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

Got 1 row and 1 column:
| ?num_attempts |
| ------------- |
| 2             |

The Mongols tried to invade Japan two times.

[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 break down the question:

1. The user wants to know how many times the Mongols attempted to invade Japan.
2. We need to identify the relevant entities: "Mongols" (likely the Mongol Empire or its forces), "Japan", and the concept of "invasion" or "attempted invasion".
3. We need to find properties related to military campaigns, invasions, or attacks, and filter for those where the Mongols were the attackers and Japan was the target.

First, let's search for the entity representing the "Mongol invasions of Japan" or similar events, as Wikidata may have a single entity or multiple entities for each invasion attempt.

[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 top result is "Mongol invasions of Japan" (wd:Q208156), which refers to the attempts by the Mongol Empire to conquer Japan. This entity likely covers the events in question.

Next, I will check if this entity has properties or linked entities that specify the number of invasion attempts or individual invasion events. This will help determine how many times the Mongols tried to invade Japan.

[Tool]
Got 293 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                                                                                |
| Mongol invasions of Japan (wd:Q208156) | participant (wdt:P710)                                      | Yuan dynasty (wd:Q7313)                                                                                |
| ...                                    | ...                                                         | ...                                                                                |

[Assistant]
The entity "Mongol invasions of Japan" (wd:Q208156) has the property "has part(s)" (wdt:P527), which links to "Battle of Kōan" (wd:Q2237622). This suggests that the invasions are composed of specific battles or events.

To get the full list of parts (i.e., individual invasion attempts or battles), I will look for all entities linked via "has part(s)" (wdt:P527) from "Mongol invasions of Japan". This should help us count the number of distinct invasion attempts.

[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]
There are two parts listed under "Mongol invasions of Japan" via the "has part(s)" property:
1. Battle of Bun'ei (wd:Q2237386)
2. Battle of Kōan (wd:Q2237622)

These correspond to the two major Mongol invasion attempts of Japan (1274 and 1281). To confirm, I will check the dates or descriptions of these battles to ensure they represent separate invasion attempts.

[Tool]
Got 87 rows and 3 columns, showing the first 10 rows below:
| subject                        | property                    | object                                                                    |
| ------------------------------ | --------------------------- | ------------------------------------------------------------------------- |
| Battle of Bun'ei (wd:Q2237386) | instance of (wdt:P31)       | battle (wd:Q178561)                                                       |
| Battle of Bun'ei (wd:Q2237386) | country (wdt:P17)           | Japan (wd:Q17)                                                            |
| Battle of Bun'ei (wd:Q2237386) | Commons category (wdt:P373) | Battle of Bun'ei                                                          |
| Battle of Bun'ei (wd:Q2237386) | image (wdt:P18)             | <http://commons.wikimedia.org/wiki/Special:FilePath/Mooko-HakataWall.jpg> |
| Battle of Bun'ei (wd:Q2237386) | part of (wdt:P361)          | Mongol invasions of Japan (wd:Q208156)                                    |
| Battle of Bun'ei (wd:Q2237386) | location (wdt:P276)         | Japan (wd:Q17)                                                            |
| Battle of Bun'ei (wd:Q2237386) | has part(s) (wdt:P527)      | Invasion of Tsushima (wd:Q97999945)                                       |
| Battle of Bun'ei (wd:Q2237386) | followed by (wdt:P156)      | Battle of Kōan (wd:Q2237622)                                              |
| Battle of Bun'ei (wd:Q2237386) | point in time (wdt:P585)    | 1274-11-01T00:00:00Z                                                      |
| Battle of Bun'ei (wd:Q2237386) | participant (wdt:P710)      | Mongol Empire (wd:Q12557)                                                 |
| ...                            | ...                         | ...                                                                       |

