PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?show ?showLabel ?startYear ?seasons WHERE {
  wd:Q23760 wdt:P161 ?show .
  ?show wdt:P31 wd:Q5398426 .
  ?show wdt:P580 ?startYear .
  ?show wdt:P1113 ?seasons .
  OPTIONAL {
    ?show rdfs:label ?showLabel .
    FILTER ( LANG( ?showLabel ) = 'en' )
  }
}

There are no TV shows in the Wikidata knowledge graph that match the criteria of starring Rowan Atkinson, having 4 seasons, and starting in 1983. The specific TV show we are looking for might not be accurately represented in the knowledge graph with the exact criteria provided.

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

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

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

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

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

[User] What is the TV-show that starred Rowan Atkinson , had 4 seasons and started in 1983?

[Assistant]
To answer this question, we need to identify the TV show that meets the following criteria:
1. Starred Rowan Atkinson
2. Had 4 seasons
3. Started in 1983

Let's start by finding the entity for Rowan Atkinson and then look for TV shows where he was a star.

[Tool]
Top 10 entity alternatives:
1. Rowan Atkinson, also known as Rowan Sebastian Atkinson (wd:Q23760): English actor, comedian, and screenwriter, electrical engineer, television director, television actor, film producer, screenwriter, manufacturer, voice actor, stage actor, film actor, comedian, actor
2. Rowan Atkinson Live (wd:Q9322991): 1992 film
3. Hubert Rowan Atkinson, also known as Hon. Hubert Rowan Atkinson (wd:Q75293963): (born 1882)
4. Sorbus, also known as rowan (wd:Q157964): genus of plants
5. Sorbus aucuparia, also known as European mountain-ash, European rowan, rowan, rowan-berry (wd:Q146198): species of plant
6. Rowan, also known as Rowan, IA, Rowan, Iowa (wd:Q533345): American city in Iowa
7. Atkinson, also known as Atkinson, NE, Atkinson, Nebraska (wd:Q965125): town in Nebraska, United States
8. Atkinson, also known as Atkinson, IL (wd:Q576062): town in Illinois, United States
9. Atkinson, also known as Atkinson, NC, Atkinson, North Carolina (wd:Q2027754): town in North Carolina
10. 4599 Rowan, also known as Rowan (wd:Q543954): asteroid

No other items found

[Assistant] The correct entity for Rowan Atkinson is `wd:Q23760`. Now, let's find the TV shows where Rowan Atkinson was a star.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that there are no direct results for TV shows where Rowan Atkinson was a star using the `P161` (cast member) property. Let's try another approach by searching for TV shows where Rowan Atkinson is known for his role.

We can use the `P453` (character portrayed) property to find TV shows where he played a character.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that there are no direct results for TV shows where Rowan Atkinson played a character using the `P453` (character portrayed) property either. Let's try a different approach by searching for TV shows where Rowan Atkinson is known for his work.

We can use the `P800` (work) property to find TV shows associated with him.

[Tool]
Got 3 rows and 3 columns:
| subject                    | property                | object                    |
| -------------------------- | ----------------------- | ------------------------- |
| Rowan Atkinson (wd:Q23760) | notable work (wdt:P800) | The Lion King (wd:Q36479) |
| Rowan Atkinson (wd:Q23760) | notable work (wdt:P800) | Mr. Bean (wd:Q484020)     |
| Rowan Atkinson (wd:Q23760) | notable work (wdt:P800) | Wonka (wd:Q108456719)     |

[Assistant]
From the results, we see that one of the notable works of Rowan Atkinson is "Mr. Bean" (`wd:Q484020`). Let's check if "Mr. Bean" has 4 seasons and started in 1983.

First, we'll verify the number of seasons and the start year of "Mr. Bean".

[Tool]
Got 1 row and 2 columns:
| ?seasons | ?startYear           |
| -------- | -------------------- |
| 14       | 1990-01-01T00:00:00Z |

[Assistant]
The results show that "Mr. Bean" (`wd:Q484020`) has 14 episodes and started in 1990, which does not match the criteria of having 4 seasons and starting in 1983.

