PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?country ?countryLabel WHERE {
  ?country wdt:P31 wd:Q849866 .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 7 rows and 2 columns:
| country                                       | countryLabel                         |
| --------------------------------------------- | ------------------------------------ |
| People's Republic of China (wd:Q148)          | People's Republic of China (en)      |
| Azerbaijan People's Government (wd:Q2305337)  | Azerbaijan People's Government (en)  |
| Cuba (wd:Q241)                                | Cuba (en)                            |
| Republic of Mahabad (wd:Q332232)              | Republic of Mahabad (en)             |
| Laos (wd:Q819)                                | Laos (en)                            |
| Gilan Socialist Soviet Republic (wd:Q8341881) | Gilan Socialist Soviet Republic (en) |
| Vietnam (wd:Q881)                             | Vietnam (en)                         |

The query returns all entities that are instances of "communist state" in Wikidata, including their English labels. These include current and historical communist countries such as People's Republic of China, Cuba, Laos, Vietnam, Azerbaijan People's Government, Republic of Mahabad, and Gilan Socialist Soviet Republic.

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

You have access to the following knowledge graphs:
wikidata at http://amur:7001

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] Give me all communist countries.

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

[Tool]
Example 1:
Question:
Give me all South American countries.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q6256 .
  ?uri wdt:P30 wd:Q18 .
}

Using entities:
South America, also known as S America, S. America, South American continent (wd:Q18): continent
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
continent (wd:P30 as wdt): continent of which the subject is a part, located in/on physical feature
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:
Show me all U.S. states.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q35657 .
}

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

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

Example 3:
Question:
Give me all federal chancellors of Germany.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P39 wd:Q4970706 .
}

Using entities:
Federal Chancellor of Germany, also known as German Federal Chancellor, prime minister of Germany, German Chancellor, Federal Chancellor of the Federal Republic of Germany, Federal Chancellor (wd:Q4970706): head of government of the Federal Republic of Germany (1949–present)

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as wdt): subject currently or formerly holds the object position or public office

[Assistant]
From the examples, we see that to get all countries of a certain type, we typically use the property "instance of" (wdt:P31) with the entity "country" (wd:Q6256). Then we filter by the political system or ideology.

Now, I need to find the entity for "communist country" or a similar concept in Wikidata. I will search for entities related to "communist country" to identify the appropriate entity or property to use.

[Tool]
Top 10 entity alternatives:
1. communist state, also known as Marxist-Leninist state, communist country (wd:Q849866): state that is administered and governed by a single communist party
2. fictional communist state, also known as fictional communist country (wd:Q82519323): communist state which only exists in a work of fiction
3. Communist Party of the Basque Country (wd:Q3750882): political party in Spain
4. Communist Party of the Valencian Country (wd:Q3326408): political party in Spain
5. Communist Movement of the Valencian Country (wd:Q11693816): political party
6. country, also known as countries (wd:Q6256): distinct territorial body or political entity
7. country music, also known as country, country and western, country & western (wd:Q83440): genre of American popular music
8. sovereign state, also known as country, nation, sovereign country, sovereign nation, Westphalian state (wd:Q3624078): state that has the highest authority over a territory
9. Dennis Rodman, also known as Rodzilla, Psycho, The Worm, Worm, Country (wd:Q201608): American basketball player, professional wrestler, basketball player, amateur wrestler, basketball coach, voice actor, film actor, actor
10. Country (wd:Q1754454): 1984 film directed by Richard Pearce

[Assistant]
The entity "communist state" (wd:Q849866) corresponds to a state that is administered and governed by a single communist party, which matches the concept of a communist country.

Next, I will write a SPARQL query to get all instances of "communist state" (wd:Q849866). This should give us all communist countries.

[Tool]
Got 7 rows and 1 column:
| country                                       |
| --------------------------------------------- |
| People's Republic of China (wd:Q148)          |
| Azerbaijan People's Government (wd:Q2305337)  |
| Cuba (wd:Q241)                                |
| Republic of Mahabad (wd:Q332232)              |
| Laos (wd:Q819)                                |
| Gilan Socialist Soviet Republic (wd:Q8341881) |
| Vietnam (wd:Q881)                             |

[Assistant]
I found 7 entities that are instances of "communist state." Some of them are historical or short-lived entities (e.g., Azerbaijan People's Government, Republic of Mahabad, Gilan Socialist Soviet Republic). The others are current or well-known communist countries (e.g., People's Republic of China, Cuba, Laos, Vietnam).

I will now enhance the query to also retrieve the labels of these countries for better readability.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?river ?riverLabel WHERE {
  wd:Q125006 wdt:P177 ?river .
  OPTIONAL {
    ?river rdfs:label ?riverLabel FILTER ( LANG( ?riverLabel ) = "en" )
  }
}

The Brooklyn Bridge crosses the East River.

[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 http://amur:7001

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

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

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

[User] Which river does the Brooklyn Bridge cross?

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

[Tool]
Example 1:
Question:
What is the longest river in China?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q4022 .
  ?uri wdt:P17 wd:Q148 .
  ?uri wdt:P2043 ?length
}
ORDER BY DESC ( ?length )
LIMIT 1

Using entities:
People's Republic of China, also known as Zhōngguó, The PRC, RPC, CHN, Red China (wd:Q148): country in East Asia
river, also known as rivers (wd:Q4022): larger natural watercourse

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
length, also known as size, long, LOA, distance, dimension (wd:P2043 as wdt): measured dimension of an object
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]
The question is about the river that the Brooklyn Bridge crosses. The Brooklyn Bridge is a bridge, and we want to find the river it crosses. So, we need to find the entity for Brooklyn Bridge and then find the property that relates it to a river it crosses.

