PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?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 examples.

[Tool]
Example 1:
Question:
what political system is in china?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P122 ?x .
}

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia

Using properties:
basic form of government, also known as form of government, government, type of government (wd:P122 as wdt): subject's government

Example 2:
Question:
who does avril lavigne play in over the hedge?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q245366 p:P725 ?p .
  ?p ps:P725 wd:Q30449 ; pq:P453 ?x .
}

Using entities:
Over the Hedge (wd:Q245366): 2006 American animated film
Avril Lavigne, also known as Avril Ramona Lavigne (wd:Q30449): Canadian singer and songwriter (born 1984), singer-songwriter, recording artist, fashion designer, philanthropist, voice actor, film actor, composer, singer, actor

Using properties:
character role, also known as role, plays, performer of, as, acting role (wd:P453 as pq): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)
voice actor, also known as voice actors, voice dubber, voice actress, voice actresses, voiced by (wd:P725 as ps/p): performer of a spoken role in a creative work such as animation,... (P453) as qualifier] [use "cast member" (P161) for live acting], film crew member, cast member

Example 3:
Question:
what did ronnie radke do?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q2019307 wdt:P106 ?x .
}

Using entities:
Ronnie Radke, also known as Ronald Joseph Radke (wd:Q2019307): American rock singer, record producer, songwriter, guitarist, singer

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] Based on the examples, we need to identify the entity for Egypt and the property for the official or spoken language. Let's start by searching for the entity "Egypt".

[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 correct entity for Egypt is `wd:Q79`. Now, let's find the property that represents the official or spoken language of a country.

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): language status identifier by Ethnologue.com using EGIDS scale
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): language in which the writer has written their work, languages spoken, written or signed

[Assistant]
The property `wd:P37` represents the official language of a country. Let's construct a SPARQL query to find the official language of Egypt (`wd:Q79`).

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

Now, let's execute this query.

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

[Assistant]
The official language of Egypt is Arabic. Here is the final SPARQL query and answer:

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

Answer: The official language of Egypt is Arabic.

[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.


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 ?model ?modelLabel WHERE {
  ?model wdt:P31 wd:Q1420 .
  ?model wdt:P176 wd:Q81965 .
  OPTIONAL {
    ?model rdfs:label ?modelLabel .
    FILTER ( LANG( ?modelLabel ) = "en" )
  }
}

Got 6 rows and 2 columns:
| model                               | modelLabel                  |
| ----------------------------------- | --------------------------- |
| 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 produces several specific models of motor cars, including the 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 examples.

[Tool]
Example 1:
Question:
what kind of currency does cuba use?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q241 wdt:P38 ?x .
}

Using entities:
Cuba, also known as cu, CUB, Republic of Cuba (wd:Q241): sovereign state situated on an island in the Caribbean Sea

Using properties:
currency, also known as base currency, used money (wd:P38 as wdt): currency used by item, uses

Example 2:
Question:
what county is morristown tn in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q962499 wdt:P131 ?x .
}

Using entities:
Morristown, also known as Morristown, New Jersey, Morristown, NJ (wd:Q962499): town in Morris County, New Jersey, United States

Using properties:
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 wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of

Example 3:
Question:
who does dominic monaghan play in lord of the rings?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q127367 p:P161 ?p .
  ?p ps:P161 wd:Q152165 ; pq:P453 ?x .
}

Using entities:
The Lord of the Rings: The Fellowship of the Ring, also known as Lord of the Rings: The Fellowship of the Ring, The Fellowship of the Ring (wd:Q127367): 2001 film by Peter Jackson
Dominic Monaghan (wd:Q152165): German-born British actor, television actor, screenwriter, stage actor, film actor, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as ps/p): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
character role, also known as role, plays, performer of, as, acting role (wd:P453 as pq): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)

[Assistant] Based on the examples, it seems like we need to identify GM (General Motors) and then look for what products or services they provide. Let's first search for the entity representing GM in the knowledge graph.

[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 top result is the correct entity for General Motors (wd:Q81965). Now, let's find out what products or services GM makes. We can look for properties such as "product" or "produces" to find this information.

I will search for properties related to "product" or "produces" for the General Motors entity.

[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 p/wdt): 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 p/wdt): specific industry of company or organization, part of
3. net profit, also known as profit, net return, net income, clear profit, clear gain (wd:P2295 as p/wdt): private entity profit after accounting for all costs
4. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
5. total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as p/wdt): income gained by an organization during a given time frame. Not to be confused with fiscal revenue
6. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as p/wdt): founder or co-founder of this organization, religion, place or entity, creator
7. operating income, also known as earnings before interest and taxes, operating profit, EBIT (wd:P3362 as p/wdt): private entity operating income
8. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
9. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as p/wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
10. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity

No other items found

[Assistant]
The property `product, material, or service produced or provided` (wd:P1056) seems to be the most relevant for finding out what GM produces. Let's construct a SPARQL query to retrieve the products or services provided by General Motors.

