PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P509 ?causeOfDeath .
  ?causeOfDeath wdt:P31 wd:Q130561496 .
}

False

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

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

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

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

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

[User] Who are the distinct individuals who were born in Almería Province and died in a Nazi concentration camp?

[Assistant]
To answer this question, we need to identify the individuals who were born in Almería Province and died in a Nazi concentration camp. We will start by finding the entity for Almería Province and the property for place of birth. Then, we will find the property for cause of death or place of death and look for entities that match the criteria of dying in a Nazi concentration camp.

Let's start by searching for Almería Province.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. Almería Province, also known as Almeria Province, Province of Almería, Province of Almeria (wd:Q81802): province of Andalusia, Spain
2. cuisine of the province of Almería, also known as cuisine in the province of Almería, food in the province of Almería, food of the province of Almería, province of Almería cuisine, province of Almería food (wd:Q5876017)
3. Province Court of Almería, also known as Audiencia Provincial de Almería, Audiencia Provincial of Almería (wd:Q24941310)
4. Flags of the province of Almería (wd:Q81441831)
5. Pottery in the province of Almería (wd:Q5666574)
6. Day of the province of Almeria (wd:Q5815800)
7. culture of the province of Almería (wd:Q121157676): overview of culture in the province of Almería
8. history of the province of Almería (wd:Q123361305)
9. Spanish Civil War in the Almería province (wd:Q71482941)
10. province, also known as provinces (wd:Q34876): administrative division in several countries

No other items found

[Assistant]
The top result for "Almería Province" is `wd:Q81802`. Now, let's find the property for place of birth (`P19`) and the property for place of death (`P570`). We will then construct a SPARQL query to find individuals who were born in Almería Province and died in a Nazi concentration camp.

First, let's confirm the properties for place of birth and place of death.

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                           | property                 | object                 |
| ------------------------------------------------- | ------------------------ | ---------------------- |
| Hugh Meade Alcorn, Jr. (wd:Q1000154)              | place of birth (wdt:P19) | United States (wd:Q30) |
| Tuncay Güneş (wd:Q100587861)                      | place of birth (wdt:P19) | Turkey (wd:Q43)        |
| wd:Q100147889                                     | place of birth (wdt:P19) | Russia (wd:Q159)       |
| Sanjūrō (wd:Q100451536)                           | place of birth (wdt:P19) | Japan (wd:Q17)         |
| Davide Furlan (wd:Q100276957)                     | place of birth (wdt:P19) | Italy (wd:Q38)         |
| Jochen Möckelmann (wd:Q100138651)                 | place of birth (wdt:P19) | Germany (wd:Q183)      |
| Jacqueline Goddet (wd:Q100148353)                 | place of birth (wdt:P19) | France (wd:Q142)       |
| wd:Q101541626                                     | place of birth (wdt:P19) | Finland (wd:Q33)       |
| José Rodolfo Villarreal Hernández (wd:Q100340209) | place of birth (wdt:P19) | Mexico (wd:Q96)        |
| Dharmakṣema (wd:Q1002321)                         | place of birth (wdt:P19) | India (wd:Q668)        |
| ...                                               | ...                      | ...                    |

[Assistant]
The property for place of birth is confirmed as `wdt:P19`. Now, let's confirm the property for place of death, which is `wdt:P570`.

Next, we need to find the entity for "Nazi concentration camp". Let's search for it.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. Nazi concentration camp, also known as Nazi German concentration camp (wd:Q328468): concentration camps operated by Nazi Germany
2. Nazi concentration camp badge (wd:Q15631700): cloth emblems; part of the system of identification in Nazi concentration camps
3. Nazi Concentration Camps (wd:Q1561119): 1945 film by George Stevens
4. Nazi concentration camp commandant, also known as camp commandant (wd:Q1719327): chief commanding position within the SS service of a Nazi concentration camp
5. Nazi concentration camp barracks, also known as block (wd:Q1520659): largely identical huts of a concentration camps
6. camp dentist, also known as Nazi Concentration Camp dentist (wd:Q85515749): medical personnel in Nazi concentration camps, physician, dentist
7. concentration camp guard, also known as camp guard, concentration camp overseer, Nazi concentration camp guard (wd:Q28789517): someone guarding a concentration camp
8. pharmacist in Nazi concentration camp, also known as Nazi concentration camp pharmacist (wd:Q85515966): pharmacist in Nazi concentration camps
9. Nazi concentration camp in Płaszów (wd:Q30161821)
10. Nazi concentration camps in Norway (wd:Q6983402)

No other items found

[Assistant]
The top result for "Nazi concentration camp" is `wd:Q328468`. Now we have all the necessary entities and properties to construct our SPARQL query.

We will query for individuals who were born in Almería Province (`wd:Q81802`) and died in a Nazi concentration camp (`wd:Q328468`).

Here is the SPARQL query:

```sparql
SELECT DISTINCT ?person ?personLabel
WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P570 ?deathPlace .
  ?deathPlace wdt:P31 wd:Q328468 .
  OPTIONAL { ?person rdfs:label ?personLabel . FILTER(LANG(?personLabel) = "en") }
}
```

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The query did not return any results, which suggests that there might not be any individuals in the knowledge graph who were born in Almería Province and died in a Nazi concentration camp.

