PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?foundingDate WHERE {
  wd:Q800176 wdt:P571 ?foundingDate .
}

The jazz club Birdland in Hamburg was founded on January 1, 1985.

[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] When was the jazz club Birdland in Hamburg founded?

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

[Tool]
Example 1:
Question:
Which museum in New York has the most visitors?

SPARQL:
SELECT ?s1 WHERE {
  ?s1 wdt:P159 wd:Q60 .
  ?s1 wdt:P31 wd:Q33506 .
  ?s1 wdt:P1174 ?o1
}
order by ?o1
limit 1

Using entities:
museum, also known as museal institution, museum-style institution (wd:Q33506): institution that holds artifacts and other objects of scientific, artistic, cultural, historical, or other importance
New York City, also known as Capital of the World, Big Apple, the five boroughs, The City So Nice They Named It Twice, NY, NY (wd:Q60): most populous city in the United States

Using properties:
visitors per year, also known as annual visitors, yearly visitors (wd:P1174 as wdt): number of people visiting a location or an event each year
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 2:
Question:
Give me all Dutch parties.

SPARQL:
SELECT ?s1 WHERE {
  ?s1 wdt:P17 wd:Q55 .
  ?s1 wdt:P31 wd:Q7278 .
}

Using entities:
Netherlands, also known as 🇳🇱, the Netherlands, NL, nl, NED (wd:Q55): country in Northwestern Europe with territories in the Caribbean
political party, also known as political parties, parties (politics), party (politics), parties in politics, party in politics (wd:Q7278): organization that seeks to influence government policy and actio...nd be elected to directly take part on government or legislation

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
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:
How many movies did Park Chan-wook direct?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) as ?count ) WHERE {
  ?uri wdt:P57 wd:Q315484 .
}

Using entities:
Park Chan-wook, also known as Park Chan-uk, Chan-wook Park (wd:Q315484): South Korean film director, screenwriter and film producer (born 1963), film director, film producer, screenwriter, film critic, politician, director

Using properties:
director, also known as film director, movie director, game director, directed by (wd:P57 as wdt): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator

[Assistant] Based on the examples, we need to identify the entity for the jazz club Birdland in Hamburg and the property for the founding date. Let's start by searching for the entity.

[Tool]
Top 10 entity alternatives:
1. Hamburg, also known as Free and Hanseatic City of Hamburg, Hamburg, Germany, Hamburg, Freie und Hansestadt (wd:Q1055): city and state in the North of Germany
2. Hamburger SV, also known as Hamburg SV, HSV, H.S.V., Hamburg Sport Club, HamburgerSV (wd:Q51974): German sports club based in Hamburg, with its largest branch being its football department
3. Hamburg, also known as Hamburg, IA, Hamburg, Iowa (wd:Q998718): city in Fremont County, Iowa, United States
4. Hamburg, also known as Hamburg, AR, Hamburg, Arkansas (wd:Q79657): city in Ashley County, Arkansas, United States
5. Hamburg, also known as Hamburg, Minnesota, Hamburg, MN (wd:Q1005682): city in Carver County, Minnesota, United States
6. Hamburg, also known as Hamburg, New Jersey, Hamburg, NJ (wd:Q1082931): borough in Sussex County, New Jersey, United States
7. Hamburg, also known as Hamburg, New York, Hamburg, NY (wd:Q742785): town in Erie County, New York, United States
8. Hamburg, also known as Hamburg, IL (wd:Q2672139): village in Calhoun County, Illinois, United States
9. Oriole Park at Camden Yards, also known as Birdland, Camden Yards, The House that Cal Built, The Yard (wd:Q1141174): baseball stadium in Baltimore, Maryland
10. Hamburg, also known as Hamburg, PA, Hamburg, Pennsylvania (wd:Q1181264): borough in Berks County, Pennsylvania, United States

[Assistant] None of these entities seem to match the jazz club Birdland in Hamburg. Let's try a more specific search.