Since "Mr. Bean" does not fit the criteria, let's try another approach by searching for TV shows that started in 1983 and had 4 seasons, and then check if Rowan Atkinson starred in any of them.

We'll start by finding TV shows that started in 1983 and had 4 seasons.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that there are no TV shows that started in 1983 and had exactly 4 seasons according to the available data. However, let's try a more flexible approach by looking for TV shows that started in 1983 and then checking if Rowan Atkinson starred in any of them.

We'll first find TV shows that started in 1983 and then filter those to see if Rowan Atkinson was a star.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that there are no TV shows that started in 1983 according to the available data. Given this, it seems that the specific TV show we are looking for might not be accurately represented in the Wikidata knowledge graph with the exact criteria provided.

However, let's try one more approach by searching for TV shows that Rowan Atkinson starred in and then checking their start year and number of seasons.

We'll start by finding TV shows where Rowan Atkinson was a star and then filter those to see if they match the criteria of starting in 1983 and having 4 seasons.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that there are no TV shows where Rowan Atkinson was a star that match the criteria of starting in 1983 and having 4 seasons according to the available data in Wikidata.

Given the lack of results, it appears that the specific TV show we are looking for might not be accurately represented in the knowledge graph with the exact criteria provided.

Therefore, I will conclude that the information required to answer the question is not available in the current state of the Wikidata knowledge graph.

I will provide a cancellation response explaining the situation.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?album ) AS ?count ) WHERE {
  ?album wdt:P175 wd:Q1744 .
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 359    |

[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] On how many albums does Madonna perform?

[Assistant] To answer the question "On how many albums does Madonna perform?", we need to identify Madonna as an artist and then count the number of albums she has performed on. Let's start by searching for Madonna as an artist in the knowledge graph.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
Got more than the maximum supported number of subject candidates (131,072)

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

No other items found

[Assistant] From the search results, the correct entity for Madonna the artist is `wd:Q1744`. Now, let's find out the property that links artists to the albums they perform on. We can search for properties related to music albums and performances.

[Tool]
Top 10 property alternatives:
1. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as p/wdt): award or recognition received by a person, organization or creative work
2. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as p/wdt): notable scientific, artistic or literary work, or other work of significance among subject's works
3. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
4. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as p/wdt): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
5. work period (start), also known as start working, flourished (start), start time for work, start date of activity, fl. (start) (wd:P2031 as p/wdt): start of period during which a person or group flourished (fl. = "floruit") in their professional activity, start time, floruit
6. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as p/wdt): specialization of a person or organization; see P106 for the occupation, interested in
7. instrument, also known as uses this instrument, teaches instrument, instruments, plays this instrument, plays instrument (wd:P1303 as p/wdt): musical instrument that a person plays or teaches or used in a music occupation, uses
8. spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as p/wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative
9. official shop URL, also known as online store, shop, shop URL, store, store URL (wd:P10225 as p/wdt): official URL where the agent sells products/merchandise, official website
10. voice type, also known as range of voice, register, tessitura, type of voice, vocal type (wd:P412 as p/wdt): person's voice type. expected values: soprano, mezzo-soprano, contralto, countertenor, tenor, baritone, bass (and derivatives)

No other items found

[Assistant]
The search for properties related to "perform" did not yield a direct property linking Madonna to the albums she performs on. However, we can try another approach by looking for properties that link artists to their albums. One common property for this relationship is `wdt:P175` (performer).

Let's check if Madonna is linked to any albums through the `wdt:P175` property.

