PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?dissolvedDate WHERE {
  wd:Q645476 wdt:P576 ?dissolvedDate
}

The production company Manglobe of Samurai Champloo was dissolved on 2015-09-30, so it is no longer existing.

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

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

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

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

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

[User] Is the production company of samurai champloo still existing?

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

[Tool]
Example 1:
Question:
How many James Bond movies do exist?

SPARQL:
SELECT ( COUNT( ?s1 ) as ?c ) WHERE {
  ?s1 wdt:P31 wd:Q11424 .
  ?s1 ?p wd:Q2009573 .
  FILTER ( ?p = wdt:P144 || ?p = wdt:P674 )
}

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
James Bond, also known as Agent 007, double o seven, 007 (wd:Q2009573): secret agent, invented by novellist Ian Fleming, 00 Agent, spy

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:
What country is Sitecore from?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q2663195 wdt:P17 ?uri
}

Using entities:
Sitecore (wd:Q2663195): company

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

Example 3:
Question:
Which states border Illinois?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1204 wdt:P47 ?uri .
}

Using entities:
Illinois, also known as The Prairie State, Illinois, United States, The Land of Lincoln, US-IL, Prairie State (wd:Q1204): state of the United States of America

Using properties:
shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as wdt): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.

[Assistant]
To answer the question "Is the production company of Samurai Champloo still existing?", I need to:

1. Identify the entity for "Samurai Champloo" (likely an anime or TV show).
2. Find the production company or companies associated with Samurai Champloo.
3. Check if the production company or companies are still existing (not dissolved or defunct).

I will start by searching for the entity "Samurai Champloo" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Samurai Champloo (wd:Q143276): Japanese anime television series
2. Samurai Champloo (wd:Q66138325): Japanese manga series
3. Samurai Champloo: Sidetracked, also known as Samurai Champloo HIPHOP Samurai Action (wd:Q3548610): 2006 video game
4. Samurai Champloo soundtrack releases, also known as Music of Samurai Champloo (wd:Q106856784): Wikipedia article covering multiple soundtrack releases
5. Samurai Champloo Music Record: Departure, also known as Departure (wd:Q7413091): 2004 soundtrack album by Nujabes / Fat Jon
6. Samurai Champloo Music Record: Impression, also known as Impression (wd:Q3149468): 2004 soundtrack album by Force of Nature / Nujabes / Fat Jon
7. Samurai Champloo Music Record: Masta, also known as Masta (wd:Q104829750): 2004 soundtrack album by FORCE OF NATURE / tsutchie
8. Samurai Champloo Music Record: Playlist (wd:Q104829743): 2004 soundtrack album by tsutchie
9. Postmodern Is Old Hat: Samurai Champloo (wd:Q56608596): article
10. samurai, also known as buke, bushi (wd:Q38142): military nobility of pre-industrial Japan

[Assistant] The entity "Samurai Champloo" as a Japanese anime television series is wd:Q143276. Next, I will find the production company or companies associated with this anime series. I will search for properties related to production company for this entity.

[Tool]
Top 10 property alternatives:
1. production company, also known as broadcasting company, theatrical troupe, theatre company, theater company, studio (wd:P272 as wdt/p): company that produced this film, audio or performing arts work
2. director, also known as film director, movie director, game director, directed by (wd:P57 as wdt/p): director(s) of film, TV-series, stageplay, video game or similar, contributor to the creative work or subject, creator
3. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt/p): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
4. derivative work, also known as is source work of, is adapted as, is the basis for, iteration, reboot (wd:P4969 as wdt/p): new work of art (film, book, software, etc.) derived from major part of this work
5. 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
6. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
7. 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
8. Kinobox film ID, also known as www.kinobox.cz/film/, kinobox.cz/film/, https://www.kinobox.cz/film/ (wd:P12096 as wdt/p): identifier for a film on the Kinobox.cz website
9. distributed by, also known as distributor (wd:P750 as wdt/p): distributor of a creative work; distributor for a record label; news agency; film distributor
10. 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

No other items found

[Assistant] The property "production company" (wdt:P272) is the relevant property for the production company of Samurai Champloo. Now I will query the production company or companies of Samurai Champloo (wd:Q143276).

[Tool]
Got 1 row and 2 columns:
| productionCompany     | productionCompanyLabel |
| --------------------- | ---------------------- |
| Manglobe (wd:Q645476) | Manglobe (en)          |

[Assistant] The production company of Samurai Champloo is Manglobe (wd:Q645476). Now I will check if Manglobe is still existing or if it is dissolved or defunct. I will look for statements about its dissolution or end of existence.

