PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?nationality WHERE {
  wd:Q192955 wdt:P27 ?nationality .
}

Got 2 rows and 1 column:
| ?nationality             |
| ------------------------ |
| United Kingdom (wd:Q145) |
| Canada (wd:Q16)          |

Steve Nash's nationalities are represented by the IRIs http://www.wikidata.org/entity/Q145 (United Kingdom) and http://www.wikidata.org/entity/Q16 (Canada).

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

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

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

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

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

[User] what is steve nash nationality?

[Assistant] To find Steve Nash's nationality, we need to identify the entity for Steve Nash and then look for the property that represents nationality. Let's start by searching for the entity corresponding to Steve Nash.

[Tool]
Top 10 entity alternatives:
1. Steve Nash, also known as Stephen John Nash (wd:Q192955): Canadian basketball player and coach, basketball player, basketball coach, businessperson, film director
2. Steve Nash (wd:Q16137022): English rugby league footballer and coach, rugby league player
3. Steve Nash (wd:Q48851147): disc jockey
4. Steve Nash (wd:Q42316961): actor
5. Steven Jackson, also known as Steven Nash Jackson (wd:Q3499240): American baseball player, baseball player
6. Steven G. Nash (wd:Q98756087): American politician, businessperson, politician
7. Steven Roy Nash, also known as Dr. Steven Roy Nash (wd:Q76187877): Peerage person ID=590989
8. Edward Mungo Steven Nash, also known as Edward Nash (wd:Q76187880): (born 1990)
9. Nash, also known as Nash, Texas, Nash, TX (wd:Q978847): city in Bowie County, Texas, United States
10. Nash, also known as Nash, OK, Nash, Oklahoma (wd:Q2321969): town in Grant County, Oklahoma, United States

[Assistant] The top result for "Steve Nash" is the Canadian basketball player and coach, which matches the context of the question. We will use this entity (wd:Q192955) to find his nationality. Next, let's search for the property that represents nationality.

[Tool]
Top 10 property alternatives:
1. 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
2. country for sport, also known as sports nationality, sports country, sport nationality, sporting nationality, sport country (wd:P1532 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): country a person or a team represents when playing a sport, country
3. 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)
4. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): most specific known birth location of a person, animal or fictional character, location
5. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): date on which the subject was born, inception
6. category for people born here, also known as category for place of birth, category of people born here (wd:P1464 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): category item that groups people born in this place, related category
7. occupation, also known as job, work, vocation, profession, employment (wd:P106 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
8. 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
9. 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
10. birthday, also known as b-day, bday, birth day (wd:P3150 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): item for day and month on which the subject was born. Used when full "date of birth" (P569) isn't known., date of birth

[Assistant]
The property "country of citizenship" (wd:P27) seems to be the most appropriate for representing nationality. Now, let's construct a SPARQL query to find Steve Nash's nationality using this property.

Here is the initial SPARQL query:

```sparql
SELECT ?nationality WHERE {
  wd:Q192955 wdt:P27 ?nationality .
}
```

Let's execute this query to see the results.

[Tool]
Got 2 rows and 1 column:
| ?nationality             |
| ------------------------ |
| United Kingdom (wd:Q145) |
| Canada (wd:Q16)          |

[Assistant]
The query returned two nationalities for Steve Nash: the United Kingdom (wd:Q145) and Canada (wd:Q16). This indicates that Steve Nash holds citizenship in both countries.

To provide a clear and concise answer, we can include the labels of these countries in the query. However, since the question does not explicitly request labels, we will stick to the IRIs for the final answer.

Here is the final SPARQL query and answer:

```sparql
SELECT ?nationality WHERE {
  wd:Q192955 wdt:P27 ?nationality .
}
```

Answer: Steve Nash's nationalities are represented by the IRIs `http://www.wikidata.org/entity/Q145` (United Kingdom) and `http://www.wikidata.org/entity/Q16` (Canada).

[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 DISTINCT ?country ?countryLabel WHERE {
  {
    wd:Q181574 wdt:P527 ?country
  } UNION {
    wd:Q181574 wdt:P1001 ?country
  }
  OPTIONAL {
    ?country rdfs:label ?countryLabel .
    FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| ?country               | ?countryLabel            |
| ---------------------- | ------------------------ |
| Canada (wd:Q16)        | Canada                   |
| United States (wd:Q30) | United States of America |
| Mexico (wd:Q96)        | Mexico                   |

The countries involved in NAFTA are: Canada, United States of America, and Mexico.

[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 countries are involved in the nafta?

[Assistant] To answer the question about which countries are involved in NAFTA, we need to identify the entity for NAFTA and then find the countries associated with it. Let's start by searching for the entity representing NAFTA in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. North American Free Trade Agreement, also known as NAFTA (wd:Q181574): trade bloc
2. Nafta, also known as Nefta (wd:Q1026298): Tunisian town
3. nafta (wd:Q1213853): chemical compound
4. Nafta (wd:Q11788993): 1961 film by Stanisław Lenartowicz
5. Nafta (wd:Q6958220): Soviet Union oil company operating abroad
6. Nafta, also known as IMO 8853946 (wd:Q52335786): bunkering tanker built in 1978
7. NAFTA, also known as NAFTA a.s. (wd:Q12772242): Slovak multinational company operating in the field of storage and extraction of hydrocarbons
8. Naftalan (wd:Q152825): city in Azerbaijan
9. Naphtali, also known as Naftali (wd:Q1771963): biblical figure and the sixth son of Jacob through his concubine Bilhah
10. United States–Mexico–Canada Agreement, also known as Canada–United States–Mexico Agreement, CUSMA, NAFTA 2.0, NAFTA Redux, New NAFTA (wd:Q56839716): North American trade bloc

[Assistant] The top result for "NAFTA" is the North American Free Trade Agreement (NAFTA), which is the correct entity we are looking for. Its identifier is `wd:Q181574`. Next, we need to find the countries that are part of this agreement. We can look for properties that might indicate membership or participation in the agreement. A common property for such relationships is "member" or "has member." Let's search for relevant properties.

