PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?chineseWritingSystem ?label WHERE {
  wd:Q8201 rdfs:label ?label .
  FILTER ( LANG( ?label ) = "en" ) BIND( wd:Q8201 AS ?chineseWritingSystem )
}

Got 1 row and 2 columns:
| chineseWritingSystem          | label                   |
| ----------------------------- | ----------------------- |
| Chinese characters (wd:Q8201) | Chinese characters (en) |

The Chinese writing system is called 'Chinese characters' (wd:Q8201).

[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 do you call the chinese writing system?

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

[Tool]
Example 1:
Question:
where do the broncos play?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q223507 wdt:P115 ?x .
}

Using entities:
Denver Broncos, also known as Broncos (wd:Q223507): National Football League franchise in Denver, Colorado

Using properties:
home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt): home stadium or venue of a sports team or applicable performing arts organization, location

Example 2:
Question:
what is the san francisco giants mascot?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q308966 wdt:P822 ?x .
}

Using entities:
San Francisco Giants, also known as Giants, Gothams, New York Giants, New York Gothams, SFG (wd:Q308966): baseball team and Major League Baseball franchise in San Francisco, California, United States

Using properties:
mascot (wd:P822 as wdt): mascot of an organization, e.g. a sports team or university

Example 3:
Question:
where is washington d.c. at?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q61 wdt:P131 ?x .
}

Using entities:
Washington, D.C., also known as D.C. Washington, District of Columbia, The District, Washington, District of Columbia (wd:Q61): capital city of the United States of America

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

[Assistant] None of these examples directly relate to the Chinese writing system, but we can use them as a reference for structuring our SPARQL query. Let's first search for the entity representing the Chinese writing system.

[Tool]
Top 10 entity alternatives:
1. Journal of Chinese Writing Systems (wd:Q58234452): journal
2. Letter-word Phrases and Chinese Writing System (wd:Q120662500): scholarly article in Journal of Xiamen University (Arts & Social Sciences) no. 6, 2010
3. Image-based anti-interference ro...Chinese character writing system (wd:Q129332745): scholarly article
4. Bibliography of the Chinese language and writing system (wd:Q125242720)
5. writing system, also known as wenzi, ven-tsee, script, graphematics (wd:Q8192): any conventional method of visually representing verbal or signed communication
6. Chinese system, also known as Chinese operating system (wd:Q118876973): program that hooks into an operating system to add the ability to display and input CJK characters
7. Conjectures on Creative Writing ...iplinary System of Chinese Style (wd:Q118678085)
8. Chinese-English biliteracy acqui...uage and writing system transfer (wd:Q52040278): scientific article published on 30 December 2004
9. Chinese writing (wd:Q125357998): Book about Chinese palaeography
10. The Chinese-based writing system of the Zhuang language (wd:Q56915490): article

[Assistant] From the search results, the most relevant entity seems to be "writing system" (wd:Q8192), but it is a general term for any writing system. We need to look for a more specific entity that represents the Chinese writing system. Let's try searching again with a more specific query.

[Tool]
Top 10 entity alternatives:
1. Chinese characters, also known as wenzi, ven-tsee, traditional Vietnamese characters, traditional Korean characters, sinographs (wd:Q8201): logographic writing system with Han origin used in the Sinospher...r Chinese, Japanese, Korean and traditional Vietnamese languages
2. Chinese Script* (wd:Q119947963): painting by unknown artist, Division of Archaeology, University of Cambridge
3. simplified Chinese characters, also known as simplified Chinese script, 簡化字, 简化字, simplified Hanzi, simplified Han characters (wd:Q185614): standardized Chinese characters used in Mainland China, Malaysia and Singapore
4. Chinese cursive script, also known as cǎoshū, grass script, cursive script (wd:Q607543): handwriting, traditional calligraphic script style of East Asian orthography
5. Chinese script style, also known as Chinese script styles (wd:Q11059241): style of writing Chinese characters
6. Chinese script reform (wd:Q2894150)
7. Chinese family of scripts, also known as Chinese scripts (wd:Q117285848): family of writing systems descended from the Chinese oracle bone script
8. Design for writing-paper for Chinese script (wd:Q65574237): print in the National Gallery of Art (NGA 54223)
9. Sound and Script in Chinese Diaspora (wd:Q56455917): article
10. Chinese Buddhist Scriptures and Indian Philosophy (wd:Q119434134)

[Assistant]
The most relevant entity from the search results is "Chinese characters" (wd:Q8201), which refers to the logographic writing system used in Chinese, Japanese, Korean, and traditional Vietnamese languages.

Now, let's construct a simple SPARQL query to confirm this and retrieve the label for the Chinese writing system.

[Tool]
Got 156 rows and 2 columns, showing the first 5 and last 5 rows below:
| chineseWritingSystem          | label                 |
| ----------------------------- | --------------------- |
| Chinese characters (wd:Q8201) | aksara Cina (bjn)     |
| Chinese characters (wd:Q8201) | Aksara Cinten (jv)    |
| Chinese characters (wd:Q8201) | Aksara Tionghoa (su)  |
| Chinese characters (wd:Q8201) | Alfabet cinesc (lld)  |
| Chinese characters (wd:Q8201) | alfabeto cinezo (vec) |
| ...                           | ...                   |
| Chinese characters (wd:Q8201) | 漢字 (yue)              |
| Chinese characters (wd:Q8201) | 漢字 (zh-hant)          |
| Chinese characters (wd:Q8201) | 漢字 (zh-hk)            |
| Chinese characters (wd:Q8201) | 漢字 (zh-mo)            |
| Chinese characters (wd:Q8201) | 漢字 (zh-tw)            |

[Assistant]
The query confirms that "Chinese characters" (wd:Q8201) is the correct entity for the Chinese writing system. The labels in various languages include "漢字" (hanzi) in Chinese, which is the term commonly used to refer to the Chinese writing system.