To ensure that we are not missing any potential matches, let's try a slightly different approach. Instead of looking for the exact place of death, we can look for individuals who were born in Almería Province and have a cause of death related to the Holocaust or Nazi persecution.

We will search for the entity for "Holocaust" and "Nazi persecution" and use these in our query.

Let's search for "Holocaust" and "Nazi persecution".

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. The Holocaust, also known as Holocaust, the Holocaust, the Shoah, Shoah (wd:Q2763): genocide of European Jews by Nazi Germany, his allied countries and local collaborators during World War II
2. The Holocaust, also known as the Shoah, the Holocaust, Shoah, Holocaust (wd:Q2763): genocide of European Jews by Nazi Germany, his allied countries and local collaborators during World War II
3. holocaust (wd:Q1148722): English translation of Greek word Holokautein (ὁλοκαυτεῖν), rare Greek sacrifice
4. Holocaust (wd:Q702858): 1978 American television miniseries directed by Marvin J. Chomsky
5. Holocaust (wd:Q1625046): Scottish heavy metal band
6. Holocaust (wd:Q133098437): in wide sense: Nazi genocides of Jews, Roma, homosexuals etc.
7. Holocaust, also known as William Rolfson, Nemesis, Genocide (wd:Q3109652): fictional character in Marvel Comics, mass murderer, terrorist
8. Holocaust (wd:Q3881907): 1978 novel by Gerald Green, book by Gerald Green
9. Holocaust / Supertankerne kommer, also known as Holocaust, Supertankerne kommer (wd:Q12715498): 1980 single by Sjølmord
10. Holocaust, also known as Leonard Smalls (wd:Q16386060): fictional character in Milestone and DC Comics, crime boss, gangster

No other items found

[Assistant]
The top result for "Holocaust" is `wd:Q2763`. Now, let's search for "Nazi persecution" to see if there is a specific entity for this concept.

Let's search for "Nazi persecution".

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. Nazi persecution (wd:Q130561496): persecution of various groups or parts of society in Nazi Germany
2. persecution of Jews in the Nazi era, also known as Nazi persecution of Jews (wd:Q112029888): persecution of Jews 1933-1945 in Hitler's Third Reich in Germany and throughout Nazi-occupied Europe
3. Nazi Persecution of Scientists (wd:Q81071445): scientific article published on 01 February 1949
4. Arolsen Archives, also known as Archives of the International Tracing Service, Archives of the ITS, International Center on Nazi Persecution (wd:Q28028118): centre for documentation, information and research on Nazi perse...abour and the Holocaust in Nazi Germany and its occupied regions
5. Memorial Book of the Jewish Victims of National Socialism in Berlin, also known as Gedenkbuch Berlins der jüdischen...ozialwissenschaftliche Forschung, Jewish Victims of Nazi Persecution, List of murdered Jews from Germany, Victims of the Persecution of Je...t Tyranny in Germany 1933 - 1945 (wd:Q59506581): list of Holocaust deaths
6. persecution of homosexuals in Nazi Germany (wd:Q718858): discrimination, torture, and murder concerning homosexual people in Nazi Germany
7. DISABILITY PARTIALLY DUE TO NAZI PERSECUTION (wd:Q76742401): scientific article published on 01 February 1964
8. Nazi persecution in Anglo-Zionist thought, 1939–1949 (wd:Q129941172): scholarly article published in 2018
9. persecution of black people in Nazi Germany (wd:Q3342759)
10. Persecution of Jehovah's Witnesses in Nazi Germany (wd:Q5644355): Nazi persecution

No other items found

[Assistant]
The top result for "Nazi persecution" is `wd:Q130561496`. Now, we can use this entity to find individuals who were born in Almería Province and had a cause of death related to Nazi persecution.

We will use the property for cause of death (`P509`) and check if it is related to `wd:Q130561496`.

Here is the updated SPARQL query:

```sparql
SELECT DISTINCT ?person ?personLabel
WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P509 ?causeOfDeath .
  ?causeOfDeath wdt:P31 wd:Q130561496 .
  OPTIONAL { ?person rdfs:label ?personLabel . FILTER(LANG(?personLabel) = "en") }
}
```

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The query still did not return any results, which suggests that there might not be any individuals in the knowledge graph who were born in Almería Province and had a cause of death related to Nazi persecution.

Given the historical context, it is possible that such individuals are not well-documented in the knowledge graph, or their data might not be available or linked in the way we are querying.

Since we have tried multiple approaches and still have not found any results, we can conclude that there are no known individuals in the knowledge graph who meet the criteria specified in the question.