Here is the SPARQL query:

```sparql
SELECT DISTINCT ?product WHERE {
  wd:Q81965 wdt:P1056 ?product .
  OPTIONAL { ?product rdfs:label ?productLabel . FILTER(LANG(?productLabel) = "en") }
}
```

This query will return the products or services provided by General Motors, along with their English labels if available.

Let's execute this query.

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

[Assistant]
The query returned "motor car" (wd:Q1420) as a product of General Motors. To provide a more comprehensive answer, let's check if there are any other specific models or types of vehicles that GM produces. We can do this by searching for objects of the property `product, material, or service produced or provided` (wdt:P1056) for General Motors.

Let's refine the query to include more specific products:

```sparql
SELECT DISTINCT ?product ?productLabel WHERE {
  wd:Q81965 wdt:P1056 ?product .
  OPTIONAL { ?product rdfs:label ?productLabel . FILTER(LANG(?productLabel) = "en") }
}
```

This query will return all products or services provided by General Motors, along with their English labels if available.

Let's execute this refined query.

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

[Assistant]
The query still returns only "motor car" (wd:Q1420) as a product of General Motors. However, this is a general category, and we might want to find more specific models or types of vehicles that GM produces.

To get more specific products, we can look for instances of "motor car" that are produced by General Motors. We can use the `instance of` property (wdt:P31) to find specific models of motor cars and then check if they are produced by GM.

Here is the updated SPARQL query:

```sparql
SELECT DISTINCT ?model ?modelLabel WHERE {
  ?model wdt:P31 wd:Q1420 .
  ?model wdt:P176 wd:Q81965 .
  OPTIONAL { ?model rdfs:label ?modelLabel . FILTER(LANG(?modelLabel) = "en") }
}
```

This query will return specific models of motor cars produced by General Motors, along with their English labels if available.

Let's execute this query.

[Tool]
Got 6 rows and 2 columns:
| model                               | modelLabel                  |
| ----------------------------------- | --------------------------- |
| 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 query returned several specific models of motor cars produced by General Motors. Here are the results:

- Buick Regal (wd:Q1002872)
- Oldsmobile Model 43 (wd:Q1310003)
- Oldsmobile Series 40 (wd:Q1340552)
- Buick LaCrosse (wd:Q498843)
- Cadillac Sixty Special (wd:Q825252)
- Buick Velite 7 (wd:Q96354603)

These are specific models of motor cars that General Motors produces.

Let's finalize the answer with the SPARQL query and the results.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the actual identifiers of the items and their labels, and the results are relevant to the question. No further refinement is needed.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?city ?population WHERE {
  VALUES ?city {
    wd:Q2807 wd:Q1492
  }
  ?city wdt:P1082 ?population .
}
ORDER BY DESC ( ?population )
LIMIT 1

The largest city in Spain 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 examples.

[Tool]
Example 1:
Question:
who were demeter's brothers and sisters?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q40730 wdt:P3373 ?x .
}

Using entities:
Demeter, also known as Dêmêtêr, Deo (wd:Q40730): Greek goddess of the harvest, grains, and agriculture

Using properties:
sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as wdt): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative

Example 2:
Question:
who played daniel larusso?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?p p:P161 ?y .
  ?y ps:P161 ?x ; pq:P453 wd:Q55315887 .
}

Using entities:
Daniel LaRusso, also known as Daniel-san (wd:Q55315887): fictional character from the Karate Kid franchise, business executive, car dealer, karateka

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as ps/p): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
character role, also known as role, plays, performer of, as, acting role (wd:P453 as pq): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)

Example 3:
Question:
what did the habsburg family control?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q65968 wdt:P1830 ?x .
}

Using entities:
House of Habsburg, also known as Habsburg, Habsburg family, Hapsburg, Hapsburg family, House of Austria (wd:Q65968): Austrian imperial house

Using properties:
owner of, also known as owns, owns property, shareholder of (wd:P1830 as wdt): entities owned by the subject

[Assistant]
Based on the examples, we need to identify the relevant entity and property for our question. We are looking for the largest city in Spain, so we need to find the entity for Spain and a property that indicates the largest city or population of cities in Spain.

First, let's search for the entity "Spain".

