PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?author WHERE {
  wd:Q44649835 wdt:P50 ?author .
}

Richard S. Zemel (wd:Q26702597)

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

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

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

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

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

[User] Who wrote "Prototypical Networks for Few-shot Learning"?

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

[Tool]
Example 1:
Question:
How many people live in Poland?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q36 wdt:P1082 ?uri
}

Using entities:
Poland, also known as Republic of Poland (wd:Q36): country in Central Europe

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject

Example 2:
Question:
When will start the final match of the football world cup 2018?

SPARQL:
SELECT DISTINCT ?val WHERE {
  wd:Q170645 wdt:P3967 ?finalEvent .
  ?finalEvent wdt:P580 ?val .
}

Using entities:
2018 FIFA World Cup, also known as FIFA 2018, FIFA World Cup 2018, World Cup 2018, World Cup 2018 in Russia, 2018 World Cup (wd:Q170645): 21st FIFA World Cup, held in Russia

Using properties:
final event (wd:P3967 as wdt): final event of a competition
start time, also known as start date, since, valid, join time, joined (wd:P580 as wdt): time an entity begins to exist or a statement starts being valid, point in time

Example 3:
Question:
Who is the mayor of Paris?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q90 wdt:P6 ?uri .
}

Using entities:
Paris, also known as City of Love, Lutetia, City of Light (wd:Q90): capital city and largest city of France

Using properties:
head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson

[Assistant] Based on the examples, we need to identify the entity for the paper "Prototypical Networks for Few-shot Learning" and then find who authored it. Let's first search for the entity of the paper.

[Tool]
Top 10 entity alternatives:
1. Prototypical Networks for Few-shot Learning (wd:Q44649835): scientific article published in January 2017
2. Hybrid Graph Neural Networks for Few-Shot Learning (wd:Q114887023): scientific article published in 2022
3. Convolutional Shrinkage Neural N...a-Learning for Few-Shot Learning (wd:Q114955057): scientific article published in 2022
4. Facial Skin Type Analysis Using ...rning with Prototypical Networks (wd:Q129793741): scholarly article published on 31 December 2023
5. Few-Shot Ensemble Learning for V...on with SlowFast Memory Networks (wd:Q130987343): scientific article published on 12 October 2020
6. Co-Learning for Few-Shot Learning (wd:Q114955104): scientific article published in 2022
7. Differential learning for learning networks (wd:Q123992464): US patent 11468327
8. Bayesian Learning for Neural Networks (wd:Q60016544): scientific article published in 1996
9. Information Maximization for Few-Shot Learning (wd:Q104090143): scientific article published in November 2020
10. Ensemble Learning for Multi-Layer Networks (wd:Q77697315): scholarly article by David Barber & Christopher M. Bishop published 1998 in Advances in Neural Information Processing Systems 10

[Assistant] The correct entity for the paper "Prototypical Networks for Few-shot Learning" is wd:Q44649835. Now, let's find out who authored this paper. We need to look for the property that indicates authorship. Let's search for the property related to authors.

[Tool]
Top 10 property alternatives:
1. theorized by, also known as author, supported by, suggested by, proposer, proposed by (wd:P8403 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): subject who suggested or proposed this concept or immaterial invention
2. lyricist, also known as text poet, writer (of song lyrics), words by, text writer, text by (wd:P676 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): author of song lyrics, author
3. author, also known as writer, written by, poet, playwright, maker (wd:P50 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/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
4. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): notable scientific, artistic or literary work, or other work of significance among subject's works
5. author given names, also known as given name, given names, first name, author first names, author first name (wd:P9687 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier for string representation of given or secondary sortin...presented in a bibliographic reference file (for example BibTeX), given name
6. used by, also known as armiger, user, played by, of (wd:P1535 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): item or concept that makes use of the subject (use sub-properties when appropriate)
7. operator, also known as offered by, operated by, provided by, run by, service operator (wd:P137 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): person, profession, organization or entity that operates the equipment, facility, or service, used by
8. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): maker of this creative work or other object (where no more specific property exists)
9. author last names, also known as author last name, surnames, surname, last names, last name (wd:P9688 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier to provide string representation of family or primary ...presented in a bibliographic reference file (for example BibTeX), family name
10. object named as, also known as original wording, place name string, printed as, reference wording, [sic] (wd:P1932 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): use as qualifier to indicate how the object's value was given in the source, name

[Assistant]
The property for authorship is `wd:P50`. Now, let's construct a SPARQL query to find the authors of the paper "Prototypical Networks for Few-shot Learning" (wd:Q44649835).