[Tool]
Got 359 rows and 3 columns, showing the first 10 rows below:
| subject                                   | property             | object             |
| ----------------------------------------- | -------------------- | ------------------ |
| Hard Candy (wd:Q483778)                   | performer (wdt:P175) | Madonna (wd:Q1744) |
| Confessions on a Dance Floor (wd:Q151880) | performer (wdt:P175) | Madonna (wd:Q1744) |
| Madonna (wd:Q177943)                      | performer (wdt:P175) | Madonna (wd:Q1744) |
| True Blue (wd:Q154467)                    | performer (wdt:P175) | Madonna (wd:Q1744) |
| MDNA (wd:Q235340)                         | performer (wdt:P175) | Madonna (wd:Q1744) |
| Like a Virgin (wd:Q250406)                | performer (wdt:P175) | Madonna (wd:Q1744) |
| Ray of Light (wd:Q155335)                 | performer (wdt:P175) | Madonna (wd:Q1744) |
| Like a Prayer (wd:Q252447)                | performer (wdt:P175) | Madonna (wd:Q1744) |
| 4 Minutes (wd:Q685457)                    | performer (wdt:P175) | Madonna (wd:Q1744) |
| Music (wd:Q155358)                        | performer (wdt:P175) | Madonna (wd:Q1744) |
| ...                                       | ...                  | ...                |

[Assistant]
The list shows that Madonna is indeed linked to several albums through the `wdt:P175` (performer) property. To get the exact count of albums, we can write a SPARQL query to count the distinct albums where Madonna is listed as a performer.

Let's execute the SPARQL query to get the count.

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 359    |

[Assistant]
The SPARQL query returned a count of 359 distinct albums where Madonna is listed as a performer.

Therefore, the answer to the question "On how many albums does Madonna perform?" is 359.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?entity ?label ?foundingDate WHERE {
  ?entity rdfs:label "Birdland" @en .
  ?entity wdt:P131 wd:Q1055 .
  OPTIONAL {
    ?entity rdfs:label ?label .
    FILTER ( LANG( ?label ) = "en" )
  }
  .
  OPTIONAL {
    ?entity wdt:P571 ?foundingDate
  }
}

The founding date of Birdland in Hamburg is not available in the knowledge graph.

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

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

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

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

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

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

[Assistant]
To answer the question about when the jazz club Birdland in Hamburg was founded, I need to identify the entity for Birdland in Hamburg and then find the property that indicates its founding date.

First, I will search for the entity representing Birdland in Hamburg.

[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

No other items found

[Assistant] It seems that the search did not return the specific entity for Birdland in Hamburg. Let me try a more targeted search by including "jazz club" in the query to narrow down the results.

[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)

No other items found

[Assistant]
The search did not return the specific entity for Birdland in Hamburg. Given that the entity might not be well-known or might not have a direct match, I will try a different approach. I will list triples where the object is "Hamburg" and the property is "located in" to see if I can find any jazz clubs in Hamburg.

Let's try this approach:

1. List triples where the object is "Hamburg" and the property is "located in".
2. Filter the results to find entities that are jazz clubs.

[Tool]
Got 2,911 rows and 3 columns, showing the first 10 rows below:
| subject                                         | property                                                    | object             |
| ----------------------------------------------- | ----------------------------------------------------------- | ------------------ |
| Elbe (wd:Q1644)                                 | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Hamburger SV (wd:Q51974)                        | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| University of Hamburg (wd:Q156725)              | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Altona (wd:Q1630)                               | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Neuengamme concentration camp (wd:Q312478)      | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Hamburg U-Bahn (wd:Q6445)                       | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Kühne Logistics University (wd:Q1683524)        | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Port of Hamburg (wd:Q3964)                      | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Alster (wd:Q1219)                               | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| Bombing of Hamburg in World War II (wd:Q316868) | located in the administrative territorial entity (wdt:P131) | Hamburg (wd:Q1055) |
| ...                                             | ...                                                         | ...                |

[Assistant]
The list of entities located in Hamburg does not include any jazz clubs. To further refine the search, I will try to find entities that are specifically labeled as jazz clubs and then check if any of them are located in Hamburg.

I will search for entities that are instances of "jazz club" and then filter those results to see if any are located in Hamburg.

Let's start by searching for entities that are instances of "jazz club".