Therefore, the answer to the question is that there are no known individuals who were born in Almería Province and died in a Nazi concentration camp or had a cause of death related to Nazi persecution.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P509 ?causeOfDeath .
  ?causeOfDeath wdt:P31 wd:Q130561496 .
}
```

The answer is: False.

Let's finalize this with the `answer` function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?member ?adminEntity ?coordinates WHERE {
  ?member wdt:P463 wd:Q96376996 .
  OPTIONAL {
    ?member wdt:P131 ?adminEntity .
    ?adminEntity wdt:P625 ?coordinates .
  }
}

The members of the Clubture Network, along with the administrative territorial entities they are located in and the coordinates of these entities, are as follows: [results of the SPARQL query]

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

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

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

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

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

[User] Who are the members of the Clubture Network, including the administrative territorial entities they are located in (if available) and the coordinates of these administrative areas (if available)?

[Assistant] To answer this question, we need to identify the entity for the Clubture Network and the properties that link it to its members. We also need to find out if there are properties that provide the location and coordinates of these members. Let's start by searching for the Clubture Network entity.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. Clubture Network, also known as Clubture.org, Clubture (wd:Q96376996): Croatian network / platform of cultural and art organizations and initiatives
2. computer network, also known as computer communication networks, computer networks, computer network technologies and services, network (wd:Q1301371): network that allows computers to share resources and communicate with each other
3. graph, also known as undirected simple graph, simple undirected graph, points and lines, graphs, graph (network) (wd:Q141488): mathematical structure made of vertices connected in pairs by edges
4. Network, also known as Union Broadcasting System (wd:Q572165): 1976 film by Sidney Lumet
5. business networking, also known as networking, network (wd:Q2360816): forming professional relationships between people, businesses, or groups
6. Slovak Conservative Party, also known as #NETWORK, NETWORK, Sieť, #SIEŤ, SIEŤ (wd:Q17115560): political party in Slovakia
7. The Network, also known as Network (wd:Q1187591): new wave side project of rock band Green Day
8. Benn Jordan, also known as Lucid16, FlexE., Human Action, Human Action Network, The Flashbulb (wd:Q3121935): American musician, musician, composer
9. network (wd:Q1900326): system of objects that are connected together to exchange something (e.g. objects, materials or information)
10. filet lace, also known as filet brodé, filet guipure, knotted netting, lacis, network (wd:Q1412659): type of embroidered lace made using darning or running stitches on a hand-knotted net ground

No other items found

[Assistant] The top result for "Clubture Network" is the Croatian network/platform of cultural and art organizations and initiatives (wd:Q96376996). Now, let's find out what properties are associated with this entity, particularly those that might link it to its members.

[Tool]
Got 115 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                                       | object                                    |
| ------------------------------- | ---------------------------------------------- | ----------------------------------------- |
| Clubture Network (wd:Q96376996) | instance of (wdt:P31)                          | non-governmental organization (wd:Q79913) |
| Clubture Network (wd:Q96376996) | main subject (wdt:P921)                        | culture (wd:Q11042)                       |
| Clubture Network (wd:Q96376996) | country (wdt:P17)                              | Croatia (wd:Q224)                         |
| Clubture Network (wd:Q96376996) | coordinate location (wdt:P625)                 | POINT(15.975538 45.807432)                |
| Clubture Network (wd:Q96376996) | Commons category (wdt:P373)                    | Clubture                                  |
| Clubture Network (wd:Q96376996) | inception (wdt:P571)                           | 2002-07-16T00:00:00Z                      |
| Clubture Network (wd:Q96376996) | official website (wdt:P856)                    | <http://Clubture.org>                     |
| Clubture Network (wd:Q96376996) | street address (wdt:P6375)                     | "Baruna Trenka 11, 10000 Zagreb"@hr       |
| Clubture Network (wd:Q96376996) | on focus list of Wikimedia project (wdt:P5008) | WikiProject CSO networks (wd:Q115120455)  |
| Clubture Network (wd:Q96376996) | member of (wdt:P463)                           | DKC-HR (wd:Q111178235)                    |
| ...                             | ...                                            | ...                                       |

[Assistant]
From the list of triples, we can see that the Clubture Network has several properties, but none directly indicate its members. However, there is a property `member of` (wdt:P463) that links the Clubture Network to another entity, DKC-HR. This suggests that the `member of` property might be used in reverse to find the members of the Clubture Network.

Let's try to find the members of the Clubture Network using the `member of` property.

[Tool]
Got 89 rows and 1 column, showing the first 5 and last 5 rows below:
| ?member                                                                            |
| ---------------------------------------------------------------------------------- |
| Queer Sport Split (wd:Q105443300)                                                  |
| HrW initiative (wd:Q108613387)                                                     |
| KLFM (wd:Q109587828)                                                               |
| Spirit Rijeka (wd:Q109621414)                                                      |
| Udruženje za razvoj kulture "URK" (wd:Q111189446)                                  |
| ...                                                                                |
| Art Workshop Lazareti (wd:Q4797154)                                                |
| Grey) (area space for contemporary and media art (wd:Q65133126)                    |
| Association for Development of ‘...t-yourself’ Culture – Makerspace (wd:Q67371330) |
| queerANarchive (wd:Q96377295)                                                      |
| Drugo more (wd:Q98930373)                                                          |

[Assistant]
We have successfully retrieved the members of the Clubture Network. Next, we need to find the administrative territorial entities where these members are located and their coordinates. The properties `located in the administrative territorial entity` (wdt:P131) and `coordinate location` (wdt:P625) are likely to be useful for this purpose.

Let's extend our query to include these properties.

[Tool]
Got more than 131,073 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?member                                                                        | ?adminEntity                        | ?coordinates               |
| ------------------------------------------------------------------------------ | ----------------------------------- | -------------------------- |
| HrW initiative (wd:Q108613387)                                                 | spatial reference system (wd:P3037) | POINT(10.261996 52.742848) |
| Udruga Domino (wd:Q111427824)                                                  | spatial reference system (wd:P3037) | POINT(10.261996 52.742848) |
| Cirkorama (wd:Q115520068)                                                      | spatial reference system (wd:P3037) | POINT(10.261996 52.742848) |
| Bunike, udruga za promicanje cirkuskih i izvedbenih umjetnosti (wd:Q125049790) | spatial reference system (wd:P3037) | POINT(10.261996 52.742848) |
| Zadar snova (wd:Q12645474)                                                     | spatial reference system (wd:P3037) | POINT(10.261996 52.742848) |
| ...                                                                            | ...                                 | ...                        |
| Udruga Domino (wd:Q111427824)                                                  | Bachs Garten (wd:Q100670546)        | POINT(10.939303 50.839810) |
| Cirkorama (wd:Q115520068)                                                      | Bachs Garten (wd:Q100670546)        | POINT(10.939303 50.839810) |
| Bunike, udruga za promicanje cirkuskih i izvedbenih umjetnosti (wd:Q125049790) | Bachs Garten (wd:Q100670546)        | POINT(10.939303 50.839810) |
| Zadar snova (wd:Q12645474)                                                     | Bachs Garten (wd:Q100670546)        | POINT(10.939303 50.839810) |
| Živ Žar Žur (wd:Q111658714)                                                    | Bachs Garten (wd:Q100670546)        | POINT(10.939303 50.839810) |

[Assistant]
It seems that the query returned a large number of rows, and some of the results are not as expected. Specifically, the `?adminEntity` and `?coordinates` values are repeated and seem to be incorrect for many entries. This suggests that the `wdt:P131` property might not be consistently used for all members, or there might be issues with the data.

To refine the query, let's ensure that we only get the correct administrative entities and coordinates by adding more constraints and checking the labels of the entities to verify their correctness.

Let's try to filter the results to only include valid administrative entities and coordinates.

[Tool]
Got 109 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?member                                                                            | ?adminEntity         | ?coordinates               |
| ---------------------------------------------------------------------------------- | -------------------- | -------------------------- |
| Queer Sport Split (wd:Q105443300)                                                  |                      |                            |
| HrW initiative (wd:Q108613387)                                                     |                      |                            |
| KLFM (wd:Q109587828)                                                               | Split (wd:Q1663)     | POINT(16.440000 43.510000) |
| Spirit Rijeka (wd:Q109621414)                                                      | Rijeka (wd:Q1647)    | POINT(14.433333 45.333333) |
| Udruženje za razvoj kulture "URK" (wd:Q111189446)                                  | Zagreb (wd:Q1435)    | POINT(15.977222 45.813056) |
| ...                                                                                | ...                  | ...                        |
| Art Workshop Lazareti (wd:Q4797154)                                                | Dubrovnik (wd:Q1722) | POINT(18.108333 42.640278) |
| Grey) (area space for contemporary and media art (wd:Q65133126)                    | Korčula (wd:Q13289)  | POINT(17.135833 42.961667) |
| Association for Development of ‘...t-yourself’ Culture – Makerspace (wd:Q67371330) | Zagreb (wd:Q1435)    | POINT(15.977222 45.813056) |
| queerANarchive (wd:Q96377295)                                                      |                      |                            |
| Drugo more (wd:Q98930373)                                                          | Rijeka (wd:Q1647)    | POINT(14.433333 45.333333) |

[Assistant]
The refined query has produced a more manageable and accurate set of results. We now have the members of the Clubture Network, along with the administrative territorial entities they are located in and the coordinates of these entities.

Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?member ?adminEntity ?coordinates WHERE {
  ?member wdt:P463 wd:Q96376996 .
  OPTIONAL {
    ?member wdt:P131 ?adminEntity .
    ?adminEntity wdt:P625 ?coordinates .
  }
}
```

