PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?height WHERE {
  wd:Q4176 wdt:P2048 ?height .
}

The height of the Cologne Cathedral is 157 meters.

[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 high is the Cologne Cathedral ?

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

[Tool]
Example 1:
Question:
how much is the elevation of Düsseldorf Airport ?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q58226 wdt:P2044 ?uri .
}

Using entities:
Düsseldorf Airport, also known as DUS, Düsseldorf International Airport, EDDL (wd:Q58226): international airport of Düsseldorf

Using properties:
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level

Example 2:
Question:
How high is the Yokohama Marine Tower?

SPARQL:
SELECT DISTINCT ?num WHERE {
  wd:Q1207989 wdt:P2048 ?num .
}

Using entities:
Yokohama Marine Tower (wd:Q1207989): lighthouse in Japan

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

Example 3:
Question:
What is the highest mountain in Germany?

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q8502 ; wdt:P2044 ?elevation ; wdt:P17 wd:Q183 .
}
ORDER BY DESC ( ?elevation )
LIMIT 1

Using entities:
Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
elevation above sea level, also known as MAMSL, MASL, height, elevation above mean sea level, elevation (wd:P2044 as wdt): height of the item (geographical object) as measured relative to sea level
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant]
From the examples, I see that the property for height is usually wdt:P2048 (height, vertical length of an entity). Also, I need to find the entity for "Cologne Cathedral" to use it in the query.

Next, I will search for the entity "Cologne Cathedral" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Cologne Cathedral, also known as High Cathedral of St. Peter (wd:Q4176): Roman-Catholic cathedral in Cologne, Germany
2. Cologne Cathedral (wd:Q20891184): painting by Oskar Kokoschka
3. Cologne Cathedral Seal (wd:Q1795817): part of Cologne Cathedral
4. Baptistry Cologne Cathedral (wd:Q106498805)
5. Cologne Cathedral, 1869 (wd:Q119111134): painting by James Webb (1835–1895), Bristol Museum & Art Gallery
6. Bells of Cologne Cathedral (wd:Q1795814)
7. Finials of Cologne Cathedral (wd:Q1408999): cologne cathedral
8. Organs of Cologne Cathedral, also known as Organs in Cologne Cathedral (wd:Q7102106)
9. The Beggar from Cologne Cathedral, also known as Beggar from Cologne Cathedral (wd:Q15241065): 1927 film by Rolf Randolf
10. Surroundings of Cologne Cathedral (wd:Q65233962): cologne Cathedral surroundings

[Assistant] The entity for "Cologne Cathedral" is wd:Q4176. The property for height is likely wdt:P2048. I will now query the height of the Cologne Cathedral using these.

[Tool]
Got 1 row and 1 column:
| height            |
| ----------------- |
| 157 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT DISTINCT ?person WHERE {
  ?person p:P39 ?statement .
  ?statement ps:P39 wd:Q1048744 .
  ?statement pq:P580 ?startDate .
  FILTER ( ?startDate < "1964-10-14T00:00:00Z" ^^ xsd:dateTime )
}

Got 2 rows and 1 column:
| person                        |
| ----------------------------- |
| Nikita Khrushchev (wd:Q35314) |
| Joseph Stalin (wd:Q855)       |

There were 2 General Secretaries of the CPSU before Leonid Brezhnev: Joseph Stalin and Nikita Khrushchev.

[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 General Secretaries of the CPSU were there before Leonid Brezhnev?

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

[Tool] No examples found

[Assistant]
There are no similar examples available, so I will proceed by identifying the relevant entities and properties for the question.

Step 1: Identify the entity for "General Secretary of the CPSU" (Communist Party of the Soviet Union).
Step 2: Identify the entity for "Leonid Brezhnev."
Step 3: Find the list of people who held the position of General Secretary of the CPSU before Leonid Brezhnev.
Step 4: Count how many such General Secretaries there were before him.

I will start by searching for the entity "General Secretary of the CPSU."

[Tool]
Top 10 entity alternatives:
1. General Secretary of the Chinese Communist Party, also known as CCP General Secretary, General Secretary of the Communist Party of China, General Secretary of the Central...e of the Chinese Communist Party, General Secretary, General Secretary of the CCP (wd:Q849418): head of the Chinese Communist Party (CCP) and de facto leader of China
2. Secretary General of the Confederación Nacional del Trabajo, also known as Secretary General of the CNT (wd:Q3393986): head of the confederation of Spanish anarcho-syndicalist labor unions
3. General Secretary of the CIS (wd:Q119716722): head of the Commonwealth Secretariat
4. Secretary General of the Universal Esperanto Association, also known as Ĝenerala Sekretario de UEA, Universal Esperanto Association Secretary General, Secretary General of the UEA, UEA Secretary General (wd:Q12357331)
5. General Secretary of the YSP (wd:Q130385190)
6. Secretary General of the CSTO (wd:Q119716745): diplomatic head of the CSTO
7. [Address of the general secretary] (wd:Q74790507): scientific article published on 01 June 2002
8. General Secretary of the Interior (wd:Q50128204)
9. General secretary of the Sea (wd:Q78862662): French official position
10. General Secretary of the CGIL, also known as General Secretary of the Italian General Confederation of Labour (wd:Q15355884): leader of the CGIL trade union of Italy

[Assistant] The search for "General Secretary of the CPSU" did not directly return the exact entity for the General Secretary of the Communist Party of the Soviet Union. I will try a more specific search query "General Secretary of the Communist Party of the Soviet Union."