Step 1: Search for the entity "Brooklyn Bridge" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Brooklyn Bridge, also known as East River Bridge, New York and Brooklyn Bridge (wd:Q125006): bridge in New York City, crossing the East River
2. Brooklyn Bridge (wd:Q929432): 1981 documentary film by Ken Burns
3. Brooklyn Bridge (wd:Q3881953): American television program in the 1990s
4. Brooklyn Bridge (wd:Q28224593): bridge in New South Wales, Australia
5. Brooklyn Bridge (wd:Q104850523): for transfering or transforming data from here to there
6. Brooklyn Bridge (wd:Q19133315): poem by Vladimir Mayakovsky
7. Brooklyn Bridge (wd:Q4974763): album by The Brooklyn Bridge
8. Brooklyn Bridge (wd:Q80080658): print by Frederick Mershimer (American, b. 1958); Printed by the artist (2013.449)
9. Brooklyn Bridge (wd:Q80084452): print by Ernest Born (American, 1898-1992); Charles Massin, Paris (2015.113)
10. Brooklyn Bridge (wd:Q90294662): item from the Cartões Postais Collection at Museu Paulista

[Assistant] The entity for the Brooklyn Bridge as a bridge in New York City crossing the East River is wd:Q125006. Now, I will look for properties of this entity that might indicate the river it crosses. I will search for properties related to "cross" or "river" for this entity.

[Tool]
Top 10 property alternatives:
1. crosses, also known as under, tunnel under, through, spanning, over (wd:P177 as wdt/p): obstacle (body of water, road, railway...) which this bridge (ferry, ford) crosses over or this tunnel goes under
2. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. plaque image, also known as memorial plaque image, inscription image, image of plaque, image of inscription, commemorative plaque image (wd:P1801 as wdt/p): image of plaque or inscription commemorating or mentioning the subject. For graves, use grave picture (P1442), image
5. width, also known as breadth, bus width, size (wd:P2049 as wdt/p): width of an object, length
6. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
7. 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
8. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdt/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
9. made from material, also known as material used, manufactured out of, manufactured from, made of material, made of (wd:P186 as wdt/p): material the subject or the object is made of or derived from (do not confuse with P10672 which is used for processes), uses
10. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

No other items found

[Assistant] The property "crosses" (wdt:P177) is the one that indicates the obstacle (body of water, road, railway, etc.) which the bridge crosses over. Now, I will query the object of the property "crosses" for the Brooklyn Bridge (wd:Q125006) to find the river it crosses.

[Tool]
Got 1 row and 2 columns:
| river                   | riverLabel      |
| ----------------------- | --------------- |
| East River (wd:Q212862) | East River (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?mountain ?mountainLabel ?elevation WHERE {
  ?mountain wdt:P31 wd:Q8502 .
  ?mountain wdt:P2044 ?elevation .
  FILTER ( ?elevation < 8091 && ?mountain != wd:Q16466024 ) OPTIONAL {
    ?mountain rdfs:label ?mountainLabel FILTER ( LANG( ?mountainLabel ) = "en" )
  }
}
ORDER BY DESC ( ?elevation )
LIMIT 1

The highest mountain after Annapurna I (8091m) according to the data is Vinegar Hill with an elevation of 8084 meters. However, this seems inconsistent with known facts about the highest mountains in the world, indicating possible data inaccuracies in the dataset.

[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 http://amur:7001

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

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

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

[User] Which mountain is the highest after the Annapurna?

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

[Tool] No examples found

[Assistant] There are no similar example queries available. Next, I will identify the entity for "Annapurna" in Wikidata to understand its properties, especially its height. Then I will find mountains and their heights to determine which mountain is the highest after Annapurna.

[Tool]
Top 10 entity alternatives:
1. Annapurna, also known as Annapurna Himal, Annapurna (Himalayas), Annapurna Massif (wd:Q159621): mountain range in the Himalayas
2. Annapurna I, also known as Annapurna, Annapurna I Main (wd:Q16466024): mountain in the Himalayas
3. Annapoorna, also known as Umamaheswari, Annapurna (wd:Q4767988): Indian Odia-language author, actor
4. Annapurna (wd:Q4767995): village development committee in Bhojpur District in the Kosi Zone of eastern Nepal
5. Annapurna (wd:Q4768000): 1960 film by V. Madhusudhan Rao
6. Annapurna (wd:Q4767994): book by Maurice Herzog
7. Annapurna (wd:Q88543102): female given name
8. Annapurna (wd:Q70575435): Kannada book by Niranjan
9. Annapurna (wd:Q12301785): Danish singer, singer
10. Annapurna, also known as Category:IMO 9014793, IMO 9014793 (wd:Q83601304): ship built in 1992

[Assistant]
The entity "Annapurna I" (wd:Q16466024) corresponds to the mountain Annapurna, which is likely the specific peak referred to in the question. Annapurna (wd:Q159621) refers to the mountain range, but since the question is about the highest mountain after Annapurna, we should consider the specific peak Annapurna I.

Next, I will check the height of Annapurna I to know its elevation. Then I will find mountains with heights less than Annapurna I and find the highest among them.

[Tool]
Got 1 row and 3 columns:
| subject                    | property                              | object             |
| -------------------------- | ------------------------------------- | ------------------ |
| Annapurna I (wd:Q16466024) | elevation above sea level (wdt:P2044) | 8091 (xsd:decimal) |

[Assistant] Annapurna I has an elevation of 8091 meters. Now I will find mountains with elevation less than 8091 meters and find the one with the highest elevation among them.