[Tool]
Got 96 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                       | object                                                                                |
| ---------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Battle of Kōan (wd:Q2237622) | instance of (wdt:P31)          | battle (wd:Q178561)                                                                                |
| Battle of Kōan (wd:Q2237622) | country (wdt:P17)              | Japan (wd:Q17)                                                                                |
| Battle of Kōan (wd:Q2237622) | coordinate location (wdt:P625) | POINT(130.332800 33.625100)                                                                                |
| Battle of Kōan (wd:Q2237622) | Commons category (wdt:P373)    | Battle of Kōan                                                                                |
| Battle of Kōan (wd:Q2237622) | image (wdt:P18)                | <http://commons.wikimedia.org/wiki/Special:FilePath/Kikuchi%20Yoosai%20-%20Mongol%20Invasion%20%28m%C5%8Dko%20sh%C5%ABrai%29%20-%20Tokyo%20National%20Museum.jpg> |
| Battle of Kōan (wd:Q2237622) | part of (wdt:P361)             | Mongol invasions of Japan (wd:Q208156)                                                                                |
| Battle of Kōan (wd:Q2237622) | location (wdt:P276)            | Northern Kyushu (wd:Q397703)                                                                                |
| Battle of Kōan (wd:Q2237622) | follows (wdt:P155)             | Battle of Bun'ei (wd:Q2237386)                                                                                |
| Battle of Kōan (wd:Q2237622) | point in time (wdt:P585)       | 1281-01-01T00:00:00Z                                                                                |
| Battle of Kōan (wd:Q2237622) | participant (wdt:P710)         | Mongol Empire (wd:Q12557)                                                                                |
| ...                          | ...                            | ...                                                                                |

[Assistant]
The "Battle of Bun'ei" occurred in 1274 and the "Battle of Kōan" in 1281. These correspond to the two historically recognized Mongol invasion attempts of Japan.

Therefore, the Mongols tried to invade Japan two times.

I will now provide a SPARQL query that counts the number of "has part(s)" (wdt:P527) for "Mongol invasions of Japan" (wd:Q208156), which gives the number of invasion attempts, and 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 ?warCount ) WHERE {
  wd:Q188712 wdt:P1344 ?war .
}

Got 1 row and 1 column:
| ?warCount |
| --------- |
| 4         |

The Empire of Japan participated in 4 wars according to Wikidata's 'participant in' property.

[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 break down the question:

1. We need to identify the entity for "Empire of Japan".
2. We need to find wars (conflicts) in which the Empire of Japan participated.
3. We need to count the number of such wars.

First, I'll 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 main entity for "Empire of Japan" is wd:Q188712, which refers to the empire in the Asia-Pacific region from 1868 to 1947.

Next, I need to find wars (conflicts) in which this entity participated. I will search for properties related to "participant" or "conflict" for this entity.

[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

[Tool]
Top 10 property alternatives:
1. diplomatic relation, also known as political relation, political relations, foreign relations, foreign relation, diplomatic relations (wd:P530 as wdt/p): diplomatic relations of the country
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. basic form of government, also known as form of government, government, type of government (wd:P122 as wdt/p): subject's government
4. 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
5. 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)
6. flag, also known as flag description (wd:P163 as wdt/p): subject's flag
7. area, also known as total area, surface area, surface, size, resolution (wd:P2046 as wdt/p): area occupied by an object
8. 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
9. 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
10. 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

No other items found

[Assistant]
The property "participant in" (wdt:P1344) is relevant here. It is used to indicate events (such as wars or conflicts) in which an entity participated.

Next, I will check which events the Empire of Japan (wd:Q188712) participated in using this property.

[Tool]
Got 4 rows and 3 columns:
| subject                      | property                   | object                                |
| ---------------------------- | -------------------------- | ------------------------------------- |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | Russo-Japanese War (wd:Q159950)       |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | Second Sino-Japanese War (wd:Q170314) |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | First Sino-Japanese War (wd:Q178687)  |
| Empire of Japan (wd:Q188712) | participant in (wdt:P1344) | Pacific War (wd:Q184425)              |

[Tool]
Got 1 row and 1 column:
| ?warCount |
| --------- |
| 4         |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?disease ?label WHERE {
  ?disease wdt:P828 wd:Q662860 .
  ?disease wdt:P31 / wdt:P279 * wd:Q12136 .
  OPTIONAL {
    ?disease rdfs:label ?label FILTER ( lang( ?label ) = "en" )
  }
}

Got 3 rows and 2 columns:
| ?disease                                    | ?label                       |
| ------------------------------------------- | ---------------------------- |
| pneumonia (wd:Q12192)                       | pneumonia                    |
| lung cancer (wd:Q47912)                     | lung cancer                  |
| smoking-related lung disease (wd:Q98608770) | smoking-related lung disease |

Diseases that can be caused by smoking include:
- pneumonia (wd:Q12192)
- lung cancer (wd:Q47912)
- smoking-related lung disease (wd:Q98608770)
These are directly linked to smoking as a cause in 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] Which diseases can be caused by smoking ?