[Tool]
Got 129 rows and 3 columns, showing the first 10 rows below:
| subject               | property                                           | object                              |
| --------------------- | -------------------------------------------------- | ----------------------------------- |
| Manglobe (wd:Q645476) | instance of (wdt:P31)                              | animation studio (wd:Q1107679)      |
| Manglobe (wd:Q645476) | country (wdt:P17)                                  | Japan (wd:Q17)                      |
| Manglobe (wd:Q645476) | inception (wdt:P571)                               | 2002-01-01T00:00:00Z (xsd:dateTime) |
| Manglobe (wd:Q645476) | official website (wdt:P856)                        | <http://www.manglobe.net>           |
| Manglobe (wd:Q645476) | headquarters location (wdt:P159)                   | Suginami-ku (wd:Q232631)            |
| Manglobe (wd:Q645476) | topic's main category (wdt:P910)                   | wd:Q7905920                         |
| Manglobe (wd:Q645476) | legal form (wdt:P1454)                             | kabushiki gaisha (wd:Q1480166)      |
| Manglobe (wd:Q645476) | industry (wdt:P452)                                | anime industry (wd:Q2575347)        |
| Manglobe (wd:Q645476) | dissolved, abolished or demolished date (wdt:P576) | 2015-09-30T00:00:00Z (xsd:dateTime) |
| Manglobe (wd:Q645476) | replaced by (wdt:P1366)                            | Geno Studio (wd:Q42216812)          |
| ...                   | ...                                                | ...                                 |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?invasion ) AS ?numberOfInvasions ) WHERE {
  wd:Q208156 wdt:P527 ?invasion .
}

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

The Mongols tried to invade Japan twice, as represented by the two parts of the Mongol invasions of Japan: the Battle of Bun'ei and the Battle of Kōan.

[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 often did the Mongols try to invade Japan?

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

[Tool]
Example 1:
Question:
What country is Mount Everest in?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q513 wdt:P17 ?uri .
}

Using entities:
Mount Everest, also known as Chomolungma, Chomolangma, Zhumulangma, World highest peak, Sagarmāthā (wd:Q513): Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas

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

Example 2:
Question:
With how many countries Iran has borders?

SPARQL:
SELECT ?uri WHERE {
  wd:Q794 wdt:P47 ?uri .
}

Using entities:
Iran, also known as ir, IRI, IRN, Islamic Rep. Iran, Islamic Republic of Iran (wd:Q794): country in Western Asia

Using properties:
shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as wdt): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.

Example 3:
Question:
Who is starring in Spanish movies produced by Benicio del Toro?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?movie wdt:P31 wd:Q11424 ; wdt:P162 wd:Q193668 ; wdt:P495 wd:Q29 ; wdt:P161 ?uri .
}

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
Benicio del Toro, also known as Benicio Del Toro (wd:Q193668): Puerto Rican actor, television actor, film director, film producer, screenwriter, film actor, actor
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa

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
producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as wdt): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]
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 examples found do not directly relate to historical events or invasions, so I will now search for entities related to "Mongol invasions of Japan" to identify the relevant historical events.

[Tool]
Top 10 entity alternatives:
1. Mongol invasions of Japan, also known as Attempted Mongol Invasions, 1274-1281, Japan--History--Attempted Mongol Invasions, 1274-1281 (wd:Q208156): attempts by the Mongol Empire to conquer Japan
2. Mongol invasions of Korea (wd:Q702266): Mongol Yuán Conquests of the Korean peninsula
3. Mongol invasions of Georgia, also known as Mongol conquests of Kingdom of Georgia (wd:Q2917513): 1220–1236 Mongol invasions of the Kingdom of Georgia
4. Mongol invasions of Vietnam (wd:Q2448644): 13th-century military campaigns of the Mongol Empire and Yuan-dynasty China in Vietnam
5. Mongol invasions of India (wd:Q2079940): history of Mongols with India
6. Mongol invasions of Sakhalin (wd:Q11344677): 1264 Invasions of the island Sakhalin by the Mongolian Yuan dynasty
7. Mongol invasions of Durdzuketi (wd:Q6899563): invasions of the territory of modern Chechnya and Ingushetia
8. Mongol invasions of Lithuania (wd:Q63859061)
9. Mongol invasions of Azerbaijan (wd:Q107354947)
10. Mongol invasions of Alania (wd:Q113130790)

[Assistant] The entity "Mongol invasions of Japan" (wd:Q208156) is exactly what we are looking for. It refers to the attempts by the Mongol Empire to conquer Japan. Next, I will check the details of this entity to find out how often the Mongols tried to invade Japan.

[Tool]
Got 294 rows and 3 columns, showing the first 10 rows below:
| subject                                | property                                                    | object                                                                                |
| -------------------------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
| Mongol invasions of Japan (wd:Q208156) | instance of (wdt:P31)                                       | conflict (wd:Q180684)                                                                                |
| Mongol invasions of Japan (wd:Q208156) | country (wdt:P17)                                           | Japan (wd:Q17)                                                                                |
| Mongol invasions of Japan (wd:Q208156) | located in the administrative territorial entity (wdt:P131) | Saikaidō (wd:Q1334003)                                                                                |
| Mongol invasions of Japan (wd:Q208156) | Commons category (wdt:P373)                                 | Mongol invasions of Japan                                                                                |
| Mongol invasions of Japan (wd:Q208156) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Japan%20in%20Marco%20Polo%27s%20travel%20record%20%282%29.JPG> |
| Mongol invasions of Japan (wd:Q208156) | part of (wdt:P361)                                          | Mongol invasions and conquests (wd:Q2092093)                                                                       |
| Mongol invasions of Japan (wd:Q208156) | location (wdt:P276)                                         | Northern Kyushu (wd:Q397703)                                                                                |
| Mongol invasions of Japan (wd:Q208156) | has part(s) (wdt:P527)                                      | Battle of Kōan (wd:Q2237622)                                                                                |
| Mongol invasions of Japan (wd:Q208156) | point in time (wdt:P585)                                    | 1274-01-01T00:00:00Z (xsd:dateTime)                                                                                |
| Mongol invasions of Japan (wd:Q208156) | participant (wdt:P710)                                      | Yuan dynasty (wd:Q7313)                                                                                |
| ...                                    | ...                                                         | ...                                                                                |

[Assistant] The entity "Mongol invasions of Japan" has parts, one of which is the "Battle of Kōan" (wd:Q2237622). This suggests there might be multiple parts or battles representing different invasion attempts. I will now check the parts of the Mongol invasions of Japan to see how many distinct invasion attempts there were.

