PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P20 ?deathPlace .
  ?deathPlace wdt:P31 / wdt:P279 * wd:Q328468 .
}

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'll start by finding the entity for Almería Province and then look for people who were born there. Next, we'll find the entity for Nazi concentration camps and check if these individuals died in such a place.

### Step 1: Find the entity for Almería Province
Let's search for the entity representing Almería Province.

[Tool]
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 correct entity for Almería Province is `wd:Q81802`.

### Step 2: Find the property for "place of birth"
We need to find the property that represents the place of birth.

[Tool]
Top 10 property alternatives:
1. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): most specific known birth location of a person, animal or fictional character, location
2. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): date on which the subject was born, inception
3. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): the object is a country that recognizes the subject as its citizen, country
4. birthday, also known as b-day, bday, birth day (wd:P3150 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): item for day and month on which the subject was born. Used when full "date of birth" (P569) isn't known., date of birth
5. birth name, also known as née, name at birth, name before marriage, ne, né (wd:P1477 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): full name of a person at birth, if different from their current, generally used name, name
6. category for people born here, also known as category for place of birth, category of people born here (wd:P1464 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): category item that groups people born in this place, related category
7. valid in place, also known as applies to country, applies to location, applies to place, country, true in (wd:P3005 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): place where a statement is valid, applies to part
8. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
9. country, also known as sovereign state, state, land, host country (wd:P17 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
10. commemorates, also known as monument to, memorial to, memorializes, marks, in memory of (wd:P547 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): what the place, monument, memorial, or holiday, commemorates, main subject

No other items found

[Assistant]
The correct property for "place of birth" is `wdt:P19`.

### Step 3: Find the property for "place of death"
Next, we need to find the property that represents the place of death.

[Tool]
Top 10 property alternatives:
1. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
2. place of burial, also known as interment, interment location, interment place, interred at, location of burial (wd:P119 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): location of grave, resting place, place of ash-scattering, etc. ...veral places: e.g., re-burials, parts of body buried separately., location
3. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): dissolved, abolished or demolished date, date on which the subject died, end time
4. category for people who died here, also known as category for place of death, category of people who died here (wd:P1465 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): category item for people who died in this location, related category
5. manner of death, also known as type of death, nature of death, death manner, death type, circumstance of death (wd:P1196 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): general circumstances of a person's death; e.g. natural causes, ...ysiological mechanism, e.g. heart attack, trauma, pneumonia etc.
6. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): total (cumulative) number of people who died since start as a direct result of an event or cause
7. category for people buried here, also known as buried here: Wikimedia category for, category of people buried here, category for place of burial, category for burials (wd:P1791 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): Wikimedia category for people with a burial site within this area, related category
8. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
9. date of burial or cremation, also known as burial date, cremation date, date of cremation, funeral date (wd:P4602 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): date on which the person was buried or cremated soon after death, point in time
10. commemorates, also known as monument to, memorial to, memorializes, marks, in memory of (wd:P547 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): what the place, monument, memorial, or holiday, commemorates, main subject

No other items found

[Assistant]
The correct property for "place of death" is `wdt:P20`.

### Step 4: Find the entity for Nazi concentration camps
Now, let's find the entity that represents Nazi concentration camps.

[Tool]
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 correct entity for Nazi concentration camps is `wd:Q328468`.

### Step 5: Construct the SPARQL query
Now that we have the necessary entities and properties, we can construct the SPARQL query to find the distinct individuals who were born in Almería Province and died in a Nazi concentration camp.

```sparql
SELECT DISTINCT ?person ?personLabel
WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P20 ?deathPlace .
  ?deathPlace wdt:P31 wd:Q328468 .
  OPTIONAL { ?person rdfs:label ?personLabel . FILTER(LANG(?personLabel) = "en") }
}
```

This query selects distinct individuals (`?person`) who were born in Almería Province (`wd:Q81802`) and died in a place that is a Nazi concentration camp (`wd:Q328468`). It also optionally retrieves the English label of the person.

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 be no 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. We can first find all individuals born in Almería Province and then check if any of them died in a place that is a subclass of a Nazi concentration camp. This might help us catch any instances where the specific type of concentration camp is a more specific subclass.

### Step 6: Refine the SPARQL query
We will use the `wdt:P279` property to check for subclasses of Nazi concentration camps.

```sparql
SELECT DISTINCT ?person ?personLabel
WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P20 ?deathPlace .
  ?deathPlace wdt:P31/wdt:P279* wd:Q328468 .
  OPTIONAL { ?person rdfs:label ?personLabel . FILTER(LANG(?personLabel) = "en") }
}
```

This query checks if the place of death is a subclass of a Nazi concentration camp. Let's execute this refined query.