[Tool]
Top 10 entity alternatives:
1. Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa
2. Spain men's national football team, also known as La Furia, The Spanish Fury, The Red One, The Red Fury, The Fury (wd:Q42267): men's national association football team representing Spain
3. Second Spanish Republic, also known as Spain (wd:Q178038): political regime established in Spain between April 14, 1931 and April 1, 1939
4. New Spain, also known as Colonial Central America, Central America under Spain, Viceroyalty of New Spain, Spanish Mexico, Spanish Central America (wd:Q170603): viceroyalty of the Spanish Empire (1535-1821)
5. Spain under the Restoration, also known as Bourbon Restoration, Spain, Second Bourbon Restoration, Restoration, Restoration (Spain) (wd:Q1044536): name given to a period between 1874 (end of First Spanish Republ...econd Spanish Republic), and the state of Spain during that time
6. Kingdom of Spain (1820-1823), also known as Kingdom of Spain, Spain, Trienio Liberal (wd:Q238809): 1820–1823 Spanish political period
7. Spain under Joseph Bonaparte, also known as Kingdom of Spain, Napoleonic Spain, Spain (wd:Q1709185): Napoleonic client state (1808–1813)
8. Sexenio Democrático, also known as Kingdom of Spain, Spain (wd:Q12411956): 1868–1874 period of fu under a provisional government
9. Ominous Decade, also known as Kingdom of Spain, second restoration of absolutism, Spain (wd:Q2378204): period in the history of Spain, corresponding to the second rest...reign of Fernando VII from August 31, 1823 to September 29, 1833
10. Hispanic Monarchy, also known as Spains, Hispanic Crown, Spain, Kingdom of Spains, Crown of Spain (wd:Q766543): (1479-1716) set of territories distributed throughout the world,...monarch and corresponding to the period of the House of Habsburg

[Assistant]
The correct entity for Spain is `wd:Q29`. Now, we need to find a property that can help us determine the largest city in Spain. We can look for properties related to cities or population.

Let's search for properties related to "largest city" or "population".

[Tool]
Top 10 property alternatives:
1. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
2. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
4. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): (list of) direct subdivisions of an administrative territorial entity, has part(s)
5. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): seat of government of a country, province, state or other type of administrative territorial entity
6. place served by transport hub, also known as serves city, train station serves, served by transport hub, city served, area served by transport hub (wd:P931 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): territorial entity or entities served by this transport hub (airport, train station, etc.), significant place
7. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
8. capital of, also known as national capital of, parish seat of, provincial capital of, seat of, state capital (wd:P1376 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): country, state, department, canton or other administrative division of which the municipality is the governmental seat, located in the administrative territorial entity
9. urban population (wd:P6343 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): number of people living within the territorial entity who live in its urban parts, population
10. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): the place where the person is or has been, resident, location

[Assistant]
None of the properties directly indicate the largest city, but we can use the property `P1082` (population) to find the city with the highest population in Spain. Let's confirm this property and then construct the query.

Let's search for the property "population" to ensure it is the correct one.

[Tool]
Top 10 property alternatives:
1. tabular population, also known as population (wd:P4179 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): same as population (P1082); used when there is data for many different points in time
2. population, also known as inhabitants, human population (wd:P1082 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): number of people inhabiting the place; number of people of subject
3. has census, also known as has human population census (wd:P8701 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): general population census of the country or region
4. urban population (wd:P6343 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): number of people living within the territorial entity who live in its urban parts, population
5. birth rate (wd:P8763 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): the total number of live births per 1,000 population
6. category populated by, also known as category filled by, category has template, cat populated by, component adding into the category, populated by (wd:P4329 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): software component (template, module, extension, MediaWiki core) populating the category
7. rural population (wd:P6344 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): number of people living within the territorial entity who live in its rural parts, population
8. female population (wd:P1539 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): number of female people inhabiting the place; number of female people of subject, population
9. male population, also known as male inhabitants, number of males (wd:P1540 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): number of male people inhabiting the place; number of male people of subject, population
10. number of households, also known as dwellings, households (wd:P1538 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): total number of households in this place, includes dwellings of all types

[Assistant]
The property `P1082` (population) is the correct one to use for finding the largest city in Spain. Now, we need to construct a SPARQL query to find the city in Spain with the highest population.

We will start by selecting cities in Spain and their populations, and then order the results by population in descending order to find the largest city.

Let's execute the following SPARQL query:

```sparql
SELECT ?city ?population WHERE {
  ?city wdt:P1082 ?population ;
        wdt:P131 wd:Q29 .
}
ORDER BY DESC(?population)
LIMIT 1
```

This query selects cities (`?city`) and their populations (`?population`) where the city is located in Spain (`wd:Q29`), orders the results by population in descending order, and limits the result to the top entry.

Let's run this query.

[Tool]
Got 1 row and 2 columns:
| city                   | population             |
| ---------------------- | ---------------------- |
| Spaniards (wd:Q160894) | 47000000 (xsd:decimal) |

[Assistant]
It seems that the query returned "Spaniards" (wd:Q160894) with a population of 47,000,000, which is not a city but rather the population of Spain itself. This suggests that the query might need to be refined to specifically target cities.

To refine the query, we need to ensure that we are only considering entities that are cities. We can do this by adding a filter to check if the entity is an instance of a city.

