PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?work ?workLabel WHERE {
  wd:Q307 wdt:P800 ?work .
  OPTIONAL {
    ?work rdfs:label ?workLabel FILTER ( LANG( ?workLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| ?work                                      | ?workLabel                   |
| ------------------------------------------ | ---------------------------- |
| equations for a falling body (wd:Q1132975) | equations for a falling body |
| Galilean transformation (wd:Q219207)       | Galilean transformation      |

Galileo Galilei was famous for his contributions such as the equations for a falling body and the Galilean transformation.

[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 galileo galilei was famous for?

[Assistant]
To answer the question about what Galileo Galilei was famous for, I first need to identify his Wikidata entity. Then, I'll look for properties that indicate reasons for fame or notable achievements.

Let's start by searching for the entity representing Galileo Galilei.

[Tool]
Top 10 entity alternatives:
1. Galileo Galilei (wd:Q307): Italian polymath (1564–1642), university teacher, mathematician, philosopher, astronomer, astrologer, scientist, physicist, engineer, inventor
2. Galileo, also known as Galileo Galilei (wd:Q3416348): 1968 film by Liliana Cavani
3. Galileo Galilei (wd:Q2483689): Japanese band
4. Galileo Galilei, also known as HMS X2 (wd:Q3757627): 1934 Archimede-class submarine
5. Galileo Galilei (wd:Q3094742): opera by Philip Glass
6. Portrait of Galileo Galilei, also known as Galileo, Galileo Galilei (wd:Q32904751): painting by Justus Sustermans circa 1636
7. Galileo Galilei (wd:Q48810529): classical lyceum in Pisa in the province of Pisa (Italy) [school id: PIPC00701T]
8. Galileo Galilei (wd:Q3757625): 1909 silent short film
9. Galileo Galilei (wd:Q3757624)
10. Galileo Galilei (wd:Q19198238): German article in Die Gartenlaube, 1855, no. 23

[Assistant]
The correct entity for Galileo Galilei is `wd:Q307`. Now, I need to find out what he was famous for. To do this, I will search for properties that might indicate his notable achievements or contributions.

I'll use the `search_property_of_entity` function to find properties related to his fame or notable achievements.

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
2. Famous Birthdays ID, also known as famousbirthdays.com ID (wd:P11194 as wdt/p): identifier for entities listed on the Famous Birthdays website
3. Notable Names Database, also known as NNDB ID, Notable Names Database people ID, Notable Names Database ID (wd:P1263 as wdt/p): identifier in the Notable Names Database, a biographical database: only for people entries
4. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
5. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt/p): date on which the subject was born, inception
6. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt/p): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
7. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): dissolved, abolished or demolished date, date on which the subject died, end time
8. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
9. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
10. depicted by, also known as acted by, shown in, seen in, portrayed by, played by (wd:P1299 as wdt/p): object depicting this subject (object being creative works such as books, films, paintings)

No other items found

[Assistant]
From the list of properties, `notable work` (`wdt:P800`) seems to be the most relevant for identifying what Galileo Galilei was famous for. I will construct a SPARQL query to retrieve his notable works.

Here is the initial SPARQL query:

```sparql
SELECT ?work ?workLabel WHERE {
  wd:Q307 wdt:P800 ?work .
  OPTIONAL { ?work rdfs:label ?workLabel }
}
```

This query will fetch the notable works of Galileo Galilei and their labels if available. Let's execute this query to see the results.

[Tool]
Got 75 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?work                                      | ?workLabel                                    |
| ------------------------------------------ | --------------------------------------------- |
| equations for a falling body (wd:Q1132975) | "Caduta"@scn                                  |
| equations for a falling body (wd:Q1132975) | "Caduta dei gravi"@it                         |
| equations for a falling body (wd:Q1132975) | "Düşen Cisim Denklemleri"@tr                  |
| equations for a falling body (wd:Q1132975) | "ecuaciones para un cuerpo en caída libre"@es |
| equations for a falling body (wd:Q1132975) | "Ecuacións da caída libre"@gl                 |
| ...                                        | ...                                           |
| Galilean transformation (wd:Q219207)       | "伽利略变换"@zh-hans                               |
| Galilean transformation (wd:Q219207)       | "伽利略变换"@zh-sg                                 |
| Galilean transformation (wd:Q219207)       | "伽利略變換"@zh-hant                               |
| Galilean transformation (wd:Q219207)       | "伽利略變換"@zh-hk                                 |
| Galilean transformation (wd:Q219207)       | "伽利略變換"@zh-tw                                 |

[Assistant]
The query returned multiple results, including various translations of the labels for notable works. To provide a clearer answer, I will modify the query to filter out duplicate works and include only the English labels if available.