[Tool]
Got 2 rows and 3 columns:
| subject                                | property               | object                         |
| -------------------------------------- | ---------------------- | ------------------------------ |
| Mongol invasions of Japan (wd:Q208156) | has part(s) (wdt:P527) | Battle of Kōan (wd:Q2237622)   |
| Mongol invasions of Japan (wd:Q208156) | has part(s) (wdt:P527) | Battle of Bun'ei (wd:Q2237386) |

[Assistant] The Mongol invasions of Japan consist of two parts: the Battle of Bun'ei and the Battle of Kōan. This indicates that the Mongols tried to invade Japan twice. I will now finalize the answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( DISTINCT ?war ) AS ?count ) WHERE {
  wd:Q188712 wdt:P1344 ?war .
  ?war wdt:P31 wd:Q198 .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 4 (xsd:int) |

The Empire of Japan participated in 4 wars according to Wikidata.

[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 wars did the Empire of Japan participate in?

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

[Tool]
Example 1:
Question:
Which U.S. state has the abbreviation MN?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q35657 .
  ?uri wdt:P1813 ?name .
  FILTER ( REGEX( ?name , "^MN$" ) )
}

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:
short name, also known as short title, shortname, abbreviation, shortened name, initialism (wd:P1813 as wdt): short name of a place, organisation, person, journal, wikidata property, etc. Used by some Wikipedia templates., name
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:
What is the highest mountain in Saxony Germany?

SPARQL:
SELECT ?res WHERE {
  ?res wdt:P31 wd:Q8502 .
  ?res wdt:P131 + wd:Q1202 .
  ?res wdt:P2044 ?height .
}
ORDER BY DESC ( ?height )
LIMIT 1

Using entities:
Saxony, also known as DE-SN, Free State of Saxony, Swobodny stat Sakska, Freistaat Sachsen, Sachsen (wd:Q1202): state in Germany
mountain, also known as mt., mtn., mountains, mount (wd:Q8502): large natural elevation of the Earth's surface

Using properties:
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): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
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)

Example 3:
Question:
When was Alberta admitted as province?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q1951 wdt:P571 ?date .
}

Using entities:
Alberta, also known as AB, Wild Rose Country, Province of Alberta, CA-AB, Alta. (wd:Q1951): province of Canada

Using properties:
inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt): time when an entity begins to exist; for date of official opening use P1619, start time

[Assistant]
From the examples, I see that to answer the question "How many wars did the Empire of Japan participate in?" I need to identify the entity for "Empire of Japan" and find the property that relates it to wars it participated in. Then I can count the number of such wars.

Step 1: Search for the entity "Empire of Japan" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Empire of Japan, also known as Great Japanese Empire, Great Empire of Japan, Japanese Empire, Imperial Japan, Japan (wd:Q188712): Empire in the Asia-Pacific region from 1868 to 1947
2. The Imperial Japanese Empire, also known as Japanese Empire, The Empire of Japan, Imperial Japanese Empire, Imperial Japan, Empire of Japan (wd:Q11435655): 1982 film directed by Toshio Masuda
3. Empire of Japan (wd:Q5374199): internal politics of the Empire of Japan
4. education in the Empire of Japan, also known as education of the Empire of Japan, Empire of Japan education (wd:Q3509524): overview of the education system in the Empire of Japan
5. Empire of Japan borders (wd:Q87763038)
6. Constitution of the Empire of Japan, also known as Meiji Constitution (wd:Q122804): constitution of Japan from 1889 to 1947
7. Empire of Japan–Russian Empire relations (wd:Q16835196): bilateral diplomatic relations
8. demographics of the Empire of Japan, also known as demography of Imperial Japan (wd:Q5256018): Japan's demographics (1868 – 1945)
9. economy of the Empire of Japan (wd:Q5374198): economy of the country
10. Map of the Empire of Japan (wd:Q100273566): 1878 map of Japan

[Assistant]
The entity "Empire of Japan" corresponds to wd:Q188712, which is the empire in the Asia-Pacific region from 1868 to 1947.

Step 2: Search for properties related to wars or conflicts that the Empire of Japan participated in. I will search for properties of the entity wd:Q188712 with a query related to "war" or "conflict".

[Tool]
Top 10 property alternatives:
1. 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
2. shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as wdt/p): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.
3. 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
4. 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
5. flag, also known as flag description (wd:P163 as wdt/p): subject's flag
6. basic form of government, also known as form of government, government, type of government (wd:P122 as wdt/p): subject's government
7. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt/p): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
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. population, also known as inhabitants, human population (wd:P1082 as wdt/p): number of people inhabiting the place; number of people of subject
10. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in

No other items found

[Assistant] The search for properties related to "war" or "conflict" for the Empire of Japan did not directly return a relevant property. I will try a more general search for properties related to "participated in" or "conflict" for the entity Empire of Japan.

