PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?work ?workLabel ?publicationDate WHERE {
  ?work wdt:P50 wd:Q207 .
  OPTIONAL {
    ?work wdt:P577 ?publicationDate
  }
  OPTIONAL {
    ?work rdfs:label ?workLabel FILTER ( LANG( ?workLabel ) = "en" )
  }
}
ORDER BY DESC ( ?publicationDate )
LIMIT 1

The newest book authored by George W. Bush is "Portraits of Courage," published in 2017.

[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 p/ps): 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 p/ps): 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]
The examples found are related to George W. Bush (wd:Q207) and his positions or birth details, but none are directly about his books. To answer the question about the title of George Bush's new book, I need to:

1. Identify the correct George Bush entity (likely George W. Bush, wd:Q207).
2. Find properties related to books authored by him.
3. Determine which book is the newest (based on publication date).
4. Retrieve the title of that newest book.

I will start by searching for properties related to books or works authored by George W. Bush (wd:Q207).

[Tool]
Top 10 property alternatives:
1. Project Gutenberg author ID, also known as Gutenberg ID, gutenberg.org/ebooks/author/, Project Gutenberg ID (author) (wd:P1938 as wdtn/p/wdt): author identifier at Project Gutenberg
2. Online Books Page author ID, also known as OnlineBooks author ID (wd:P4629 as p/wdt): identifier for an author, at the Online Books Page website
3. Penguin Random House author ID, also known as Penguin Random House author identifier (wd:P9802 as p/wdt): identifier for an author on the Penguin Random House website
4. NACSIS-CAT author ID, also known as NII (Japan), NII ID, CiNii Books author ID, CiNii author ID (books), CiNii author identifier (wd:P271 as wdtn/p/wdt): identifier for book authors in NACSIS-CAT, the union catalog for university libraries and research institutions in Japan
5. Goodreads author ID, also known as njirimara onye odee na webụsaịtị Goodreads (wd:P2963 as p/wdt): identifier of an author on the Goodreads website
6. Commons Creator page, also known as creator page, creator template (wd:P1472 as p/wdt): name of Commons Infobox template residing in "Creator" namespace on Wikimedia Commons
7. Babelio author ID (wd:P3630 as p/wdt): identifier for an author on the literature website Babelio
8. SBN author ID, also known as ICCU, SBN, SBN identifier, Servizio Bibliotecario Nazionale (wd:P396 as p/wdt): identifier issued by National Library Service (SBN) of Italy
9. BAnQ authority ID, also known as B2Q author ID, B2Q authority ID, B2Q subject ID, B2Q work ID, BanQ ID (wd:P3280 as p/wdt): identifier for an authority record for authors, works, and subje...ngs in the Bibliothèque et Archives nationales du Québec (BAnQ).
10. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as wdtn/p/wdt): identifier from an international authority file of names, subjects, and organizations

No other items found

[Assistant] The search for properties related to books authored by George W. Bush did not directly return a property for "author of" or "written work." Instead, it returned various author identifier properties. To find the books authored by George W. Bush, I will try searching for the property "author" (P50) which is commonly used to link works to their authors. I will check if George W. Bush (wd:Q207) is linked as an author to any works using this property.

[Tool] Got no rows and 3 columns

[Assistant]
There are no triples with George W. Bush (wd:Q207) as the subject and the property "author" (wdt:P50). This means the property "author" is not used in this direction for George W. Bush. Usually, the property "author" points from a work to the author, so I should look for works where George W. Bush is the object of the "author" property.

I will now search for works where George W. Bush (wd:Q207) is the author using the property "author" (wdt:P50) with George W. Bush as the object.