Here is the refined SPARQL query:

```sparql
SELECT DISTINCT ?work ?workLabel WHERE {
  wd:Q307 wdt:P800 ?work .
  OPTIONAL { ?work rdfs:label ?workLabel FILTER(LANG(?workLabel) = "en") }
}
```

This query will fetch distinct notable works of Galileo Galilei and their English labels if available. Let's execute this query to see the results.

[Tool]
Got 2 rows and 2 columns:
| ?work                                      | ?workLabel                   |
| ------------------------------------------ | ---------------------------- |
| equations for a falling body (wd:Q1132975) | equations for a falling body |
| Galilean transformation (wd:Q219207)       | Galilean transformation      |

[Assistant]
The refined query has returned two notable works of Galileo Galilei:

1. Equations for a falling body (`wd:Q1132975`)
2. Galilean transformation (`wd:Q219207`)

These are some of the key contributions that made Galileo Galilei famous.

Now, I will provide the final SPARQL query and the answer based on the results.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?timezone ?timezoneLabel WHERE {
  wd:Q8646 wdt:P421 ?timezone .
  OPTIONAL {
    ?timezone rdfs:label ?timezoneLabel FILTER ( LANG( ?timezoneLabel ) = "en" )
  }
}

The current timezone for Hong Kong is UTC+08:00.

[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 time in china hong kong?

[Assistant] To answer the question about the current time in China Hong Kong, we need to find the timezone information for Hong Kong and then formulate a SPARQL query to retrieve it. Let's start by searching for the entity representing Hong Kong in the Wikidata knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Hong Kong, also known as Hong Kong SAR, Hong Kong Special Administrative...f the People's Republic of China, Hong Kong Special Administrative Region, Hong Kong SAR, China, Hong Kong, China (wd:Q8646): city and special administrative region of China
2. British Hong Kong, also known as British Administration, British HK, colonial Hong Kong, Hongkong, Hong Kong (wd:Q1054923): British colony and dependent territory from 1841 to 1997
3. Hong Kong (wd:Q2735819): 1994 live album by Jean Michel Jarre
4. Hong Kong (wd:Q3795969): 1952 film by Lewis R. Foster
5. Hong Kong (wd:Q3140041): ABC adventure/drama series
6. Hong Kong (wd:Q22704538): Wikimedia permanent duplicate item
7. Rosa 'Hong Kong', also known as Rosa 'Hong-Kong', Hong-Kong, Hong Kong (wd:Q16626341): rose cultivar
8. Hong Kong (wd:Q18688799): Finnish variety store chain
9. Hong Kong Flight Information Region, also known as HKFIR, Hong Kong, Hong Kong FIR (wd:Q106861520): Area Control Centre
10. Hong Kong, also known as Hon Kon (wd:Q11277867): Japanese owarai tarento, owarai tarento, actor

[Assistant] The correct entity for Hong Kong, the special administrative region of China, is `wd:Q8646`. Next, we need to identify the property that represents the timezone of a place. We can search for properties related to "timezone" in the Wikidata knowledge graph.

[Tool]
Top 10 property alternatives:
1. located in time zone, also known as TZ, time zone, timezone (wd:P421 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): time zone for this item
2. refine date, also known as date refinement, exact time, hour, local time, quarter (wd:P4241 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): qualifier to narrow down the precision of a date not fully specified by the Wikidata time datatype precision field
3. time index, also known as time coordinate, timestamp, temporal coordinate, relative time (wd:P4895 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): indicate when something happens relative to its beginning
4. point in time, also known as at time, as of, year, when, time of event (wd:P585 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
5. UTC timezone offset, also known as offset from UTC, UTC offset of timezone (wd:P2907 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): difference between Coordinated Universal Time (UTC) and this timezone, numeric value
6. term length of position, also known as term length, time in position, term of position, position term, length of term (wd:P2097 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): length of time in years (unit: Q577) a person (usually a politician) is to serve in a particular position. Do not add bounds., duration
7. start time, also known as start date, since, valid, join time, joined (wd:P580 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): time an entity begins to exist or a statement starts being valid, point in time
8. duration, also known as time length, timespan, time span, time interval, runtime (wd:P2047 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): length of time of an event or process
9. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
10. IANA timezone ID (wd:P6687 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): identifier of a time zone in the IANA database

[Assistant]
The relevant property for the timezone of a place is `wdt:P421` (located in time zone). Now, let's construct a SPARQL query to retrieve the timezone of Hong Kong (`wd:Q8646`).

The query will select the timezone of Hong Kong and include optional clauses to retrieve the labels of the timezone for better readability.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant]
It seems there was an issue with the SERVICE request to retrieve labels. Since we cannot rely on the SERVICE request, we will modify the query to directly fetch the timezone and include optional clauses to retrieve the labels using `rdfs:label`.