[Tool]
Top 10 property alternatives:
1. member of political party, also known as political party member, political party, party membership, party, member of party (wd:P102 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): the political party of which a person is or has been a member or otherwise affiliated, member of
2. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
3. used by, also known as armiger, user, played by, of (wd:P1535 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): item or concept that makes use of the subject (use sub-properties when appropriate)
4. operator, also known as offered by, operated by, provided by, run by, service operator (wd:P137 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): person, profession, organization or entity that operates the equipment, facility, or service, used by
5. theorized by, also known as author, supported by, suggested by, proposer, proposed by (wd:P8403 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): subject who suggested or proposed this concept or immaterial invention
6. lyricist, also known as text poet, writer (of song lyrics), words by, text writer, text by (wd:P676 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): author of song lyrics, author
7. author, also known as writer, written by, poet, playwright, maker (wd:P50 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
8. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): sports teams or clubs that the subject represents or represented, member of
9. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
10. member count, also known as total members, number of members, count of members, amount of members, membership (wd:P2124 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): number of entities that are part of an organization at a particular time

[Assistant]
The property "member of" (wd:P463) seems to be the most appropriate for finding the countries that are part of NAFTA. Let's use this property to construct a SPARQL query to find the countries involved in NAFTA.

Here is the initial SPARQL query:

```sparql
SELECT ?country ?countryLabel
WHERE {
  wd:Q181574 wdt:P463 ?country .
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") }
}
```

This query will select the countries that are members of NAFTA and their English labels. Let's execute this query to see the results.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the query did not return any results, which suggests that the property `wdt:P463` (member of) might not be the correct property for this specific context. Let's try another approach by looking at the properties and statements associated with the NAFTA entity to see if there are other properties that might indicate the member countries.

We can use the `list` function to explore the properties and objects associated with the NAFTA entity.

[Tool]
Got 508 rows and 3 columns, showing the first 10 rows below:
| subject                                          | property                                       | object                                                  |
| ------------------------------------------------ | ---------------------------------------------- | ------------------------------------------------------- |
| North American Free Trade Agreement (wd:Q181574) | instance of (wdt:P31)                          | trade bloc (wd:Q1129645)                                |
| North American Free Trade Agreement (wd:Q181574) | Commons category (wdt:P373)                    | North American Free Trade Agreement                     |
| North American Free Trade Agreement (wd:Q181574) | inception (wdt:P571)                           | 1994-01-01T00:00:00Z                                    |
| North American Free Trade Agreement (wd:Q181574) | short name (wdt:P1813)                         | "ALENA"@fr                                              |
| North American Free Trade Agreement (wd:Q181574) | has part(s) (wdt:P527)                         | United States (wd:Q30)                                  |
| North American Free Trade Agreement (wd:Q181574) | official website (wdt:P856)                    | <http://www.nafta-sec-alena.org/>                       |
| North American Free Trade Agreement (wd:Q181574) | follows (wdt:P155)                             | Canada–United States Free Trade Agreement (wd:Q2665587) |
| North American Free Trade Agreement (wd:Q181574) | followed by (wdt:P156)                         | United States–Mexico–Canada Agreement (wd:Q56839716)    |
| North American Free Trade Agreement (wd:Q181574) | on focus list of Wikimedia project (wdt:P5008) | wd:Q6173448                                             |
| North American Free Trade Agreement (wd:Q181574) | applies to jurisdiction (wdt:P1001)            | United States (wd:Q30)                                  |
| ...                                              | ...                                            | ...                                                     |

[Assistant]
From the list of properties and objects associated with the NAFTA entity, we can see that the property `wdt:P527` (has part(s)) and `wdt:P1001` (applies to jurisdiction) both seem to indicate the countries involved in NAFTA.

Let's use these properties to construct a new SPARQL query to find the countries involved in NAFTA.

Here is the updated SPARQL query:

```sparql
SELECT ?country ?countryLabel
WHERE {
  { wd:Q181574 wdt:P527 ?country }
  UNION
  { wd:Q181574 wdt:P1001 ?country }
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") }
}
```

This query will select the countries that are parts of NAFTA or to which NAFTA applies, along with their English labels. Let's execute this query to see the results.

[Tool]
Got 6 rows and 2 columns:
| ?country               | ?countryLabel            |
| ---------------------- | ------------------------ |
| Canada (wd:Q16)        | Canada                   |
| Canada (wd:Q16)        | Canada                   |
| United States (wd:Q30) | United States of America |
| United States (wd:Q30) | United States of America |
| Mexico (wd:Q96)        | Mexico                   |
| Mexico (wd:Q96)        | Mexico                   |

[Assistant]
The query returned the countries involved in NAFTA, but there are duplicate entries for each country. To remove the duplicates, we can use the `DISTINCT` keyword in the SPARQL query.

