PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?book ?title ?date WHERE {
  ?book wdt:P50 wd:Q207 ; wdt:P1476 ?title ; wdt:P577 ?date .
}
ORDER BY DESC ( ?date )
LIMIT 1

The title of George W. Bush's new book is 'Portraits of Courage'.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 title of george bush's new book?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
when did george w bush take office?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q207 p:P39 ?y .
  ?y ps:P39 wd:Q11696 ; pq:P580 ?x .
}

Using entities:
President of the United States, also known as US President, U.S. President, United States President, the president of the United States, President of the U.S. (wd:Q11696): head of state and head of government of the United States of America
George W. Bush, also known as GWB, POTUS 43, George Bush, President Bush, Bush Jr. (wd:Q207): President of the United States from 2001 to 2009, motivational speaker, rugby union player, military officer, autobiographer, businessperson, statesperson, politician, financier, painter

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time

Example 2:
Question:
where is george bush from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q207 wdt:P19 ?x .
}

Using entities:
George W. Bush, also known as GWB, POTUS 43, George Bush, President Bush, Bush Jr. (wd:Q207): President of the United States from 2001 to 2009, motivational speaker, rugby union player, military officer, autobiographer, businessperson, statesperson, politician, financier, painter

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 3:
Question:
when was george w bush sworn in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q207 p:P39 ?y .
  ?y ps:P39 wd:Q11696 ; pq:P580 ?x .
}

Using entities:
President of the United States, also known as US President, U.S. President, United States President, the president of the United States, President of the U.S. (wd:Q11696): head of state and head of government of the United States of America
George W. Bush, also known as GWB, POTUS 43, George Bush, President Bush, Bush Jr. (wd:Q207): President of the United States from 2001 to 2009, motivational speaker, rugby union player, military officer, autobiographer, businessperson, statesperson, politician, financier, painter

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time

[Assistant] None of these examples directly address the question about George Bush's new book, but they provide useful information about George W. Bush and how to structure a query involving him. Let's first identify the entity for George W. Bush.

[Tool]
Top 10 entity alternatives:
1. George W. Bush, also known as GWB, POTUS 43, George Bush, President Bush, Bush Jr. (wd:Q207): President of the United States from 2001 to 2009, motivational speaker, rugby union player, military officer, autobiographer, businessperson, statesperson, politician, financier, painter
2. George Washington Bush, also known as George W. Bush (wd:Q5545944): American pioneer, husbandry worker, employee, pioneer, trapper, farmer
3. George W. Bush (wd:Q104844437): 2020 television documentary film
4. George W. Bush (wd:Q111279895): episode of Dokument utifrån
5. George W. Bush (wd:Q47513370): painting by Robert A. Anderson
6. George W. Bush, also known as George Washington Bush (wd:Q94496272): operator of George W. Bush & Son, a shipping business in Delaware, United States (1824-1900), consignor, director, merchant
7. George H. W. Bush, also known as G H W Bush, George W. Bush Senior, GHWB, GHW Bush, G.H.W. Bush (wd:Q23505): President of the United States from 1989 to 1993, baseball player, aircraft pilot, autobiographer, naval officer, statesperson, entrepreneur, politician, diplomat
8. George W. Bush Presidential Center, also known as GWB Library, GWB Presidential Library, George W. Bush Presidential Library, George W. Bush Library, Bush Presidential Library (wd:Q1508430): Presidential library and museum for U.S. President George W. Bush, located in Dallas, Texas
9. George W. Bush Administration cabinet, also known as Bush Cabinet, George W. Bush Cabinet, Cabinet of George W. Bush, Cabinet George W. Bush, Cabinet Bush (wd:Q28464532): most senior appointed officers of the executive branch of the fe...ent of the United States under the George W. Bush Administration
10. USS George W. Bush (wd:Q131753450): planned Gerald R. Ford-class aircraft carrier

[Assistant] The correct entity for George W. Bush is `wd:Q207`. Now, let's find out if there is a property that indicates books written by someone.

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): notable scientific, artistic or literary work, or other work of significance among subject's works
2. object named as, also known as original wording, place name string, printed as, reference wording, [sic] (wd:P1932 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): use as qualifier to indicate how the object's value was given in the source, name
3. author name string, also known as short author name, writer named as, writer name, writer indicated as, songwriting credits string (wd:P2093 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): stores unspecified author or editor name for publications; use i...or editor (P98) does not exist or is not known. Do not use both.
4. author given names, also known as given name, given names, first name, author first names, author first name (wd:P9687 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): qualifier for string representation of given or secondary sortin...presented in a bibliographic reference file (for example BibTeX), given name
5. theorized by, also known as author, supported by, suggested by, proposer, proposed by (wd:P8403 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): subject who suggested or proposed this concept or immaterial invention
6. developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): organization or person that developed the item, creator
7. screenwriter, also known as story by, teleplay by, video game writer, writer (of screenplays), writing credits (wd:P58 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): person(s) who wrote the script for subject item, film crew member, author
8. composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator
9. author, also known as writer, written by, poet, playwright, maker (wd:P50 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
10. author last names, also known as author last name, surnames, surname, last names, last name (wd:P9688 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): qualifier to provide string representation of family or primary ...presented in a bibliographic reference file (for example BibTeX), family name

[Assistant] The property `wd:P50` (author) seems to be the most relevant for indicating the author of a book. We can use this property to find books authored by George W. Bush. Let's construct a query to find the books written by George W. Bush.

[Tool] Got no rows and 1 columns

[Assistant] It appears that the query did not return any results, which suggests that there might be a different way to find books written by George W. Bush. Let's try another approach by searching for books where George W. Bush is listed as the author using the `wdt:P50` property in a different direction.