[Tool]
Top 10 entity alternatives:
1. Iridium Jazz Club (wd:Q1331383): jazz club in New York City
2. Reduta Jazz Club (wd:Q12049057): music club
3. Smalls Jazz Club (wd:Q2294913): jazz club located in New York City
4. Vortex Jazz Club, also known as The Vortex Jazz Club (wd:Q7941919): jazz club in London Borough of Hackney, UK
5. Andy's Jazz Club (wd:Q4760329)
6. PizzaExpress Jazz Club, also known as pizza express jazz club (wd:Q7199945): jazz club in London, England
7. Akwarium Jazz Club (wd:Q9143790): jazz club in Warsaw, Poland
8. Dakota Jazz Club (wd:Q5210022): jazz club in Minneapolis, Minnesota, US
9. Cory Weeds’ Cellar Jazz Club, also known as Cellar Jazz Club (wd:Q15857151): jazz club in Vancouver, Canada
10. Constellation Jazz Club (wd:Q18706206)

No other items found

[Assistant]
The search for jazz clubs did not return any specific entity for Birdland in Hamburg. However, it did return several jazz clubs, which suggests that the entity for Birdland in Hamburg might exist but is not well-known or indexed under the exact name.

To further refine the search, I will try to find entities that are jazz clubs and are located in Hamburg. I will use the property "located in" (P131) to filter the results.

Let's execute a SPARQL query to find jazz clubs located in Hamburg and their founding dates.

```sparql
SELECT ?jazzClub ?foundingDate WHERE {
  ?jazzClub wdt:P31 wd:Q1684522 ;  # Instance of jazz club
          wdt:P131 wd:Q1055 .      # Located in Hamburg
  OPTIONAL { ?jazzClub wdt:P571 ?foundingDate }  # Optional: founding date
}
```

This query will help us identify any jazz clubs in Hamburg and their founding dates.

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| ?jazzClub     | ?foundingDate |
| ------------- | ------------- |
| wd:Q106087772 |               |

[Assistant]
The query returned one jazz club in Hamburg, but it does not have a recorded founding date. The entity is `wd:Q106087772`.

To confirm if this entity is indeed Birdland in Hamburg, I will fetch more details about this entity, such as its label and any other relevant information.

Let's execute a SPARQL query to get more details about this entity.

```sparql
SELECT ?jazzClub ?label ?foundingDate WHERE {
  ?jazzClub wdt:P31 wd:Q1684522 ;  # Instance of jazz club
          wdt:P131 wd:Q1055 .      # Located in Hamburg
  OPTIONAL { ?jazzClub wdt:P571 ?foundingDate }  # Optional: founding date
  OPTIONAL { ?jazzClub rdfs:label ?label . FILTER(LANG(?label) = "en") }  # Optional: English label
}
```

This query will provide the label and any other relevant information about the jazz club in Hamburg.

Let's execute this query.

[Tool]
Got 1 row and 3 columns:
| ?jazzClub     | ?label | ?foundingDate |
| ------------- | ------ | ------------- |
| wd:Q106087772 |        |               |

[Assistant]
The query returned the entity `wd:Q106087772` for a jazz club in Hamburg, but it does not have a label or a founding date. This suggests that the entity might not be well-documented in Wikidata.

Given the lack of information, I will try one more approach to find the founding date of Birdland in Hamburg. I will search for the entity directly using the name "Birdland Hamburg" and see if it has any properties related to its founding date.

Let's execute a search for the entity "Birdland Hamburg" and then check its properties.

```sparql
SELECT ?entity ?label ?foundingDate WHERE {
  ?entity rdfs:label "Birdland"@en .
  ?entity wdt:P131 wd:Q1055 .  # Located in Hamburg
  OPTIONAL { ?entity rdfs:label ?label . FILTER(LANG(?label) = "en") }  # Optional: English label
  OPTIONAL { ?entity wdt:P571 ?foundingDate }  # Optional: founding date
}
```

This query will search for an entity with the label "Birdland" located in Hamburg and retrieve its label and founding date if available.

Let's execute this query.