[Tool]
Got 400 rows and 3 columns, showing the first 10 rows below:
| subject                                                      | property         | object                   |
| ------------------------------------------------------------ | ---------------- | ------------------------ |
| Decision Points (wd:Q942966)                                 | author (wdt:P50) | George W. Bush (wd:Q207) |
| Mission Accomplished speech (wd:Q3316780)                    | author (wdt:P50) | George W. Bush (wd:Q207) |
| Executive Order 13224 (wd:Q5419884)                          | author (wdt:P50) | George W. Bush (wd:Q207) |
| Executive Order 13470 (wd:Q1383597)                          | author (wdt:P50) | George W. Bush (wd:Q207) |
| 41: A Portrait of My Father (wd:Q18786479)                   | author (wdt:P50) | George W. Bush (wd:Q207) |
| Proclamation 7750 of the President of the USA (wd:Q18330735) | author (wdt:P50) | George W. Bush (wd:Q207) |
| Presidential Radio Address - 26 July 2008 (wd:Q19052002)     | author (wdt:P50) | George W. Bush (wd:Q207) |
| Portraits of Courage (wd:Q29097574)                          | author (wdt:P50) | George W. Bush (wd:Q207) |
| A Charge to Keep (wd:Q4655824)                               | author (wdt:P50) | George W. Bush (wd:Q207) |
| Executive Order 13233 (wd:Q5419885)                          | author (wdt:P50) | George W. Bush (wd:Q207) |
| ...                                                          | ...              | ...                      |

[Assistant]
George W. Bush (wd:Q207) is the author of several works including books and speeches. Some of the books are "Decision Points" (wd:Q942966), "41: A Portrait of My Father" (wd:Q18786479), "Portraits of Courage" (wd:Q29097574), and "A Charge to Keep" (wd:Q4655824).

Next, I will try to find the publication dates of these works to determine which one is the newest. I will query the publication date (P577) for these works authored by George W. Bush.

[Tool]
Got 10 rows and 3 columns:
| work                                       | workLabel                        | publicationDate                     |
| ------------------------------------------ | -------------------------------- | ----------------------------------- |
| Portraits of Courage (wd:Q29097574)        | Portraits of Courage (en)        | 2017-01-01T00:00:00Z (xsd:dateTime) |
| 41: A Portrait of My Father (wd:Q18786479) | 41: A Portrait of My Father (en) | 2014-11-11T00:00:00Z (xsd:dateTime) |
| Decision Points (wd:Q942966)               | Decision Points (en)             | 2010-11-09T00:00:00Z (xsd:dateTime) |
| Proclamation 8325 (wd:Q43983370)           | Proclamation 8325 (en)           | 2008-12-02T00:00:00Z (xsd:dateTime) |
| Executive Order 13470 (wd:Q1383597)        | Executive Order 13470 (en)       | 2008-08-04T00:00:00Z (xsd:dateTime) |
| Proclamation 8207 (wd:Q44615465)           | Proclamation 8207 (en)           | 2007-12-04T00:00:00Z (xsd:dateTime) |
| Proclamation 8124 (wd:Q19085418)           | Proclamation 8124 (en)           | 2007-04-11T00:00:00Z (xsd:dateTime) |
| Proclamation 8112 (wd:Q19085354)           | Proclamation 8112 (en)           | 2007-03-06T00:00:00Z (xsd:dateTime) |
| Executive Order 13406 (wd:Q19104840)       | Executive Order 13406 (en)       | 2006-06-28T00:00:00Z (xsd:dateTime) |
| Proclamation 8031 (wd:Q19084913)           | Proclamation 8031 (en)           | 2006-06-26T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?placeOfBirth ?placeOfBirthLabel WHERE {
  wd:Q36844 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    ?placeOfBirth rdfs:label ?placeOfBirthLabel FILTER ( LANG( ?placeOfBirthLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| placeOfBirth               | placeOfBirthLabel  |
| -------------------------- | ------------------ |
| Saint Michael (wd:Q819170) | Saint Michael (en) |

Rihanna was raised in Saint Michael, which is also her place of birth according to Wikidata.

[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]
The similar examples indicate that the entity for Rihanna is wd:Q36844. The property for place of birth is wdt:P19, but the question is about where Rihanna was raised, which may be different from place of birth.

I will now search for properties related to "raised" or "childhood" for the entity Rihanna (wd:Q36844) to find the most appropriate property for where she was raised.

[Tool]
Top 10 property alternatives:
1. wears, also known as wore, wearing, uniform, outfit, garment (wd:P3828 as p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): work where this item is described, attested in
5. height, also known as heighth, size, depth over terrain (wd:P2048 as p/wdt): vertical length of an entity, length
6. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as p/wdt): 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 p/wdt): entities owned by the subject
8. child, also known as has son, kids, mother of, kid, issue (wd:P40 as p/wdt): 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 p/wdt): 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 p/wdt): language(s) that a person or a people speaks, writes or signs, including the native language(s)

No other items found

[Assistant]
There is no direct property for "raised" or "childhood location" for Rihanna in the properties of the entity. The closest relevant property is "place of birth" (wdt:P19).