Here is the revised SPARQL query:

```sparql
SELECT DISTINCT ?country ?countryLabel
WHERE {
  { wd:Q181574 wdt:P527 ?country }
  UNION
  { wd:Q181574 wdt:P1001 ?country }
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") }
}
```

This query will ensure that each country is listed only once. Let's execute this query to see the results.

[Tool]
Got 3 rows and 2 columns:
| ?country               | ?countryLabel            |
| ---------------------- | ------------------------ |
| Canada (wd:Q16)        | Canada                   |
| United States (wd:Q30) | United States of America |
| Mexico (wd:Q96)        | Mexico                   |

[Assistant]
The query has successfully returned the three countries involved in NAFTA: Canada, the United States of America, and Mexico. Each country is listed only once, and their English labels are included.

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

```sparql
SELECT DISTINCT ?country ?countryLabel
WHERE {
  { wd:Q181574 wdt:P527 ?country }
  UNION
  { wd:Q181574 wdt:P1001 ?country }
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") }
}
```

The countries involved in NAFTA are:
- Canada
- United States of America
- Mexico

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

[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 ?placeOfBirth ?placeOfBirthLabel WHERE {
  wd:Q1744 wdt:P19 ?placeOfBirth .
  OPTIONAL {
    ?placeOfBirth rdfs:label ?placeOfBirthLabel .
    FILTER ( LANG( ?placeOfBirthLabel ) = 'en' )
  }
}

Got 1 row and 2 columns:
| ?placeOfBirth         | ?placeOfBirthLabel |
| --------------------- | ------------------ |
| Bay City (wd:Q746820) | Bay City           |

Madonna grew up in Bay City, Michigan.

[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 madonna grew up?

[Assistant] To answer the question "Where did Madonna grow up?", we need to identify the entity for Madonna and then find the place where she grew up. Let's start by searching for the entity representing Madonna in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Madonna, also known as Madonna Ciccone, Madonna Louise Veronica Ciccone, Madonna Louise Ciccone, Madonna L. Ciccone (wd:Q1744): American singer, songwriter, and actress (born 1958), songwriter, singer, actor
2. Mary, also known as Mother of God, Mother of Jesus, Notre Dame, Our Lady, Queen of the Universe (wd:Q345): mother of Jesus
3. Madonna, also known as Madonna: The First Album (wd:Q177943): 1983 self-titled debut studio album by Madonna
4. Madonna (wd:Q1989780): painting composition by Edvard Munch, in different versions
5. Madonna, also known as Madonna in art, The Madonna in art, Virgin Mary in art (wd:Q926743): artistic representation of Mary, either alone or with her child Jesus
6. Madonna (wd:Q19794647): 2015 South Korean film directed by Shin Su-won
7. Madonna (wd:Q1147459): album by …And You Will Know Us by the Trail of Dead
8. Madonna (wd:Q486604): 2010 EP by Secret
9. Madonna, also known as Madonna, MD, Madonna, Maryland (wd:Q6728358): unincorporated community in Harford County, Maryland
10. Virgin and Child with an Angel (Madonna del Latte), also known as Madonna, Madonna with child and John the Baptist (wd:Q3842618): painting by Antonio da Correggio from 1520

[Assistant] The top result for "Madonna" is the American singer, songwriter, and actress Madonna Ciccone (wd:Q1744). Now, let's find the place where she grew up. We need to look for a property that indicates the place of birth or childhood location. Let's search for relevant properties.

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

[Assistant] The property "place of birth" (wd:P19) seems to be the most relevant for finding where Madonna grew up. Let's use this property to query the place of birth for Madonna (wd:Q1744).

[Tool]
Got 1 row and 1 column:
| ?placeOfBirth         |
| --------------------- |
| Bay City (wd:Q746820) |

[Assistant] The place of birth for Madonna is Bay City (wd:Q746820). To provide a more complete answer, let's also include the label for Bay City in the query.

[Tool]
Got 1 row and 2 columns:
| ?placeOfBirth         | ?placeOfBirthLabel |
| --------------------- | ------------------ |
| Bay City (wd:Q746820) | Bay City           |

[Assistant]
The place where Madonna grew up is Bay City, Michigan (wd:Q746820).

Here is the final SPARQL query and answer:

```sparql
SELECT ?placeOfBirth ?placeOfBirthLabel
WHERE {
  wd:Q1744 wdt:P19 ?placeOfBirth .
  OPTIONAL { ?placeOfBirth rdfs:label ?placeOfBirthLabel . FILTER(LANG(?placeOfBirthLabel) = 'en') }
}
```

Answer: Madonna grew up in Bay City, Michigan.

[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 ?governmentForm ?governmentFormLabel WHERE {
  wd:Q928 wdt:P122 ?governmentForm .
  OPTIONAL {
    ?governmentForm rdfs:label ?governmentFormLabel .
    FILTER ( LANG( ?governmentFormLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| ?governmentForm     | ?governmentFormLabel |
| ------------------- | -------------------- |
| republic (wd:Q7270) | republic             |

The form of government in the Philippines is a republic.

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

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

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

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

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

[User] what form of government do we have in the philippines?

[Assistant] To answer the question about the form of government in the Philippines, I need to identify the entity for the Philippines and the property that describes its form of government. Let's start by searching for the entity representing the Philippines.

