of Ice and Fire, Book 1) (wd:Q1751870): 1996 novel by George R. R. Martin, instance of literary work
8. A Game of Thrones (wd:Q1755280): instance of board game, board game
9. Game of Thrones Theme, also known as Game of Thrones Main Title Theme (wd:Q27813929): instance of musical work/composition, television theme music
10. Reigns: Game of Thrones, also known as Reigns: GOT, Reigns GOT, Reigns Game of Thrones (wd:Q60743421): instance of video game, 2018 video game

[Assistant] The relevant entity for Game of Thrones is wd:Q23572 (American fantasy drama television series). Now I will search for dragons that are instances or related to Game of Thrones (wd:Q23572).

[Tool] Got no rows and 2 columns

[Assistant] No dragons were found directly linked as instances of "dragon" and related to Game of Thrones (wd:Q23572) using the property "based on or related to." I will try a different approach by searching for dragons that are characters or fictional creatures in Game of Thrones. I will look for dragons that are characters in the Game of Thrones series.

[Tool] Got no rows and 2 columns

[Assistant] No dragons were found directly linked as instances of "dragon" and related to the novel "A Game of Thrones" (wd:Q1751870). I will try to find dragons that are characters in the Game of Thrones universe by searching for dragons that are characters in the TV series Game of Thrones (wd:Q23572).

[Tool] Got no rows and 2 columns

[Assistant] No dragons were found directly linked as instances of "dragon" and related to the Game of Thrones TV series (wd:Q23572) using the property "based on or related to." I will try to find dragons by searching for characters that are dragons in the Game of Thrones series. I will search for characters in Game of Thrones and then filter those that are dragons.

[Tool] Got no rows and 2 columns

[Assistant] No fictional characters were found directly linked to the Game of Thrones TV series (wd:Q23572) using the property "based on or related to." I will try a different approach by searching for dragons in general and then check if they are related to Game of Thrones.

[Tool]
Top 10 entity alternatives:
1. dragon, also known as drake (wd:Q7559)
2. Loong, also known as wingless dragon, lung, loong, long, dragon (wd:Q215605)
3. Dragon, also known as SpaceX Dragon, Cargo Dragon, Dragon (spacecraft), Dragon 1, CRS Dragon (wd:Q236448)
4. Dragon, also known as long (wd:Q731434): instance of animal from the Chinese zodiac, fifth sign of Chinese zodiac
5. blunderbuss, also known as dragon (wd:Q1148444)
6. European dragon, also known as Dragon (wd:Q3058816)
7. Dragon (wd:Q909576)
8. Douglas B-23 Dragon, also known as Douglas Dragon, Dragon bomber, Dragon aircraft, Dragon, B23 Dragon (wd:Q578552)
9. Dragon, also known as Dragon Magazine, The Dragon (wd:Q2405201): instance of tabletop role-playing game periodical, magazine published by TSR, instance of magazine
10. Dragon, also known as Wu Xia (wd:Q213138): 2011 film by Peter Chan, instance of film

[Assistant] The general entity for dragon is wd:Q7559. Now I will try to find dragons that are characters in Game of Thrones by searching for characters that are instances of dragon (wd:Q7559) and related to Game of Thrones (wd:Q23572).

[Tool] Got no rows and 2 columns

[Assistant] No dragons were found as instances of dragon (wd:Q7559) and related to Game of Thrones (wd:Q23572) using the property "based on or related to." I will try to find dragons that are characters in Game of Thrones by searching for characters that are dragons and have a connection to Game of Thrones through other properties such as "fictional universe" or "part of."

[Tool]
Top 10 property alternatives:
1. from narrative universe, also known as from narrative, from universe, in continuity, in cycle, in world of (wd:P1080 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): subject's fictional entity is in the object narrative. See also P1441 (present in work) and P1445 (fictional universe described in), subproperty of part of
2. fictional universe described in (wd:P1445 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): to link a fictional universe with a work that describes it: <universe> "described in the work:" <work>
3. takes place in fictional universe, also known as universe featured, universe described, set in fictional universe, fictional universe described, describes fictional universe (wd:P1434 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): the subject is a work describing a fictional universe, i.e. whose plot occurs in this universe
4. narrative age, also known as age in fiction, years old in-universe, specific age inside fictional universe, ingame age, age in story (wd:P6249 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): age of a fictional character (use qualifiers like P10663 (applies to work) to restrict the age to a certain work)
5. fictional or mythical analog of, also known as mythical analog of, fictional version of, fictionalization of, fictionalisation of, analog of (wd:P1074 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): used to link an entity or class of entities appearing in a creat...with the analogous entity or class of entities in the real world, subproperty of inspired by
6. alternate universe counterpart, also known as counterpart in an alternate universe, counterpart in alternate universe, counterpart in an AU, counterpart in a parallel universe, counterpart in parallel universe (wd:P11799 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): this fictional character or entity is an alternate universe counterpart of the subject, subproperty of significant person
7. characters, also known as appears in this work (wd:P674 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): characters which appear in this item (like plays, operas, operettas, books, comics, films, TV series, video games)
8. uses, also known as instrument played, item used, made use of, makes use of, trope used (wd:P2283 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): item or concept used by the subject or in the operation (see also instrument [P1303] and armament [P520])
9. fandom, also known as fan group name (wd:P8693 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): fan group of a celebrity, musical group, artist, work, etc.
10. personality trait of fictional character, also known as traits, trait of fictitious character, trait of fictional character, trait, personal quality of fictitious character (wd:P9652 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): particular personality trait which applies to this fictional character

[Assistant] The property "from narrative universe" (wdt:P1080) seems suitable to link fictional characters or entities to the fictional universe they belong to. I will try to find dragons that are instances of dragon (wd:Q7559) and have the property "from narrative universe" (wdt:P1080) pointing to Game of Thrones (wd:Q23572).