[Tool]
Top 10 entity alternatives:
1. acid jazz, also known as club jazz, dancefloor jazz, dance-jazz (wd:Q221772): genre of music, mixing elements of funk, soul, jazz and electronic music
2. jazz club, also known as jazz venue (wd:Q1684522): type of club, music venue, dedicated to jazz music
3. clubjazz, also known as club jazz (wd:Q105637106): music genre
4. Hamburger SV, also known as Hamburg SV, HSV, H.S.V., Hamburg Sport Club, HamburgerSV (wd:Q51974): German sports club based in Hamburg, with its largest branch being its football department
5. FC Jazz, also known as Football Club Jazz (wd:Q1387226): association football club in Finland
6. Gypsy jazz, also known as Gypsy swing, hot club jazz, jazz manouche, manouche jazz, sinti jazz (wd:Q844245): music genre
7. Iridium Jazz Club (wd:Q1331383): jazz club in New York City
8. Reduta Jazz Club (wd:Q12049057): music club
9. JFC Jazz Club, also known as JFC-Club.spb (wd:Q123555148): Jazz Hall
10. Ealing Jazz Club (wd:Q5325487)

[Assistant]
It seems like the specific entity for the jazz club Birdland in Hamburg is not present in the top 10 results. However, we can try another approach by searching for the property related to the founding date and then constructing a query to see if we can find the jazz club and its founding date together.

First, let's search for the property related to the founding date.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): time when an entity begins to exist; for date of official opening use P1619, start time
2. location of formation, also known as place of incorporation, originates from, place of establishment, place of formation, place of foundation (wd:P740 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): location where a group or organization was formed, location of creation
3. date of incorporation, also known as date incorporated, incorporated, incorporated on date, incorporation date, year incorporated (wd:P10786 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): date when company, organization or city was incorporated
4. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): notable scientific, artistic or literary work, or other work of significance among subject's works
5. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): founder or co-founder of this organization, religion, place or entity, creator
6. foundational text, also known as main text, treaty, organic law, founding document, establishing document (wd:P457 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): text through which an institution or object has been created or established
7. time of discovery or invention, also known as invented in, time of invention, time of discovery, invention time, invented on (wd:P575 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): date or point in time when the item was discovered or invented
8. introduced on, also known as bill introduced on, introduction date, introduced date (wd:P9448 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): date when a bill was introduced into a legislature, inception
9. start time, also known as start date, since, valid, join time, joined (wd:P580 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): time an entity begins to exist or a statement starts being valid, point in time
10. earliest date, also known as no earlier than, terminus post quem, start date (earliest), on or after this date, minimum date (wd:P1319 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): earliest date at which an event could have happened. Use as qualifier for other date properties

[Assistant]
The property `inception` (P571) seems to be the most relevant for the founding date. Now, let's construct a SPARQL query to find the jazz club Birdland in Hamburg and its founding date.

We will use the property `inception` and search for entities that are jazz clubs and are located in Hamburg.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that there are no results for the jazz club Birdland in Hamburg with the given query. Let's try a different approach by searching for the entity "Birdland" and then checking if it has a founding date and is located in Hamburg.

First, let's search for the entity "Birdland" again.

[Tool]
Top 10 entity alternatives:
1. Oriole Park at Camden Yards, also known as Birdland, Camden Yards, The House that Cal Built, The Yard (wd:Q1141174): baseball stadium in Baltimore, Maryland
2. Birdland (wd:Q256347): New York City jazz club
3. Birdland (wd:Q2904435): album by The Yardbirds
4. Birdland (wd:Q784489): song
5. Birdland (wd:Q800176): jazz club in Hamburg
6. Birdland (wd:Q55652970): 2018 film directed by Peter Lynch
7. Birdland (wd:Q11854283)
8. Birdland, also known as Birdland, San Diego (wd:Q4916004): neighborhood of San Diego, California, United States
9. Oliver Ho, also known as Raudive, Seeker, Veil, Zov Zov, Life (wd:Q1722141): British DJ and electronic music producer, disc jockey, DJ producer
10. Birdland (wd:Q118905393): erotic comic book series

[Assistant]
The entity for the jazz club Birdland in Hamburg is `wd:Q800176`. Now, let's construct a SPARQL query to find the founding date of this specific jazz club.

We will use the entity `wd:Q800176` and the property `inception` (P571) to get the founding date.

[Tool]
Got 1 row and 1 column:
| foundingDate                        |
| ----------------------------------- |
| 1985-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The jazz club Birdland in Hamburg was founded on January 1, 1985.

Here is the final SPARQL query and answer:

```sparql
SELECT ?foundingDate WHERE {
  wd:Q800176 wdt:P571 ?foundingDate .
}
```

Answer: The jazz club Birdland in Hamburg was founded on January 1, 1985.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?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 representing 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 top result is the entity for the paper "Prototypical Networks for Few-shot Learning" (wd:Q44649835). Now, let's find out who authored this paper. We will look for the property that indicates authorship, which is typically `P50` (author).