[Tool]
Top 10 entity alternatives:
1. Philippines, also known as ph, The Philippines, RP, Republic of the Philippines, Philippine Islands (wd:Q928): archipelagic country in Southeast Asia
2. Philippines (wd:Q70886687): scientific article published on 01 January 1966
3. Philippines (wd:Q116754458): scientific article published on 15 November 2001
4. Philippines (wd:Q47409215): scientific article published in August 1986
5. Philippines (wd:Q57715247): entry in Encyclopedia of Global Warming & Climate Change
6. Philippines. (wd:Q39611725): scientific article published on September 1983
7. Philippines (wd:L1259985-S1)
8. Philippines (wd:L476739-F2)
9. Philippines (wd:L254248-F1)
10. Philippines (wd:L254248)

[Assistant] The top result for the Philippines is `wd:Q928`, which represents the archipelagic country in Southeast Asia. Next, I need to find the property that describes the form of government. Let's search for properties related to "government" or "form of government."

[Tool]
Top 10 property alternatives:
1. basic form of government, also known as form of government, government, type of government (wd:P122 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): subject's government
2. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): seat of government of a country, province, state or other type of administrative territorial entity
3. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
4. office held by head of government, also known as title of head of government, position of head of government, office of head of government, head of government's office, position held by head of government (wd:P1313 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): political office that is fulfilled by the head of the government of this item
5. authority, also known as executive authority, governed by, governing authority, governing body, has authority (wd:P797 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): entity having executive power on given entity
6. voting system, also known as electoral system, voting system, type of election (wd:P8196 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): system used for this election or vote
7. Classification of the Functions of Government, also known as COFOG ID, COFOG number (wd:P9798 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): UN classification to compare government agencies across countries
8. office held by head of state, also known as head of state, position held by head of state, state headed by (wd:P1906 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): political office that is fulfilled by the head of state of this item
9. country, also known as sovereign state, state, land, host country (wd:P17 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
10. foundational text, also known as main text, treaty, organic law, founding document, establishing document (wd:P457 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): text through which an institution or object has been created or established

[Assistant]
The property `wd:P122` (basic form of government) seems to be the most relevant for describing the form of government. Now, let's construct a SPARQL query to find the form of government for the Philippines (`wd:Q928`).

I'll start with a simple query to see if this property returns the expected result.

```sparql
SELECT ?governmentForm
WHERE {
  wd:Q928 wdt:P122 ?governmentForm .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| ?governmentForm     |
| ------------------- |
| republic (wd:Q7270) |

[Assistant]
The query returned that the form of government in the Philippines is a republic, represented by the entity `wd:Q7270`. To provide a more complete answer, I will include the label of the government form in the query.

Here is the refined SPARQL query:

```sparql
SELECT ?governmentForm ?governmentFormLabel
WHERE {
  wd:Q928 wdt:P122 ?governmentForm .
  OPTIONAL { ?governmentForm rdfs:label ?governmentFormLabel . FILTER(LANG(?governmentFormLabel) = "en") }
}
```

Let's execute this query to get both the IRI and the label.

[Tool]
Got 1 row and 2 columns:
| ?governmentForm     | ?governmentFormLabel |
| ------------------- | -------------------- |
| republic (wd:Q7270) | republic             |

[Assistant]
The query confirms that the form of government in the Philippines is a republic. Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?governmentForm ?governmentFormLabel
WHERE {
  wd:Q928 wdt:P122 ?governmentForm .
  OPTIONAL { ?governmentForm rdfs:label ?governmentFormLabel . FILTER(LANG(?governmentFormLabel) = "en") }
}
```

The form of government in the Philippines is a republic.

[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 ?movie ?movieLabel WHERE {
  ?movie wdt:P57 wd:Q103646 .
  OPTIONAL {
    ?movie rdfs:label ?movieLabel .
    FILTER ( LANG( ?movieLabel ) = "en" )
  }
}

Got 44 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?movie                             | ?movieLabel         |
| ---------------------------------- | ------------------- |
| Thirteen Lives (wd:Q105962844)     | Thirteen Lives      |
| Apollo 13 (wd:Q106428)             | Apollo 13           |
| Cocoon (wd:Q1105463)               | Cocoon              |
| Splash (wd:Q1127184)               | Splash              |
| Parenthood (wd:Q1137831)           | Parenthood          |
| ...                                | ...                 |
| Frost/Nixon (wd:Q691672)           | Frost/Nixon         |
| Ransom (wd:Q752362)                | Ransom              |
| Far and Away (wd:Q839341)          | Far and Away        |
| Rebuilding Paradise (wd:Q96314873) | Rebuilding Paradise |
| Night Shift (wd:Q997228)           | Night Shift         |

The movies directed by Ron Howard include: Thirteen Lives, Apollo 13, Cocoon, Splash, Parenthood, Frost/Nixon, Ransom, Far and Away, Rebuilding Paradise, Night Shift, and more.

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

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

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

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

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

[User] what movies did ron howard director?