Here is the SPARQL query:

```sparql
SELECT ?author WHERE {
  wd:Q44649835 wdt:P50 ?author .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| author                          |
| ------------------------------- |
| Richard S. Zemel (wd:Q26702597) |

[Assistant]
The author of the paper "Prototypical Networks for Few-shot Learning" is Richard S. Zemel (wd:Q26702597).

Let's finalize the answer with the SPARQL query and the result.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly identifies the author of the paper 'Prototypical Networks for Few-shot Learning' and returns the IRI of the author along with the label.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?university WHERE {
  ?university wdt:P31 wd:Q3918 ; wdt:P2196 ?students .
  FILTER ( ?students > 200000 )
}

Got 7 rows and 1 column:
| university                                                 |
| ---------------------------------------------------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) |
| University of Phoenix (wd:Q1889100)                        |
| Cairo University (wd:Q194445)                              |
| National Autonomous University of Mexico (wd:Q222738)      |
| Ain Shams University (wd:Q2723670)                         |
| Allama Iqbal Open University (wd:Q3694529)                 |
| Universitas Terbuka (wd:Q7894421)                          |

The universities with more than 200,000 students are: National Open University of Nigeria, Lafia, University of Phoenix, Cairo University, National Autonomous University of Mexico, Ain Shams University, Allama Iqbal Open University, and Universitas Terbuka.

[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] Which universities have more than 200000 students?

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

[Tool]
Example 1:
Question:
How heavy is Jupiter's lightest moon?

SPARQL:
SELECT * WHERE {
  ?res wdt:P31 wd:Q61702557 .
  ?res wdt:P2067 ?mass .
}
ORDER BY ?mass
LIMIT 1

Using entities:
moon of Jupiter, also known as Jovial moon, Jovian satellite, moons of Jupiter, natural satellite of Jupiter (wd:Q61702557): natural satellite orbiting Jupiter

Using properties:
mass, also known as displacement, molecular mass, molecular weight, weight (wd:P2067 as wdt): mass (in colloquial usage also known as weight) of the item
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
Which U.S. state has been admitted latest?

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q35657 .
  ?uri wdt:P571 ?date
}
ORDER BY DESC ( ?date )
LIMIT 1

Using entities:
U.S. state, also known as U.S. states, US state, USA state, U.S.A. state, United States states (wd:Q35657): constituent political entity of the United States of America

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt): time when an entity begins to exist; for date of official opening use P1619, start time

Example 3:
Question:
List all the musicals with music by Leonard Bernstein.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q2743 ; wdt:P86 wd:Q152505 .
}

Using entities:
Leonard Bernstein (wd:Q152505): American conductor and composer (1918–1990), film score composer, musicologist, conductor, composer, pianist
musical, also known as musical play, stage musical, musical theatre, musical theater, lyric drama (wd:Q2743): stage work that combines songs, music, spoken dialogue, acting, and dance

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
composer, also known as music by, written by, songwriter, musical score by, film score by (wd:P86 as wdt): person(s) who wrote the music [for lyricist, use "lyrics by" (P676)], creator

[Assistant] Based on the examples, we need to identify the entity for university and the property for number of students. Let's first search for the entity "university".

[Tool]
Top 10 entity alternatives:
1. university, also known as uni, universities (wd:Q3918): academic institution for further education
2. University, also known as Suitcase City, University, FL (wd:Q2463874): human settlement in Hillsborough County, Florida, USA
3. medieval university, also known as mediaeval university, university (wd:Q1143413): corporation organized during the High Middle Ages
4. University, also known as University, Orange County, University, FL, University, Florida (wd:Q7894478): Orange County, Florida, USA
5. University, also known as University CDP, University Census Designated Place, University, Mississippi, University, MS (wd:Q3612349): census-designated place in Mississippi, United States
6. Expo Park/USC, also known as University (wd:Q1953496): Los Angeles Metro station
7. University Avenue, also known as University, University Ave (wd:Q3551335): street in Toronto, Ontario
8. Universidad, also known as Universidad station, University (wd:Q25442975): subway station in San Juan, Puerto Rico
9. University (wd:Q7894493): constituency of the Central and Western District Council of Hong Kong
10. Hochschule, also known as technical university, university (wd:Q21028957): German term for "higher schooling", i.e. institutions of tertiar... which may or may not include vocational or professional schools

[Assistant] The correct entity for "university" is wd:Q3918. Now let's search for the property "number of students".

[Tool]
Top 10 property alternatives:
1. count of students, also known as count of pupils, count of students, enrollment, number of pupils, number of pupils or students (wd:P2196 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of pupils or students of any type in an educational organ...evious years normal rank (not deprecated rank). Don't overwrite.
2. student, also known as teacher to, teacher of, had student, students, pupils (wd:P802 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): notable student(s) of the subject individual, significant person
3. number of out-of-school children (wd:P2573 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of out-of-school children reported for a place
4. number of teachers, also known as faculty count (wd:P10610 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of teachers at an educational facility
5. school class, also known as class, cohort, grade, school cohort, stage (wd:P7174 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): (qualifier) class of a given year/time period to which the person belonged in an educational institution
6. number of children, also known as children, children (number), how many children, issue, kids (wd:P1971 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of children of the person, animal, or character
7. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): educational institution attended by subject, affiliation
8. student retention rate (wd:P11698 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): proportion of first-year students at the educational institution who return for their second year
9. number of participants, also known as number of competitors, number of teams, participant count, participants, qty participants (wd:P1132 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): number of participants of an event, e.g. people or groups of people that take part in the event (NO units), quantity
10. educational stage, also known as student classification (wd:P7374 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): educational stage of a specific school; qualifier of "educated at" (P69): the education stage of someone's attendance of school

[Assistant]
The correct property for "number of students" is wd:P2196. We can now construct a basic SPARQL query to list universities with more than 200,000 students.

Let's try the following query:

```sparql
SELECT ?university WHERE {
  ?university wdt:P31 wd:Q3918 ;
              wdt:P2196 ?students .
  FILTER(?students > 200000)
}
```

This query selects universities (instances of wd:Q3918) that have more than 200,000 students according to the property wd:P2196. Let's execute it and see the results.

[Tool] Got no rows and 1 columns

[Assistant]
It seems there was an error in the query. The issue might be with the property `wdt:P3918` instead of `wdt:P31`. Let's correct this and re-run the query:

```sparql
SELECT ?university WHERE {
  ?university wdt:P31 wd:Q3918 ;
              wdt:P2196 ?students .
  FILTER(?students > 200000)
}
```

Let's execute this corrected query.

[Tool]
Got 7 rows and 1 column:
| university                                                 |
| ---------------------------------------------------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) |
| University of Phoenix (wd:Q1889100)                        |
| Cairo University (wd:Q194445)                              |
| National Autonomous University of Mexico (wd:Q222738)      |
| Ain Shams University (wd:Q2723670)                         |
| Allama Iqbal Open University (wd:Q3694529)                 |
| Universitas Terbuka (wd:Q7894421)                          |

[Assistant]
The query has returned the expected results. Here are the universities with more than 200,000 students:

1. National Open University of Nigeria, Lafia (wd:Q131290725)
2. University of Phoenix (wd:Q1889100)
3. Cairo University (wd:Q194445)
4. National Autonomous University of Mexico (wd:Q222738)
5. Ain Shams University (wd:Q2723670)
6. Allama Iqbal Open University (wd:Q3694529)
7. Universitas Terbuka (wd:Q7894421)

I will now provide the final SPARQL query and the answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected results. The universities listed are indeed those with more than 200,000 students according to the provided data. No further refinements are necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?holiday ) AS ?count ) WHERE {
  wd:Q980 wdt:P832 ?holiday .
}

Got 1 row and 1 column:
| count        |
| ------------ |
| 13 (xsd:int) |

[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] How many public holidays does the state Bavaria have in a year?

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

[Tool]
Example 1:
Question:
How tall is Claudia Schiffer?

SPARQL:
SELECT DISTINCT ?val WHERE {
  wd:Q76717 wdt:P2048 ?val .
}

Using entities:
Claudia Schiffer, also known as Claudia Maria Schiffer de Vere Drummond, Claudia Maria Vaughn, Claudia Schiffer de Vere Drummond, Claudia Vaughn, Claudia Maria Schiffer (wd:Q76717): German model, film actor, model, actor

Using properties:
height, also known as heighth, size, depth over terrain (wd:P2048 as wdt): vertical length of an entity, length

Example 2:
Question:
Who was called Rodzilla?

SPARQL:
SELECT * WHERE {
  ?uri skos:altLabel "Rodzilla" @en .
}

Example 3:
Question:
Give me all Swedish holidays.

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q1401522 .
}

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] From the examples, we see that the third example is relevant to our question as it deals with holidays. However, we need to adapt it to specifically look for public holidays in Bavaria. Let's first identify the entity for Bavaria and the property for public holidays.

[Tool]
Top 10 entity alternatives:
1. Bavaria, also known as Bayern, Free State of Bavaria, Freistaat Bayern, Freistoot Bayern (wd:Q980): federated state in the south of Germany
2. Kingdom of Bavaria, also known as Bavaria, Kinereich Baiern, Kinereich Bayern, Königreich Baiern, Königreich Bayern (wd:Q154195): kingdom in Central Europe between 1806 and 1918, from January 1871 part of the German Empire
3. Electorate of Bavaria, also known as Bavaria (wd:Q256961): state in the Holy Roman Empire
4. 301 Bavaria, also known as (301) Bavaria, Bavaria (wd:Q150590): main-belt asteroid
5. Bavaria, also known as Bavaria statue (wd:Q707669): Statue of Bavaria in Munich, Germany
6. Bavaria (wd:Q874953): former train on the Trans-Europ-Express connection Zurich-Munich
7. Bavaria, also known as Bavaria, Kansas, Bavaria, KS (wd:Q4873636): unincorporated community in Kansas, USA
8. Bavaria Brewery, also known as Bavaria S.A., Bavaria (wd:Q2946947): Colombian brewery company
9. Bavaria Fluggesellschaft, also known as Bavaria (wd:Q811995): former German airline
10. Bavaria (wd:Q117323380): female figure symbolising Bavaria

[Assistant] The correct entity for Bavaria is the first one, `wd:Q980`. Now let's find the property for public holidays.

[Tool]
Top 10 property alternatives:
1. public holiday, also known as bank holiday, legal holiday, national holiday (wd:P832 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): official public holiday that occurs in this place in its honor, usually a non-working day, significant event
2. closed on, also known as closed days, days not open, except (wd:P3026 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): exceptions to open days (P3025), usually special dates
3. open days, also known as open on days, days open, opening days (wd:P3025 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): days in the week subject item is open within the opening season
4. day in year for periodic occurrence, also known as date, day, day in year, day of periodic occurrence, day of year (wd:P837 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): when a specific holiday or periodic event occurs. Can be used as property or qualifier
5. day of week, also known as day of the week, days of week (wd:P2894 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): day of the week on which this item occurs, applies to or is valid
6. date of official closure, also known as closed, official closure, date of closure, closure date, closing date (wd:P3999 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date of official closure of a building or event, end time
7. set during recurring event, also known as narrative event, narrative holiday, set during event, set during holiday (wd:P9215 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): recurring event (e.g. season, month, holiday) during which this fictional work is set
8. opening time, also known as hour opens, opens at, opening hour, open hour (wd:P8626 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): time when an establishment or attraction opens, qualifier for P3025
9. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date or point in time a place, organization, or event officially opened, start time, inception
10. created for, also known as cause of creation, commissioned for, made for, occasion, on the occasion of (wd:P9883 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): a work, event, etc. for which the subject work was created (e.g.... play was created for; a film that a screenplay was written for)

[Assistant]
The property for public holidays is `wd:P832`. Now, we can construct a basic SPARQL query to find out how many public holidays Bavaria has in a year. We will start with a simple query and then refine it if necessary.

Let's try the following query:

```sparql
SELECT (COUNT(?holiday) AS ?count)
WHERE {
  wd:Q980 wdt:P832 ?holiday .
}
```

This query counts the number of public holidays associated with Bavaria. Let's execute it.

[Tool]
Got 1 row and 1 column:
| count        |
| ------------ |
| 13 (xsd:int) |

[Assistant]
The SPARQL query returned that Bavaria has 13 public holidays in a year.

Therefore, the answer to the question "How many public holidays does the state Bavaria have in a year?" is 13.

Here is the final SPARQL query and answer:

```sparql
SELECT (COUNT(?holiday) AS ?count)
WHERE {
  wd:Q980 wdt:P832 ?holiday .
}
```

Answer: 13

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. It counts the number of public holidays in Bavaria by querying the public holiday property (P832) of the entity representing Bavaria (Q980).


PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  ?place wdt:P17 wd:Q39 .
  ?place wdt:P625 ?coords .
  BIND( STRAFTER( STRBEFORE( STR( ?coords ) , " " ) , "POINT(" ) AS ?latStr ) BIND( xsd:float ( ?latStr ) AS ?lat ) FILTER ( ?lat > 47.8 )
}

False

[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] Does Switzerland have a place north of the Point (8.57 47.8)?

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

[Tool]
Example 1:
Question:
Give me all companies in Munich.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q4830453 .
  ?uri wdt:P31 ?type ; wdt:P159 wd:Q1726 .
}

Using entities:
Munich, also known as Monachium, Münich, München, Munchen, Muenchen (wd:Q1726): capital and most populous city of Bavaria, Germany
business (wd:Q4830453): organization undertaking commercial, industrial, or professional activity

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
Which musician wrote the most books?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q639669 .
  ?x wdt:P31 wd:Q571 ; wdt:P50 ?uri .
}
GROUP BY ?uri
ORDER BY DESC ( COUNT( ?x ) )
LIMIT 1

Using entities:
book, also known as books (wd:Q571): medium for recording information (words or images) typically on bound pages or more abstractly in electronic or audio form
musician, also known as musical artist (wd:Q639669): person who composes, conducts or performs music

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): 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

Example 3:
Question:
When did princess Diana die?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q9685 wdt:P570 ?date .
}

Using entities:
Diana, Princess of Wales, also known as Lady Diana Spencer, Princess Diana, Princess Di, Lady Diana Frances Spencer, The People's Princess (wd:Q9685): member of the British royal family and Princess of Wales (1961–1997), mental health advocate, kindergarten teacher, HIV/AIDS activist, environmentalist, peace activist, philanthropist, humanitarian, aristocrat, benefactor, socialite

Using properties:
date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt): dissolved, abolished or demolished date, date on which the subject died, end time

[Assistant] Based on the examples, we need to find a way to express the geographical relationship "north of" in our SPARQL query. We will likely need to use the geospatial properties and functions provided by the GeoSPARQL vocabulary. Let's first identify the relevant entities and properties for Switzerland and geographic points.

[Tool]
Top 10 entity alternatives:
1. Switzerland, also known as Confoederatio Helvetica, Swiss, Swiss Confederation (wd:Q39): country in Central Europe
2. Old Swiss Confederacy, also known as Swiss Confederation, Switzerland (wd:Q435583): confederation of cantons (1291-1798)
3. Old Swiss Confederacy, also known as Swiss Confederation, Switzerland (wd:Q435583): confederation of cantons (1291-1798)
4. Switzerland at the 1900 Summer Olympics, also known as Switzerland (wd:Q141744): sporting event delegation
5. federation of eight cantons, also known as Confederacy of eight cantons, Switzerland, Swiss Confederation, federation of 8 cantons, Old Swiss Confederacy (wd:Q340787)
6. Federation of thirteen cantons, also known as Federation of 13 cantons, Swiss Confederacy, Swiss Confederation, Switzerland (wd:Q351537)
7. Switzerland, also known as Switzerland, SC (wd:Q2373348): community in South Carolina, USA
8. Switzerland (wd:Q80282): community in Florida, United States
9. Switzerland in the Eurovision Song Contest 2006, also known as Switzerland (wd:Q7659174)
10. Switzerland (wd:Q420049): album by rock band Electric Six

[Assistant] The correct entity for Switzerland is `wd:Q39`. Now let's find the property that represents the geographic coordinates of a place.

[Tool]
Top 10 property alternatives:
1. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
2. this zoological name is coordinate with, also known as coordinate name (wd:P2743 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): links coordinate zoological names, different from
3. coordinates of depicted place, also known as depicted item geolocation, geolocation of object, object geolocation (wd:P9149 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): coordinates of the place depicted in the work (or Commons image)
4. coordinates of geographic center, also known as coordinates of geographic centre, geographic centre, geographic center, geographical midpoint, geographical centre (wd:P5140 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): coordinates of the center of an area. Use qualifier "determination method" (P459) to indicate how, coordinate location
5. time index, also known as time coordinate, timestamp, temporal coordinate, relative time (wd:P4895 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): indicate when something happens relative to its beginning
6. genomic start, also known as start coordinate, genomic from, Genloc From, from, Genloc Start (wd:P644 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): genomic starting coordinate of the biological sequence (e.g. a gene)
7. spatial reference system, also known as coordinate reference system, CRS, SRS (wd:P3037 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): frame of reference used to produce a map
8. coordinates of the point of view, also known as camera location, camera coordinates, viewpoint coordinates, coordinates of point of view, coordinates of pov (wd:P1259 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): point from which the scene depicted by the element is seen (element can be a photo, a painting, etc.)
9. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): person or organization occupying property, used by
10. named place on map, also known as name on map, place on map (wd:P9664 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): property linking a map to a place named on it

[Assistant]
The correct property for geographic coordinates is `wdt:P625`. Next, we need to check if there is a way to filter places based on their geographic coordinates. We will use the `geof:sfWithin` function from GeoSPARQL to check if a place is north of the given point.

Let's construct a SPARQL query to find places in Switzerland that are north of the point (8.57, 47.8).

### Step-by-Step Query Construction

1. **Identify the coordinates of the point**: (8.57, 47.8)
2. **Use the `geof:sfWithin` function** to filter places that are north of this point.
3. **Filter places in Switzerland** using the `wdt:P17` property (country).

Here is the initial SPARQL query:

```sparql
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>

SELECT ?place WHERE {
  ?place wdt:P17 wd:Q39 .  # Place is in Switzerland
  ?place wdt:P625 ?coords .  # Coordinates of the place
  FILTER (geof:sfWithin(?coords, "POINT(8.57 47.8)"^^geo:wktLiteral))  # Place is north of the point
}
```

Let's execute this query to see if it returns any results.

[Tool]
SPARQL execution failed:
Not supported: Function "<http://www.opengis.net/def/function/geosparql/sfWithin>" is currently not supported by QLever.

[Assistant]
It appears that the `geof:sfWithin` function is not supported by the QLever SPARQL endpoint. Instead, we can use a simpler approach to filter places based on their latitude. We will compare the latitude of the place with the latitude of the given point (47.8).

### Updated Query

1. **Extract the latitude from the coordinates**.
2. **Filter places with a latitude greater than 47.8**.

Here is the updated SPARQL query:

```sparql
PREFIX geo: <http://www.opengis.net/ont/geosparql#>

SELECT ?place WHERE {
  ?place wdt:P17 wd:Q39 .  # Place is in Switzerland
  ?place wdt:P625 ?coords .  # Coordinates of the place
  BIND(STRAFTER(STRBEFORE(STR(?coords), " "), "POINT(") AS ?latStr)
  BIND(xsd:float(?latStr) AS ?lat)
  FILTER (?lat > 47.8)  # Place is north of the point (8.57, 47.8)
}
LIMIT 10
```

Let's execute this query to see if it returns any results.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the query did not return any results, which suggests that there might be no places in Switzerland with a latitude greater than 47.8. However, to confirm this, we should use an ASK query to check if there exists any place in Switzerland north of the point (8.57, 47.8).

### ASK Query

We will use the same logic to extract the latitude and filter places, but this time we will use an ASK query to return a boolean result.

```sparql
PREFIX geo: <http://www.opengis.net/ont/geosparql#>

ASK WHERE {
  ?place wdt:P17 wd:Q39 .  # Place is in Switzerland
  ?place wdt:P625 ?coords .  # Coordinates of the place
  BIND(STRAFTER(STRBEFORE(STR(?coords), " "), "POINT(") AS ?latStr)
  BIND(xsd:float(?latStr) AS ?lat)
  FILTER (?lat > 47.8)  # Place is north of the point (8.57, 47.8)
}
```

Let's execute this ASK query to get a boolean result.

[Tool] False

[Assistant]
The ASK query returned `False`, which means there are no places in Switzerland that are north of the point (8.57, 47.8).

Therefore, the answer to the question "Does Switzerland have a place north of the Point (8.57 47.8)?" is **False**.

Let's finalize this with the answer function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated to check if there is any place in Switzerland with a latitude greater than 47.8. The result of the query is 'False', which means no such place exists according to the data in Wikidata. The answer is therefore correct and complete.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?entrepreneur ?netWorth WHERE {
  ?entrepreneur wdt:P106 wd:Q131524 .
  ?entrepreneur wdt:P2218 ?netWorth .
}
ORDER BY DESC ( ?netWorth )
LIMIT 1

The most wealthy entrepreneur is Elon Musk.

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

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

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

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

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

[User] Who is the most wealthy entrepreneur?

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

[Tool]
Example 1:
Question:
Which country has the most official languages?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P37 ?language .
}
GROUP BY ?uri
ORDER BY DESC ( COUNT( ?language ) )
LIMIT 1

Using entities:
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdt): language designated as official by this item, language used

Example 2:
Question:
Who is the mayor of Tel Aviv?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q33935 wdt:P6 ?uri .
}

Using entities:
Tel Aviv, also known as Tel Aviv City, TLV, Tel Aviv-Jafo, Tel-Aviv Jaffa, Tel Aviv-Jaffa (wd:Q33935): city in Israel

Using properties:
head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson

Example 3:
Question:
When did Michael Jackson die?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q2831 wdt:P570 ?o1 .
}

Using entities:
Michael Jackson, also known as M.J., MJ, M. J., The King of Pop, King of Pop (wd:Q2831): American singer, songwriter, and dancer (1958–2009), singer-songwriter, recording artist, record producer, businessperson, philanthropist, music arranger, autobiographer, film director, choreographer, art collector, entrepreneur, screenwriter, voice actor, songwriter, film actor, composer, dancer, singer, actor, model, poet

Using properties:
date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt): dissolved, abolished or demolished date, date on which the subject died, end time

[Assistant] Based on the examples, we need to identify the entity for "entrepreneur" and a property that indicates wealth. Let's start by searching for the entity "entrepreneur".

[Tool]
Top 10 entity alternatives:
1. entrepreneur, also known as undertaking (wd:Q131524): person who owns and operates a business
2. Entrepreneur (wd:Q5380740): business magazine for entrepreneurs
3. The Entrepreneur, also known as Entrepreneur (wd:Q3204987): 2011 film by Giuliano Montaldo
4. Entrepreneur (wd:Q30598698): 1997 video game
5. Entrepreneur (wd:Q5380739): British Thoroughbred racehorse
6. Entrepreneur (wd:Q98669462): 2020 single by Pharrell Williams featuring Jay-Z
7. Entrepreneur, also known as Yrittäjä (wd:Q104145164): 2018 film directed by Virpi Suutari
8. Darren Lopes, also known as Entrepreneur (wd:Q112113075): entrepreneur, Entrepreneur
9. Adil Sami, also known as Entrepreneur (wd:Q111692090): Entrepreneur, entrepreneur
10. Alee Club Miss & Mr Teen India, also known as Entrepreneur (wd:Q113526755): entrepreneur

[Assistant] The correct entity for "entrepreneur" is `wd:Q131524`. Next, let's search for a property that indicates wealth, such as "net worth" or "wealth".

[Tool]
Top 10 property alternatives:
1. net worth, also known as declared patrimony, estimated net worth, net wealth, net worth estimate, personal assets (wd:P2218 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): totality of wealth possessed by a person
2. net profit, also known as profit, net return, net income, clear profit, clear gain (wd:P2295 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): private entity profit after accounting for all costs
3. estimated value, also known as valuation, traded value, traded cap, price estimation, estimated worth (wd:P8340 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): estimated value of a house, company, patent, etc
4. total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): income gained by an organization during a given time frame. Not to be confused with fiscal revenue
5. item of property constraint, also known as claim value, value, qid, property value, item qualifier (wd:P2305 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier to define a property constraint in combination with "property constraint" (P2302)
6. prize money, also known as money, purse, value (wd:P2121 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): amount in a specific currency
7. price, also known as cost, exchange rate, list price, MSRP, published price (wd:P2284 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): published price listed or paid for a product (use with unit of currency)
8. numeric value, also known as numerical value, value (wd:P1181 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): numerical value of a number, a mathematical constant, or a physical constant
9. total assets, also known as assets, balance sheet total (wd:P2403 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): value of assets held by a private or public entity
10. box office, also known as earnings (wd:P2142 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): box office takings accumulated by a film, total revenue

[Assistant]
The correct property for "net worth" is `wd:P2218`. Now, we need to find the most wealthy entrepreneur. We will use the `wdt:P2218` property to filter and sort the results by net worth in descending order.

Let's construct the SPARQL query to find the most wealthy entrepreneur:

```sparql
SELECT ?entrepreneur ?netWorth WHERE {
  ?entrepreneur wdt:P106 wd:Q131524 .
  ?entrepreneur wdt:P2218 ?netWorth .
}
ORDER BY DESC(?netWorth)
LIMIT 1
```

This query selects the entrepreneur and their net worth, filters for those who are entrepreneurs, and orders the results by net worth in descending order, limiting the result to the top one.

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| entrepreneur           | netWorth                   |
| ---------------------- | -------------------------- |
| Elon Musk (wd:Q317521) | 270100000000 (xsd:decimal) |

[Assistant]
The most wealthy entrepreneur is Elon Musk, with a net worth of approximately 270.1 billion USD.

Here is the final SPARQL query and answer:

```sparql
SELECT ?entrepreneur ?netWorth WHERE {
  ?entrepreneur wdt:P106 wd:Q131524 .
  ?entrepreneur wdt:P2218 ?netWorth .
}
ORDER BY DESC(?netWorth)
LIMIT 1
```

Answer: The most wealthy entrepreneur is Elon Musk.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is well-constructed and correctly identifies the most wealthy entrepreneur based on the available data. The answer provided is accurate and complete.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK {
  wd:Q283 wdt:P462 wd:Q1396399
}

True

[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] Is water colorless?

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

[Tool]
Example 1:
Question:
Give me the grandchildren of Elvis Presley.

SPARQL:
SELECT DISTINCT ?o2 WHERE {
  wd:Q303 wdt:P40 ?o1 .
  ?o1 wdt:P40 ?o2
}

Using entities:
Elvis Presley, also known as The King of Rock'n'Roll, Elvis A. Presley, King of Rock'n'Roll, Elvis, Elvis Aaron Presley (wd:Q303): American singer and actor (1935–1977), military personnel, military officer, philanthropist, film actor, composer, activist, singer, actor

Using properties:
child, also known as has son, kids, mother of, kid, issue (wd:P40 as wdt): subject has object as child. Do not use for stepchildren—use "relative" (P1038), qualified with "type of kinship" (P1039), significant person, relative

Example 2:
Question:
In which UK city are the headquarters of the MI6?

SPARQL:
SELECT DISTINCT ?o1 WHERE {
  wd:Q184560 wdt:P159 ?o1 .
  ?o1 wdt:P31 wd:Q515 .
}

Using entities:
Secret Intelligence Service, also known as British intelligence agency, SIS, Secret Intelligence Service of the United Kingdom, Military Intelligence, Section 6, MI6 Secret Intelligence Service (wd:Q184560): British intelligence agency
city, also known as cities (wd:Q515): large human settlement

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
Which companies have more than 1 million employees?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?type wdt:P279 * wd:Q4830453 .
  ?uri wdt:P31 ?type ; wdt:P1128 ?num .
  FILTER ( ?num > 1000000 )
}

Using entities:
business (wd:Q4830453): organization undertaking commercial, industrial, or professional activity

Using properties:
employees, also known as workers, staff, personnel, number of employees, employee count (wd:P1128 as wdt): total number of employees of a company at a given "point in time...not deprecated rank). Add data for recent years, don't overwrite
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] None of these examples directly relate to the property of water being colorless. However, we can try to find relevant information about water and its properties in the knowledge graph. Let's start by searching for the entity representing water.

[Tool]
Top 10 entity alternatives:
1. water, also known as dihydrogen oxide, hydrogen hydroxide, dihydrogen monoxide, hydrogen oxide, oxidane (wd:Q283): chemical compound whose molecules are formed by two hydrogen atoms and one oxygen atom
2. water, also known as dihydridooxygen, pure water, oxidane, hydrogen oxide, hydrogen hydroxide (wd:Q283): chemical compound whose molecules are formed by two hydrogen atoms and one oxygen atom
3. ice, also known as crystalline water, frozen ice, frozen water, solid water, water (wd:Q23392): water frozen into the solid state
4. drinking water, also known as drinkable water, improved drinking water, potable water, water (wd:Q7892): water safe for consumption
5. Water, also known as Water (2005), Water (2005 movie) (wd:Q617395): 2005 film by Deepa Mehta
6. water (wd:Q2637515): classical element in philosophy and cosmology
7. Water (wd:Q123169436): 2023 single by Tyla
8. ☵, also known as water, U+2635, TRIGRAM FOR WATER, kan3, k’an (wd:Q2785811): trigram for water (U+2635): kan3, kǎn, k’an
9. liquid water, also known as water (wd:Q29053744): form of water
10. Water, also known as Water (Elitsa & Stoyan song) (wd:Q2735297): song by Elitsa Todorova and Stoyan Yankoulov

[Assistant] The top result for "water" is the chemical compound H2O, which is what we're interested in. Now let's look for properties related to color or appearance that might apply to this entity.

[Tool]
Top 10 property alternatives:
1. color, also known as with the colour, with the color, pigment, of the colour, of the color (wd:P462 as wdt/p): color of subject
2. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as wdt/p): inherent or distinguishing quality or feature of the entity. Use a more specific property when possible
5. has use, also known as utility, used in, used for, used as, use (wd:P366 as wdt/p): main use of the subject (includes current and former usage)
6. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
7. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
8. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category
9. mass, also known as displacement, molecular mass, molecular weight, weight (wd:P2067 as wdt/p): mass (in colloquial usage also known as weight) of the item
10. name (image), also known as name as image, label image, label as image, image of name, image as label (wd:P7407 as wdt/p): name of something, when it's not fully supported by Unicode or contains characters that are not widely supported, icon

No other items found

[Assistant] The property `color` (wdt:P462) seems to be the most relevant for our query. Let's check if this property is associated with the water entity (wd:Q283).

[Tool]
Got 5 rows and 3 columns:
| subject         | property         | object                       |
| --------------- | ---------------- | ---------------------------- |
| water (wd:Q283) | color (wdt:P462) | green (wd:Q3133)             |
| water (wd:Q283) | color (wdt:P462) | white (wd:Q23444)            |
| water (wd:Q283) | color (wdt:P462) | turquoise (wd:Q5960345)      |
| water (wd:Q283) | color (wdt:P462) | color of water (wd:Q5148636) |