[Tool]
Top 10 entity alternatives:
1. General Secretary of the Communist Party of the Soviet Union, also known as First Secretary of the Communist Party of the Soviet Union, General Secretary of the Soviet Union, Gensek of the CPSU (wd:Q1048744): de facto head of state of the Soviet Union and dirigent of the Communist Party of the Soviet Union
2. Secretary of the Communist Party of the Soviet Union (wd:Q115147301)
3. General Department of the Communist Party of the Soviet Union (wd:Q16682297): organization
4. General Secretary Xi Jin-ping’s ...and Their Practical Significance (wd:Q120857259): scholarly article in Studies on Mao Zedong and Deng Xiaoping Theories, no. 9, 2019
5. Communist Party of the Soviet Union, also known as Bolshevik Party, CPSU, RCP(b), Russian Communist Party (Bolsheviks), All-Union Communist Party (Bolsheviks) (wd:Q79854): Marxist–Leninist founding and ruling party of the Soviet Union
6. Communist Party of the Soviet Union (wd:Q4229911): Political party founded in 2001
7. Communist Party of the Soviet Union (wd:Q3701765): Political party founded in 1992
8. Appeal to the General Secretary ...States of America, Ronald Reagan (wd:Q43929775): scientific article published on July 13, 1985
9. General Secretary of the Chinese Communist Party, also known as CCP General Secretary, General Secretary of the Communist Party of China, General Secretary of the Central...e of the Chinese Communist Party, General Secretary, General Secretary of the CCP (wd:Q849418): head of the Chinese Communist Party (CCP) and de facto leader of China
10. Congress of the Communist Party of the Soviet Union (wd:Q2415599): supreme decision-making body of the Communist Party of the Soviet Union

[Assistant] The entity for "General Secretary of the Communist Party of the Soviet Union" is wd:Q1048744. Next, I will search for the entity "Leonid Brezhnev."

[Tool]
Top 10 entity alternatives:
1. Leonid Brezhnev, also known as Brezhnev, Leonid Ilyich Brezhnev (wd:Q765165): General Secretary of the Communist Party of the Soviet Union (1906–1982), military personnel, politician, engineer, surveyor
2. Admiral Kuznetsov, also known as Admiral Flota Sovetskovo Soyuza Kuznetsov, aircraft carrier Admiral Kuznetsov, Leonid Brezhnev, Riga, Tbilisi (wd:Q274924): 1985 Kuznetsov-class aircraft carrier
3. Starry Metropolis, also known as Neptune, Olivia, IMO 7359498, Kareliya, Leonid Brezhnev (wd:Q22968553): ship built in 1976
4. Leonid Brezhnev (wd:Q47510509): painting by Boris Chaliapin
5. Leonid Brezhnev, also known as Whiskey on the Rocks Leonid Brezhnev (wd:Q131583282): Leonid Brezhnev as depicted in television series Whiskey on the Rocks, politician
6. Leonid Brezhnev (wd:Q58345911): Scientific article (April 2014)
7. Leonid Brezhnev (wd:Q47511657): painting by Harvey Simpson
8. Leonid Brezhnev (wd:Q47510378): painting by Burton Philip Silverman
9. Leonid Brezhnev (wd:Q47509621): painting by Boris Artzybasheff
10. Yuri Brezhnev, also known as Yuri Leonidovich Brezhnev (wd:Q8061482): Soviet politician (1933-2013), politician

[Assistant]
The entity for Leonid Brezhnev is wd:Q765165. Now I will find the people who held the position of General Secretary of the CPSU (wd:Q1048744) before Leonid Brezhnev (wd:Q765165).

I will first find the start date of Leonid Brezhnev's term as General Secretary to use it as a cutoff date.

[Tool]
Got 1 row and 1 column:
| startDate                           |
| ----------------------------------- |
| 1964-10-14T00:00:00Z (xsd:dateTime) |

[Assistant] Leonid Brezhnev started his term as General Secretary of the CPSU on 14 October 1964. Now I will find all the General Secretaries of the CPSU who started their term before this date.

[Tool]
Got 2 rows and 1 column:
| person                        |
| ----------------------------- |
| Nikita Khrushchev (wd:Q35314) |
| Joseph Stalin (wd:Q855)       |

[Assistant]
There were 2 General Secretaries of the CPSU before Leonid Brezhnev: Joseph Stalin and Nikita Khrushchev.

I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?dateOfDeath WHERE {
  wd:Q6711 wdt:P570 ?dateOfDeath .
}

Allen Ginsberg died on April 5, 1997.

[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 did Allen Ginsberg die?

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

[Tool]
Example 1:
Question:
When was the death of Shakespeare?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q692 wdt:P570 ?uri .
}

Using entities:
William Shakespeare, also known as Bard of Avon, The Bard of Avon, The Bard, Swan of Avon (wd:Q692): English playwright and poet (1564–1616), playwright

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

Example 2:
Question:
What did Bruce Carver die from?

SPARQL:
SELECT DISTINCT ?o1 WHERE {
  wd:Q4977246 wdt:P509 ?o1 .
}

Using entities:
Bruce Carver, also known as Bruce E. Carver, Bruce Eugene Carver (wd:Q4977246): Video game designer and programmer, video game director, businessperson, engineer

Using properties:
cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide

Example 3:
Question:
Where did Hillel Slovak die?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q186924 wdt:P20 ?uri .
}

Using entities:
Hillel Slovak (wd:Q186924): Israeli-American musician (1962–1988), songwriter, guitarist, musician