[Assistant]
Let's break down the question:

1. We need to identify diseases (entities of type "disease").
2. We need to find a property that links diseases to their causes, specifically where the cause is "smoking".

First, I'll search for the entity representing "smoking" in Wikidata.

[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 most relevant entity for "smoking" as a cause of disease is wd:Q662860 ("smoking, also known as smoke: breathing in smoke after burning a substance and inhaling, done in order to be tasted and absorbed into the bloodstream").

Next, I need to find a property that links diseases to their causes, specifically where the cause is "smoking". I'll search for properties related to "cause" in Wikidata.

[Tool]
Top 10 property alternatives:
1. has cause, also known as implied by, originated due to, initial cause, outcome of, reason (wd:P828 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): underlying cause, entity that ultimately resulted in this effect, follows
2. has effect, also known as has symptom, implies, leads to, led to, outcome (wd:P1542 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): effect of this item, followed by
3. end cause, also known as cause of ending, ended by, end has cause, end reason, reason for end (wd:P1534 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): qualifier used together with the end date qualifier (P582) to specify the reason for the end
4. has immediate cause, also known as immediate result of, is immediately caused by, is proximately caused by, proximate effect of, proximately caused by (wd:P1478 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): nearest, proximate thing that directly resulted in the subject a...ause) and 'has contributing factor'. See 'Help:Modeling causes'., has cause
5. constraint clarification, also known as why this constraint?, reason, rationale, constraint reason, property constraint comment (wd:P6607 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): qualifier to provide details on the function or purpose of a property constraint on a given property, Wikidata usage instructions
6. perpetrator, also known as robber, perpetrators, perpetrated by, perp, offender (wd:P8031 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): person or organization who carried out this harmful, illegal, or immoral act, participant
7. created for, also known as cause of creation, commissioned for, made for, occasion, on the occasion of (wd:P9883 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): a work, event, etc. for which the subject work was created (e.g.... play was created for; a film that a screenplay was written for)
8. cause of destruction, also known as destroyed by, reason of failure (wd:P770 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): item which caused the destruction of the subject item, has cause
9. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
10. has contributing factor, also known as contributing factor, significant contributing factor, is/was indirectly caused by, indirectly caused by, indirect cause (wd:P1479 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): thing that significantly influenced, but did not directly cause,...use' and 'has immediate cause'.  See '[[Help:Modeling causes]]'., has cause

[Assistant]
The most relevant property to link a disease to its cause is "has cause" (wdt:P828).

Now, I need to find diseases (likely instances of "disease" or subclasses thereof) where "has cause" is "smoking" (wd:Q662860). To ensure we get only diseases, I'll check the entity for "disease" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. disease, also known as ailments, sickness, other disease, medical disorder, MedicalCondition (wd:Q12136): abnormal condition negatively affecting organisms
2. infectious bursal disease, also known as disease, infectious bursitis, IBD, infectious avian nephrosis (wd:Q119150394): viral disease
3. Disease (wd:Q130601340): 2024 single by Lady Gaga
4. illness, also known as disease, sickness (wd:Q814207): the state of being unwell
5. medical state, also known as disease, health condition, health status, medical condition, medical status (wd:Q6806605): one's current state of health, usually within a hospital
6. Disease (wd:Q57243972): Beartooth album
7. Disease (wd:Q5282114): album by G G F H
8. Disease (wd:Q60838658)
9. Disease (wd:Q10474229): The Ark's song
10. Disease (wd:Q12530894)

[Assistant]
The main entity for "disease" is wd:Q12136.

Now, I will construct a SPARQL query to find all entities that are instances of (or subclasses of) "disease" (wd:Q12136) and have "smoking" (wd:Q662860) as a cause (wdt:P828). I will also optionally retrieve their labels for readability.

Here's the query I will use:

```sparql
SELECT ?disease WHERE {
  ?disease wdt:P828 wd:Q662860 .
  ?disease wdt:P31/wdt:P279* wd:Q12136 .
  OPTIONAL { ?disease rdfs:label ?label FILTER (lang(?label) = "en") }
}
```

I will now execute this query to see which diseases are caused by smoking.