[Tool] Got no rows and 2 columns

[Assistant]
The refined query also did not return any results, which suggests that there are no individuals in the knowledge graph who were born in Almería Province and died in a Nazi concentration camp.

Given the lack of results, it is likely that such individuals are not represented in the Wikidata knowledge graph, or the data is incomplete. Therefore, we can conclude that there are no known individuals who meet the criteria specified in the question.

### Final Answer
Since the query did not return any results, 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 according to the available data in Wikidata.

```sparql
ASK WHERE {
  ?person wdt:P19 wd:Q81802 .
  ?person wdt:P20 ?deathPlace .
  ?deathPlace wdt:P31/wdt:P279* wd:Q328468 .
}
```

This ASK query confirms that no such individuals exist in the knowledge graph.

Let's provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?member WHERE {
  wd:Q96376996 wdt:P190 ?member
}

After searching for the Clubture Network entity and attempting to find its members using various properties (P527 and P190), and even searching for entities that are described as members of the Clubture Network, no relevant data was found in Wikidata. This suggests that the information about the members of the Clubture Network is not available in the knowledge graph.

[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 then find its members. Additionally, we need to find the administrative territorial entities where these members are located and their coordinates.

### Step 1: Identify the Clubture Network Entity
First, let's search for the Clubture Network entity in Wikidata.

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "position": "subject",
    "query": "Clubture Network"
  }
}
```

### Step 2: Find Members of the Clubture Network
Once we have the entity for the Clubture Network, we can find its members by looking for the property that indicates membership.

### Step 3: Find Administrative Territorial Entities and Coordinates
For each member, we need to find the administrative territorial entities they are located in and the coordinates of these entities.

Let's start with the first step.

[Tool]
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]
From the search results, the most relevant entity for the Clubture Network is:

- **Clubture Network** (wd:Q96376996): Croatian network / platform of cultural and art organizations and initiatives

Next, we need to find the members of this network. We will look for properties that indicate membership, such as `P527` (has part) or `P190` (member of).

### Step 2: Find Members of the Clubture Network
Let's list the triples where the Clubture Network (wd:Q96376996) is the subject and the property indicates membership.

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "subject": "wd:Q96376996",
    "property": "wdt:P527"
  }
}
```

If this does not yield results, we will try another property.

Let's execute this query.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that the property `P527` (has part) did not yield any results for the Clubture Network. Let's try another property, such as `P190` (member of), to see if it provides the members.

### Step 2: Try Another Property for Membership
Let's list the triples where the Clubture Network (wd:Q96376996) is the subject and the property indicates membership using `P190`.

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "subject": "wd:Q96376996",
    "property": "wdt:P190"
  }
}
```

Let's execute this query.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that neither `P527` (has part) nor `P190` (member of) yielded any results for the Clubture Network. This suggests that the members of the Clubture Network might not be directly linked to the network entity in Wikidata.

### Step 3: Search for Members Indirectly
We can try searching for entities that are described as members of the Clubture Network or are associated with it in some way. Let's search for entities that are described as members of the Clubture Network.

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "position": "object",
    "query": "Clubture Network",
    "constraints": {
      "property": "wdt:P190"
    }
  }
}
```

This search will look for entities that have the Clubture Network as a member of (i.e., they are members of the Clubture Network).

Let's execute this query.

[Tool]
No entity items found

No other items found

No literal items found

[Assistant]
It seems that there are no direct or indirect links indicating the members of the Clubture Network in Wikidata. This suggests that the information about the members of the Clubture Network might not be available in the knowledge graph.

Given this, we cannot construct a SPARQL query to answer the question as the required data is not present in Wikidata.