Let's try the updated query:

```sparql
SELECT ?timezone ?timezoneLabel WHERE {
  wd:Q8646 wdt:P421 ?timezone .
  OPTIONAL { ?timezone rdfs:label ?timezoneLabel FILTER(LANG(?timezoneLabel) = "en") }
}
```

This query will retrieve the timezone of Hong Kong and its label in English if available.

[Tool]
Got 1 row and 2 columns:
| ?timezone            | ?timezoneLabel |
| -------------------- | -------------- |
| UTC+08:00 (wd:Q6985) | UTC+08:00      |

[Assistant]
The SPARQL query successfully retrieved the timezone for Hong Kong, which is `UTC+08:00` (wd:Q6985).

Now, let's provide the final answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?arena WHERE {
  wd:Q204623 wdt:P115 ?arena
}

The Florida Panthers play at Amerant Bank Arena (wd:Q795526).

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

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

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

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

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

[User] where do florida panthers play?

[Assistant] To answer the question "Where do Florida Panthers play?", I need to identify the entity representing the Florida Panthers and then find the property that links them to their home arena. Let's start by searching for the Florida Panthers entity in the Wikidata knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Florida Panthers (wd:Q204623): National Hockey League team in Sunrise, Florida
2. FIU Panthers, also known as FIU Golden Panthers, Florida International Golden Panthers, Florida International Panthers (wd:Q3110096): intercollegiate sports teams of Florida International University
3. FIU Panthers men's basketball, also known as FIU Golden Panthers men's basketball, FIU Panthers basketball, Florida International Golden Panthers men's basketball, Florida International Panthers, Florida International Panthers basketball (wd:Q5425988): men's college basketball team
4. Florida Panthers IceDen (wd:Q124007705)
5. Florida Tech Panthers (wd:Q16155409): collegiate sports club in the United States
6. 2010–11 Florida Panthers season (wd:Q3467872): national Hockey League team season
7. 2007–08 Florida Panthers season (wd:Q4610005): national Hockey League team season
8. 2009–10 Florida Panthers season (wd:Q4615762): national Hockey League team season
9. 2008–09 Florida Panthers season (wd:Q4612635): national Hockey League team season
10. 2013–14 Florida Panthers season (wd:Q16245182): national Hockey League team season

[Assistant] The first result, `Florida Panthers` (wd:Q204623), seems to be the correct entity for the NHL team. Next, I need to find the property that links the Florida Panthers to their home arena. I'll search for properties related to "home arena" or "venue" for this entity.

[Tool]
Top 10 property alternatives:
1. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as wdt/p): home stadium or venue of a sports team or applicable performing arts organization, location
2. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as wdt/p): league or competition in which team or player has played, or in which an event occurs, participant in
3. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
4. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
5. sport, also known as play, plays, sport played, sports (wd:P641 as wdt/p): sport that the subject participates or participated in or is associated with
6. competition won, also known as won event, won, winner of, victory (wd:P2522 as wdt/p): competition or sports event won by the subject, participant in
7. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
8. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
9. owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt/p): owner of the subject
10. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt/p): 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

No other items found

[Assistant] The property `home venue` (wdt:P115) seems to be the correct one to link the Florida Panthers to their home arena. Now, I will construct a SPARQL query to retrieve the home arena of the Florida Panthers.

[Tool]
Got 1 row and 1 column:
| ?arena                          |
| ------------------------------- |
| Amerant Bank Arena (wd:Q795526) |

[Assistant]
The SPARQL query returned the home arena of the Florida Panthers as "Amerant Bank Arena" (wd:Q795526).

Now, I will provide the final answer with the SPARQL query and the identifier of the home arena.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?country WHERE {
  wd:Q1215310 wdt:P17 ?country .
}

The Battle of San Juan Hill took place in Cuba (wd:Q241).

[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 country did the battle of san juan hill take place?

[Assistant] To answer the question about the country where the Battle of San Juan Hill took place, I first need to identify the entity representing the Battle of San Juan Hill in the Wikidata knowledge graph. I'll use the `search_entity` function to find it.