[Tool]
Top 10 property alternatives:
1. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as wdt/p): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
2. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
3. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as wdt/p): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
4. 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
5. replaces, also known as supersedes, succeeds, replaced, previous job holder, preceded by (wd:P1365 as wdt/p): person, state or item replaced. Use "structure replaces" (P1398)...item was not replaced or predecessor and successor are identical, follows
6. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt/p): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
7. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as wdt/p): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
8. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
9. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdt/p): language widely used (spoken or written) in this place or at this event or organisation
10. replaced by, also known as heir, superseded by, successor, succeeded by, next job holder (wd:P1366 as wdt/p): 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

No other items found

[Assistant]
The property "participant in" (wdt:P1344) seems to be the relevant property that indicates events in which the Empire of Japan participated. Wars are typically modeled as events in Wikidata.

Step 3: Use this property to find the wars the Empire of Japan participated in and count them. I will write a SPARQL query to count the number of wars (instances of "war") in which the Empire of Japan (wd:Q188712) was a participant.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 4 (xsd:int) |

[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 DISTINCT ?disease ?diseaseLabel WHERE {
  ?disease wdt:P5642 wd:Q662860 .
  ?disease wdt:P31 wd:Q12136 .
  OPTIONAL {
    ?disease rdfs:label ?diseaseLabel FILTER ( LANG( ?diseaseLabel ) = "en" )
  }
}
LIMIT 100

Got 15 rows and 2 columns, showing the first 5 and last 5 rows below:
| disease                                         | diseaseLabel                          |
| ----------------------------------------------- | ------------------------------------- |
| toothache (wd:Q143925)                          | toothache (en)                        |
| arteriosclerotic heart disease (wd:Q1444550)    | arteriosclerotic heart disease (en)   |
| lung neoplasm (wd:Q15124212)                    | lung neoplasm (en)                    |
| clavicle fracture (wd:Q1746068)                 | clavicle fracture (en)                |
| sinus arrhythmia (wd:Q2583597)                  | sinus arrhythmia (en)                 |
| ...                                             | ...                                   |
| airway obstruction (wd:Q4330029)                | airway obstruction (en)               |
| gingival squamous cell carcinoma (wd:Q53678376) | gingival squamous cell carcinoma (en) |
| multiple pulmonary nodules (wd:Q54972627)       | multiple pulmonary nodules (en)       |
| no reflow phenomenon (wd:Q7045267)              | no reflow phenomenon (en)             |
| tennis elbow (wd:Q736776)                       | tennis elbow (en)                     |

Diseases that can be caused by smoking (i.e., smoking is a risk factor) include toothache, arteriosclerotic heart disease, lung neoplasm, clavicle fracture, sinus arrhythmia, airway obstruction, gingival squamous cell carcinoma, multiple pulmonary nodules, no reflow phenomenon, and tennis elbow, among others.

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

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

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

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

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

[User] Which diseases can be caused by smoking ?

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

[Tool]
Example 1:
Question:
What is the most frequent cause of death?

SPARQL:
SELECT ?uri WHERE {
  ?s wdt:P509 ?uri .
}
GROUP BY ?uri
ORDER BY DESC ( COUNT( DISTINCT ?s ) )
LIMIT 1

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 2:
Question:
Which spaceflights were launched from Baikonur?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P1427 wd:Q177477
}

Using entities:
Baikonur Cosmodrome, also known as 5 GIK, Baykonur Cosmodrome, GIK-5, State Test Range No. 5, Tyuratam (wd:Q177477): cosmodrome in Kazakhstan, used by Russia

Using properties:
start point, also known as start location, launch pad, launch site, starting place, starting point (wd:P1427 as wdt): starting place of this journey, flight, voyage, trek, migration etc., source of transfer, location

Example 3:
Question:
Give me all actors who were born in Paris after 1950.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q33999 ; wdt:P19 wd:Q90 ; wdt:P569 ?birthDate .
  BIND( YEAR( ?birthDate ) as ?birthYear ) .
  FILTER ( ?birthYear > 1950 )
}

Using entities:
actor, also known as actress, actresses, movie actress, movie actresses, actors (wd:Q33999): person who acts in a dramatic or comic production and works in film, television, theatre, or radio
Paris, also known as City of Love, Lutetia, City of Light (wd:Q90): capital city and largest city of France

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
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
date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt): date on which the subject was born, inception

[Assistant]
From the examples, I see that the property "cause of death" (wdt:P509) is used to indicate causes related to death. However, the question is about diseases caused by smoking, which is more about causation or risk factors rather than cause of death directly.

I will now search for the entity "smoking" to find its Wikidata identifier. Then I will look for properties related to diseases caused by or associated with smoking.