### Conclusion
I will provide a detailed explanation of why I could not find a satisfactory SPARQL query and my best attempt so far.

```json
{
  "name": "cancel",
  "arguments": {
    "explanation": "After searching for the Clubture Network entity and attempting to find its members using various properties (P527 and P190), and even searching for entities that are described as members of the Clubture Network, no relevant data was found in Wikidata. This suggests that the information about the members of the Clubture Network is not available in the knowledge graph.",
    "best_attempt": {
      "sparql": "SELECT ?member WHERE { wd:Q96376996 wdt:P190 ?member }",
      "kg": "wikidata"
    }
  }
}
```

Let's execute this cancellation.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?item ?label WHERE {
  ?item wdt:P279 wd:Q13442814 .
  OPTIONAL {
    ?item rdfs:label ?label .
    FILTER ( LANG( ?label ) = "en" )
  }
}
ORDER BY ?label

Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?item                                    | ?label                    |
| ---------------------------------------- | ------------------------- |
| 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 as follows:

| ?item                                    | ?label                    |
| ---------------------------------------- | ------------------------- |
| 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             |

[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 items that are subclasses of scholarly articles and then order them by their labels. Let's start by finding the IRI for "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

No other items found

[Assistant]
From the search results, the most appropriate IRI for "scholarly article" seems to be `wd:Q13442814`. Now, let's find the items that are subclasses of `wd:Q13442814` and order them by their labels.

We will use the `wdt:P279` property, which represents "subclass of," to find the subclasses of `wd:Q13442814`. We will also use `rdfs:label` to get the labels of these items and order them alphabetically.

Let's construct and execute the SPARQL query:

```sparql
SELECT ?item ?label
WHERE {
  ?item wdt:P279 wd:Q13442814 .
  OPTIONAL { ?item rdfs:label ?label . FILTER(LANG(?label) = "en") }
}
ORDER BY ?label
```

This query will select the items that are subclasses of `wd:Q13442814` and their labels, and then order the results by the labels.

Now, let's execute this query.

[Tool]
Got 30 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?item                                    | ?label                    |
| ---------------------------------------- | ------------------------- |
| 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 has successfully returned the items that are subclasses of "scholarly article" (`wd:Q13442814`) and ordered them by their labels. Here are the first few and last few results:

| ?item                                    | ?label                    |
| ---------------------------------------- | ------------------------- |
| 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             |

These results provide the items that are subclasses of scholarly articles, ordered by their labels.

Now, let's finalize the answer with the SPARQL query and the results.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?airport ?airportLabel ( COUNT( ?runway ) AS ?runwayCount ) WHERE {
  ?airport wdt:P238 ?iataCode .
  ?airport wdt:P529 ?runway .
  OPTIONAL {
    ?airport rdfs:label ?airportLabel .
    FILTER ( LANG( ?airportLabel ) = "en" )
  }
}
GROUP BY ?airport ?airportLabel
HAVING ( COUNT( ?runway ) = 1 )
ORDER BY ?airportLabel

Got 2,580 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?airport                                      | ?airportLabel                   | ?runwayCount |
| --------------------------------------------- | ------------------------------- | ------------ |
| A. A. Bere Tallo Airport (wd:Q3889241)        | A. A. Bere Tallo Airport        | 1            |
| Aachen-Merzbrück airfield (wd:Q571434)        | Aachen-Merzbrück airfield       | 1            |
| Aasiaat Airport (wd:Q2983335)                 | Aasiaat Airport                 | 1            |
| Abakan International Airport (wd:Q558204)     | Abakan International Airport    | 1            |
| Abéché Airport (wd:Q3495936)                  | Abéché Airport                  | 1            |
| ...                                           | ...                             | ...          |
| Zona da Mata Regional Airport (wd:Q8073848)   | Zona da Mata Regional Airport   | 1            |
| Zonguldak Airport (wd:Q582306)                | Zonguldak Airport               | 1            |
| Zuwarah Airport (wd:Q1433954)                 | Zuwarah Airport                 | 1            |
| Zvartnots International Airport (wd:Q1124922) | Zvartnots International Airport | 1            |
| Zweibrücken airfield (wd:Q819200)             | Zweibrücken airfield            | 1            |

The international airports with exactly one runway are listed in the query results.

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

You have access to the following knowledge graphs:
wikidata at 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 international airports and the number of runways they have. We will start by searching for the property that indicates the number of runways an airport has and then proceed to construct a query that filters out the airports with exactly one runway. Let's begin by finding the relevant property.

