PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?language WHERE {
  VALUES ?ethnicGroup {
    wd:Q794 wd:Q12223 wd:Q201501
  }
  ?ethnicGroup wdt:P37 | wdt:P1412 ?language .
}

The languages spoken in Iran include Persian (Farsi), Kurdish, Arabic, Turkish, Balochi, and Urdu.

[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 the languages spoken in Iran, we need to identify the entity for Iran and then find the properties related to languages spoken in that country. 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 `wd:Q794`, which represents the country Iran. Next, we need to find the property that indicates the languages spoken in a country. Let's search for properties related to "language" or "languages spoken".

[Tool]
Top 10 property alternatives:
1. programmed in, also known as language, programming language, written in (wd:P277 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): 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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): dialect, graphic or phonetic variety that this lexeme, form or sense is used in

[Assistant]
The property `wd:P37` (official language) seems to be the most relevant for finding the languages spoken in a country. Let's use this property to query the languages spoken in Iran (`wd:Q794`).

I'll construct a simple SPARQL query to retrieve the languages spoken in Iran and execute it.

```sparql
SELECT ?language
WHERE {
  wd:Q794 wdt:P37 ?language .
}
```

Let's run this query.

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

[Assistant]
The query returned that Persian (Farsi) is the official language of Iran. However, to provide a more comprehensive answer, we should also check if there are other languages spoken in Iran. We can use the property `wd:P1412` (languages spoken, written or signed) to find additional languages.

Let's modify the query to include both official and spoken languages:

```sparql
SELECT ?language
WHERE {
  VALUES ?property { wdt:P37 wdt:P1412 }
  wd:Q794 ?property ?language .
}
```

Let's run this updated query.

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

[Assistant]
The updated query still only returns Persian (Farsi) as the language spoken in Iran. This suggests that either the data in Wikidata is limited or that other languages spoken in Iran are not linked through these properties.

To ensure we are not missing any information, let's also check if there are any other properties that might indicate languages spoken in Iran. We can list the properties of Iran to see if there are any other relevant properties.

Let's run a list query to see the properties of Iran.

```sparql
SELECT ?property ?value
WHERE {
  wd:Q794 ?property ?value .
}
LIMIT 100
```

Let's execute this query.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property          | ?value                        |
| ------------------ | ----------------------------- |
| schema:description | "állam Délnyugat-Ázsiában"@hu |
| schema:description | "Batı Asya'da bir ülke"@tr    |
| schema:description | country in Western Asia       |
| schema:description | country in Western Asia       |
| schema:description | country in Western Asia       |
| ...                | ...                           |
| schema:description | "서아시아의 국가"@ko                 |
| schema:description | "中东地区国家"@zh-hans              |
| schema:description | "中東一國"@yue                    |
| schema:description | "亚洲国家"@zh-cn                  |
| schema:description | "亚洲国家"@zh-my                  |

[Assistant]
The list query shows various properties and values for Iran, but it does not immediately reveal any additional languages spoken in the country beyond Persian (Farsi). However, we can see that there are multiple descriptions in different languages, which might indicate that other languages are spoken in Iran.

To further investigate, let's specifically look for properties related to ethnic groups or demographics, as these might provide more context about the languages spoken in Iran. We can search for properties related to "ethnic group" or "demographics".

Let's search for these properties.

[Tool]
Top 10 property alternatives:
1. ethnic group, also known as ethnic or cultural origin, tribal affiliation, race, people, ethnicity (wd:P172 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
2. diaspora, also known as diaspora of, part of diaspora (wd:P3833 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): diaspora that a cultural group belongs to
3. tribe, also known as community, member in tribe, member of tribe, tribal affiliation (wd:P12011 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): recognised membership in a society, mainly denoted by shared cultural heritage; for ethnicity use P172
4. classification of race, also known as classification of cycling race (wd:P2502 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): race for which this classification applies
5. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): the object is a country that recognizes the subject as its citizen, country
6. demographics of topic, also known as demography of topic (wd:P9241 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): item that deals with demographics of the subject
7. population, also known as inhabitants, human population (wd:P1082 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of people inhabiting the place; number of people of subject
8. ancestral home, also known as native village, native place, ancestral village, ancestral homeplace, ancestral place (wd:P66 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): place of origin for ancestors of subject, significant place
9. number of speakers, writers, or signers, also known as signers of a language, speakers of language, writers of a language (wd:P1098 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of people who use a language
10. blood type, also known as blood group (wd:P1853 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): blood type of the human or animal

[Assistant]
The property `wd:P172` (ethnic group) seems relevant for finding the ethnic groups in Iran, which might help us identify other languages spoken in the country. Let's use this property to find the ethnic groups in Iran and then check the languages associated with those groups.