[Tool]
Top 10 entity alternatives:
1. Battle of San Juan Hill, also known as Battle for the San Juan Heights (wd:Q1215310): 1898 decisive battle of the Spanish–American War
2. The Battle of San Juan Hill (wd:Q3985806): 1913 film by Francis Ford
3. San Juan Hill order of battle (wd:Q7414586)
4. Battle of San Juan and Chorrillos, also known as Battle of Chorrillos, Battle of San Juan (wd:Q3772977): 1881 battle of the War of the Pacific
5. Battle of San Juan (wd:Q656046): 1797 battle
6. Battle of San Juan (wd:Q4872281): Spanish victory during the Anglo–Spanish War
7. Battle of San Juan (wd:Q4872279): 1625 battle in Puerto Rico
8. Battle of San Juan (wd:Q4872278): 1598 English force of 20 ships took San Juan from Spain for 65 days
9. Battle of San Juan Bautista (wd:Q4872282): 1864 battle
10. Second Battle of San Juan (wd:Q7443113): naval battle during the Spanish-American War (1898)

[Assistant] The correct entity for the Battle of San Juan Hill is the first one listed: "Battle of San Juan Hill, also known as Battle for the San Juan Heights" (wd:Q1215310). Now, I need to find out in which country this battle took place. To do this, I will look for a property that indicates the location of the battle. I'll use the `search_property_of_entity` function to find such a property.

[Tool]
Top 10 property alternatives:
1. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
3. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
4. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
5. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
6. point in time, also known as at time, as of, year, when, time of event (wd:P585 as wdt/p): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)

No other items found

[Assistant]
The property "located in the administrative territorial entity" (wdt:P131) seems like a good candidate to find the location of the Battle of San Juan Hill. However, since we're interested in the country, I'll check if the property "country" (wdt:P17) is directly linked to the battle. If not, I'll use wdt:P131 to find the location and then trace it back to the country.

I will now construct a SPARQL query to find the country where the Battle of San Juan Hill took place.

[Tool]
Got 1 row and 1 column:
| ?country       |
| -------------- |
| Cuba (wd:Q241) |

[Assistant]
The SPARQL query returned that the Battle of San Juan Hill took place in Cuba (wd:Q241).

Now, I can provide the final answer.

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT ?coach WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
}

The following individuals have coached the Carolina Panthers: Ron Rivera (wd:Q1991889), Perry Fewell (wd:Q7169776), Chris Tabor (wd:Q57242442), Steven Wilks (wd:Q7615391), John Fox (wd:Q719030), Dave Canales (wd:Q47456134), Matt Rhule (wd:Q14950994), and Frank Reich (wd:Q5489177).

[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 coached the carolina panthers?

[Assistant] To answer the question "Who has coached the Carolina Panthers?", I need to identify the entity for the Carolina Panthers and the property that represents coaching a team. Let's start by searching for the entity representing the Carolina Panthers.

[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 correct entity for the Carolina Panthers is `wd:Q330120`. Next, I need to find the property that indicates who coaches a sports team. I'll search for properties related to coaching.

[Tool]
Top 10 property alternatives:
1. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): 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 pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): 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 pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): 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 pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): identifier for a coach in the French basketball championship
5. trained by, also known as personal trainer, trainer (wd:P10449 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): personal trainer of the athlete, student of
6. LevskiSofia.info coach ID (wd:P6410 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): identifier for a coach at the LevskiSofia.info
7. TuttoCalciatori.net coach ID (wd:P7035 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): identifier of a football coach in the website TuttoCalciatori.net
8. Soccerdonna coach ID, also known as Soccerdonna trainer ID (wd:P8134 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): identifier for a coach at the Soccerdonna
9. LegaBasket.it coach ID (wd:P5612 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): 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 pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): identifier for a volleyball player on the website of the German Volleyball Federation

[Assistant]
The relevant property for identifying the coach of a sports team is `wdt:P286` (head coach). Now, I can construct a SPARQL query to find out who has coached the Carolina Panthers (`wd:Q330120`).

Let's execute the query:

```sparql
SELECT ?coach ?coachLabel WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
```

However, since we agreed not to use `SERVICE wikibase:label`, let's adjust the query accordingly:

```sparql
SELECT ?coach WHERE {
  wd:Q330120 p:P286 ?statement .
  ?statement ps:P286 ?coach .
}
```

I will now execute this adjusted query.

[Tool]
Got 8 rows and 1 column:
| ?coach                      |
| --------------------------- |
| Ron Rivera (wd:Q1991889)    |
| Perry Fewell (wd:Q7169776)  |
| Chris Tabor (wd:Q57242442)  |
| Steven Wilks (wd:Q7615391)  |
| John Fox (wd:Q719030)       |
| Dave Canales (wd:Q47456134) |
| Matt Rhule (wd:Q14950994)   |
| Frank Reich (wd:Q5489177)   |

[Assistant]
The SPARQL query returned several individuals who have coached the Carolina Panthers. Here are the coaches:

- Ron Rivera (wd:Q1991889)
- Perry Fewell (wd:Q7169776)
- Chris Tabor (wd:Q57242442)
- Steven Wilks (wd:Q7615391)
- John Fox (wd:Q719030)
- Dave Canales (wd:Q47456134)
- Matt Rhule (wd:Q14950994)
- Frank Reich (wd:Q5489177)

Now, I will provide the final SPARQL query and the list of coaches as the answer.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?language ?label WHERE {
  {
    wd:Q794 wdt:P37 ?language .
  } UNION {
    wd:Q794 wdt:P1412 ?language .
  }
  OPTIONAL {
    ?language rdfs:label ?label .
    FILTER ( LANG( ?label ) = 'en' )
  }
}

Got 1 row and 2 columns:
| ?language          | ?label  |
| ------------------ | ------- |
| Persian (wd:Q9168) | Persian |

The primary language spoken in Iran is Persian (wd:Q9168).

