PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?currency WHERE {
  wd:Q1028 wdt:P38 ?currency .
}

The currency you should take to Morocco is the Moroccan dirham.

[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 currency should you take to morocco?

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

[Tool]
Example 1:
Question:
what currency in dominican republic should i bring?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q786 wdt:P38 ?x .
}

Using entities:
Dominican Republic, also known as 🇩🇴, DO, Dominicana, DomRep, DR (wd:Q786): island sovereign state 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 currency should i take to jamaica?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q766 wdt:P38 ?x .
}

Using entities:
Jamaica, also known as JA, JM, JAM, Commonwealth of Jamaica (wd:Q766): island state in the Caribbean Sea

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

Example 3:
Question:
what currency should i bring to italy?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q38 wdt:P38 ?x .
}

Using entities:
Italy, also known as Italia, Republic of Italy, Repubblica Italiana, Italian Republic, ITA (wd:Q38): country in Southern Europe

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

[Assistant] Based on the examples, we need to identify Morocco and the currency property to construct our query.

[Tool]
Top 10 entity alternatives:
1. Morocco, also known as ⵎⵓⵔⴰⴽⵓⵛ, Marocco, MAR, ma, Lmaġrib (wd:Q1028): sovereign state in North Africa
2. Morocco, also known as al-Maġrib, Kingdom of Morocco, ⵎⵓⵔⴰⴽⵓⵛ, Lmaġrib, ma (wd:Q1028): sovereign state in North Africa
3. Morocco (wd:Q577754): 1930 film by Josef von Sternberg and Henry Hathaway
4. Morocco, also known as Morocco, IN, Morocco, Indiana (wd:Q2374422): town in Indiana, United States
5. Morocco leather, also known as Levant, Morocco (wd:Q2118555): soft, pliable leather used for bookbinding, wallets, linings for luggage, gloves and shoes
6. Morocco at the 1960 Summer Olympics, also known as 1960 Summer Olympics, Morocco (wd:Q2149869): sporting event delegation
7. Exit Marrakech, also known as Morocco (wd:Q14949869): 2013 film by Caroline Link
8. Sharifian Empire, also known as Morocco (wd:Q12183875): historical name given to Morocco
9. blouza, also known as Morocco (wd:Q2907091): traditional dress of Morocco and Algeria, traditional dress of Algeria
10. Morocco titular see, also known as Marocco, Marruecos titular see, Morocco, Titular see of Marocco (wd:Q3708053): titular see

[Assistant] The first result is the correct Morocco. Now let's check the currency property.

[Tool]
Top 10 property alternatives:
1. currency, also known as base currency, used money (wd:P38 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): currency used by item, uses
2. ISO 4217 code, also known as currency code, ISO 4217, ISO 4217 alphabetic code, ISO currency code (wd:P498 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): identifier for a currency per ISO 4217
3. price, also known as cost, exchange rate, list price, MSRP, published price (wd:P2284 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): published price listed or paid for a product (use with unit of currency)
4. prize money, also known as money, purse, value (wd:P2121 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): amount in a specific currency
5. cash (wd:P2232 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): the value of cash and securities held by a state government
6. currency symbol description (wd:P489 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): item with description of currency symbol, notation
7. face value, also known as coin value, numeric value of coin, value of coin (wd:P3934 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): value printed on a coin, stamp or banknote by the issuing authority
8. quantity, also known as amount, total number, total, qty, occurrences (wd:P1114 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): number of instances of this subject in the universe of the subje...e actual number of instances in Wikidata may be lower or higher)
9. item of property constraint, also known as claim value, value, qid, property value, item qualifier (wd:P2305 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): qualifier to define a property constraint in combination with "property constraint" (P2302)
10. numeric value, also known as numerical value, value (wd:P1181 as pr/psv/prv/wdtn/pqv/pq/pqn/wdt/psn/ps/prn/p): numerical value of a number, a mathematical constant, or a physical constant

[Assistant]
The first result is the correct currency property. We can now construct the SPARQL query.

Let's test the query:

```sparql
SELECT DISTINCT ?currency WHERE {
  wd:Q1028 wdt:P38 ?currency .
}
```

This query selects the currency used in Morocco.

[Tool]
Got 1 row and 1 column:
| currency                     |
| ---------------------------- |
| Moroccan dirham (wd:Q200192) |

[Assistant]
The query returns the correct result. The currency used in Morocco is the Moroccan dirham.

Now, let's provide the final answer.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?language WHERE {
  wd:Q148 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?label .
    FILTER ( LANG( ?label ) = "en" )
  }
}

Got 3 rows and 1 column:
| language                        |
| ------------------------------- |
| Standard Chinese (wd:Q727694)   |
| Chinese (wd:Q7850)              |
| languages of China (wd:Q835866) |

The primary language of China is Standard Chinese (Mandarin).