Let's modify the query to include this filter:

```sparql
SELECT ?city ?population WHERE {
  ?city wdt:P1082 ?population ;
        wdt:P131 wd:Q29 ;
        wdt:P31 wd:Q515 .
}
ORDER BY DESC(?population)
LIMIT 1
```

Here, `wdt:P31 wd:Q515` ensures that the entity is an instance of a city (`wd:Q515`).

Let's run this updated query.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the query did not return any results, which suggests that there might be an issue with the constraints or the data in the knowledge graph. Let's try a different approach to ensure we are correctly identifying cities in Spain.

We can first list the cities in Spain and then filter by population. This will help us verify that we are correctly identifying cities.

Let's list the cities in Spain:

```sparql
SELECT ?city WHERE {
  ?city wdt:P131 wd:Q29 ;
        wdt:P31 wd:Q515 .
}
```

This query will list all cities in Spain. Once we have the list, we can then filter by population.

Let's run this query to list the cities.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query to list cities in Spain did not return any results. This suggests that there might be an issue with the way we are identifying cities or the data in the knowledge graph.

Let's try a different approach by listing entities that are located in Spain and are instances of a city, and then manually inspect the results to see if we can identify the largest city.

We can use the following query to list entities that are located in Spain:

```sparql
SELECT ?entity WHERE {
  ?entity wdt:P131 wd:Q29 .
}
```

This query will list all entities located in Spain. We can then inspect the results to see if they include cities.

Let's run this query.

[Tool]
Got 499 rows and 1 column, showing the first 5 and last 5 rows below:
| entity                                             |
| -------------------------------------------------- |
| EroticFeel (wd:Q101125400)                         |
| agustina Silva líder del cartel cali (wd:Q1026717) |
| Portuguese Way (wd:Q1029533)                       |
| region of Spain (wd:Q104117291)                    |
| Old Castile (wd:Q1043594)                          |
| ...                                                |
| Viaducto de Ormaiztegi (wd:Q9093145)               |
| wd:Q922362                                         |
| Limbo Starr (wd:Q94139194)                         |
| Slovenian Genealogy (wd:Q96680880)                 |
| Sesame Time (wd:Q98214958)                         |

[Assistant]
The results include a variety of entities, not just cities. To narrow down the results to cities, we need to ensure that we are only selecting entities that are instances of a city (`wd:Q515`).

Let's modify the query to include the instance of a city constraint:

```sparql
SELECT ?city WHERE {
  ?city wdt:P131 wd:Q29 ;
        wdt:P31 wd:Q515 .
}
```

This query will list all cities in Spain. Let's run this query again to ensure we get the correct list of cities.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that even with the additional constraint to filter for cities, the query is still not returning any results. This suggests that there might be an issue with the data in the knowledge graph or the way we are querying it.

Let's try a different approach by searching for the largest city in Spain using a more direct method. We can search for the entity "Madrid" and "Barcelona" and compare their populations.

First, let's search for the entity "Madrid" and "Barcelona".

### Search for Madrid
```sparql
SELECT ?madrid WHERE {
  ?madrid rdfs:label "Madrid"@en ;
          wdt:P31 wd:Q515 .
}
```

### Search for Barcelona
```sparql
SELECT ?barcelona WHERE {
  ?barcelona rdfs:label "Barcelona"@en ;
             wdt:P31 wd:Q515 .
}
```

Let's run these queries to find the IRIs for Madrid and Barcelona.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query to find the IRI for Madrid did not return any results. This suggests that there might be an issue with the label or the way we are querying it. Let's try a different approach by using the `search_entity` function to find the correct IRI for Madrid and Barcelona.

### Search for Madrid
```json
{
  "name": "search_entity",
  "arguments": {
    "kg": "wikidata",
    "query": "Madrid"
  }
}
```

### Search for Barcelona
```json
{
  "name": "search_entity",
  "arguments": {
    "kg": "wikidata",
    "query": "Barcelona"
  }
}
```

Let's run these searches to find the correct IRIs for Madrid and Barcelona.

[Tool]
Top 10 entity alternatives:
1. Madrid (wd:Q2807): capital of Spain
2. Madrid (wd:Q2807): capital of Spain
3. Madrid (wd:Q824651): municipality of Spain
4. Madrid, also known as Madrid, Iowa (wd:Q1934324): city in Iowa, United States
5. Madrid (wd:Q66470): city in Alabama, United States
6. Madrid, also known as Madrid, New Mexico, Madrid, NM (wd:Q1331390): census-designated place in Santa Fe County, New Mexico, United States
7. Madrid, also known as Madrid, NE (wd:Q2323298): village in Iowa, United States
8. Madrid (wd:Q2316396): Colombian municipality of the department of Cundinamarca
9. Madrid, also known as Municipality of Madrid (wd:Q155605): municipality of the Philippines in the province of Surigao del Sur
10. Madrid, also known as Madrid, New York, Madrid, NY (wd:Q3708806): city in New York, United States

[Assistant]
The correct entity for Madrid is `wd:Q2807`. Now, let's find the IRI for Barcelona.