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

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

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

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

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

[User] what kind of language do they speak in iran?

[Assistant] To answer the question about what kind of language is spoken in Iran, I need to identify the entity representing Iran and the property linking countries to their languages. Let's start by searching for the entity representing Iran.

[Tool]
Top 10 entity alternatives:
1. Iran, also known as ir, IRI, IRN, Islamic Rep. Iran, Islamic Republic of Iran (wd:Q794): country in Western Asia
2. Qajar Iran, also known as Guarded Domains of Persia, Sublime State of Persia, Qajar Persia, Persia, Iran (wd:Q63158027): country in Western Asia (1789–1925)
3. Iran (wd:Q2043606): village in North Ossetia, Russia
4. Iran, also known as Īrān (wd:Q10859757): Iranian newspaper
5. IRAN, also known as Iran: Journal of the British Institute of Persian Studies (wd:Q5973372): academic journal
6. Irane Man, also known as Iran (wd:Q100939484): 2018 studio by Homayoun Shajarian & Sohrab Pournazeri
7. IRAN (wd:Q108434787): herbarium
8. Iran, also known as Iran (first name), Iran (given name) (wd:Q21286079): male given name
9. Iran, also known as IMO 5327817 (wd:Q101778560): ship built in 1941
10. Iran (wd:Q96382820): About Iran

[Assistant] The top result for "Iran" is the country in Western Asia (wd:Q794). Next, I'll search for the property that links countries to their official or widely spoken languages.

[Tool]
Top 10 property alternatives:
1. programmed in, also known as language, programming language, written in (wd:P277 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): the programming language(s) in which the software is developed
2. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): language(s) that a person or a people speaks, writes or signs, including the native language(s)
3. 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 pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): language designated as official by this item, language used
5. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): language widely used (spoken or written) in this place or at this event or organisation
6. language style, also known as manner, term register, obsolete, register, sense register (wd:P6191 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): to denote of the way a sense of a word is used, e.g., slang, vulgarism, babytalk, colloquialism, etc.
7. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): language or languages a person has learned from early childhood, languages spoken, written or signed
8. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): language in which the writer has written their work, languages spoken, written or signed
9. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): language status identifier by Ethnologue.com using EGIDS scale
10. variety of lexeme, form or sense, also known as dialect, used in dialect, variety of form (wd:P7481 as pq/psn/pr/prn/pqv/psv/wdtn/pqn/wdt/p/ps/prv): dialect, graphic or phonetic variety that this lexeme, form or sense is used in

[Assistant] The property "languages spoken, written or signed" (wdt:P1412) seems appropriate for identifying the languages spoken in a country. Now, I will construct a SPARQL query to find out the languages spoken in Iran (wd:Q794).

[Tool] Got no rows and 1 columns

[Assistant]
It appears that the direct query did not return any results, which might indicate that the property `wdt:P1412` is not directly linked to the country entity for Iran. I will try another approach by looking for the official language property (`wdt:P37`) since it's common for countries to have an official language listed.

Let's modify the query to check for the official language of Iran.

[Tool]
Got 1 row and 1 column:
| ?language          |
| ------------------ |
| Persian (wd:Q9168) |

[Assistant]
The query returned Persian (wd:Q9168) as the official language of Iran. However, to provide a more comprehensive answer, we should also consider other widely spoken languages in Iran. Let's extend our query to include both the official language and other languages spoken in Iran.

I will use the `wdt:P1412` property again to find other languages spoken in Iran, combining it with the official language query.

[Tool]
Got 1 row and 2 columns:
| ?language          | ?label  |
| ------------------ | ------- |
| Persian (wd:Q9168) | Persian |

[Assistant]
The query returned Persian (wd:Q9168) as the primary language spoken in Iran. Since only one language was returned, it suggests that Persian is the main language spoken in Iran, and there might not be additional widely recognized languages listed under the same properties.