[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 primary language of china?

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

[Tool]
Example 1:
Question:
what is the official language in china?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?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:
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 type of language does china speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?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:
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 kind of language do china speak?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148 wdt:P37 ?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:
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

[Assistant] Based on the examples, it seems like the property `wdt:P37` (official language) is used to find the primary language of a country. Let's confirm this by checking the property details.

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

[Assistant] The property `wdt:P37` (official language) is indeed the correct property to use for finding the primary language of a country. Now, let's identify the entity for China.

[Tool]
Top 10 entity alternatives:
1. People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia
2. porcelain, also known as china, fine china (wd:Q130693): ceramic material
3. China, also known as All under Heaven, Zhonghua, Cathay, Zhongguo, Tianxia (wd:Q29520): cultural region, ancient civilization, and nation in East Asia, ...in political situation and rarely refer to the Republic of China
4. China, also known as Sinae, Zhonghua, Zhongguo, Tianxia, Middle Kingdom (wd:Q29520): cultural region, ancient civilization, and nation in East Asia, ...in political situation and rarely refer to the Republic of China
5. 1125 China, also known as (1125) China, China (wd:Q136211): asteroid
6. China, also known as China, TX, China, Texas (wd:Q288864): city in Jefferson County, Texas, United States
7. China, also known as China, Maine, China, ME (wd:Q3447500): town in Kennebec County, Maine, United States
8. China (wd:Q932423): town in Ōshima district, Kagoshima prefecture, Japan
9. Tianxia, also known as All under Heaven, Tien Hsia, T'ien Hsia, Tien-hsia, T'ien-hsia (wd:Q955223): Chinese cultural, philosophical, and political concept
10. China (wd:Q942154): municipality in the Nuevo León, Mexico

[Assistant]
The entity `wd:Q148` (People's Republic of China) is the correct entity for China. Now, let's construct a SPARQL query to find the primary language of China using the property `wdt:P37`.

Here is the SPARQL query:

```sparql
SELECT DISTINCT ?language WHERE {
  wd:Q148 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

Let's execute this query.

[Tool]
Got 3 rows and 1 column:
| language                        |
| ------------------------------- |
| Standard Chinese (wd:Q727694)   |
| Chinese (wd:Q7850)              |
| languages of China (wd:Q835866) |

[Assistant]
The query returned three results: Standard Chinese, Chinese, and languages of China. To provide a clear and concise answer, we need to determine which one is the primary language. Given the context, "Standard Chinese" (Mandarin) is typically considered the primary language of China.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?language WHERE {
  wd:Q148 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
```

The primary language of China is **Standard Chinese** (Mandarin).

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?owner WHERE {
  wd:Q156578 wdt:P127 ?owner
}

Got 3 rows and 1 column:
| owner                                               |
| --------------------------------------------------- |
| Hannoversche Beteiligungsgesellschaft (wd:Q1529289) |
| Qatar Investment Authority (wd:Q1571175)            |
| Porsche Automobil Holding SE (wd:Q2104551)          |

The owners of Volkswagen Group include: Hannoversche Beteiligungsgesellschaft, Qatar Investment Authority, and Porsche Automobil Holding SE.

[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] who owns volkswagen car company?

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

[Tool]
Example 1:
Question:
who owns nissan car manufacturer?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q20165 wdt:P112 ?x .
}

Using entities:
Nissan Motor Co. Ltd., also known as Nissan Motors, Nissan Motor Company Ltd, Nissan Motor Company, Nissan Motor Co., Ltd., NISSAN (wd:Q20165): Japanese company

Using properties:
founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as wdt): founder or co-founder of this organization, religion, place or entity, creator

Example 2:
Question:
who owns aston martin 2012?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q63049904 wdt:P127 ?x .
}

Using entities:
Aston Martin (wd:Q63049904): British luxury sports car marque

Using properties:
owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt): owner of the subject

Example 3:
Question:
who owns chrysler corporation 2012?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29610 wdt:P127 ?x .
}

Using entities:
Chrysler, also known as Chrysler brand (wd:Q29610): US automotive brand

Using properties:
owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt): owner of the subject

[Assistant] Based on the examples, we can see that the property `owned by` (P127) is used to find the owner of a company. Let's first identify the entity for Volkswagen.