This query returns the members of the Clubture Network, the administrative territorial entities they are located in, and the coordinates of these entities.

[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 ?subclass ?subclassLabel WHERE {
  ?subclass wdt:P279 wd:Q13442814 .
  OPTIONAL {
    ?subclass rdfs:label ?subclassLabel .
    FILTER ( LANG( ?subclassLabel ) = 'en' )
  }
}
ORDER BY ?subclassLabel

Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?subclass                                | ?subclassLabel            |
| ---------------------------------------- | ------------------------- |
| academic conference paper (wd:Q10885494) | academic conference paper |
| academic journal article (wd:Q18918145)  | academic journal article  |
| Annus Mirabilis papers (wd:Q51497)       | Annus Mirabilis papers    |
| A-publication (wd:Q2774197)              | A-publication             |
| classic article (wd:Q58898396)           | classic article           |
| ...                                      | ...                       |
| sleeping beauty (wd:Q115528532)          | sleeping beauty           |
| survey article (wd:Q60535861)            | survey article            |
| systematic review (wd:Q1504425)          | systematic review         |
| type catalogue (wd:Q121763407)           | type catalogue            |
| working paper (wd:Q1228945)              | working paper             |

The items that are subclasses of scholarly articles, ordered by their labels, are: academic conference paper, academic journal article, Annus Mirabilis papers, A-publication, classic article, ... , sleeping beauty, survey article, systematic review, type catalogue, working paper.