[Tool]
Top 10 property alternatives:
1. runway, also known as RWY (wd:P529 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): name (direction) of runway at airport/airfield/airstrip
2. ICAO airport code, also known as ICAO aerodrome code, ICAO airfield code, International Civil Aviation Organization airport code (wd:P239 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): four-character alphanumeric identifier for designating airports (for airlines, see P230)
3. aerodrome reference point, also known as airfield reference point, airport reference point, ARP (wd:P2786 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, coordinate location
4. number of lanes, also known as number of lanes of that road in a direction, lanes number (wd:P10367 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): number of lanes on this road in a given direction
5. number of platform tracks, also known as platform tracks, Tracks (wd:P1103 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): number of tracks served by the platforms at a railway station
6. number of branches, also known as branches, store count, shop count, number of supermakerts, number of shops (wd:P8368 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): number of branches or stores affiliated with a chain, brand or company
7. image of design plans, also known as plan view image, plan view, plan image, floor plan, design plan (wd:P3311 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): image representing the plan of a building or place, detail map
8. takeoff and landing capability (wd:P1956 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): type of takeoff and landing the aircraft is designed to operate
9. wheelbase, also known as wheelbases (wd:P3039 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): horizontal distance between the centers of the front and rear wheels
10. number of parts of this work, also known as number of movements, number of parts of this work of art, number of pictures, number of scenes, number of segments of anthology film (wd:P2635 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): the number of parts that a work comprises

No other items found

[Assistant]
None of the top 10 property alternatives directly mention the number of runways. However, the first option, "runway" (wd:P529), seems to be the closest match. Let's check the usage of this property to confirm if it can help us find the number of runways for an airport.

I will list some examples of how the "runway" property is used.

[Tool]
Got 13,474 rows and 3 columns, showing the first 10 rows below:
| subject                                         | property          | object  |
| ----------------------------------------------- | ----------------- | ------- |
| Tan Son Nhat International Airport (wd:Q478595) | runway (wdt:P529) | 07L/25R |
| Da Nang International Airport (wd:Q773330)      | runway (wdt:P529) | 17L/35R |
| Cam Ranh International Airport (wd:Q25226)      | runway (wdt:P529) | 02L/20R |
| Cà Mau Airport (wd:Q900492)                     | runway (wdt:P529) | 9L/27R  |
| Heathrow Airport (wd:Q8691)                     | runway (wdt:P529) | 09L/27R |
| Amsterdam Airport Schiphol (wd:Q9694)           | runway (wdt:P529) | 04/22   |
| Paris-Charles de Gaulle Airport (wd:Q46280)     | runway (wdt:P529) | 08L/26R |
| Frankfurt Airport (wd:Q46033)                   | runway (wdt:P529) | 07C/25C |
| Domodedovo International Airport (wd:Q186614)   | runway (wdt:P529) | 14L/32R |
| Ben Gurion Airport (wd:Q181479)                 | runway (wdt:P529) | 03/21   |
| ...                                             | ...               | ...     |

[Assistant]
The "runway" property (wdt:P529) lists individual runways for airports, but it does not directly provide the total number of runways. To find the number of runways, we can count the distinct values of the "runway" property for each airport.

Next, we need to identify the property that indicates whether an airport is an international airport. Let's search for this property.

[Tool]
Top 10 property alternatives:
1. airline hub, also known as hub airport, hub (wd:P113 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): airport that serves as a hub for an airline
2. IATA airport code, also known as International Air Transport Association airport code, uio, IATA station code, IATA location identifier (wd:P238 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): three-letter identifier for designating airports, railway stations or cities (for airlines, see P229)
3. aerodrome reference point, also known as airfield reference point, airport reference point, ARP (wd:P2786 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): official coordinates for airport. Aeronautical publications use format with degrees, minutes and seconds concatenated, coordinate location
4. image of design plans, also known as plan view image, plan view, plan image, floor plan, design plan (wd:P3311 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): image representing the plan of a building or place, detail map
5. ICAO airport code, also known as ICAO aerodrome code, ICAO airfield code, International Civil Aviation Organization airport code (wd:P239 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): four-character alphanumeric identifier for designating airports (for airlines, see P230)
6. FAA airport code, also known as FAA LID (wd:P240 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): three-letter or four-letter alphanumeric code identifying United States airports
7. Ex-USSR internal airport code, also known as russian airport codes (wd:P5851 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): airport code similar to IATA codes but only for Russian and CEI airports
8. UTC date of spacecraft landing, also known as landing time, time of spacecraft landing, spacecraft landing time, spacecraft landing date, landing date (wd:P620 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): date of spacecraft landing in UTC
9. location of landing, also known as landing site, landing location (wd:P1158 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): location where the craft landed, significant place, location
10. Tierstimmenarchiv ID, also known as TSA, TSA ID (wd:P5397 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): identifier for a species in the Animal Sound Archive Berlin

No other items found

[Assistant]
None of the top 10 property alternatives directly indicate whether an airport is an international airport. However, the presence of an IATA airport code (wd:P238) is a strong indicator that an airport is an international airport, as IATA codes are primarily used for international travel.

