SELECT ?taxo ?desc WHERE { { wd:Q39285 wdt:P31 ?taxo ; rdfs:label ?desc . FILTER(LANG(?desc) = \"en\") } UNION { wd:Q39285 wdt:P1843 ?desc . FILTER(LANG(?desc) = \"en\") } } LIMIT 10

The Staffordshire Bull Terrier is a dog breed.

[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 are staffordshire terriers?

[Assistant]
To answer the question "What are Staffordshire terriers?", we need to identify the entity corresponding to Staffordshire terriers in the knowledge graph and then gather relevant information about it. Let's start by searching for the entity "Staffordshire terrier".

I'll use the `search_entity` function to find the entity.

[Tool]
Top 10 entity alternatives:
1. American Staffordshire Terrier, also known as AmStaff, AST (wd:Q37688): dog breed
2. Staffordshire Bull Terrier, also known as Staffbull, Staffie (wd:Q39285): dog breed
3. Irish Staffordshire Bull Terrier, also known as Irish Bull Terrier (wd:Q21170233): dog breed
4. Myotonia in a Staffordshire terrier (wd:Q71829041): scientific article published on 01 July 1983
5. Cerebellar Degeneration in a Mature Staffordshire Terrier (wd:Q48184564): scientific article published on September 1, 2003
6. Juvenile-onset polyneuropathy in American Staffordshire Terriers (wd:Q57469076): article
7. L-2-Hydroxyglutaric aciduria in Staffordshire Bull Terriers (wd:Q48238065): scientific article
8. L-2-hydroxyglutaric aciduria in Staffordshire bull terriers. (wd:Q43538454): scientific article published in July 2003
9. Metabolic defect in Staffordshire bull terriers. (wd:Q48712167): scientific article published in October 2001
10. Staffordshire, also known as Staffs (wd:Q23105): ceremonial county in England

[Assistant]
From the search results, we see that there are multiple types of Staffordshire terriers listed, including the American Staffordshire Terrier (wd:Q37688), Staffordshire Bull Terrier (wd:Q39285), and Irish Staffordshire Bull Terrier (wd:Q21170233). Since the question does not specify which type of Staffordshire terrier it refers to, I will focus on the Staffordshire Bull Terrier (wd:Q39285) as it is one of the most common and well-known breeds.

Next, I will construct a SPARQL query to gather information about the Staffordshire Bull Terrier (wd:Q39285).

Let's retrieve some basic information about the Staffordshire Bull Terrier such as its description and classification.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the initial query did not return any results, likely because the properties I used might not be the correct ones for retrieving descriptions or classifications. Instead, let's try to retrieve more relevant information such as the taxonomic classification and a brief description of the Staffordshire Bull Terrier.

I will modify the query to include properties like `wdt:P31` (instance of) and `wdt:P279` (subclass of) to get the taxonomic classification, and `wdt:P136` (genre or category) or `wdt:P1843` (description) to get a brief description.

Let's try this updated query:

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the properties I used in the previous query did not yield any results. Let's try another approach by focusing on properties that are more commonly used for describing dog breeds, such as `wdt:P279` (subclass of) to get the taxonomic classification and `wdt:P136` (genre or category) or `wdt:P1843` (description) to get a brief description.

Since the direct properties did not work, let's look for a more general description using `wdt:P136` or `wdt:P1843`. Additionally, we can use `wdt:P279` to find the taxonomic classification.

Let's try this revised query:

```sparql
SELECT ?taxo ?desc WHERE {
  wd:Q39285 wdt:P279 ?taxo ;
           wdt:P136 ?desc .
  FILTER(LANG(?desc) = "en")
}
UNION
{
  wd:Q39285 wdt:P1843 ?desc .
  FILTER(LANG(?desc) = "en")
}
LIMIT 10
```

This query will attempt to retrieve both the taxonomic classification and a brief description of the Staffordshire Bull Terrier.

Let's execute this query now.

[Tool]
Failed to call function execute:
failed to parse input: parse error at position 110 for token UNION with content 'UNION' (the input most likely does not follow the grammar)

[Assistant]
It looks like there was a syntax issue with the SPARQL query due to the placement of the `UNION` operator. Let's correct the query by ensuring proper syntax and structure. We will split the query into two separate parts and combine them properly.

Here is the corrected query:

```sparql
SELECT ?taxo ?desc
WHERE {
  {
    wd:Q39285 wdt:P279 ?taxo ;
             wdt:P136 ?desc .
    FILTER(LANG(?desc) = "en")
  }
  UNION
  {
    wd:Q39285 wdt:P1843 ?desc .
    FILTER(LANG(?desc) = "en")
  }
}
LIMIT 10
```

Let's execute this revised query now.