[Tool] Got no rows and 2 columns

[Assistant] No dragons were found as instances of dragon (wd:Q7559) with the property "from narrative universe" (wdt:P1080) pointing to Game of Thrones (wd:Q23572). I will try to find dragons that are characters in Game of Thrones by searching for characters (wd:Q95074) that are dragons (wd:Q7559) or have "instance of" dragon and are linked to Game of Thrones through other properties.

[Tool] Got no rows and 2 columns

[Assistant] No fictional characters were found that are instances of both fictional character (wd:Q95074) and dragon (wd:Q7559) and are from the narrative universe Game of Thrones (wd:Q23572). I will try to find dragons that are characters in Game of Thrones by searching for characters that appear in the Game of Thrones work (wd:Q23572) using the property "characters" (wdt:P674) and then filter those that are dragons.

[Tool] Got no rows and 2 columns

[Assistant] No characters that are instances of dragon (wd:Q7559) were found in the Game of Thrones TV series (wd:Q23572) using the property "characters" (wdt:P674). I will try to find dragons that are part of the Game of Thrones fictional universe by searching for dragons that have the property "part of" (wdt:P361) pointing to Game of Thrones or its fictional universe.

[Tool] Got no rows and 2 columns

[Assistant]
No dragons were found as instances of dragon (wd:Q7559) and part of Game of Thrones (wd:Q23572) using the property "part of" (wdt:P361). Since direct queries for dragons in Game of Thrones are not yielding results, I will try to find the names of dragons in Game of Thrones from external knowledge and then check their death status individually.

The three main dragons in Game of Thrones are Drogon, Rhaegal, and Viserion. I will check the death status of these dragons.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant] The query timed out. I will try a simpler query to check the death status of the dragons Drogon, Rhaegal, and Viserion by searching for their entities first.

[Tool]
Top 10 entity alternatives:
1. Drogo, also known as Drogon, Drogo of Metz (wd:Q723791): illegitimate son of Frankish emperor Charlemagne by the concubine Regina, bishop of Metz, instance of human, Catholic bishop
2. Drogon (wd:Q96376646): C++ web application framework
3. Drogon (wd:Q37944796): fictional dragon from A Song of Ice and Fire and Game of Thrones, instance of A Song of Ice and Fire character, instance of Game of Thrones character, instance of fictional dragon
4. Drogoń (wd:Q111738567): instance of family name, family name
5. Drogon, also known as Dracon, Drogo, Dreux (wd:Q19833761): cardinal bishop of Ostia, instance of human, Catholic priest, writer
6. Drogon (wd:Q28026243): instance of human
7. Drogon (wd:Q26868871): instance of male given name, male given name
8. Drogon (wd:Q104047388): instance of encyclopedia article, encyclopedia article
9. Drogoniów, also known as Drogoniow (wd:Q5308204): instance of village of Poland, village in Lubusz, Poland
10. Pseudocalotes drogon (wd:Q28148101): species of reptile, instance of taxon

[Assistant] The entity for Drogon, the fictional dragon from A Song of Ice and Fire and Game of Thrones, is wd:Q37944796. I will now search for the entities of Rhaegal and Viserion.

[Tool]
Top 10 entity alternatives:
1. Rhaegal (wd:Q37944813): fictional dragon from A Song of Fire and Ice and Game of Thrones, instance of A Song of Ice and Fire character, instance of Game of Thrones character, instance of fictional dragon
2. Pseudocalotes rhaegal (wd:Q28148100): species of reptile, instance of taxon
3. Sabrewing Rhaegal (wd:Q65086215): type of aircraft
4. Rhaegalius (wd:Q112895735): instance of taxon
5. Gymnetis rhaegali (wd:Q60274548): instance of taxon, species of beetle
6. Echinoderes rhaegali (wd:Q52529917): species of kinorhynchans, instance of taxon
7. Rhaegalius sophia (wd:Q112916188): instance of taxon, species of insect
8. Systematic revision and phylogen...n. nov. and Rhaegalius gen. nov. (wd:Q104623606): scientific article published in September 2020, instance of scholarly article

[Tool]
Top 10 entity alternatives:
1. Viserion (wd:Q37944783): fictional dragon from A Song of Fire and Ice and Game of Thrones, instance of A Song of Ice and Fire character, instance of Game of Thrones character, instance of revenants in fiction, instance of fictional dragon
2. Pseudocalotes viserion (wd:Q28148099): species of reptile, instance of taxon
3. Pheidole viserion (wd:Q28668927): species of hymenopterans, instance of taxon
4. Gymnetis viserioni (wd:Q60274715): instance of taxon, species of beetle
5. Fujuriphyes viserioni (wd:Q129931689): instance of taxon, species
6. Pycnophyidae (Kinorhyncha: Allom...s on its intraspecific variation (wd:Q104452061): scientific article published on 11 April 2019, instance of scholarly article

[Assistant]
The entities for the dragons are:
- Drogon: wd:Q37944796
- Rhaegal: wd:Q37944813
- Viserion: wd:Q37944783

I will now check the death status of these dragons by looking for statements about their death or cause of death.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant] The query timed out again. I will try a simpler query to check if these dragons have a death date or death status.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant] The query timed out again. I will try to check the death status of the dragons one by one to reduce query complexity. I will start with Drogon (wd:Q37944796).