Let's construct a SPARQL query to find international airports with exactly one runway. We will use the IATA airport code to filter international airports and count the number of runways using the "runway" property (wdt:P529).

Here is the initial query:

```sparql
SELECT ?airport ?airportLabel (COUNT(?runway) AS ?runwayCount)
WHERE {
  ?airport wdt:P238 ?iataCode .
  ?airport wdt:P529 ?runway .
  OPTIONAL { ?airport rdfs:label ?airportLabel . FILTER(LANG(?airportLabel) = "en") }
}
GROUP BY ?airport ?airportLabel
HAVING (COUNT(?runway) = 1)
ORDER BY ?airportLabel
```

Let's execute this query to see the results.

[Tool]
Got 2,580 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?airport                                      | ?airportLabel                   | ?runwayCount |
| --------------------------------------------- | ------------------------------- | ------------ |
| A. A. Bere Tallo Airport (wd:Q3889241)        | A. A. Bere Tallo Airport        | 1            |
| Aachen-Merzbrück airfield (wd:Q571434)        | Aachen-Merzbrück airfield       | 1            |
| Aasiaat Airport (wd:Q2983335)                 | Aasiaat Airport                 | 1            |
| Abakan International Airport (wd:Q558204)     | Abakan International Airport    | 1            |
| Abéché Airport (wd:Q3495936)                  | Abéché Airport                  | 1            |
| ...                                           | ...                             | ...          |
| Zona da Mata Regional Airport (wd:Q8073848)   | Zona da Mata Regional Airport   | 1            |
| Zonguldak Airport (wd:Q582306)                | Zonguldak Airport               | 1            |
| Zuwarah Airport (wd:Q1433954)                 | Zuwarah Airport                 | 1            |
| Zvartnots International Airport (wd:Q1124922) | Zvartnots International Airport | 1            |
| Zweibrücken airfield (wd:Q819200)             | Zweibrücken airfield            | 1            |

[Assistant]
The query has successfully returned a list of international airports with exactly one runway, along with their labels and the number of runways. The results are ordered by the airport label.

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

```sparql
SELECT ?airport ?airportLabel (COUNT(?runway) AS ?runwayCount)
WHERE {
  ?airport wdt:P238 ?iataCode .
  ?airport wdt:P529 ?runway .
  OPTIONAL { ?airport rdfs:label ?airportLabel . FILTER(LANG(?airportLabel) = "en") }
}
GROUP BY ?airport ?airportLabel
HAVING (COUNT(?runway) = 1)
ORDER BY ?airportLabel
```

The answer to the user's question is the list of international airports with exactly one runway, as provided by the query results.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?company ( COUNT( ?item ) AS ?count ) WHERE {
  ?item wdt:P272 ?company .
  ?company wdt:P31 wd:Q11396960 .
}
GROUP BY ?company
HAVING ( COUNT( ?item ) > 100 )
ORDER BY DESC ( ?count )

Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?company                                            | ?count |
| --------------------------------------------------- | ------ |
| Schauspielhaus Zürich (wd:Q40313234)                | 3203   |
| 20th Century Studios (wd:Q434841)                   | 1171   |
| Fox Film Corporation (wd:Q2644123)                  | 610    |
| Arthur Lodge Productions (wd:Q130311794)            | 386    |
| Westdeutscher Rundfunk (wd:Q203453)                 | 384    |
| ...                                                 | ...    |
| Associated British Picture Corporation (wd:Q200976) | 173    |
| Barrandov Studios (wd:Q790756)                      | 136    |
| Hammer Film Productions (wd:Q1433745)               | 129    |
| fABULEUS (wd:Q17428187)                             | 117    |
| Nea Skini Technis (wd:Q60929381)                    | 112    |

The production companies that have produced more than 100 items, ordered by the number of items produced in descending order, are as follows:
1. Schauspielhaus Zürich (wd:Q40313234) - 3203 items
2. 20th Century Studios (wd:Q434841) - 1171 items
3. Fox Film Corporation (wd:Q2644123) - 610 items
4. Arthur Lodge Productions (wd:Q130311794) - 386 items
5. Westdeutscher Rundfunk (wd:Q203453) - 384 items
...
14. Associated British Picture Corporation (wd:Q200976) - 173 items
15. Barrandov Studios (wd:Q790756) - 136 items
16. Hammer Film Productions (wd:Q1433745) - 129 items
17. fABULEUS (wd:Q17428187) - 117 items
18. Nea Skini Technis (wd:Q60929381) - 112 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 then filter out the companies that have produced more than 100 items and order the results by the number of items produced in descending order.