[Tool]
Got 3 rows and 1 column:
| ?disease                                    |
| ------------------------------------------- |
| pneumonia (wd:Q12192)                       |
| lung cancer (wd:Q47912)                     |
| smoking-related lung disease (wd:Q98608770) |

[Tool]
Got 3 rows and 2 columns:
| ?disease                                    | ?label                       |
| ------------------------------------------- | ---------------------------- |
| pneumonia (wd:Q12192)                       | pneumonia                    |
| lung cancer (wd:Q47912)                     | lung cancer                  |
| smoking-related lung disease (wd:Q98608770) | smoking-related lung disease |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?article ?date WHERE {
  ?article wdt:P31 wd:Q13442814 ; wdt:P921 wd:Q6557482 ; wdt:P577 ?date .
}
ORDER BY ASC ( ?date )
LIMIT 1

The first article about Liquid State Machines was published on August 1, 2007. The article is 'An Application of Spike-Timing-Dependent Plasticity to an Analog CMOS Circuit for Liquid State Machine' (wd:Q57961191).

[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 break down the question:

1. The user wants to know the publication date of the first article about "Liquid State Machines".
2. I need to identify the entity "Liquid State Machine" in Wikidata.
3. Then, I need to find articles (likely scholarly articles or papers) about this entity.
4. Finally, I need to retrieve the publication date of the earliest such article.

First, I'll search for the entity "Liquid State Machine" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. liquid state machine, also known as LSM (wd:Q6557482): type of artificial neural network
2. Energy Constraints Improve Liquid State Machine Performance (wd:Q130947914): scientific article published on 28 July 2020
3. The cerebellum as a liquid state machine (wd:Q47287092): scientific article
4. Reinforcement Learning With Low-Complexity Liquid State Machines (wd:Q90049673): scientific article published on 27 August 2019
5. finite-state machine, also known as FA, finite automata, finite automaton, finite-state automaton, FSM (wd:Q176452): mathematical model of computation; abstract machine that can be in exactly one of a finite number of states at any given time
6. Liquid State (wd:Q3242295): song by English rock band Muse
7. Finite-state machine, also known as State Machine (wd:Q6035379)
8. Liquid State, also known as Liquid State Limited (wd:Q112312670): record label
9. Hardware-aware liquid state mach... 2D/3D Network-on-Chip platforms (wd:Q113863454): scientific article published in 2022
10. Effects of synaptic connectivity...liquid state machine performance (wd:Q47267043): scientific article published on November 17, 2012

[Assistant]
The main entity for "Liquid State Machine" is wd:Q6557482, which is a type of artificial neural network. The other results are specific articles about Liquid State Machines.

Next, I need to find articles (likely scholarly articles) about wd:Q6557482 and retrieve their publication dates, then determine the earliest one.

To do this, I will look for properties that link articles to their main subject (likely "main subject" or "about") and properties for publication date. I'll start by searching for properties related to "main subject".

[Tool]
Top 10 property alternatives:
1. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): primary topic of a work (see also P180: depicts), depicts
2. facet of, also known as subitem of, topic of, subtopic of, subject in, section in (wd:P1269 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): topic of which this item is an aspect; item that offers a broader perspective on the same topic
3. category's main topic, also known as item for this category, cmt, main article in category, main category topic, main subject of category (wd:P301 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): primary topic of the subject Wikimedia category, main subject
4. academic major, also known as academic subject, college major, field of study, major, studied (wd:P812 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): major someone studied at college/university
5. depicts, also known as portrays, presents, preserves, quotes, represents (wd:P180 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): entity visually depicted in an image, literarily described in a ...o an audiovisual or other medium; see also P921,  'main subject', has part(s)
6. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): specialization of a person or organization; see P106 for the occupation, interested in
7. curriculum topics, also known as included subject, included topic, subject included, syllabus includes, topic included (wd:P8263 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): topic or subject included in the curriculum
8. Wikidata item of this property, also known as associated item, Wikidata property subject item, Wikidata item for this property, subject of this property, subject of property (wd:P1629 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): item corresponding to the concept represented by the property
9. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): main Wikimedia category
10. statement is subject of, also known as statement is the subject of, subject of the statement, subject of the claim, subject of, related item of statement (wd:P805 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): (qualifying) item that describes the relation identified in this statement

[Assistant]
The property "main subject" (wd:P921) is used to link articles to their primary topic, which is what we need.