To provide a clear and concise answer, we can use the English label for the Chinese writing system.

Here is the final SPARQL query and answer:

```sparql
SELECT ?chineseWritingSystem ?label WHERE {
  wd:Q8201 rdfs:label ?label .
  FILTER(LANG(?label) = "en")
  BIND(wd:Q8201 AS ?chineseWritingSystem)
}
```

The answer to the question "What do you call the Chinese writing system?" is "Chinese characters" (wd:Q8201).

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the IRI of the Chinese writing system along with its English label. The answer provided is accurate and matches the user's question.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?coach WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
  OPTIONAL {
    ?statement pq:P580 ?start .
  }
  OPTIONAL {
    ?statement pq:P582 ?end .
  }
  FILTER ( ?start <= '2013-12-31' ^^ xsd:date && ?end >= '2013-01-01' ^^ xsd:date )
}

Ron Rivera (wd:Q1991889) was the head coach of the Carolina Panthers in 2013.

[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 will coach carolina panthers in 2013?

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

[Tool]
Example 1:
Question:
what sport does rebecca adlington compete in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q49287 wdt:P641 ?x .
}

Using entities:
Rebecca Adlington, also known as Becky Adlington (wd:Q49287): British swimmer, Olympic gold medallist, swimmer

Using properties:
sport, also known as play, plays, sport played, sports (wd:P641 as wdt): sport that the subject participates or participated in or is associated with

Example 2:
Question:
who did stephenie meyer play in twilight?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q160071 p:P161 ?p .
  ?p ps:P161 wd:Q160219 ; pq:P453 ?x .
}

Using entities:
Twilight (wd:Q160071): 2008 American film directed by Catherine Hardwicke
Stephenie Meyer, also known as Stephenie Morgan Meyer (wd:Q160219): science fiction writer, film screenwriter, children's writer, American author, businessperson, film producer, screenwriter, entrepreneur, novelist, writer, 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)

Example 3:
Question:
in what timezone is arizona?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q816 wdt:P421 ?x .
}

Using entities:
Arizona, also known as Ariz., US-AZ, The Grand Canyon State, State of Arizona, Grand Canyon State (wd:Q816): 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 relevant entity (Carolina Panthers) and the property (coach) for the year 2013. Let's first search for the Carolina Panthers entity.

[Tool]
Top 10 entity alternatives:
1. Carolina Panthers (wd:Q330120): American football team
2. history of the Carolina Panthers, also known as Carolina Panthers history, the Carolina Panthers history (wd:Q5869201): aspect of history
3. Carolina Panthers Cheerleaders (wd:Q130385213): cheerleading squad of the Carolina Panthers
4. 2021 Carolina Panthers season (wd:Q104869609): 27th season in franchise history
5. 2022 Carolina Panthers season (wd:Q110445399): 28th season in franchise history
6. 2020 Carolina Panthers season (wd:Q80995958): 26th season in franchise history
7. 2018 Carolina Panthers season (wd:Q48843395): 24th season in franchise history
8. 2015 Carolina Panthers season (wd:Q18701993): NFL team 21st season(lost Super Bowl)
9. 2019 Carolina Panthers season (wd:Q60525825): 25th season in franchise history
10. 2023 Carolina Panthers season (wd:Q116170055): 29th season in franchise history

[Assistant] The top result is the Carolina Panthers (wd:Q330120). Now, let's search for the property that represents the coach of a team.

[Tool]
Top 10 property alternatives:
1. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person
2. coach of sports team, also known as trainer of sports team, club manager of sports team, trainer of, team manager of sports team, team manager of (wd:P6087 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): sports club or team for which this person is or was on-field manager or coach
3. PlaymakerStats.com coach ID, also known as PlaymakerStats.com manager ID, TheFinalBall coach ID (wd:P6316 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for an association football (soccer) coach at PlaymakerStats.com (formerly TheFinalBall.com)
4. LNB Coach id, also known as LNB Pro A coach ID (wd:P6743 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for a coach in the French basketball championship
5. trained by, also known as personal trainer, trainer (wd:P10449 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): personal trainer of the athlete, student of
6. LevskiSofia.info coach ID (wd:P6410 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for a coach at the LevskiSofia.info
7. TuttoCalciatori.net coach ID (wd:P7035 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier of a football coach in the website TuttoCalciatori.net
8. Soccerdonna coach ID, also known as Soccerdonna trainer ID (wd:P8134 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for a coach at the Soccerdonna
9. LegaBasket.it coach ID (wd:P5612 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for a coach at legabasket.it
10. VBL people ID, also known as VBL coach ID, VBL player ID, VBL staff ID (wd:P4298 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for a volleyball player on the website of the German Volleyball Federation

[Assistant]
The most relevant property for the coach of a sports team is `head coach` (wd:P286). We will use this property to find out who coached the Carolina Panthers in 2013.

To ensure we get the correct coach for the year 2013, we need to check if there is a specific property or qualifier that indicates the year. Let's look at the properties of the Carolina Panthers to see if there is a way to filter by year.

[Tool]
Top 10 property alternatives:
1. 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
2. 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
3. history of topic, also known as history of the topic, timeline of subject, timeline of the subject, timeline of the topic, timeline of topic (wd:P2184 as p/wdt): item about the historical development of a subject's topic, exam... To list key events of the topic, use "significant event" (P793), studied in
4. 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
5. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as p/wdt): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of

No other items found

[Assistant] It seems that there isn't a direct property for the year in the context of the Carolina Panthers. However, we can use the `head coach` property (wd:P286) and check if there are any qualifiers that indicate the year. Let's try to find the head coach of the Carolina Panthers and see if there are any qualifiers for the year 2013.