Given this information, I can now provide the final answer.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?college ?collegeLabel WHERE {
  wd:Q134262 wdt:P69 ?college .
  OPTIONAL {
    ?college rdfs:label ?collegeLabel FILTER ( LANG( ?collegeLabel ) = "en" )
  }
}

Got 6 rows and 2 columns:
| ?college                                               | ?collegeLabel                            |
| ------------------------------------------------------ | ---------------------------------------- |
| University of Iowa (wd:Q182973)                        | University of Iowa                       |
| University of Missouri (wd:Q579968)                    | University of Missouri                   |
| The New School (wd:Q599316)                            | New School                               |
| Soldan International Studies High School (wd:Q7557201) | Soldan International Studies High School |
| Washington University in St. Louis (wd:Q777403)        | Washington University in St. Louis       |
| University City High School (wd:Q7894583)              | University City High School              |

Tennessee Williams attended several colleges including the University of Iowa, University of Missouri, The New School, Soldan International Studies High School, Washington University in St. Louis, and University City High School.

[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 tennessee williams go to college?

[Assistant]
To answer the question about where Tennessee Williams went to college, I first need to identify the correct entity for Tennessee Williams in the knowledge graph. Then, I'll look for a property that indicates the educational institution he attended.

I'll start by searching for the entity representing Tennessee Williams.

[Tool]
Top 10 entity alternatives:
1. Tennessee Williams, also known as Thomas Lanier Williams III, Thomas Williams (wd:Q134262): American playwright (1911–1983), screenwriter, playwright, novelist, writer, poet
2. Tennessee Williams (wd:Q55896325)
3. Tennessee Williams (wd:Q47513341): painting by Buffie Johnson
4. Tennessee Williams (wd:Q47510960): painting by Bernard Safran
5. Tennessee Williams (wd:Q33979787): scientific article
6. Williamsport, also known as Williamsport, Tennessee, Williamsport, TN (wd:Q8021260): unincorporated community in Maury County, Tennessee
7. allée Tennessee Williams (wd:Q111588984): alley in Thiais, France
8. Tennessee Williams Canal (wd:Q49269468): canal in St. James Parish, Louisiana, United States of America
9. NOTEBOOKS: TENNESSEE WILLIAMS (wd:Q113218921): scientific article published on 19 May 2022
10. NOTEBOOKS: TENNESSEE WILLIAMS (wd:Q113214712): scientific article published on 05 February 2022

[Assistant] The correct entity for Tennessee Williams is `wd:Q134262`. Now, I need to find out which property represents the educational institution he attended. A common property for this kind of information is `wdt:P69` (educated at). I will check if this property is used for Tennessee Williams.

[Tool]
Got 6 rows and 3 columns:
| subject                         | property              | object                                                 |
| ------------------------------- | --------------------- | ------------------------------------------------------ |
| Tennessee Williams (wd:Q134262) | educated at (wdt:P69) | Washington University in St. Louis (wd:Q777403)        |
| Tennessee Williams (wd:Q134262) | educated at (wdt:P69) | University of Iowa (wd:Q182973)                        |
| Tennessee Williams (wd:Q134262) | educated at (wdt:P69) | University of Missouri (wd:Q579968)                    |
| Tennessee Williams (wd:Q134262) | educated at (wdt:P69) | The New School (wd:Q599316)                            |
| Tennessee Williams (wd:Q134262) | educated at (wdt:P69) | Soldan International Studies High School (wd:Q7557201) |
| Tennessee Williams (wd:Q134262) | educated at (wdt:P69) | University City High School (wd:Q7894583)              |

[Assistant]
Based on the list of educational institutions, it appears that Tennessee Williams attended multiple colleges. To provide a complete answer, I will construct a SPARQL query to retrieve all the colleges he attended along with their labels.

Here's the SPARQL query:

```sparql
SELECT ?college ?collegeLabel WHERE {
  wd:Q134262 wdt:P69 ?college .
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
```

However, since we agreed not to use `SERVICE wikibase:label`, I will modify the query to include optional clauses for labels:

```sparql
SELECT ?college ?collegeLabel WHERE {
  wd:Q134262 wdt:P69 ?college .
  OPTIONAL { ?college rdfs:label ?collegeLabel FILTER(LANG(?collegeLabel) = "en") }
}
```

Now, I will execute this query to confirm the results.

[Tool]
Failed to call function execute:
failed to parse input: no matching token found from position 146: '\"en\") }
}'

[Assistant]
It seems there was an issue with the query due to improper escaping of the double quotes. Let me correct the query and try again.

Here is the corrected SPARQL query:

```sparql
SELECT ?college ?collegeLabel WHERE {
  wd:Q134262 wdt:P69 ?college .
  OPTIONAL { ?college rdfs:label ?collegeLabel FILTER(LANG(?collegeLabel) = "en") }
}
```

Let's execute this query now.