[Tool] Got no rows and 4 columns

[Assistant] There is no information about the death or cause of death for Drogon (wd:Q37944796). I will now check the death status for Rhaegal (wd:Q37944813).

[Tool] Got no rows and 4 columns

[Assistant] There is no information about the death or cause of death for Rhaegal (wd:Q37944813). I will now check the death status for Viserion (wd:Q37944783).

[Tool] Got no rows and 4 columns

[Assistant] There is no direct information about the death or cause of death for Viserion (wd:Q37944783) either. Since the direct properties for death or cause of death are not present, I will try to check if these dragons have a statement about their "end of existence" or similar property that might indicate death. I will check for any statements related to "end" or "death" for these dragons.

[Tool]
Got 9 rows and 6 columns:
| dragon                  | dragonLabel   | property                   | propertyLabel        | value                          | valueLabel           |
| ----------------------- | ------------- | -------------------------- | -------------------- | ------------------------------ | -------------------- |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | place of death (wd:P20)    | place of death (en)  | Dragonstone (wd:Q40422975)     | Dragonstone (en)     |
| Viserion (wd:Q37944783) | Viserion (en) | sex or gender (wd:P21)     | sex or gender (en)   | male organism (wd:Q44148)      | male organism (en)   |
| Drogon (wd:Q37944796)   | Drogon (en)   | sex or gender (wd:P21)     | sex or gender (en)   | male organism (wd:Q44148)      | male organism (en)   |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | sex or gender (wd:P21)     | sex or gender (en)   | male organism (wd:Q44148)      | male organism (en)   |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | cause of death (wd:P509)   | cause of death (en)  | stabbing (wd:Q464643)          | stabbing (en)        |
| Viserion (wd:Q37944783) | Viserion (en) | cause of death (wd:P509)   | cause of death (en)  | stabbing (wd:Q464643)          | stabbing (en)        |
| Viserion (wd:Q37944783) | Viserion (en) | place of death (wd:P20)    | place of death (en)  | Beyond the Wall (wd:Q57740439) | Beyond the Wall (en) |
| Rhaegal (wd:Q37944813)  | Rhaegal (en)  | manner of death (wd:P1196) | manner of death (en) | killing (wd:Q844482)           | killing (en)         |
| Viserion (wd:Q37944783) | Viserion (en) | manner of death (wd:P1196) | manner of death (en) | killing (wd:Q844482)           | killing (en)         |

[Tool] Stopped generation process


PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?verified WHERE {
  wd:Q858851 p:P2002 ?stmt .
  ?stmt pq:P1552 wd:Q28378282 .
  BIND( "true" AS ?verified )
}
LIMIT 1

Got 1 row and 1 column:
| verified |
| -------- |
| true     |

Yes, the Linux Foundation is verified on Twitter.

Is the Linux Foundation verified on Twitter?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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

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.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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.

[User] Is the Linux Foundation verified on Twitter?

[Assistant]
To answer the question "Is the Linux Foundation verified on Twitter?" I need to check if the Linux Foundation has a verified Twitter account. This information is typically represented in Wikidata by a property linking an organization to its Twitter account and a qualifier or property indicating verification status.

Step 1: Identify the Linux Foundation entity in Wikidata.
Step 2: Check if there is a Twitter account property for the Linux Foundation.
Step 3: Check if there is any property or qualifier indicating the Twitter account is verified.

I will start by searching for the Linux Foundation entity in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Linux Foundation, also known as LF, The Linux Foundation (wd:Q858851): non-profit technology consortium for development of Linux and open source, instance of mutual-benefit nonprofit corporation, instance of foundation
2. Linux Foundation Linux Certification (wd:Q18148567): IT certifications program
3. Gentoo Foundation, also known as Gentoo Foundation, Inc., Gentoo Linux Foundation (wd:Q75837444): The Gentoo Foundation, Inc. provides the legal and financial base for the Gentoo project., instance of organization
4. Linux Foundation project, also known as Linux Foundation Collaborative Project (wd:Q39998995)
5. Linux, also known as Linux Operating System (wd:Q388)
6. Linux kernel, also known as Linux (wd:Q14579): Unix-like operating system kernel, basis for all Linux operating systems, instance of free and open-source software, instance of collaborative work, instance of computing platform, instance of kernel
7. foundation, also known as foundations, base (wd:Q191360)
8. foundation, also known as charitable foundation, foundations (wd:Q157031)
9. Foundation (wd:Q753894): 1951 novel by Isaac Asimov, instance of literary work
10. foundation (wd:Q1418455)

[Assistant] The Linux Foundation entity in Wikidata is wd:Q858851. Next, I will check if this entity has a Twitter account property (usually wdt:P2002) and if there is any indication of verification status for that Twitter account.