[Tool]
Got 1 row and 1 column:
| coach                    |
| ------------------------ |
| Ron Rivera (wd:Q1991889) |

[Assistant]
The SPARQL query has returned that Ron Rivera (wd:Q1991889) was the head coach of the Carolina Panthers in 2013.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?coach WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
  OPTIONAL { ?statement pq:P580 ?start . }
  OPTIONAL { ?statement pq:P582 ?end . }
  FILTER(?start <= '2013-12-31'^^xsd:date && ?end >= '2013-01-01'^^xsd:date)
}
```

The coach of the Carolina Panthers in 2013 was Ron Rivera.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies Ron Rivera as the head coach of the Carolina Panthers in 2013 by using the appropriate filters for the start and end dates of his tenure.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT ?spouse WHERE {
  wd:Q42869 wdt:P26 ?spouse .
  FILTER NOT EXISTS {
    wd:Q42869 p:P26 [ ps:P26 ?spouse ; pq:P582 ?endTime ]
  }
}

Nicolas Cage is currently married to Riko Shibata.

[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 is nicolas cage married too?

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

[Tool]
Example 1:
Question:
what are the official languages in spain?

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

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

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

Example 2:
Question:
who killed harvey milk?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q17141 wdt:P157 ?x .
}

Using entities:
Harvey Milk, also known as Harvey Bernard Milk (wd:Q17141): American gay rights activist (1930–1978), human rights activist, LGBTQ rights activist, naval officer, politician

Using properties:
killed by, also known as slaughtered by, slain by, shot dead by, murderer, murdered by (wd:P157 as wdt): person or organization who killed the subject, significant person

Example 3:
Question:
where was alice paul born?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q127328 wdt:P19 ?x .
}

Using entities:
Alice Paul, also known as Alice Stokes Paul (wd:Q127328): American suffragist, feminist, and activist (1885–1977), women's rights activist, suffragette, jurist

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

[Assistant]
Based on the examples, we can see that to answer a question about a person's relationship status, such as who Nicolas Cage is married to, we need to identify the relevant entity (Nicolas Cage) and the property that represents marriage (spouse).

First, let's search for the entity "Nicolas Cage" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Nicolas Cage, also known as Nicolas Coppola, Nicolas Kim Coppola, Nic Cage (wd:Q42869): television presenter, television producer, executive producer, character actor, American actor, film director, film producer, screenwriter, voice actor, film actor, director, writer, actor
2. Nicolas Cage/Bobby Brown, also known as Saturday Night Live S18E1, Saturday Night Live, season 18 episode 1 (wd:Q110035758): episode of Saturday Night Live (S18 E1)
3. Weekend Update: Nicolas Cage (wd:Q110060114): sketch from Saturday Night Live
4. Cage's Kiss: The Nicolas Cage Podcast (wd:Q110580467): podcast
5. Cage Rage - A Nicolas Cage Podcast (wd:Q110575223): podcast
6. Weekend Update: Nicolas Cage and Bradley Cooper (wd:Q110060014): sketch from Saturday Night Live
7. cage (wd:Q129211): enclosure used to confine, contain or protect something or someone
8. China Zorrilla, also known as Nicolás (wd:Q458175): Uruguayan actress (1922–2014), stage actor, film actor, actor
9. Frida Kalho, also known as Nicolás (wd:Q458175): Uruguayan actress (1922–2014), stage actor, film actor, actor
10. The Cage, also known as Cage, Cage, The, Pilot: The Cage (wd:Q20909): 1986 episode of Star Trek: The Original Series directed by Robert Butler

[Assistant]
The top result for "Nicolas Cage" is `wd:Q42869`, which is the correct entity for the American actor Nicolas Cage.

Next, we need to find the property that represents marriage or spouse. Let's search for the property "spouse" in the knowledge graph.

[Tool]
Top 10 property alternatives:
1. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
2. relative, also known as relatives, grandparent, relation, non-binary parent, niece (wd:P1038 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): family member (qualify with "kinship to subject", P1039; for direct family member please use specific property), significant person
3. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
4. married name, also known as full name (married) (wd:P2562 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): name or surname adopted by a person upon marriage, name
5. place of marriage, also known as marriage location, wedding location, wed at, married in, married at (wd:P2842 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): location where the marriage was celebrated. Use as qualifier for property "spouse" (P26), significant place
6. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): full name of a person at birth, if different from their current, generally used name, name
7. connects with, also known as contacts, couples with, intersects at grade, links to, mates with (wd:P2789 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): item with which the item is physically connected, physically interacts with
8. companion of, also known as has astronomical companion, has companion, companion, astronomic companion of, astronomic companion (wd:P399 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): two or more astronomic bodies of the same type relating to each other
9. end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time
10. relation (wd:P2309 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): qualifier to define a property constraint in combination with P2...h the property "relative" is "type of kinship" (P1039), not this

[Assistant]
The top result for the property "spouse" is `wd:P26`. This property is used to indicate the spouse of a person.

Now, let's construct a SPARQL query to find out who Nicolas Cage is married to. We will use the entity `wd:Q42869` and the property `wdt:P26`.

Here is the SPARQL query:

```sparql
SELECT ?spouse WHERE {
  wd:Q42869 wdt:P26 ?spouse .
}
```

Let's execute this query to get the result.

[Tool]
Got 5 rows and 1 column:
| spouse                          |
| ------------------------------- |
| Erika Koike (wd:Q109355321)     |
| Riko Shibata (wd:Q109355504)    |
| Alice Kim Cage (wd:Q12606061)   |
| Patricia Arquette (wd:Q215976)  |
| Lisa Marie Presley (wd:Q237324) |

[Assistant]
The SPARQL query returned multiple results, indicating that Nicolas Cage has been married to several people. Here are the individuals he has been married to:

1. Erika Koike (wd:Q109355321)
2. Riko Shibata (wd:Q109355504)
3. Alice Kim Cage (wd:Q12606061)
4. Patricia Arquette (wd:Q215976)
5. Lisa Marie Presley (wd:Q237324)

To provide a clear answer, we can list these individuals. However, since the question asks who Nicolas Cage is married to, it would be more accurate to specify his current spouse if known. Let's check if there is a way to filter the results to show only the current spouse.

We can use the property `wdt:P582` (end time) to filter out past marriages. If a marriage has an end time, it means the marriage is no longer active.

Here is the updated SPARQL query:

```sparql
SELECT ?spouse WHERE {
  wd:Q42869 wdt:P26 ?spouse .
  FILTER NOT EXISTS { wd:Q42869 p:P26 [ ps:P26 ?spouse ; pq:P582 ?endTime ] }
}
```

Let's execute this query to get the current spouse.

[Tool]
Got 1 row and 1 column:
| spouse                       |
| ---------------------------- |
| Riko Shibata (wd:Q109355504) |

[Assistant]
The updated SPARQL query has returned Riko Shibata (wd:Q109355504) as Nicolas Cage's current spouse.

Therefore, the answer to the question "Who is Nicolas Cage married to?" is Riko Shibata.

Here is the final SPARQL query and answer:

```sparql
SELECT ?spouse WHERE {
  wd:Q42869 wdt:P26 ?spouse .
  FILTER NOT EXISTS { wd:Q42869 p:P26 [ ps:P26 ?spouse ; pq:P582 ?endTime ] }
}
```

Answer: Nicolas Cage is currently married to Riko Shibata.

Let's finalize this with the answer function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query successfully identifies Nicolas Cage's current spouse, Riko Shibata, by filtering out any previous marriages that have ended.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?birthPlace WHERE {
  wd:Q206939 wdt:P19 ?birthPlace .
}

Got 1 row and 1 column:
| birthPlace      |
| --------------- |
| London (wd:Q84) |

Elvis Costello was born in London (wd:Q84).

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] where was elvis costello born?

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

[Tool]
Example 1:
Question:
who did kat graham play in parent trap?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q729782 p:P161 ?p .
  ?p ps:P161 wd:Q151118 ; pq:P453 ?x .
}

Using entities:
Kat Graham, also known as Katerina Graham, Katerina Alexandre Hartford Graham (wd:Q151118): American actress and singer (born 1989), executive producer, singer-songwriter, television actor, record producer, philanthropist, voice actor, film actor, dancer
The Parent Trap, also known as Parent Trap (wd:Q729782): 1998 romantic comedy film directed by Nancy Meyers

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 2:
Question:
what kind government does canada have?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q16 wdt:P122 ?x .
}

Using entities:
Canada, also known as CA, CAN, Dominion of Canada, ISO 3166-1:CA (wd:Q16): country in North America

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

Example 3:
Question:
where is the sistine chapel by michelangelo?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q2943 wdt:P131 ?x .
}

Using entities:
Sistine Chapel (wd:Q2943): chapel in the Apostolic Palace, Vatican City

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

[Assistant] Based on the examples, we need to identify the entity for Elvis Costello and the property for place of birth. Let's start by searching for Elvis Costello in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Elvis Costello, also known as Declan MacManus, Declan Patrick MacManus (wd:Q206939): English singer-songwriter (born 1954), record producer, songwriter, guitarist, singer
2. Elvis Costello (wd:Q107119978): lithograph at the Museum of Modern Art; unknown designer
3. Elvis Costello (wd:Q125462686): written work by Eduardo Margaretto Kohrmann
4. Elvis Costello (wd:Q125462696): 1993 edition of written work by Eduardo Margaretto Kohrmann
5. Elvis Costello (wd:Q28052324): painting by Adam Birtwistle
6. Spectacle: Elvis Costello with... (wd:Q7575075): television series
7. Elvis Costello Band concert (wd:Q99322922): Elvis Costello Band concert performed during Montreux Jazz Festival the 1989-07-11
8. Elvis Costello Solo concert (wd:Q99429372): Elvis Costello Solo concert performed during Montreux Jazz Festival the 1989-07-11
9. Elvis Costello & the Attractions, also known as Elvis Costello and the Attractions, Nick Lowe And His Sound (wd:Q7714866): UK musical group
10. Elvis Costello with Steve Nieve (wd:Q105091759): print by Jenny Gill

[Assistant] The first result, Elvis Costello (wd:Q206939), seems to be the correct entity for the English singer-songwriter. Next, let's search for the property related to the place of birth.

[Tool]
Top 10 property alternatives:
1. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): most specific known birth location of a person, animal or fictional character, location
2. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): date on which the subject was born, inception
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): the object is a country that recognizes the subject as its citizen, country
4. birthday, also known as b-day, bday, birth day (wd:P3150 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): item for day and month on which the subject was born. Used when full "date of birth" (P569) isn't known., date of birth
5. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): full name of a person at birth, if different from their current, generally used name, name
6. category for people born here, also known as category for place of birth, category of people born here (wd:P1464 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): category item that groups people born in this place, related category
7. valid in place, also known as applies to country, applies to location, applies to place, country, true in (wd:P3005 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): place where a statement is valid, applies to part
8. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
9. country, also known as sovereign state, state, land, host country (wd:P17 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
10. commemorates, also known as monument to, memorial to, memorializes, marks, in memory of (wd:P547 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): what the place, monument, memorial, or holiday, commemorates, main subject

[Assistant]
The property "place of birth" (wd:P19) is the correct property for our query. Now, let's construct a simple SPARQL query to find out where Elvis Costello was born.