[Tool]
Top 10 entity alternatives:
1. smoking, also known as smoke (wd:Q662860): breathing in smoke after burning a substance and inhaling, done in order to be tasted and absorbed into the bloodstream
2. smoking, also known as food smoking (wd:Q193713): exposing food to the smoke to flavour or preserve it
3. Smoking/No Smoking, also known as Smoking / No Smoking (wd:Q674929): 1993 film by Alain Resnais
4. Smoking (wd:Q7546026): brand of rolling papers
5. 🚬, also known as cigarette emoji, smoking emoji, SMOKING SYMBOL, U+1F6AC (wd:Q87581997): Unicode character which may be used to communicate 'cigarette'
6. Smoking (wd:Q55525268)
7. pottery smoking, also known as Smoking (wd:Q123929966): ceramic finishing technique for waterproofing, involving firing plant elements to enhance impermeability
8. [Smoking--passive smoking] (wd:Q69790331): scientific article published on 01 January 1985
9. Smoking (wd:Q68339472): scientific article published on 01 February 1988
10. Smoking (wd:Q75942855): scientific article published on 01 December 1951

[Assistant] The entity "smoking" as the act of breathing in smoke after burning a substance is wd:Q662860. Now I want to find properties related to diseases caused by or associated with smoking. I will search for properties related to "cause" or "caused by" for this entity.

[Tool]
Top 10 property alternatives:
1. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as wdt/p): effect of this item, followed by
2. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
3. risk factor (wd:P5642 as wdt/p): factor associated with a high prevalence of a particular gene, d...ountry of citizenship, race, gender, occupation, anamnesis, etc.
4. 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
5. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
6. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as wdt/p): inherent or distinguishing quality or feature of the entity. Use a more specific property when possible
7. 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
8. different from, also known as not to be confused with, to be distinguished from, similar to, see also, rejected match (wd:P1889 as wdt/p): item that is different from another item, with which it may be confused
9. page banner, also known as article banner, WV banner, Women in Red banner, WIR banner, Wikivoyage banner (wd:P948 as wdt/p): image at top of an article about the topic, mainly used by Wikivoyage and Women in Red, image
10. 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)

No other items found

[Assistant] The property "risk factor" (wdt:P5642) seems relevant as smoking is a risk factor for many diseases. I will now search for diseases that have smoking as a risk factor. To do this, I will find entities that have the property "risk factor" with the value "smoking" (wd:Q662860).

[Tool]
Got 15 rows and 2 columns, showing the first 5 and last 5 rows below:
| disease                                         | diseaseLabel                          |
| ----------------------------------------------- | ------------------------------------- |
| toothache (wd:Q143925)                          | toothache (en)                        |
| arteriosclerotic heart disease (wd:Q1444550)    | arteriosclerotic heart disease (en)   |
| lung neoplasm (wd:Q15124212)                    | lung neoplasm (en)                    |
| clavicle fracture (wd:Q1746068)                 | clavicle fracture (en)                |
| sinus arrhythmia (wd:Q2583597)                  | sinus arrhythmia (en)                 |
| ...                                             | ...                                   |
| airway obstruction (wd:Q4330029)                | airway obstruction (en)               |
| gingival squamous cell carcinoma (wd:Q53678376) | gingival squamous cell carcinoma (en) |
| multiple pulmonary nodules (wd:Q54972627)       | multiple pulmonary nodules (en)       |
| no reflow phenomenon (wd:Q7045267)              | no reflow phenomenon (en)             |
| tennis elbow (wd:Q736776)                       | tennis elbow (en)                     |

[Assistant] I found diseases that have smoking as a risk factor. Some examples are toothache, arteriosclerotic heart disease, lung neoplasm, clavicle fracture, sinus arrhythmia, airway obstruction, gingival squamous cell carcinoma, multiple pulmonary nodules, no reflow phenomenon, and tennis elbow. I will now provide the SPARQL query and the answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?article ?articleLabel ?publicationDate WHERE {
  ?article wdt:P31 wd:Q13442814 ; wdt:P921 wd:Q6557482 ; wdt:P577 ?publicationDate .
  OPTIONAL {
    ?article rdfs:label ?articleLabel FILTER ( LANG( ?articleLabel ) = "en" )
  }
}
ORDER BY ?publicationDate
LIMIT 1

The first article about Liquid State Machines was published on August 1, 2007. The article is titled "An Application of Spike-Timing-D...Circuit for Liquid State Machine."

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

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

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

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

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

[User] When was the first article about Liquid State Machines published?

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

[Tool]
Example 1:
Question:
Which bridges cross the Seine?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q12280 ; wdt:P177 wd:Q1471 .
}

Using entities:
bridge, also known as bridge (engineering), bridges, bridge (structure) (wd:Q12280): structure that spans and provides a passage over a road, railway, river, or some other obstacle
Seine, also known as Seine River, river Seine (wd:Q1471): river in France

Using properties:
crosses, also known as under, tunnel under, through, spanning, over (wd:P177 as wdt): obstacle (body of water, road, railway...) which this bridge (ferry, ford) crosses over or this tunnel goes under
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:
List all games by GMT.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 / wdt:P279 * wd:Q11410 ; wdt:P123 wd:Q928726 .
}

Using entities:
game, also known as games, plays and games, recreational game, recreational games (wd:Q11410): structured form of play
GMT Games (wd:Q928726): American board game publisher

Using properties:
publisher, also known as video game publisher, software publisher, comic book publisher, sheet music publisher, publishing house (wd:P123 as wdt): organization or person responsible for publishing books, periodicals, printed music, podcasts, games or software
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 3:
Question:
how big is the total area of North Rhine- Westphalia?