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

[Assistant] From the examples, the property for date of death is wdt:P570. Now, I need to find the entity for Allen Ginsberg to use it in the query. I will search for the entity "Allen Ginsberg" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Allen Ginsberg, also known as Irwin Allen Ginsberg (wd:Q6711): American poet and writer (1926–1997), autobiographer, photographer, screenwriter, playwright, musician, diarist, teacher, writer, poet
2. Allen Ginsberg (wd:Q102249265): Ph.D. Rutgers University, New Brunswick 1986
3. Allen Ginsberg Library (wd:Q117833470): this entity is a library in the state of colorado. it was identified and verified by the chr task force
4. Untitled (Allen Ginsberg) (wd:Q76558858): print in the National Gallery of Art (NGA 155538)
5. Allen Ginsberg no number (wd:Q64163104): photograph in the National Gallery of Art (NGA 88333)
6. Allen Ginsberg no number (wd:Q64163625): photograph in the National Gallery of Art (NGA 88331)
7. Allen Ginsberg no number (wd:Q64163102): photograph in the National Gallery of Art (NGA 88336)
8. Allen Ginsberg no number (wd:Q64163079): photograph in the National Gallery of Art (NGA 88334)
9. Allen Ginsberg no number (wd:Q64162725): photograph in the National Gallery of Art (NGA 88330)
10. Allen Ginsberg no number (wd:Q64162264): photograph in the National Gallery of Art (NGA 88332)

[Assistant] The entity for Allen Ginsberg is wd:Q6711. The property for date of death is wdt:P570. I will now construct a SPARQL query to get the date of death for Allen Ginsberg.

[Tool]
Got 1 row and 1 column:
| dateOfDeath                         |
| ----------------------------------- |
| 1997-04-05T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?award ) AS ?totalOscars ) WHERE {
  ?movie wdt:P57 wd:Q55400 ; wdt:P166 ?award .
  ?award wdt:P31 wd:Q19020 .
}

Got 1 row and 1 column:
| totalOscars |
| ----------- |
| 2 (xsd:int) |

All the movies directed by Hayao Miyazaki combined have won 2 Oscars.

[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 Oscars did all the movies directed by Hayao Miyazaki combined win?

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

[Tool]
Example 1:
Question:
Which movies did Kurosawa direct?

SPARQL:
SELECT DISTINCT * WHERE {
  ?uri wdt:P57 wd:Q8006 .
}

Using entities:
Akira Kurosawa, also known as Kurosawa Akira, Kurosawa (wd:Q8006): Japanese filmmaker (1910–1998), film producer, film director, screenwriter, film editor, producer, director, painter, writer

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

Example 2:
Question:
How many films did Leonardo DiCaprio star in?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P31 wd:Q11424 ; wdt:P161 wd:Q38111 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Leonardo DiCaprio (wd:Q38111): American actor and film producer (born 1974), television actor, environmentalist, film producer, screenwriter, stage actor, film actor, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
Give me all movies directed by Francis Ford Coppola.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 / wdt:P279 * wd:Q11424 ; wdt:P57 wd:Q56094 .
}

Using entities:
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock
Francis Ford Coppola, also known as Francis Coppola (wd:Q56094): American filmmaker, executive producer, businessperson, film producer, film director, screenwriter, film editor, winegrower, film actor, producer, director, composer, writer, actor

Using properties:
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
director, also known as film director, movie director, game director, directed by (wd:P57 as wdt): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator

[Assistant]
From the examples, I see that to find movies directed by a specific director, we can use the property "director" (wdt:P57) with the director's entity. Then to count Oscars won by those movies, we need to find the property that links movies to Oscars won.

Step 1: Find the Wikidata entity for Hayao Miyazaki.
Step 2: Find movies directed by Hayao Miyazaki.
Step 3: Find the property that indicates Oscars won by a movie.
Step 4: Count the total number of Oscars won by all movies directed by Hayao Miyazaki.

I will start by searching for the entity of Hayao Miyazaki.

[Tool]
Top 10 entity alternatives:
1. Hayao Miyazaki, also known as Miyazaki Hayao, Tsutomu Teruki (wd:Q55400): Japanese animator, film director, and mangaka (born 1941), mechanical designer, character designer, animation director, environmentalist, film producer, film director, comics artist, screenwriter, film editor, illustrator, animator, director, lyricist, mangaka
2. Hayao Miyazaki (wd:Q11454632): Japanese baseball player, baseball player
3. Hayao Miyazaki bibliography (wd:Q130265400)
4. Never-Ending Man: Hayao Miyazaki (wd:Q42859983): 2016 film
5. Hayao Miyazaki's Daydream Data Notes, also known as Miyazaki Hayao no Zassō Nōto (wd:Q888819): collection of manga and illustrated writings by Hayao Miyazaki
6. Hayao Miyazaki and the Heron (wd:Q130287351): 2024 documentary film
7. Miyazaki Prefecture, also known as Miyazaki (wd:Q130300): prefecture of Japan
8. Miyazaki (wd:Q204308): city in Miyazaki prefecture, Japan
9. 6905 Miyazaki, also known as Miyazaki (wd:Q717728): asteroid
10. Hayao (wd:Q60786238): Japanese male given name (はやお)

[Assistant] The entity for Hayao Miyazaki as a Japanese animator and film director is wd:Q55400. Next, I will find movies directed by Hayao Miyazaki using the property "director" (wdt:P57).

[Tool] Got no rows and 1 columns

[Assistant] It seems the query returned no results. I will try to check if the property for director is correct and if the instance of movie is correct. I will first check some movies directed by Hayao Miyazaki to verify the properties used.

[Tool] Got no rows and 3 columns

[Assistant] It seems that the property wdt:P57 (director) is not used with Hayao Miyazaki as subject. I will try to find properties of Hayao Miyazaki to see if there is a property linking him to movies he directed.