[Tool]
Got 400 rows and 1 column, showing the first 5 and last 5 rows below:
| book                                                                                |
| ----------------------------------------------------------------------------------- |
| Remarks by US President George W...n Ceremony in Washington DC, USA (wd:Q112243677) |
| Executive Order 13470 (wd:Q1383597)                                                 |
| Proclamation 7750 of the President of the USA (wd:Q18330735)                        |
| 41: A Portrait of My Father (wd:Q18786479)                                          |
| Freedom and Fear Are at War (wd:Q19036080)                                          |
| ...                                                                                |
| Executive Order 13438 (wd:Q5419892)                                                 |
| Executive Order 13440 (wd:Q5419893)                                                 |
| A Charge to Keep (wd:Q60416508)                                                     |
| Transcript of President Bush's address (wd:Q61743801)                               |
| Decision Points (wd:Q942966)                                                        |

[Assistant]
The query returned several books written by George W. Bush. To find the title of his new book, we need to filter the results to include only the most recent ones. We can use the publication date property (`wdt:P577`) to sort the results and select the most recent book.

Let's modify the query to include the publication date and sort the results by date in descending order.

[Tool]
Got 1 row and 3 columns:
| book                                | title                     | date                                |
| ----------------------------------- | ------------------------- | ----------------------------------- |
| Portraits of Courage (wd:Q29097574) | Portraits of Courage (en) | 2017-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The most recent book written by George W. Bush is titled "Portraits of Courage," published in 2017.

Here is the final SPARQL query and answer:

```sparql
SELECT ?book ?title ?date WHERE {
  ?book wdt:P50 wd:Q207 ;
        wdt:P1476 ?title ;
        wdt:P577 ?date .
} ORDER BY DESC(?date) LIMIT 1
```

The title of George W. Bush's new book is "Portraits of Courage."

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query effectively retrieves the most recent book authored by George W. Bush along with its title and publication date. The answer provided matches the information obtained from the query.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?placeOfBirth ?countryOfCitizenship WHERE {
  wd:Q36844 wdt:P19 ?placeOfBirth .
  wd:Q36844 wdt:P27 ?countryOfCitizenship .
}

Got 1 row and 2 columns:
| placeOfBirth               | countryOfCitizenship |
| -------------------------- | -------------------- |
| Saint Michael (wd:Q819170) | Barbados (wd:Q244)   |

Based on the available information, Rihanna was likely raised in Barbados, possibly in or around Saint Michael, where she was born.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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] where was rihanna raised?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
where did rihanna grow up?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q36844 wdt:P19 ?x .
}

Using entities:
Rihanna, also known as Barbadian singer, Rihana, Robyn Fenty, Robyn Rihanna Fenty (wd:Q36844): Barbadian singer and actress, singer

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 2:
Question:
where did rihanna come from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q36844 wdt:P19 ?x .
}

Using entities:
Rihanna, also known as Barbadian singer, Rihana, Robyn Fenty, Robyn Rihanna Fenty (wd:Q36844): Barbadian singer and actress, singer

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 3:
Question:
what county is rihanna from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q36844 wdt:P27 ?x .
}

Using entities:
Rihanna, also known as Barbadian singer, Rihana, Robyn Fenty, Robyn Rihanna Fenty (wd:Q36844): Barbadian singer and actress, singer

Using properties:
country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt): the object is a country that recognizes the subject as its citizen, country

[Assistant] From the examples, we see that the place of birth (P19) and country of citizenship (P27) properties are used to determine where Rihanna is from. However, these properties might not directly tell us where she was raised. Let's first confirm Rihanna's entity and then look for more relevant properties.

[Tool]
Top 10 entity alternatives:
1. Rihanna, also known as Barbadian singer, Rihana, Robyn Fenty, Robyn Rihanna Fenty (wd:Q36844): Barbadian singer and actress, singer
2. Rihanna (wd:Q751012): 2010 photo-book
3. Rihanna (wd:Q17136804): female given name
4. Rihanna (wd:Q65090823): 2017 single by Yxng Bane
5. Rihanna (wd:Q17146308): single  by Orezi
6. Rihanna (wd:Q120757612): original song written by Camille Dhont, and performed by Camille
7. Rihanna (wd:Q119197664)
8. Rihanna (wd:Q119197546)
9. Rihanna (wd:Q120757609): vocal track by Camille; 2023 studio recording
10. Rihanna Twitter account, also known as @rihanna, twitter.com/rihanna (wd:Q65676172): Twitter account of Rihanna

[Assistant] The top result for "Rihanna" is the Barbadian singer and actress, which is the correct entity we are looking for. Her IRI is `wd:Q36844`. Now let's check if there are any properties that specifically mention where she was raised or grew up.

[Tool]
Top 10 property alternatives:
1. wears, also known as wore, wearing, uniform, outfit, garment (wd:P3828 as wdt/p): clothing or accessory worn on subject's body, item operated
2. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt/p): date on which the subject was born, inception
4. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
5. height, also known as heighth, size, depth over terrain (wd:P2048 as wdt/p): vertical length of an entity, length
6. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
7. owner of, also known as owns, owns property, shareholder of (wd:P1830 as wdt/p): entities owned by the subject
8. child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt/p): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
9. influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt/p): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”
10. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)

No other items found

[Assistant]
It seems that there isn't a specific property that directly mentions where Rihanna was raised. However, the place of birth (P19) and country of citizenship (P27) properties might still provide useful information. Let's start with the place of birth and see if it gives us any clues.

I'll run a query to get Rihanna's place of birth.

[Tool]
Got 1 row and 1 column:
| placeOfBirth               |
| -------------------------- |
| Saint Michael (wd:Q819170) |

[Assistant]
Rihanna's place of birth is Saint Michael, which is a parish in Barbados. However, this doesn't necessarily mean she was raised there. To get a more comprehensive understanding, let's also check her country of citizenship and any other relevant information that might indicate where she was raised.

I'll run a query to get her country of citizenship and any other relevant locations.

[Tool]
Got 1 row and 2 columns:
| placeOfBirth               | countryOfCitizenship |
| -------------------------- | -------------------- |
| Saint Michael (wd:Q819170) | Barbados (wd:Q244)   |

[Assistant]
Rihanna's place of birth is Saint Michael, a parish in Barbados, and her country of citizenship is Barbados. This suggests that she was likely raised in Barbados, although it doesn't specify the exact location within Barbados.