I will now check the place of birth for Rihanna (wd:Q36844) to see if that might be the answer or if there is any other property that might indicate where she was raised.

[Tool]
Got 1 row and 2 columns:
| placeOfBirth               | placeOfBirthLabel  |
| -------------------------- | ------------------ |
| Saint Michael (wd:Q819170) | Saint Michael (en) |

[Assistant]
Rihanna was born in Saint Michael. Since there is no direct property for where she was raised, it is common to use the place of birth as the location where she was raised unless otherwise specified.

I will now check if there is any property related to "hometown" or "residence" for Rihanna that might indicate where she was raised.

[Tool]
Top 10 property alternatives:
1. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
2. name in native language, also known as native name (wd:P1559 as p/wdt): name of a person in their native language, name
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country
4. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
5. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as p/wdt): language or languages a person has learned from early childhood, languages spoken, written or signed
6. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
7. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as p/wdt): full name of a person at birth, if different from their current, generally used name, name
8. family name, also known as clan name, last name, surname (wd:P734 as p/wdt): part of full name of person, name
9. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
10. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as p/wdt): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name

No other items found

[Assistant]
There is no specific property for "hometown" or "raised in" for Rihanna in the knowledge graph. The best available information is the place of birth, which is Saint Michael.

I will now provide the final answer based on this information.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT DISTINCT ?start ?end WHERE {
  wd:Q91 p:P39 ?statement .
  ?statement ps:P39 wd:Q11696 ; pq:P580 ?start ; pq:P582 ?end .
}

Abraham Lincoln was president from March 4, 1861 to April 15, 1865.

[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 p/ps): 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 p/ps): 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, I see that Abraham Lincoln is represented by wd:Q91 and the position "President of the United States" is wd:Q11696. The property "position held" is wd:P39, and the qualifiers "start time" and "end time" are wd:P580 and wd:P582 respectively.

I will now write a query to get the start and end time of Abraham Lincoln's presidency.

[Tool]
Got 1 row and 2 columns:
| start                               | end                                 |
| ----------------------------------- | ----------------------------------- |
| 1861-03-04T00:00:00Z (xsd:dateTime) | 1865-04-15T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?language ?languageLabel WHERE {
  wd:Q79 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| language           | languageLabel |
| ------------------ | ------------- |
| Arabic (wd:Q13955) | Arabic (en)   |

Egypt speaks Arabic as its official language.

[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] The similar examples indicate that the property for the language spoken or official language of a country is wdt:P37. The entity for Egypt is wd:Q79. I will now write a SPARQL query to get the languages spoken in Egypt using these identifiers.

[Tool]
Got 1 row and 2 columns:
| language           | languageLabel |
| ------------------ | ------------- |
| Arabic (wd:Q13955) | Arabic (en)   |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?product ?productLabel WHERE {
  ?product wdt:P176 wd:Q81965 .
  OPTIONAL {
    ?product rdfs:label ?productLabel FILTER ( LANG( ?productLabel ) = "en" )
  }
}
LIMIT 20

Got 20 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)   |
| ...                              | ...                     |
| Buick Park Avenue (wd:Q1002838)  | Buick Park Avenue (en)  |
| Buick Reatta (wd:Q1002842)       | Buick Reatta (en)       |
| Buick Rainier (wd:Q1002843)      | Buick Rainier (en)      |
| Buick Rendezvous (wd:Q1002847)   | Buick Rendezvous (en)   |
| Buick Serie D-Four (wd:Q1002854) | Buick Serie D-Four (en) |

General Motors (GM) manufactures various products, mainly cars such as Buick Estate, Buick Electra, Buick Invicta, Buick Limited, Buick Master Six, Buick Park Avenue, Buick Reatta, Buick Rainier, Buick Rendezvous, and Buick Serie D-Four.