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

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

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

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

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

[User] What are the items that are subclasses of scholarly articles, ordered by their labels?

[Assistant] To answer this question, we need to identify the entity for "scholarly articles" and the property for "subclass of". Then, we can construct a SPARQL query to retrieve the items that are subclasses of scholarly articles and order them by their labels.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

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

No other items found

[Assistant] From the search results, it seems that the most relevant item for "scholarly article" is "scholarly article" (wd:Q13442814). Let's confirm this and also find the property for "subclass of".

[Tool]
Got 592 rows and 3 columns, showing the first 10 rows below:
| subject                          | property                              | object                                                                          |
| -------------------------------- | ------------------------------------- | ------------------------------------------------------------------------------- |
| scholarly article (wd:Q13442814) | Commons category (wdt:P373)           | Scientific articles                                                             |
| scholarly article (wd:Q13442814) | image (wdt:P18)                       | <http://commons.wikimedia.org/wiki/Special:FilePath/Scientific%20citations.png> |
| scholarly article (wd:Q13442814) | subclass of (wdt:P279)                | article (wd:Q191067)                                                            |
| scholarly article (wd:Q13442814) | different from (wdt:P1889)            | white paper (wd:Q223729)                                                        |
| scholarly article (wd:Q13442814) | maintained by WikiProject (wdt:P6104) | WikiProject Academic Journals (wd:Q21597731)                                    |
| scholarly article (wd:Q13442814) | topic's main category (wdt:P910)      | wd:Q20024402                                                                    |
| scholarly article (wd:Q13442814) | exact match (wdt:P2888)               | <https://citationstyles.org/ontology/type/article-journal>                      |
| scholarly article (wd:Q13442814) | uses (wdt:P2283)                      | scholarly peer review (wd:Q7096397)                                             |
| scholarly article (wd:Q13442814) | topic's main template (wdt:P1424)     | wd:Q26062603                                                                    |
| scholarly article (wd:Q13442814) | partially coincident with (wdt:P1382) | technical article (wd:Q106645507)                                               |
| ...                              | ...                                   | ...                                                                             |

[Assistant]
The property "subclass of" is represented by `wdt:P279`. Now, let's construct a SPARQL query to find the items that are subclasses of "scholarly article" (wd:Q13442814) and order them by their labels.

We will start with a basic query to retrieve the subclasses and then add the label retrieval and ordering.

[Tool]
Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?subclass                                | ?subclassLabel            |
| ---------------------------------------- | ------------------------- |
| academic conference paper (wd:Q10885494) | academic conference paper |
| academic journal article (wd:Q18918145)  | academic journal article  |
| Annus Mirabilis papers (wd:Q51497)       | Annus Mirabilis papers    |
| A-publication (wd:Q2774197)              | A-publication             |
| classic article (wd:Q58898396)           | classic article           |
| ...                                      | ...                       |
| sleeping beauty (wd:Q115528532)          | sleeping beauty           |
| survey article (wd:Q60535861)            | survey article            |
| systematic review (wd:Q1504425)          | systematic review         |
| type catalogue (wd:Q121763407)           | type catalogue            |
| working paper (wd:Q1228945)              | working paper             |

[Assistant]
The SPARQL query successfully retrieved the items that are subclasses of "scholarly article" and ordered them by their labels. Here are the results:

1. academic conference paper
2. academic journal article
3. Annus Mirabilis papers
4. A-publication
5. classic article
...
26. sleeping beauty
27. survey article
28. systematic review
29. type catalogue
30. working paper

Now, let's finalize the answer with the SPARQL query and the list of subclasses.

[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 ?airport ?airportLabel WHERE {
  ?airport wdt:P31 wd:Q124880 .
  OPTIONAL {
    ?airport rdfs:label ?airportLabel .
    FILTER ( LANG( ?airportLabel ) = "en" )
  }
}
ORDER BY ?airportLabel

The question asks for international airports that have exactly one runway, along with their labels and the number of runways, ordered by the airport label. However, after multiple attempts to find relevant properties in Wikidata, it became clear that there is no direct or indirect property linking an airport to its runways or indicating the number of runways. Therefore, it is not possible to construct a SPARQL query that can accurately answer this question using the available data 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 international airports have exactly one runway? Please provide their labels and the number of runways, and order by the airport label.

[Assistant] To answer this question, we need to identify the properties for international airports and the number of runways. We will start by searching for the property that indicates the number of runways an airport has.

[Tool]
No property items found

No other items found

[Assistant] It seems that there is no direct property for "number of runways" in Wikidata. However, we can try to find a property that links to the runways of an airport and then count the number of runways. Let's search for a property that links an airport to its runways.

[Tool]
No property items found

No other items found

[Assistant]
It appears that there is no direct property linking an airport to its runways either. However, we can try a different approach by looking for airports that have a specific type of runway and then counting the number of such runways. Let's search for the property that links an airport to its runways again, but this time let's look for specific types of runways.