To provide a more precise answer, let's check if there are any other properties or statements that might give us more details about where she was raised. We can look for properties related to her early life or childhood.

I'll run a query to find any additional information about her early life or childhood.

[Tool]
Got 3 rows and 2 columns:
| property                         | value                               |
| -------------------------------- | ----------------------------------- |
| place of birth (wdt:P19)         | Saint Michael (wd:Q819170)          |
| country of citizenship (wdt:P27) | Barbados (wd:Q244)                  |
| date of birth (wdt:P569)         | 1988-02-20T00:00:00Z (xsd:dateTime) |

[Assistant]
The query results confirm that Rihanna was born in Saint Michael, Barbados, and her country of citizenship is Barbados. However, there is no additional information about her early life or where she was raised beyond her place of birth and citizenship.

Given the information available, it is reasonable to conclude that Rihanna was raised in Barbados, likely in or around Saint Michael, where she was born.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query provides the place of birth and country of citizenship, which is useful, but the question specifically asks where Rihanna was raised. We should refine the query to include information about where she spent her childhood or grew up. If such information is not directly available, we can infer that she was likely raised in the same place she was born, but this should be noted in the answer.

[Assistant]
Thank you for the feedback. Let's refine the query to include more information about where Rihanna spent her childhood or grew up. If such information is not directly available, we will infer that she was likely raised in the same place she was born and note this in the answer.

First, let's check if there are any properties or statements that specifically mention where she spent her childhood or grew up.

I'll run a query to find any additional properties or statements related to her early life or childhood.

[Tool]
Got 3 rows and 2 columns:
| property                         | value                               |
| -------------------------------- | ----------------------------------- |
| place of birth (wdt:P19)         | Saint Michael (wd:Q819170)          |
| country of citizenship (wdt:P27) | Barbados (wd:Q244)                  |
| date of birth (wdt:P569)         | 1988-02-20T00:00:00Z (xsd:dateTime) |

[Assistant]
The query results show that there is no additional information about where Rihanna spent her childhood or grew up. The only relevant properties are her place of birth (Saint Michael, Barbados), country of citizenship (Barbados), and date of birth.

Given this information, we can reasonably infer that Rihanna was likely raised in Barbados, possibly in or around Saint Michael, where she was born. However, to be thorough, we should note that this is an inference based on the available data.

Here is the refined SPARQL query and answer:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query provides the place of birth and country of citizenship, which is useful, but it does not directly state where Rihanna was raised. To better answer the question, we should include information about her place of residence during her childhood or early life. We can try to find this information by looking for properties related to residence or living places.

[Assistant]
Thank you for the feedback. Let's refine the query to include information about Rihanna's place of residence during her childhood or early life. We will look for properties related to residence or living places.

First, let's search for properties related to residence or living places.

I'll run a query to find properties related to residence or living places.

[Tool]
Top 10 property alternatives:
1. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the place where the person is or has been, resident, location
2. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): person or organization occupying property, used by
3. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): location where persons or organisations were actively participating in employment, business or other work, significant place, location
4. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): seat of government of a country, province, state or other type of administrative territorial entity
5. official residence, also known as resides at, lives in, palace, presidential mansion, presidential palace (wd:P263 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the residence at which heads of government and other senior figures officially reside, residence
6. number of households, also known as dwellings, households (wd:P1538 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): total number of households in this place, includes dwellings of all types
7. number of housing units, also known as dwelling-places, housing units, number of dwelling-places (wd:P10814 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): number of housing units (dwellings) in a specific place (such as city, county, state, country etc.)
8. family, also known as royal house, noble family, member of family, house, dynasty (wd:P53 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): family, including dynasty and nobility houses. Not family name (use P734 for family name)., member of
9. property, also known as allowed qualifier (wd:P2306 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): qualifier to define a property constraint in combination with P2...traint), or to limit the scope of Q44292881 (wikidata statement)
10. Wikidata property, also known as main property, property for this item, property, has property (wd:P1687 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): main Wikidata property for this item

[Assistant]
The property `residence` (P551) seems to be the most relevant for finding where Rihanna lived during her childhood or early life. Let's check if there are any statements about her residence.

I'll run a query to find any statements about Rihanna's residence.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that there are no statements about Rihanna's residence in the Wikidata knowledge graph. Given this, we can infer that the most specific information we have about where she was raised is her place of birth and country of citizenship.

Since there is no direct information about her place of residence during her childhood, we can reasonably infer that she was likely raised in Barbados, possibly in or around Saint Michael, where she was born.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT DISTINCT ?startTime WHERE {
  wd:Q91 p:P39 ?statement .
  ?statement ps:P39 wd:Q11696 ; pq:P580 ?startTime .
}

Abraham Lincoln became president on March 4, 1861.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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] when was abe lincoln president?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
when president lincoln was elected?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q91 p:P39 ?y .
  ?y ps:P39 wd:Q1467287 ; pq:P580 ?x .
}

Using entities:
President-elect of the United States, also known as President-Elect (wd:Q1467287): winning candidate of the U.S. presidential election in the time before inauguration
Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time

Example 2:
Question:
what date was abraham lincoln inaugurated?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q91 p:P39 ?y .
  ?y ps:P39 wd:Q11696 ; pq:P580 ?x .
}

Using entities:
President of the United States, also known as US President, U.S. President, United States President, the president of the United States, President of the U.S. (wd:Q11696): head of state and head of government of the United States of America
Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office
start time, also known as start date, since, valid, join time, joined (wd:P580 as pq): time an entity begins to exist or a statement starts being valid, point in time

Example 3:
Question:
what is president abraham lincoln known for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q91 wdt:P106 ?x .
}

Using entities:
Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)

[Assistant]
From the examples, we can see that the property `position held` (P39) is used to indicate the positions held by Abraham Lincoln, and the qualifier `start time` (P580) is used to specify when he started holding a particular position. We need to find the start time for the position of President of the United States (Q11696).

Let's construct a SPARQL query to get this information.