```sparql
SELECT ?birthPlace WHERE {
  wd:Q206939 wdt:P19 ?birthPlace .
}
```

I'll execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| birthPlace      |
| --------------- |
| London (wd:Q84) |

[Assistant]
The SPARQL query has returned that Elvis Costello was born in London. To provide a complete answer, I will include the IRI of London in the final SPARQL query.

Here is the final SPARQL query and answer:

```sparql
SELECT ?birthPlace WHERE {
  wd:Q206939 wdt:P19 ?birthPlace .
}
```

Elvis Costello was born in London (wd:Q84).

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the birthplace of Elvis Costello, and the answer provided is accurate and matches the execution result.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?movie WHERE {
  ?movie wdt:P161 wd:Q187038 ; wdt:P31 wd:Q11424 .
}

Got 104 rows and 1 column, showing the first 5 and last 5 rows below:
| movie                             |
| --------------------------------- |
| Think Big (wd:Q1031705)           |
| Death Race (wd:Q1033191)          |
| The Misfit Brigade (wd:Q10478647) |
| Deathsport (wd:Q1134750)          |
| Crank: High Voltage (wd:Q1138916) |
| ...                               |
| Mean Streets (wd:Q841335)         |
| True Legend (wd:Q910896)          |
| Kill Bill: Volume 2 (wd:Q928019)  |
| Big Stan (wd:Q939728)             |
| Behind Enemy Lines (wd:Q946863)   |

David Carradine played in the following movies: Think Big (wd:Q1031705), Death Race (wd:Q1033191), The Misfit Brigade (wd:Q10478647), Deathsport (wd:Q1134750), Crank: High Voltage (wd:Q1138916), Mean Streets (wd:Q841335), True Legend (wd:Q910896), Kill Bill: Volume 2 (wd:Q928019), Big Stan (wd:Q939728), Behind Enemy Lines (wd:Q946863), and many others.

[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 movies did david carradine play in?

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

[Tool]
Example 1:
Question:
what films have johnny depp been in?

SPARQL:
SELECT DISTINCT ?x WHERE {
  ?x wdt:P161 wd:Q37175 ; wdt:P31 / wdt:P279 * wd:Q11424 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Johnny Depp, also known as John Christopher "Johnny" Depp II, John Christopher Depp, John Christopher Depp II, John Depp, John C. Depp II (wd:Q37175): American actor (born 1963), television actor, nightclub owner, film director, film producer, screenwriter, voice actor, film actor, guitarist, musician, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
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)

Example 2:
Question:
where's wayne state?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q7976637 wdt:P131 ?x .
}

Using entities:
Wayne State College, also known as Nebraska State Teachers College at Wayne, wsc.edu, WSC (wd:Q7976637): public college in Wayne, Nebraska, 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:
what team did deion sanders play for in baseball?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q954184 wdt:P54 ?x .
}

Using entities:
Deion Sanders, also known as Deion Luwynn Sanders Sr., Prime Time, Neon Deion (wd:Q954184): American football and baseball player and football coach, American football player, American football coach, 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

[Assistant] Based on the examples, we can see that to find out what movies an actor has been in, we need to use the property `cast member` (P161). We also need to identify David Carradine as an entity in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. David Carradine, also known as John Arthur Carradine (wd:Q187038): American actor (1936–2009), television producer, television actor, character actor, film director, film producer, screenwriter, voice actor, film actor, director, actor
2. David Carradine, also known as David M. Carradine, D. Carradine (wd:Q123597734): structural engineer at BRANZ in New Zealand, structural engineer
3. David Carradine, Actor, Is Dead at 72 (wd:Q123708760): The New York Times article (June 5, 2009)
4. David, also known as David, King of Israel, King David, Dawud re d'Israele, Nabi Dawud King of Israel, sweet singer of Israel (wd:Q41370): king of Israel and Judah, instrumentalist, monarch, warlord, herder, ruler, poet
5. Jacques-Louis David, also known as J.-L. David, Louis David, L. David, J.L. David, J. L. David (wd:Q83155): architectural draftsperson, French painter (1748–1825), politician, painter
6. David, also known as David by Michelangelo, Michelangelo's David, David of Michelangelo, Michaelangelo's David (wd:Q179900): sculpture by Michelangelo
7. Saint David, also known as St. David, St David, David of Wales, Dewi, David (wd:Q331697): patron saint of Wales, Catholic bishop, Catholic priest
8. David, also known as David, Chiriquí, San José de David (wd:Q279009): capital of the province of Chiriquí, Panama
9. Gerard David, also known as Gheeraert Davidt, Gheeraedt David, Gheeraert David, Gheeraedt Davit, Gheeraedt Davidt (wd:Q333380): Early Netherlandish painter, (c. 1460–1523), draftsperson, illuminator, painter, artist
10. Armand David, also known as Pere David, Jean-Pierre-Armand David, David, Père David, Jean Pierre Armand David (wd:Q346939): French Catholic priest, zoologist, and botanist (1826–1900), Catholic priest, ornithologist, missionary, zoologist, botanist, explorer

[Assistant]
The correct entity for David Carradine is `wd:Q187038`. Now, let's construct a SPARQL query to find the movies he played in. We will use the property `cast member` (P161) and filter for items that are instances of `film` (Q11424).

Let's try the following query:

```sparql
SELECT DISTINCT ?movie WHERE {
  ?movie wdt:P161 wd:Q187038 ;
         wdt:P31 wd:Q11424 .
}
```

This query selects distinct movies where David Carradine is listed as a cast member and the item is an instance of a film.

Let's execute this query.

[Tool]
Got 104 rows and 1 column, showing the first 5 and last 5 rows below:
| movie                             |
| --------------------------------- |
| Think Big (wd:Q1031705)           |
| Death Race (wd:Q1033191)          |
| The Misfit Brigade (wd:Q10478647) |
| Deathsport (wd:Q1134750)          |
| Crank: High Voltage (wd:Q1138916) |
| ...                               |
| Mean Streets (wd:Q841335)         |
| True Legend (wd:Q910896)          |
| Kill Bill: Volume 2 (wd:Q928019)  |
| Big Stan (wd:Q939728)             |
| Behind Enemy Lines (wd:Q946863)   |

[Assistant]
The query has successfully returned a list of movies in which David Carradine played a role. Here are some of the movies:

- Think Big (wd:Q1031705)
- Death Race (wd:Q1033191)
- The Misfit Brigade (wd:Q10478647)
- Deathsport (wd:Q1134750)
- Crank: High Voltage (wd:Q1138916)
- Mean Streets (wd:Q841335)
- True Legend (wd:Q910896)
- Kill Bill: Volume 2 (wd:Q928019)
- Big Stan (wd:Q939728)
- Behind Enemy Lines (wd:Q946863)

We can now finalize the answer with the SPARQL query and the list of movies.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query returns the IRIs of the movies David Carradine played in, and the human-readable answer lists several of these movies. No further refinement is 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 ?politicalSystem ?politicalSystemLabel WHERE {
  wd:Q155 wdt:P122 ?politicalSystem .
  OPTIONAL {
    ?politicalSystem rdfs:label ?politicalSystemLabel .
    FILTER ( LANG( ?politicalSystemLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| politicalSystem                       | politicalSystemLabel          |
| ------------------------------------- | ----------------------------- |
| representative democracy (wd:Q188759) | representative democracy (en) |
| presidential system (wd:Q49892)       | presidential system (en)      |
| federal republic (wd:Q512187)         | federal republic (en)         |

Brazil has a political system that includes representative democracy, a presidential system, and a federal republic.

[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 political system is brazil?

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

[Tool]
Example 1:
Question:
where did president eisenhower go to college?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q9916 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
higher education institution, also known as HEI, Higher Education school, institution of higher education, Superior Institute (wd:Q38723): institution that provides higher education (e.g., universities, ...es, liberal arts colleges, institutes of technology, and others)
Dwight D. Eisenhower, also known as Ike, Eisenhower, Dwight Eisenhower, Dwight David Eisenhower, Ike Eisenhower (wd:Q9916): president of the United States from 1953 to 1961, military personnel, military officer, army officer, statesperson, politician, writer

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)
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

Example 2:
Question:
what disease did helen keller?

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

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

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

Example 3:
Question:
what are the names of michael jackson children?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q2831 wdt:P40 ?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:
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

[Assistant] From the examples, we can see that the SPARQL queries typically involve selecting a specific entity and then querying for a certain property or relationship. In our case, we need to identify Brazil and the property that describes its political system. Let's start by searching for the entity "Brazil".

[Tool]
Top 10 entity alternatives:
1. Brazil, also known as Federative Republic of Brazil, Brasil, br (wd:Q155): country in South America
2. Brazil, also known as br, Brasil, Federative Republic of Brazil (wd:Q155): country in South America
3. Brazil (wd:Q25057): 1985 dystopian film by Terry Gilliam
4. Brazil, also known as Brazil, Indiana, Brazil, IN (wd:Q1998364): city in Clay County, Indiana, United States
5. State of Brazil, also known as Brazil, Colonial Brazil, Portuguese Brazil (wd:Q11876909): state of the Portuguese Empire
6. Brazil, also known as Stars and Guitars (wd:Q4958616): 1944 film by Joseph Santley
7. Brazil, also known as Brazil, IA (wd:Q17511873): human settlement in Appanoose County, Iowa, United States
8. Viceroyalty of Brazil, also known as Brasil, Brazil (wd:Q2920081): Portuguese viceroyalty in Brazil 1763-1815
9. Brazil (wd:Q13581616): 1991 song by Bebi Dol
10. Brazil (wd:Q4095706): 1994 novel by John Updike

[Assistant] The top result for "Brazil" is the country in South America, which is what we're interested in. Its identifier is `wd:Q155`. Next, we need to find the property that describes the political system of a country. We can search for properties related to "political system" or "government" to find the appropriate property.

[Tool]
Top 10 property alternatives:
1. voting system, also known as electoral system, voting system, type of election (wd:P8196 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): system used for this election or vote
2. basic form of government, also known as form of government, government, type of government (wd:P122 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): subject's government
3. diplomatic relation, also known as political relation, political relations, foreign relations, foreign relation, diplomatic relations (wd:P530 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): diplomatic relations of the country
4. member of political party, also known as political party member, political party, party membership, party, member of party (wd:P102 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): the political party of which a person is or has been a member or otherwise affiliated, member of
5. position held, also known as function, held position, holds position, job, office held (wd:P39 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): subject currently or formerly holds the object position or public office
6. political alignment, also known as political position, alignment (wd:P1387 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): political position within the left–right political spectrum
7. political coalition, also known as electoral block, political bloc, political alliance, electoral list, electoral alliance (wd:P5832 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): agreement for cooperation between different political parties, member of
8. political ideology, also known as editorial viewpoint, has political ideology, ideology, official ideology, official political ideology (wd:P1142 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): political ideology of an organization or person or of a work (such as a newspaper)
9. authority, also known as executive authority, governed by, governing authority, governing body, has authority (wd:P797 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): entity having executive power on given entity
10. parliamentary group, also known as faction, party organization, party (wd:P4100 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): parliamentary group to which a member of a parliament belongs, member of

[Assistant]
The property "basic form of government" (wd:P122) seems to be the most relevant for describing the political system of a country. Now, let's construct a simple SPARQL query to find the value of this property for Brazil (wd:Q155).