[Tool]
Got 6 rows and 2 columns:
| ?college                                               | ?collegeLabel                            |
| ------------------------------------------------------ | ---------------------------------------- |
| University of Iowa (wd:Q182973)                        | University of Iowa                       |
| University of Missouri (wd:Q579968)                    | University of Missouri                   |
| The New School (wd:Q599316)                            | New School                               |
| Soldan International Studies High School (wd:Q7557201) | Soldan International Studies High School |
| Washington University in St. Louis (wd:Q777403)        | Washington University in St. Louis       |
| University City High School (wd:Q7894583)              | University City High School              |

[Assistant]
The SPARQL query returned the colleges that Tennessee Williams attended, along with their labels. Based on the results, Tennessee Williams attended several institutions including the University of Iowa, University of Missouri, The New School, Soldan International Studies High School, Washington University in St. Louis, and University City High School.

Now, I will provide the final answer.

[Tool] Stopped generation process


SELECT ?river ?riverLabel ?coord WHERE {
  wd:Q8321 rdfs:label ?riverLabel .
  wd:Q8321 wdt:P625 ?coord .
  BIND(wd:Q8321 AS ?river)
}
OPTIONAL {
  ?river rdfs:label ?riverLabel .
  FILTER(LANG(?riverLabel) = 'en')
}

The Illinois River (wd:Q8321) has the following coordinates: POINT(-90.454200 38.970300) and POINT(-88.260300 41.393600).

[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 is the illinois river on a map?

[Assistant]
To answer the question "Where is the Illinois River on a map?", I need to first identify the entity representing the Illinois River in the knowledge graph. Then, I'll look for properties that might provide geographical coordinates or a location description for the river.

First, I will search for the entity representing the Illinois River.

[Tool]
Top 10 entity alternatives:
1. Illinois River (wd:Q8321): Illinois tributary of the Mississippi River in the United States
2. Illinois River (wd:Q1309881): tributary of the Rogue River in Curry and Josephine counties in Oregon, United States
3. Illinois River (wd:Q5999628): Colorado tributary of the Michigan River
4. Illinois River (wd:Q5999623): river in Arkansas and Oklahoma
5. Chicago River, also known as Chicago river, Chicago (river, Illinois), Chicago River main stem, Chicago River (United States) (wd:Q643284): river in Chicago, Illinois, United States
6. Wood River, also known as Wood River, IL, Wood River (Illinois), Wood River, Illinois (wd:Q185601): city in Illinois, United States
7. River Forest, also known as River Forest, IL, River Forest, Illinois (wd:Q2299339): village in Cook County, Illinois, United States
8. River Grove, also known as River Grove, IL, River Grove, Illinois (wd:Q783421): village in Cook County, Illinois, United States of America
9. Apple River, also known as Apple River, Illinois (wd:Q621346): human settlement in Illinois, United States of America
10. Riverside, also known as Riverside, Illinois, Riverside IL, Riverside, IL (wd:Q2462183): village in Cook County, Illinois, United States

[Assistant]
The correct entity for the Illinois River is `wd:Q8321`. Now, I need to find out the geographical location of the Illinois River. Typically, rivers are associated with properties like `wdt:P625` (coordinate location) or `wdt:P17` (country). I will check if these properties are available for the Illinois River.

I will start by checking if the Illinois River has a coordinate location (`wdt:P625`).

[Tool]
Got 2 rows and 3 columns:
| subject                   | property                       | object                      |
| ------------------------- | ------------------------------ | --------------------------- |
| Illinois River (wd:Q8321) | coordinate location (wdt:P625) | POINT(-90.454200 38.970300) |
| Illinois River (wd:Q8321) | coordinate location (wdt:P625) | POINT(-88.260300 41.393600) |

[Assistant]
The Illinois River has two coordinate locations listed: `POINT(-90.454200 38.970300)` and `POINT(-88.260300 41.393600)`. These points represent different parts of the river's course.

Now, I will construct a SPARQL query to retrieve these coordinates along with the identifier and label of the Illinois River.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?work ?workLabel WHERE {
  wd:Q587156 wdt:P106 ?work .
  OPTIONAL {
    ?work rdfs:label ?workLabel .
    FILTER ( LANG( ?workLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| ?work                           | ?workLabel       |
| ------------------------------- | ---------------- |
| television actor (wd:Q10798782) | television actor |
| film actor (wd:Q10800557)       | film actor       |
| actor (wd:Q33999)               | actor            |

Kim Richards was a television actor (wd:Q10798782), film actor (wd:Q10800557), and actor (wd:Q33999).

[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 was kim richards in?