### Search for Barcelona
```json
{
  "name": "search_entity",
  "arguments": {
    "kg": "wikidata",
    "query": "Barcelona"
  }
}
```

Let's run this search to find the correct IRI for Barcelona.

[Tool]
Top 10 entity alternatives:
1. Barcelona, also known as Barcelona, Catalonia, Barcelona, Spain, BCN, B'na (wd:Q1492): capital city of Catalonia, Spain
2. FC Barcelona, also known as Football Club Barcelona, F.C.B., Fútbol Club Barcelona, Futbol Club Barcelona, Barça (wd:Q7156): multisports club in Barcelona, Spain
3. FC Barcelona, also known as Fútbol Club Barcelona, Futbol Club Barcelona, Football Club of Barcelona, Football Club Barcelona, FC Barcelona Football (wd:Q7156): multisports club in Barcelona, Spain
4. Barcelona (wd:Q391221): capital of Anzoátegui State, Venezuela
5. Barcelona (wd:Q807917): song by Freddie Mercury and Montserrat Caballé
6. Barcelona, also known as Barcelona, Rio Grande do Norte (wd:Q2011622): municipality in Rio Grande do Norte, Brazil
7. Barcelona (wd:Q279991): album by Freddie Mercury
8. Barcelona, also known as Barcelona, Sorsogon (wd:Q174114): municipality of the Philippines in the province of Sorsogon
9. Barcelona (wd:Q4078526): 1994 film by Whit Stillman
10. Barcelona, also known as Barcelona, Cornwall (wd:Q4859837): hamlet in Cornwall, England

[Assistant]
The correct entity for Barcelona is `wd:Q1492`.

Now that we have the IRIs for Madrid (`wd:Q2807`) and Barcelona (`wd:Q1492`), we can query their populations and compare them to determine the largest city in Spain.

Let's write a SPARQL query to get the populations of Madrid and Barcelona:

```sparql
SELECT ?city ?population WHERE {
  VALUES ?city { wd:Q2807 wd:Q1492 }
  ?city wdt:P1082 ?population .
}
ORDER BY DESC(?population)
LIMIT 1
```

This query will select the city and its population for Madrid and Barcelona, order the results by population in descending order, and limit the result to the top entry.

Let's run this query.

[Tool]
Got 1 row and 2 columns:
| city              | population            |
| ----------------- | --------------------- |
| Madrid (wd:Q2807) | 3416771 (xsd:decimal) |

[Assistant]
The query results show that Madrid (wd:Q2807) has a population of 3,416,771, making it the largest city in Spain.

Therefore, the largest city in Spain is Madrid.

Let's provide the final answer.

```sparql
SELECT ?city ?population WHERE {
  VALUES ?city { wd:Q2807 wd:Q1492 }
  ?city wdt:P1082 ?population .
}
ORDER BY DESC(?population)
LIMIT 1
```

The largest city in Spain is Madrid.

[Tool] Stopped generation process

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


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?causeOfDeath WHERE {
  wd:Q40933 wdt:P509 ?causeOfDeath .
}

Cervical cancer (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 examples.

[Tool]
Example 1:
Question:
who plays lois in family guy?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5930 p:P725 ?p .
  ?p ps:P725 ?x ; pq:P453 wd:Q853073 .
}

Using entities:
Family Guy (wd:Q5930): American adult animated sitcom
Lois Griffin, also known as Lois Pewterschmidt Griffin, Lois Pewterschmidt, Lois Patrice Pewterschmidt, Lois Patrice Griffin (wd:Q853073): fictional character from the Family Guy franchise, housewife

Using properties:
character role, also known as role, plays, performer of, as, acting role (wd:P453 as pq): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)
voice actor, also known as voice actors, voice dubber, voice actress, voice actresses, voiced by (wd:P725 as ps/p): performer of a spoken role in a creative work such as animation,... (P453) as qualifier] [use "cast member" (P161) for live acting], film crew member, cast member

Example 2:
Question:
what illnesses did george washington suffer from?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q23 wdt:P1050 ?x .
}

Using entities:
George Washington, also known as Father of the United States (wd:Q23): Founding Father and first U.S. president (1789–1797), revolutionary, statesperson, army officer, cartographer, politician, engineer, geometer, writer, farmer

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

Example 3:
Question:
who did rufus wainwright married?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q192402 wdt:P26 ?x .
}

Using entities:
Rufus Wainwright, also known as Rufus McGarrigle Wainwright (wd:Q192402): American-Canadian singer-songwriter and composer, singer-songwriter, television actor, voice actor, film actor, musician, composer, pianist, singer

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

[Assistant] Based on the examples, it seems that we need to find the medical condition (P1050) of Eva Peron (Q1543). Let's start by searching for Eva Peron in the knowledge graph.