[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]
From the similar examples, we see two main approaches to answer the question "what does gm make?":

1. If we consider GM as General Motors (an automotive manufacturing corporation), we can look for its subsidiaries or products it makes using the property "has part(s)" or "has subsidiary" (wdt:P355).
2. Another approach is to look for items that have General Motors as their manufacturer or producer using the property "manufacturer" or "production company" (wdt:P176 or wdt:P272).

I will first identify the entity for GM (General Motors) and then query what it makes or produces using the appropriate properties. Let's start by searching for the entity "GM".

[Tool] No entity items found

[Assistant] The search for the entity "GM" did not return any results. I will try searching for "General Motors" instead, as GM is commonly known as General Motors.

[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 entity for General Motors is wd:Q81965. Now, I will check what General Motors makes or produces. From the examples, the property "has subsidiary" (wdt:P355) was used to find what a company has as subsidiaries. I will also check for products or manufactured items using the property "manufacturer" (wdt:P176) or "production company" (wdt:P272). Let's start by querying the subsidiaries of General Motors.

[Tool]
Got 34 rows and 2 columns, showing the first 5 and last 5 rows below:
| item                                              | itemLabel                              |
| ------------------------------------------------- | -------------------------------------- |
| General Motors do Brasil (wd:Q10752027)           | General Motors do Brasil (en)          |
| General Motors Specialty Vehicles (wd:Q112728701) | General Motors Specialty Vehicles (en) |
| General Motors China (wd:Q112874402)              | General Motors China (en)              |
| General Motors (China) Investment (wd:Q112874411) | General Motors (China) Investment (en) |
| General Aviation Corp. (wd:Q116951874)            | General Aviation Corp. (en)            |
| ...                                               | ...                                    |
| General Motors de México (wd:Q5876989)            | General Motors de México (en)          |
| Vauxhall (wd:Q59187)                              | Vauxhall (en)                          |
| Cadillac Europe (wd:Q72371228)                    | Cadillac Europe (en)                   |
| Marquette (wd:Q939291)                            | Marquette (en)                         |
| General Motors Japan (wd:Q968076)                 | General Motors Japan (en)              |

[Assistant]
General Motors has several subsidiaries such as General Motors do Brasil, General Motors Specialty Vehicles, General Motors China, Vauxhall, Cadillac Europe, Marquette, and General Motors Japan.

Now, I will check what products or items General Motors manufactures. For this, I will query items that have General Motors as their manufacturer using the property "manufacturer" (wdt:P176).

[Tool]
Got 20 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)   |
| ...                              | ...                     |
| Buick Park Avenue (wd:Q1002838)  | Buick Park Avenue (en)  |
| Buick Reatta (wd:Q1002842)       | Buick Reatta (en)       |
| Buick Rainier (wd:Q1002843)      | Buick Rainier (en)      |
| Buick Rendezvous (wd:Q1002847)   | Buick Rendezvous (en)   |
| Buick Serie D-Four (wd:Q1002854) | Buick Serie D-Four (en) |

[Assistant]
General Motors manufactures various products, mainly cars such as Buick Estate, Buick Electra, Buick Invicta, Buick Limited, Buick Master Six, Buick Park Avenue, Buick Reatta, Buick Rainier, Buick Rendezvous, and Buick Serie D-Four.

I will now provide the final answer with a SPARQL query that retrieves the products General Motors manufactures.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?city ?cityLabel WHERE {
  ?city wdt:P31 wd:Q2074737 ; wdt:P1082 ?population .
  OPTIONAL {
    ?city rdfs:label ?cityLabel FILTER ( LANG( ?cityLabel ) = "en" )
  }
}
ORDER BY DESC ( ?population )
LIMIT 1

The largest city in Spain by population is Madrid.

[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] From the examples, I see that Spain is represented by wd:Q29 in Wikidata. Now, I need to find the property that indicates the largest city in a country. Usually, the largest city can be identified by population or by a specific property indicating the largest city. I will search for properties related to "largest city" or "largest settlement" for Spain.