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt/p): notable scientific, artistic or literary work, or other work of significance among subject's works
2. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
3. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt/p): date on which the subject was born, inception
4. represented by, also known as agent, managed by, manager, managing agent, rep (wd:P1875 as wdt/p): person or agency that represents or manages the subject
5. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt/p): educational institution attended by subject, affiliation
6. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdt/p): language(s) that a person or a people speaks, writes or signs, including the native language(s)
7. NDL Authority ID, also known as NDLSH (NDL Subject Headings), NDLNA (NDL Name Authority), NDLNAId, NDL identifier, NDL ID (wd:P349 as wdt/p/wdtn): identifier for authority control per the National Diet Library of Japan
8. employer, also known as working at, working for, working place, works at, works for (wd:P108 as wdt/p): person or organization for which the subject works or worked, affiliation
9. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
10. influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt/p): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

No other items found

[Assistant] The property "notable work" (wdt:P800) seems to be used for works by Hayao Miyazaki. I will try to find movies that are notable works of Hayao Miyazaki.

[Tool] Got no rows and 1 columns

[Assistant] It seems that the notable work property does not directly link to movies either. I will try to find movies where Hayao Miyazaki is listed as director using the property "director" (wdt:P57) but with the movie as subject.

[Tool]
Got 10 rows and 1 column:
| movie                                |
| ------------------------------------ |
| Kujiratori (wd:Q1083425)             |
| Koro no Daisanpo (wd:Q1083462)       |
| Sherlock Hound (wd:Q1150791)         |
| Piyopiyo Baba (wd:Q122390248)        |
| Tacolator (wd:Q122390707)            |
| Spirited Away (wd:Q155653)           |
| Farewell, Lupin (wd:Q17209543)       |
| Princess Mononoke (wd:Q186572)       |
| Kiki's Delivery Service (wd:Q196602) |
| Ponyo (wd:Q236728)                   |

[Assistant] I found movies directed by Hayao Miyazaki using the property "director" (wdt:P57) with the movie as subject. Now I will find the property that indicates Oscars won by a movie. I will search for properties related to "Oscar" for one of these movies, e.g., Spirited Away (wd:Q155653).

[Tool]
Top 10 property alternatives:
1. Academy Awards Database film ID, also known as Oscar film ID (wd:P6145 as wdt/p): identifier for a film in the official Academy Awards database
2. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as wdt/p): award or recognition received by a person, organization or creative work
3. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as wdt/p): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
4. film editor, also known as edited by, editing by, editor (wd:P1040 as wdt/p): person who works with the raw footage, selecting shots and combining them into sequences to create a finished motion picture, film crew member
5. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as wdt/p): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
6. box office, also known as earnings (wd:P2142 as wdt/p): box office takings accumulated by a film, total revenue
7. screenwriter, also known as story by, teleplay by, video game writer, writer (of screenplays), writing credits (wd:P58 as wdt/p): person(s) who wrote the script for subject item, film crew member, author
8. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as wdt/p): method (or type) of distribution for the subject
9. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category
10. voice actor, also known as voice actors, voice dubber, voice actress, voice actresses, voiced by (wd:P725 as wdt/p): performer of a spoken role in a creative work such as animation,... (P453) as qualifier] [use "cast member" (P161) for live acting], film crew member, cast member

No other items found

[Assistant] The property "award received" (wdt:P166) is used for awards won by a movie. I will now count the total number of Oscars won by all movies directed by Hayao Miyazaki. The Oscars are instances of the Academy Award (wd:Q19020). I will count the awards received by the movies that are instances of Academy Award.

[Tool]
Got 1 row and 1 column:
| totalOscars |
| ----------- |
| 2 (xsd:int) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX schema: <http://schema.org/>
SELECT ?generation ?description WHERE {
  wd:Q3044693 schema:description ?description .
  FILTER ( LANG( ?description ) = "en" )
}

People of the Silent Generation belong to the age group born from 1928 to 1945.

[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] People of what age belong to the Silent Generation ?

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

[Tool]
Example 1:
Question:
List the children of Margaret Thatcher.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q7416 wdt:P40 ?uri .
}

Using entities:
Margaret Thatcher, also known as Margaret Hilda Thatcher, Margaret Roberts, Margaret Hilda Roberts, Mrs. T, Mrs. Thatcher (wd:Q7416): British stateswoman and prime minister (1925–2013), research scientist, business executive, autobiographer, statesperson, politician, barrister, chemist

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

[Assistant] The Silent Generation is a demographic cohort, so it is likely represented as a Wikidata entity with properties indicating the birth years or age range of people belonging to it. I will search for the entity "Silent Generation" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Silent Generation, also known as Lucky Few Generation (wd:Q3044693): cohort succeeding the Greatest Generation, born from 1928 to 1945
2. The Silent Generation (wd:Q7764217): album by The Dimes
3. The Silent Generation (wd:Q112669391): 2020 film
4. Silent zone generation (wd:Q124647370): US patent 11495205
5. Winter’s Children - The Silent Generation (wd:Q85671231): 2005 documentary film by Jens Schanze
6. China Witness, also known as China Witness: Voices from a Silent Generation. (wd:Q113681807): History of China in the 20th century
7. Comparison of Silent Navigator Waveform Generation Methods (wd:Q92381831): scientific article published on 30 May 2019
8. AI-assisted sound effect generation for silent video (wd:Q122763863): US patent 11381888
9. From the silent generation to the involved generation (wd:Q40372593): scientific article published on April 1, 1978
10. Retirement income decisions: the silent generation speaks. (wd:Q40344803): scientific article published on January 2005

[Assistant] The entity "Silent Generation" (wd:Q3044693) is described as a cohort succeeding the Greatest Generation, born from 1928 to 1945. This matches the concept of a demographic cohort with an age range. I will now check the properties of this entity to find the birth years or age range information.