[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 top result is the correct entity for Eva Peron (Q40933). Now let's check if there is a property that indicates the type of cancer she had. We can search for the property "cancer" or "medical condition" related to Eva Peron.

[Tool]
Top 10 property alternatives:
1. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as p/wdt): dissolved, abolished or demolished date, date on which the subject died, end time
2. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as p/wdt): religion of a person, organization or religious building, or associated with this subject
3. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as p/wdt): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
4. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as p/wdt): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
5. 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
6. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as p/wdt): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
7. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as p/wdt): specialization of a person or organization; see P106 for the occupation, interested in
8. 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
9. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
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] The property "cause of death" (P509) seems to be the most relevant for finding out what type of cancer Eva Peron had. Let's execute a SPARQL query to retrieve the cause of death for Eva Peron.

[Tool]
Got 1 row and 1 column:
| causeOfDeath                       |
| ---------------------------------- |
| cervix uterine cancer (wd:Q160105) |

[Assistant]
Eva Peron had cervical cancer, which is also known as cervix uterine cancer.

Here is the final SPARQL query and answer:

```sparql
SELECT ?causeOfDeath WHERE { wd:Q40933 wdt:P509 ?causeOfDeath . }
```

Answer: Cervical cancer (cervix uterine cancer).

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the cause of death for Eva Perón, which is cervical cancer (cervix uterine cancer).


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:P37 wd:Q34271 ; wdt:P31 wd:Q6256 .
  } UNION {
    ?place wdt:P2936 wd:Q34271 ; wdt:P31 wd:Q6256 .
  }
  ?place rdfs:label ?placeLabel .
  FILTER ( LANG( ?placeLabel ) = "en" )
}

Got 3 rows and 2 columns:
| place                                | placeLabel                      |
| ------------------------------------ | ------------------------------- |
| People's Republic of China (wd:Q148) | People's Republic of China (en) |
| India (wd:Q668)                      | India (en)                      |
| Nepal (wd:Q837)                      | Nepal (en)                      |

Thank you for the feedback. To address the issue, we will include the property `wdt:P2936` (languages used) and filter out historical regions by focusing on current administrative divisions. We can use the property `wdt:P31` (instance of) to filter for modern regions or countries.

Let's update the query:

```sparql
SELECT DISTINCT ?place ?placeLabel WHERE {
  {
    ?place wdt:P37 wd:Q34271 ;
           wdt:P31 wd:Q6256 .
  } UNION {
    ?place wdt:P2936 wd:Q34271 ;
           wdt:P31 wd:Q6256 .
  }
  ?place rdfs:label ?placeLabel .
  FILTER(LANG(?placeLabel) = "en")
}
```

This query selects places where Tibetan is either an official language (`wdt:P37`) or a widely used language (`wdt:P2936`), and ensures that these places are instances of a country or region (`wdt:P31 wd:Q6256`). It also retrieves the English labels of these places.

Let's execute this updated query.
<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT DISTINCT ?place ?placeLabel WHERE {   {     ?place wdt:P37 wd:Q34271 ;            wdt:P31 wd:Q6256 .   } UNION {     ?place wdt:P2936 wd:Q34271 ;            wdt:P31 wd:Q6256 .   }   ?place rdfs:label ?placeLabel .   FILTER(LANG(?placeLabel) = \\"en\\") }"}}
</tool_call>

[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 examples.

[Tool]
Example 1:
Question:
who was the apostle paul considered to be?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q9200 wdt:P1559 ?x .
}

Using entities:
Paul the Apostle, also known as Saul of Tarsus, Saulus, Šā'ûl, Saul, Saint Paulus (wd:Q9200): Early Christian apostle and missionary (c. AD 5 – c. 64/65), religious figure, missionary, theologian, traveler, artisan, writer, rabbi

Using properties:
name in native language, also known as native name (wd:P1559 as wdt): name of a person in their native language, name

Example 2:
Question:
what countries around the world speak french?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P37 wd:Q150 ; wdt:P31 / wdt:P279 * wd:Q6256 .
}

Using entities:
French, also known as (fr), fr, fra, French language (wd:Q150): Romance language
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
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 did lucille ball die of?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q209662 wdt:P509 ?x .
}

Using entities:
Lucille Ball, also known as Lucille Désirée Ball, Lucille Desiree Ball, Lucille Ball Arnaz, Queen of the B Movies,, Lucy Ball (wd:Q209662): American actress (1911–1989), film studio executive, television producer, television actor, film director, film actor, producer, director, comedian, 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

[Assistant] Based on the examples, we can see that to find where a language is spoken, we need to look for the property "official language" (P37) and the entity "Tibetan language" (Q9119). Let's first confirm the entity for Tibetan language.

[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 top result for "Tibetan language" is `wd:Q34271`. Now let's confirm 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): language status identifier by Ethnologue.com using EGIDS scale
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): 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 pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): language in which the writer has written their work, languages spoken, written or signed

[Assistant]
The top result for "official language" is `wd:P37`. Now we can construct a SPARQL query to find the countries or regions where Tibetan is an official language.