[Tool]
Top 10 entity alternatives:
1. Volkswagen, also known as VW (wd:Q246): German automotive brand; manufacturing subsidiary of Volkswagen Group
2. Volkswagen Group, also known as Volkswagen, VOLKSWAGEN AG, Volkswagen Aktiengesellschaft, VOLKSWAGEN AKTIENGESELLSCHAFT, VW (wd:Q156578): German automotive manufacturing conglomerate
3. .volkswagen (wd:Q24033243): top-level domain
4. Volkswagen (wd:Q30256185): company in Verona, Italy
5. Volkswagen (wd:Q98823874): postcard by Louise Lawler (MET, 2014.679.3)
6. Volkswagen Beetle, also known as VW Bug, VW Beetle, Volkswagen Bug, Volkswagen Type 1 (wd:Q152946): compact car selling
7. Volkswagen Golf, also known as Volkswagen Caribe, Volkswagen Rabbit (wd:Q247): small family car manufactured by Volkswagen
8. Volkswagen Passat, also known as VW Passat (wd:Q216754): Car model series
9. Volkswagen Polo, also known as Volkswagen Derby (wd:Q247819): Series of automobiles manufactured by Volkswagen
10. Volkswagen Arena (wd:Q155603): football stadium in Wolfsburg, Germany

[Assistant] The correct entity for Volkswagen car company is `Volkswagen Group` (wd:Q156578). Now let's use the property `owned by` (P127) to find out who owns Volkswagen Group.

[Tool]
Got 3 rows and 1 column:
| owner                                               |
| --------------------------------------------------- |
| Hannoversche Beteiligungsgesellschaft (wd:Q1529289) |
| Qatar Investment Authority (wd:Q1571175)            |
| Porsche Automobil Holding SE (wd:Q2104551)          |

[Assistant]
The owners of Volkswagen Group include:

- Hannoversche Beteiligungsgesellschaft (wd:Q1529289)
- Qatar Investment Authority (wd:Q1571175)
- Porsche Automobil Holding SE (wd:Q2104551)

Let's provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?team WHERE {
  wd:Q706908 wdt:P54 ?team .
}

Got 5 rows and 1 column:
| team                              |
| --------------------------------- |
| New York Yankees (wd:Q213417)     |
| Texas Rangers (wd:Q329816)        |
| Detroit Tigers (wd:Q650855)       |
| Washington Nationals (wd:Q825838) |
| Houston Astros (wd:Q848117)       |

Pudge Rodriguez has played for the New York Yankees, Texas Rangers, Detroit Tigers, Washington Nationals, and Houston Astros.

[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] who has pudge rodriguez played for?

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

[Tool]
Example 1:
Question:
what team did albert pujols play for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q148726 wdt:P54 ?x .
}

Using entities:
Albert Pujols, also known as Jose Alberto Pujols Alcantara, José Alberto Pujols Alcántara, Alberto Pujols Alcántara, Alberto Pujols Alcantara, Alberto Alcántara (wd:Q148726): Dominican-American baseball player, baseball player

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

Example 2:
Question:
who do nate robinson play for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q315104 wdt:P54 ?x .
}

Using entities:
Nate Robinson (wd:Q315104): American basketball player, American football player, basketball player, businessperson, restaurateur

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

Example 3:
Question:
what teams has lionel messi played for?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q615 wdt:P54 ?x .
}

Using entities:
Lionel Messi, also known as Messi, Lionel Andrés Messi Cuccittini, Lionel Andrés Messi, Lionel Andres Messi, Leo Messi (wd:Q615): Argentine association football player (born 1987), association football player

Using properties:
member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt): sports teams or clubs that the subject represents or represented, member of

[Assistant] Based on the examples, it looks like we need to find the entity for Pudge Rodriguez and then use the `P54` property to find the teams he has played for. Let's first search for the entity for Pudge Rodriguez.

[Tool]
Top 10 entity alternatives:
1. Iván Rodríguez, also known as Pudge Rodríguez, Pudge Rodriguez, Pudge, Iván Rodríguez Torres, Ivan Rodriguez Torres (wd:Q706908): Puerto Rican Hall of Fame baseball player, baseball player
2. Ivan ‘Pudge' Rodríguez Field (wd:Q115284691): baseball field in Annandale, Virginia
3. Rodrigues, also known as Rodrigues Islands, Rodriguez, Rodriguez Island (wd:Q208668): Mauritian island
4. Sixto Rodriguez, also known as Rodriguez, Sixto Diaz Rodriguez, Sixto Díaz Rodríguez, Sixto Rodriquez, Sugar Man (wd:Q1894125): American musician (1942–2023), singer-songwriter, guitarist, musician
5. Rodriguez, also known as Municipality of Rodriguez, Montalban (wd:Q106825): municipality of the Philippines in the province of Rizal
6. David Valdes, also known as Rodríguez, Samuel Róbinson (wd:Q1358685): Venezuelan educator, philosopher and politician, philosopher, pedagogue, writer
7. Simón Rodríguez, also known as Samuel Róbinson, Rodríguez (wd:Q1358685): Venezuelan educator, philosopher and politician, philosopher, pedagogue, writer
8. 13760 Rodriguez, also known as (13760) Rodriguez, Rodriguez (wd:Q1090306): asteroid
9. Rodríguez (wd:Q1654058): family name
10. Rodríguez, also known as Rodriguez (wd:Q32453): small town in the San José Department of southern Uruguay

[Assistant]
The top result for "Pudge Rodriguez" is Iván Rodríguez (wd:Q706908). Now, let's use the `P54` property to find the teams he has played for.