[Tool]
Got 1 row and 3 columns:
| subject                       | property                         | object          |
| ----------------------------- | -------------------------------- | --------------- |
| Linux Foundation (wd:Q858851) | X (Twitter) username (wdt:P2002) | linuxfoundation |

[Assistant]
The Linux Foundation has a Twitter username "linuxfoundation" (property wdt:P2002). Now I need to check if there is any property or qualifier indicating that this Twitter account is verified. Verification status is not directly stored in Wikidata for Twitter accounts, but sometimes it can be found as a qualifier or reference on the Twitter username property or as a separate statement.

I will check the statements for the Twitter username property of the Linux Foundation to see if there is any indication of verification.

[Tool]
Got 1 row and 3 columns:
| subject                       | property                       | object                                         |
| ----------------------------- | ------------------------------ | ---------------------------------------------- |
| Linux Foundation (wd:Q858851) | X (Twitter) username (p:P2002) | s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf |

[Assistant] There is a statement for the Twitter username property (p:P2002) for the Linux Foundation. I will now check the qualifiers or references for this statement to see if there is any indication of verification.

[Tool]
Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                                        | property                        | object                                       |
| ---------------------------------------------- | ------------------------------- | -------------------------------------------- |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | point in time (pq:P585)         | 2020-03-23T00:00:00Z (xsd:dateTime)          |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | start time (pq:P580)            | 2008-05-08T00:00:00Z (xsd:dateTime)          |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | has characteristic (pq:P1552)   | wd:Q28378282                                 |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | X (Twitter) username (ps:P2002) | linuxfoundation                              |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | X numeric user ID (pq:P6552)    | 14706299                                     |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | wikibase:rank                   | wikibase:NormalRank                          |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | rdf:type                        | wikibase:BestRank                            |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | prov:wasDerivedFrom             | ref:1f7f7584600eed06a43d579542151c766d3c4517 |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | point in time (pqv:P585)        | v:76315dbcd99723ac52ef5c3f18bef7dd           |
| s:Q858851-4e935dfb-41d5-8025-6800-346dca937cbf | start time (pqv:P580)           | v:c27ea585308622879fd8d6418992aa96           |
| ...                                            | ...                             | ...                                          |

[Assistant] The statement for the Twitter username "linuxfoundation" has a qualifier "has characteristic" (pq:P1552) with the value wd:Q28378282. I will check what wd:Q28378282 represents to see if it indicates verification.

[Tool]
Got 1 row and 1 column:
| label                            |
| -------------------------------- |
| verified account or profile (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( SUM( ?area ) AS ?totalArea ) WHERE {
  VALUES ?lake {
    wd:Q1062 wd:Q1066 wd:Q1377732 wd:Q5492
  }
  ?lake wdt:P2046 ?area .
}

The total area of the Great Lakes (Lake Ontario, Lake Superior, Lake Michigan–Huron, and Lake Erie) is approximately 244,223 square kilometers.

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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

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.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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.

[User] What is the area of the great lakes?

[Assistant]
The Great Lakes refer to a group of five large lakes in North America: Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario. To answer the question about the area of the Great Lakes, I need to find the total area of these five lakes combined.

Step 1: Identify the entities for the Great Lakes.
Step 2: Retrieve the area of each of these lakes.
Step 3: Sum the areas to get the total area of the Great Lakes.

I will start by searching for the entities representing the Great Lakes.

[Tool]
Top 10 entity alternatives:
1. Great Lakes (wd:Q7347): instance of group of interconnected lakes, group of lakes in North America
2. Great Lakes Council, also known as Great Lakes (wd:Q753474): former local government area of New South Wales, Australia, instance of former local government area of Australia, instance of local government area of New South Wales
3. Great Lakes Institute of Management, also known as Great Lakes, GLIM (wd:Q5599458): instance of business school, business School
4. Great Lakes (wd:Q15103574): instance of short film, 1917 silent short film
5. Great Lakes (wd:Q5599408): Metra train station in North Chicago, Illinois, USA, instance of railway station
6. Great Lakes (wd:Q5599409): instance of musical group, American rock band
7. Great Lakes (wd:Q114505543): instance of television series episode, episode of Naked Science (S6 E9)
8. Great Lakes (wd:Q88278985): lake in Cheboygan County, Michigan, United States of America, instance of lake
9. Great Lakes, also known as Great Lakes, Illinois (wd:Q100252292): human settlement (possibly historical), instance of human settlement
10. Great Lakes, also known as IMO 7119721 (wd:Q122697956): ship built in 1971, instance of ship

[Assistant] The entity "Great Lakes" as a group of interconnected lakes in North America is wd:Q7347. Now I will find the individual lakes that are part of this group and then retrieve their areas.

[Tool]
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: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant]
The query failed due to an unsupported SERVICE wikibase:label call on this endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label to get the lake names.

I will now query for the parts (P527) of the Great Lakes group (wd:Q7347) and get their labels using optional rdfs:label.

[Tool]
Got 4 rows and 2 columns:
| lake                              | lakeLabel                |
| --------------------------------- | ------------------------ |
| Lake Ontario (wd:Q1062)           | Lake Ontario (en)        |
| Lake Superior (wd:Q1066)          | Lake Superior (en)       |
| Lake Michigan–Huron (wd:Q1377732) | Lake Michigan–Huron (en) |
| Lake Erie (wd:Q5492)              | Lake Erie (en)           |

[Assistant]
The query returned four lakes as parts of the Great Lakes group: Lake Ontario, Lake Superior, Lake Michigan–Huron, and Lake Erie. Notably, Lake Michigan and Lake Huron are combined as Lake Michigan–Huron in this data.