[Assistant] To answer the question about the movies directed by Ron Howard, we need to identify the entity for Ron Howard and the property that indicates he directed a movie. Let's start by searching for the entity "Ron Howard" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Ron Howard, also known as Ronald William Howard (wd:Q103646): American film director, producer, and actor, television producer, television director, television actor, film producer, film director, screenwriter, voice actor, film actor, director, actor
2. Ron Howard (wd:Q4883): American basketball player, basketball player
3. Ron Howard, also known as Ronald Ford Howard (wd:Q3940969): American football player (born 1951), American football player, basketball player
4. Ron Howard, also known as Ronald John Howard (wd:Q19874445): Australian rules footballer (1940-2023), Australian rules football player
5. Ron Howard, also known as Ron Dean Howard (wd:Q107646515): politician
6. Ron Howard (wd:Q112407850)
7. Ron Howard (wd:Q100982902): college basketball player (1986–1986) U.S. International, basketball player
8. Ronald Howard (wd:Q2165643): British actor (1918–1996), television actor, stage actor, actor
9. Ronald A. Howard, also known as Ronald Arthur Howard, Ronald Howard (wd:Q7364615): management engineer, university teacher, American professor, academic
10. Ronald Howard, also known as Vice-Admiral Ronald Howard (wd:Q75253869): (1878-1959)

[Assistant] The top result for "Ron Howard" is the American film director, producer, and actor, which matches our context. We will use this entity (wd:Q103646) to find the movies he directed. Next, we need to identify the property that indicates a person directed a movie. Let's search for the property "directed" in the knowledge graph.

[Tool]
Top 10 property alternatives:
1. organization directed by the office or position, also known as leader of, head of, leads, organization directed by, organization directed from the office (wd:P2389 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): organization which is directed by this office
2. has goal, also known as intention, mission, objective, objective of project or action, project of (wd:P3712 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): desired result or outcome
3. director, also known as film director, movie director, game director, directed by (wd:P57 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator
4. editor-in-chief, also known as lead editor, managing editor, executive editor, editor in chief, directed by (wd:P5769 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): a publication's editorial leader who has final responsibility fo...publishing articles or books in collections or periodical papers
5. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): effect of this item, followed by
6. position held by head of the organization, also known as position held by head of the organisation, secretary's position, president's position, position held by head of the organization, office held by head of the organisation (wd:P2388 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): position of the head of this item
7. director / manager, also known as leader, manager, manager/director, managing director, Officer-in-Charge (wd:P1037 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): person who manages any kind of group, significant person
8. agent of action, also known as being done by, initiator, initiated by, done by, doer (wd:P12995 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): particular item that initiates this action or class of actions
9. depicted by, also known as acted by, shown in, seen in, portrayed by, played by (wd:P1299 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): object depicting this subject (object being creative works such as books, films, paintings)
10. direction (wd:P560 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): relative direction of an entity

[Assistant]
The property "director" (wd:P57) is the most relevant for indicating that a person directed a movie. Now, let's construct a SPARQL query to find the movies directed by Ron Howard (wd:Q103646).

We will start with a simple query to list the movies and their IRIs:

```sparql
SELECT ?movie
WHERE {
  wd:Q103646 wdt:P57 ?movie .
}
```

Let's execute this query to see the results.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the initial query did not return any results. This might be because the property `wdt:P57` is used to indicate the director of a movie, but it is typically used in the form where the movie is the subject and the director is the object. Let's try reversing the direction of the property to see if we can get the correct results:

```sparql
SELECT ?movie
WHERE {
  ?movie wdt:P57 wd:Q103646 .
}
```

Let's execute this revised query.

[Tool]
Got 44 rows and 1 column, showing the first 5 and last 5 rows below:
| ?movie                             |
| ---------------------------------- |
| Thirteen Lives (wd:Q105962844)     |
| Apollo 13 (wd:Q106428)             |
| Cocoon (wd:Q1105463)               |
| Splash (wd:Q1127184)               |
| Parenthood (wd:Q1137831)           |
| ...                                |
| Frost/Nixon (wd:Q691672)           |
| Ransom (wd:Q752362)                |
| Far and Away (wd:Q839341)          |
| Rebuilding Paradise (wd:Q96314873) |
| Night Shift (wd:Q997228)           |

[Assistant]
The revised query returned the expected results, listing the movies directed by Ron Howard. To provide a more complete answer, we can include the labels of the movies in the results. However, since the labels are optional, we will use an `OPTIONAL` clause to fetch them.

Here is the final SPARQL query:

```sparql
SELECT ?movie ?movieLabel
WHERE {
  ?movie wdt:P57 wd:Q103646 .
  OPTIONAL { ?movie rdfs:label ?movieLabel . FILTER(LANG(?movieLabel) = "en") }
}
```

Let's execute this final query.

[Tool]
Got 44 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?movie                             | ?movieLabel         |
| ---------------------------------- | ------------------- |
| Thirteen Lives (wd:Q105962844)     | Thirteen Lives      |
| Apollo 13 (wd:Q106428)             | Apollo 13           |
| Cocoon (wd:Q1105463)               | Cocoon              |
| Splash (wd:Q1127184)               | Splash              |
| Parenthood (wd:Q1137831)           | Parenthood          |
| ...                                | ...                 |
| Frost/Nixon (wd:Q691672)           | Frost/Nixon         |
| Ransom (wd:Q752362)                | Ransom              |
| Far and Away (wd:Q839341)          | Far and Away        |
| Rebuilding Paradise (wd:Q96314873) | Rebuilding Paradise |
| Night Shift (wd:Q997228)           | Night Shift         |

[Assistant]
The final query successfully returned the movies directed by Ron Howard along with their English labels. Here is the SPARQL query and the answer to the user's question:

```sparql
SELECT ?movie ?movieLabel
WHERE {
  ?movie wdt:P57 wd:Q103646 .
  OPTIONAL { ?movie rdfs:label ?movieLabel . FILTER(LANG(?movieLabel) = "en") }
}
```

The movies directed by Ron Howard include:
- Thirteen Lives
- Apollo 13
- Cocoon
- Splash
- Parenthood
- Frost/Nixon
- Ransom
- Far and Away
- Rebuilding Paradise
- Night Shift
- and more.