| water (wd:Q283) | color (wdt:P462) | colorless (wd:Q1396399)      |

[Assistant]
The results show that water is associated with multiple colors, including "green," "white," "turquoise," and "colorless." To determine if water is colorless, we need to focus on the "colorless" entry.

Let's construct an ASK query to check if water is indeed colorless.

[Tool] True

[Assistant]
The ASK query confirms that water is indeed colorless.

Therefore, the answer to the question "Is water colorless?" is **True**.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query correctly checks if water (wd:Q283) has the property of being colorless (wd:Q1396399) using the color property (wdt:P462). The result is True, which accurately answers the question.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?name WHERE {
  wd:Q30 rdfs:label ?name .
  FILTER ( LANG( ?name ) = 'tr' )
}

Got 1 row and 1 column:
| name                             |
| -------------------------------- |
| Amerika Birleşik Devletleri (tr) |

Amerika Birleşik Devletleri

[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] How do people from Turkey call the official name of the united states of america?

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

[Tool]
Example 1:
Question:
In which studio did the Beatles record their first album?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?album wdt:P31 wd:Q482994 ; wdt:P175 wd:Q1299 ; wdt:P577 ?date ; wdt:P483 ?uri .
}
ORDER BY ?date
LIMIT 1

Using entities:
The Beatles, also known as Fab Four, The Lads from Liverpool, John, Paul, George, and Ringo (wd:Q1299): English pop rock band (1960–1970)
album, also known as full album, record album, music album, full-length album, audio album (wd:Q482994): grouping of album releases by an artist usually released at the ...racks but in different formats for consumption (digital, CD, LP)

Using properties:
performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as wdt): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
recorded at studio or venue, also known as venue, studio, recording venue, recording studio, recorded at event (wd:P483 as wdt): studio or location where a musical composition/release was recorded, recording location
publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt): date or point in time when a work was first published or released, point in time

Example 2:
Question:
Which animals are critically endangered?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P141 wd:Q219127
}

Using entities:
Critically Endangered, also known as CR, critically endangered species (wd:Q219127): IUCN conservation category

Using properties:
IUCN conservation status, also known as conservation status (wd:P141 as wdt): conservation status assigned by the International Union for Conservation of Nature

Example 3:
Question:
List all boardgames by GMT.

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q131436 .
}

Using entities:
board game, also known as boardgame (wd:Q131436): game that involves counters or pieces moved or placed on a pre-marked surface or "board", according to a set of rules

Using properties:
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] Based on the examples, we need to identify the relevant entities and properties for our question. We are looking for the official name of the United States of America as it is called in Turkish. Let's start by searching for the entity representing the United States of America.

[Tool]
Top 10 entity alternatives:
1. United States, also known as the U.S. of A, United States of America, the US of America, United States of North America, U. S. (wd:Q30): country primarily located in North America
2. Istadus Unidus the America, also known as United States of America (wd:Q29045433): Wikimedia permanent duplicate item
3. United States of America (wd:Q100270731): scholarly article
4. United States of America (wd:Q103971355): encyclopedic article; Grove Art Online
5. United States of America (wd:Q105870683): vocal track by Kikki Danielsson; 1980 studio recording
6. United States of America (wd:Q105870692): song written and composed by Donna Fargo, originally recorded by Donna Fargo and released in 1974
7. United States of America (wd:Q106315054): vocal track by Donna Fargo; 1974 studio recording
8. United States of America, also known as Star Trek USA, USA (wd:Q99281400): country of the United States of America as depicted in Star Trek
9. United States of America (wd:L350484-S1)
10. United States (of America) (wd:L399000-S1)

[Assistant] The correct entity for the United States of America is `wd:Q30`. Next, we need to find the property that represents the official name of a country in a specific language. Let's search for a property related to the name of a country in a specific language.

[Tool]
Top 10 property alternatives:
1. name in native language, also known as native name (wd:P1559 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): name of a person in their native language, name
2. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
3. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): label for the items in their official language  or their original language, name
4. name in kana, also known as kana reading, reading in kana, yomigana, kana name, kana (wd:P1814 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): the reading of a Japanese name in kana, transliteration or transcription, name
5. vocalized name, also known as nikud, ḥarakāt (wd:P4239 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): item name with full vowel marks
6. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
7. author given names, also known as given name, given names, first name, author first names, author first name (wd:P9687 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier for string representation of given or secondary sortin...presented in a bibliographic reference file (for example BibTeX), given name
8. author last names, also known as author last name, surnames, surname, last names, last name (wd:P9688 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier to provide string representation of family or primary ...presented in a bibliographic reference file (for example BibTeX), family name
9. family name, also known as clan name, last name, surname (wd:P734 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): part of full name of person, name
10. applies to name of subject, also known as applies to subject's name, for the name, involved name, limited to the name, valid for the name (wd:P5168 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): (qualifier) label or alias name to which the claim applies (subj...er to name of the value, use "applies to name of object" (P8338), subject named as