[Tool]
Got 1 row and 1 column:
| startTime                           |
| ----------------------------------- |
| 1861-03-04T00:00:00Z (xsd:dateTime) |

[Assistant]
The start time for Abraham Lincoln's presidency is March 4, 1861.

Let's format this as the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the exact start date of Abraham Lincoln's presidency. The answer provided is accurate and complete.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?language WHERE {
  wd:Q79 wdt:P37 ?language .
}

The official language of Egypt is Arabic.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 kind of language does egypt speak?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what language do they speak in egyptian?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q79 wdt:P37 ?x .
}

Using entities:
Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia

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

Example 2:
Question:
what languages do people speak in egypt?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q79 wdt:P37 ?x .
}

Using entities:
Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia

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

Example 3:
Question:
what language does egyptians use?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q79 wdt:P37 ?x .
}

Using entities:
Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia

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

[Assistant] Based on the similar examples, it seems like we need to find the official language of Egypt. Let's confirm the entity for Egypt and the property for official language.

[Tool]
Top 10 entity alternatives:
1. Egypt, also known as Republic of Egypt, Rep. Egypt, EGY, eg, Arab Republic of Egypt (wd:Q79): country in Northeast Africa and Southwest Asia
2. Khedivate of Egypt, also known as Egypt, Egyptian Khedivate (wd:Q127861): 1867-1914 monarchy in Northeastern Africa
3. Egypt Eyalet, also known as QASR of Egypt, Pashalik of Egypt, Egypt, Mısır Eyalet, Beylerbeyilik of Egypt (wd:Q491507): Egyptian State (1517–1867)
4. Egypt, also known as Egypt, AK, Egypt, AR (wd:Q2083973): town in Arkansas
5. Egypt, also known as Egypt, Etowah County, Alabama, Egypt, AL (wd:Q5348296): census-designated place in Etowah County, Alabama
6. Egypt, also known as Egypt, Pennsylvania (wd:Q5348306): human settlement in Pennsylvania, United States
7. Egypt 1156 B.C., also known as Egypt, Egypt 1156 B.C.: Tomb of the Pharaoh (wd:Q3586212): 1999 video game
8. Egypt, also known as Egypt, WV (wd:Q5348310): unincorporated community in Jefferson County, West Virginia
9. Egypt (wd:Q5348317): 2004 album by Youssou N'Dour
10. Egypt, also known as Egypt, IN, Egypt, Indiana (wd:Q5348297): unincorporated community in Jasper County, Minnesota

[Assistant] The first result is the correct one for the country of Egypt. Now let's verify the property for official language.

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language designated as official by this item, language used
2. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): label for the items in their official language  or their original language, name
3. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language status identifier by Ethnologue.com using EGIDS scale
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): regulatory body of a language
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language or languages a person has learned from early childhood, languages spoken, written or signed
7. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language widely used (spoken or written) in this place or at this event or organisation
8. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
9. programmed in, also known as language, programming language, written in (wd:P277 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the programming language(s) in which the software is developed
10. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): language in which the writer has written their work, languages spoken, written or signed

[Assistant]
The first result is the correct one for the property of official language. We can now construct our SPARQL query to find the official language of Egypt.

Let's run the SPARQL query:

```sparql
SELECT DISTINCT ?language WHERE {
  wd:Q79 wdt:P37 ?language .
}
```

This query selects the official language of Egypt (wd:Q79) using the property wdt:P37.

Now, let's execute the query.

[Tool]
Got 1 row and 1 column:
| language           |
| ------------------ |
| Arabic (wd:Q13955) |

[Assistant]
The official language of Egypt is Arabic (wd:Q13955).

Let's provide the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the official language of Egypt as Arabic, which is the expected answer.


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 DISTINCT ?product ?productLabel WHERE {
  ?product wdt:P176 wd:Q81965 .
  ?product wdt:P31 wd:Q1420 .
  OPTIONAL {
    ?product rdfs:label ?productLabel .
    FILTER ( LANG( ?productLabel ) = 'en' )
  }
}

Got 6 rows and 2 columns:
| product                             | productLabel                |
| ----------------------------------- | --------------------------- |
| Buick Regal (wd:Q1002872)           | Buick Regal (en)            |
| Oldsmobile Model 43 (wd:Q1310003)   | Oldsmobile Model 43 (en)    |
| Oldsmobile Series 40 (wd:Q1340552)  | Oldsmobile Series 40 (en)   |
| Buick LaCrosse (wd:Q498843)         | Buick LaCrosse (en)         |
| Cadillac Sixty Special (wd:Q825252) | Cadillac Sixty Special (en) |
| Buick Velite 7 (wd:Q96354603)       | Buick Velite 7 (en)         |

General Motors (GM) produces a wide range of car models, including: Buick Regal, Oldsmobile Model 43, Oldsmobile Series 40, Buick LaCrosse, Cadillac Sixty Special, and Buick Velite 7.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 does gm make?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what other cars does gm make?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q81965 wdt:P355 ?x .
}

Using entities:
General Motors, also known as GM, General Motors Corporation (wd:Q81965): automotive manufacturing corporation based in Detroit, Michigan, USA, American automotive manufacturing corporation

Using properties:
has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as wdt): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of

Example 2:
Question:
what does pixar produce?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P272 wd:Q127552 .
}

Using entities:
Pixar, also known as Graphics Group, Pixar Studios (wd:Q127552): American computer animation studio

Using properties:
production company, also known as broadcasting company, theatrical troupe, theatre company, theater company, studio (wd:P272 as wdt): company that produced this film, audio or performing arts work

Example 3:
Question:
what did george eastman make?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P61 wd:Q189147 .
}

Using entities:
George Eastman, also known as geog. eastman, g. eastman, Geo. Eastman (wd:Q189147): American entrepreneur, inventor and photographer (1854–1932), patron of the arts, philanthropist, photographer, entrepreneur, inventor

Using properties:
discoverer or inventor, also known as inventor or discoverer, inventor, invented by, invented, introduced by (wd:P61 as wdt): subject who discovered, first described, invented, or developed this discovery or invention or scientific hypothesis or theory

[Assistant] Based on the examples, it seems like we need to find what products or subsidiaries GM (General Motors) makes. We can use the `P355` (has subsidiary) property as seen in Example 1, or we might need to look for products that GM produces. Let's first confirm the entity for GM and then proceed with the query.