Let's start by finding the property that links production companies to the items they have produced.

[Tool]
Top 10 property alternatives:
1. produced by, also known as secreted by (wd:P2849 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): links a class of biological features (such as a biochemical compound) with another class of biological entities known to produce it, has immediate cause
2. manufacturer, also known as manufactured by, provider, provided by, product of, producer (wd:P176 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): (main or final) manufacturer or producer of this product
3. fabrication method, also known as process used, method of cooking, preparation method, obtained by, method used (wd:P2079 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): method, process or technique used to grow, cook, weave, build, assemble, manufacture the item, uses
4. producer, also known as music producer, record producer, produced by, film producer, creative producer (wd:P162 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): person(s) who produced the film, musical work, theatrical produc...etc.) [for production company, use P272, video games - use P178]
5. 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/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): 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
6. 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/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): 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
7. created for, also known as cause of creation, commissioned for, made for, occasion, on the occasion of (wd:P9883 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): 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. production company, also known as broadcasting company, theatrical troupe, theatre company, theater company, studio (wd:P272 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): company that produced this film, audio or performing arts work
9. working title, also known as code name, codename, production name, production title, working name (wd:P1638 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): temporary name of a product or project used during its development
10. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as prv/psv/ps/p/psn/pr/pq/wdt/pqv/wdtn/pqn/prn): maker of this creative work or other object (where no more specific property exists)

No other items found

[Assistant] From the search results, the property `wd:P272` (production company) seems to be the most relevant for linking items to their production companies. Let's confirm this by listing some examples of how this property is used.

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                | property                      | object                    |
| -------------------------------------- | ----------------------------- | ------------------------- |
| The Truth of the Moment (wd:Q12837618) | production company (wdt:P272) | Russia (wd:Q159)          |
| The Naked Maja (wd:Q1407653)           | production company (wdt:P272) | Italy (wd:Q38)            |
| Underdogs (wd:Q5664643)                | production company (wdt:P272) | Spain (wd:Q29)            |
| wd:Q113005344                          | production company (wdt:P272) | United Kingdom (wd:Q145)  |
| If (wd:Q56875931)                      | production company (wdt:P272) | United Nations (wd:Q1065) |
| wd:Q123298751                          | production company (wdt:P272) | Kazakhstan (wd:Q232)      |
| Fitil (wd:Q4485081)                    | production company (wdt:P272) | Soviet Union (wd:Q15180)  |
| wd:Q3724908                            | production company (wdt:P272) | Barcelona (wd:Q1492)      |
| Harvey Beaks (wd:Q19263671)            | production company (wdt:P272) | YouTube (wd:Q866)         |
| İki Genç Kız (wd:Q232081)              | production company (wdt:P272) | April 29 (wd:Q2535)       |
| ...                                    | ...                           | ...                       |

[Assistant]
It looks like the property `wdt:P272` (production company) is indeed used to link items to their production companies. However, some of the objects listed are not actual production companies but rather countries or other entities. This suggests that we need to filter the results to only include actual production companies.

To do this, we can use the `wdt:P31` (instance of) property to ensure that the objects are instances of a production company. Let's try to find the IRI for "production company" and then construct our query.

First, let's search for the IRI of "production company".

[Tool]
Top 10 entity alternatives:
1. production company, also known as production agency (wd:Q11396960): company or studio that produces media
2. television production company, also known as TV show producing company, TV production company, TV produceur, television production companies, TV producer company (wd:Q10689397): company that produces TV programs
3. production company, also known as ballet company, theatrical troupe, theatre company, theater company, studio (wd:P272): company that produced this film, audio or performing arts work
4. film studio, also known as film production company, movie studio, motion picture studio, movie production company (wd:Q375336): major entertainment company or motion picture company that has i...e used to make films, which is handled by the production company
5. animation studio, also known as animation production company (wd:Q1107679): production company that creates animation
6. film production company, also known as movie production company, film company, movie company (wd:Q1762059): company that produces films
7. theatre company, also known as acting company, actors' collective, theater company, theatrical company, theatrical production company (wd:Q11812394): organization that produces theatrical performances
8. Konidela production company (wd:Q27963181): film
9. Transatlantic Pictures, also known as Transatlantic (production company) (wd:Q1265201): film production company
10. Indiana Production Company (wd:Q24760226)

No other items found

No literal items found

[Assistant]
The IRI for "production company" is `wd:Q11396960`. Now we can construct a SPARQL query to find production companies that have produced more than 100 items, and order the results by the number of items produced in descending order.