I'll construct a simple SPARQL query to retrieve this information.

[Tool]
Got 5 rows and 1 column:
| team                              |
| --------------------------------- |
| New York Yankees (wd:Q213417)     |
| Texas Rangers (wd:Q329816)        |
| Detroit Tigers (wd:Q650855)       |
| Washington Nationals (wd:Q825838) |
| Houston Astros (wd:Q848117)       |

[Assistant]
The SPARQL query successfully retrieved the teams that Pudge Rodriguez has played for. Here is the final SPARQL query and the answer:

```sparql
SELECT DISTINCT ?team WHERE {
  wd:Q706908 wdt:P54 ?team .
}
```

Pudge Rodriguez has played for the following teams:
- New York Yankees
- Texas Rangers
- Detroit Tigers
- Washington Nationals
- Houston Astros

Now, I will provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?x WHERE {
  wd:Q5930 p:P725 ?p .
  ?p ps:P725 ?x ; pq:P453 wd:Q908772 .
}

Mila Kunis

[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] who originally voiced meg on family guy?

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

[Tool]
Example 1:
Question:
who was the original voice of meg on family guy?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5930 p:P725 ?p .
  ?p ps:P725 ?x ; pq:P453 wd:Q908772 .
}

Using entities:
Family Guy (wd:Q5930): American adult animated sitcom
Meg Griffin, also known as Megan Griffin, Megatron "Meg" Harvey Oswald Griffin, Megatron Harvey Oswald Griffin, Megan "Meg" Griffin (wd:Q908772): fictional character from the Family Guy franchise, high school student

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:
who plays meg in family guy?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5930 p:P725 ?p .
  ?p ps:P725 ?x ; pq:P453 wd:Q908772 .
}

Using entities:
Family Guy (wd:Q5930): American adult animated sitcom
Meg Griffin, also known as Megan Griffin, Megatron "Meg" Harvey Oswald Griffin, Megatron Harvey Oswald Griffin, Megan "Meg" Griffin (wd:Q908772): fictional character from the Family Guy franchise, high school student

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:
who is the voice of family guys meg griffin?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5930 p:P725 ?p .
  ?p ps:P725 ?x ; pq:P453 wd:Q908772 .
}

Using entities:
Family Guy (wd:Q5930): American adult animated sitcom
Meg Griffin, also known as Megan Griffin, Megatron "Meg" Harvey Oswald Griffin, Megatron Harvey Oswald Griffin, Megan "Meg" Griffin (wd:Q908772): fictional character from the Family Guy franchise, high school student

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

[Assistant] All three examples provide the same SPARQL query to answer the question. Let's try executing it.

[Tool]
Got 1 row and 1 column:
| x                      |
| ---------------------- |
| Mila Kunis (wd:Q37628) |

[Assistant]
The original voice of Meg on Family Guy is Mila Kunis.

I'll now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?place WHERE {
  wd:Q714185 wdt:P20 ?place .
}

They found Jenni Rivera's body in Iturbide.

[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 did they find jenni rivera's body?

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

[Tool]
Example 1:
Question:
where did reagan die?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q9960 wdt:P20 ?x .
}

Using entities:
Ronald Reagan, also known as Ronald W Reagan, Ronald W. Reagan, R. W. Reagan, President Reagan, President Ronald Reagan (wd:Q9960): president of the United States from 1981 to 1989 and actor, television actor, military officer, character actor, game show host, autobiographer, anti-communist, trade unionist, art collector, statesperson, screenwriter, voice actor, film actor, politician, announcer, lifeguard, diarist, actor

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

Example 2:
Question:
what did brittany murphy died of?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q168847 wdt:P509 ?x .
}

Using entities:
Brittany Murphy, also known as Brittany Bertolotti, Brittany Murphy-Monjack, Brittany Anne Murphy-Monjack, Brittany Anne Murphy (wd:Q168847): American actress and singer (1977–2009), voice actor, musician, 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 3:
Question:
where did the casey anthony case take place?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1056362 wdt:P276 ?x .
}