First, let's find the ethnic groups in Iran:

```sparql
SELECT ?ethnicGroup
WHERE {
  wd:Q794 wdt:P172 ?ethnicGroup .
}
```

Let's execute this query.

[Tool]
Got 8 rows and 1 column:
| ?ethnicGroup                       |
| ---------------------------------- |
| Kurds (wd:Q12223)                  |
| Baloch (wd:Q201501)                |
| Iranian Azerbaijanis (wd:Q2426677) |
| Iranian Arabs (wd:Q2859361)        |
| Iranian Turkmen (wd:Q4466166)      |
| Persians (wd:Q484206)              |
| Lurs (wd:Q508194)                  |
| Turkic peoples (wd:Q83328)         |

[Assistant]
The query returned several ethnic groups in Iran. Now, let's find the languages associated with these ethnic groups. We can use the property `wd:P37` (official language) or `wd:P1412` (languages spoken, written or signed) to find the languages spoken by these groups.

Let's construct a query to find the languages associated with these ethnic groups:

```sparql
SELECT ?ethnicGroup ?language
WHERE {
  VALUES ?ethnicGroup { wd:Q12223 wd:Q201501 wd:Q2426677 wd:Q2859361 wd:Q4466166 wd:Q484206 wd:Q508194 wd:Q83328 }
  ?ethnicGroup wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool]
Got 7 rows and 2 columns:
| ?ethnicGroup        | ?language           |
| ------------------- | ------------------- |
| Kurds (wd:Q12223)   | Arabic (wd:Q13955)  |
| Kurds (wd:Q12223)   | Turkish (wd:Q256)   |
| Kurds (wd:Q12223)   | Kurdish (wd:Q36368) |
| Kurds (wd:Q12223)   | Persian (wd:Q9168)  |
| Baloch (wd:Q201501) | Urdu (wd:Q1617)     |
| Baloch (wd:Q201501) | Balochi (wd:Q33049) |
| Baloch (wd:Q201501) | Persian (wd:Q9168)  |

[Assistant]
The query returned the languages associated with some of the ethnic groups in Iran. Here are the languages spoken by these groups:

- **Kurds**: Arabic, Turkish, Kurdish, Persian
- **Baloch**: Urdu, Balochi, Persian

However, the query did not return languages for all the ethnic groups listed. To ensure we have a comprehensive list, let's also check the languages associated with the remaining ethnic groups: Iranian Azerbaijanis, Iranian Arabs, Iranian Turkmen, Persians, Lurs, and Turkic peoples.

We can modify the query to include these groups as well:

```sparql
SELECT ?ethnicGroup ?language
WHERE {
  VALUES ?ethnicGroup { wd:Q12223 wd:Q201501 wd:Q2426677 wd:Q2859361 wd:Q4466166 wd:Q484206 wd:Q508194 wd:Q83328 }
  ?ethnicGroup wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query again to ensure we capture all the languages.

[Tool]
Got 7 rows and 2 columns:
| ?ethnicGroup        | ?language           |
| ------------------- | ------------------- |
| Kurds (wd:Q12223)   | Arabic (wd:Q13955)  |
| Kurds (wd:Q12223)   | Turkish (wd:Q256)   |
| Kurds (wd:Q12223)   | Kurdish (wd:Q36368) |
| Kurds (wd:Q12223)   | Persian (wd:Q9168)  |
| Baloch (wd:Q201501) | Urdu (wd:Q1617)     |
| Baloch (wd:Q201501) | Balochi (wd:Q33049) |
| Baloch (wd:Q201501) | Persian (wd:Q9168)  |

[Assistant]
The query results show the languages associated with the Kurds and Baloch ethnic groups in Iran. However, it did not return any languages for the other ethnic groups: Iranian Azerbaijanis, Iranian Arabs, Iranian Turkmen, Persians, Lurs, and Turkic peoples.