[Tool]
Top 10 entity alternatives:
1. mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface
2. Mountain (wd:Q1255792): American band
3. Mountain, also known as Mountain, ND, Mountain, North Dakota (wd:Q2032181): city in North Dakota, USA
4. Mountain, also known as Town of Mountain (wd:Q1885759): civil town in Oconto County, Wisconsin
5. The Mountain, also known as Mountain (wd:Q910112): 1956 film by Edward Dmytryk
6. ☶, also known as U+2636, gèn, TRIGRAM FOR MOUNTAIN, mountain, immobility (wd:Q2068902): trigram for mountain (U+2636), immobility, or stability: gen4, gèn, ken
7. The Mountain, also known as Mountain (wd:Q1192451): 1991 film by Markus Imhoof
8. 4-8-2, also known as 241, 2D1, 4/7, Mohawk, Mountain (wd:Q1473477): locomotive wheel arrangement
9. Mountain (wd:Q13422857): human settlement in Wisconsin, United States of America
10. Mountain (wd:Q29293936): 2014 simulation video game

[Assistant] The entity for mountain is wd:Q8502. I will now find mountains with elevation less than 8091 meters and order them by elevation in descending order to find the highest mountain after Annapurna.

[Tool]
Got 1 row and 3 columns:
| mountain                    | mountainLabel     | elevation          |
| --------------------------- | ----------------- | ------------------ |
| Vinegar Hill (wd:Q39074795) | Vinegar Hill (en) | 8084 (xsd:decimal) |

[Assistant] The highest mountain after Annapurna I (8091m) according to the query result is Vinegar Hill with an elevation of 8084 meters. This seems unexpected as Vinegar Hill is not known as one of the highest mountains in the world. I will refine the query to ensure we are only considering mountains that are part of the highest mountain category or mountain ranges known for high peaks. I will check for mountains that are instance of "mountain" and also part of the Himalayas or Karakoram ranges, which contain the highest peaks.

[Tool]
Top 10 entity alternatives:
1. Himalayas, also known as Himalaya, Himálaya, Himalaya Mountains, Himálaya Mountains, Himalayan Mountains (wd:Q5451): mountain range in Asia
2. The Himalayas, also known as Himalayas (wd:Q21546296): 2015 film directed by Lee Seok-hoon
3. Himalayas (wd:Q85766587): Welsh rock band
4. Himalayas (wd:Q100353676): painting by Gaganendranath Tagore
5. Himalayas (wd:Q100374811)
6. Himalayas (wd:Q100403347): painting collection of National Gallery of Modern Art, Bangalore
7. Himalayas (wd:Q20441754): painting by Eliot Clark
8. Himalayas (wd:Q20469856): painting by Sidney Nolan
9. Himalayas (wd:Q20469983): painting by Sidney Nolan (AGNSW, 96.1989)
10. Himalayas (wd:L1125498-S1)

[Tool]
Top 10 entity alternatives:
1. Karakoram, also known as Karakoram Mountains, Karakoram Range, Karakoroum, Karakoroum Mountains, Karakorum (wd:Q5469): major mountain range spanning the borders between India, Pakistan, and China
2. Karakoram (wd:Q16108247): 1937 film by Marcel Ichac
3. Karakoram (wd:Q120497757): Pakistani alternative rock band
4. Karakoram, also known as IMO 7408938 (wd:Q122783444): ship built in 1975
5. Karakoram Highway, also known as KKH, N-35 (wd:Q217099): international highway running through Pakistan, China
6. Karakoram Pass (wd:Q784798): Mountain pass in India
7. Baltoro Muztagh, also known as Baltoro Karakoram (wd:Q805805): mountain range
8. Hongdu JL-8, also known as JL-8, K-8, Karakoram 8, Karakorum 8, Nanchang Karakoram (wd:Q731183): family of military training aircraft
9. Hispar Muztagh, also known as Hispar Karakoram, Hisper Muztagh (wd:Q745507): mountain range in Gilgit-Baltistan, Pakistan
10. Karakoram International University, also known as Karakoram University, KIU, بین الاقوامی جامعہ قراقرم (wd:Q6368138): university in Gilgit-Baltistan, Pakistan

[Assistant] The Himalayas (wd:Q5451) and Karakoram (wd:Q5469) are the major mountain ranges containing the highest peaks. I will now find mountains that are instances of mountain (wd:Q8502) and are located in either the Himalayas or Karakoram ranges, then order them by elevation to find the highest mountain after Annapurna I.

[Tool]
Got 1 row and 3 columns:
| mountain                 | mountainLabel  | elevation          |
| ------------------------ | -------------- | ------------------ |
| Padmanabh (wd:Q20191132) | Padmanabh (en) | 7030 (xsd:decimal) |

[Assistant] The highest mountain after Annapurna I in the Himalayas or Karakoram ranges according to the query is Padmanabh with an elevation of 7030 meters. This seems lower than expected for the second highest mountain after Annapurna. I will try a different approach by checking the highest mountains in the world and then filtering out Annapurna I to find the next highest.

[Tool]
Got 10 rows and 3 columns:
| mountain                             | mountainLabel              | elevation          |
| ------------------------------------ | -------------------------- | ------------------ |
| Vinegar Hill (wd:Q39074795)          | Vinegar Hill (en)          | 8084 (xsd:decimal) |
| Gasherbrum (wd:Q187138)              | Gasherbrum (en)            | 8080 (xsd:decimal) |
| Mount Hardy (wd:Q49052914)           | Mount Hardy (en)           | 8080 (xsd:decimal) |
| Corteo Peak (wd:Q49022207)           | Corteo Peak (en)           | 8080 (xsd:decimal) |
| Hozomeen Mountain (wd:Q3141668)      | Hozomeen Mountain (en)     | 8071 (xsd:decimal) |
| El Capitan (wd:Q5350921)             | El Capitan (en)            | 8064 (xsd:decimal) |
| Mount Ina Coolbrith (wd:Q6921409)    | Mount Ina Coolbrith (en)   | 8064 (xsd:decimal) |
| Broad Peak (wd:Q180996)              | Broad Peak (en)            | 8051 (xsd:decimal) |
| Blue Mountain Plateau (wd:Q49014482) | Blue Mountain Plateau (en) | 8048 (xsd:decimal) |
| The Blues (wd:Q49082179)             | The Blues (en)             | 8042 (xsd:decimal) |

[Assistant]
The results include many mountains with elevations close to Annapurna I but some of these are not known as the highest mountains in the world (e.g., Vinegar Hill, El Capitan). This suggests that the data might include some inaccuracies or that the classification of these entities as mountains might be broad.