[Tool]
Top 10 entity alternatives:
1. General Motors, also known as GM, General Motors Corporation (wd:Q81965): automotive manufacturing corporation based in Detroit, Michigan, USA, American automotive manufacturing corporation
2. General Motors railway station, also known as General Motors station, General Motors (wd:Q5532064): closed railway station in Dandenong South, Victoria, Australia
3. General Motors (wd:Q16891615): former Rochester Industrial and Rapid Transit Railway station and streetcar carhouse in Rochester, New York
4. General Motors (wd:Q84480751): scientific article published on 01 November 1983
5. General Motors, also known as General Motors (Poland) (wd:Q30338742): Polish subsidary of General Motors
6. General motors (wd:Q84481962): scientific article published on 01 February 1983
7. Grumman TBF Avenger, also known as TBM Avenger, TBM, Grumman Torpedo Bomber Design 1, Grumman TBM, Grumman Tarpon (wd:Q734103): 1941 torpedo bomber aircraft model by Grumman
8. Rogers Arena, also known as GM Place, General Motors Place (wd:Q517318): sports arena in Vancouver, British Columbia, Canada
9. GM Korea, also known as General Motors Korea, GM Daewoo, 한국GM (wd:Q497193): South Korean subsidiary of General Motors
10. General Motors EV1, also known as EV1 (wd:Q152013): electric compact car model

[Assistant] The correct entity for General Motors is `wd:Q81965`. Now, let's check if GM has any subsidiaries using the `P355` property.

[Tool]
Got 34 rows and 1 column, showing the first 5 and last 5 rows below:
| subsidiary                                        |
| ------------------------------------------------- |
| General Motors do Brasil (wd:Q10752027)           |
| General Motors Specialty Vehicles (wd:Q112728701) |
| General Motors China (wd:Q112874402)              |
| General Motors (China) Investment (wd:Q112874411) |
| General Aviation Corp. (wd:Q116951874)            |
| ...                                               |
| General Motors de México (wd:Q5876989)            |
| Vauxhall (wd:Q59187)                              |
| Cadillac Europe (wd:Q72371228)                    |
| Marquette (wd:Q939291)                            |
| General Motors Japan (wd:Q968076)                 |

[Assistant]
The query returned several subsidiaries of General Motors. However, this doesn't directly tell us what products GM makes. To find out what GM produces, we should look for properties related to products or manufacturing. One such property could be `P272` (production company), which was used in Example 2.

Let's try to find products that GM produces using the `P272` property.

[Tool]
Got 3 rows and 1 column:
| product                         |
| ------------------------------- |
| Ballet Robotique (wd:Q15891058) |
| Master Hands (wd:Q2185080)      |
| Strange Holiday (wd:Q7621406)   |

[Assistant]
The query returned some results, but they seem to be films or other media rather than the products that GM manufactures. Since GM is primarily known for manufacturing vehicles, we should look for properties related to vehicle production.

Let's try to find properties related to vehicle production for GM.

[Tool]
Top 10 property alternatives:
1. product, material, or service produced or provided, also known as products, product or service provided, product, material, or service produced or provided, product, output (wd:P1056 as wdt/p): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
2. industry, also known as branch, economic branch, field of action, field of exercise, sector (wd:P452 as wdt/p): specific industry of company or organization, part of
3. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
4. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
5. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
6. employees, also known as workers, staff, personnel, number of employees, employee count (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
7. logo image, also known as masthead, wordmark, trademark, trade mark, sign (wd:P154 as wdt/p): graphic mark or emblem commonly used by commercial enterprises, organizations and products, icon
8. net profit, also known as profit, net return, net income, clear profit, clear gain (wd:P2295 as wdt/p): private entity profit after accounting for all costs
9. operating income, also known as earnings before interest and taxes, operating profit, EBIT (wd:P3362 as wdt/p): private entity operating income
10. total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as wdt/p): income gained by an organization during a given time frame. Not to be confused with fiscal revenue

No other items found

[Assistant] The property `P1056` (product, material, or service produced or provided) seems relevant for finding the products that GM produces. Let's use this property in our query to find the products manufactured by GM.

[Tool]
Got 1 row and 1 column:
| product              |
| -------------------- |
| motor car (wd:Q1420) |

[Assistant]
The query returned "motor car" as a product produced by GM. However, this is a general category. To get more specific products, we can look for specific models or brands produced by GM.

Let's try to find specific products or models produced by GM using the `P1056` property again, but this time we will include labels to get more detailed information.

[Tool]
Got 1 row and 2 columns:
| product              | productLabel   |
| -------------------- | -------------- |
| motor car (wd:Q1420) | motor car (en) |

[Assistant]
The query still returns "motor car" as a general category. To get more specific products, we need to look for specific models or brands produced by GM. We can try to find specific car models or brands associated with GM.

Let's search for specific car models or brands produced by GM using the `P176` (manufacturer) property, which indicates the manufacturer of a product.

[Tool]
Got 793 rows and 2 columns, showing the first 5 and last 5 rows below:
| product                          | productLabel                   |
| -------------------------------- | ------------------------------ |
| Buick Estate (wd:Q1002745)       | Buick Estate (en)              |
| Buick Electra (wd:Q1002748)      | Buick Electra (en)             |
| Buick Invicta (wd:Q1002761)      | Buick Invicta (en)             |
| Buick Limited (wd:Q1002771)      | Buick Limited (en)             |
| Buick Master Six (wd:Q1002780)   | Buick Master Six (en)          |
| ...                              | ...                            |
| Cadillac ATS (wd:Q979003)        | Cadillac ATS (en)              |
| T18 Boarhound (wd:Q979464)       | T18 Boarhound (en)             |
| wd:Q991211                       | list of Opel concept cars (en) |
| Pontiac Trans Sport (wd:Q991256) | Pontiac Trans Sport (en)       |
| Isuzu 117 Coupé (wd:Q993674)     | Isuzu 117 Coupé (en)           |

[Assistant]
The query returned a list of specific car models and brands produced by General Motors. This includes various Buick, Cadillac, and other models.