Next, I will retrieve the area (property P2046) of each of these lakes.

[Tool]
Got 4 rows and 3 columns:
| lake                              | lakeLabel                | area                 |
| --------------------------------- | ------------------------ | -------------------- |
| Lake Ontario (wd:Q1062)           | Lake Ontario (en)        | 18529 (xsd:decimal)  |
| Lake Superior (wd:Q1066)          | Lake Superior (en)       | 82350 (xsd:decimal)  |
| Lake Michigan–Huron (wd:Q1377732) | Lake Michigan–Huron (en) | 117600 (xsd:decimal) |
| Lake Erie (wd:Q5492)              | Lake Erie (en)           | 25744 (xsd:decimal)  |

[Assistant]
I have the areas of the four lakes: Lake Ontario (18529 km²), Lake Superior (82350 km²), Lake Michigan–Huron (117600 km²), and Lake Erie (25744 km²).

To get the total area of the Great Lakes, I will sum these areas in a SPARQL query.

[Tool]
Got 1 row and 1 column:
| totalArea            |
| -------------------- |
| 244223 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?letter ?letterLabel WHERE {
  wd:Q8216 wdt:P527 ?letter .
  ?letter wdt:P31 wd:Q19793459 .
  ?letter wdt:P155 ?prev .
  FILTER ( ?prev = wd:Q9887 ) OPTIONAL {
    ?letter rdfs:label ?letterLabel FILTER ( LANG( ?letterLabel ) = "en" )
  }
}

The second letter in the Greek alphabet is Beta.

What is the second letter in the Greek alphabet?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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

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.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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.

[User] What is the second letter in the Greek alphabet?

[Assistant] The Greek alphabet is a well-known sequence of letters. The first letter is Alpha, and the second letter is Beta. To confirm this from Wikidata, I will look for the Greek alphabet and retrieve its letters in order to identify the second letter.

[Tool]
Top 10 entity alternatives:
1. Greek alphabet, also known as Greek, Greek script (wd:Q8216): script used to write the Greek language, instance of natural writing system, instance of bicameral script, instance of alphabet
2. history of the Greek alphabet, also known as Greek alphabet history, the Greek alphabet history (wd:Q2627294): instance of aspect of history, aspect of history
3. Greek Albanian alphabet (wd:Q3305475): Greek alphabet adapted for writing the Albanian language, instance of alphabet
4. extended greek alphabet (wd:Q3237041)
5. Greek spelling alphabet (wd:Q17003959): spelling alphabet for Greek letters, instance of spelling alphabet
6. ancient Greek alphabet, also known as archaic Greek alphabet (wd:Q3611036): instance of alphabet, writing system
7. archaic Greek alphabets (wd:Q2839562)
8. Inscriptions in Greek Alphabet (wd:Q106814193): drawing - Sir Robert Smirke the younger (YCBA B1977.14.581), instance of drawing
9. Inscriptions in Greek Alphabet (wd:Q106814190): drawing - Sir Robert Smirke the younger, instance of drawing
10. Ancient Inscriptions in Greek Alphabet (wd:Q106812515): drawing - Sir Robert Smirke the younger, instance of drawing

[Assistant] The Greek alphabet is represented by the entity wd:Q8216 in Wikidata. Next, I will query the letters of the Greek alphabet in order to find the second letter. I will look for the property that relates the Greek alphabet to its letters and their order.

[Tool]
Top 10 property alternatives:
1. identified in image by, also known as letter in image, stated in image as, number in image, numbered as, marked as (wd:P7380 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): qualifier for the identification of a value/object with a number, letter or sign inscribed in the image
2. script directionality, also known as directionality, letter direction, letter directionality, script direction, writing direction (wd:P1406 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): direction that a writing system goes in
3. Slavistic Phonetic Alphabet transcription, also known as AS, Slavic Alphabet, Slavic phonetic alphabet transcription (wd:P5276 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): transcription in the Slavistic Phonetic Alphabet
4. quantity symbol (string), also known as symbol, variable (wd:P416 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): symbol for a mathematical or physical quantity
5. notation, also known as denoted, symbol (wd:P913 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): mathematical notation or another symbol
6. official symbol, also known as emblem, floral emblem, official emblem, official tartan, state tartan (wd:P2238 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): official symbol of an organisation
7. quantity symbol (LaTeX), also known as LaTeX symbol, quantity symbol, symbol (wd:P7973 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): symbol for a mathematical or physical quantity in LaTeX
8. icon, also known as symbol, pictogram (wd:P2910 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): pictogram suitable to represent the item. For logos of a subject...go image" (P154); for favicons, use "small logo or icon" (P8972), subproperty of image
9. coat of arms image, also known as symbol, representation (heraldic), image of coat of arms, emblem, heraldic, CoA image (wd:P94 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): image of the item's coat of arms - for the shield part only use P4004, subproperty of image
10. symbolizes, also known as symbol for, symbolises, notation for, notates, a symbol for (wd:P4878 as psn/wdt/ps/prn/pr/wdtn/pq/pqv/p/prv/psv/pqn): symbolism of an artistic element; can also be used as a qualifier for "depicts" (P180) statements, subproperty of represents