Here is the initial query:

```sparql
SELECT ?company (COUNT(?item) AS ?count)
WHERE {
  ?item wdt:P272 ?company .
  ?company wdt:P31 wd:Q11396960 .
}
GROUP BY ?company
HAVING (COUNT(?item) > 100)
ORDER BY DESC(?count)
```

Let's execute this query to see the results.

[Tool]
Got 18 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?company                                            | ?count |
| --------------------------------------------------- | ------ |
| Schauspielhaus Zürich (wd:Q40313234)                | 3203   |
| 20th Century Studios (wd:Q434841)                   | 1171   |
| Fox Film Corporation (wd:Q2644123)                  | 610    |
| Arthur Lodge Productions (wd:Q130311794)            | 386    |
| Westdeutscher Rundfunk (wd:Q203453)                 | 384    |
| ...                                                 | ...    |
| Associated British Picture Corporation (wd:Q200976) | 173    |
| Barrandov Studios (wd:Q790756)                      | 136    |
| Hammer Film Productions (wd:Q1433745)               | 129    |
| fABULEUS (wd:Q17428187)                             | 117    |
| Nea Skini Technis (wd:Q60929381)                    | 112    |

[Assistant]
The 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. Schauspielhaus Zürich (wd:Q40313234) - 3203 items
2. 20th Century Studios (wd:Q434841) - 1171 items
3. Fox Film Corporation (wd:Q2644123) - 610 items
4. Arthur Lodge Productions (wd:Q130311794) - 386 items
5. Westdeutscher Rundfunk (wd:Q203453) - 384 items

Bottom 5:
1. Associated British Picture Corporation (wd:Q200976) - 173 items
2. Barrandov Studios (wd:Q790756) - 136 items
3. Hammer Film Productions (wd:Q1433745) - 129 items
4. fABULEUS (wd:Q17428187) - 117 items
5. Nea Skini Technis (wd:Q60929381) - 112 items

The query appears to be correct and provides the desired results. I will now provide the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?item ?partOf WHERE {
  {
    ?item wdt:P31 wd:Q11660 .
  } UNION {
    ?item wdt:P279 wd:Q11660 .
  }
  ?item wdt:P361 ?partOf .
}

Got 7 rows and 2 columns:
| ?item                                                       | ?partOf                                    |
| ----------------------------------------------------------- | ------------------------------------------ |
| artificial intelligence in material science (wd:Q112500260) | materials science (wd:Q228736)             |
| machine unlearning (wd:Q118970497)                          | artificial intelligence (wd:Q11660)        |
| machine unlearning (wd:Q118970497)                          | mathematics (wd:Q395)                      |
| open-source artificial intelligence (wd:Q120785614)         | open knowledge (wd:Q2251455)               |
| open-source artificial intelligence (wd:Q120785614)         | free and open-source software (wd:Q506883) |
| machine learning (wd:Q2539)                                 | artificial intelligence (wd:Q11660)        |
| machine learning (wd:Q2539)                                 | mathematics (wd:Q395)                      |

The items that are instances or subclasses of artificial intelligence, along with the objects they are part of, are as follows:

| Item                                                       | Part Of                                    |
|------------------------------------------------------------|--------------------------------------------|
| artificial intelligence in material science (wd:Q112500260) | materials science (wd:Q228736)             |
| machine unlearning (wd:Q118970497)                         | artificial intelligence (wd:Q11660)        |
| machine unlearning (wd:Q118970497)                         | mathematics (wd:Q395)                      |
| open-source artificial intelligence (wd:Q120785614)        | open knowledge (wd:Q2251455)               |
| open-source artificial intelligence (wd:Q120785614)        | free and open-source software (wd:Q506883) |
| machine learning (wd:Q2539)                                | artificial intelligence (wd:Q11660)        |
| machine learning (wd:Q2539)                                | mathematics (wd:Q395)                      |

[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 then find the objects they are part of. We will start by searching for the entity representing "artificial intelligence" and then look for items that are instances or subclasses of it. After that, we will find the objects these items are part of.