SPARQL:
SELECT DISTINCT ?val WHERE {
  wd:Q1198 wdt:P2046 ?val .
}

Using entities:
North Rhine-Westphalia, also known as Nordrhein-Westfalen, NRW (wd:Q1198): state in Germany

Using properties:
area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt): area occupied by an object

[Assistant]
The question is about the publication date of the first article about "Liquid State Machines." To answer this, I need to identify the entity for "Liquid State Machines" and then find the earliest publication date of an article about it.

Step 1: Search for the entity "Liquid State Machines" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Reinforcement Learning With Low-Complexity Liquid State Machines (wd:Q90049673): scientific article published on 27 August 2019
2. Liquid State (wd:Q3242295): song by English rock band Muse
3. Liquid State, also known as Liquid State Limited (wd:Q112312670): record label
4. liquid state machine, also known as LSM (wd:Q6557482): type of artificial neural network
5. Liquid State Physics (wd:Q59024975)
6. Liquid State Physics (wd:Q59003637)
7. Liquid State Physics (wd:Q59003068): scientific article published in Nature
8. Liquid state physics (wd:Q81190251): scientific article published on 01 July 1977
9. Liquid state machines and cultur...tworks: the separation property. (wd:Q47371667): scientific article published on 13 August 2008
10. Liquid crystalline state (wd:Q71918074): journal article; published in Nature on 1978-1

[Assistant]
I found an entity "liquid state machine" (wd:Q6557482) which is a type of artificial neural network. This seems to be the relevant concept. Now I need to find articles about this entity and their publication dates to determine the first article published about Liquid State Machines.

Step 2: Search for articles related to "liquid state machine" (wd:Q6557482) and their publication dates. I will look for scientific articles that mention or are about this entity.

[Tool]
Got 1 row and 3 columns:
| article                                                                            | articleLabel                                                             | publicationDate                     |
| ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | ----------------------------------- |
| An Application of Spike-Timing-D...Circuit for Liquid State Machine (wd:Q57961191) | An Application of Spike-Timing-D...Circuit for Liquid State Machine (en) | 2007-08-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The earliest scientific article about Liquid State Machines found is "An Application of Spike-Timing-D...Circuit for Liquid State Machine" published on August 1, 2007.

I will now provide the final answer with the SPARQL query used.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?numberOfSeats WHERE {
  wd:Q33091469 wdt:P1342 ?numberOfSeats .
}

Got 1 row and 1 column:
| numberOfSeats     |
| ----------------- |
| 733 (xsd:decimal) |

The current German Bundestag (20th German Bundestag) has 733 seats.

[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 seats are there in the current German Bundestag ?

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

[Tool]
Example 1:
Question:
Which state of the United States of America has the highest density?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q35657 ; wdt:P2046 ?area ; wdt:P1082 ?population .
  BIND( ( ?population / ?area ) AS ?density ) .
}
ORDER BY DESC ( ?density )
LIMIT 1

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

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt): area occupied by 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)

Example 2:
Question:
Which television shows were created by John Cleese?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q5398426 .
  {
    ?uri wdt:P170 wd:Q25014 .
  } UNION {
    ?uri wdt:P170 / wdt:P527 wd:Q25014 .
  }
}

Using entities:
John Cleese, also known as John Marwood Cleese (wd:Q25014): English comedian and actor (born 1939), television actor, autobiographer, film producer, film director, screenwriter, stage actor, voice actor, film actor, comedian, writer, actor
television series, also known as TV show, TV-series, television show, TV series (wd:Q5398426): connected set of television program episodes under the same title

Using properties:
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)
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)
has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

Example 3:
Question:
Who is the governor of Texas?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1439 wdt:P1313 wd:Q5589725 .
  wd:Q5589725 wdt:P1308 ?uri .
}

Using entities:
Texas, also known as Lone Star State, US-TX, TX, Texas, United States, Tex. (wd:Q1439): state of the United States of America
Governor of Texas, also known as Governor of the State of Texas, Texas Governor (wd:Q5589725): head of state and of government of the U.S. state of Texas

Using properties:
position holder, also known as incumbent, noble title holder, office holder, position held by (wd:P1308 as wdt): persons who hold and/or held the position
office held by head of government, also known as title of head of government, position of head of government, office of head of government, head of government's office, position held by head of government (wd:P1313 as wdt): political office that is fulfilled by the head of the government of this item

[Assistant]
The question is about the number of seats in the current German Bundestag. The Bundestag is the federal parliament of Germany. We need to find the entity for the German Bundestag and then find the property that indicates the number of seats or members.

Step 1: Search for the entity "German Bundestag" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. German Bundestag, also known as Bundestag of Germany, German parliament (wd:Q154797): legislature of Germany; parliament
2. 21st German Bundestag, also known as Twenty-first German Bundestag (wd:Q124661964): legislative term from 2025 to 2029 (expected)
3. 20th German Bundestag, also known as Twentieth German Bundestag (wd:Q33091469): legislative term from 2021 to 2025
4. 1st German Bundestag, also known as First German Bundestag (wd:Q30542687): legislative term from 1949 to 1953
5. 2nd German Bundestag, also known as Second German Bundestag (wd:Q30542689): legislative term from 1953 to 1957
6. 19th German Bundestag, also known as Nineteenth German Bundestag (wd:Q30579723): legislative term from 2017 to 2021
7. 18th German Bundestag, also known as Eighteenth German Bundestag (wd:Q15081430): legislative term from 2013 to 2017
8. 10th German Bundestag, also known as Tenth German Bundestag (wd:Q30542755): legislative term from 1983 to 1987
9. 17th German Bundestag, also known as Seventeenth German Bundestag (wd:Q30542764): legislative term from 2009 to 2013
10. 16th German Bundestag, also known as Sixteenth German Bundestag (wd:Q30542763): legislative term from 2005 to 2009

[Assistant]
The entity for the German Bundestag is wd:Q154797. However, the question asks for the current German Bundestag, which refers to the current legislative term. The 20th German Bundestag (wd:Q33091469) is the legislative term from 2021 to 2025, which is the current one.