I will now specifically look for the highest mountains in the world by checking for mountains that are part of the "eight-thousanders" (mountains above 8000 meters) and exclude Annapurna I to find the next highest mountain after it.

[Tool]
Top 10 entity alternatives:
1. The Eight-Thousanders (wd:Q110516210): short story by Jason Sanford
2. List of Finnish mountaineers who have conquered eight-thousanders (wd:Q130530941): Finnish mountaineers who have conquered eight-thousanders

[Assistant] The entity for the group of mountains known as "Eight-Thousanders" (mountains above 8000 meters) is not directly found. I will try searching for "mountains above 8000 meters" or "eight-thousand meter peaks" to find a relevant category or list.

[Tool]
Top 10 entity alternatives:
1. Above the Mountains, also known as Boven de bergen (wd:Q2257136): 1992 film by Digna Sinke
2. mountains above Megara (wd:Q124644621): unlocated water source for Megara attested by Pausanias (1.41.2)
3. elevation above sea level in metres, also known as elevation above sea level in meters, meters above mean sea level, meters above sea level, metres above mean sea level, metres above sea level (wd:Q110272053): legacy term combining quantity and unit frequently abbreviated i...ing the unit metre; elevation above sea level measured in metres
4. Three Meters Above the Sky (wd:Q2253999): novel by Federico Moccia
5. Summertime, also known as Three Meters Above the Sky (wd:Q92490540): Italian drama web television series
6. Air Above Mountains (Buildings Within) (wd:Q4697710): 1978 live album by Cecil Taylor
7. Hawk Above Village in Mountains (wd:Q64549189): painting by Gu Jianlong
8. Clouds Rising above Mountains and Stream (wd:Q49420423): painting by Chen Danzhong (jinshi 1643)
9. A Winged Sorceress Soaring above Mountains (wd:Q64630034): drawing in the National Gallery of Art (NGA 155588)
10. New Moon above the Riesengebirge Mountains (wd:Q18602437): painting by Caspar David Friedrich

[Assistant] There is no direct entity for "mountains above 8000 meters" or "eight-thousanders" found. I will try a different approach by searching for the property "elevation above sea level" and filter for mountains with elevation greater than 8000 meters, then exclude Annapurna I and find the next highest mountain.

[Tool]
Got 1 row and 3 columns:
| mountain                   | mountainLabel   | elevation           |
| -------------------------- | --------------- | ------------------- |
| Peak 17400 (wd:Q123374100) | Peak 17400 (en) | 17450 (xsd:decimal) |

[Assistant]
The result shows "Peak 17400" with an elevation of 17450 meters, which is incorrect as it exceeds the height of any known mountain on Earth. This suggests data inaccuracies or errors in the dataset.

I will refine the query to exclude obviously incorrect data by limiting the elevation to a maximum of 9000 meters and find the highest mountain after Annapurna I.

[Tool]
Got 1 row and 3 columns:
| mountain                     | mountainLabel       | elevation          |
| ---------------------------- | ------------------- | ------------------ |
| Telegraph Peak (wd:Q7696249) | Telegraph Peak (en) | 8989 (xsd:decimal) |

[Assistant]
The highest mountain after Annapurna I with elevation between 8000 and 9000 meters is Telegraph Peak with an elevation of 8989 meters. This elevation is higher than Annapurna I's 8091 meters, which is inconsistent with the question's premise.

I will now check the elevation of some of the known highest mountains such as Mount Everest and K2 to verify the data and then find the highest mountain after Annapurna I.

[Tool]
Got 1 row and 3 columns:
| mountain                | mountainLabel      | elevation             |
| ----------------------- | ------------------ | --------------------- |
| Mount Everest (wd:Q513) | Mount Everest (en) | 8848.86 (xsd:decimal) |

[Assistant]
The elevation of Mount Everest (wd:Q513) is 8848.86 meters, which is correct. The data for Telegraph Peak showing 8989 meters seems to be an error.

I will now find the highest mountain with elevation less than Annapurna I's 8091 meters, excluding Annapurna I itself, to find the highest mountain after Annapurna.

[Tool]
Got 1 row and 3 columns:
| mountain                    | mountainLabel     | elevation          |
| --------------------------- | ----------------- | ------------------ |
| Vinegar Hill (wd:Q39074795) | Vinegar Hill (en) | 8084 (xsd:decimal) |

[Assistant]
The highest mountain after Annapurna I according to the data is Vinegar Hill with an elevation of 8084 meters. However, Vinegar Hill is not known as one of the highest mountains in the world, which suggests some data inconsistencies.

Given the known facts, the second highest mountain after Annapurna I (8091m) should be Dhaulagiri (8167m) or Manaslu (8163m), but these are higher than Annapurna I, so they cannot be "after" Annapurna in height.

I will now specifically check the elevation of Dhaulagiri and Manaslu to verify their heights.

[Tool] Got no rows and 3 columns

[Assistant]
The query returned no results for Dhaulagiri (wd:Q1913) and Manaslu (wd:Q1935). It seems these entities might not be correctly identified or do not have elevation data in the dataset.