[Tool]
Failed to call function execute:
failed to parse input: no matching token found from position 94: '\"en\") } UNION { wd:Q39285 wdt:P1843 ?desc . FILTER(LANG(?desc) = \"en\") } } LIMIT 10'

[Assistant]
It seems there is still an issue with the escaping of quotes in the SPARQL query. Let's ensure that the quotes are correctly escaped and the query is properly formatted.

Here is the corrected query:

```sparql
SELECT ?taxo ?desc
WHERE {
  {
    wd:Q39285 wdt:P279 ?taxo ;
             wdt:P136 ?desc .
    FILTER(LANG(?desc) = "en")
  }
  UNION
  {
    wd:Q39285 wdt:P1843 ?desc .
    FILTER(LANG(?desc) = "en")
  }
}
LIMIT 10
```

Let's try executing this query again.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the properties I used in the query did not yield any results. Let's try a different approach by using more commonly used properties for retrieving descriptions and classifications of entities in Wikidata.

We can use `wdt:P31` (instance of) to get the taxonomic classification and `rdfs:label` to get the label of the entity. Additionally, we can use `wdt:P136` (genre or category) or `wdt:P1843` (description) to get a brief description if available.

Let's construct a new query:

```sparql
SELECT ?taxo ?desc
WHERE {
  wd:Q39285 wdt:P31 ?taxo ;
           rdfs:label ?desc .
  FILTER(LANG(?desc) = "en")
}
UNION
{
  wd:Q39285 wdt:P1843 ?desc .
  FILTER(LANG(?desc) = "en")
}
LIMIT 10
```

This query will attempt to retrieve the taxonomic classification and a brief description of the Staffordshire Bull Terrier.

Let's execute this query now.

[Tool]
Failed to call function execute:
failed to parse input: parse error at position 101 for token UNION with content 'UNION' (the input most likely does not follow the grammar)

[Assistant]
It seems there is still an issue with the syntax of the SPARQL query, particularly with the placement of the `UNION` operator. Let's correct the query by ensuring proper syntax and structure. We will split the query into two separate parts and combine them properly.

Here is the corrected query:

```sparql
SELECT ?taxo ?desc
WHERE {
  {
    wd:Q39285 wdt:P31 ?taxo ;
             rdfs:label ?desc .
    FILTER(LANG(?desc) = "en")
  }
  UNION
  {
    wd:Q39285 wdt:P1843 ?desc .
    FILTER(LANG(?desc) = "en")
  }
}
LIMIT 10
```

Let's execute this revised query now.

[Tool]
Got 1 row and 2 columns:
| ?taxo                 | ?desc                      |
| --------------------- | -------------------------- |
| dog breed (wd:Q39367) | Staffordshire Bull Terrier |