```sparql
SELECT ?politicalSystem WHERE {
  wd:Q155 wdt:P122 ?politicalSystem .
}
```

Let's execute this query to see the result.

[Tool]
Got 3 rows and 1 column:
| politicalSystem                       |
| ------------------------------------- |
| representative democracy (wd:Q188759) |
| presidential system (wd:Q49892)       |
| federal republic (wd:Q512187)         |

[Assistant]
The query returned three types of political systems for Brazil:
1. Representative democracy (wd:Q188759)
2. Presidential system (wd:Q49892)
3. Federal republic (wd:Q512187)

To provide a more complete answer, we can include the labels of these political systems. Let's modify the query to include the labels:

```sparql
SELECT ?politicalSystem ?politicalSystemLabel WHERE {
  wd:Q155 wdt:P122 ?politicalSystem .
  OPTIONAL { ?politicalSystem rdfs:label ?politicalSystemLabel . FILTER(LANG(?politicalSystemLabel) = "en") }
}
```

Let's execute this updated query.

[Tool]
Got 3 rows and 2 columns:
| politicalSystem                       | politicalSystemLabel          |
| ------------------------------------- | ----------------------------- |
| representative democracy (wd:Q188759) | representative democracy (en) |
| presidential system (wd:Q49892)       | presidential system (en)      |
| federal republic (wd:Q512187)         | federal republic (en)         |

[Assistant]
The query has successfully returned the labels for the political systems of Brazil:

1. **Representative democracy** (wd:Q188759)
2. **Presidential system** (wd:Q49892)
3. **Federal republic** (wd:Q512187)

These labels provide a clear and human-readable description of Brazil's political system.

Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?politicalSystem ?politicalSystemLabel WHERE {
  wd:Q155 wdt:P122 ?politicalSystem .
  OPTIONAL { ?politicalSystem rdfs:label ?politicalSystemLabel . FILTER(LANG(?politicalSystemLabel) = "en") }
}
```

Answer: Brazil has a political system that includes representative democracy, a presidential system, and a federal republic.

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

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is well-constructed and returns the correct information about the political system of Brazil. The answer provided is accurate and complete based on the data retrieved from the knowledge graph.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?date WHERE {
  wd:Q211386 wdt:P585 ?date
}

The big earthquake hit San Francisco on April 18, 1906.

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

You can use the following SPARQL prefixes implicitly in all functions:
cc: <http://creativecommons.org/ns#
data: <https://www.wikidata.org/wiki/Special:EntityData/
dct: <http://purl.org/dc/terms/
geo: <http://www.opengis.net/ont/geosparql#
geof: <http://www.opengis.net/def/function/geosparql/
imdb: <https://www.imdb.com/
math: <http://www.w3.org/2005/xpath-functions/math#
ontolex: <http://www.w3.org/ns/lemon/ontolex#
owl: <http://www.w3.org/2002/07/owl#
p: <http://www.wikidata.org/prop/
pq: <http://www.wikidata.org/prop/qualifier/
pqn: <http://www.wikidata.org/prop/qualifier/value-normalized/
pqv: <http://www.wikidata.org/prop/qualifier/value/
pr: <http://www.wikidata.org/prop/reference/
prn: <http://www.wikidata.org/prop/reference/value-normalized/
prov: <http://www.w3.org/ns/prov#
prv: <http://www.wikidata.org/prop/reference/value/
ps: <http://www.wikidata.org/prop/statement/
psn: <http://www.wikidata.org/prop/statement/value-normalized/
psv: <http://www.wikidata.org/prop/statement/value/
qfn: <http://qlever.cs.uni-freiburg.de/function#
ql: <http://qlever.cs.uni-freiburg.de/builtin-functions/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
ref: <http://www.wikidata.org/reference/
s: <http://www.wikidata.org/entity/statement/
schema: <http://schema.org/
skos: <http://www.w3.org/2004/02/skos/core#
v: <http://www.wikidata.org/value/
wd: <http://www.wikidata.org/entity/
wdno: <http://www.wikidata.org/prop/novalue/
wdt: <http://www.wikidata.org/prop/direct/
wdtn: <http://www.wikidata.org/prop/direct-normalized/
wikibase: <http://wikiba.se/ontology#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
dc: <http://purl.org/dc/elements/1.1/
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] when did the big earthquake hit san francisco?

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

[Tool]
Example 1:
Question:
what state did roger sherman represent at the constitutional convention?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q405475 p:P710 ?p .
  ?p ps:P710 wd:Q354721 ; pq:P768 ?x .
}

Using entities:
Roger Sherman (wd:Q354721): early American lawyer and politician, Founding Father of the United States (1721-1793), statesperson, politician, diplomat, lawyer, judge
Constitutional Convention, also known as Federal Convention, Grand Convention at Philadelphia, Philadelphia Convention (wd:Q405475): event taking place from May 25 to September 17, 1787 in Philadel...ania, that led to the creation of the United States Constitution

Using properties:
participant, also known as suspect, player, party, participants, has participant (wd:P710 as ps/p): person, group of people or organization (object) that actively t...ct has role" (P3831). Use P1923 for participants that are teams., significant person
electoral district, also known as riding, seat, ward, electoral area, electorate (wd:P768 as pq): electoral district this person is representing, or of the office... or "office contested" (P541) or "candidacy in election" (P3602)

Example 2:
Question:
where does ryan lochte live now?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q201517 wdt:P551 ?x .
}

Using entities:
Ryan Lochte, also known as Ryan Steven Lochte (wd:Q201517): American swimmer, swimmer

Using properties:
residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt): the place where the person is or has been, resident, location

Example 3:
Question:
which college did barack obama attend?

SPARQL:
SELECT DISTINCT ?x WHERE {
  wd:Q76 wdt:P69 ?x .
  ?x wdt:P31 / wdt:P279 * wd:Q38723 .
}

Using entities:
higher education institution, also known as HEI, Higher Education school, institution of higher education, Superior Institute (wd:Q38723): institution that provides higher education (e.g., universities, ...es, liberal arts colleges, institutes of technology, and others)
Barack Obama, also known as Honorable Barack Obama, BHO, Obama, Barry O Bomber, President Obama (wd:Q76): president of the United States from 2009 to 2017, community organizer, political writer, statesperson, politician, podcaster, memoirist, academic, lawyer, jurist

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)
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

[Assistant] Based on the examples, we need to identify the entity for San Francisco and the property for the date of the earthquake. Let's start by searching for the entity "San Francisco".