[Assistant]
The property `wd:P1559` (name in native language) seems to be the closest match for what we need. However, it specifically refers to the name in the native language. We need the name in Turkish. Let's check if there is a property that specifies the name in a specific language.

Since `wd:P1559` is not exactly what we need, let's try searching for a property that directly relates to the name in a specific language.

[Tool]
Top 10 property alternatives:
1. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
2. name in native language, also known as native name (wd:P1559 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): name of a person in their native language, name
3. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): label for the items in their official language  or their original language, name
4. applies to name of subject, also known as applies to subject's name, for the name, involved name, limited to the name, valid for the name (wd:P5168 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): (qualifier) label or alias name to which the claim applies (subj...er to name of the value, use "applies to name of object" (P8338), subject named as
5. name in kana, also known as kana reading, reading in kana, yomigana, kana name, kana (wd:P1814 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): the reading of a Japanese name in kana, transliteration or transcription, name
6. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
7. author given names, also known as given name, given names, first name, author first names, author first name (wd:P9687 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): qualifier for string representation of given or secondary sortin...presented in a bibliographic reference file (for example BibTeX), given name
8. given name version for other gender, also known as personal name version for other gender, other genders given name, male version of this female given name, gender inflection of given name, female version of this male given name (wd:P1560 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): equivalent name (with respect to the meaning of the name) in the...n of a female first name. Add primarily the closest matching one
9. vocalized name, also known as nikud, ḥarakāt (wd:P4239 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): item name with full vowel marks
10. stylized name, also known as stylized as, stylised name, stylised as (wd:P9989 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): a widely used but non-standard form of a name (rather than a different name entirely or a pseudonym), name

[Assistant]
The property `wd:P407` (language of work or name) seems to be the most appropriate for specifying the language of a name. We can use this property to find the name of the United States of America in Turkish.

Let's construct a SPARQL query to find the name of the United States of America in Turkish.

First, we need to find the entity representing the Turkish language. Let's search for the Turkish language entity.

[Tool]
Top 10 entity alternatives:
1. Turkish, also known as Anatolian Turkish, Istanbul Turkish, tr, tur, Türkçe (wd:Q256): Oghuz Turkic language of the Turkish people
2. Turkish language (wd:L615174-S1)
3. Ottoman Turkish, also known as ota, Ottoman, Ottoman language, Ottoman Turkish language (wd:Q36730): language that was used in the Ottoman Empire
4. Turkish Language Association, also known as Türk Dil Kurumu (wd:Q1569712): official regulatory body of the Turkish language
5. Turkish Sign Language, also known as TID, TİD, tsm (wd:Q36885): sign language
6. Turkish bird language, also known as kuş dili (wd:Q64210939): whistled language in Turkey’s Eastern Black Sea Region – inscrib...st of Intangible Heritage in Need of Urgent Safeguarding in 2017
7. Turkish Language Olympiads (wd:Q1060833): student competition
8. Turkish language reform (wd:Q3458382)
9. Turkish phonology, also known as Turkish language phonology (wd:Q3242271): phonology of the Turkish language
10. Thesaurus:penis (Turkish language), also known as Thesaurus:erkeklik organı (wd:Q30715151)

[Assistant]
The correct entity for the Turkish language is `wd:Q256`. Now we can use this entity along with the property `wd:P407` to find the name of the United States of America in Turkish.

Let's construct the SPARQL query to find the name of the United States of America in Turkish.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the property `p:P1448` (official name) with the qualifier `pq:P407` (language of work or name) did not yield any results. Let's try another approach by using the property `p:P1705` (native label) with the qualifier `pq:P407` to see if it provides the name in Turkish.

Let's construct the SPARQL query using `p:P1705`.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that the property `p:P1705` (native label) with the qualifier `pq:P407` (language of work or name) also did not yield any results. Let's try another property that might be more suitable for finding the name in a specific language.

We can try using the property `p:P1559` (name in native language) with the qualifier `pq:P407` to see if it provides the name in Turkish.

Let's construct the SPARQL query using `p:P1559`.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the property `p:P1559` (name in native language) with the qualifier `pq:P407` (language of work or name) also did not yield any results.

Given that these approaches have not worked, let's try a different strategy. We can look for the label of the United States of America in Turkish directly using the `rdfs:label` property, which often includes labels in multiple languages.

Let's construct the SPARQL query to find the label of the United States of America in Turkish using `rdfs:label`.

[Tool]
Got 1 row and 1 column:
| name                             |
| -------------------------------- |
| Amerika Birleşik Devletleri (tr) |

[Assistant]
The SPARQL query successfully returned the name of the United States of America in Turkish. The name is "Amerika Birleşik Devletleri".

Here is the final SPARQL query and the answer:

```sparql
SELECT ?name WHERE { wd:Q30 rdfs:label ?name . FILTER(LANG(?name) = 'tr') }
```

Answer: Amerika Birleşik Devletleri

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query retrieves the Turkish label for the United States of America, which is 'Amerika Birleşik Devletleri'. The query follows all the rules and returns the expected result.