Let's try searching for "runway" in the context of an airport.

[Tool]
No property items found

No other items found

[Assistant]
It seems that there is no direct property linking an airport to its runways in Wikidata. However, we can try a different approach by looking for airports that have a specific type of runway and then counting the number of such runways. Let's search for the property that links an airport to its runways again, but this time let's look for specific types of runways.

Since the direct approach didn't work, let's try to find a property that might indirectly link an airport to its runways. One possibility is to look for properties related to airport facilities or infrastructure.

Let's search for properties related to airport facilities or infrastructure.

[Tool]
No property items found

No other items found

[Assistant]
It seems that there are no properties directly linking an airport to its runways or facilities in the way we expected. Given this, we might need to consider a different approach or accept that the data required to answer this question is not available in the current form in Wikidata.

However, let's try one more approach by searching for properties related to airport infrastructure or specific airport features that might indirectly indicate the presence of runways.

Let's search for properties related to airport infrastructure or features.

[Tool]
No property items found

No other items found

[Assistant]
It appears that there are no properties in Wikidata that directly or indirectly link an airport to its runways or infrastructure in a way that would allow us to count the number of runways. Given this, it is not possible to construct a SPARQL query that answers the question as posed.

I will now provide a detailed explanation and my best attempt at a SPARQL query, even though it will not fully answer the question.

### Explanation
The question asks for international airports that have exactly one runway, along with their labels and the number of runways, ordered by the airport label. However, after multiple attempts to find relevant properties in Wikidata, it became clear that there is no direct or indirect property linking an airport to its runways or indicating the number of runways. Therefore, it is not possible to construct a SPARQL query that can accurately answer this question using the available data in Wikidata.

### Best Attempt at a SPARQL Query
Since we cannot find the necessary properties, the best we can do is list international airports and their labels, but we cannot filter or count the runways.

```sparql
SELECT ?airport ?airportLabel
WHERE {
  ?airport wdt:P31 wd:Q124880.  # International airport
  OPTIONAL { ?airport rdfs:label ?airportLabel . FILTER(LANG(?airportLabel) = "en") }
}
ORDER BY ?airportLabel
```

This query will list international airports and their labels, but it does not include the number of runways or filter for airports with exactly one runway.

### Final Answer
I am unable to find a satisfactory SPARQL query that answers the question as posed due to the lack of relevant properties in Wikidata.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?productionCompany ( COUNT( ?item ) AS ?count ) WHERE {
  ?item wdt:P272 ?productionCompany .
}
GROUP BY ?productionCompany
HAVING ( COUNT( ?item ) > 100 )
ORDER BY DESC ( ?count )

Got 210 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?productionCompany                        | ?count |
| ----------------------------------------- | ------ |
| Theater Basel (wd:Q391991)                | 7865   |
| Schauspielhaus Zürich (wd:Q40313234)      | 3203   |
| Vitagraph Studios (wd:Q1418400)           | 2551   |
| Essanay Studios (wd:Q1368540)             | 1974   |
| Universal Pictures (wd:Q168383)           | 1614   |
| ...                                       | ...    |
| Laika (wd:Q54805138)                      | 103    |
| C-mine Cultuurcentrum Genk (wd:Q33100443) | 103    |
| Constantin Film (wd:Q564960)              | 103    |
| STV (wd:Q4403233)                         | 102    |
| Disney Television Animation (wd:Q1392453) | 102    |

The production companies that have produced more than 100 items, ordered by the number of items produced in descending order, are as follows:

Top 5:
1. Theater Basel (wd:Q391991) - 7865 items
2. Schauspielhaus Zürich (wd:Q40313234) - 3203 items
3. Vitagraph Studios (wd:Q1418400) - 2551 items
4. Essanay Studios (wd:Q1368540) - 1974 items
5. Universal Pictures (wd:Q168383) - 1614 items

Bottom 5:
1. Laika (wd:Q54805138) - 103 items
2. C-mine Cultuurcentrum Genk (wd:Q33100443) - 103 items
3. Constantin Film (wd:Q564960) - 103 items
4. STV (wd:Q4403233) - 102 items
5. Disney Television Animation (wd:Q1392453) - 102 items

[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 production companies have produced more than 100 items, ordered by the number of items produced in descending order?

[Assistant] To answer this question, we need to identify the production companies and count the number of items they have produced. We can start by searching for the property that indicates the production company of an item.

[Tool] Invalid SPARQL query: Variable ?search must occurr in the WHERE clause at least once

[Assistant]
It seems I made a mistake in the SPARQL query. Instead of using `p:P361` and `ps:P361`, I should have used `wdt:P361` to directly link the item to the production company. Let me correct this and try again.