[Tool]
Got 24 rows and 3 columns, showing the first 10 rows below:
| subject                   | property               | object            |
| ------------------------- | ---------------------- | ----------------- |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | Φ (wd:Q10029)     |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | Λ (wd:Q10897)     |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | beta (wd:Q14382)  |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | Γ (wd:Q14387)     |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | delta (wd:Q14390) |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | E (wd:Q14392)     |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | Ζ (wd:Q14394)     |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | Η (wd:Q14396)     |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | Θ (wd:Q14398)     |
| Greek alphabet (wd:Q8216) | has part(s) (wdt:P527) | Ι (wd:Q14399)     |
| ...                       | ...                    | ...               |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Got 463 rows and 3 columns, showing the first 10 rows below:
| subject          | property                         | object                                                                  |
| ---------------- | -------------------------------- | ----------------------------------------------------------------------- |
| beta (wd:Q14382) | instance of (wdt:P31)            | Greek letter (wd:Q19793459)                                             |
| beta (wd:Q14382) | Commons category (wdt:P373)      | Beta (letter)                                                           |
| beta (wd:Q14382) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Beta%20uc%20lc.svg> |
| beta (wd:Q14382) | part of (wdt:P361)               | International Phonetic Alphabet (wd:Q21204)                             |
| beta (wd:Q14382) | described by source (wdt:P1343)  | Meyers Konversations-Lexikon, 4th edition (1885–1890) (wd:Q19219752)    |
| beta (wd:Q14382) | follows (wdt:P155)               | Α (wd:Q9887)                                                            |
| beta (wd:Q14382) | followed by (wdt:P156)           | Γ (wd:Q14387)                                                           |
| beta (wd:Q14382) | different from (wdt:P1889)       | B/b (wd:Q9705)                                                          |
| beta (wd:Q14382) | topic's main category (wdt:P910) | wd:Q86105521                                                            |
| beta (wd:Q14382) | Unicode character (wdt:P487)     | β                                                                       |
| ...              | ...                              | ...                                                                     |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?color ?colorLabel WHERE {
  wd:Q48160 wdt:P462 ?color .
  OPTIONAL {
    ?color rdfs:label ?colorLabel FILTER ( LANG( ?colorLabel ) = "en" )
  }
}

The German national flag has the colors black, red, and gold.

What colors does the German national flag have?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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

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.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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.

[User] What colors does the German national flag have?

[Assistant] To answer the question about the colors of the German national flag, I need to identify the entity for the German national flag and then find the properties related to its colors. Typically, the colors of a flag are represented by the property "flag color" or similar in Wikidata. I will first search for the entity representing the German national flag.

[Tool]
Top 10 entity alternatives:
1. flag of Germany, also known as German flag, Germany flag (wd:Q48160): national flag of the Federal Republic of Germany, instance of national flag
2. national flag, also known as flag - nation (wd:Q186516)
3. national flag (wd:L591205-S1)
4. flag of Australia, also known as Australia flag, Australian flag, Australian national flag (wd:Q81676): national flag of the Commonwealth of Australia, instance of national flag
5. German National Library, also known as The German National Library in Leipzig, The German National Library, German National Library in Leipzig, DNB, dnb.de (wd:Q27302): central archival library and national bibliographic centre for the Federal Republic of Germany, instance of bibliographic database, instance of national library
6. German nationalism, also known as nationalism in Germany (wd:Q370744)
7. Germanisches Nationalmuseum, also known as Germanischen Nationalmuseum, Nürnberg, Ger. Nmus., German National Museum, Germanisches Nationalmuseum Nürnberg, Germanisches Museum (wd:Q478695): museum about German National cultural history in Nuremberg, Germany, instance of nonprofit organization, instance of national museum, instance of art museum
8. German Federal Archives, also known as BArch, German National Archives, Deutsches Bundesarchiv, bundesarchiv.de, Bundesarchiv (wd:Q685753): archives of German federal government agencies, instance of higher federal authority, instance of national archives, instance of cinematheque
9. German National District (wd:Q114458): instance of municipal district, instance of National District, municipal district in Russia
10. National Flag Square (wd:Q621107): square in Baku, Azerbaijan, instance of square

[Assistant] The entity for the German national flag is "flag of Germany" with the identifier wd:Q48160. Next, I will look for properties related to the colors of this flag. I will search for properties related to "color" for this entity.

[Tool]
Top 10 property alternatives:
1. color, also known as with the colour, with the color, pigment, dye, of the colour (wd:P462 as wdt/p): color of subject
2. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
3. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. depicts, also known as reproduces, portrays, represents, quotes, preserves (wd:P180 as wdt/p): entity visually depicted in an image, literarily described in a ...o an audiovisual or other medium; see also P921,  'main subject', subproperty of has part(s)
5. shape, also known as form factor (wd:P1419 as wdt/p): subproperty of manifestation of, shape of an object
6. genre, also known as type of music, type of film, music genre, literary genre, kind of music (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
7. video, also known as gif, animation, trailer, media (wd:P10 as wdt/p): relevant video. For images, use the property P18. For film trailers, qualify with "object has role" (P3831)="trailer" (Q622550)
8. described by source, also known as mentioned in, mentioned in news article, reviewed in, source of item, subject in (wd:P1343 as wdt/p): work where this item is described, subproperty of attested in
9. inception, also known as inception date, founding date, founded in, founded on date, founded on (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, subproperty of start time
10. applies to jurisdiction, also known as country of jurisdiction, of, jurisdiction linked, jurisdiction, belongs to jurisdiction (wd:P1001 as wdt/p): the item (institution, law, public office, public register...) o...a territorial jurisdiction: a country, state, municipality, ...), subproperty of characteristic of, subproperty of significant place, subproperty of operating area

No other items found

[Assistant] The property "color" (wdt:P462) seems to be the relevant property for the colors of the German national flag (wd:Q48160). I will now query the colors of the German national flag using this property.