Here is the final SPARQL query and the answer to the question:

**Final SPARQL Query:**
```sparql
SELECT DISTINCT ?product ?productLabel WHERE {
  ?product wdt:P176 wd:Q81965 .
  OPTIONAL { ?product rdfs:label ?productLabel . FILTER(LANG(?productLabel) = 'en') }
}
```

**Answer:**
General Motors (GM) produces a wide range of car models and brands, including:
- Buick Estate
- Buick Electra
- Buick Invicta
- Buick Limited
- Buick Master Six
- Cadillac ATS
- T18 Boarhound
- Pontiac Trans Sport
- Isuzu 117 Coupé
- And many more.

These are just a few examples of the specific products and models produced by GM.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct, but it includes a row that is not a product (list of Opel concept cars). This row should be filtered out to ensure only actual products are returned. Consider adding a filter to exclude non-product items or to only include items with a specific property that indicates they are a product.

[Assistant]
Thank you for the feedback. To refine the query, we can add a filter to exclude non-product items. One way to do this is to ensure that the items have a specific property that indicates they are a product, such as `P31` (instance of) with values like "automobile" or "vehicle."

Let's update the query to include this filter.

[Tool]
Got 6 rows and 2 columns:
| product                             | productLabel                |
| ----------------------------------- | --------------------------- |
| Buick Regal (wd:Q1002872)           | Buick Regal (en)            |
| Oldsmobile Model 43 (wd:Q1310003)   | Oldsmobile Model 43 (en)    |
| Oldsmobile Series 40 (wd:Q1340552)  | Oldsmobile Series 40 (en)   |
| Buick LaCrosse (wd:Q498843)         | Buick LaCrosse (en)         |
| Cadillac Sixty Special (wd:Q825252) | Cadillac Sixty Special (en) |
| Buick Velite 7 (wd:Q96354603)       | Buick Velite 7 (en)         |

[Assistant]
The updated query now returns only items that are instances of "motor car" (wd:Q1420), effectively filtering out non-product items like "list of Opel concept cars."

Here is the refined final SPARQL query and the answer to the question:

**Final SPARQL Query:**
```sparql
SELECT DISTINCT ?product ?productLabel WHERE {
  ?product wdt:P176 wd:Q81965 .
  ?product wdt:P31 wd:Q1420 .
  OPTIONAL { ?product rdfs:label ?productLabel . FILTER(LANG(?productLabel) = 'en') }
}
```

**Answer:**
General Motors (GM) produces a wide range of car models, including:
- Buick Regal
- Oldsmobile Model 43
- Oldsmobile Series 40
- Buick LaCrosse
- Cadillac Sixty Special
- Buick Velite 7

These are specific examples of the products and models produced by GM.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies products (cars) manufactured by General Motors (GM) and returns their labels in English. The results are relevant and match the user's question.


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 ?city ?cityLabel ?population WHERE {
  ?city wdt:P31 wd:Q515 ; wdt:P17 wd:Q29 ; wdt:P1082 ?population .
  ?city wdt:P31 / wdt:P279 * wd:Q486972 .
  OPTIONAL {
    ?city rdfs:label ?cityLabel .
    FILTER ( LANG( ?cityLabel ) = "en" )
  }
}
ORDER BY DESC ( ?population )
LIMIT 1

The largest city in Spain is Barcelona.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 the largest city in spain?

[Assistant] Let's start by looking at some similar examples.

[Tool]
Example 1:
Question:
what is the name of the capital of spain?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29 wdt:P36 ?x .
}

Using entities:
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa

Using properties:
capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt): seat of government of a country, province, state or other type of administrative territorial entity

Example 2:
Question:
what are the main languages spoken in spain?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29 wdt:P37 ?x .
}

Using entities:
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa

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

Example 3:
Question:
what are the official languages in spain?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29 wdt:P37 ?x .
}

Using entities:
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa

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

[Assistant] Based on the examples, we can see that the entity for Spain is `wd:Q29`. However, the property used in these examples does not directly relate to the largest city. We need to find a property that relates to the population of cities in Spain to determine the largest one. Let's search for such a property.

[Tool]
Top 10 property alternatives:
1. population, also known as inhabitants, human population (wd:P1082 as wdt/p): number of people inhabiting the place; number of people of subject
2. urban population (wd:P6343 as wdt/p): number of people living within the territorial entity who live in its urban parts, population
3. birth rate (wd:P8763 as wdt/p): the total number of live births per 1,000 population
4. rural population (wd:P6344 as wdt/p): number of people living within the territorial entity who live in its rural parts, population
5. female population (wd:P1539 as wdt/p): number of female people inhabiting the place; number of female people of subject, population
6. male population, also known as male inhabitants, number of males (wd:P1540 as wdt/p): number of male people inhabiting the place; number of male people of subject, population
7. demographics of topic, also known as demography of topic (wd:P9241 as wdt/p): item that deals with demographics of the subject
8. total fertility rate (wd:P4841 as wdt/p): average number of children that would be born to a woman over her lifetime who lives in this territorial entity
9. nominal GDP, also known as GDP, GDP, nominal, gross domestic product, nominal GDP, nominal gross domestic product (wd:P2131 as wdt/p): market value of all officially recognized final goods and services produced within a country in a given period of time
10. nominal GDP per capita, also known as GDP per capita, nominal, Gross domestic product per capita, gdp per capita, GDP (nominal) per capita (wd:P2132 as wdt/p): country's total GDP divided by the population

No other items found

[Assistant]
The property `population` (wd:P1082) seems relevant for our purpose. To find the largest city in Spain, we need to identify cities in Spain and then sort them by their population. Let's first list some cities in Spain to ensure we have the correct entities.

I will use the property `located in the administrative territorial entity` (wdt:P131) to find cities located in Spain.

Let's search for this property.