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

[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 ?highSchool ?highSchoolLabel WHERE {
  wd:Q19526 wdt:P69 ?highSchool .
  OPTIONAL {
    ?highSchool rdfs:label ?highSchoolLabel .
    FILTER ( LANG( ?highSchoolLabel ) = 'en' )
  }
}

Got 2 rows and 2 columns:
| ?highSchool                                 | ?highSchoolLabel              |
| ------------------------------------------- | ----------------------------- |
| California Labor School (wd:Q16850567)      | California Labor School       |
| George Washington High School (wd:Q5545977) | George Washington High School |

Maya Angelou attended both the California Labor School and George Washington High School.

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

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

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

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

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

[User] what high school did maya angelou go to?

[Assistant]
To answer the question about which high school Maya Angelou attended, we need to identify the entity for Maya Angelou and then find the property that indicates her high school education.

### Step 1: Identify the entity for Maya Angelou
Let's start by searching for the entity representing Maya Angelou in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Maya Angelou, also known as Marguerite Annie Johnson, Marguerite Ann Johnson, Marguerite Johnson, Marguerite Anne Johnson (wd:Q19526): American poet, author, and civil rights activist (1928–2014), human rights activist, television producer, television actor, autobiographer, film producer, film director, screenwriter, stage actor, playwright, journalist, politician, songwriter, professor, essayist, novelist, dancer, singer, writer
2. Maya Angelou, also known as Marguerite Ann Johnson, Marguerite Annie Johnson, Marguerite Johnson, Marguerite Anne Johnson (wd:Q19526): American poet, author, and civil rights activist (1928–2014), human rights activist, television producer, television actor, autobiographer, film director, film producer, screenwriter, stage actor, journalist, playwright, songwriter, politician, professor, essayist, novelist, writer, singer, dancer
3. Maya Angelou (wd:Q108467711): magazine article from 'American Funeral Director' published in 2014
4. Maya Angelou (wd:Q47513615): painting by Ross R. Rossin
5. Maya Angelou Academy (wd:Q14692192): American alternative school
6. Maya Angelou Birthplace, also known as Birthplace of Maya Angelou (wd:Q132261323): The house in St. Louis, Missouri where Maya Angelou was born
7. Maya Angelou, 1928-2014 (wd:Q116228315): journal article
8. Maya Angelou Library (wd:Q66364225): public library in Stockton, California, USA
9. Maya Angelou Northeast (wd:Q69781127): public library in Wichita, Kansas, USA
10. rue Maya Angelou (wd:Q125778051)

[Assistant]
The top result for Maya Angelou is `wd:Q19526`, which represents the American poet, author, and civil rights activist. We will use this entity to find out which high school she attended.

### Step 2: Identify the property for high school education
Next, we need to find the property that indicates the high school or secondary education of a person. Let's search for properties related to "high school" or "secondary education".

[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. student, also known as teacher to, teacher of, had student, students, pupils (wd:P802 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): notable student(s) of the subject individual, significant person
4. school class, also known as class, cohort, grade, school cohort, stage (wd:P7174 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): (qualifier) class of a given year/time period to which the person belonged in an educational institution
5. 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
6. CEEB K-12 school code, also known as CEEB code, K–12 (wd:P7801 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): code issued by the College Board for K-12 schools
7. athletics program, also known as school district athletics program, school district sports program, school sports program, sports program, university athletics program (wd:P10607 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): the name of a school's athletics program, as opposed to individual teams in particular sports, represented by
8. Historic Environment Scotland ID, also known as HB number, HES ID, HESID, HES number, HS ID (wd:P709 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): identifier for a building in the Historic Environment Scotland database
9. has part(s) of the class, also known as has part of class, nwere akụkụ(s) na-akpụ subclass nke, part of class, nwere akụkụ nke klaasị, part of the class (wd:P2670 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): the subject has one or more parts of the object class, has part(s)
10. student of, also known as professor, studied under, pupil of, tutor, mentor (wd:P1066 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): person who has taught this person, significant person

[Assistant]
The property `wd:P69` (educated at) seems to be the most relevant for indicating the educational institutions a person attended, including high schools. We will use this property to find out which high school Maya Angelou attended.

### Step 3: Construct and execute the SPARQL query
Now, let's construct a SPARQL query to find the high school Maya Angelou attended.

[Tool]
Got 2 rows and 1 column:
| ?highSchool                                 |
| ------------------------------------------- |
| California Labor School (wd:Q16850567)      |
| George Washington High School (wd:Q5545977) |

[Assistant]
The SPARQL query returned two high schools that Maya Angelou attended:

1. California Labor School (wd:Q16850567)
2. George Washington High School (wd:Q5545977)

To provide a more complete answer, we can include the labels of these high schools in the query.

### Step 4: Include labels in the SPARQL query
Let's modify the query to include the labels of the high schools.

[Tool]
Got 2 rows and 2 columns:
| ?highSchool                                 | ?highSchoolLabel              |
| ------------------------------------------- | ----------------------------- |
| California Labor School (wd:Q16850567)      | California Labor School       |
| George Washington High School (wd:Q5545977) | George Washington High School |

[Assistant]
The SPARQL query has successfully returned the high schools that Maya Angelou attended along with their labels:

1. **California Labor School** (wd:Q16850567)
2. **George Washington High School** (wd:Q5545977)

### Final Answer
Maya Angelou attended both the California Labor School and George Washington High School.

Here is the final SPARQL query and answer:

```sparql
SELECT ?highSchool ?highSchoolLabel
WHERE {
  wd:Q19526 wdt:P69 ?highSchool .
  OPTIONAL { ?highSchool rdfs:label ?highSchoolLabel . FILTER(LANG(?highSchoolLabel) = 'en') }
}
```

Answer: Maya Angelou attended both the California Labor School and George Washington High School.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?college WHERE {
  wd:Q89925 wdt:P69 ?college
}

The data in Wikidata does not contain information about Otto Frank's educational institution. The SPARQL query to find this information returned no results, indicating that the information is not available in the knowledge graph.