I will search for Dhaulagiri and Manaslu entities again to verify their correct identifiers.

[Tool]
Top 10 entity alternatives:
1. Dhaulagiri, also known as Dhaulagiri I, Dhwalagiri (wd:Q165440): mountain in Nepal
2. Dhaulagiri Journal of Sociology and Anthropology, also known as Dhaulagiri (wd:Q50808137): Academic journal published by Tribhuvan University , covering th... Recreation: Anthropology | Social Sciences: Sociology (General)
3. Dhaulagiri Zone, also known as Dhawalagiri Zone (wd:Q9088): former administrative zone of Nepal
4. Putha Hiunchuli, also known as Dhaulagiri VII (wd:Q1377280): mountain
5. Beni, also known as Beni, Dhaulagiri, Beni, Dhawalagiri, Beni, Myagdi (wd:Q2096921): human settlement in Nepal
6. Dhaulagiri II (wd:Q1207614): mountain
7. Dhaulagiri Himal, also known as Dhaulāgiri Himāl, Dhaulagiri Himalaya (wd:Q1207615): mountain range in Nepal
8. Trox dhaulagiri (wd:Q2624402): species of insect
9. Glyptotrox dhaulagiri (wd:Q110420300): species of beetle
10. Dhaulagiri IV (wd:Q31653016)

[Tool]
Top 10 entity alternatives:
1. Manaslu, also known as კუტანგი (wd:Q170070): mountain in Nepal; eighth highest mountain in the world
2. Hotel Manaslu, also known as Manaslu (wd:Q111862181): hotel in Kathmandu, Nepal
3. Mansiri Himal, also known as Manaslu Himal (wd:Q1763730): Mansiri Himal is a small, high subrange of the Himalaya in north-central Nepal
4. Manaslu Circuit, also known as Manaslu Trek (wd:Q14223863): long-distance trail in Nepal
5. Hydraena manaslu (wd:Q14929719): species of insect
6. Cyphon manaslu (wd:Q21265730): species of insect
7. Someira manaslu (wd:Q124277275): species of insect
8. Berge Manaslu, also known as IMO 9000352 (wd:Q117007882): bulk carrier built in 1992
9. Manaslu Conservation Area, also known as Manasalu Conservation Area (wd:Q6747003): protected area of Nepal
10. Meconopsis manasluensis (wd:Q6804864): species of plant

[Assistant]
The correct entities are:
- Dhaulagiri (wd:Q165440)
- Manaslu (wd:Q170070)

I will now check their elevations.

[Tool]
Got 2 rows and 3 columns:
| mountain                | mountainLabel   | elevation          |
| ----------------------- | --------------- | ------------------ |
| Dhaulagiri (wd:Q165440) | Dhaulagiri (en) | 8167 (xsd:decimal) |
| Manaslu (wd:Q170070)    | Manaslu (en)    | 8163 (xsd:decimal) |

[Assistant]
Dhaulagiri has an elevation of 8167 meters and Manaslu has an elevation of 8163 meters, both higher than Annapurna I's 8091 meters. So they cannot be "after" Annapurna in height.

I will now check the elevation of the mountain that is the third highest after Mount Everest and K2 to find the highest mountain after Annapurna. I will check the elevation of Kangchenjunga (third highest mountain) and then find the next highest after Annapurna.

[Tool]
Top 10 entity alternatives:
1. Kanchenjunga, also known as Kañcanjaṅghā, Kangchendzonga, Kangchenjunga (wd:Q82019): third highest mountain in the world, in Nepal and India
2. Kangchenjunga (wd:Q120478276): film directed by Ján Piroh
3. Kangchenjunga South (wd:Q1723730): summit of Kangchenjunga
4. Pterostichus kangchenjunga (wd:Q14214363): species of insect
5. Kangchenjunga Central (wd:Q1723729): summit of Kangchenjunga
6. Berge Kangchenjunga, also known as IMO 9073438 (wd:Q117010196): bulk carrier built in 1994
7. Kangchenjunga Climbed (wd:Q56050363)
8. 1905 Kanchenjunga Expedition, also known as 1905 Kangchenjunga expedition (wd:Q4558367): Failed attempt to climb the Kangchenjunga in 1905
9. Russula kangchenjungae (wd:Q95575384): species of fungi in the genus Russula
10. Conquering the Himalayas: Kangchenjunga expedition, 1930, also known as The Kangchenjunga Adventure (wd:Q105078436): 1932 Payot edition

[Tool]
Got 1 row and 3 columns:
| mountain                 | mountainLabel     | elevation          |
| ------------------------ | ----------------- | ------------------ |
| Kanchenjunga (wd:Q82019) | Kanchenjunga (en) | 8586 (xsd:decimal) |

[Assistant]
Kangchenjunga has an elevation of 8586 meters, which is higher than Annapurna I's 8091 meters.

Now I will find the highest mountain with elevation less than Annapurna I's 8091 meters, excluding Annapurna I itself, to find the highest mountain after Annapurna.

[Tool]
Got 1 row and 3 columns:
| mountain                    | mountainLabel     | elevation          |
| --------------------------- | ----------------- | ------------------ |
| Vinegar Hill (wd:Q39074795) | Vinegar Hill (en) | 8084 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?capital ?capitalLabel ?population WHERE {
  wd:Q408 wdt:P36 ?capital .
  ?capital wdt:P1082 ?population .
  OPTIONAL {
    ?capital rdfs:label ?capitalLabel FILTER ( LANG( ?capitalLabel ) = "en" )
  }
}

The capital of Australia is Canberra, and its population is 381,488.

[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 http://amur:7001

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 people live in the capital of Australia?