Using entities:
death of Caylee Anthony, also known as Caylee Anthony, Caylee Anthony death, Caylee Anthony's death, Caylee Marie Anthony (wd:Q1056362): 2008 death in Orlando, Florida, United States

Using properties:
location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object

[Assistant] Based on these examples, we need to identify the entity for Jenni Rivera and the property for the location where her body was found. Let's start by searching for Jenni Rivera in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Jenni Rivera, also known as Jenny Rivera, Dolores Janney Rivera Saavedra (wd:Q714185): American singer (1969–2012), television producer, singer-songwriter, recording artist, record producer, businessperson, film actor, composer, singer, actor
2. Jennifer Rivera (wd:Q22088654): American opera singer, opera singer
3. Jennifer Rivera (wd:Q99230893): rhythmic gymnast
4. Jenni Rivera Fashion (wd:Q18359270): web-based fashion boutique
5. Jenni Rivera: Mariposa de Barrio, also known as Mariposa de Barrio (wd:Q29267690): television series
6. Jenniffer López, also known as Jenniffer Katherine Lopez Rivera (wd:Q26131407): weightlifter
7. Jenni Rivera, Mexican-American Singer, Dies at 43 (wd:Q123654230): The New York Times article (December 10, 2012)
8. Rivera (wd:Q646498): city in Uruguay
9. Rivera (wd:Q1526269): Colombian municipality of the department of Huila
10. Rivera, also known as Rivera, Switzerland, Rivera TI (wd:Q663061): village and former municipality in Mendrisio in the canton of Ticino, Switzerland

[Assistant] The correct entity for Jenni Rivera is `wd:Q714185`. Now, we need to find the property that indicates the location where her body was found. Based on the examples, it seems like the property `P20` (place of death) might be relevant. Let's check if this property is used for Jenni Rivera.

[Tool]
Got 1 row and 3 columns:
| subject                   | property                 | object                   |
| ------------------------- | ------------------------ | ------------------------ |
| Jenni Rivera (wd:Q714185) | place of death (wdt:P20) | Iturbide (wd:Q116918876) |

[Assistant]
The place where Jenni Rivera's body was found is Iturbide, which is represented by the IRI `wd:Q116918876`.

Now, let's construct the SPARQL query to retrieve this information.

```sparql
SELECT ?place WHERE {
  wd:Q714185 wdt:P20 ?place .
}
```

I will execute this query to confirm the result.

[Tool]
Got 1 row and 1 column:
| place                    |
| ------------------------ |
| Iturbide (wd:Q116918876) |

[Assistant]
The SPARQL query confirms that the place where Jenni Rivera's body was found is Iturbide, represented by the IRI `wd:Q116918876`.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?place WHERE {
  wd:Q714185 wdt:P20 ?place .
}
```

Answer: They found Jenni Rivera's body in Iturbide.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?currency WHERE {
  wd:Q29 wdt:P38 ?currency .
}

The currency used in Spain is the Euro.

[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 money does spain use?

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

[Tool]
Example 1:
Question:
what is the name of the money used in spain?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29 wdt:P38 ?x .
}

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

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

Example 2:
Question:
what is the name of currency used in spain?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29 wdt:P38 ?x .
}

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

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

Example 3:
Question:
what is the money of spain called?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q29 wdt:P38 ?x .
}

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

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

[Assistant] Based on the examples, we can see that the property `wdt:P38` is used to represent the currency of a country. Let's confirm that the entity for Spain is `wd:Q29`.

[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 entity for Spain is indeed `wd:Q29`. Now let's construct a SPARQL query to find out what currency Spain uses.

[Tool]
Got 1 row and 1 column:
| currency        |
| --------------- |
| Euro (wd:Q4916) |

[Assistant] The currency used in Spain is the Euro.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?language WHERE {
  wd:Q34 wdt:P37 ?language .
}

Swedish

[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 language do they speak in sweden yahoo?

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

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

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q414 wdt:P37 ?x .
}

Using entities:
Argentina, also known as Argentine Republic, Republic of Argentina, Republica Argentina (wd:Q414): sovereign state in South America

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 timezone does sweden have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q34 wdt:P421 ?x .
}

Using entities:
Sweden, also known as Swedish Kingdom, Konungariket Sverige, SWE, se, Kingdom of Sweden (wd:Q34): country in Northern Europe

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

Example 3:
Question:
what language do they speak in iceland wikipedia?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q189 wdt:P37 ?x .
}

Using entities:
Iceland, also known as Republic of Iceland, ISL (wd:Q189): Nordic island country in the North Atlantic Ocean

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

[Assistant] Based on the examples, we can see that the property `wdt:P37` is used to denote the official language of a country. Let's identify the entity for Sweden.

[Tool]
Top 10 entity alternatives:
1. Sweden, also known as Swedish Kingdom, Konungariket Sverige, SWE, se, Kingdom of Sweden (wd:Q34): country in Northern Europe
2. Sweden, also known as Kingdom of Sweden, Konungariket Sverige, se, Swedish Kingdom, Sverige (wd:Q34): country in Northern Europe
3. Sweden, also known as Sweden, Maine, Sweden, ME (wd:Q3450805): town in Oxford County, Maine, USA
4. Sweden, also known as Sweden, NY, Sweden, New York (wd:Q3709888): town in New York, USA
5. Sweden (wd:Q7654287): constituency of the European Parliament
6. Sweden, also known as Sweden, AR, Sweden, Arkansas (wd:Q7654280): unincorporated community in Jefferson County, Arkansas
7. Sweden, also known as Sweden, Missouri, Sweden, MO (wd:Q27985021): unincorporated community in Missouri
8. Sweden, also known as Sweden, Georgia (wd:Q65049788)
9. Curators of Sweden, also known as @sweden, twitter.com/sweden (wd:Q5194619): Swedish social media campaign, Twitter account
10. Sweden (wd:Q12004480): musical group from Oslo, Norway

[Assistant]
The correct entity for Sweden is `wd:Q34`. Now let's construct a SPARQL query to find the official language of Sweden using the property `wdt:P37`.