Step 2: Search for properties related to the number of seats or members for the entity "20th German Bundestag" (wd:Q33091469).

[Tool]
Top 10 property alternatives:
1. number of seats, also known as membership quantity, number of seats (members), quantity of members, seats (members) (wd:P1342 as wdt/p): total number of seats/members in an assembly (legislative house or similar)
2. chairperson, also known as dean, headed by, leader, president, chairwoman (wd:P488 as wdt/p): presiding member of an organization, group or body, director / manager, significant person
3. elected in, also known as re-elected in, election (wd:P2715 as wdt/p): election in which a person gained a position (as qualifier), or a position or legislative term is/was elected (as main value)
4. meeting of, also known as term of, body meeting, session of, organization meeting, legislative term of (wd:P13188 as wdt/p): subject is a meeting or session of this organization
5. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
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. applies to jurisdiction, also known as belongs to jurisdiction, applies to territorial jurisdiction, applies to region, applies to place, applies to geographic place (wd:P1001 as wdt/p): the item (institution, law, public office, public register...) o...a territorial jurisdiction: a country, state, municipality, ...), significant place, characteristic of, operating area
8. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as wdt/p): significant or notable events associated with the subject
9. Commons category, also known as category on Wikimedia Commons, Wikimedia Commons category, Wikimedia Commons cat, Commons cat, commonscat (wd:P373 as wdt/p): name of the Wikimedia Commons category containing files related to this item (without the prefix "Category:")
10. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time

No other items found

[Assistant]
The property "number of seats" (wdt:P1342) is the relevant property for the total number of seats/members in an assembly.

Step 3: Construct a SPARQL query to get the number of seats in the current German Bundestag (20th German Bundestag, wd:Q33091469).

[Tool]
Got 1 row and 1 column:
| numberOfSeats     |
| ----------------- |
| 733 (xsd:decimal) |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
SELECT ( ( ?maxPop2022 - ?pop2010 ) / ?pop2010 * 100 AS ?growthPercentage ) ( ( ?maxPop2022 - ?pop2010 ) / ?pop2010 * 100 >= 1 AS ?atLeastOnePercent ) WHERE {
  SELECT ( MAX( ?pop2022 ) AS ?maxPop2022 ) ?pop2010 WHERE {
    wd:Q183 p:P1082 ?popStatement2010 .
    ?popStatement2010 ps:P1082 ?pop2010 .
    ?popStatement2010 pq:P585 ?date2010 .
    FILTER ( YEAR( ?date2010 ) = 2010 ) wd:Q183 p:P1082 ?popStatement2022 .
    ?popStatement2022 ps:P1082 ?pop2022 .
    ?popStatement2022 pq:P585 ?date2022 .
    FILTER ( YEAR( ?date2022 ) = 2022 )
  }
  GROUP BY ?pop2010
}

Got 1 row and 2 columns:
| growthPercentage      | atLeastOnePercent  |
| --------------------- | ------------------ |
| 3.18872 (xsd:decimal) | true (xsd:boolean) |

Yes, Germany had a population growth of at least 1% since 2010. The population growth percentage from 2010 to 2022 is approximately 3.19%.

[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] Did Germany have a population growth of at least 1% since 2010?

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

[Tool]
Example 1:
Question:
When was Jack Wolfskin founded?

SPARQL:
SELECT ?date WHERE {
  wd:Q536133 wdt:P571 ?date .
}

Using entities:
Jack Wolfskin, also known as Jack Wolfskin Ausrüstung für Draussen GmbH & Co. KGaA (wd:Q536133): German outdoors clothing and equipment manufacturer and retail chain

Using properties:
inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt): time when an entity begins to exist; for date of official opening use P1619, start time

Example 2:
Question:
In which country is Mecca located?

SPARQL:
SELECT DISTINCT ?res WHERE {
  wd:Q5806 wdt:P131 + ?res .
  ?res wdt:P31 wd:Q6256 .
}

Using entities:
Mecca, also known as Mother of All Settlements, Umm al-Qurā, The Holy City of Mecca, Mecca (Saudi Arabia), mecca (wd:Q5806): Saudi Arabian city and capital of the Makkah province
country, also known as countries (wd:Q6256): distinct territorial body or political entity

Using properties:
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): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
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:
When did the Ming dynasty dissolve?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q9903 wdt:P576 ?date .
}

Using entities:
Ming dynasty, also known as Ming period, Ming imperium, Ming China, Ming-era, Ming Empire (wd:Q9903): imperial dynasty of China from 1368 to 1644

Using properties:
dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time

[Assistant]
The question is about the population growth of Germany since 2010. We need to find the population of Germany in 2010 and the population of Germany in a recent year (or the latest available year) and then check if the growth is at least 1%.