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

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

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

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

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

Example 2:
Question:
What is the capital of Cameroon?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1009 wdt:P36 ?uri
}

Using entities:
Cameroon, also known as CAM, CM, CMR, Republic of Cameroon (wd:Q1009): sovereign state in West-Central Africa

Using properties:
capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt): seat of government of a country, province, state or other type of administrative territorial entity

[Assistant]
From the examples, we see that the property for population is wdt:P1082 and the property for capital is wdt:P36. We need to find the capital of Australia (wd:Q408) and then find the population of that capital.

Next, I will write a SPARQL query to get the population of the capital of Australia.

[Tool]
Got 1 row and 3 columns:
| capital             | capitalLabel  | population           |
| ------------------- | ------------- | -------------------- |
| Canberra (wd:Q3114) | Canberra (en) | 381488 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?city ?cityLabel WHERE {
  wd:Q9696 wdt:P20 ?placeOfDeath .
  ?placeOfDeath wdt:P131 ?city .
  ?city wdt:P31 wd:Q515 .
  OPTIONAL {
    ?city rdfs:label ?cityLabel FILTER ( LANG( ?cityLabel ) = "en" )
  }
}

John F. Kennedy died in the city of Dallas.

[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 http://amur:7001

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] In which city did John F. Kennedy die?

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

[Tool]
Example 1:
Question:
Who became president after JFK died?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q9696 p:P39 ?position .
  ?position ps:P39 wd:Q11696 .
  ?position pq:P1366 ?uri .
}

Using entities:
President of the United States, also known as US President, U.S. President, United States President, the president of the United States, President of the U.S. (wd:Q11696): head of state and head of government of the United States of America
John F. Kennedy, also known as John Kennedy, John Fitzgerald Kennedy, John F Kennedy, Kennedy, President Kennedy (wd:Q9696): President of the United States from 1961 to 1963, anti-communist, naval officer, statesperson, journalist, politician, writer

Using properties:
replaced by, also known as heir, superseded by, successor, succeeded by, next job holder (wd:P1366 as pq): other person or item which continues the item by replacing it in... nor identical, but adds to the series (e.g. books in a series)., followed by
position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p): subject currently or formerly holds the object position or public office

Example 2:
Question:
In which city did Nikos Kazantzakis die?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q214622 wdt:P20 ?uri
}

Using entities:
Nikos Kazantzakis (wd:Q214622): Greek writer, poet and philosopher (1883-1957), children's writer, correspondent, travel writer, lexicographer, prose writer, screenwriter, philosopher, politician, translator, playwright, journalist, novelist, traveler, writer, poet

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

Example 3:
Question:
In which city does Sylvester Stallone live?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q40026 wdt:P551 ?uri .
  ?uri wdt:P31 wd:Q515 .
}

Using entities:
Sylvester Stallone, also known as Sylvester Gardenzio Stallone, Sly Stallone, Michael Sylvester Gardenzio Stallone (wd:Q40026): American actor and film director, film director, film producer, screenwriter, film actor
city, also known as cities (wd:Q515): large human settlement

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)
residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt): the place where the person is or has been, resident, location

[Assistant]
From the similar examples, I see that the property "place of death" (wdt:P20) is used to indicate the city where a person died. Also, John F. Kennedy is identified by the entity wd:Q9696.

I will now write a SPARQL query to find the city where John F. Kennedy died using the property "place of death" (wdt:P20).

[Tool] Got no rows and 2 columns

[Assistant]
The query returned no results when filtering the place of death to be an instance of a city (wd:Q515). It is possible that the place of death is recorded as a more specific location (e.g., hospital) or a different type of entity.

I will try a simpler query without restricting the place of death to be a city and just get the place of death for John F. Kennedy.

[Tool]
Got 1 row and 2 columns:
| placeOfDeath                             | placeOfDeathLabel               |
| ---------------------------------------- | ------------------------------- |
| Parkland Memorial Hospital (wd:Q3896131) | Parkland Memorial Hospital (en) |

[Assistant] The place of death for John F. Kennedy is Parkland Memorial Hospital. Since the question asks for the city, I will now find the city where Parkland Memorial Hospital is located.

[Tool] Got no rows and 2 columns

[Assistant]
The query to find the city containing Parkland Memorial Hospital using the property "located in the administrative territorial entity" (wdt:P131) did not return any results. It is possible that the hospital is directly linked to a different type of administrative entity.

I will try to find any administrative territorial entity (wdt:P131) for the hospital without restricting it to a city.

[Tool]
Got 1 row and 2 columns:
| adminEntity        | adminEntityLabel |
| ------------------ | ---------------- |
| Dallas (wd:Q16557) | Dallas (en)      |

[Assistant]
Parkland Memorial Hospital is located in Dallas. Dallas is a city.

I will now provide the final answer that John F. Kennedy died in the city of Dallas.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?currency ?currencyLabel WHERE {
  wd:Q213 wdt:P38 ?currency .
  OPTIONAL {
    ?currency rdfs:label ?currencyLabel FILTER ( LANG( ?currencyLabel ) = "en" )
  }
}

The currency of the Czech Republic is the Czech koruna.

[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 http://amur:7001

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

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

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

[User] What is the currency of the Czech Republic?

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

[Tool]
Example 1:
Question:
Show me all Czech movies.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q11424 .
  {
    ?uri wdt:P495 wd:Q213 .
  } UNION {
    ?uri wdt:P495 wd:Q33946 .
  }
}

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
Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe
Czechoslovakia, also known as TCH, People's Republic of Czechoslovakia, CSFR, Federation of Czechoslovakia, ČSFR (wd:Q33946): country in Central Europe, 1918–1992

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)
country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country

[Assistant]
The example does not directly answer the question about the currency of the Czech Republic, but it shows how to use the entity for the Czech Republic (wd:Q213) and properties related to countries.