Next, I need to find scholarly articles (likely instances of "scholarly article" or similar) where the main subject is wd:Q6557482 (Liquid State Machine), and retrieve their publication dates. I'll search for the property for "publication date" to use in the query.

[Tool]
Top 10 property alternatives:
1. publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): date or point in time when a work was first published or released, point in time
2. dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time
3. place of publication, also known as published in (place), where published, release region, released in, published from (wd:P291 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): geographical place of publication of the edition (use 1st edition when referring to works), significant place
4. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): larger work that a given work was published in, like a book, journal or music album, part of
5. announcement date, also known as released, date teased, made public on, public announcement date, published (wd:P6949 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): time of the first public presentation of a subject by the creator, of information by the media, point in time
6. edition number, also known as number of event, software version number, version number (wd:P393 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of an edition (first, second, ... as 1, 2, ...) or event, series ordinal
7. publisher, also known as video game publisher, software publisher, comic book publisher, sheet music publisher, publishing house (wd:P123 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): organization or person responsible for publishing books, periodicals, printed music, podcasts, games or software
8. distribution format, also known as book format, type of media, publication format, method of distribution, medium (wd:P437 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): method (or type) of distribution for the subject
9. publication interval, also known as frequency of publication, interval of publication, publication cycle, publication frequency, update frequency (wd:P2896 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): standard publication interval for daily or periodical publications, event interval
10. platform, also known as computing platform, runtime, published on, platforms, hardware (wd:P400 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): platform for which a work was developed or released, or the specific platform version of a software product

[Assistant]
The property "publication date" (wd:P577) is the correct one to use for retrieving the publication date of articles.

Now, I need to identify the class for "scholarly article" to restrict the search to articles. I'll search for the entity "scholarly article" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. scholarly article, also known as scientific papers, scientific paper, scientific article, scholarly paper, scholarly articles (wd:Q13442814): article in an academic publication, usually peer reviewed
2. academic journal article, also known as academic article, academic paper, journal article, scholarly article (wd:Q18918145): article published in an academic journal
3. More rain, more virus, also known as Scholarly article (wd:Q60067355): scientific article published in Nature
4. Nine axes of merit for technosignature searches, also known as scholarly article (wd:Q122939269): scientific article published on 19 November 2019
5. News censorship in online social...rcumvention in the commentsphere, also known as scholarly article (wd:Q116784910): scientific article published on 3 June 2016
6. The importance of peer group ("crowd") affiliation in adolescence, also known as scholarly article (wd:Q126361512)
7. The possibility of converting lipids into carbohydrates in man., also known as scholarly article (wd:Q78576861): scientific article published on 01 June 1954
8. fictional scholarly article, also known as fictional scientific article (wd:Q116527851): scientific article in a work of fiction
9. medical scholarly article, also known as medical journal article (wd:Q82969330): scholarly article in the field of medicine, often published in a medical journal
10. Marc-André Selosse (scholarly article), also known as Marc-André Selosse (wd:Q48058861): scientific article published in January 2015

[Assistant]
The entity for "scholarly article" is wd:Q13442814.

Now, I can construct a SPARQL query to find all items that:
- are instances of "scholarly article" (wd:Q13442814)
- have "Liquid State Machine" (wd:Q6557482) as their main subject (wd:P921)
- have a publication date (wd:P577)

I will then order the results by publication date and select the earliest one.

[Tool]
Got 1 row and 2 columns:
| ?article                                                                           | ?date                |
| ---------------------------------------------------------------------------------- | -------------------- |
| An Application of Spike-Timing-D...Circuit for Liquid State Machine (wd:Q57961191) | 2007-08-01T00:00:00Z |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?seats WHERE {
  wd:Q154797 wdt:P1342 ?seats
}

Got 1 row and 1 column:
| ?seats |
| ------ |
| 733    |

There are 733 seats in the current German Bundestag.

[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 break down the question:

1. The user wants to know the number of seats in the current German Bundestag.
2. We need to identify the entity for the "German Bundestag" and the property for "number of seats" or "current number of seats".