[Tool]
Got 124 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                                              | object                                                                                |
| ------------------------------- | ----------------------------------------------------- | -------------------------------------------------------------------------------------- |
| Silent Generation (wd:Q3044693) | instance of (wdt:P31)                                 | cultural generation of western society (wd:Q15925549)                                  |
| Silent Generation (wd:Q3044693) | image (wdt:P18)                                       | <http://commons.wikimedia.org/wiki/Special:FilePath/Girl%20listening%20to%20radio.gif> |
| Silent Generation (wd:Q3044693) | subclass of (wdt:P279)                                | Great Power Saeculum (wd:Q113207359)                                                   |
| Silent Generation (wd:Q3044693) | location (wdt:P276)                                   | worldwide (wd:Q13780930)                                                               |
| Silent Generation (wd:Q3044693) | follows (wdt:P155)                                    | Greatest Generation (wd:Q27995878)                                                     |
| Silent Generation (wd:Q3044693) | followed by (wdt:P156)                                | Baby Boomers (wd:Q797819)                                                              |
| Silent Generation (wd:Q3044693) | Freebase ID (wdt:P646)                                | /m/02zksw                                                                              |
| Silent Generation (wd:Q3044693) | Quora topic ID (wdt:P3417)                            | Silent-Generation                                                                      |
| Silent Generation (wd:Q3044693) | Encyclopædia Britannica Online ID (wdt:P1417)         | topic/Silent-Generation                                                                |
| Silent Generation (wd:Q3044693) | Encyclopedia of China (Third Edition) ID (wdt:P10565) | 125938                                                                                |
| ...                             | ...                                                   | ...                                                                                |

[Assistant] The entity "Silent Generation" has properties indicating it is a cultural generation of western society, its location is worldwide, and it follows the Greatest Generation and is followed by the Baby Boomers. However, I do not see direct birth year or age range properties in the first 10 results. I will look specifically for properties related to birth year or time period.

[Tool]
Top 10 property alternatives:
1. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
2. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
3. follows, also known as prev, successor to, succeeds to, succeeds, split from (wd:P155 as wdt/p): immediately prior item in a series of which the subject is a par... preceding item, e.g. political offices, use "replaces" (P1365)]
4. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
5. followed by, also known as continued as, then, successor, succeeded by, sequel is (wd:P156 as wdt/p): immediately following item in a series of which the subject is a...een replaced, e.g. political offices, use "replaced by" (P1366)]
6. Encyclopædia Britannica Online ID, also known as Encyclopaedia Britannica Online ID, Encyclopedia Britannica Online ID, EBID, EB ID, Britannica online ID (wd:P1417 as wdt/p): identifier for an article in the online version of Encyclopædia Britannica
7. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
8. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain

No other items found

[Assistant] The search for properties related to "birth year" for the Silent Generation entity did not return any direct properties indicating birth years or age range. I will now try to find properties related to "start time" or "end time" which might indicate the time period of this generation.

[Tool]
Top 10 property alternatives:
1. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. followed by, also known as continued as, then, successor, succeeded by, sequel is (wd:P156 as wdt/p): immediately following item in a series of which the subject is a...een replaced, e.g. political offices, use "replaced by" (P1366)]
3. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
4. follows, also known as prev, successor to, succeeds to, succeeds, split from (wd:P155 as wdt/p): immediately prior item in a series of which the subject is a par... preceding item, e.g. political offices, use "replaces" (P1365)]
5. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
6. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
7. Encyclopædia Britannica Online ID, also known as Encyclopaedia Britannica Online ID, Encyclopedia Britannica Online ID, EBID, EB ID, Britannica online ID (wd:P1417 as wdt/p): identifier for an article in the online version of Encyclopædia Britannica

No other items found

[Assistant] There are no direct properties like "start time" or "end time" for the Silent Generation entity. Since the initial description mentioned the birth years from 1928 to 1945, I will now try to confirm this by querying the entity's description or statement that might mention these years. I will try to get the English description of the entity.

[Tool]
Got 1 row and 1 column:
| description                                                            |
| ---------------------------------------------------------------------- |
| cohort succeeding the Greatest Generation, born from 1928 to 1945 (en) |

[Assistant] The description of the Silent Generation entity confirms that it is a cohort born from 1928 to 1945. This answers the question about the age range of people belonging to the Silent Generation. I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?founder ?founderLabel WHERE {
  wd:Q479361 wdt:P112 ?founder .
  OPTIONAL {
    ?founder rdfs:label ?founderLabel FILTER ( LANG( ?founderLabel ) = "en" )
  }
}

Got 3 rows and 2 columns:
| founder                      | founderLabel         |
| ---------------------------- | -------------------- |
| Fritz Mackensen (wd:Q448400) | Fritz Mackensen (en) |
| Hans am Ende (wd:Q561091)    | Hans am Ende (en)    |
| Otto Modersohn (wd:Q71425)   | Otto Modersohn (en)  |

The Worpswede artist colony was founded by Fritz Mackensen, Hans am Ende, and Otto Modersohn.

[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 founded the Worpswede artist colony ?

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

[Tool]
Example 1:
Question:
Where is the birthplace of Goethe?