[Tool]
Top 10 entity alternatives:
1. San Francisco, also known as San Francisco County, California, The Golden City, The City by the Bay, SFO, SF (wd:Q62): consolidated city and county in California, United States
2. Wake Island, also known as Wake Atoll, St Francois, St Francis Island, St Francis, San Francisco Island (wd:Q43296): coral atoll in the North Pacific Ocean administered by the US and also claimed by the Marshall Islands
3. San Francisco (wd:Q975641): Colombian municipality of the department of Antioquia
4. San Francisco (wd:Q316332): municipality of the Philippines in the province of Cebu
5. San Francisco (wd:Q130769): 1936 film by D. W. Griffith, W. S. Van Dyke
6. San Francisco (wd:Q627190): municipality of the Philippines in the province of Agusan del Sur
7. San Francisco, also known as San Francisco (Argentina), San Francisco, Córdoba (wd:Q949535): city in Argentina
8. San Francisco, also known as San Francisco, Quezon, Municipality of San Francisco (wd:Q104063): municipality of the Philippines in the province of Quezon
9. San Francisco (wd:Q1576269): Colombian municipality of the department of Cundinamarca
10. San Francisco (wd:Q155864): municipality of the Philippines in the province of Surigao del Norte

[Assistant] The correct entity for San Francisco is `wd:Q62`. Now, let's search for the property related to the date of the earthquake.

[Tool]
Top 10 property alternatives:
1. earthquake magnitude on the moment magnitude scale, also known as magnitude of an earthquake, Mw, moment magnitude scale value, magnitude, earthquake magnitude (wd:P2527 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): magnitude of an earthquake according to the moment magnitude scale
2. earthquake magnitude on the Richter magnitude scale, also known as earthquake magnitude, magnitude, magnitude of an earthquake, ML, Richter scale value (wd:P2528 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): magnitude of an earthquake according to the Richter magnitude scale
3. USGS earthquake ID, also known as ANSS, USGS Advanced National Seismic System, USGS-ANSS, USGS-ANSS event page (wd:P3196 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for an earthquake or other seismic event, in the United States Geological Survey database
4. GEOFON earthquake ID (wd:P6419 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): ID of an earthquake in GEOFON earthquake ID
5. fault, also known as fault zone, seismic fault (wd:P5095 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): geological faults involved in the event
6. aftershocks, also known as number of aftershocks, quantity of aftershocks (wd:P2320 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): number of smaller earthquakes which follow a significant earthquake, quantity
7. date depicted, also known as depicted date, temporal coverage, shown as at date, depicts date, depicted time (wd:P2913 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): date represented in a work (for narrative works use P2408)
8. announcement date, also known as released, date teased, made public on, public announcement date, published (wd:P6949 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): time of the first public presentation of a subject by the creator, of information by the media, point in time
9. point in time, also known as at time, as of, year, when, time of event (wd:P585 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
10. Italian National Earthquake Center ID (wd:P4207 as pqv/pqn/prn/wdtn/wdt/pq/prv/p/psn/psv/ps/pr): identifier for an earthquake in the Italian National Earthquake Centre database

[Assistant]
The property that seems most relevant for the date of the earthquake is `wd:P585` (point in time). Now, let's find the specific earthquake event that affected San Francisco. We will look for events where San Francisco is mentioned and the property `wd:P585` is used.

Let's list the triples where San Francisco (`wd:Q62`) is the subject and the property `wd:P585` is used.

[Tool] Got no rows and 3 columns

[Assistant] It seems there are no direct triples linking San Francisco to a specific earthquake event using the `wd:P585` property. Instead, let's search for the specific earthquake event that affected San Francisco. We can use the search_entity function to find the entity for the "1906 San Francisco earthquake".

[Tool]
Top 10 entity alternatives:
1. 1906 San Francisco earthquake, also known as Great San Francisco Earthquake and Fire, San Francisco earthquake of 1906, San Francisco earthquake and fire, 1906, Great 1906 San Francisco Earthquake, earthquake of 1906 (wd:Q211386): major earthquake that struck San Francisco and the coast of Northern California
2. 1906 San Francisco earthquake, also known as 1906 San Francisco earthquake and fire, earthquake of 1906, San Francisco earthquake of 1906, Great 1906 San Francisco Earthquake, Great San Francisco Earthquake and Fire (wd:Q211386): major earthquake that struck San Francisco and the coast of Northern California
3. The San Francisco earthquake of 1906 (wd:Q58176314): scholarly article by Dennis R. Dean published November 1993 in Annals of Science
4. Simulations of the 1906 San Francisco Earthquake (wd:Q57871965): scholarly article published 16 December 2005
5. The 1906 San Francisco Earthquake and Fire (wd:Q133012217): online exhibition from National Archives and Records Administration (United States)
6. San Francisco Earthquake & Fire: April 18, 1906, also known as San Francisco Earthquake and Fire, April 18, 1906 (wd:Q19599178): 1906 film
7. Postseismic deformation following the 1906 San Francisco earthquake (wd:Q97865309): scientific article published on 10 June 2000
8. Source mechanism of the 1906 San Francisco earthquake (wd:Q98594973): scientific article published in September 1978
9. The 1906 San Francisco earthquake: a centennial contemplation. (wd:Q45948972): scientific article published in May 2006
10. Strain release mechanism of the 1906 san francisco earthquake (wd:Q57140054): scientific article

[Assistant]
The correct entity for the 1906 San Francisco earthquake is `wd:Q211386`. Now, let's find the date of this earthquake using the property `wd:P585`.

We will execute a SPARQL query to retrieve the date of the 1906 San Francisco earthquake.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 1906-04-18T00:00:00Z (xsd:dateTime) |

[Assistant]
The 1906 San Francisco earthquake occurred on April 18, 1906.

Here is the final SPARQL query and answer:

```sparql
SELECT ?date WHERE {
  wd:Q211386 wdt:P585 ?date
}
```

Answer: The big earthquake hit San Francisco on April 18, 1906.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly retrieves the date of the 1906 San Francisco earthquake from Wikidata. The answer provided is accurate and matches the execution result.