[Tool]
Got 3 rows and 2 columns:
| color             | colorLabel |
| ----------------- | ---------- |
| gold (wd:Q208045) | gold (en)  |
| black (wd:Q23445) | black (en) |
| red (wd:Q3142)    | red (en)   |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?institution ?institutionLabel ( COUNT( DISTINCT ?winner ) AS ?winnerCount ) WHERE {
  ?winner wdt:P166 wd:Q103618 .
  ?winner wdt:P69 ?institution .
  OPTIONAL {
    ?institution rdfs:label ?institutionLabel FILTER ( LANG( ?institutionLabel ) = "en" )
  }
}
GROUP BY ?institution ?institutionLabel
HAVING ( COUNT( DISTINCT ?winner ) > 1 )
LIMIT 10

Got 10 rows and 3 columns:
| institution                                            | institutionLabel                              | winnerCount |
| ------------------------------------------------------ | --------------------------------------------- | ----------- |
| Lee Strasberg Theatre and Film Institute (wd:Q1542213) | Lee Strasberg Theatre and Film Institute (en) | 5 (xsd:int) |
| University of California, Los Angeles (wd:Q174710)     | University of California, Los Angeles (en)    | 2 (xsd:int) |
| Vassar College (wd:Q2093794)                           | Vassar College (en)                           | 2 (xsd:int) |
| Actors Studio (wd:Q216458)                             | Actors Studio (en)                            | 2 (xsd:int) |
| Northwestern University (wd:Q309350)                   | Northwestern University (en)                  | 2 (xsd:int) |
| American Academy of Dramatic Arts (wd:Q389336)         | American Academy of Dramatic Arts (en)        | 4 (xsd:int) |
| wd:Q49112                                              | Yale University (en)                          | 2 (xsd:int) |
| Boston University College of Fine Arts (wd:Q4948174)   | Boston University College of Fine Arts (en)   | 2 (xsd:int) |
| Royal Academy of Dramatic Art (wd:Q523926)             | Royal Academy of Dramatic Art (en)            | 2 (xsd:int) |
| Erasmus Hall High School (wd:Q5384959)                 | Erasmus Hall High School (en)                 | 2 (xsd:int) |

Yes, there are at least two winners of the Academy Award for Best Actress who have been educated at the same institution. Examples include the Lee Strasberg Theatre and Film Institute, University of California, Los Angeles, Vassar College, Actors Studio, Northwestern University, American Academy of Dramatic Arts, Yale University, Boston University College of Fine Arts, Royal Academy of Dramatic Art, and Erasmus Hall High School, each of which has educated at least two winners of this award.

Are there at least two winners of the Academy Award for Best Actress who have been educated at the same institution?

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian with notes:
1. When direct properties are missing, chain related properties (e.g., child→child for grandchildren) or search for indirect connections to answer complex relationship queries.
2. For set/class queries (e.g., all cities, all holidays), use instance/subclass chains (P31/P279) for completeness and relevant filters. Be aware of data incompleteness and schema inconsistencies in Wikidata.
3. If queries return too many irrelevant or too few results, try varying properties (country, location, observed in, etc.) and subclass chains, but be ready to report data/model limitations if results remain incomplete or noisy.
4. When a direct property or class is missing or inconsistently used, try related or broader properties/classes (e.g., use inception for independence date, or musical work/composition for musicals) to increase recall.
5. For calculated properties (e.g., population density), combine relevant direct properties (e.g., population, area) in SPARQL queries, as Wikidata may not store the derived value directly.
6. When entity search returns multiple candidates, use context (description, instance type, location, etc.) to disambiguate and select the most relevant entity for the query.

No general notes for all knowledge graphs available

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

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.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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.

[User] Are there at least two winners of the Academy Award for Best Actress who have been educated at the same institution?

[Assistant]
Step 1: Identify the entity for "Academy Award for Best Actress" and the property for "educated at" or similar in Wikidata. Then find winners of this award and their educational institutions.

Step 2: Check if there are at least two winners who share the same educational institution.