[Tool]
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 entity for "artificial intelligence" is `wd:Q11660`. We will use this entity to find items that are instances or subclasses of artificial intelligence. Let's start by finding the items that are instances of artificial intelligence.

We will use the `wdt:P31` property for "instance of" and the `wdt:P279` property for "subclass of".

Let's first list the items that are instances of artificial intelligence:

```sparql
SELECT ?item WHERE {
  ?item wdt:P31 wd:Q11660 .
}
```

Then, we will list the items that are subclasses of artificial intelligence:

```sparql
SELECT ?item WHERE {
  ?item wdt:P279 wd:Q11660 .
}
```

Finally, we will combine these results and find the objects they are part of using the `wdt:P361` property for "part of".

Let's start with the instances:

[Tool]
Got 8 rows and 1 column:
| ?item                                                       |
| ----------------------------------------------------------- |
| You.com (wd:Q111598768)                                     |
| NotebookLM (wd:Q124559107)                                  |
| wd:Q125545918                                               |
| artificial intelligence in higher education (wd:Q126198166) |
| AI washing (wd:Q127449019)                                  |
| wd:Q18337489                                                |
| artificial neural network (wd:Q192776)                      |
| AlphaFold (wd:Q60827595)                                    |

[Assistant]
We have found the items that are instances of artificial intelligence. Now, let's find the items that are subclasses of artificial intelligence:

```sparql
SELECT ?item WHERE {
  ?item wdt:P279 wd:Q11660 .
}
```

After that, we will combine these results and find the objects they are part of using the `wdt:P361` property for "part of".

[Tool]
Got 54 rows and 1 column, showing the first 5 and last 5 rows below:
| ?item                                               |
| --------------------------------------------------- |
| 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]
We have found the items that are subclasses of artificial intelligence. Now, let's combine the instances and subclasses of artificial intelligence and find the objects they are part of using the `wdt:P361` property for "part of".

Here is the combined SPARQL query:

```sparql
SELECT ?item ?partOf WHERE {
  {
    ?item wdt:P31 wd:Q11660 .
  } UNION {
    ?item wdt:P279 wd:Q11660 .
  }
  ?item wdt:P361 ?partOf .
}
```

This query will return the items that are instances or subclasses of artificial intelligence along with the objects they are part of.

Let's execute this query:

[Tool]
Got 7 rows and 2 columns:
| ?item                                                       | ?partOf                                    |
| ----------------------------------------------------------- | ------------------------------------------ |
| artificial intelligence in material science (wd:Q112500260) | materials science (wd:Q228736)             |
| machine unlearning (wd:Q118970497)                          | artificial intelligence (wd:Q11660)        |
| machine unlearning (wd:Q118970497)                          | mathematics (wd:Q395)                      |
| open-source artificial intelligence (wd:Q120785614)         | open knowledge (wd:Q2251455)               |
| open-source artificial intelligence (wd:Q120785614)         | free and open-source software (wd:Q506883) |
| machine learning (wd:Q2539)                                 | artificial intelligence (wd:Q11660)        |
| machine learning (wd:Q2539)                                 | mathematics (wd:Q395)                      |

[Assistant]
The SPARQL query has returned the items that are instances or subclasses of artificial intelligence along with the objects they are part of. Here are the results:

| Item                                                       | Part Of                                    |
|------------------------------------------------------------|--------------------------------------------|
| artificial intelligence in material science (wd:Q112500260) | materials science (wd:Q228736)             |
| machine unlearning (wd:Q118970497)                         | artificial intelligence (wd:Q11660)        |
| machine unlearning (wd:Q118970497)                         | mathematics (wd:Q395)                      |
| open-source artificial intelligence (wd:Q120785614)        | open knowledge (wd:Q2251455)               |
| open-source artificial intelligence (wd:Q120785614)        | free and open-source software (wd:Q506883) |
| machine learning (wd:Q2539)                                | artificial intelligence (wd:Q11660)        |
| machine learning (wd:Q2539)                                | mathematics (wd:Q395)                      |

These results show the items that are instances or subclasses of artificial intelligence and the objects they are part of.

Now, let's provide the final answer:

```sparql
SELECT ?item ?partOf WHERE {
  {
    ?item wdt:P31 wd:Q11660 .
  } UNION {
    ?item wdt:P279 wd:Q11660 .
  }
  ?item wdt:P361 ?partOf .
}
```

The answer to the question is the set of items and their corresponding parts as shown above.

[Tool] Stopped generation process