Let's execute a SPARQL query to find the authors of the paper.

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

I will now run 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).

To provide a complete and final answer, I will include the SPARQL query and the answer.

[Tool] Stopped generation process


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

Got 7 rows and 2 columns:
| university                                                 | numberOfStudents      |
| ---------------------------------------------------------- | --------------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | 500000 (xsd:decimal)  |
| University of Phoenix (wd:Q1889100)                        | 420000 (xsd:decimal)  |
| Cairo University (wd:Q194445)                              | 257200 (xsd:decimal)  |
| National Autonomous University of Mexico (wd:Q222738)      | 360883 (xsd:decimal)  |
| Ain Shams University (wd:Q2723670)                         | 220000 (xsd:decimal)  |
| Allama Iqbal Open University (wd:Q3694529)                 | 1326266 (xsd:decimal) |
| Universitas Terbuka (wd:Q7894421)                          | 525360 (xsd:decimal)  |

The universities with more than 200,000 students are: 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), and Universitas Terbuka (wd:Q7894421).

[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 relevant entities and properties for our question. We are looking for universities and their number of students. Let's start by searching 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 first result, "university" (wd:Q3918), seems to be the most relevant entity for our query. Next, let's search for the property "number of students" to see if it exists in the knowledge graph.

[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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): notable student(s) of the subject individual, significant person
3. number of out-of-school children (wd:P2573 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): number of out-of-school children reported for a place
4. number of teachers, also known as faculty count (wd:P10610 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): number of teachers at an educational facility
5. school class, also known as class, cohort, grade, school cohort, stage (wd:P7174 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): (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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): educational institution attended by subject, affiliation
8. student retention rate (wd:P11698 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): educational stage of a specific school; qualifier of "educated at" (P69): the education stage of someone's attendance of school

[Assistant]
The property "count of students" (wd:P2196) appears to be the most relevant for our query. Now, let's construct a SPARQL query to find universities that have more than 200,000 students.

We will start with a basic query to list universities and their number of students, then filter the results to only include those with more than 200,000 students.

Here is the initial query:

```sparql
SELECT ?university ?numberOfStudents WHERE {
  ?university wdt:P31 wd:Q3918 .
  ?university wdt:P2196 ?numberOfStudents .
}
```

Let's execute this query to see the results.

[Tool]
Got 2,233 rows and 2 columns, showing the first 5 and last 5 rows below:
| university                                    | numberOfStudents    |
| --------------------------------------------- | ------------------- |
| University of Aizu (wd:Q1007777)              | 1371 (xsd:decimal)  |
| Bryant University (wd:Q1012699)               | 3338 (xsd:decimal)  |
| Bryant University (wd:Q1012699)               | 3674 (xsd:decimal)  |
| Osaka Metropolitan University (wd:Q101579967) | 15991 (xsd:decimal) |
| California College of the Arts (wd:Q1026804)  | 1800 (xsd:decimal)  |
| ...                                           | ...                 |
| Malmö University (wd:Q977781)                 | 15625 (xsd:decimal) |
| Santa Clara University (wd:Q992830)           | 8095 (xsd:decimal)  |
| Santa Clara University (wd:Q992830)           | 8616 (xsd:decimal)  |
| Bryn Mawr College (wd:Q995265)                | 1676 (xsd:decimal)  |
| Bryn Mawr College (wd:Q995265)                | 1719 (xsd:decimal)  |

[Assistant]
The initial query returns a list of universities and their number of students. However, we need to filter the results to only include universities with more than 200,000 students. Let's add the filter condition to the query.

Here is the updated query:

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

Let's execute this query to get the desired results.

[Tool]
Got 7 rows and 2 columns:
| university                                                 | numberOfStudents      |
| ---------------------------------------------------------- | --------------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | 500000 (xsd:decimal)  |
| University of Phoenix (wd:Q1889100)                        | 420000 (xsd:decimal)  |
| Cairo University (wd:Q194445)                              | 257200 (xsd:decimal)  |
| National Autonomous University of Mexico (wd:Q222738)      | 360883 (xsd:decimal)  |
| Ain Shams University (wd:Q2723670)                         | 220000 (xsd:decimal)  |
| Allama Iqbal Open University (wd:Q3694529)                 | 1326266 (xsd:decimal) |
| Universitas Terbuka (wd:Q7894421)                          | 525360 (xsd:decimal)  |

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

- National Open University of Nigeria, Lafia (wd:Q131290725) with 500,000 students
- University of Phoenix (wd:Q1889100) with 420,000 students
- Cairo University (wd:Q194445) with 257,200 students
- National Autonomous University of Mexico (wd:Q222738) with 360,883 students
- Ain Shams University (wd:Q2723670) with 220,000 students
- Allama Iqbal Open University (wd:Q3694529) with 1,326,266 students
- Universitas Terbuka (wd:Q7894421) with 525,360 students

These are the universities that meet the criteria of having more than 200,000 students.