[Tool] Got no rows and 3 columns

[Assistant]
The query did not return any results, indicating that there is no entity in Wikidata with the label "Birdland" located in Hamburg that has a recorded founding date.

Given the lack of information in Wikidata, it appears that the data for Birdland in Hamburg is either not present or not well-documented in the knowledge graph.

Since I cannot find the founding date of Birdland in Hamburg using the available data, I will have to conclude that the information is not available in Wikidata.

I will provide a response indicating that the founding date of Birdland in Hamburg is not available in the knowledge graph.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?author ?authorLabel WHERE {
  wd:Q44649835 wdt:P50 ?author .
  OPTIONAL {
    ?author rdfs:label ?authorLabel .
    FILTER ( LANG( ?authorLabel ) = 'en' )
  }
}

Richard S. Zemel

[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]
To answer the question about who wrote "Prototypical Networks for Few-shot Learning," we need to identify the paper and then find the authors associated with it.

### Step 1: Identify the Paper
First, let's search for the paper "Prototypical Networks for Few-shot Learning" in the knowledge graph.

### Step 2: Find the Authors
Once we have the paper, we can look for the authors associated with it.

Let's start by searching for 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

No other items found

[Assistant]
The search results show that the paper "Prototypical Networks for Few-shot Learning" is identified as `wd:Q44649835`. Now, let's find the authors associated with this paper.

### Step 2: Find the Authors
We will use the property `P50` (author) to find the authors of the paper.

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

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

[Assistant]
The SPARQL query returned one author, Richard S. Zemel, for the paper "Prototypical Networks for Few-shot Learning."

However, it is common for research papers to have multiple authors. Let's refine the query to ensure we capture all authors associated with the paper.

### Step 3: Refine the Query to Capture All Authors
We will modify the query to include all authors and their labels for better readability.

Let's execute the refined SPARQL query.

[Tool]
Got 1 row and 2 columns:
| ?author                         | ?authorLabel     |
| ------------------------------- | ---------------- |
| Richard S. Zemel (wd:Q26702597) | Richard S. Zemel |

[Assistant]
The refined SPARQL query still only returned one author, Richard S. Zemel. However, it is possible that the paper has multiple authors, and we might need to check if there are any additional authors listed under different properties or qualifiers.

### Step 4: Check for Additional Authors
Let's check if there are any additional authors listed under different properties or qualifiers.

We will use the property `P50` (author) and also check for any qualifiers that might list additional authors.

Let's execute the query to check for additional authors.

[Tool]
Got 1 row and 2 columns:
| ?author                         | ?authorLabel     |
| ------------------------------- | ---------------- |
| Richard S. Zemel (wd:Q26702597) | Richard S. Zemel |

[Assistant]
The query to check for additional authors using qualifiers also only returned one author, Richard S. Zemel. This suggests that the paper "Prototypical Networks for Few-shot Learning" is indeed authored by Richard S. Zemel, and no other authors are listed in the knowledge graph.

### Final Answer
Based on the SPARQL query results, the author of "Prototypical Networks for Few-shot Learning" is Richard S. Zemel.

Let's provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?university ?universityLabel ?studentCount WHERE {
  ?university wdt:P2196 ?studentCount .
  FILTER ( ?studentCount > 200000 ) OPTIONAL {
    ?university rdfs:label ?universityLabel .
    FILTER ( LANG( ?universityLabel ) = 'en' )
  }
}

Got 27 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?university                                                | ?universityLabel                           | ?studentCount |
| ---------------------------------------------------------- | ------------------------------------------ | ------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | National Open University of Nigeria, Lafia | 500000        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| University of California (wd:Q184478)                      | University of California                   | 295573        |
| University of Phoenix (wd:Q1889100)                        | University of Phoenix                      | 420000        |
| University of Buenos Aires (wd:Q194223)                    | University of Buenos Aires                 | 262932        |
| ...                                                        | ...                                        | ...           |
| Universitas Terbuka (wd:Q7894421)                          | Universitas Terbuka                        | 525360        |
| national school (wd:Q96386155)                             | national school                            | 511320        |
| national school (wd:Q96386155)                             | national school                            | 2039229       |
| national school (wd:Q96386155)                             | national school                            | 2044299       |
| national school (wd:Q96386155)                             | national school                            | 2071890       |

The universities with more than 200,000 students include: National Open University of Nigeria, Lafia (500,000 students), Damascus University (210,929 students), University of California (295,573 students), University of Phoenix (420,000 students), University of Buenos Aires (262,932 students), Universitas Terbuka (525,360 students), and national school (511,320 students).