[Tool]
Top 10 property alternatives:
1. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as p/wdt): (list of) direct subdivisions of an administrative territorial entity, has part(s)
2. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as p/wdt): seat of government of a country, province, state or other type of administrative territorial entity
3. urban population (wd:P6343 as p/wdt): number of people living within the territorial entity who live in its urban parts, population
4. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as p/wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
5. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as p/wdt): significant or notable events associated with the subject
6. population, also known as inhabitants, human population (wd:P1082 as p/wdt): number of people inhabiting the place; number of people of subject
7. 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 p/wdt): country's total GDP divided by the population
8. located in/on physical feature, also known as located on the terrain feature, location (terrain feature), loc (terr), on, on geographical feature (wd:P706 as p/wdt): located on the specified (geo)physical feature. Should not be us...nly political/administrative (P131) or a mountain range (P4552)., location, part of
9. nominal GDP, also known as GDP, GDP, nominal, gross domestic product, nominal GDP, nominal gross domestic product (wd:P2131 as p/wdt): market value of all officially recognized final goods and services produced within a country in a given period of time
10. highest point, also known as tallest point, zenith, summit, pinnacle, highest peak (wd:P610 as p/wdt): point with highest elevation in a region or path, has part(s)

No other items found

[Assistant] There is no direct property for "largest city" of Spain. However, the property "population" (wdt:P1082) can be used to find the city with the largest population in Spain. I will now search for cities in Spain and order them by population to find the largest one. First, I need to confirm the property for "located in the administrative territorial entity" to filter cities located in Spain.

[Tool]
Top 10 property alternatives:
1. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. 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 wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
3. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): person or organization occupying property, used by
4. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
5. adjacent building, also known as next to, neighboring building, located next to, adjoining building (wd:P3032 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): building adjacent to the item, connects with
6. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
7. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): environment (geographic feature, building type, etc.) the narrative work is set in, significant place, depicts
8. narrative location, also known as set in location, set in, setting, setting location, setting of work (wd:P840 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): the narrative of the work is set in this location, significant place, depicts
9. 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 wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): 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
10. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdtn/pq/wdt/prv/pqv/psv/pr/prn/pqn/psn/p/ps): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

[Assistant] The property "located in the administrative territorial entity" (wdt:P131) is suitable to filter cities located in Spain. Now I will write a SPARQL query to find the city in Spain with the largest population. I will look for entities that are instances of city or human settlement, located in Spain, and order them by population in descending order, limiting the result to the top one.

[Tool]
Got 1 row and 3 columns:
| city               | cityLabel    | population          |
| ------------------ | ------------ | ------------------- |
| Melilla (wd:Q5831) | Melilla (en) | 85811 (xsd:decimal) |

[Assistant] The query result shows Melilla as the largest city in Spain by population, which seems unexpected since Madrid is generally known as the largest city. This might be because Melilla is an autonomous city and listed differently. I will try to refine the query to consider only cities that are part of the country Spain (wd:Q29) directly or its subdivisions, excluding autonomous cities like Melilla. I will check the subclasses of city to include human settlements and towns.

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

[Assistant] The refined query result shows Barcelona as the largest city in Spain by population when considering the country property. However, Madrid is generally known as the largest city in Spain. I will check the population of Madrid to compare.

[Tool]
Got 1 row and 1 column:
| population            |
| --------------------- |
| 3416771 (xsd:decimal) |

[Assistant] Madrid has a population of 3,416,771 which is larger than Barcelona's population of 1,702,547. It seems the previous query missed Madrid. I will adjust the query to include all human settlements in Spain and order by population to find the largest city correctly.

[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 result shows the Madrid Metropolitan Area as the largest human settlement in Spain by population, with 5,682,111 people. However, the question asks for the largest city, not metropolitan area. I will now specifically check for the city Madrid itself and confirm it is the largest city by population in Spain.

[Tool]
Got 5 rows and 3 columns:
| city                          | cityLabel           | population            |
| ----------------------------- | ------------------- | --------------------- |
| Barcelona (wd:Q1492)          | Barcelona (en)      | 1702547 (xsd:decimal) |
| Barcelona City (wd:Q31913048) | Barcelona City (en) | 1660122 (xsd:decimal) |
| Zaragoza City (wd:Q31926034)  | Zaragoza City (en)  | 666880 (xsd:decimal)  |
| Palma (wd:Q8826)              | Palma (en)          | 431521 (xsd:decimal)  |
| Alicante (wd:Q11959)          | Alicante (en)       | 358720 (xsd:decimal)  |

[Assistant] The query result shows Barcelona as the city with the largest population in Spain, followed by other cities. Madrid is not listed as an instance of city with country Spain in this result, which is unexpected. I will check the instance of Madrid to understand its classification.