```sparql
SELECT DISTINCT ?language WHERE {
  wd:Q34 wdt:P37 ?language .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| language           |
| ------------------ |
| Swedish (wd:Q9027) |

[Assistant]
The official language spoken in Sweden is Swedish. Here is the final SPARQL query:

```sparql
SELECT DISTINCT ?language WHERE {
  wd:Q34 wdt:P37 ?language .
}
```

And the answer is: **Swedish**.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?genre ?genreLabel WHERE {
  wd:Q1203 wdt:P136 ?genre .
  OPTIONAL {
    ?genre rdfs:label ?genreLabel .
    FILTER ( LANG( ?genreLabel ) = 'en' )
  }
}

Got 11 rows and 2 columns, showing the first 5 and last 5 rows below:
| genre                            | genreLabel              |
| -------------------------------- | ----------------------- |
| rock music (wd:Q11399)           | rock music (en)         |
| experimental music (wd:Q1640319) | experimental music (en) |
| experimental rock (wd:Q1641839)  | experimental rock (en)  |
| blues rock (wd:Q193355)          | blues rock (en)         |
| psychedelic rock (wd:Q206159)    | psychedelic rock (en)   |
| ...                              | ...                     |
| pop music (wd:Q37073)            | pop music (en)          |
| pop rock (wd:Q484641)            | pop rock (en)           |
| beat music (wd:Q546264)          | beat music (en)         |
| rock and roll (wd:Q7749)         | rock and roll (en)      |
| hard rock (wd:Q83270)            | hard rock (en)          |

John Lennon sang various types of music including rock music, experimental music, experimental rock, blues rock, psychedelic rock, pop music, pop rock, beat music, rock and roll, and hard rock.

[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 music did john lennon sing?

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

[Tool]
Example 1:
Question:
what kind of music does john mayer play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q215215 wdt:P136 ?x .
}

Using entities:
John Mayer (wd:Q215215): American musician (born 1977), singer-songwriter, record producer, visual artist, tattoo artist, music artist, guitarist, composer, blogger, artist, singer, actor

Using properties:
genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic

Example 2:
Question:
what kind of music did michael jackson sing?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q2831 wdt:P136 ?x .
}

Using entities:
Michael Jackson, also known as M.J., MJ, M. J., The King of Pop, King of Pop (wd:Q2831): American singer, songwriter, and dancer (1958–2009), singer-songwriter, recording artist, record producer, businessperson, philanthropist, music arranger, autobiographer, film director, choreographer, art collector, entrepreneur, screenwriter, voice actor, songwriter, film actor, composer, dancer, singer, actor, model, poet

Using properties:
genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic

Example 3:
Question:
what type of music does ella fitzgerald sing?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q1768 wdt:P136 ?x .
}

Using entities:
Ella Fitzgerald, also known as The First Lady of Jazz, The First Lady of Song, Lady Ella, Queen of Jazz (wd:Q1768): American jazz singer (1917–1996), singer-songwriter, recording artist, jazz musician, bandleader, film actor, conductor, composer, singer