[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] To answer this question, we need to identify the universities and their number of students. We can start by searching for the property that represents the number of students in a university.

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

No other items found

[Assistant]
From the search results, the property `count of students` (wd:P2196) seems to be the most relevant for representing the number of students in a university. Now, let's find the universities that have more than 200,000 students using this property.

I will construct a SPARQL query to retrieve the universities and their number of students where the number of students is greater than 200,000.

Let's execute the query.

[Tool]
Got 27 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?university                                                | ?universityLabel                           | ?studentCount |
| ---------------------------------------------------------- | ------------------------------------------ | ------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | National Open University of Nigeria, Lafia | 500000        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| University of California (wd:Q184478)                      | University of California                   | 295573        |
| University of Phoenix (wd:Q1889100)                        | University of Phoenix                      | 420000        |
| University of Buenos Aires (wd:Q194223)                    | University of Buenos Aires                 | 262932        |
| ...                                                        | ...                                        | ...           |
| Universitas Terbuka (wd:Q7894421)                          | Universitas Terbuka                        | 525360        |
| national school (wd:Q96386155)                             | national school                            | 511320        |
| national school (wd:Q96386155)                             | national school                            | 2039229       |
| national school (wd:Q96386155)                             | national school                            | 2044299       |
| national school (wd:Q96386155)                             | national school                            | 2071890       |

[Assistant]
The SPARQL query has returned the universities that have more than 200,000 students along with their names and the number of students. Here are some of the results:

- **National Open University of Nigeria, Lafia**: 500,000 students
- **Damascus University**: 210,929 students
- **University of California**: 295,573 students
- **University of Phoenix**: 420,000 students
- **University of Buenos Aires**: 262,932 students
- **Universitas Terbuka**: 525,360 students
- **national school**: 511,320 students (Note: This entry appears multiple times with different student counts, which might indicate data inconsistencies or different campuses/schools under the same name.)

Now, I will provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


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

Got 1 row and 1 column:
| ?numberOfHolidays |
| ----------------- |
| 13                |

[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] To answer this question, we need to identify the entity for Bavaria and the property that represents public holidays. Let's start by searching for the entity representing 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

No other items found

[Assistant] The top result for Bavaria is the Free State of Bavaria (wd:Q980), which is the correct entity we are looking for. Next, we need to find the property that represents public holidays. We can search for properties related to public holidays and see if any of them are associated with Bavaria.

[Tool]
Top 10 property alternatives:
1. public holiday, also known as bank holiday, legal holiday, national holiday (wd:P832 as p/wdt): official public holiday that occurs in this place in its honor, usually a non-working day, significant event
2. located in time zone, also known as TZ, time zone, timezone (wd:P421 as p/wdt): time zone for this item
3. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
4. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as p/wdt): language designated as official by this item, language used
5. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as wdtn/p/wdt): identifier from an international authority file of names, subjects, and organizations
6. basic form of government, also known as form of government, government, type of government (wd:P122 as p/wdt): subject's government
7. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as p/wdt): label for the items in their official language  or their original language, name
8. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as p/wdt): seat of government of a country, province, state or other type of administrative territorial entity
9. anthem, also known as national anthem, team anthem, official song, official hymn, club anthem (wd:P85 as p/wdt): subject's official anthem
10. country, also known as sovereign state, state, land, host country (wd:P17 as p/wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity

No other items found

[Assistant]
The property `public holiday` (wd:P832) seems to be the most relevant for our query. However, it appears that this property is used to indicate specific public holidays rather than the total number of public holidays in a year. To find the number of public holidays, we need to count the distinct values of this property for Bavaria.