[Tool]
Got 2 rows and 3 columns:
| subject           | property              | object                              |
| ----------------- | --------------------- | ----------------------------------- |
| Madrid (wd:Q2807) | instance of (wdt:P31) | municipality of Spain (wd:Q2074737) |
| Madrid (wd:Q2807) | instance of (wdt:P31) | tourist destination (wd:Q1200957)   |

[Assistant] Madrid is classified as a "municipality of Spain" (wd:Q2074737) rather than a direct instance of "city" (wd:Q515). I will now check if "municipality of Spain" is a subclass of "city" or "human settlement" to understand its classification better.

[Tool]
Got 5 rows and 3 columns:
| subject                             | property               | object                                                   |
| ----------------------------------- | ---------------------- | -------------------------------------------------------- |
| municipality of Spain (wd:Q2074737) | subclass of (wdt:P279) | municipality (wd:Q15284)                                 |
| municipality of Spain (wd:Q2074737) | subclass of (wdt:P279) | administrative territorial entity of Spain (wd:Q3183364) |
| municipality of Spain (wd:Q2074737) | subclass of (wdt:P279) | municipio (wd:Q1763214)                                  |
| municipality of Spain (wd:Q2074737) | subclass of (wdt:P279) | LAU 2 (wd:Q11618417)                                     |
| municipality of Spain (wd:Q2074737) | subclass of (wdt:P279) | third-order administrative division (wd:Q48909258)       |

[Assistant] The "municipality of Spain" is a subclass of "municipality" (wd:Q15284) and other administrative territorial entities, but it is not explicitly a subclass of "city" or "human settlement." To include Madrid and similar entities, I will adjust the query to consider instances of "municipality of Spain" located in Spain and order by population to find the largest city or municipality.

[Tool]
Got 1 row and 3 columns:
| city              | cityLabel   | population            |
| ----------------- | ----------- | --------------------- |
| Madrid (wd:Q2807) | Madrid (en) | 3416771 (xsd:decimal) |

[Assistant] The query result shows Madrid as the largest municipality of Spain by population with 3,416,771 people. This aligns with the general knowledge that Madrid is the largest city in Spain. I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q40933 wdt:P509 ?causeOfDeath .
  OPTIONAL {
    ?causeOfDeath rdfs:label ?causeOfDeathLabel FILTER ( LANG( ?causeOfDeathLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| causeOfDeath                       | causeOfDeathLabel          |
| ---------------------------------- | -------------------------- |
| cervix uterine cancer (wd:Q160105) | cervix uterine cancer (en) |

Eva Perón had cervix uterine cancer.

[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 type of cancer did eva peron have?

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

[Tool]
Example 1:
Question:
what type of cancer did donna summer died from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q908933 wdt:P509 ?x .
}

Using entities:
Donna Summer, also known as Donna Sommer, LaDonna Gaines (wd:Q908933): American singer (1948–2012), recording artist, songwriter, musician, composer, singer, actor

Using properties:
cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide

Example 2:
Question:
what cancer did audrey hepburn died of?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q42786 wdt:P509 ?x .
}

Using entities:
Audrey Hepburn, also known as Edda Kathleen Ruston, Audrey Kathleen Ruston, Edda van Heemstra, Audrey Kathleen Hepburn-Ruston (wd:Q42786): British actress (1929–1993), philanthropist, ballet dancer, humanitarian, stage actor, film actor, dancer, actor, model

Using properties:
cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide

Example 3:
Question:
what disease did helen keller?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q38203 wdt:P1050 ?x .
}

Using entities:
Helen Keller, also known as Helen Adams Keller (wd:Q38203): American author and activist (1880–1968), political activist, autobiographer, peace activist, trade unionist, suffragette, essayist, linguist, writer, orator

Using properties:
medical condition, also known as disability, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as wdt): any state relevant to the health of an organism, including diseases and positive conditions

[Assistant]
From the examples, it appears that the property "cause of death" (wdt:P509) is used to indicate the cause of death, which can include the type of cancer. Also, the property "medical condition" (wdt:P1050) can be used to indicate diseases a person had.

Next, I will search for the entity "Eva Peron" in Wikidata to find her identifier.