To ensure we have a comprehensive list, let's manually check the languages associated with these groups by running individual queries for each group.

### Iranian Azerbaijanis
```sparql
SELECT ?language
WHERE {
  wd:Q2426677 wdt:P37|wdt:P1412 ?language .
}
```

### Iranian Arabs
```sparql
SELECT ?language
WHERE {
  wd:Q2859361 wdt:P37|wdt:P1412 ?language .
}
```

### Iranian Turkmen
```sparql
SELECT ?language
WHERE {
  wd:Q4466166 wdt:P37|wdt:P1412 ?language .
}
```

### Persians
```sparql
SELECT ?language
WHERE {
  wd:Q484206 wdt:P37|wdt:P1412 ?language .
}
```

### Lurs
```sparql
SELECT ?language
WHERE {
  wd:Q508194 wdt:P37|wdt:P1412 ?language .
}
```

### Turkic peoples
```sparql
SELECT ?language
WHERE {
  wd:Q83328 wdt:P37|wdt:P1412 ?language .
}
```

Let's execute these queries one by one.

### Iranian Azerbaijanis
```sparql
SELECT ?language
WHERE {
  wd:Q2426677 wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
The query for Iranian Azerbaijanis did not return any languages. Let's move on to the next group.

### Iranian Arabs
```sparql
SELECT ?language
WHERE {
  wd:Q2859361 wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
The query for Iranian Arabs also did not return any languages. Let's move on to the next group.

### Iranian Turkmen
```sparql
SELECT ?language
WHERE {
  wd:Q4466166 wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
The query for Iranian Turkmen did not return any languages. Let's move on to the next group.

### Persians
```sparql
SELECT ?language
WHERE {
  wd:Q484206 wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
The query for Persians did not return any languages. Let's move on to the next group.

### Lurs
```sparql
SELECT ?language
WHERE {
  wd:Q508194 wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
The query for Lurs did not return any languages. Let's move on to the last group.

### Turkic peoples
```sparql
SELECT ?language
WHERE {
  wd:Q83328 wdt:P37|wdt:P1412 ?language .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
The query for Turkic peoples also did not return any languages.

Given that the queries for the remaining ethnic groups did not return any languages, we can conclude that the data in Wikidata is limited for these specific groups. However, based on the information we have, the languages spoken in Iran include:

- **Persian (Farsi)**: Official language
- **Kurdish**
- **Arabic**
- **Turkish**
- **Balochi**
- **Urdu**

To provide a comprehensive answer, we can include these languages and note that there may be other languages spoken by smaller ethnic groups that are not represented in the data.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?language
WHERE {
  VALUES ?ethnicGroup { wd:Q794 wd:Q12223 wd:Q201501 }
  ?ethnicGroup wdt:P37|wdt:P1412 ?language .
}
```

The languages spoken in Iran include Persian (Farsi), Kurdish, Arabic, Turkish, Balochi, and Urdu.

Let's finalize the answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?college ?collegeLabel WHERE {
  wd:Q134262 wdt:P69 ?college .
  FILTER NOT EXISTS {
    ?college wdt:P31 wd:Q9826
  }
  OPTIONAL {
    ?college rdfs:label ?collegeLabel .
    FILTER ( LANG( ?collegeLabel ) = "en" )
  }
}

Got 4 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                         |
| Washington University in St. Louis (wd:Q777403) | Washington University in St. Louis |

Tennessee Williams attended the following colleges and universities: University of Iowa, University of Missouri, New School, and Washington University in St. Louis.

[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 need to first identify the entity for Tennessee Williams and then look for the property that indicates his education or college attendance. Let's 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 top result for Tennessee Williams is `wd:Q134262`, which represents the American playwright. Now, I will search for properties related to education or college attendance to find out where he went to college.

[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 prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): educational institution attended by subject, affiliation
2. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
3. Commons gallery, also known as gallery (wd:P935 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): name of the Wikimedia Commons gallery page(s) related to this item (is suitable to allow multiple links to more gallery pages)
4. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): primary topic of a work (see also P180: depicts), depicts
5. culture, also known as archaeological culture, civilisation, civilization (wd:P2596 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): human culture or people (or several cultures) associated with this item, part of
6. ethnic group, also known as ethnic or cultural origin, tribal affiliation, race, people, ethnicity (wd:P172 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): subject's ethnicity (consensus is that a VERY high standard of p...agreed on by scholars, or 3) is fictional and portrayed as such)
7. platform, also known as computing platform, runtime, published on, platforms, hardware (wd:P400 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): platform for which a work was developed or released, or the specific platform version of a software product
8. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
9. product, material, or service produced or provided, also known as products, product or service provided, product, material, or service produced or provided, product, output (wd:P1056 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
10. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): significant or notable events associated with the subject

[Assistant]
The property `wd:P69` (educated at) seems to be the most relevant for finding out where Tennessee Williams went to college. Now, I will construct a SPARQL query to retrieve the educational institutions he attended.

Let's execute the query:

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

This query will return the colleges or universities that Tennessee Williams attended, along with their labels in English.

[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 several educational institutions that Tennessee Williams attended. These include:

- University of Iowa
- University of Missouri
- New School
- Soldan International Studies High School
- Washington University in St. Louis
- University City High School

To provide a concise answer, I will list the colleges and universities he attended, excluding high schools.

Here is the final SPARQL query and answer:

```sparql
SELECT ?college ?collegeLabel
WHERE {
  wd:Q134262 wdt:P69 ?college .
  FILTER NOT EXISTS { ?college wdt:P31 wd:Q3914 }  # Exclude high schools
  OPTIONAL { ?college rdfs:label ?collegeLabel . FILTER(LANG(?collegeLabel) = "en") }
}
```

This query filters out high schools by checking if the institution is an instance of a high school (`wd:Q3914`).

Let's execute this refined query.

[Tool]
SPARQL execution failed:
failed to parse input: parse error at position 225 for token EOF with content '' (the input most likely does not follow the grammar)

[Assistant]
It looks like there was a syntax error in the SPARQL query. The comment symbol `#` is causing the issue because it comments out the rest of the line. I will remove the comment and re-execute the query.

Here is the corrected SPARQL query:

```sparql
SELECT ?college ?collegeLabel
WHERE {
  wd:Q134262 wdt:P69 ?college .
  FILTER NOT EXISTS { ?college wdt:P31 wd:Q3914 }
  OPTIONAL { ?college rdfs:label ?collegeLabel . FILTER(LANG(?collegeLabel) = "en") }
}
```

Let's execute this query again.

[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]
It seems that the filter to exclude high schools did not work as expected. Let's verify the instances of the educational institutions to ensure that we are correctly identifying high schools and excluding them.

I will check the instances of the educational institutions to see if they are indeed high schools.

Let's execute a query to check the instances:

```sparql
SELECT ?college ?collegeLabel ?instanceOf
WHERE {
  wd:Q134262 wdt:P69 ?college .
  OPTIONAL { ?college rdfs:label ?collegeLabel . FILTER(LANG(?collegeLabel) = "en") }
  OPTIONAL { ?college wdt:P31 ?instanceOf . ?instanceOf rdfs:label ?instanceOfLabel . FILTER(LANG(?instanceOfLabel) = "en") }
}
```

This query will return the colleges and their instances, along with labels in English.

[Tool]
Got 14 rows and 4 columns, showing the first 5 and last 5 rows below:
| ?college                                               | ?collegeLabel                            | ?instanceOf                                                        | ?instanceOfLabel                                    |
| ------------------------------------------------------ | ---------------------------------------- | ------------------------------------------------------------------ | --------------------------------------------------- |
| University of Iowa (wd:Q182973)                        | University of Iowa                       | university (wd:Q3918)                                              | university                                          |
| University of Iowa (wd:Q182973)                        | University of Iowa                       | public educational institution of the United States (wd:Q23002039) | public educational institution of the United States |
| University of Iowa (wd:Q182973)                        | University of Iowa                       | public research university (wd:Q62078547)                          | public research university                          |
| University of Missouri (wd:Q579968)                    | University of Missouri                   | public educational institution of the United States (wd:Q23002039) | public educational institution of the United States |
| University of Missouri (wd:Q579968)                    | University of Missouri                   | public research university (wd:Q62078547)                          | public research university                          |
| ...                                                    | ...                                      | ...                                                                | ...                                                 |
| The New School (wd:Q599316)                            | New School                               | private university (wd:Q902104)                                    | private university                                  |
| Soldan International Studies High School (wd:Q7557201) | Soldan International Studies High School | high school (wd:Q9826)                                             | high school                                         |
| Washington University in St. Louis (wd:Q777403)        | Washington University in St. Louis       | university (wd:Q3918)                                              | university                                          |
| Washington University in St. Louis (wd:Q777403)        | Washington University in St. Louis       | private not-for-profit educational institution (wd:Q23002054)      | private not-for-profit educational institution      |
| University City High School (wd:Q7894583)              | University City High School              | high school (wd:Q9826)                                             | high school                                         |

[Assistant]
From the results, we can see that the institutions are correctly identified as either universities or high schools. Specifically:

- **University of Iowa** is a university.
- **University of Missouri** is a university.
- **New School** is a private university.
- **Soldan International Studies High School** is a high school.
- **Washington University in St. Louis** is a university.
- **University City High School** is a high school.

Now, I will refine the SPARQL query to exclude high schools by filtering out instances of `wd:Q9826` (high school).

Here is the final SPARQL query:

```sparql
SELECT ?college ?collegeLabel
WHERE {
  wd:Q134262 wdt:P69 ?college .
  FILTER NOT EXISTS { ?college wdt:P31 wd:Q9826 }
  OPTIONAL { ?college rdfs:label ?collegeLabel . FILTER(LANG(?collegeLabel) = "en") }
}
```

Let's execute this query to get the final list of colleges and universities that Tennessee Williams attended.

[Tool]
Got 4 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                         |
| Washington University in St. Louis (wd:Q777403) | Washington University in St. Louis |

[Assistant]
The final SPARQL query has successfully filtered out the high schools and returned the colleges and universities that Tennessee Williams attended. These are:

- University of Iowa
- University of Missouri
- New School
- Washington University in St. Louis

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

```sparql
SELECT ?college ?collegeLabel
WHERE {
  wd:Q134262 wdt:P69 ?college .
  FILTER NOT EXISTS { ?college wdt:P31 wd:Q9826 }
  OPTIONAL { ?college rdfs:label ?collegeLabel . FILTER(LANG(?collegeLabel) = "en") }
}
```

Tennessee Williams attended the following colleges and universities:
- University of Iowa
- University of Missouri
- New School
- Washington University in St. Louis

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?coordinates WHERE {
  wd:Q8321 wdt:P625 ?coordinates
}

The Illinois River is located at the following coordinates: POINT(-90.454200 38.970300) and POINT(-88.260300 41.393600). These coordinates can be used to locate the Illinois River on a map.

[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 about where the Illinois River is located on a map, we need to find the geographical coordinates of the Illinois River. We can start by searching for the entity "Illinois River" in the knowledge graph.

Let's search for the entity "Illinois River" in Wikidata.

[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 top result for "Illinois River" is `wd:Q8321`, which is the Illinois tributary of the Mississippi River in the United States. This is likely the Illinois River we are looking for.

Next, we need to find the geographical coordinates of the Illinois River. We can use the property `wdt:P625` (coordinate location) to get the coordinates.

Let's execute a SPARQL query to get the coordinates of the Illinois River.

[Tool]
Got 2 rows and 1 column:
| ?coordinates                |
| --------------------------- |
| POINT(-90.454200 38.970300) |
| POINT(-88.260300 41.393600) |

[Assistant]
The Illinois River has two sets of coordinates:

1. `POINT(-90.454200 38.970300)`
2. `POINT(-88.260300 41.393600)`

These coordinates represent different points along the river. To provide a more precise answer, we can include both coordinates in the response.

Here is the final SPARQL query and answer:

```sparql
SELECT ?coordinates WHERE {
  wd:Q8321 wdt:P625 ?coordinates
}
```

The Illinois River is located at the following coordinates:
- `POINT(-90.454200 38.970300)`
- `POINT(-88.260300 41.393600)`

These coordinates can be used to locate the Illinois River on a map.

Now, let's finalize the answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?work WHERE {
  ?work wdt:P161 wd:Q587156
}

Got 12 rows and 1 column, showing the first 5 and last 5 rows below:
| ?work                                              |
| -------------------------------------------------- |
| The Car (wd:Q1197355)                              |
| Escape to Witch Mountain (wd:Q1212511)             |
| Return from Witch Mountain (wd:Q1925695)           |
| Raid on Entebbe (wd:Q2305094)                      |
| Tuff Turf (wd:Q2362823)                            |
| ...                                                |
| Black Snake Moan (wd:Q468660)                      |
| Hello, Larry (wd:Q5708520)                         |
| No Deposit, No Return (wd:Q7044096)                |
| The Real Housewives of Beverly Hills (wd:Q7759591) |
| Race to Witch Mountain (wd:Q922006)                |

Kim Richards has appeared in several works including 'The Car,' 'Escape to Witch Mountain,' 'Return from Witch Mountain,' 'Raid on Entebbe,' 'Tuff Turf,' 'Black Snake Moan,' 'Hello, Larry,' 'No Deposit, No Return,' 'The Real Housewives of Beverly Hills,' and 'Race to Witch Mountain.'

[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 about what Kim Richards was in, we need to identify Kim Richards as an entity and then find out the roles or projects she was involved in. Let's start by searching for Kim Richards in the knowledge graph.

[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] From the search results, the most relevant entity for Kim Richards is the American actress and television personality (wd:Q587156). Next, we need to find out the roles or projects she was involved in. We can look for properties such as "occupation," "notable work," or "filmography." Let's start by listing the triples where Kim Richards is the subject to see what information is available.

[Tool]
Got 297 rows and 3 columns, showing the first 10 rows below:
| subject                   | property                         | object                                                                                |
| ------------------------- | -------------------------------- | -------------------------------------------------------------------------------------------------------------- |
| Kim Richards (wd:Q587156) | instance of (wdt:P31)            | human (wd:Q5)                                                                                |
| Kim Richards (wd:Q587156) | occupation (wdt:P106)            | actor (wd:Q33999)                                                                                |
| Kim Richards (wd:Q587156) | sex or gender (wdt:P21)          | female (wd:Q6581072)                                                                                |
| Kim Richards (wd:Q587156) | given name (wdt:P735)            | Kim (wd:Q637123)                                                                                |
| Kim Richards (wd:Q587156) | date of birth (wdt:P569)         | 1964-09-19T00:00:00Z                                                                                |
| Kim Richards (wd:Q587156) | Commons category (wdt:P373)      | Kim Richards                                                                                |
| Kim Richards (wd:Q587156) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Nanny%20and%20the%20Professor%20Kim%20Richards%201970.jpg> |
| Kim Richards (wd:Q587156) | country of citizenship (wdt:P27) | United States (wd:Q30)                                                                                |
| Kim Richards (wd:Q587156) | family name (wdt:P734)           | Richards (wd:Q608991)                                                                                |
| Kim Richards (wd:Q587156) | place of birth (wdt:P19)         | Mineola (wd:Q2349502)                                                                                |
| ...                       | ...                              | ...                                                                                |

[Assistant] From the list of triples, we can see that Kim Richards is an actor. To find out the specific roles or projects she was involved in, we need to look for properties such as "notable work" or "filmography." Let's search for these properties to see if they are available.

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): notable scientific, artistic or literary work, or other work of significance among subject's works
2. present in work, also known as is shown in, in work, in narrative, character in, from work (wd:P1441 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): this (fictional or fictionalized) entity, place, or person appea...r works, P1343 for entities described in non-fictional accounts)
3. notable role, also known as notable character role, notable for character role, notable for role (wd:P10606 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): role in a theatrical, motion picture, television, or other work that a performer is particularly known for, notable work
4. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
5. product, material, or service produced or provided, also known as products, product or service provided, product, material, or service produced or provided, product, output (wd:P1056 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
6. significant place, also known as sign. place, significant location, significant landmark, place associated with the subject, notable place (wd:P7153 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): significant or notable places associated with the subject, location
7. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): significant or notable events associated with the subject
8. exemplar of, also known as contains work, includes work, contents (wd:P1574 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): Links an individual copy of a work to the item for that work or edition, instance of
9. presented works, also known as displayed works, performed works (wd:P13360 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): works of art or creative works performed, displayed or presented at a given event
10. contributed to creative work, also known as contributor to, contributor, contributed to, collaborator on, contributed to work (wd:P3919 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): person is cited as contributing to some creative or published work or series (qualify with "subject has role", P2868)

[Assistant] The property "notable work" (wd:P800) seems to be the most relevant for finding the roles or projects Kim Richards was involved in. Let's use this property in a SPARQL query to find out the notable works of Kim Richards.

[Tool] Got no rows and 1 columns

[Assistant] It appears that the property "notable work" (wd:P800) does not yield any results for Kim Richards. Let's try another approach by looking for properties related to her roles in films or television shows. The property "notable role" (wd:P10606) might be more appropriate. Let's check if this property provides any results.

[Tool] Got no rows and 1 columns

[Assistant] The property "notable role" (wd:P10606) also did not yield any results. Let's try a different approach by looking for properties related to her filmography or the works she has been part of. We can use the property "cast member" (wd:P161) to find the films or TV shows she has appeared in. Let's execute a query using this property.

[Tool]
Got 12 rows and 1 column, showing the first 5 and last 5 rows below:
| ?work                                              |
| -------------------------------------------------- |
| The Car (wd:Q1197355)                              |
| Escape to Witch Mountain (wd:Q1212511)             |
| Return from Witch Mountain (wd:Q1925695)           |
| Raid on Entebbe (wd:Q2305094)                      |
| Tuff Turf (wd:Q2362823)                            |
| ...                                                |
| Black Snake Moan (wd:Q468660)                      |
| Hello, Larry (wd:Q5708520)                         |
| No Deposit, No Return (wd:Q7044096)                |
| The Real Housewives of Beverly Hills (wd:Q7759591) |
| Race to Witch Mountain (wd:Q922006)                |

[Assistant]
The query using the property "cast member" (wd:P161) returned several works in which Kim Richards has appeared. These include films and TV shows such as "The Car," "Escape to Witch Mountain," "Return from Witch Mountain," "Raid on Entebbe," "Tuff Turf," "Black Snake Moan," "Hello, Larry," "No Deposit, No Return," "The Real Housewives of Beverly Hills," and "Race to Witch Mountain."

Let's finalize the SPARQL query and provide the answer to the user question.

[Tool] Stopped generation process


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

Got 154 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?chineseWritingSystem         | ?label                |
| ----------------------------- | --------------------- |
| Chinese characters (wd:Q8201) | "aksara Cina"@bjn     |
| Chinese characters (wd:Q8201) | "Aksara Cinten"@jv    |
| Chinese characters (wd:Q8201) | "Aksara Tionghoa"@su  |
| Chinese characters (wd:Q8201) | "Alfabet cinesc"@lld  |
| Chinese characters (wd:Q8201) | "alfabeto cinezo"@vec |
| ...                           | ...                   |
| Chinese characters (wd:Q8201) | "漢字"@yue              |
| Chinese characters (wd:Q8201) | "漢字"@zh-hant          |
| Chinese characters (wd:Q8201) | "漢字"@zh-hk            |
| Chinese characters (wd:Q8201) | "漢字"@zh-mo            |
| Chinese characters (wd:Q8201) | "漢字"@zh-tw            |

The Chinese writing system is called Chinese characters.

[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 what the Chinese writing system is called, we need to identify the relevant entity in the knowledge graph. Let's start by searching for the term "Chinese writing system" to find the corresponding entity.

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

[Assistant]
From the search results, the most relevant entity appears to be "writing system, also known as wenzi, ven-tsee, script, graphemics" (wd:Q8192). However, this entity seems to be a general term for any writing system. We need to find a more specific entity that refers to the Chinese writing system.

Let's try searching again with a more specific query, such as "Chinese script" or "Chinese characters."

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

[Assistant]
The most relevant entity from the search results is "Chinese characters, also known as wenzi, ven-tsee, traditional Vietnamese characters, traditional Korean characters, sinographs" (wd:Q8201). This entity specifically refers to the logographic writing system used in Chinese and other East Asian languages.

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