Step 1: Identify the entity for Germany.
Step 2: Identify the property for population.
Step 3: Find the population of Germany in 2010.
Step 4: Find the population of Germany in the latest available year.
Step 5: Calculate the growth and check if it is at least 1%.

Let's start by searching for the entity "Germany".

[Tool]
Top 10 entity alternatives:
1. Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
2. Weimar Republic, also known as German Republic, Weimar Germany, Republic of Germany, Germany, German Reich (wd:Q41304): Germany in the years 1918/1919–1933
3. German Reich, also known as Deutsches Reich, German Empire, German Realm, Germany, Imperial Germany (wd:Q1206012): official name for the German nation state from 1871 to 1945, and name of Germany until 1949
4. Burg Drachenfels, also known as Germany (wd:Q1011486): castle
5. Germany (wd:Q5552078): European Parliament constituency
6. Geilenkirchen-Heinsberg, also known as Germany (wd:Q2268218)
7. Germany, also known as Germany, IN, Germany, Indiana (wd:Q18151573): unincorporated community in Clark County, Indiana
8. Germany (wd:Q19147844): poem by Vladimir Mayakovsky
9. Germany, also known as Ludwig, West (wd:Q3839160): Hetalia character
10. Germany, also known as Germany, Texas, Germany, TX (wd:Q15221573): unincorporated community in Houston County, Texas, United States

[Assistant] The entity for Germany as a country is wd:Q183. Next, I will search for the property related to population.

[Tool]
Top 10 property alternatives:
1. tabular population, also known as population (wd:P4179 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): same as population (P1082); used when there is data for many different points in time
2. population, also known as inhabitants, human population (wd:P1082 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): number of people inhabiting the place; number of people of subject
3. has census, also known as has human population census (wd:P8701 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): general population census of the country or region
4. urban population (wd:P6343 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): number of people living within the territorial entity who live in its urban parts, population
5. birth rate (wd:P8763 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): the total number of live births per 1,000 population
6. category populated by, also known as category filled by, category has template, cat populated by, component adding into the category, populated by (wd:P4329 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): software component (template, module, extension, MediaWiki core) populating the category
7. rural population (wd:P6344 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): number of people living within the territorial entity who live in its rural parts, population
8. female population (wd:P1539 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): number of female people inhabiting the place; number of female people of subject, population
9. male population, also known as male inhabitants, number of males (wd:P1540 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): number of male people inhabiting the place; number of male people of subject, population
10. number of households, also known as dwellings, households (wd:P1538 as pr/wdtn/pq/pqn/pqv/p/prn/psn/wdt/psv/prv/ps): total number of households in this place, includes dwellings of all types

[Assistant] The property for population is wd:P1082. Now I will write a query to get the population of Germany (wd:Q183) for the year 2010 and the latest available year. I will use the population property (wdt:P1082) with a qualifier for the point in time (P585).

[Tool]
Got 22 rows and 2 columns, showing the first 5 and last 5 rows below:
| year           | population             |
| -------------- | ---------------------- |
| 2010 (xsd:int) | 81752000 (xsd:decimal) |
| 2012 (xsd:int) | 80500000 (xsd:decimal) |
| 2014 (xsd:int) | 81083600 (xsd:decimal) |
| 2015 (xsd:int) | 81292400 (xsd:decimal) |
| 2015 (xsd:int) | 82175700 (xsd:decimal) |
| ...            | ...                    |
| 2021 (xsd:int) | 83222442 (xsd:decimal) |
| 2022 (xsd:int) | 84358845 (xsd:decimal) |
| 2022 (xsd:int) | 84270625 (xsd:decimal) |
| 2022 (xsd:int) | 83695430 (xsd:decimal) |
| 2022 (xsd:int) | 84079811 (xsd:decimal) |

[Assistant]
We have population data for Germany for the years 2010 and 2022 among others. The population in 2010 was approximately 81,752,000 and in 2022 it ranges around 83,695,430 to 84,358,845 (multiple values likely from different sources or estimates).

Next, I will calculate the population growth percentage from 2010 to 2022 using the maximum population value in 2022 to check if it is at least 1%. I will write a SPARQL query to do this calculation and return whether the growth is at least 1%.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: Variable ?pop2010 is selected but not aggregated. All non-aggregated variables must be part of the GROUP BY clause. Note: The GROUP BY in this query is implicit because an aggregate expression was used in the SELECT clause

[Assistant] The previous query failed because I used an aggregate function without grouping. I will fix the query by using GROUP BY for the non-aggregated variable.

[Tool]
Got 1 row and 2 columns:
| maxPop2022             | pop2010                |
| ---------------------- | ---------------------- |
| 84358845 (xsd:decimal) | 81752000 (xsd:decimal) |

[Assistant]
The population of Germany in 2010 was 81,752,000 and the maximum population in 2022 was 84,358,845. Now I will calculate the growth percentage and check if it is at least 1%. The growth percentage is ((84,358,845 - 81,752,000) / 81,752,000) * 100.

I will write a SPARQL query to calculate this and return whether the growth is at least 1%.

[Tool]
Got 1 row and 2 columns:
| growthPercentage      | atLeastOnePercent  |
| --------------------- | ------------------ |
| 3.18872 (xsd:decimal) | true (xsd:boolean) |

[Tool] Stopped generation process