[Tool]
Top 10 entity alternatives:
1. Eva Perón, also known as Evita Perón, Maria Eva Duarte de Peron, Evita, María Eva Duarte de Perón, Maria Eva Duarte Peron (wd:Q40933): Argentine actress and politician (1919–1952), women's rights activist, trade unionist, stage actor, suffragist, politician, film actor
2. Eva Perón: The True Story, also known as Eva Peron, Eva Perón (wd:Q953441): 1996 film by Juan Carlos Desanzo
3. Fray Luis Beltrán, also known as Eva Perón, Eva Perón tanker, Fray Luis Beltrán tanker (wd:Q113059537): tanker
4. Eva Perón (wd:Q113030601): tanker
5. Hospital Lagomarsino, also known as Eva Perón (wd:Q5902632): hospital in Merlo, Buenos Aires
6. Uruguay, also known as Eva Perón (wd:Q113330807): 1951 passenger ship
7. Eva Perón (wd:Q47512378): painting by Boris Chaliapin
8. Estadio Eva Perón, also known as Estadio Eva Peron (wd:Q5400194): football stadium in Junín, Argentina
9. Puerto Eva Perón (wd:Q972928): ciudad en la provincia de Chaco, Argentina
10. Eva Perón Foundation (wd:Q2004004): former Argentine foundation

[Assistant] The entity for Eva Perón is wd:Q40933. Now I will query the cause of death (wdt:P509) for Eva Perón to see if it includes the type of cancer she had.

[Tool]
Got 1 row and 2 columns:
| causeOfDeath                       | causeOfDeathLabel          |
| ---------------------------------- | -------------------------- |
| cervix uterine cancer (wd:Q160105) | cervix uterine cancer (en) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?place ?placeLabel WHERE {
  ?place wdt:P2936 wd:Q34271 .
  ?place wdt:P31 / wdt:P279 * wd:Q56061 .
  OPTIONAL {
    ?place rdfs:label ?placeLabel FILTER ( LANG( ?placeLabel ) = "en" )
  }
}

Got 14 rows and 2 columns, showing the first 5 and last 5 rows below:
| place                                | placeLabel                      |
| ------------------------------------ | ------------------------------- |
| Arunachal Pradesh (wd:Q1162)         | Arunachal Pradesh (en)          |
| Assam (wd:Q1164)                     | Assam (en)                      |
| Himachal Pradesh (wd:Q1177)          | Himachal Pradesh (en)           |
| Delhi (wd:Q1353)                     | Delhi (en)                      |
| People's Republic of China (wd:Q148) | People's Republic of China (en) |
| ...                                  | ...                             |
| Mustang (wd:Q28307)                  | Mustang (en)                    |
| Xinjiang (wd:Q34800)                 | Xinjiang (en)                   |
| Kingdom of Sikkim (wd:Q3960459)      | Kingdom of Sikkim (en)          |
| India (wd:Q668)                      | India (en)                      |
| Nepal (wd:Q837)                      | Nepal (en)                      |

Tibetan is spoken in several administrative regions and countries including China, India, Nepal, Arunachal Pradesh, Assam, Himachal Pradesh, Delhi, Xinjiang, Mustang, and the Kingdom of Sikkim.

[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 do they speak tibetan?

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

[Tool]
Example 1:
Question:
what do tibetan people speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q187985 wdt:P2936 ?x .
}

Using entities:
Tibetan people, also known as Bhod-pa, Sifan, Sifen, Tibetans, Xifan (wd:Q187985): East Asian ethnic group native to Tibet

Using properties:
language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdt): language widely used (spoken or written) in this place or at this event or organisation

Example 2:
Question:
where do people speak burmese?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P2936 wd:Q9228 .
}

Using entities:
Burmese, also known as Myanmar language, Burmese language (wd:Q9228): Sino-Tibetan language of Myanmar

Using properties:
language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdt): language widely used (spoken or written) in this place or at this event or organisation

Example 3:
Question:
what do they speak in austria?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q40 wdt:P37 ?x .
}

Using entities:
Austria, also known as Republik Österreich, Republic of Austria, Österreich (wd:Q40): country in Central Europe

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]
From the similar examples, I see two main approaches to answer the question "where do they speak Tibetan?":

1. Query places where the language Tibetan is used (property wdt:P2936 "language used").
2. Query places where Tibetan is an official language (property wdt:P37 "official language").