SPARQL:
SELECT ?res WHERE {
  wd:Q5879 wdt:P19 ?res .
}

Using entities:
Johann Wolfgang von Goethe, also known as Johann Wolfgang Von Goethe, Goethe (wd:Q5879): German writer, artist, natural scientist and politician (1749–1832), theatre manager, autobiographer, art historian, travel writer, visual artist, statesperson, mineralogist, art theorist, music critic, philosopher, playwright, theologian, art critic, politician, librettist, librarian, physicist, zoologist, diplomat, aphorist, polymath, botanist, composer, novelist, literary, diarist, painter, writer, jurist, lawyer, poet

Using properties:
place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt): most specific known birth location of a person, animal or fictional character, location

Example 2:
Question:
To which artistic movement did the painter of The Three Dancers belong?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q3235867 wdt:P170 ?painter .
  ?painter wdt:P135 ?uri .
}

Using entities:
The Three Dancers (wd:Q3235867): painting by Pablo Picasso

Using properties:
movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as wdt): maker of this creative work or other object (where no more specific property exists)

Example 3:
Question:
Who was Vincent van Gogh inspired by?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q5582 wdt:P737 ?uri .
}

Using entities:
Vincent van Gogh (wd:Q5582): Dutch painter (1853–1890), draftsperson, printmaker, painter

Using properties:
influenced by, also known as favorite player, has influence, informed by, role model (wd:P737 as wdt): this person, idea, etc. is informed by that other person, idea, etc., e.g. “Heidegger was influenced by Aristotle”

[Assistant]
The question is about the founder of the Worpswede artist colony. From the examples, we see that to find the founder or creator of something, we often use the property "creator" (wdt:P170) or "founder" (wdt:P112).

Step 1: Search for the entity "Worpswede artist colony" to get its Wikidata ID.
Step 2: Query the founder or creator of this entity using the appropriate property.

I will start by searching for the entity "Worpswede artist colony".

[Tool]
Top 10 entity alternatives:
1. Worpswede artist colony, also known as Worpswede circle (wd:Q479361): Artist's colony founded in Worpswede in 1889
2. art colony, also known as artist colony, artists' colony (wd:Q1558054): place where artists live and interact with each other
3. Worpswede colony (wd:Q103974815): encyclopedia article
4. Nagybánya art colony, also known as Nagybanya art colony, Nagybánya artists' colony, Nagybanya School, Nagybánya School (wd:Q591229)
5. Önningeby artists colony, also known as Önningebykolonin (wd:Q2687905): Finnish artists colony
6. Ekensund Artists' Colony (wd:Q1669854)
7. Ein Hod artist colony, also known as Artists Village of Ein Hod, Ein Hod artists village (wd:Q124390656)
8. Nishimui Art Village, also known as Nishimui Art Colony, Nishimui Artists' Colony (wd:Q38277516)
9. New Rochelle artist colony (wd:Q19877130)
10. Kirkcudbright Artists' Colony (wd:Q20713695): artists' community

[Assistant] The entity "Worpswede artist colony" corresponds to wd:Q479361. Now I will search for the founder or creator of this entity using the properties "founder" (wdt:P112) and "creator" (wdt:P170). I will start with "founder" (wdt:P112).

[Tool]
Got 3 rows and 2 columns:
| founder                      | founderLabel         |
| ---------------------------- | -------------------- |
| Fritz Mackensen (wd:Q448400) | Fritz Mackensen (en) |
| Hans am Ende (wd:Q561091)    | Hans am Ende (en)    |
| Otto Modersohn (wd:Q71425)   | Otto Modersohn (en)  |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?university WHERE {
  VALUES ?founder {
    wd:Q1739008 wd:Q15934341 wd:Q20689208 wd:Q18653685 wd:Q18659347
  }
  ?founder wdt:P69 ?university .
}
GROUP BY ?university
HAVING ( COUNT( ?founder ) >= 2 )

Got 2 rows and 1 column:
| university                           |
| ------------------------------------ |
| Shenzhen University (wd:Q1945756)    |
| Shenzhen Middle School (wd:Q7494378) |

Yes, at least two of the founders of Tencent were educated at the same university, Shenzhen University.

[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] Were at least two of the founders of tencent educated at the same university?

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

[Tool]
Example 1:
Question:
Which electronics companies were founded in Beijing?

SPARQL:
SELECT DISTINCT ?company WHERE {
  ?company wdt:P452 wd:Q581105 ; wdt:P740 wd:Q956 .
}

Using entities:
consumer electronics, also known as home electronics, household electronics (wd:Q581105): electronic equipment intended for everyday use, typically in private homes
Beijing, also known as Pei-ping, Peip'ing, Peiping, Pekin, Pe-king (wd:Q956): capital city of China

Using properties:
industry, also known as branch, economic branch, field of action, field of exercise, sector (wd:P452 as wdt): specific industry of company or organization, part of
location of formation, also known as place of incorporation, originates from, place of establishment, place of formation, place of foundation (wd:P740 as wdt): location where a group or organization was formed, location of creation

[Assistant] The example found is not directly related to the question about the founders of Tencent and their education. I will now search for the entity "Tencent" to identify its founders. Then I will check the educational institutions of these founders to see if at least two of them were educated at the same university.

[Tool]
Top 10 entity alternatives:
1. Tencent, also known as Téngxùn Kònggǔ Yǒuxiàn Gōngsī, Teng xun (firma), Tencent Group, Téngxùn, Tengxun (wd:Q860580): Chinese multinational investment holding conglomerate
2. Tencent QQ, also known as QQ, Qq, QICQ, OPEN-ICQ, OpenICQ (wd:Q857640): instant messaging software service
3. Tencent Video, also known as v.qq.com, TencentVideo, WeTV (wd:Q28136991): video streaming service from China
4. Tencent Pictures (wd:Q22080095): Chinese film production company
5. Tencent Games, also known as Tencent Game (wd:Q39057776): division of Tencent Holdings
6. Tencent Music, also known as tencentmusic.com, Tencent Music Entertainment, Tencent Music Entertainment Group, TME (wd:Q56011958): Chinese music distribution company
7. Tencent Weibo (wd:Q3983400): Chinese microblogging website launched by Tencent in April 2010
8. WeGame, also known as Tencent Game Platform, Tencent Games Platform, Tencent Gaming Platform, Tencent WeGame, TGP (wd:Q10852223): video gaming platform of Tencent
9. QQ browser, also known as tbs, TBS, Tencent Browsing Service, Tencent Explorer, Tencent Traveler (wd:Q6862273): web browser
10. Tencent Comic (wd:Q22101513): Chinese animation and comics network

[Assistant] The entity "Tencent" is identified as wd:Q860580. I will now look for the founders of Tencent (wd:Q860580).