Let's try the following query:

```sparql
SELECT DISTINCT ?place ?placeLabel WHERE {
  ?place wdt:P37 wd:Q34271 ;
         rdfs:label ?placeLabel .
  FILTER(LANG(?placeLabel) = "en")
}
```

This query selects places where Tibetan (`wd:Q34271`) is an official language (`wdt:P37`) and retrieves their English labels.

Let's execute this query.
<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT DISTINCT ?place ?placeLabel WHERE {   ?place wdt:P37 wd:Q34271 ;          rdfs:label ?placeLabel .   FILTER(LANG(?placeLabel) = \\"en\\") }"}}
</tool_call>

[User]
Feedback (status=refine):
The query returns historical regions and dynasties where Tibetan was an official language, but it does not include modern regions or countries. To improve the answer, we should also consider the property `wdt:P2936` (languages of the country or region) to find modern regions where Tibetan is spoken. Additionally, we should filter out historical regions and focus on current administrative divisions.

[Assistant]
Thank you for the feedback. To address the issue, we will include the property `wdt:P2936` (languages used) and filter out historical regions by focusing on current administrative divisions. We can use the property `wdt:P31` (instance of) to filter for modern regions or countries.

Let's update the query:

```sparql
SELECT DISTINCT ?place ?placeLabel WHERE {
  {
    ?place wdt:P37 wd:Q34271 ;
           wdt:P31 wd:Q6256 .
  } UNION {
    ?place wdt:P2936 wd:Q34271 ;
           wdt:P31 wd:Q6256 .
  }
  ?place rdfs:label ?placeLabel .
  FILTER(LANG(?placeLabel) = "en")
}
```

This query selects places where Tibetan is either an official language (`wdt:P37`) or a widely used language (`wdt:P2936`), and ensures that these places are instances of a country or region (`wdt:P31 wd:Q6256`). It also retrieves the English labels of these places.

Let's execute this updated query.
<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT DISTINCT ?place ?placeLabel WHERE {   {     ?place wdt:P37 wd:Q34271 ;            wdt:P31 wd:Q6256 .   } UNION {     ?place wdt:P2936 wd:Q34271 ;            wdt:P31 wd:Q6256 .   }   ?place rdfs:label ?placeLabel .   FILTER(LANG(?placeLabel) = \\"en\\") }"}}
</tool_call>

[User]
Feedback (status=done):
The updated SPARQL query correctly identifies the places where Tibetan is spoken, both as an official language and a widely used language. The results include relevant countries such as the People's Republic of China, India, and Nepal. The query is well-structured and follows the rules for SPARQL queries. No further refinements are needed.


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 ?disease ?diseaseLabel WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
  OPTIONAL {
    ?disease rdfs:label ?diseaseLabel .
    FILTER ( LANG( ?diseaseLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| disease                       | diseaseLabel            |
| ----------------------------- | ----------------------- |
| multiple sclerosis (wd:Q8277) | multiple sclerosis (en) |

The query returned multiple labels for the disease "multiple sclerosis" in different languages. Since we are primarily interested in the English label, we can filter the results to only include the English label.

### Step 3: Filter for English Label
Let's modify the query to filter for the English label.

```sparql
SELECT ?disease ?diseaseLabel WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
  OPTIONAL { ?disease rdfs:label ?diseaseLabel . FILTER(LANG(?diseaseLabel) = "en") }
}
```

Let's execute this query to see the results.

<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT ?disease ?diseaseLabel WHERE {\n  wd:Q1345645 wdt:P1050 ?disease .\n  OPTIONAL { ?disease rdfs:label ?diseaseLabel . FILTER(LANG(?diseaseLabel) = \\"en\\") }\n}"}}
</tool_call>

[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 disease does montel williams have?

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

[Tool]
Example 1:
Question:
where was governor cuomo born?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q11673 wdt:P19 ?x .
}

Using entities:
Andrew Cuomo, also known as Andrew Mark Cuomo (wd:Q11673): Governor of New York from 2011 to 2021, politician, lawyer

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 queen isabella live?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q45859 wdt:P19 ?x .
}

Using entities:
Isabella I of Castile, also known as la Católica, Queen of Spain Isabel I, Queen Isabella, Queen Isabella of Spain, Queen of Castile and Leon Isabella, Queen of Castile and León Isabella (wd:Q45859): queen of the Crown of Castile, from 1474 to 1504; first queen of a dynastically-united Spain, from 1479 to 1504, ruler

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 is my timezone in california?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q99 wdt:P421 ?x .
}

Using entities:
California, also known as US-CA, State of California, The Golden State, Calif., Cali. (wd:Q99): state of the United States of America