First, I will 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 main entity for the German Bundestag is wd:Q154797. However, the current legislative term is the "20th German Bundestag" (wd:Q33091469), which covers 2021 to 2025.

Since the user asked about the "current" Bundestag, it's best to focus on the 20th German Bundestag (wd:Q33091469).

Next, I will search for a property related to "number of seats" to find the correct property to use.

[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 ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): total number of seats/members in an assembly (legislative house or similar)
2. number of seats in assembly, also known as number of delegates, number of representatives, number of seats, seats (wd:P1410 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of seats a political party, faction, or group has in a given assembly
3. maximum capacity, also known as capacity, complement, crew, number of seats, seating capacity (wd:P1083 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of people allowed for a venue or vehicle
4. number of constituencies, also known as num_constituencies (wd:P4253 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of constituencies related to a legislative body
5. electoral district, also known as riding, seat, ward, electoral area, electorate (wd:P768 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): electoral district this person is representing, or of the office... or "office contested" (P541) or "candidacy in election" (P3602)
6. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): seat of government of a country, province, state or other type of administrative territorial entity
7. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
8. member count, also known as total members, number of members, count of members, amount of members, membership (wd:P2124 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of entities that are part of an organization at a particular time
9. position held, also known as function, held position, holds position, job, office held (wd:P39 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): subject currently or formerly holds the object position or public office
10. candidate number, also known as candidate ID, election candidate number, ID of candidate, number of candidate (wd:P4243 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of the candidate in an election

[Assistant]
The most relevant property for the total number of seats in a legislative body is "number of seats" (wdt:P1342).

Now, I will check if the "20th German Bundestag" (wd:Q33091469) has a value for "number of seats" (wdt:P1342).

[Tool] Got no rows and 1 columns

[Assistant]
It appears that the "20th German Bundestag" (wd:Q33091469) does not have a direct value for "number of seats" (wdt:P1342) in Wikidata.

As an alternative, I will check if the main entity "German Bundestag" (wd:Q154797) has a value for "number of seats" (wdt:P1342), as this property might be attached to the general institution rather than a specific legislative term.

[Tool]
Got 1 row and 1 column:
| ?seats |
| ------ |
| 733    |

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK {
  {
    SELECT ( MAX( ?pop2010 ) AS ?pop2010Max ) ( MAX( ?popRecent ) AS ?popRecentMax ) WHERE {
      wd:Q183 p:P1082 ?statement2010 .
      ?statement2010 ps:P1082 ?pop2010 ; pq:P585 ?date2010 .
      FILTER ( YEAR( ?date2010 ) = 2010 ) wd:Q183 p:P1082 ?statementRecent .
      ?statementRecent ps:P1082 ?popRecent ; pq:P585 ?dateRecent .
      FILTER ( YEAR( ?dateRecent ) = 2022 )
    }
  }
  FILTER ( ( ?popRecentMax - ?pop2010Max ) / ?pop2010Max >= 0.01 )
}

Yes, Germany had a population growth of at least 1% since 2010.

[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 break down the question:

1. We need to identify the entity for Germany.
2. We need to find the property for population.
3. We need to get the population of Germany in 2010 and the most recent available year.
4. We need to calculate the percentage growth between 2010 and the most recent year.
5. We need to check if the growth is at least 1%.

First, let's search for the entity for 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 correct entity for Germany (the country) is wd:Q183.

Next, let's search for the property related to population.

[Tool]
Top 10 property alternatives:
1. tabular population, also known as population (wd:P4179 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): 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 ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of people inhabiting the place; number of people of subject
3. has census, also known as has human population census (wd:P8701 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): general population census of the country or region
4. urban population (wd:P6343 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of people living within the territorial entity who live in its urban parts, population
5. birth rate (wd:P8763 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): 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 ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): software component (template, module, extension, MediaWiki core) populating the category
7. rural population (wd:P6344 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): number of people living within the territorial entity who live in its rural parts, population
8. female population (wd:P1539 as ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): 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 ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): 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 ps/p/psn/pqv/psv/pr/pq/prv/pqn/prn/wdtn/wdt): total number of households in this place, includes dwellings of all types

[Assistant]
The main property for population is wd:P1082.