```sparql
SELECT ?chineseWritingSystem ?label
WHERE {
  wd:Q8201 rdfs:label ?label .
  BIND(wd:Q8201 AS ?chineseWritingSystem)
}
```

This query will return the IRI and label of the Chinese writing system.

[Tool]
Got 154 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?chineseWritingSystem         | ?label                |
| ----------------------------- | --------------------- |
| Chinese characters (wd:Q8201) | "aksara Cina"@bjn     |
| Chinese characters (wd:Q8201) | "Aksara Cinten"@jv    |
| Chinese characters (wd:Q8201) | "Aksara Tionghoa"@su  |
| Chinese characters (wd:Q8201) | "Alfabet cinesc"@lld  |
| Chinese characters (wd:Q8201) | "alfabeto cinezo"@vec |
| ...                           | ...                   |
| Chinese characters (wd:Q8201) | "漢字"@yue              |
| Chinese characters (wd:Q8201) | "漢字"@zh-hant          |
| Chinese characters (wd:Q8201) | "漢字"@zh-hk            |
| Chinese characters (wd:Q8201) | "漢字"@zh-mo            |
| Chinese characters (wd:Q8201) | "漢字"@zh-tw            |

[Assistant]
The query returned multiple labels for the Chinese writing system in different languages. The most common and widely recognized term in English is "Chinese characters."

Therefore, the Chinese writing system is commonly referred to as **Chinese characters**.

Here is the final SPARQL query and answer:

```sparql
SELECT ?chineseWritingSystem ?label
WHERE {
  wd:Q8201 rdfs:label ?label .
  BIND(wd:Q8201 AS ?chineseWritingSystem)
}
```

The Chinese writing system is called **Chinese characters**.

[Tool] Stopped generation process


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

Ron Rivera

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

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

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

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

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

[User] who will coach carolina panthers in 2013?

[Assistant]
To answer the question about who coached the Carolina Panthers in 2013, we need to identify the following:

1. The entity for the Carolina Panthers.
2. The property for the coach of a team.
3. The specific coach for the Carolina Panthers in 2013.

Let's start by searching for the entity for 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 top result for the Carolina Panthers is `wd:Q330120`, which represents the American football team. Next, we need to find the property for the coach of a team. Let's search for this property.

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

[Assistant]
The most relevant property for the coach of a team is `wd:P286`, which stands for "head coach." Now, we need to find out who the head coach of the Carolina Panthers was in 2013. We will use the `wd:Q330120` entity for the Carolina Panthers and the `wd:P286` property to find the coach. Additionally, we need to filter the results to the year 2013.

Let's construct and execute a SPARQL query to find the head coach of the Carolina Panthers in 2013.

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

[Assistant]
The head coach of the Carolina Panthers in 2013 was Ron Rivera.

Here is the final SPARQL query and answer:

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

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?spouse WHERE {
  wd:Q42869 wdt:P26 ?spouse
}

Nicolas Cage is married to Riko Shibata.

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

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

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

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

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

[User] who is nicolas cage married too?

[Assistant]
To answer the question about who Nicolas Cage is married to, we need to identify the entity for Nicolas Cage and then find the property that indicates his current spouse or marriage status.

### Step 1: Identify Nicolas Cage's entity
Let's start by searching for Nicolas Cage in the knowledge graph.

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

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

### Step 2: Identify the property for marriage or spouse
Next, we need to find the property that indicates a person's spouse or marriage status. Let's search for properties related to "spouse" or "marriage."

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

[Assistant]
The property `wd:P26` is the correct one for indicating a person's spouse. Now, let's construct a SPARQL query to find out who Nicolas Cage is married to.

### Step 3: Construct the SPARQL query
We will query for the value of the `wd:P26` property for the entity `wd:Q42869` (Nicolas Cage).

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

[Assistant]
The SPARQL query returned that Nicolas Cage is married to Riko Shibata, represented by the entity `wd:Q109355504`.

### Final Answer
Nicolas Cage is married to Riko Shibata.

Let's provide the final answer with the SPARQL query and the result.

[Tool] Stopped generation process