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

[Assistant] Based on the examples, we need to identify the entity Montel Williams and the property that describes his medical condition or disease. Let's start by searching for Montel Williams in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Montel Williams, also known as Montel Brian Anthony Williams (wd:Q1345645): American television host and actor, television presenter, television producer, radio personality, television actor, military officer, journalist, politician, actor
2. The Montel Williams Show (wd:Q2412902): television series
3. Montel Williams MS Foundation (wd:Q30289093): nonprofit organization in New York, United States
4. Williams Racing, also known as Williams, Williams Grand Prix Engineering Limited, Williams F1 (wd:Q171337): European Formula One motor racing team and constructor
5. Williams, also known as Williams, AZ, Williams, Arizona (wd:Q79864): city in Coconino County, Arizona, United States
6. Williams, also known as Williams, CA, Williams, California (wd:Q2685585): city in Colusa County, California, United States
7. Williams, also known as Williams, IA, Williams, Iowa (wd:Q1012283): city in Hamilton County, Iowa, United States
8. Williams, also known as Williams, MN, Williams, Minnesota (wd:Q963315): city in Lake of the Woods County, Minnesota, United States
9. Williams College, also known as Williams (wd:Q49166): liberal arts college in Massachusetts
10. Williams, also known as Williams, SC, Williams, South Carolina (wd:Q2562748): town in Collation County, South Carolina, United States

[Assistant] The top result is the correct entity for Montel Williams. Now let's search for properties related to diseases or medical conditions that might apply to him.

[Tool]
Top 10 property alternatives:
1. medical condition, also known as disability, suffers from, suffer from, sickness, paralympic disability (wd:P1050 as p/wdt): any state relevant to the health of an organism, including diseases and positive conditions
2. 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
3. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as p/wdt): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
4. 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
5. 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)
6. 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)
7. 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
8. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
9. 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
10. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)

No other items found

[Assistant]
The property `medical condition` (wd:P1050) seems to be the most relevant for finding out what kind of disease Montel Williams has. Let's construct a SPARQL query to retrieve this information.

### Step 1: Simple Query
We'll start with a simple query to check if Montel Williams has any medical conditions listed.

```sparql
SELECT ?disease WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
}
```

Let's execute this query to see the results.

[Tool]
Got 1 row and 1 column:
| disease                       |
| ----------------------------- |
| multiple sclerosis (wd:Q8277) |

[Assistant]
The query returned that Montel Williams has multiple sclerosis. To provide a more complete answer, we can include the label of the disease in the query. However, since the label is optional, we will use an `OPTIONAL` clause to fetch it.

### Step 2: Include Label
Let's modify the query to include the label of the disease.

```sparql
SELECT ?disease ?diseaseLabel WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
  OPTIONAL { ?disease rdfs:label ?diseaseLabel . }
}
```

Let's execute this query to see the results.

[Tool]
Got 84 rows and 2 columns, showing the first 5 and last 5 rows below:
| disease                       | diseaseLabel                       |
| ----------------------------- | ---------------------------------- |
| multiple sclerosis (wd:Q8277) | dağınıq skleroz (az)               |
| multiple sclerosis (wd:Q8277) | Encephalomyelitis disseminata (la) |
| multiple sclerosis (wd:Q8277) | esclerose múltipla (pt)            |
| multiple sclerosis (wd:Q8277) | Esclerose múltipla (pt-br)         |
| multiple sclerosis (wd:Q8277) | Esclerose múltiple (gl)            |
| ...                           | ...                                |
| multiple sclerosis (wd:Q8277) | 多发性硬化症 (zh)                        |
| multiple sclerosis (wd:Q8277) | 多発性硬化症 (ja)                        |
| multiple sclerosis (wd:Q8277) | 多發性硬化症 (yue)                       |
| multiple sclerosis (wd:Q8277) | 多發性硬化症 (zh-hant)                   |
| multiple sclerosis (wd:Q8277) | 多發性硬化症 (zh-tw)                     |

[Assistant]
The query returned multiple labels for the disease "multiple sclerosis" in different languages. Since we are primarily interested in the English label, we can filter the results to only include the English label.

### Step 3: Filter for English Label
Let's modify the query to filter for the English label.

```sparql
SELECT ?disease ?diseaseLabel WHERE {
  wd:Q1345645 wdt:P1050 ?disease .
  OPTIONAL { ?disease rdfs:label ?diseaseLabel . FILTER(LANG(?diseaseLabel) = "en") }
}
```

Let's execute this query to see the results.

<tool_call>
{"name": "execute", "arguments": {"kg": "wikidata", "sparql": "SELECT ?disease ?diseaseLabel WHERE {\n  wd:Q1345645 wdt:P1050 ?disease .\n  OPTIONAL { ?disease rdfs:label ?diseaseLabel . FILTER(LANG(?diseaseLabel) = \\"en\\") }\n}"}}
</tool_call>

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. The disease that Montel Williams has is multiple sclerosis, and the query filters for the English label as requested.