[Tool]
Got 5 rows and 3 columns:
| subject              | property              | object                       |
| -------------------- | --------------------- | ---------------------------- |
| Tencent (wd:Q860580) | founded by (wdt:P112) | Ma Huateng (wd:Q1739008)     |
| Tencent (wd:Q860580) | founded by (wdt:P112) | Zhang Zhidong (wd:Q15934341) |
| Tencent (wd:Q860580) | founded by (wdt:P112) | Chen Yidan (wd:Q20689208)    |
| Tencent (wd:Q860580) | founded by (wdt:P112) | Zeng Liqing (wd:Q18653685)   |
| Tencent (wd:Q860580) | founded by (wdt:P112) | Daniel Xu (wd:Q18659347)     |

[Assistant] Tencent has five founders: Ma Huateng (wd:Q1739008), Zhang Zhidong (wd:Q15934341), Chen Yidan (wd:Q20689208), Zeng Liqing (wd:Q18653685), and Daniel Xu (wd:Q18659347). I will now check the educational institutions of these founders to see if at least two of them were educated at the same university.

[Tool]
Got 2 rows and 3 columns:
| subject                  | property              | object                               |
| ------------------------ | --------------------- | ------------------------------------ |
| Ma Huateng (wd:Q1739008) | educated at (wdt:P69) | Shenzhen University (wd:Q1945756)    |
| Ma Huateng (wd:Q1739008) | educated at (wdt:P69) | Shenzhen Middle School (wd:Q7494378) |

[Tool]
Got 2 rows and 3 columns:
| subject                      | property              | object                                            |
| ---------------------------- | --------------------- | ------------------------------------------------- |
| Zhang Zhidong (wd:Q15934341) | educated at (wdt:P69) | Shenzhen University (wd:Q1945756)                 |
| Zhang Zhidong (wd:Q15934341) | educated at (wdt:P69) | South China University of Technology (wd:Q835366) |

[Tool]
Got 2 rows and 3 columns:
| subject                   | property              | object                               |
| ------------------------- | --------------------- | ------------------------------------ |
| Chen Yidan (wd:Q20689208) | educated at (wdt:P69) | Shenzhen University (wd:Q1945756)    |
| Chen Yidan (wd:Q20689208) | educated at (wdt:P69) | Shenzhen Middle School (wd:Q7494378) |

[Tool]
Got 1 row and 3 columns:
| subject                    | property              | object                         |
| -------------------------- | --------------------- | ------------------------------ |
| Zeng Liqing (wd:Q18653685) | educated at (wdt:P69) | Xidian University (wd:Q649906) |

[Tool]
Got 2 rows and 3 columns:
| subject                  | property              | object                            |
| ------------------------ | --------------------- | --------------------------------- |
| Daniel Xu (wd:Q18659347) | educated at (wdt:P69) | Nanjing University (wd:Q683580)   |
| Daniel Xu (wd:Q18659347) | educated at (wdt:P69) | Shenzhen University (wd:Q1945756) |

[Assistant]
Ma Huateng, Zhang Zhidong, Chen Yidan, and Daniel Xu were educated at Shenzhen University. Zeng Liqing was educated at Xidian University. Since at least two founders (in fact, four) were educated at the same university (Shenzhen University), the answer to the question is "Yes."

I will now provide the final answer with a SPARQL query that confirms this.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?airport ) AS ?count ) WHERE {
  ?airport wdt:P31 wd:Q644371 ; wdt:P131 * wd:Q1055 .
}

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

There is 1 international airport located within the city of Hamburg or its administrative subdivisions.

[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 international airports are located within the city of Hamburg ?

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

[Tool]
Example 1:
Question:
How many airlines are there?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?uriCount ) WHERE {
  ?uri wdt:P31 wd:Q46970
}

Using entities:
airline, also known as air company, air carrier (wd:Q46970): company that provides air transport services for traveling passengers and freight

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

Example 2:
Question:
Which airports does Air China serve?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q51747 wdt:P113 ?uri .
}

Using entities:
Air China, also known as CCA, Air China Limited, CA (wd:Q51747): flag carrier of the People's Republic of China

Using properties:
airline hub, also known as hub airport, hub (wd:P113 as wdt): airport that serves as a hub for an airline

Example 3:
Question:
Give me all companies in Munich.

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

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

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

[Assistant]
From the examples, we see that to count airports in a city, we need to find entities that are instances of airports and are located in the city. We need to identify the entity for Hamburg and the class for international airports or airports in general.

Step 1: Search for the entity "Hamburg" in Wikidata.
Step 2: Search for the class or entity representing "international airport" or "airport."
Step 3: Construct a query to count airports located in Hamburg.

Let's start with step 1 and search for the entity "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. Hamburg, also known as Hamburg, PA, Hamburg, Pennsylvania (wd:Q1181264): borough in Berks County, Pennsylvania, United States
10. Hamburg, also known as Hamburgh (wd:Q939795): breed of chicken developed in Germany and Holland prior to 1700

[Assistant]
The entity for the city and state in the North of Germany is wd:Q1055 for Hamburg.

Next, let's search for the class or entity representing "international airport" or "airport."