Let's construct a SPARQL query to count the number of public holidays in Bavaria.

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

This query will count the distinct public holidays associated with Bavaria. Let's execute this query to get the result.

[Tool]
Got 1 row and 1 column:
| ?numberOfHolidays |
| ----------------- |
| 13                |

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

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

Here is the final SPARQL query:

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

And the answer is 13.

[Tool] Stopped generation process


PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  ?place wdt:P625 ?coordinates .
  ?place wdt:P17 wd:Q39 .
  BIND( REPLACE( STR( ?coordinates ) , 'Point\\(.* ([0-9.-]+)\\)' , '$1' ) AS ?latitude ) FILTER ( xsd:float ( ?latitude ) > 47.8 )
}

False

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

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

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

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

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

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

[Assistant] To answer this question, we need to find out if there is any place in Switzerland that has a latitude greater than 47.8. We can achieve this by querying for places in Switzerland and then checking their geographic coordinates. Let's start by finding the IRI 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

No other items found

[Assistant] The correct IRI for Switzerland is `wd:Q39`. Now, let's find the property for the geographic location of a place.

[Tool]
Top 10 property alternatives:
1. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): 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 p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): subject's ordinal position as qualitatively evaluated relative to other members of a group

No other items found

[Assistant]
The property for the geographic location of a place is `wdt:P625` (coordinate location). Now, we can construct a SPARQL query to check if there is any place in Switzerland with a latitude greater than 47.8.

Let's write and execute the SPARQL query.

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

[Assistant]
It seems that the `geof:lat` function is not supported by the QLever endpoint. Instead, we can use the `str` function to extract the latitude from the coordinate string and then compare it. Let's modify the query accordingly.

Here’s the updated SPARQL query:

```sparql
ASK WHERE {
  ?place wdt:P625 ?coordinates .
  ?place wdt:P17 wd:Q39 .
  BIND(REPLACE(STR(?coordinates), 'Point\\(.* ([0-9.-]+)\\)', '$1') AS ?latitude)
  FILTER(xsd:float(?latitude) > 47.8)
}
```

This query will extract the latitude from the coordinate string and check if it is greater than 47.8.

Let's execute this query.

[Tool] False

[Assistant]
The SPARQL query returned `False`, which means there is no place in Switzerland with a latitude greater than 47.8.

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

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


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

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] To determine who is the most wealthy entrepreneur, we need to identify individuals who are entrepreneurs and then compare their wealth. We'll start by searching for the property that represents being an entrepreneur and the property that represents net worth or wealth.

[Tool]
Top 10 property alternatives:
1. copyright holder, also known as licensor, proprietor, copyright owner, copyright ‘owner’ (wd:P3931 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): person or organisation who holds the copyright of a work according to the Berne Convention, owned by
2. owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): owner of the subject
3. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): founder or co-founder of this organization, religion, place or entity, creator
4. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): notable scientific, artistic or literary work, or other work of significance among subject's works
5. discoverer or inventor, also known as inventor or discoverer, inventor, invented by, invented, introduced by (wd:P61 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): subject who discovered, first described, invented, or developed this discovery or invention or scientific hypothesis or theory
6. investor, also known as funder, private investor, investors (wd:P1951 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): individual or organization which invests money in the item for the purpose of obtaining financial return on their investment
7. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): time when an entity begins to exist; for date of official opening use P1619, start time
8. partner in business or sport, also known as professional partner, sport partner, sports partner, team mate, team-mate (wd:P1327 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): professional collaborator, significant person
9. franchisor, also known as franchise, franchiser (wd:P10680 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): one who licenses some or all of its know-how, procedures, intell...rights to sell its branded products and services to a franchisee
10. collection creator, also known as creator, archive creator, collector, archives of, archive of (wd:P6241 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): entity (person, organization, etc.) that caused a record or collection to be produced or gathered

No other items found

[Assistant]
None of the top 10 properties directly represent being an entrepreneur. However, we can try searching for the occupation or profession of being an entrepreneur.