Let's provide the final answer using the `answer` function.

[Tool] Stopped generation process


Failed to generate SPARQL query

[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] Based on the examples, we need to identify the entity for Bavaria and the property for public holidays. Let's start by searching for the entity "Bavaria".

[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, "Bavaria, also known as Bayern, Free State of Bavaria, Freistaat Bayern, Freistoot Bayern (wd:Q980)". Now let's search for the property "public holiday" or something similar.

[Tool]
Top 10 property alternatives:
1. public holiday, also known as bank holiday, legal holiday, national holiday (wd:P832 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): exceptions to open days (P3025), usually special dates
3. open days, also known as open on days, days open, opening days (wd:P3025 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 "public holiday" (wd:P832) seems to be the most relevant. We will now try to construct a query to find out how many public holidays Bavaria has in a year.

We will start with a basic query to see what kind of data we get.


Failed to generate SPARQL query

[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 the entity for Switzerland and the property for geographic location. We will then construct a query to check if there is any place in Switzerland that is located north of the given point (8.57, 47.8).

First, let's find the entity for Switzerland.

[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 for geographic location.

[Tool]
Top 10 property alternatives:
1. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): location where persons or organisations were actively participating in employment, business or other work, significant place, location
2. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
4. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person or organization occupying property, used by
5. recorded at studio or venue, also known as venue, studio, recording venue, recording studio, recorded at event (wd:P483 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): studio or location where a musical composition/release was recorded, recording location
6. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): home stadium or venue of a sports team or applicable performing arts organization, location
7. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): larger work that a given work was published in, like a book, journal or music album, part of
8. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
9. directions, also known as locality or place (wd:P2795 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): describe how to find the subject - directions, objects along way, comments
10. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): subject's ordinal position as qualitatively evaluated relative to other members of a group

[Assistant]
The property for geographic location is `wdt:P625` (coordinate location). We will use this property to find places in Switzerland and their coordinates.

Next, we need to construct a query to check if there is any place in Switzerland that is located north of the given point (8.57, 47.8).

We will use the `geof:sfWithin` function to check if a point is within a bounding box defined by the coordinates of Switzerland and the given point.

Let's start by finding the bounding box for Switzerland.

First, we need to find the coordinates of Switzerland.


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 first result, "entrepreneur" (wd:Q131524), seems to be the correct entity for our purpose. 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): estimated value of a house, company, patent, etc
4. total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier to define a property constraint in combination with "property constraint" (P2302)
6. prize money, also known as money, purse, value (wd:P2121 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): amount in a specific currency
7. price, also known as cost, exchange rate, list price, MSRP, published price (wd:P2284 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): value of assets held by a private or public entity
10. box office, also known as earnings (wd:P2142 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): box office takings accumulated by a film, total revenue

[Assistant]
The property "net worth" (wd:P2218) is the most relevant for our query. Now, we can construct a SPARQL query to find the entrepreneur with the highest net worth. We'll start by selecting the top 10 entrepreneurs based on their net worth to ensure we get the most wealthy one.

Here is the initial SPARQL query:

```sparql
SELECT ?entrepreneur ?netWorth WHERE {
  ?entrepreneur wdt:P106 wd:Q131524 .
  ?entrepreneur wdt:P2218 ?netWorth .
}
ORDER BY DESC(?netWorth)
LIMIT 10
```

Let's execute this query to see the results.