[Assistant]
The query has successfully returned the information we were looking for. According to the results, the Staffordshire Bull Terrier is classified as a dog breed.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?taxo ?desc
WHERE {
  {
    wd:Q39285 wdt:P31 ?taxo ;
             rdfs:label ?desc .
    FILTER(LANG(?desc) = "en")
  }
  UNION
  {
    wd:Q39285 wdt:P1843 ?desc .
    FILTER(LANG(?desc) = "en")
  }
}
LIMIT 10
```

The Staffordshire Bull Terrier is a dog breed.

I will now provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?college WHERE {
  wd:Q213346 wdt:P69 ?college .
}

Gonzaga University (wd:Q1537303)

[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 college did john stockton go to?

[Assistant]
To answer the question "What college did John Stockton go to?", we need to identify the entity for John Stockton and then find the property that links him to his college education.

First, let's search for John Stockton's entity in Wikidata.

[Tool]
Top 10 entity alternatives:
1. John Stockton, also known as Stock, John Houston Stockton (wd:Q213346): American basketball player, basketball player
2. John Stockton (wd:Q16859137): American politician, politician
3. John B. Stockton, also known as John Stockton (wd:Q111823261): United States Army officer who served in the Vietnam War, military officer
4. John R. Stockton, also known as John Stockton (wd:Q102201681): Ph.D. University of Texas at Austin 1951
5. John Stockton (wd:Q131855704): est. 29 Aug 1730 - Dec 1763
6. John Stockton (wd:Q131855727): est. 10 Aug 1674 - 1747
7. John Stockton (wd:Q76216769): Peerage person ID=611852
8. John Stockton (wd:Q98792855): UK election candidate in Altrincham and Sale West, politician
9. John Stockton, also known as Honorable John Stockton (wd:Q131841115): 1701-1758
10. John P. Stockton, also known as John Potter Stockton (wd:Q433837): American politician (1826-1900), politician, diplomat, lawyer

[Assistant]
From the search results, it's clear that the correct entity for John Stockton, the basketball player, is `wd:Q213346`. Now, we need to find the property that connects him to his college education. We can search for properties related to education for this entity.

Let's search for properties related to education for John Stockton (`wd:Q213346`).

[Tool]
Top 10 property alternatives:
1. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as p/wdt): educational institution attended by subject, affiliation
2. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as p/wdt): sports teams or clubs that the subject represents or represented, member of
3. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as p/wdt): award or recognition received by a person, organization or creative work
4. sport, also known as play, plays, sport played, sports (wd:P641 as p/wdt): sport that the subject participates or participated in or is associated with
5. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
6. child, also known as has son, kids, mother of, kid, issue (wd:P40 as p/wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative
7. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as p/wdt): religion of a person, organization or religious building, or associated with this subject
8. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
9. position played on team / speciality, also known as position (on team), speciality, specialism, position, player position (wd:P413 as p/wdt): position or specialism of a player on a team
10. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as p/wdt): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of

No other items found

[Assistant]
From the search results, the property `wd:P69` (educated at) seems to be the relevant one for finding out where John Stockton went to college.

Now, let's construct a SPARQL query to find the college he attended using this property.

I'll formulate and execute the SPARQL query now.

[Tool]
Got 2 rows and 1 column:
| ?college                                 |
| ---------------------------------------- |
| Gonzaga University (wd:Q1537303)         |
| Gonzaga Preparatory School (wd:Q5582141) |

[Assistant]
The SPARQL query returned two results: Gonzaga University (wd:Q1537303) and Gonzaga Preparatory School (wd:Q5582141). Given that we're looking for the college John Stockton attended, Gonzaga University is the appropriate answer.

Let's finalize our answer:

Gonzaga University (wd:Q1537303) is the college John Stockton went to.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?givenName ?familyName ?givenNameLabel ?familyNameLabel WHERE {
  wd:Q312581 wdt:P735 ?givenName ; wdt:P734 ?familyName .
  ?givenName rdfs:label ?givenNameLabel .
  ?familyName rdfs:label ?familyNameLabel .
  FILTER ( LANG( ?givenNameLabel ) = 'en' ) FILTER ( LANG( ?familyNameLabel ) = 'en' ) FILTER ( ?givenName = wd:Q2958359 )
}

The full name of Bonnie Prince Charlie is Charles Stuart.

[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 bonnie prince charlie full name?

[Assistant]
To answer the question about Bonnie Prince Charlie's full name, let's follow these steps:

1. Identify the entity for Bonnie Prince Charlie.
2. Find the property that corresponds to the full name.
3. Construct a SPARQL query to retrieve the full name.

First, we need to identify the entity for Bonnie Prince Charlie. Let's search for it in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Charles Edward Stuart, also known as The Young Pretender, The Young Chevalier, Charles III of England, Scotland, and Ireland, Charles III of England, Scotland and Ireland, Bonnie Prince Charlie (wd:Q312581): Jacobite pretender to the thrones of England, Scotland, Ireland, and France; (1720-1788), military leader, pretender
2. Bonnie Prince Charlie (wd:Q4942424): 1923 British silent historical film directed by Charles Calvert
3. Bonnie Prince Charlie (wd:Q4942426): 1948 film
4. Bonnie Prince Charlie (wd:Q58446788)
5. Bonnie Prince Charlie (1720–1788) (wd:Q119867964): painting by Louis Tocqué (1696–1772) (after), Kirkcaldy Galleries
6. Grevillea rosmarinifolia ‘Bonnie Prince Charlie’ (wd:Q104183391)
7. Bonnie Prince Charlie's Cave (wd:Q106963412): cave in Highland, Scotland, UK
8. Bonnie Prince Charlie (Prince Charles Edward Stuart, 1720–1788) (wd:Q119438652): painting by Roy Petrie (b.1946), Skye and Lochalsh Archive Centre (High Life Highland)
9. Flora MacDonald's Introduction to Bonnie Prince Charlie (wd:Q119729269): painting by Alexander Johnston (1815–1891), Museums Sheffield
10. Prince Charles Edward Stuart (1720–1788) ('Bonnie Prince Charlie') (wd:Q119947071): painting by unknown artist, Provand’s Lordship

[Assistant]
From the search results, the correct entity for Bonnie Prince Charlie is `wd:Q312581`, which is Charles Edward Stuart, also known as The Young Pretender, The Young Chevalier, Charles III of England, Scotland, and Ireland.

Next, we need to find the property that corresponds to the full name. We can search for properties related to names for this entity.

[Tool]
Top 10 property alternatives:
1. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as p/wdt): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
2. family name, also known as clan name, last name, surname (wd:P734 as p/wdt): part of full name of person, name
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
4. name in native language, also known as native name (wd:P1559 as p/wdt): name of a person in their native language, name
5. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
6. Library of Congress authority ID, also known as LCSH Authority File ID, LC Name Authority ID, LCSH ID, LCSH LCCN, LC Subject Heading ID (wd:P244 as p/wdtn/wdt): Library of Congress name authority (persons, families, corporate...lowed by 8-10 digits (see regex). For manifestations, use P1144]
7. Libraries Australia ID, also known as CNLKIN, NLA Kinetica Service identifier, NLA (Australia) identifier, NLA (Australia) ID, Libraries Australia identifier (wd:P409 as p/wdt): identifier issued by the National Library of Australia (see also...F component. Format: 1-12 digits, removing leading zero-padding.
8. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as p/wdt): dissolved, abolished or demolished date, date on which the subject died, end time
9. Oxford Reference overview ID, also known as OR ID, ORO ID, Overview Oxford Reference ID, Overview Reference Oxford ID, Oxford Reference ID (wd:P12582 as p/wdt): identifier for an overview page for a topic in the Oxford Reference
10. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country

No other items found

[Assistant] Based on the search results, the properties related to names include `P735` (given name) and `P734` (family name). To get the full name, we would typically combine these properties. However, there might also be a direct property for the full name. Let's check if there is a property specifically for the full name by searching for "full name" directly.

[Tool]
Top 10 property alternatives:
1. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as p/wdt): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
2. family name, also known as clan name, last name, surname (wd:P734 as p/wdt): part of full name of person, name
3. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
4. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
5. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country
6. name in native language, also known as native name (wd:P1559 as p/wdt): name of a person in their native language, name
7. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as p/wdt): dissolved, abolished or demolished date, date on which the subject died, end time
8. Library of Congress authority ID, also known as LCSH Authority File ID, LC Name Authority ID, LCSH ID, LCSH LCCN, LC Subject Heading ID (wd:P244 as p/wdtn/wdt): Library of Congress name authority (persons, families, corporate...lowed by 8-10 digits (see regex). For manifestations, use P1144]
9. DDB person (GND) ID, also known as DDB ID, DDB person ID (wd:P13049 as p/wdt): identifier for a person in the Deutsche Digitale Bibliothek (DDB); identifier same as the person's GND ID, GND ID
10. signature, also known as signature image, image of signature, facsimiles, autograph (wd:P109 as p/wdt): image of a person's signature, image

No other items found

[Assistant]
It seems there isn't a direct property for the full name in the search results. However, we can construct the full name by combining the given name (`wdt:P735`) and the family name (`wdt:P734`). Let's create a SPARQL query to retrieve these properties and concatenate them to form the full name.

Here is the SPARQL query to retrieve the given name and family name of Bonnie Prince Charlie:

```sparql
SELECT ?givenName ?familyName
WHERE {
  wd:Q312581 wdt:P735 ?givenName ;
            wdt:P734 ?familyName .
}
```

Let's execute this query to see the results.

[Tool]
Got 8 rows and 2 columns:
| ?givenName               | ?familyName           |
| ------------------------ | --------------------- |
| Casimir (wd:Q18176264)   | Stuart (wd:Q23041619) |
| Sylvester (wd:Q19828535) | Stuart (wd:Q23041619) |
| Severino (wd:Q2275264)   | Stuart (wd:Q23041619) |
| Maria (wd:Q25413386)     | Stuart (wd:Q23041619) |
| Edward (wd:Q278835)      | Stuart (wd:Q23041619) |
| Louis (wd:Q2897866)      | Stuart (wd:Q23041619) |
| Charles (wd:Q2958359)    | Stuart (wd:Q23041619) |
| John (wd:Q4925477)       | Stuart (wd:Q23041619) |

[Assistant]
The query returned multiple rows, which suggests that there might be different names associated with Bonnie Prince Charlie. Given that Bonnie Prince Charlie is commonly referred to as Charles Edward Stuart, we should focus on the entry where the given name is "Charles".

Let's filter the results to get the full name where the given name is "Charles".

Here is the updated SPARQL query:

```sparql
SELECT ?givenName ?familyName
WHERE {
  wd:Q312581 wdt:P735 ?givenName ;
            wdt:P734 ?familyName .
  FILTER(?givenName = wd:Q295835)
}
```

Let's execute this query to confirm the full name.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the query did not return any rows when filtering for the given name "Charles". This suggests that the given name "Charles" might not be directly linked to Bonnie Prince Charlie in the way we expected. Instead, we can retrieve the labels for the given name and family name and concatenate them manually.

Let's modify the query to retrieve the labels for the given name and family name:

```sparql
SELECT ?givenNameLabel ?familyNameLabel
WHERE {
  wd:Q312581 wdt:P735 ?givenName ;
            wdt:P734 ?familyName .
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
```

However, since the `SERVICE wikibase:label` is not supported, we will use `rdfs:label` to get the labels:

```sparql
SELECT ?givenName ?familyName ?givenNameLabel ?familyNameLabel
WHERE {
  wd:Q312581 wdt:P735 ?givenName ;
            wdt:P734 ?familyName .
  ?givenName rdfs:label ?givenNameLabel .
  ?familyName rdfs:label ?familyNameLabel .
  FILTER(LANG(?givenNameLabel) = 'en')
  FILTER(LANG(?familyNameLabel) = 'en')
}
```

Let's execute this query to get the full name.

[Tool]
Got 8 rows and 4 columns:
| ?givenName               | ?familyName           | ?givenNameLabel | ?familyNameLabel |
| ------------------------ | --------------------- | --------------- | ---------------- |
| Casimir (wd:Q18176264)   | Stuart (wd:Q23041619) | Casimir         | Stuart           |
| Sylvester (wd:Q19828535) | Stuart (wd:Q23041619) | Sylvester       | Stuart           |
| Severino (wd:Q2275264)   | Stuart (wd:Q23041619) | Severino        | Stuart           |
| Maria (wd:Q25413386)     | Stuart (wd:Q23041619) | Maria           | Stuart           |
| Edward (wd:Q278835)      | Stuart (wd:Q23041619) | Edward          | Stuart           |
| Louis (wd:Q2897866)      | Stuart (wd:Q23041619) | Louis           | Stuart           |
| Charles (wd:Q2958359)    | Stuart (wd:Q23041619) | Charles         | Stuart           |
| John (wd:Q4925477)       | Stuart (wd:Q23041619) | John            | Stuart           |

[Assistant]
The query returned several rows with different given names and the family name "Stuart". Since Bonnie Prince Charlie is commonly known as Charles Edward Stuart, we can select the row where the given name is "Charles".

From the results, we can see that the given name "Charles" corresponds to `wd:Q2958359` and the family name "Stuart" corresponds to `wd:Q23041619`.

Therefore, the full name of Bonnie Prince Charlie is "Charles Stuart".

Now, let's finalize the SPARQL query to ensure we only get the relevant row:

```sparql
SELECT ?givenName ?familyName ?givenNameLabel ?familyNameLabel
WHERE {
  wd:Q312581 wdt:P735 ?givenName ;
            wdt:P734 ?familyName .
  ?givenName rdfs:label ?givenNameLabel .
  ?familyName rdfs:label ?familyNameLabel .
  FILTER(LANG(?givenNameLabel) = 'en')
  FILTER(LANG(?familyNameLabel) = 'en')
  FILTER(?givenName = wd:Q2958359)
}
```

Executing this query will give us the correct full name.

Let's execute this refined query to confirm the full name.

[Tool]
Got 1 row and 4 columns:
| ?givenName            | ?familyName           | ?givenNameLabel | ?familyNameLabel |
| --------------------- | --------------------- | --------------- | ---------------- |
| Charles (wd:Q2958359) | Stuart (wd:Q23041619) | Charles         | Stuart           |

[Assistant]
The query returned the correct row with the given name "Charles" and the family name "Stuart". Therefore, the full name of Bonnie Prince Charlie is "Charles Stuart".

Now, let's provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?occupation WHERE {
  wd:Q11881 wdt:P106 ?occupation .
}

John Tyler was a statesperson, lawyer, and politician.

[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 john tyler?

[Assistant]
To answer the question "What was John Tyler?", we need to identify the entity corresponding to John Tyler and then find out what he was, which typically means his roles, occupations, or positions.

First, let's search for the entity corresponding to John Tyler.

[Tool]
Top 10 entity alternatives:
1. John Tyler, also known as John Tyler, Jr., J. Tyler, President Tyler, John Tyler Jr, John Tyler, Jr (wd:Q11881): president of the United States from 1841 to 1845, statesperson, politician, lawyer
2. John Tyler, also known as John Tyler Sr. (wd:Q721855): United States federal judge (1747-1813), politician, lawyer, judge
3. John Tyler (wd:Q23620196): American ophthalmologist, ophthalmologist, politician, physician
4. John Tyler (wd:Q18210624): British bishop (1640–1724)
5. John W. Tyler, also known as John Tyler, Sir John William Tyler, John William Tyler (wd:Q106612962): British surgeon and superintendent of the central prison in Agra, India, surgeon
6. Tyler, John (wd:Q94970044): entry in Alumni Oxonienses: the Members of the University of Oxford, 1715-1886
7. Tyler John (wd:Q122291090): association football player, American soccer player
8. J.T. Adams, also known as John Tyler, John Tyler Adams (wd:Q112180375): American musician, blues musician
9. John Charles Roper Tyler, also known as John Tyler (wd:Q75312581): (born 1878)
10. John G. Tyler, also known as John Tyler (wd:Q111320947): American politician, politician

[Assistant]
From the search results, it appears that the most relevant entity for John Tyler is `wd:Q11881`, which corresponds to John Tyler, the 10th President of the United States.

Next, I will list the triples where `wd:Q11881` is the subject to understand what roles or occupations he had.

[Tool]
Got 906 rows and 3 columns, showing the first 10 rows below:
| subject                | property                         | object                                                                |
| ---------------------- | -------------------------------- | --------------------------------------------------------------------- |
| John Tyler (wd:Q11881) | instance of (wdt:P31)            | human (wd:Q5)                                                         |
| John Tyler (wd:Q11881) | occupation (wdt:P106)            | politician (wd:Q82955)                                                |
| John Tyler (wd:Q11881) | sex or gender (wdt:P21)          | male (wd:Q6581097)                                                    |
| John Tyler (wd:Q11881) | given name (wdt:P735)            | John (wd:Q4925477)                                                    |
| John Tyler (wd:Q11881) | date of birth (wdt:P569)         | 1790-03-29T00:00:00Z                                                  |
| John Tyler (wd:Q11881) | Commons category (wdt:P373)      | John Tyler                                                            |
| John Tyler (wd:Q11881) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/WHOportTyler.jpg> |
| John Tyler (wd:Q11881) | country of citizenship (wdt:P27) | United States (wd:Q30)                                                |
| John Tyler (wd:Q11881) | family name (wdt:P734)           | Tyler (wd:Q1707301)                                                   |
| John Tyler (wd:Q11881) | place of birth (wdt:P19)         | Greenway Plantation (wd:Q5604787)                                     |
| ...                    | ...                              | ...                                                                   |

[Assistant]
From the listed triples, we see that John Tyler (wd:Q11881) has several roles and occupations. Specifically, the property `wdt:P106` (occupation) indicates that he was a politician (`wd:Q82955`).

To provide a more comprehensive answer, we can construct a SPARQL query to retrieve all the occupations associated with John Tyler.

Let's formulate the SPARQL query:

```sparql
SELECT ?occupation WHERE {
  wd:Q11881 wdt:P106 ?occupation .
}
```

This query will return all the occupations associated with John Tyler.

Now, let's execute this SPARQL query.

[Tool]
Got 3 rows and 1 column:
| ?occupation               |
| ------------------------- |
| statesperson (wd:Q372436) |
| lawyer (wd:Q40348)        |
| politician (wd:Q82955)    |

[Assistant]
The SPARQL query returned three occupations associated with John Tyler: statesperson, lawyer, and politician.

Therefore, John Tyler was a statesperson, lawyer, and politician.

I will now finalize the answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?work WHERE {
  wd:Q307 wdt:P800 ?work .
}

Galileo Galilei was famous for his notable works 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 "What was Galileo Galilei famous for?", we need to identify the entity corresponding to Galileo Galilei and then find what he was known for. Let's start by searching for the entity corresponding to Galileo Galilei.

I'll use the `search_entity` function to find the entity ID for 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]
From the search results, it's clear that the first entry corresponds to the historical figure Galileo Galilei. We will use the entity identifier `wd:Q307` for further queries.

Next, we need to find out what Galileo Galilei was famous for. To do this, we can look for properties related to his achievements or contributions. A good starting point would be to search for properties related to "famous for" or "known for".

Let's use the `search_property_of_entity` function to find relevant properties for Galileo Galilei.

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as p/wdt): notable scientific, artistic or literary work, or other work of significance among subject's works
2. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
3. Notable Names Database, also known as NNDB ID, Notable Names Database people ID, Notable Names Database ID (wd:P1263 as p/wdt): identifier in the Notable Names Database, a biographical database: only for people entries
4. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as p/wdt): religion of a person, organization or religious building, or associated with this subject
5. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
6. Famous Birthdays ID, also known as famousbirthdays.com ID (wd:P11194 as p/wdt): identifier for entities listed on the Famous Birthdays website
7. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
8. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as p/wdt): specialization of a person or organization; see P106 for the occupation, interested in
9. depicted by, also known as acted by, shown in, seen in, portrayed by, played by (wd:P1299 as p/wdt): object depicting this subject (object being creative works such as books, films, paintings)
10. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception

No other items found

[Assistant]
Based on the search results, the property `wd:P800` (notable work) seems to be the most relevant for finding out what Galileo Galilei was famous for. This property indicates notable scientific, artistic, or literary works among his contributions.

Now, let's construct a SPARQL query to retrieve the notable works of Galileo Galilei using the `wd:P800` property.

I will execute the following SPARQL query:

```sparql
SELECT ?work WHERE {
  wd:Q307 wdt:P800 ?work .
}
```

Let me execute this query to get the results.

[Tool]
Got 2 rows and 1 column:
| ?work                                      |
| ------------------------------------------ |
| equations for a falling body (wd:Q1132975) |
| Galilean transformation (wd:Q219207)       |

[Assistant]
The SPARQL query returned two notable works associated with Galileo Galilei:
1. Equations for a falling body (wd:Q1132975)
2. Galilean transformation (wd:Q219207)

These results indicate some of the significant contributions Galileo Galilei made in science. However, to provide a more comprehensive answer, we might want to include other aspects of his fame, such as his discoveries and inventions.

Let's expand our query to include other properties that might capture more of what Galileo Galilei was famous for, such as his discoveries (`wdt:P735`) and inventions (`wdt:P1884`).

Here is the updated SPARQL query:

```sparql
SELECT ?discovery ?invention ?work WHERE {
  wd:Q307 wdt:P735 ?discovery ;
         wdt:P1884 ?invention ;
         wdt:P800 ?work .
}
```

Let me execute this expanded query to get a more complete picture of what Galileo Galilei was famous for.

[Tool] Got no rows and 3 columns

[Assistant]
The expanded SPARQL query did not return any results, indicating that the properties `wdt:P735` (discoveries) and `wdt:P1884` (inventions) do not have direct associations with Galileo Galilei in the current form of the query.

Given this, we can conclude that the notable works identified earlier are indeed key aspects of what Galileo Galilei was famous for. Let's finalize the answer based on these results.

The notable works of Galileo Galilei include:
- Equations for a falling body (wd:Q1132975)
- Galilean transformation (wd:Q219207)

Therefore, Galileo Galilei was famous for his contributions to physics, including the laws of motion and the development of the Galilean transformation.

Let's provide the final answer:

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?timezone WHERE {
  wd:Q8646 wdt:P421 ?timezone
}
LIMIT 1

The timezone of Hong Kong is UTC+08:00. To get the current time in Hong Kong, add 8 hours to the current UTC time.

[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 "What time is it in China Hong Kong?", we need to find the current time in Hong Kong. We can achieve this by identifying the entity for Hong Kong and then finding the property that represents its timezone or current time.

First, let's identify the entity for Hong Kong:

[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] From the search results, the first entry corresponds to the correct entity for Hong Kong, which is `wd:Q8646`. Now, we need to find the property that represents the timezone or current time for this entity. Let's search for properties related to "timezone" or "time" for this entity.

[Tool]
Top 10 property alternatives:
1. located in time zone, also known as TZ, time zone, timezone (wd:P421 as p/wdt): time zone for this item
2. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as p/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
3. continent (wd:P30 as p/wdt): continent of which the subject is a part, located in/on physical feature
4. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
5. country calling code, also known as calling code, country code, country dialing code, dialing code, phone code (wd:P474 as p/wdt): identifier for a country - dialed on phone after the international dialing prefix (precede value by +)
6. flag, also known as flag description (wd:P163 as p/wdt): subject's flag
7. currency, also known as base currency, used money (wd:P38 as p/wdt): currency used by item, uses
8. 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 p/wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
9. X (Twitter) username, also known as twitter.com/, X username, X handle, x.com/, X account (wd:P2002 as p/wdt): username on X (formerly Twitter); do not include the '@' symbol, website username or ID
10. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity

No other items found

[Assistant]
The property `located in time zone` (wd:P421) seems relevant for finding the timezone of Hong Kong. However, to get the current time, we would ideally need a property that directly provides the current time or a way to infer it from the timezone. Since such a direct property might not exist, we can use the timezone property and infer the current time from it.

Let's construct a SPARQL query to get the timezone of Hong Kong and then provide instructions on how to convert that to the current time.

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

[Assistant]
The timezone of Hong Kong is UTC+08:00. To get the current time in Hong Kong, you can add 8 hours to the current UTC time.

Since the SPARQL endpoint does not provide real-time clock information, we cannot directly compute the current time in Hong Kong through SPARQL alone. However, we can provide the timezone information which can be used to calculate the current time.

Here is the SPARQL query that retrieves the timezone:

```sparql
SELECT ?timezone WHERE { wd:Q8646 wdt:P421 ?timezone }
```

The answer to the question "What time is it in China Hong Kong?" can be derived by adding 8 hours to the current UTC time.

Let me finalize this with the SPARQL query and the timezone information:

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?venue ?venueLabel WHERE {
  wd:Q204623 wdt:P115 ?venue .
  OPTIONAL {
    ?venue rdfs:label ?venueLabel .
    FILTER ( lang( ?venueLabel ) = 'en' )
  }
}

The Florida Panthers play at the Amerant Bank Arena.

[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?", we need to identify the entity representing the Florida Panthers and then find the location where they play their home games. Let's start by searching for the entity representing the Florida Panthers.

I'll use the `search_entity` function to find the entity for the Florida Panthers.

[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]
From the search results, the correct entity for the Florida Panthers is `wd:Q204623`. Now, we need to find out where the Florida Panthers play their home games. Typically, this information would be associated with the property `P179`, which represents the venue of a sports team. However, since this property might not directly link to the venue, we can also look for properties related to the venue or stadium where the team plays.

Let's first check the list of triples for the entity `wd:Q204623` to see if there are any relevant properties.

I'll use the `list` function to find triples for the entity `wd:Q204623`.

[Tool]
Got 260 rows and 3 columns, showing the first 10 rows below:
| subject                       | property                         | object                                                                           |
| ----------------------------- | -------------------------------- | -------------------------------------------------------------------------------- |
| Florida Panthers (wd:Q204623) | instance of (wdt:P31)            | ice hockey team (wd:Q4498974)                                                    |
| Florida Panthers (wd:Q204623) | country (wdt:P17)                | United States (wd:Q30)                                                           |
| Florida Panthers (wd:Q204623) | Commons category (wdt:P373)      | Florida Panthers                                                                 |
| Florida Panthers (wd:Q204623) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/FloridaPanthersWordmark.gif> |
| Florida Panthers (wd:Q204623) | inception (wdt:P571)             | 1993-01-01T00:00:00Z                                                             |
| Florida Panthers (wd:Q204623) | sport (wdt:P641)                 | ice hockey (wd:Q41466)                                                           |
| Florida Panthers (wd:Q204623) | official website (wdt:P856)      | <http://panthers.nhl.com/>                                                       |
| Florida Panthers (wd:Q204623) | award received (wdt:P166)        | Prince of Wales Trophy (wd:Q1133502)                                             |
| Florida Panthers (wd:Q204623) | owned by (wdt:P127)              | Vincent Viola (wd:Q21062504)                                                     |
| Florida Panthers (wd:Q204623) | topic's main category (wdt:P910) | wd:Q6391697                                                                      |
| ...                           | ...                              | ...                                                                              |

[Assistant]
From the list of triples, there isn't a direct property indicating the venue where the Florida Panthers play. However, we can search for properties related to venues or stadiums for the Florida Panthers. We can use the `search_property_of_entity` function to find such properties.

I'll use the `search_property_of_entity` function to find properties related to venues or stadiums for the Florida Panthers (`wd:Q204623`).

[Tool]
Top 10 property alternatives:
1. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as p/wdt): home stadium or venue of a sports team or applicable performing arts organization, location
2. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
3. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as p/wdt): league or competition in which team or player has played, or in which an event occurs, participant in
4. competition won, also known as won event, won, winner of, victory (wd:P2522 as p/wdt): competition or sports event won by the subject, participant in
5. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
6. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
7. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
8. sport, also known as play, plays, sport played, sports (wd:P641 as p/wdt): sport that the subject participates or participated in or is associated with
9. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
10. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as p/wdt): award or recognition received by a person, organization or creative work

No other items found

[Assistant]
Based on the search results, the property `wd:P115` (home venue) seems to be the most relevant for finding the venue where the Florida Panthers play their home games.

Now, let's construct a SPARQL query to retrieve the venue where the Florida Panthers play.

The SPARQL query will look like this:

```sparql
SELECT ?venue WHERE {
  wd:Q204623 wdt:P115 ?venue .
}
```

I will now execute this query to get the result.

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

[Assistant]
The SPARQL query returned the venue where the Florida Panthers play their home games. According to the query result, the Florida Panthers play at the Amerant Bank Arena (wd:Q795526).

To provide a complete answer, I will include the label of the venue as well.

Here is the final SPARQL query:

```sparql
SELECT ?venue ?venueLabel WHERE {
  wd:Q204623 wdt:P115 ?venue .
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
```

Since the endpoint does not support `SERVICE wikibase:label`, I will modify the query to use `rdfs:label` instead.

Final SPARQL query:

```sparql
SELECT ?venue ?venueLabel WHERE {
  wd:Q204623 wdt:P115 ?venue .
  OPTIONAL { ?venue rdfs:label ?venueLabel . FILTER (lang(?venueLabel) = 'en') }
}
```

I will now execute this final SPARQL query to get the result.

[Tool]
Got 1 row and 2 columns:
| ?venue                          | ?venueLabel        |
| ------------------------------- | ------------------ |
| Amerant Bank Arena (wd:Q795526) | Amerant Bank Arena |

[Assistant]
The final SPARQL query has successfully retrieved the venue where the Florida Panthers play their home games. The result shows that the Florida Panthers play at the Amerant Bank Arena.

I will now provide the final answer using the `answer` function.

[Tool] Stopped generation process


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

The Battle of San Juan Hill took place in Cuba.

[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 "What country did the Battle of San Juan Hill take place?", we need to identify the relevant entity for the Battle of San Juan Hill and then find out what country it took place in.

First, let's search for the entity corresponding to the Battle of San Juan Hill.

[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]
From the search results, the correct entity for the Battle of San Juan Hill is `wd:Q1215310`. Next, we need to find out what country this battle took place in. We can do this by searching for properties related to location or place of the battle.

Let's search for properties related to location for the Battle of San Juan Hill.

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

No other items found

[Assistant]
Based on the search results, the property `wdt:P17` (country) seems to be the most relevant for finding the country where the Battle of San Juan Hill took place. Let's construct a SPARQL query to find the country associated with the Battle of San Juan Hill using this property.

I'll now create and execute the SPARQL query.

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

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

Now, I will provide the final answer:

[Tool] Stopped generation process