Using properties:
genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic

[Assistant] Based on the examples, we can see that the property `genre` (P136) is used to indicate the type of music an artist sings. Let's first identify John Lennon's entity in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. John Lennon, also known as John Winston Ono Lennon, John Ono Lennon (wd:Q1203): British musician (1940–1980), political activist, singer-songwriter, recording artist, record producer, peace activist, public figure, film director, film producer, prose writer, film actor, guitarist, composer, painter, pianist, singer, writer, actor, poet
2. Liverpool John Lennon Airport, also known as John Lennon, John Lennon airport, Liverpool Airport, Liverpool (John Lennon), LPL (wd:Q8727): airport in Liverpool, England
3. John Lennon Silva Santos, also known as John Lennon (wd:Q6244563): association football player, Brazilian footballer
4. John Brown Lennon, also known as John B. Lennon, John Lennon (wd:Q6223555): American labor leader (1850–1923), trade unionist
5. John Lennon, also known as J. John Lennon, Justin John Lennon (wd:Q11979004): Scottish tourism researcher, university teacher, tourism scholar
6. John Lennon (wd:Q65548791): Irish hurler born 1996
7. John Lennon (wd:Q6244553): British naval captain, military personnel
8. John Lennon (wd:Q12871620): 1855 - 3 Aug 1921
9. John Lennon (wd:Q125643070): 2005 edition of written work by Jordi Sierra i Fabra
10. John Lennon (wd:Q106781630): 2015 song by SCH

[Assistant]
The correct entity for John Lennon is `wd:Q1203`. Now, let's use the property `genre` (P136) to find out what type of music John Lennon sang.

I will construct a simple SPARQL query to retrieve the genres associated with John Lennon.

[Tool]
Got 11 rows and 1 column, showing the first 5 and last 5 rows below:
| genre                            |
| -------------------------------- |
| rock music (wd:Q11399)           |
| experimental music (wd:Q1640319) |
| experimental rock (wd:Q1641839)  |
| blues rock (wd:Q193355)          |
| psychedelic rock (wd:Q206159)    |
| ...                              |
| pop music (wd:Q37073)            |
| pop rock (wd:Q484641)            |
| beat music (wd:Q546264)          |
| rock and roll (wd:Q7749)         |
| hard rock (wd:Q83270)            |

[Assistant]
The query has returned multiple genres associated with John Lennon. To provide a clear and concise answer, I will list the genres and include their labels for better readability.

Let's modify the query to include the labels of the genres.

[Tool]
Got 11 rows and 2 columns, showing the first 5 and last 5 rows below:
| genre                            | genreLabel              |
| -------------------------------- | ----------------------- |
| rock music (wd:Q11399)           | rock music (en)         |
| experimental music (wd:Q1640319) | experimental music (en) |
| experimental rock (wd:Q1641839)  | experimental rock (en)  |
| blues rock (wd:Q193355)          | blues rock (en)         |
| psychedelic rock (wd:Q206159)    | psychedelic rock (en)   |
| ...                              | ...                     |
| pop music (wd:Q37073)            | pop music (en)          |
| pop rock (wd:Q484641)            | pop rock (en)           |
| beat music (wd:Q546264)          | beat music (en)         |
| rock and roll (wd:Q7749)         | rock and roll (en)      |
| hard rock (wd:Q83270)            | hard rock (en)          |

[Assistant]
The query has successfully retrieved the genres and their labels associated with John Lennon. Here are the types of music he sang:

- Rock music
- Experimental music
- Experimental rock
- Blues rock
- Psychedelic rock
- Pop music
- Pop rock
- Beat music
- Rock and roll
- Hard rock

Now, I will provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?placeOfDeath ?placeOfDeathLabel WHERE {
  wd:Q39607 wdt:P20 ?placeOfDeath .
  OPTIONAL {
    ?placeOfDeath rdfs:label ?placeOfDeathLabel .
    FILTER ( LANG( ?placeOfDeathLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| placeOfDeath                      | placeOfDeathLabel         |
| --------------------------------- | ------------------------- |
| place de la Concorde (wd:Q189503) | place de la Concorde (en) |

Place de la Concorde

[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 did antoine lavoisier die?