[Tool]
Got 10 rows and 2 columns:
| entrepreneur                    | netWorth                   |
| ------------------------------- | -------------------------- |
| Elon Musk (wd:Q317521)          | 270100000000 (xsd:decimal) |
| Bernard Arnault (wd:Q32055)     | 207800000000 (xsd:decimal) |
| Jeff Bezos (wd:Q312556)         | 204600000000 (xsd:decimal) |
| Bernard Arnault (wd:Q32055)     | 143000000000 (xsd:decimal) |
| Bill Gates (wd:Q5284)           | 89800000000 (xsd:decimal)  |
| Radovan Vítek (wd:Q23664043)    | 86000000000 (xsd:decimal)  |
| Mukesh Ambani (wd:Q298547)      | 83600000000 (xsd:decimal)  |
| Warren Buffett (wd:Q47213)      | 82500000000 (xsd:decimal)  |
| Patrik Tkáč (wd:Q20120426)      | 80000000000 (xsd:decimal)  |
| Daniel Křetínský (wd:Q11774225) | 78000000000 (xsd:decimal)  |

[Assistant]
The query returned the top 10 wealthiest entrepreneurs based on their net worth. The most wealthy entrepreneur is Elon Musk with a net worth of 270,100,000,000 USD.

To finalize the answer, we can use an ASK query to confirm that Elon Musk is indeed the most wealthy entrepreneur. However, since the initial query already provides the answer, we can directly provide the final answer.

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
```

The most wealthy entrepreneur is Elon Musk.

[Tool] Stopped generation process


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 color of water, but we can see how to structure a query to check a property of an entity. Let's first find the entity for 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 a property that describes the color of water.

[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 p/wdt): 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 p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): 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 p/wdt): main Wikimedia category
9. mass, also known as displacement, molecular mass, molecular weight, weight (wd:P2067 as p/wdt): 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 p/wdt): 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` (P462) seems to be the most relevant for our purpose. Let's check if water (Q283) has a value for this property.

[Tool]
Got 5 rows and 1 column:
| color                        |
| ---------------------------- |
| colorless (wd:Q1396399)      |
| white (wd:Q23444)            |
| green (wd:Q3133)             |
| color of water (wd:Q5148636) |
| turquoise (wd:Q5960345)      |

[Assistant] Water is listed as having multiple colors, including "colorless". However, since the question specifically asks if water is colorless, we need to check if "colorless" is one of the values associated with water. We can use an ASK query to confirm this.

[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


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

Got 1 row and 1 column:
| turkishLabel                     |
| -------------------------------- |
| 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] None of these examples directly relate to our question, but we can learn from them. We need to find out how people from Turkey call the official name of the United States of America. This likely involves finding the official name of the USA and then translating it into Turkish. Let's start by identifying the entity for 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`. Now, let's find the property that represents the official name of a country. We can search for properties related to "official name" or "name".

[Tool]
Top 10 property alternatives:
1. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): label for the items in their official language  or their original language, name
2. official name, also known as long title, name, official title, registered name, full title (wd:P1448 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): official name of the subject in its official language(s), name
3. officialized by, also known as made official by, officialised by (wd:P5194 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the item which officialized this lexeme
4. short name, also known as short title, shortname, abbreviation, shortened name, initialism (wd:P1813 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): short name of a place, organisation, person, journal, wikidata property, etc. Used by some Wikipedia templates., name
5. official symbol, also known as symbol, state tartan, official tartan, floral emblem, official emblem (wd:P2238 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): official symbol of an organisation
6. nickname, also known as informal name, moniker, other name, othername, other_names (wd:P1449 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): informal name (for a pseudonym use P742), name
7. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
8. anthem, also known as national anthem, team anthem, official song, official hymn, club anthem (wd:P85 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): subject's official anthem
9. courtesy name, also known as courtesy name, style name, zi (wd:P1782 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): name bestowed upon a person at adulthood in addition to one's given name, mostly in East Asia, name
10. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name

[Assistant]
The property `wd:P1448` represents the official name of a country. Next, we need to find the official name of the United States of America using this property. Once we have the official name, we can then look for its translation into Turkish.

Let's start by retrieving the official name of the United States of America.

[Tool]
Got 1 row and 1 column:
| officialName                      |
| --------------------------------- |
| the United States of America (en) |

[Assistant]
The official name of the United States of America is "the United States of America" in English. Now, we need to find the Turkish translation of this name. We can use the `rdfs:label` property to get the label in Turkish.

Let's retrieve the Turkish label for the United States of America.

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

[Assistant]
The Turkish label for the United States of America is "Amerika Birleşik Devletleri".

Therefore, the official name of the United States of America in Turkish is "Amerika Birleşik Devletleri".

Here is the final SPARQL query:

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

And the answer to the question is "Amerika Birleşik Devletleri".

[Tool] Stopped generation process