Next, I will search for the entity "Czech Republic" to confirm its identifier in Wikidata. Then I will search for properties related to currency for that entity.

[Tool]
Top 10 entity alternatives:
1. Czech Republic, also known as CZR, CZE, Czechia, CZ (wd:Q213): country in Central Europe
2. Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe
3. Czech Republic (wd:Q3496079): member state of the Czech and Slovak Federal Republic (1990–1992)
4. Czech Republic (wd:Q5201764): constituency of the European Parliament
5. Czech Republic, also known as Star Trek Czech Republic (wd:Q99288382): the country of the Czech Republic as depicted in Star Trek
6. CZECH REPUBLIC (wd:Q60164570)
7. Czech Republic (wd:Q59760445): scholarly article by LUKAS LINEK & ZDENKA MANSFELDOVA published December 2004 in European Journal of Political Research
8. Czech Republic (wd:Q59212257): scholarly article by Aleš Kudrnáč et al published December 2016 in European Journal of Political Research Political Data Yearbook
9. Czech Republic (wd:Q59212246): scholarly article by IVAN PETÚŠEK & ALEŠ KUDRNÁČ published 5 Jul...n European Journal of Political Research Political Data Yearbook
10. Czech Republic (wd:Q57542866): scholarly article

[Assistant] The entity for the Czech Republic is wd:Q213. Now I will search for properties related to currency for this entity.

[Tool]
Top 10 property alternatives:
1. currency, also known as base currency, used money (wd:P38 as wdt/p): currency used by item, uses
2. nominal GDP, also known as GDP, GDP, nominal, gross domestic product, nominal GDP, nominal gross domestic product (wd:P2131 as wdt/p): market value of all officially recognized final goods and services produced within a country in a given period of time
3. central bank (wd:P1304 as wdt/p): country's central bank
4. flag, also known as flag description (wd:P163 as wdt/p): subject's flag
5. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
6. minimum wage (wd:P6794 as wdt/p): lowest wage which can be paid legally in a state for working
7. VAT rate, also known as goods and services tax rate, GST, sales tax, taxation rate for sales, tax rate for sales (wd:P2855 as wdt/p): percentage value-added tax in this country or region. Do not use for sales tax. For specialised rates, use qualifiers
8. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as wdt/p): religion of a person, organization or religious building, or associated with this subject
9. The New Yorker topic ID, also known as TNY ID, The NewYorker topic ID, New Yorker topic ID, NewYorker topic ID, newyorker.com (wd:P13317 as wdt/p): identifier of a topic on The New Yorker website
10. 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)

No other items found

[Assistant] The property for currency is wd:P38. Now I will write a SPARQL query to get the currency of the Czech Republic using the entity wd:Q213 and property wd:P38.

[Tool]
Got 1 row and 2 columns:
| currency                  | currencyLabel     |
| ------------------------- | ----------------- |
| Czech koruna (wd:Q131016) | Czech koruna (en) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?episode ?episodeLabel WHERE {
  wd:Q5274359 wdt:P527 ?episode .
  ?episode wdt:P31 wd:Q21191270 .
  OPTIONAL {
    ?episode rdfs:label ?episodeLabel FILTER ( LANG( ?episodeLabel ) = "en" )
  }
}
ORDER BY ?episodeLabel

Got 13 rows and 2 columns, showing the first 5 and last 5 rows below:
| episode                                          | episodeLabel                            |
| ------------------------------------------------ | --------------------------------------- |
| 46 Long (wd:Q3499084)                            | 46 Long (en)                            |
| A Hit Is a Hit (wd:Q3435602)                     | A Hit Is a Hit (en)                     |
| Boca (wd:Q3435595)                               | Boca (en)                               |
| College (wd:Q2422019)                            | College (en)                            |
| Denial, Anger, Acceptance (wd:Q3442861)          | Denial, Anger, Acceptance (en)          |
| ...                                              | ...                                     |
| Meadowlands (wd:Q1750968)                        | Meadowlands (en)                        |
| Nobody Knows Anything (wd:Q3442850)              | Nobody Knows Anything (en)              |
| Pax Soprana (wd:Q3442891)                        | Pax Soprana (en)                        |
| The Legend of Tennessee Moltisanti (wd:Q3499074) | The Legend of Tennessee Moltisanti (en) |
| The Sopranos (wd:Q1434187)                       | The Sopranos (en)                       |

The 13 episodes of the first season of the HBO television series The Sopranos are: 46 Long, A Hit Is a Hit, Boca, College, Denial, Anger, Acceptance, Meadowlands, Nobody Knows Anything, Pax Soprana, The Legend of Tennessee Moltisanti, The Sopranos, and others as listed in the query results.

[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 http://amur:7001

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] List all episodes of the first season of the HBO television series The Sopranos.

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

[Tool] No examples found

[Assistant] There are no direct examples available for this question. I will now search for the entity "The Sopranos" in Wikidata to identify its identifier.

[Tool]
Top 10 entity alternatives:
1. The Sopranos (wd:Q23628): American crime drama television series (1999–2007)
2. The Sopranos (wd:Q1434187): episode of The Sopranos (S1 E1)
3. The Sopranos (wd:Q7765311): 1998 novel by Alan Warner
4. The Sopranos (wd:Q7765312): pinball machine based on the television series The Sopranos
5. The Sopranos Diaries (wd:Q110055728): sketch from Saturday Night Live
6. The Sopranos soundtrack releases, also known as Music on The Sopranos (wd:Q206205): Wikipedia article covering multiple soundtrack releases
7. The Sopranos, season 1 (wd:Q5274359): season of television series
8. The Sopranos, season 6, also known as sixth and final season (wd:Q3727044): season of television series
9. The Sopranos, season 5 (wd:Q3727041): season of television series
10. The Sopranos, season 4 (wd:Q3727040): season of television series

[Assistant] The Sopranos television series is identified as wd:Q23628. The first season of The Sopranos is identified as wd:Q5274359. I will now find all episodes of the first season (wd:Q5274359). Typically, episodes are linked to a season by the property "part of the series" or "season" or "part of" or similar. I will search for properties related to this season entity to find how episodes are linked.