However, it's also possible that `P361` is not the correct property for indicating the production company. Let me search for the correct property first.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 property alternatives:
1. production company, also known as broadcasting company, theatrical troupe, theatre company, theater company, studio (wd:P272 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): company that produced this film, audio or performing arts work
2. working title, also known as code name, codename, production name, production title, working name (wd:P1638 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): temporary name of a product or project used during its development
3. produced by, also known as secreted by (wd:P2849 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): links a class of biological features (such as a biochemical compound) with another class of biological entities known to produce it, has immediate cause
4. manufacturer, also known as manufactured by, provider, provided by, product of, producer (wd:P176 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): (main or final) manufacturer or producer of this product
5. fabrication method, also known as process used, method of cooking, preparation method, obtained by, method used (wd:P2079 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): method, process or technique used to grow, cook, weave, build, assemble, manufacture the item, uses
6. producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]
7. location of creation, also known as manufactured in, manufacturing location, mint, minted at, photographed at (wd:P1071 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): place where the item was conceived or made; where applicable, location of final assembly, significant place
8. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
9. product, material, or service produced or provided, also known as products, product or service provided, product, material, or service produced or provided, product, output (wd:P1056 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of
10. created for, also known as cause of creation, commissioned for, made for, occasion, on the occasion of (wd:P9883 as prv/pr/psn/ps/wdtn/pqv/pq/wdt/prn/psv/p/pqn): 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)

No other items found

[Assistant]
From the search results, the property `wd:P272` (production company) seems to be the most relevant for our query. Now, let's construct a SPARQL query to find the production companies that have produced more than 100 items and order them by the number of items produced in descending order.

Here is the initial query:

```sparql
SELECT ?productionCompany (COUNT(?item) AS ?count)
WHERE {
  ?item wdt:P272 ?productionCompany .
}
GROUP BY ?productionCompany
HAVING (COUNT(?item) > 100)
ORDER BY DESC(?count)
```

Let's execute this query to see the results.

[Tool]
Got 210 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?productionCompany                        | ?count |
| ----------------------------------------- | ------ |
| Theater Basel (wd:Q391991)                | 7865   |
| Schauspielhaus Zürich (wd:Q40313234)      | 3203   |
| Vitagraph Studios (wd:Q1418400)           | 2551   |
| Essanay Studios (wd:Q1368540)             | 1974   |
| Universal Pictures (wd:Q168383)           | 1614   |
| ...                                       | ...    |
| Laika (wd:Q54805138)                      | 103    |
| C-mine Cultuurcentrum Genk (wd:Q33100443) | 103    |
| Constantin Film (wd:Q564960)              | 103    |
| STV (wd:Q4403233)                         | 102    |
| Disney Television Animation (wd:Q1392453) | 102    |

[Assistant]
The SPARQL query has successfully returned the production companies that have produced more than 100 items, ordered by the number of items produced in descending order. Here are the top 5 and bottom 5 results:

Top 5:
1. **Theater Basel** (wd:Q391991) - 7865 items
2. **Schauspielhaus Zürich** (wd:Q40313234) - 3203 items
3. **Vitagraph Studios** (wd:Q1418400) - 2551 items
4. **Essanay Studios** (wd:Q1368540) - 1974 items
5. **Universal Pictures** (wd:Q168383) - 1614 items

Bottom 5:
1. **Laika** (wd:Q54805138) - 103 items
2. **C-mine Cultuurcentrum Genk** (wd:Q33100443) - 103 items
3. **Constantin Film** (wd:Q564960) - 103 items
4. **STV** (wd:Q4403233) - 102 items
5. **Disney Television Animation** (wd:Q1392453) - 102 items

This query provides the required information accurately. I will now provide the final answer.

[Tool] Stopped generation process


Failed to generate SPARQL query

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

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

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

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

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

[User] What are the items that are instances or subclasses of artificial intelligence, along with the objects they are part of?

[Assistant] To answer this question, we need to identify the items that are instances or subclasses of artificial intelligence and also find the objects they are part of. We can start by searching for the item representing "artificial intelligence" and then look for its instances and subclasses. After that, we can find the objects these items are part of.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
SPARQL query contains no IRIs to constrain with

Top 10 entity alternatives:
1. artificial intelligence, also known as Machine thought, Machine understanding, Machines that can think, Machine intelligence, Simulated intelligence (wd:Q11660): field of computer science that develops and studies intelligent machines; also referring to such software
2. artificial intelligence, also known as Artificial Intelligence, a.i., Intelligent machine, Machine thought, Artificial Intelligence (AI), Artificially intelligent (wd:Q11660): field of computer science that develops and studies intelligent machines; also referring to such software
3. A.I. Artificial Intelligence, also known as Artificial Intelligence: A.I., Artificial Intelligence, AI, A.I., A.I.: Artificial Intelligence (wd:Q221113): 2001 film directed by Steven Spielberg
4. Artificial Intelligence (wd:Q128447): 1985 studio album by John Cale
5. Artificial Intelligence (wd:Q4801033): 1992 compilation album; various artists
6. Artificial Intelligence (wd:Q2865784): series of albums by Warp Records
7. Artificial Intelligence, also known as AIJ, Journal of Artificial Intelligence (wd:Q4801030): journal published by Elsevier
8. Artificial Intelligence (wd:Q28451730): song performed by OneRepublic
9. ISO/IEC JTC 1/SC 42: Artificial intelligence, also known as Artificial intelligence (wd:Q98148785): subcommittee of a technical committee
10. Artificial Intelligence (wd:Q125635217): book by Patrick Henry Winston

No other items found

[Assistant]
From the search results, the most relevant item for "artificial intelligence" is `wd:Q11660`. Now, let's find the instances and subclasses of this item. We can use the properties `wdt:P31` (instance of) and `wdt:P279` (subclass of) to achieve this.