[Tool]
Top 10 property alternatives:
1. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
2. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): (list of) direct subdivisions of an administrative territorial entity, has part(s)
3. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
4. located in the statistical territorial entity, also known as located in statistical territorial entity, statistical territorial entity (wd:P8138 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): statistical territorial entity in which a place is located or is...nty is split into or part of several regions: add several values, location
5. territory overlaps, also known as partially overlaps, partially coincident with, partly coincident with, partly located in territorial entity, partly overlaps (wd:P3179 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): part or all of the area associated with (this) entity overlaps part or all of the area associated with that entity, partially coincident with
6. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
7. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): seat of government of a country, province, state or other type of administrative territorial entity
8. located in the religious territorial entity, also known as Deanery, Vicariate, religious territorial entity, Parish, located in the ecclesiastical territorial entity (wd:P5607 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): the item is located on the territory of the following religious entity. Use P708 (diocese) for dioceses, location, part of
9. capital of, also known as national capital of, parish seat of, provincial capital of, seat of, state capital (wd:P1376 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): country, state, department, canton or other administrative division of which the municipality is the governmental seat, located in the administrative territorial entity
10. contains the statistical territorial entity, also known as contains statistical territorial entity (wd:P10888 as pqn/pqv/wdt/wdtn/pr/prv/psn/psv/ps/pq/prn/p): (list of) statistical territorial entities which are direct subd...ions of another statistical or administrative territorial entity, has part(s)

[Assistant]
The property `located in the administrative territorial entity` (wdt:P131) is the correct one to use for finding cities located in Spain. Now, let's construct a SPARQL query to find cities in Spain and sort them by their population to determine the largest city.

Here's the initial query to find cities in Spain:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P131 wd:Q29 ;  # Located in Spain
        wdt:P1082 ?population .  # Population
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

This query selects cities in Spain, their populations, and their labels, and orders them by population in descending order, limiting the result to the top one.

Let's execute this query.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that the query did not return any results. This could be due to a few reasons, such as incorrect property usage or data availability issues. Let's try a different approach by first listing some cities in Spain and then checking their populations.

First, let's list some cities in Spain using the `located in the administrative territorial entity` property (wdt:P131) and the `instance of` property (wdt:P31) to filter for cities (wd:Q515).

Here's the query to list cities in Spain:

```sparql
SELECT ?city ?cityLabel
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P131 wd:Q29 .  # Located in Spain
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
LIMIT 10
```

Let's execute this query to see if we can find some cities in Spain.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query to list cities in Spain did not return any results either. This suggests that there might be an issue with the properties or the way they are being used. Let's verify the properties and entities again.

First, let's confirm that the entity for Spain (wd:Q29) and the property for "city" (wd:Q515) are correct. Then, let's check the property for "located in the administrative territorial entity" (wdt:P131) to ensure it is being used correctly.

Let's list some triples involving Spain to see how it is used in the knowledge graph.

```sparql
SELECT ?property ?object
WHERE {
  wd:Q29 ?property ?object .
}
LIMIT 10
```

This will give us an idea of the properties and objects associated with Spain.

Let's execute this query.

[Tool]
Got 10 rows and 2 columns:
| property           | object                                                         |
| ------------------ | -------------------------------------------------------------- |
| schema:description | Afrika'da toprakları bulunan güneybatı Avrupa'da bir ülke (tr) |
| schema:description | állam Délnyugat-Európában (hu)                                 |
| schema:description | Cənubi Avropada ölkə (az)                                      |
| schema:description | çheer ayns sheear yiass yn Oarpey (gv)                         |
| schema:description | civitas Europae (la)                                           |
| schema:description | constitutional monarchy in Southwest Europe (en-ca)            |
| schema:description | constitutional monarchy in Southwest Europe (en-gb)            |
| schema:description | country in southwestern Europe with territories in Africa (en) |
| schema:description | država u jugozapadnoj Europi (hr)                              |
| schema:description | država u jugozapadnoj Evropi (sr-el)                           |

[Assistant]
The results show that the entity for Spain (wd:Q29) is correct, but they are mostly descriptions in different languages. Let's look at the properties and objects associated with cities to ensure we are using the correct properties.

We can list some triples involving the "city" entity (wd:Q515) to see how it is used in the knowledge graph.

```sparql
SELECT ?property ?object
WHERE {
  wd:Q515 ?property ?object .
}
LIMIT 10
```

This will give us an idea of the properties and objects associated with cities.

Let's execute this query.

[Tool]
Got 10 rows and 2 columns:
| property           | object                                                                   |
| ------------------ | ------------------------------------------------------------------------ |
| schema:description | agglomération, avec souvent une ...dizaines de milliers d'habitants (fr) |
| schema:description | aneddiad mawr a pharhaol (cy)                                            |
| schema:description | büyük yerleşim yeri (tr)                                                 |
| schema:description | daerah pemukiman penduduk (id)                                           |
| schema:description | dakkel ken permanente a pagnaedan (ilo)                                  |
| schema:description | e groossi Sidlig mit ere gwüsse Aazahl Iiwohner (gsw)                    |
| schema:description | entidad de población urbana grande (es)                                  |
| schema:description | entidade urbana con alta densida...lmente a industria e os servizos (gl) |
| schema:description | entidat di poblashon (pap)                                               |
| schema:description | espas kote yon popilasyon abite (ht)                                     |

[Assistant]
The results show that the entity for "city" (wd:Q515) is also correct, but they are mostly descriptions in different languages. Let's now focus on the property `located in the administrative territorial entity` (wdt:P131) to ensure it is being used correctly.

To find cities in Spain, we need to ensure that the cities are indeed linked to Spain through the `located in the administrative territorial entity` property. Let's list some cities and their locations to verify this.

```sparql
SELECT ?city ?cityLabel ?location ?locationLabel
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P131 ?location .  # Located in
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
  OPTIONAL { ?location rdfs:label ?locationLabel . FILTER(LANG(?locationLabel) = "en") }
}
LIMIT 10
```

This query will list some cities and their locations to help us verify the properties.

Let's execute this query.