[Tool]
Top 10 entity alternatives:
1. international airport (wd:Q644371): airport serving international destinations
2. International Airport (wd:Q55614529): 1985 film directed by Don Chaffey
3. Domodedovo International Airport, also known as Moscow-Domodedovo Airport, UUDD, Moscow International Airport, Moscow Domodedovo Airport, Moscow Airport (wd:Q186614): international airport serving Moscow, Russia
4. John F. Kennedy International Airport, also known as Kennedy International Airport, New York International Airport, KJFK, JFK International Airport, Kennedy International (wd:Q8685): international airport in New York, United States
5. John F. Kennedy International Airport, also known as Idlewild Airport, Kennedy International, JFK International Airport, KJFK, JFK Airport (wd:Q8685): international airport in New York, United States
6. Sheremetyevo International Airport, also known as Moscow Airport, Moscow International Airport, Sheremetyevo Airport, SVO, UUEE (wd:Q192733): international airport serving Moscow, Russia
7. Istanbul-Atatürk, also known as LTBA, Istanbul Atatürk Airport, Istanbul Ataturk Airport, ISL, Atatürk International Airport (wd:Q86473): international airport in Istanbul, Turkey
8. Dubai International Airport, also known as DXB, OMDB (wd:Q193439): international airport in Dubai, United Arab Emirates
9. Singapore Changi Airport, also known as WSSS, Changi Airport Singapore, SIN, Changi Airport, Changi International Airport (wd:Q32159): international airport in Singapore
10. Tokyo International Airport, also known as Haneda Airport, HND, RJTT (wd:Q204853): international airport serving Tokyo, Japan

[Assistant]
The entity for "international airport" is wd:Q644371.

Now, let's construct a query to count how many international airports (wd:Q644371) are located within the city of Hamburg (wd:Q1055). We will use the property "located in the administrative territorial entity" (wdt:P131) to filter airports located in Hamburg.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The query returned 0, which means no international airports are directly located within the administrative territorial entity of Hamburg using the property wdt:P131.

It is possible that the airports are located in subdivisions or boroughs of Hamburg rather than directly in Hamburg itself. We can try to include airports located in any administrative territorial entity that is part of Hamburg by using a property path with wdt:P131* (zero or more levels of administrative territorial entity).

Let's try this refined query.

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

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?discoveryDate WHERE {
  wd:Q15787 wdt:P575 ?discoveryDate .
}

HIV was first identified in the year 1983.

[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 HIV first identified?

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

[Tool] No examples found

[Assistant] There are no directly similar example questions available. Now, I will search for the entity "HIV" in Wikidata to identify its identifier. This will help us find the relevant properties related to its identification date.

[Tool]
Top 10 entity alternatives:
1. HIV, also known as AIDS virus, human immunodeficiency virus (wd:Q15787): human retrovirus, cause of AIDS
2. Hiv (wd:Q5959052): village in Iran
3. .hiv (wd:Q20573587): top level domain
4. human immunodeficiency virus infectious disease, also known as HIV+, HIV infection, HIV infections, HIV-positive (wd:Q18556697): disease affecting the human immune system
5. HIV/AIDS in Ethiopia, also known as HIV (wd:Q65409016): Ethiopia faces an epidemic among sub-populations and geographic areas
6. HyperChem Input File, also known as HIV, .hin, HyperChem molecule file format, HIV molecule file format (wd:Q28530466): file format
7. HIV (wd:Q87558374): scientific article published on 01 August 2014
8. HIV (wd:Q93531156)
9. HIV. (wd:Q43247940): scientific article published in February 1998
10. HIV. (wd:Q45982420): scientific article published in February 2001

[Assistant] The entity "HIV" is identified as wd:Q15787 in Wikidata, which corresponds to the human immunodeficiency virus. Now, I will search for properties related to the identification or discovery date of HIV. I will search for properties of the entity wd:Q15787 with a query related to "identification" or "discovery date."

[Tool]
Top 10 property alternatives:
1. time of discovery or invention, also known as invented in, time of invention, time of discovery, invention time, invented on (wd:P575 as p/wdt): date or point in time when the item was discovered or invented
2. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
3. ScienceDirect topic ID, also known as Science Direct topic ID, Science Direct topic identifier, ScienceDirect topic identifier (wd:P10376 as p/wdt): identifier for a topic on the research publication website ScienceDirect
4. Library of Congress authority ID, also known as LCSH Authority File ID, LC Name Authority ID, LCSH ID, LCSH LCCN, LC Subject Heading ID (wd:P244 as p/wdtn/wdt): Library of Congress name authority (persons, families, corporate...lowed by 8-10 digits (see regex). For manifestations, use P1144]
5. GND ID, also known as Deutschen Nationalbibliothek ID, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as p/wdtn/wdt): identifier from an international authority file of names, subjects, and organizations
6. FAST ID, also known as Faceted Application of Subject Terminology ID, FAST subject heading, fst, worldcat.org/fast (wd:P2163 as p/wdtn/wdt): authority control identifier in WorldCat's “FAST Linked Data” authority file
7. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
8. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
9. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as p/wdt): effect of this item, followed by
10. NL CR AUT ID, also known as Czech National Authority Database, NLCR AUT ID (wd:P691 as p/wdt): identifier in the Czech National Authority Database of National Library of the Czech Republic (NL CR)

No other items found

[Assistant] The property "time of discovery or invention" (wdt:P575) seems to be the most relevant for the question about when HIV was first identified. I will now construct a SPARQL query to get the time of discovery or invention for the entity HIV (wd:Q15787).

[Tool]
Got 1 row and 1 column:
| discoveryDate                       |
| ----------------------------------- |
| 1983-01-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process