[Assistant] To answer the question "What was Kim Richards in?", I need to first identify the entity corresponding to "Kim Richards". After that, I will look for works or roles she has been involved in. Let's start by searching for the entity "Kim Richards".

[Tool]
Top 10 entity alternatives:
1. Kim Richards, also known as Kimberley Richards, Kim Erica Richards (wd:Q587156): American actress and television personality (born 1964), television actor, film actor, actor
2. Kim Richards, also known as Kim Elizabeth Richards (wd:Q44115773): Australian politician, politician
3. Kim Richards, also known as NanoKim, NanoSounds (wd:Q123745576): content creator, Twitch streamer
4. Kim Richards (wd:Q76151754): Peerage person ID=566462
5. Kim Richardson, also known as Kimberly Ann Richardson (wd:Q19668126): Canadian actor and singer, jazz musician, actor
6. Kim Lahaie Richards, also known as Kim Lahaie (wd:Q26276511): American drag racer, crew chief, racing automobile driver
7. Kim Michele Richardson (wd:Q100598285): American writer, literary critic, writer
8. Sibel Kekilli, also known as Dilara, Sybel, Kim (wd:Q63682): German actress, actor
9. Kim, also known as Kim, CO (wd:Q2275070): Statutory Town in Las Animas County, Colorado, United States
10. Kim (wd:Q589868): picaresque novel by Rudyard Kipling

[Assistant] The search returned several entities named "Kim Richards". The first result, "Kim Richards" (wd:Q587156), is an American actress and television personality, which seems to be the most likely candidate for the question. Next, I'll search for the properties related to her roles or works.

[Tool]
Top 10 property alternatives:
1. occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt/p): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
2. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. work period (start), also known as start working, flourished (start), start time for work, start date of activity, fl. (start) (wd:P2031 as wdt/p): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, start time, floruit
5. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as wdt/p): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
6. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as wdt/p): the object is a country that recognizes the subject as its citizen, country
7. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt/p): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
8. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
9. child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt/p): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
10. sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as wdt/p): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative

No other items found

[Assistant] Based on the search results, the property "occupation" (wdt:P106) seems to be the most relevant to find out what Kim Richards was in. Now, I will construct a SPARQL query to retrieve the occupations of Kim Richards (wd:Q587156).

[Tool]
Got 3 rows and 1 column:
| ?work                           |
| ------------------------------- |
| television actor (wd:Q10798782) |
| film actor (wd:Q10800557)       |
| actor (wd:Q33999)               |

[Assistant] The SPARQL query returned three occupations for Kim Richards: "television actor", "film actor", and "actor". To provide a more comprehensive answer, I will include these occupations along with their respective IRIs.

[Tool] Stopped generation process


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

The Chinese writing system is referred to as 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] To answer the question about the Chinese writing system, I first need to identify the relevant entity or concept in the knowledge graph. I'll use the `search_entity` function to look for entities related to "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, it seems that the term "writing system" is quite general and has returned multiple entries that aren't specifically about the Chinese writing system. However, the fifth entry mentions "wenzi," which is a term associated with the Chinese writing system. To clarify, I will search for "Chinese characters" since it's a more specific term often used to describe the Chinese writing system.

[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. traditional Chinese characters, also known as Chinese characters, Hant, Hanzi, traditional characters, traditional Hanzi (wd:Q178528): late imperial Chinese characters still used for official purposes on Taiwan and in Hong Kong and Macau and informally elsewhere
3. Chinese Characters (wd:Q122342671): 2022 video game
4. 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
5. variant sinogram, also known as variant Chinese characters, folk variant, semantic variant (wd:Q837611): different form of Han characters that equivalent in pronunciatio...application such as non-standard abbreviated or simplified forms
6. Singapore Chinese characters (wd:Q391025): writing system for Mandarin Chinese, as used in Singapore
7. abbreviated Chinese character, also known as abbreviated Chinese characters, abbreviated CJK ideograph, abbreviated CJK ideographs, CJKV simplified character, CJKV simplified characters (wd:Q125440195): abbreviated variant of a CJK ideograph
8. Ryukyuan Chinese characters, also known as Ryukyuan Kanji (wd:Q12594322)
9. Wu Chinese characters, also known as wuu-Hans, wuu-Hant (wd:Q6435454): Chinese characters used in the Wu Chinese language
10. Chinese whole characters (wd:Q122761535): complete characters in Chinese writing

[Assistant]
The first result clearly defines "Chinese characters" (wd:Q8201) as the logographic writing system with Han origin used in the Sinosphere for Chinese, Japanese, Korean, and traditional Vietnamese languages. This is the entity we're looking for to answer the question about the Chinese writing system.

Now, I will construct a SPARQL query to retrieve the identifier and label of this entity.

[Tool] Stopped generation process