[Tool]
Got 10 rows and 4 columns:
| city                     | cityLabel       | location                             | locationLabel                |
| ------------------------ | --------------- | ------------------------------------ | ---------------------------- |
| Mayumba (wd:Q2738426)    | Mayumba (en)    | Gabon (wd:Q1000)                     | Gabon (en)                   |
| Pana (wd:Q30897875)      | Pana (en)       | Gabon (wd:Q1000)                     | Gabon (en)                   |
| Deventer (wd:Q26296883)  | Deventer (en)   | Deventer (wd:Q10001)                 | Deventer (en)                |
| Patna (wd:Q80484)        | Patna (en)      | Patna district (wd:Q100077)          | Patna district (en)          |
| Purnia (wd:Q1771762)     | Purnia (en)     | Purnia district (wd:Q100082)         | Purnia district (en)         |
| Sasaram (wd:Q1949410)    | Sasaram (en)    | Rohtas district (wd:Q100085)         | Rohtas district (en)         |
| Chaman (wd:Q1017644)     | Chaman (en)     | Killa Abdullah District (wd:Q100106) | Killa Abdullah District (en) |
| Samastipur (wd:Q1925191) | Samastipur (en) | Samastipur district (wd:Q100117)     | Samastipur district (en)     |
| Saharsa (wd:Q1949422)    | Saharsa (en)    | Saharsa district (wd:Q100120)        | Saharsa district (en)        |
| Hajipur (wd:Q1949403)    | Hajipur (en)    | Vaishali district (wd:Q100130)       | Vaishali district (en)       |

[Assistant]
It appears that the cities listed are not in Spain, which suggests that the `located in the administrative territorial entity` property (wdt:P131) might not be the best choice for this specific query. Instead, let's try using the `country` property (wdt:P17) to find cities in Spain.

Here's the revised query to find cities in Spain and their populations:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P17 wd:Q29 ;   # Country (Spain)
        wdt:P1082 ?population .  # Population
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

This query selects cities in Spain, their populations, and their labels, and orders them by population in descending order, limiting the result to the top one.

Let's execute this query.

[Tool]
Got 1 row and 3 columns:
| city                 | cityLabel      | population            |
| -------------------- | -------------- | --------------------- |
| Barcelona (wd:Q1492) | Barcelona (en) | 1702547 (xsd:decimal) |

[Assistant]
The query has returned the largest city in Spain, which is Barcelona, with a population of 1,702,547.

Therefore, the largest city in Spain is Barcelona.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P17 wd:Q29 ;   # Country (Spain)
        wdt:P1082 ?population .  # Population
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

The largest city in Spain is **Barcelona**.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is almost correct, but it could be improved to ensure that only inhabited places are considered. The current query does not explicitly check if the city is an instance of an inhabited place. Please add a filter to ensure that the city is an instance of an inhabited place (wd:Q486972).

[Assistant]
Thank you for the feedback. To ensure that the city is an instance of an inhabited place, we can add a filter to check if the city is an instance of `wd:Q486972` (inhabited place).

Here is the revised SPARQL query:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P17 wd:Q29 ;   # Country (Spain)
        wdt:P1082 ?population .  # Population
  ?city wdt:P31 wd:Q486972 .  # Ensure the city is an inhabited place
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

Let's execute this query to ensure it returns the correct result.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that adding the filter to ensure the city is an instance of an inhabited place (wd:Q486972) did not return any results. This suggests that the cities in Spain might not be explicitly marked as instances of both `wd:Q515` (city) and `wd:Q486972` (inhabited place).

Let's try a different approach by using the `subclass of` property (wdt:P279) to include all subclasses of inhabited places. This way, we can capture cities that are instances of subclasses of inhabited places.

Here is the revised SPARQL query:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31/wdt:P279* wd:Q486972 ;  # City or subclass of inhabited place
        wdt:P17 wd:Q29 ;                # Country (Spain)
        wdt:P1082 ?population .         # Population
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

This query uses the `wdt:P31/wdt:P279*` pattern to match cities that are instances of `wd:Q486972` or any of its subclasses.

Let's execute this query to see if it returns the correct result.

[Tool]
Got 1 row and 3 columns:
| city                                  | cityLabel                     | population            |
| ------------------------------------- | ----------------------------- | --------------------- |
| Madrid Metropolitan Area (wd:Q959356) | Madrid Metropolitan Area (en) | 5682111 (xsd:decimal) |

[Assistant]
The query has returned the Madrid Metropolitan Area as the largest inhabited place in Spain, with a population of 5,682,111. However, the user is likely looking for the largest city, not the metropolitan area.

To ensure we get the largest city, we should filter out metropolitan areas and focus on cities specifically. We can do this by ensuring the city is an instance of `wd:Q515` (city) and is also an inhabited place.

Here is the revised SPARQL query:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P17 wd:Q29 ;   # Country (Spain)
        wdt:P1082 ?population .  # Population
  ?city wdt:P31/wdt:P279* wd:Q486972 .  # Ensure the city is an inhabited place or a subclass
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

Let's execute this query to ensure it returns the correct result.

[Tool]
Got 1 row and 3 columns:
| city                 | cityLabel      | population            |
| -------------------- | -------------- | --------------------- |
| Barcelona (wd:Q1492) | Barcelona (en) | 1702547 (xsd:decimal) |

[Assistant]
The revised query has returned the largest city in Spain, which is Barcelona, with a population of 1,702,547.

Therefore, the largest city in Spain is Barcelona.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?city ?cityLabel ?population
WHERE {
  ?city wdt:P31 wd:Q515 ;  # City (instance of)
        wdt:P17 wd:Q29 ;   # Country (Spain)
        wdt:P1082 ?population .  # Population
  ?city wdt:P31/wdt:P279* wd:Q486972 .  # Ensure the city is an inhabited place or a subclass
  OPTIONAL { ?city rdfs:label ?cityLabel . FILTER(LANG(?cityLabel) = "en") }
}
ORDER BY DESC(?population)
LIMIT 1
```

The largest city in Spain is **Barcelona**.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query effectively finds the largest city in Spain based on population, and the result correctly identifies Barcelona as the largest city.