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

[Tool]
Example 1:
Question:
where did antoine laurent lavoisier die?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q39607 wdt:P20 ?x .
}

Using entities:
Antoine Lavoisier, also known as Lavoisier, Antoine Laurent Lavoisier, Antoine Laurent de Lavoisier, Antoine-Laurent de Lavoisier (wd:Q39607): French chemist, administrator, astronomer, biologist, physicist, economist, academic, chemist, lawyer, writer

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

Example 2:
Question:
where did thomas jefferson died?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q11812 wdt:P20 ?x .
}

Using entities:
Thomas Jefferson, also known as T. Jefferson, President Jefferson (wd:Q11812): president of the United States from 1801 to 1809, paleontologist, cryptographer, archaeologist, planter class, statesperson, philosopher, politician, architect, inventor, diplomat, teacher, jurist, writer, lawyer

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

Example 3:
Question:
where did pablo picasso die?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q5593 wdt:P20 ?x .
}

Using entities:
Pablo Picasso, also known as Pablo Ruiz Y Picasso, Pablo Ruys, Pablo Ruiz Picasso, Pablo Ruiz y Picasso, Picasso (wd:Q5593): Spanish painter and sculptor (1881–1973), architectural draftsperson, assemblage artist, graphic designer, jewelry designer, costume designer, graphic artist, poster artist, choreographer, visual artist, scenographer, screenwriter, photographer, draftsperson, illustrator, ceramicist, printmaker, collagist, designer, muralist, sculptor, painter, artist

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

[Assistant] From the examples, we can see that the property `wdt:P20` is used to represent the place of death. Let's now search for the entity representing Antoine Lavoisier.

[Tool]
Top 10 entity alternatives:
1. Antoine Lavoisier, also known as Lavoisier, Antoine Laurent Lavoisier, Antoine Laurent de Lavoisier, Antoine-Laurent de Lavoisier (wd:Q39607): French chemist, administrator, astronomer, biologist, physicist, economist, academic, chemist, lawyer, writer
2. Antoine Lavoisier (wd:Q59073688): scientific article published in Nature
3. Antoine Lavoisier (wd:Q18147111): sculpture by Jacques-Léonard Maillet
4. Antoine-Laurent Lavoisier (wd:Q21685186): encyclopedic article
5. résidence Antoine Lavoisier (wd:Q113413611): street in Pierrefitte-sur-Seine, France
6. Antoine de Lavoisier (wd:Q108838701)
7. Antoine Laurent Lavoisier (wd:Q74018917): scientific article published on 01 May 1956
8. Antoine Laurent Lavoisier (wd:Q69311401): scientific article published on 01 July 1973
9. Antoine Laurent Lavoisier (wd:Q41051602): scientific article
10. rue Antoine de Lavoisier (wd:Q112065256): street in Rungis, France

[Assistant]
The first result, `wd:Q39607`, seems to be the correct entity for Antoine Lavoisier. We can now construct a simple SPARQL query to find where he died.

Let's execute the following SPARQL query:

```sparql
SELECT DISTINCT ?placeOfDeath WHERE {
  wd:Q39607 wdt:P20 ?placeOfDeath .
}
```

This query will return the place of death for Antoine Lavoisier.

[Tool]
Got 1 row and 1 column:
| placeOfDeath                      |
| --------------------------------- |
| place de la Concorde (wd:Q189503) |

[Assistant]
The SPARQL query has returned the place of death for Antoine Lavoisier as `place de la Concorde (wd:Q189503)`.

To provide a complete answer, let's include the label of the place of death in the final SPARQL query.

Here is the final SPARQL query:

```sparql
SELECT DISTINCT ?placeOfDeath ?placeOfDeathLabel WHERE {
  wd:Q39607 wdt:P20 ?placeOfDeath .
  OPTIONAL { ?placeOfDeath rdfs:label ?placeOfDeathLabel . FILTER(LANG(?placeOfDeathLabel) = "en") }
}
```

Executing this query will give us both the IRI and the English label of the place of death.

Let's execute the final SPARQL query:

```sparql
SELECT DISTINCT ?placeOfDeath ?placeOfDeathLabel WHERE {
  wd:Q39607 wdt:P20 ?placeOfDeath .
  OPTIONAL { ?placeOfDeath rdfs:label ?placeOfDeathLabel . FILTER(LANG(?placeOfDeathLabel) = "en") }
}
```

Now, let's run this query.

[Tool]
Got 1 row and 2 columns:
| placeOfDeath                      | placeOfDeathLabel         |
| --------------------------------- | ------------------------- |
| place de la Concorde (wd:Q189503) | place de la Concorde (en) |

[Assistant]
The final SPARQL query has returned the place of death for Antoine Lavoisier as `place de la Concorde (wd:Q189503)` with the English label `place de la Concorde`.

Therefore, the answer to the question "Where did Antoine Lavoisier die?" is:

**Place de la Concorde**

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?placeOfDeath ?placeOfDeathLabel WHERE {
  wd:Q39607 wdt:P20 ?placeOfDeath .
  OPTIONAL { ?placeOfDeath rdfs:label ?placeOfDeathLabel . FILTER(LANG(?placeOfDeathLabel) = "en") }
}
```

Answer: Place de la Concorde

Let's finalize this with the `answer` function.

[Tool] Stopped generation process