Next, we will find the objects these items are part of using the property `wdt:P361` (part of).

Let's start by finding the instances and subclasses of `wd:Q11660`.

[Tool]
Got 62 rows and 1 column, showing the first 5 and last 5 rows below:
| ?item                                                       |
| ----------------------------------------------------------- |
| You.com (wd:Q111598768)                                     |
| NotebookLM (wd:Q124559107)                                  |
| wd:Q125545918                                               |
| artificial intelligence in higher education (wd:Q126198166) |
| AI washing (wd:Q127449019)                                  |
| ...                                                         |
| Marketing and artificial intelligence (wd:Q6770893)         |
| neurorobotics (wd:Q7002613)                                 |
| autonomic computing (wd:Q788172)                            |
| Artificial Chemist (wd:Q96246713)                           |
| Associatron (wd:Q97208985)                                  |

[Assistant]
We have successfully retrieved the instances and subclasses of `wd:Q11660` (artificial intelligence). Now, let's find the objects these items are part of using the property `wdt:P361` (part of).

We will construct a SPARQL query to retrieve both the items and the objects they are part of.

[Tool]
Got 65 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?item                                               | ?partOf |
| --------------------------------------------------- | ------- |
| automated music production (wd:Q100148106)          |         |
| autonomous navigation (wd:Q100712173)               |         |
| Green AI (wd:Q103845039)                            |         |
| Red AI (wd:Q103873243)                              |         |
| artificial intelligence in hiring (wd:Q104878955)   |         |
| ...                                                 | ...     |
| Marketing and artificial intelligence (wd:Q6770893) |         |
| neurorobotics (wd:Q7002613)                         |         |
| autonomic computing (wd:Q788172)                    |         |
| Artificial Chemist (wd:Q96246713)                   |         |
| Associatron (wd:Q97208985)                          |         |

[Assistant]
The query has returned the items that are instances or subclasses of artificial intelligence (`wd:Q11660`) along with the objects they are part of. However, it appears that many of these items do not have a `part of` relationship specified in the data.

To ensure we capture all relevant information, we can include the labels of the items and the objects they are part of, if available. This will make the results more readable and informative.

Let's modify the query to include the labels of the items and the objects they are part of.

[Tool]
SPARQL execution failed:
Tried to allocate 3.3 GB, but only 2.5 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
It seems that the query is too complex or resource-intensive for the current environment, causing a memory allocation issue. To address this, we can simplify the query by removing the label retrieval for now and focusing on the core relationships. We can then add the label retrieval back in a more controlled manner.

Let's first retrieve the items and their `part of` relationships without the labels, and then we can add the label retrieval in a separate step if needed.

[Tool]
Got 65 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?item                                               | ?partOf |
| --------------------------------------------------- | ------- |
| automated music production (wd:Q100148106)          |         |
| autonomous navigation (wd:Q100712173)               |         |
| Green AI (wd:Q103845039)                            |         |
| Red AI (wd:Q103873243)                              |         |
| artificial intelligence in hiring (wd:Q104878955)   |         |
| ...                                                 | ...     |
| Marketing and artificial intelligence (wd:Q6770893) |         |
| neurorobotics (wd:Q7002613)                         |         |
| autonomic computing (wd:Q788172)                    |         |
| Artificial Chemist (wd:Q96246713)                   |         |
| Associatron (wd:Q97208985)                          |         |

[Assistant]
The simplified query has successfully retrieved the items that are instances or subclasses of artificial intelligence (`wd:Q11660`) along with the objects they are part of. As we can see, many of these items do not have a `part of` relationship specified in the data.

Since the query is working, we can now add the label retrieval for the items and the objects they are part of to make the results more readable. Let's try this again with the label retrieval included, but we will limit the number of results to ensure it doesn't cause a memory issue.

[Tool]
SPARQL execution failed:
Tried to allocate 3.3 GB, but only 1.1 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
It seems that even with the limit, the query is still too resource-intensive. To address this, we can break down the query into smaller parts. First, we will retrieve the items and their `part of` relationships without labels. Then, we will retrieve the labels in a separate query.

Let's start by retrieving the items and their `part of` relationships without labels.

[Tool]
Got 50 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?item                                                | ?partOf |
| ---------------------------------------------------- | ------- |
| automated music production (wd:Q100148106)           |         |
| autonomous navigation (wd:Q100712173)                |         |
| Green AI (wd:Q103845039)                             |         |
| Red AI (wd:Q103873243)                               |         |
| artificial intelligence in hiring (wd:Q104878955)    |         |
| ...                                                  | ...     |
| industrial artificial intelligence (wd:Q30673782)    |         |
| distributed artificial intelligence (wd:Q3153007)    |         |
| decision support system (wd:Q330268)                 |         |
| knowledge representation and reasoning (wd:Q3478658) |         |
| Automated Mathematician (wd:Q3851731)                |         |