Let's search for the occupation "entrepreneur" as an object.

[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. entrepreneurship, also known as entrepreneurial approach, entrepreneurship approach (wd:Q3908516): creation or extraction of economic value
4. talent manager, also known as artist manager, manager, pop entrepreneur (wd:Q1320883): person who guides the career of an artist
5. female entrepreneur, also known as entrepreneure, female entrepreneurs (wd:Q5442755): women who organize and manage an enterprise
6. theatre entrepreneur, also known as impresario (wd:Q17994313): profession
7. social entrepreneur (wd:Q12336252): occupation
8. serial entrepreneur (wd:Q2273346): entrepreneur who continually starts new businesses
9. fashion entrepreneur (wd:Q5436768)
10. Internet entrepreneur (wd:Q52228055): entrepreneur who applies innovation to create new businesses on the Internet

No other items found

No literal items found

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

No other items found

[Assistant]
We found the property "net worth" (wd:P2218). Now, we can construct a SPARQL query to find the most wealthy entrepreneur by combining the occupation "entrepreneur" (wd:Q131524) and the property "net worth" (wd:P2218).

Let's write the SPARQL query to find the most wealthy entrepreneur.

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

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

Here is the final SPARQL query:

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

And the answer is Elon Musk.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  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]
To determine if water is colorless, we need to find the relevant property in Wikidata that describes the color of substances, and then check if water has this property set to "colorless."
Let's start by searching for the property that describes the color of a substance.

[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/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): color of subject
2. colorist, also known as colouring by, colours by, colourist, colors by, coloring by (wd:P6338 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): artist in charge of adding colors to drawings, comics, movie film, etc., illustrator
3. hair color, also known as color of hair, colour of hair, haircolor, hair colour, haircolour (wd:P1884 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): person's hair color. Use P585 as qualifier if there's more than one value, color
4. color produced, also known as colour produced (wd:P7253 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): color or color range produced by a dye, alone or with a mordant
5. for color scheme, also known as color mode, uses colour scheme, uses color scheme, theme, prefers colour scheme (wd:P8798 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): which color scheme this statement is designed for
6. sRGB color hex triplet, also known as color hex triplet, sRGB, RGB, hex color, hexcode (wd:P465 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): sRGB hex triplet format for subject color (e.g. 7FFFD4) specifying the 8-bit red, green and blue components
7. eye color, also known as eyecolour, eye colour, eyecolor (wd:P1340 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): color of the irises of a person's eyes, color
8. color index (wd:P1458 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): color index of an astronomical object
9. official color, also known as official colors, signature colors, signature color (wd:P6364 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): official colors chosen to represent or identify an item
10. flower color, also known as bloom color, flower colour, colour of the flower, color of the flower, bloom colour (wd:P2827 as p/pqn/psv/prn/ps/pr/wdt/psn/wdtn/pq/pqv/prv): colour of flowers of a plant species, hybrid or cultivar, color

No other items found

[Assistant]
From the search results, the property `color` (wd:P462) seems to be the most relevant for describing the color of a substance. Next, we need to find the entity for water and check if it has this property set to "colorless."

Let's search for the entity representing water.

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

No other items found

[Assistant]
The entity for water is `wd:Q283`. Now, let's check if this entity has the property `color` (wd:P462) and if it is set to "colorless."

We will execute a SPARQL query to find out.

[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]
The results show that water (wd:Q283) has multiple values for the color property (wd:P462), including "colorless" (wd:Q1396399). This indicates that water is indeed considered colorless.

To confirm this, we can use an ASK query to check if water has the color "colorless."

Let's execute the ASK query.

[Tool] True

[Assistant]
The ASK query confirms that water (wd:Q283) has the color "colorless" (wd:Q1396399).

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

Here is the final SPARQL query and answer:

[Tool] Stopped generation process