Now, we need to get the population of Germany (wd:Q183) for the year 2010 and the most recent available year. Let's check how population data is structured for Germany using this property.

[Tool]
Got 1 row and 3 columns:
| subject           | property               | object   |
| ----------------- | ---------------------- | -------- |
| Germany (wd:Q183) | population (wdt:P1082) | 84358845 |

[Assistant]
The direct property wdt:P1082 for Germany only gives the current population (84,358,845), but does not provide historical data for 2010.

Wikidata often stores time-specific population data as qualifiers on the population statement, or uses the "tabular population" property (wd:P4179). Let's check if there are statements with qualifiers for different years for Germany's population.

[Tool]
Got 50 rows and 3 columns, showing the first 10 rows below:
| subject           | property             | object                                      |
| ----------------- | -------------------- | ------------------------------------------- |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-0951cc4e-43f0-a15a-7635-dd36e8f57f65 |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-09ae7f11-481a-8e24-b7eb-470bee80d342 |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-09e61e34-4083-a417-1433-7c603b95e738 |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-0a770b9f-44e2-1116-1d0d-8478fd0b8d2d |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-0f96c6b8-4180-d803-282f-15e438bbbd66 |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-13562147-451c-2575-c022-fc24bb1b37d4 |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-19bf2b0c-4e49-2acb-519b-93922bfe2d77 |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-23af582d-43b2-2d04-7ffe-7f8692ca71e1 |
| Germany (wd:Q183) | population (p:P1082) | s:Q183-2591593a-453b-d37c-8ddf-ad3ab5612473 |
| ...               | ...                  | ...                                         |

[Assistant]
There are multiple population statements for Germany, each with a unique statement node. These likely have qualifiers for the point in time (year).

Next, let's check what qualifiers are present on one of these population statements, specifically looking for the qualifier indicating the year (likely "point in time").

[Tool]
Got 8 rows and 3 columns:
| subject                                     | property                                   | object                                       |
| ------------------------------------------- | ------------------------------------------ | -------------------------------------------- |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | population (ps:P1082)                      | 77780000                                     |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | point in time (pq:P585)                    | 1986-01-01T00:00:00Z                         |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | determination method or standard (pq:P459) | _:bn836161                                   |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | wikibase:rank                              | wikibase:NormalRank                          |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | rdf:type                                   | wikibase:Statement                           |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | prov:wasDerivedFrom                        | ref:8ce9b989c9fd499f6800e88eb3792b76704d4323 |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | population (psv:P1082)                     | v:b7465ba5e42f5ba4a2be7ae31283bb0a           |
| s:Q183-01a71cd2-4829-bb4d-a453-e55151ab016a | point in time (pqv:P585)                   | v:ac0b76e1b2fe93b1e50091c9879a2f38           |

[Assistant]
The population statement includes a "point in time" qualifier (pq:P585) indicating the year.

To answer the question, we need to:
- Select all population statements for Germany with their population value and point in time.
- Find the population for 2010 and the most recent year.
- Calculate the percentage growth.
- Check if the growth is at least 1%.

Let's write a SPARQL query to get all population values for Germany with their corresponding years.

[Tool]
Got 50 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?year | ?population |
| ----- | ----------- |
| 1954  | 70945000    |
| 1956  | 70943000    |
| 1958  | 72031000    |
| 1960  | 73147000    |
| 1962  | 74383000    |
| ...   | ...         |
| 2021  | 83222442    |
| 2022  | 84270625    |
| 2022  | 84358845    |
| 2022  | 83695430    |
| 2022  | 84079811    |

[Assistant]
We have population data for Germany for multiple years, including 2010 and several values for 2022 (the most recent year). To answer the question, we need to:
- Select the population for 2010.
- Select the maximum population for the most recent year (2022).
- Calculate the percentage growth between 2010 and 2022.
- Check if this growth is at least 1%.

Let's write an ASK query that checks if the population growth from 2010 to 2022 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 error is due to the use of an aggregate (MAX) without a GROUP BY clause. To fix this, we need to group by the 2010 population. Let's adjust the query to use GROUP BY for ?pop2010.

Additionally, since there may be multiple 2010 population values, we should use MAX for both years to ensure we get the highest values for comparison.

Let's try again with the corrected query.

[Tool] True

[Tool] Stopped generation process