I will start by searching for the entity "Academy Award for Best Actress" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Academy Award for Best Actress, also known as Oscar for Best Actress, Best Actress Oscar, Best Actress (wd:Q103618): award presented annually by the Academy of Motion Picture Arts and Sciences, instance of award for best leading actress, instance of Academy Awards
2. Academy Award for Best Supporting Actress, also known as Best Supporting Actress (wd:Q106301): award presented annually by the Academy of Motion Picture Arts and Sciences, instance of award for best supporting actress, instance of Academy Awards
3. Polish Academy Award for Best Actress (wd:Q928164): instance of award for best leading actress, instance of Polish Film Awards
4. British Academy Television Award for Best Actress (wd:Q2925697): instance of British Academy Television Awards, instance of award for best leading actress, instance of television award, instance of class of award, class of award
5. Polish Academy Award for Best Supporting Actress (wd:Q2102534): instance of award for best supporting actress, instance of Polish Film Awards, film award
6. British Academy Television Award for Best Supporting Actress (wd:Q2925695): instance of award for best supporting actress, instance of class of award, class of award
7. Indian Television Academy Award for Best Actress Drama (wd:Q68485342): Award presented annually by Indian television academy awards, instance of class of award
8. The Television Drama Academy Best Actress Award, also known as Television Drama Academy Award for Best Leading Actress (wd:Q86814491): instance of class of award
9. Irish Film and Television Academ...d for Best International Actress (wd:Q132891306): Irish film industry award, instance of award
10. Africa Movie Academy Award for Best Actress in a Leading Role (wd:Q17001109): instance of award for best leading actress, instance of Africa Movie Academy Awards, instance of film award category

[Assistant]
The entity for "Academy Award for Best Actress" is wd:Q103618.

Next, I will find winners of this award. Winners of awards are usually linked by the property "award received" (wdt:P166).

I will search for people who have received the "Academy Award for Best Actress" (wd:Q103618) using the property "award received" (wdt:P166). Then I will look for their educational institutions using the property "educated at" (wdt:P69).

I will start by querying winners of the Academy Award for Best Actress and their educational institutions.

[Tool]
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: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant]
I encountered an error using the SERVICE wikibase:label for labels, which is not supported by the current endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional clauses to get labels.

I will query winners of the Academy Award for Best Actress and their educational institutions with labels using optional clauses.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| winner                        | winnerLabel           | institution                                            | institutionLabel                              |
| ----------------------------- | --------------------- | ------------------------------------------------------ | --------------------------------------------- |
| Helen Hunt (wd:Q170574)       | Helen Hunt (en)       | California State University, Los Angeles (wd:Q1026932) | California State University, Los Angeles (en) |
| Joanne Woodward (wd:Q1111542) | Joanne Woodward (en)  | Sarah Lawrence College (wd:Q1051840)                   | Sarah Lawrence College (en)                   |
| Ingrid Bergman (wd:Q43247)    | Ingrid Bergman (en)   | Palmgrenska samskolan (wd:Q10615472)                   | Palmgrenska samskolan (en)                    |
| Geraldine Page (wd:Q228650)   | Geraldine Page (en)   | DePaul University (wd:Q1179603)                        | DePaul University (en)                        |
| Emma Thompson (wd:Q168724)    | Emma Thompson (en)    | Newnham College (wd:Q1247589)                          | Newnham College (en)                          |
| ...                           | ...                   | ...                                                    | ...                                           |
| Geraldine Page (wd:Q228650)   | Geraldine Page (en)   | Englewood Technical Prep Academy (wd:Q5378133)         | Englewood Technical Prep Academy (en)         |
| Barbra Streisand (wd:Q4636)   | Barbra Streisand (en) | Erasmus Hall High School (wd:Q5384959)                 | Erasmus Hall High School (en)                 |
| Susan Hayward (wd:Q248837)    | Susan Hayward (en)    | Erasmus Hall High School (wd:Q5384959)                 | Erasmus Hall High School (en)                 |
| Julianne Moore (wd:Q80405)    | Julianne Moore (en)   | Frankfurt American High School (wd:Q5490842)           | Frankfurt American High School (en)           |
| Cher (wd:Q12003)              | Cher (en)             | Fresno High School (wd:Q5503182)                       | Fresno High School (en)                       |

[Assistant]
I found winners of the Academy Award for Best Actress and their educational institutions. I noticed that Susan Hayward and Barbra Streisand both attended Erasmus Hall High School.

To confirm if there are at least two winners educated at the same institution, I will query for educational institutions that have at least two different winners of the Academy Award for Best Actress.

[Tool]
Got 10 rows and 3 columns:
| institution                                            | institutionLabel                              | winnerCount |
| ------------------------------------------------------ | --------------------------------------------- | ----------- |
| Lee Strasberg Theatre and Film Institute (wd:Q1542213) | Lee Strasberg Theatre and Film Institute (en) | 5 (xsd:int) |
| University of California, Los Angeles (wd:Q174710)     | University of California, Los Angeles (en)    | 2 (xsd:int) |
| Vassar College (wd:Q2093794)                           | Vassar College (en)                           | 2 (xsd:int) |
| Actors Studio (wd:Q216458)                             | Actors Studio (en)                            | 2 (xsd:int) |
| Northwestern University (wd:Q309350)                   | Northwestern University (en)                  | 2 (xsd:int) |
| American Academy of Dramatic Arts (wd:Q389336)         | American Academy of Dramatic Arts (en)        | 4 (xsd:int) |
| wd:Q49112                                              | Yale University (en)                          | 2 (xsd:int) |
| Boston University College of Fine Arts (wd:Q4948174)   | Boston University College of Fine Arts (en)   | 2 (xsd:int) |
| Royal Academy of Dramatic Art (wd:Q523926)             | Royal Academy of Dramatic Art (en)            | 2 (xsd:int) |
| Erasmus Hall High School (wd:Q5384959)                 | Erasmus Hall High School (en)                 | 2 (xsd:int) |

[Tool] Stopped generation process