I will first search for the entity representing the Tibetan language. Then I will query places where this language is used.

[Tool]
Top 10 entity alternatives:
1. Tibetan, also known as bo, bod, Lhasa Tibetan, Standard Tibetan, Tibetan language (wd:Q34271): Tibeto-Burman language
2. Tibetan (language) (wd:L612746-S1)
3. Tibetan language (wd:L1020969-S1)
4. Tibetic, also known as Tibetan languages, Tibetic languages (wd:Q1641150): group of Bodic languages spoken by Tibetans in the Eastern Tibetan Plateau and in northern areas of the Indian subcontinent
5. Central Tibetan, also known as Dbus, Ü-Tsang, Central Tibetan language, Ü (wd:Q5061915): language and dialects ensemble from Central Tibet
6. Central Tibetan, also known as Ü-Tsang, Ü, Dbus, Central Tibetan language (wd:Q5061915): language and dialects ensemble from Central Tibet
7. Khams Tibetan, also known as Khams Tibetan language, khg (wd:Q56601): language
8. Amdo Tibetan, also known as adx, Amdo Tibetan language (wd:Q56509): Tibetic language of Amdo
9. Tibetan Sign Language (wd:Q15936110): deaf sign language of Tibet
10. Humla, also known as Humla Bhotiya, Humla language, Humla Tibetan, Humla Tibetan language, Humli Tamang (wd:Q17588918): Sino-Tibetan language

[Assistant] The entity for the Tibetan language is wd:Q34271. Now I will query places where this language is used (property wdt:P2936).

[Tool]
Got 21 rows and 2 columns, showing the first 5 and last 5 rows below:
| place                            | placeLabel              |
| -------------------------------- | ----------------------- |
| CNR Tibetan Radio (wd:Q10874976) | CNR Tibetan Radio (en)  |
| Arunachal Pradesh (wd:Q1162)     | Arunachal Pradesh (en)  |
| Assam (wd:Q1164)                 | Assam (en)              |
| Himachal Pradesh (wd:Q1177)      | Himachal Pradesh (en)   |
| All India Radio (wd:Q1321122)    | All India Radio (en)    |
| ...                              | ...                     |
| Kingdom of Sikkim (wd:Q3960459)  | Kingdom of Sikkim (en)  |
| Radio Free Asia (wd:Q40322)      | Radio Free Asia (en)    |
| India (wd:Q668)                  | India (en)              |
| Qinghai Television (wd:Q7267827) | Qinghai Television (en) |
| Nepal (wd:Q837)                  | Nepal (en)              |

[Assistant] The query returns places and some media organizations where Tibetan is used. To focus on geographic places where Tibetan is spoken, I will filter out media organizations and focus on administrative regions or countries. I will refine the query to include only instances of administrative territorial entities or countries.

[Tool]
Got 14 rows and 2 columns, showing the first 5 and last 5 rows below:
| place                                | placeLabel                      |
| ------------------------------------ | ------------------------------- |
| Arunachal Pradesh (wd:Q1162)         | Arunachal Pradesh (en)          |
| Assam (wd:Q1164)                     | Assam (en)                      |
| Himachal Pradesh (wd:Q1177)          | Himachal Pradesh (en)           |
| Delhi (wd:Q1353)                     | Delhi (en)                      |
| People's Republic of China (wd:Q148) | People's Republic of China (en) |
| ...                                  | ...                             |
| Mustang (wd:Q28307)                  | Mustang (en)                    |
| Xinjiang (wd:Q34800)                 | Xinjiang (en)                   |
| Kingdom of Sikkim (wd:Q3960459)      | Kingdom of Sikkim (en)          |
| India (wd:Q668)                      | India (en)                      |
| Nepal (wd:Q837)                      | Nepal (en)                      |

[Assistant]
The refined query returns administrative regions and countries where Tibetan is used, including China, India, Nepal, and specific regions like Arunachal Pradesh, Assam, Himachal Pradesh, Delhi, Xinjiang, Mustang, and the Kingdom of Sikkim.

I will now finalize the answer with this information.

[Tool] Stopped generation process