[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 otto frank go to college?

[Assistant]
To answer the question about where Otto Frank went to college, we need to identify the entity for Otto Frank and then find the property that indicates his educational institution.

### Step 1: Identify the entity for Otto Frank
Let's start by searching for the entity "Otto Frank" in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Frank Otto (wd:Q1443909): German water polo player, water polo player
2. Otto Frank, also known as Otto Friedrich Frank, Friedrich Wilhelm Ferdinand Otto Frank (wd:Q89925): German physiologist (1865–1944), university teacher, cardiologist, physiologist, physician
3. Frank Otto (wd:Q1443902): German media entrepreneur, film director, geologist, director
4. Frank Otto (wd:Q16104521): American linguist (1936-2017), linguist
5. Frank Otto (wd:Q1443905): German historian of modern age, historian of Modern Age, writer
6. Otto Frank (wd:Q36977791): German FDP politician (1900-1963), politician
7. Otto Frank (wd:Q94791903): military officer, cartographer
8. Frank Otto (wd:Q96110480): researcher
9. Otto Frank (wd:Q94941069): engineer
10. Otto Frank (wd:Q116960730): Austrian architect, architect

[Assistant]
From the search results, the entity that matches "Otto Frank" and is associated with the context of the question is "Otto Frank, also known as Otto Friedrich Frank, Friedrich Wilhelm Ferdinand Otto Frank" (wd:Q89925). This entity is described as a German physiologist, university teacher, cardiologist, physiologist, and physician, which aligns with the historical figure Otto Frank.

### Step 2: Identify the property for educational institution
Next, we need to find the property that indicates the educational institution where Otto Frank studied. Let's search for properties related to education or 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. parent organization, also known as parent company, parent institution, parent organisation, parent unit, part of (wd:P749 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): parent organization of an organization, opposite of subsidiaries (P355), owned by, part of
3. Commons Institution page, also known as Institution template (wd:P1612 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): name of the institutions's page on Wikimedia Commons (without the prefix "Institution")
4. Educational Institution Identification Number, also known as E.I.I.N, E.I.I.N., EIIN, Educational Institute Identification Number (wd:P6390 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): identifier for an educational institution issued by the Bangladesh Bureau of Educational Information and Statistics
5. Biodiversity Repository ID, also known as GRSciCol, GrSciColl, GrSciColl ID, institution code (wd:P4090 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): identifier of a repository, in the Biodiversity Repository database
6. category for alumni of educational institution, also known as alumni category, category for alumnae of educational institution (wd:P3876 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): category containing people who studied at this institution, topic's main category
7. studied in, also known as is studied by, studied by, science, that studies, researched in, researched by (wd:P2579 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): subject is studied by this science or domain, field of work
8. 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.
9. conferred by, also known as presented by, officiated by, issued by, granted by, given by (wd:P1027 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): person or organization who grants an award, certification, grant, or role
10. EHIS educational institution ID (wd:P13246 as prv/p/psn/psv/pqn/wdtn/wdt/prn/pqv/pq/ps/pr): identifier for an educational institution in the Estonian Education Information System

[Assistant]
The property that indicates the educational institution where someone studied is "educated at" (wd:P69).

### Step 3: Construct the SPARQL query
Now, let's construct a SPARQL query to find the educational institution where Otto Frank (wd:Q89925) studied.

[Tool] Got no rows and 1 columns

[Assistant]
The initial query did not return any results, which suggests that the entity wd:Q89925 (Otto Frank) might not have the property wdt:P69 (educated at) directly linked to it.

Let's try another approach by looking at the triples associated with the entity wd:Q89925 to see if there are any other properties or statements that might indicate his educational background.

[Tool]
Got 215 rows and 3 columns, showing the first 10 rows below:
| subject                | property                         | object                     |
| ---------------------- | -------------------------------- | -------------------------- |
| Otto Frank (wd:Q89925) | instance of (wdt:P31)            | human (wd:Q5)              |
| Otto Frank (wd:Q89925) | occupation (wdt:P106)            | physician (wd:Q39631)      |
| Otto Frank (wd:Q89925) | sex or gender (wdt:P21)          | male (wd:Q6581097)         |
| Otto Frank (wd:Q89925) | given name (wdt:P735)            | Otto (wd:Q18029644)        |
| Otto Frank (wd:Q89925) | date of birth (wdt:P569)         | 1865-06-21T00:00:00Z       |
| Otto Frank (wd:Q89925) | Commons category (wdt:P373)      | Otto Frank (physiologist)  |
| Otto Frank (wd:Q89925) | country of citizenship (wdt:P27) | German Reich (wd:Q1206012) |
| Otto Frank (wd:Q89925) | family name (wdt:P734)           | Frank (wd:Q13560458)       |
| Otto Frank (wd:Q89925) | place of birth (wdt:P19)         | Groß-Umstadt (wd:Q516474)  |
| Otto Frank (wd:Q89925) | date of death (wdt:P570)         | 1944-11-12T00:00:00Z       |
| ...                    | ...                              | ...                        |

[Assistant]
The list of triples for Otto Frank (wd:Q89925) does not include any information about his educational institution. This suggests that the specific information about where he went to college might not be directly available in the Wikidata knowledge graph.