[Tool]
Top 10 property alternatives:
1. part of the series, also known as series, video game series, subseries, part of series, listed in (wd:P179 as wdt/p): series which contains the subject, part of
2. number of episodes, also known as series length, no. of episodes, episodes, episode count (wd:P1113 as wdt/p): number of episodes in a film/TV/radio series, number of parts of this work
3. original broadcaster, also known as TV channel, television channel, radio station, radio network, original network (wd:P449 as wdt/p): network(s) or service(s) that originally broadcast a radio or television program, broadcast by
4. TheTVDB season ID, also known as TheTVDB.com season ID (wd:P12397 as wdt/p): identifier for a TV season on the website TheTVDB.com
5. end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as wdt/p): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time
6. start time, also known as start date, since, valid, join time, joined (wd:P580 as wdt/p): time an entity begins to exist or a statement starts being valid, point in time
7. TMDB season ID, also known as The Movie Database season ID (wd:P12558 as wdt/p): identifier for a television series season at The Movie Database
8. 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)
9. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
10. original language of film or TV show, also known as media language, original language, language of the original work, created on language (wd:P364 as wdt/p): language in which a film or a performance work was originally cr... works and songs; use P407 ("language of work or name") instead., language of work or name

No other items found

[Assistant] The property "part of the series" (wdt:P179) seems relevant to link episodes to the series or season. I will check if episodes of the first season (wd:Q5274359) are linked by this property. I will query for entities that have "part of the series" (wdt:P179) as the first season (wd:Q5274359) to find the episodes.

[Tool] Got no rows and 2 columns

[Assistant] No episodes were found using the property "part of the series" (wdt:P179) linked to the first season (wd:Q5274359). I will try another approach. I will check if the first season (wd:Q5274359) has a property "has part" or similar that lists the episodes. I will list properties of the first season entity.

[Tool]
Got 107 rows and 3 columns, showing the first 10 rows below:
| subject                              | property                                        | object                                 |
| ------------------------------------ | ----------------------------------------------- | -------------------------------------- |
| The Sopranos, season 1 (wd:Q5274359) | instance of (wdt:P31)                           | television series season (wd:Q3464665) |
| The Sopranos, season 1 (wd:Q5274359) | has part(s) (wdt:P527)                          | College (wd:Q2422019)                  |
| The Sopranos, season 1 (wd:Q5274359) | country of origin (wdt:P495)                    | United States (wd:Q30)                 |
| The Sopranos, season 1 (wd:Q5274359) | official website (wdt:P856)                     | <https://www.hbo.com/the-sopranos>     |
| The Sopranos, season 1 (wd:Q5274359) | part of the series (wdt:P179)                   | The Sopranos (wd:Q23628)               |
| The Sopranos, season 1 (wd:Q5274359) | start time (wdt:P580)                           | 1999-01-10T00:00:00Z (xsd:dateTime)    |
| The Sopranos, season 1 (wd:Q5274359) | end time (wdt:P582)                             | 1999-04-04T00:00:00Z (xsd:dateTime)    |
| The Sopranos, season 1 (wd:Q5274359) | original language of film or TV show (wdt:P364) | English (wd:Q1860)                     |
| The Sopranos, season 1 (wd:Q5274359) | original broadcaster (wdt:P449)                 | HBO (wd:Q23633)                        |
| The Sopranos, season 1 (wd:Q5274359) | number of episodes (wdt:P1113)                  | 13 (xsd:decimal)                       |
| ...                                  | ...                                             | ...                                    |

[Assistant] The first season (wd:Q5274359) has the property "has part(s)" (wdt:P527) linking to "College" (wd:Q2422019), which is likely an episode. I will now query all entities that are linked by "has part(s)" (wdt:P527) to the first season (wd:Q5274359) and check if they are TV episodes.

[Tool]
Got 13 rows and 2 columns, showing the first 5 and last 5 rows below:
| episode                                          | episodeLabel                            |
| ------------------------------------------------ | --------------------------------------- |
| 46 Long (wd:Q3499084)                            | 46 Long (en)                            |
| A Hit Is a Hit (wd:Q3435602)                     | A Hit Is a Hit (en)                     |
| Boca (wd:Q3435595)                               | Boca (en)                               |
| College (wd:Q2422019)                            | College (en)                            |
| Denial, Anger, Acceptance (wd:Q3442861)          | Denial, Anger, Acceptance (en)          |
| ...                                              | ...                                     |
| Meadowlands (wd:Q1750968)                        | Meadowlands (en)                        |
| Nobody Knows Anything (wd:Q3442850)              | Nobody Knows Anything (en)              |
| Pax Soprana (wd:Q3442891)                        | Pax Soprana (en)                        |
| The Legend of Tennessee Moltisanti (wd:Q3499074) | The Legend of Tennessee Moltisanti (en) |
| The Sopranos (wd:Q1434187)                       | The Sopranos (en)                       |

[Assistant] I have found 13 episodes linked to the first season of The Sopranos (wd:Q5274359) using the property "has part(s)" (wdt:P527). These are the episodes of the first season. I will now finalize the answer with the list of these episodes including their labels.

[Tool] Stopped generation process