Given this, we can conclude that there is no direct information in Wikidata about the educational institution where Otto Frank studied. Therefore, we cannot provide a definitive answer to the question based on the available data.

### Final Answer
Since the data in Wikidata does not contain information about Otto Frank's educational institution, we cannot provide a specific answer to the question.

Here is the best attempt at a SPARQL query:

```sparql
SELECT ?college WHERE { wd:Q89925 wdt:P69 ?college }
```

However, this query returns no results, indicating that the information is not available in the knowledge graph.

Therefore, the answer to the question "Where did Otto Frank go to college?" is that the information is not available in the Wikidata knowledge graph.

[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 ?education WHERE {
  wd:Q52651 wdt:P69 ?education .
  OPTIONAL {
    ?education rdfs:label ?educationLabel .
    FILTER ( LANG( ?educationLabel ) = "en" )
  }
}

Got 3 rows and 1 column:
| ?education                               |
| ---------------------------------------- |
| Ohio State University (wd:Q309331)       |
| East Technical High School (wd:Q5329490) |
| North Platte High School (wd:Q7056353)   |

Jesse Owens attended Ohio State University, East Technical High School, and North Platte High School.

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

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

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

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

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

[User] what was jesse owens education?

[Assistant]
To answer the question about Jesse Owens' education, we need to identify the entity for Jesse Owens and then find the property that represents his education.

### Step 1: Identify Jesse Owens
First, let's search for Jesse Owens in the knowledge graph.

```json
{
  "name": "search_entity",
  "arguments": {
    "kg": "wikidata",
    "query": "Jesse Owens"
  }
}
```

### Step 2: Identify the Property for Education
Once we have the entity for Jesse Owens, we need to find the property that represents education.

```json
{
  "name": "search_property",
  "arguments": {
    "kg": "wikidata",
    "query": "education"
  }
}
```

Let's start with the first step.

[Tool]
Top 10 entity alternatives:
1. Jesse Owens, also known as James Cleveland "Jesse" Owens, James Cleveland Owens (wd:Q52651): American track and field athlete (1913–1980), athletics competitor, long jumper, sprinter
2. Jesse Owens (wd:Q6186721): 2012 film
3. Jesse Owens Award (wd:Q3540423): awards given out by USA Track and Field
4. Jesse Hickman, also known as Jesse Owens Hickman (wd:Q6186412): American baseball player, baseball player
5. The Jesse Owens Story, also known as Jesse Owens Story, Jessie Owens Story (wd:Q7743138): 1984 television film
6. Jesse Owens Park (wd:Q49507611): park in Pima County, Arizona, United States of America
7. Jesse Owens Park (wd:Q49507612): park in Los Angeles County, California, United States of America
8. Jesse Owens Plaza (wd:Q49507617): park in Franklin County, Ohio, United States of America
9. Jesse Owens Playground (wd:Q49507615): park in Kings County, New York, United States of America
10. Jesse Owens Park, also known as Stony Island Park (wd:Q49566705): park in the United States of America

[Assistant]
The top result for Jesse Owens is `wd:Q52651`, which is the correct entity for the American track and field athlete. Now, let's find the property that represents education.

```json
{
  "name": "search_property",
  "arguments": {
    "kg": "wikidata",
    "query": "education"
  }
}
```

Let's proceed with this step.

[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 top result for the property "education" is `wd:P69`, which represents "educated at" or "place of education". Now, let's construct a SPARQL query to find the educational institutions Jesse Owens attended.

### Step 3: Construct the SPARQL Query
We will use the entity `wd:Q52651` for Jesse Owens and the property `wd:P69` to find his educational institutions.

```sparql
SELECT ?education WHERE {
  wd:Q52651 wdt:P69 ?education .
  OPTIONAL { ?education rdfs:label ?educationLabel . FILTER(LANG(?educationLabel) = "en") }
}
```

Let's execute this query to get the result.

```json
{
  "name": "execute",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?education WHERE { wd:Q52651 wdt:P69 ?education . OPTIONAL { ?education rdfs:label ?educationLabel . FILTER(LANG(?educationLabel) = \"en\") } }"
  }
}
```

Let's proceed with this step.

[Tool]
Got 3 rows and 1 column:
| ?education                               |
| ---------------------------------------- |
| Ohio State University (wd:Q309331)       |
| East Technical High School (wd:Q5329490) |
| North Platte High School (wd:Q7056353)   |

[Assistant]
The SPARQL query has returned the educational institutions that Jesse Owens attended:

1. Ohio State University (wd:Q309331)
2. East Technical High School (wd:Q5329490)
3. North Platte High School (wd:Q7056353)

These are the institutions where Jesse Owens received his education.

### Final Answer
Jesse Owens attended Ohio State University, East Technical High School, and North Platte High School.

```json
{
  "name": "answer",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?education WHERE { wd:Q52651 wdt:P69 ?education . OPTIONAL { ?education rdfs:label ?educationLabel . FILTER(LANG(?educationLabel) = \"en\") } }",
    "answer": "Jesse Owens attended Ohio State University, East Technical High School, and North Platte High School."
  }
}
```

This completes the process.

[Tool] Stopped generation process