PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?productionCompany ?productionCompanyLabel ( COUNT( ?item ) AS ?itemCount ) WHERE {
  ?item wdt:P272 ?productionCompany .
  ?productionCompany rdfs:label ?productionCompanyLabel .
  FILTER ( LANG( ?productionCompanyLabel ) = "en" )
}
GROUP BY ?productionCompany ?productionCompanyLabel
HAVING ( COUNT( ?item ) > 100 )
ORDER BY DESC ( ?itemCount )
LIMIT 100

Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| productionCompany                       | productionCompanyLabel         | itemCount      |
| --------------------------------------- | ------------------------------ | -------------- |
| Theater Basel (wd:Q391991)              | Theater Basel (en)             | 7865 (xsd:int) |
| Schauspielhaus Zürich (wd:Q40313234)    | Schauspielhaus Zürich (en)     | 3203 (xsd:int) |
| Vitagraph Studios (wd:Q1418400)         | Vitagraph Studios (en)         | 2551 (xsd:int) |
| Essanay Studios (wd:Q1368540)           | Essanay Studios (en)           | 1974 (xsd:int) |
| Universal Pictures (wd:Q168383)         | Universal Pictures (en)        | 1625 (xsd:int) |
| ...                                     | ...                            | ...            |
| Muziektheater Transparant (wd:Q2747185) | Muziektheater Transparant (en) | 197 (xsd:int)  |
| Troma Entertainment (wd:Q640786)        | Troma Entertainment (en)       | 194 (xsd:int)  |
| Mitteldeutscher Rundfunk (wd:Q158948)   | Mitteldeutscher Rundfunk (en)  | 193 (xsd:int)  |
| Bavaria Film (wd:Q648034)               | Bavaria Film (en)              | 190 (xsd:int)  |
| Mongolkino (wd:Q21663353)               | Mongolkino (en)                | 189 (xsd:int)  |

The query results show production companies that have produced more than 100 items, ordered by the number of items produced in descending order. For example, Theater Basel produced 7865 items, Schauspielhaus Zürich produced 3203 items, Vitagraph Studios produced 2551 items, Essanay Studios produced 1974 items, and Universal Pictures produced 1625 items. The full list contains 100 production companies meeting the criteria.

[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, I need to identify the property that links production companies to the items they have produced. Then, I will count the number of items produced by each production company and filter those with more than 100 items. Finally, I will order the results by the number of items produced in descending order.

Step 1: Identify the entity for "production company."
Step 2: Identify the property that links production companies to the items they have produced.
Step 3: Write a SPARQL query to count the number of items produced by each production company, filter those with more than 100 items, and order the results.

I will start by searching for the entity "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)

[Assistant]
The entity "production company" is wd:Q11396960, which represents a company or studio that produces media. There is also a property wd:P272 which is described as "production company" and is used to indicate the company that produced a film, audio, or performing arts work.

Next, I will verify that wd:P272 is the correct property to link items to their production companies by checking some examples of its usage.

[Tool]
Got 65,536 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)                       |
| wd:Q113005344                               | production company (wdt:P272) | United Kingdom (wd:Q145)             |
| wd:Q123298751                               | production company (wdt:P272) | Kazakhstan (wd:Q232)                 |
| wd:Q132192931                               | production company (wdt:P272) | YouTube (wd:Q866)                    |
| wd:Q15623534                                | production company (wdt:P272) | World Health Organization (wd:Q7817) |
| Developer_Direct (wd:Q124222084)            | production company (wdt:P272) | Microsoft (wd:Q2283)                 |
| Bad Sisters (wd:Q111943988)                 | production company (wdt:P272) | Apple (wd:Q312)                      |
| Thomas and the Magic Railroad (wd:Q1635070) | production company (wdt:P272) | Isle of Man (wd:Q9676)               |
| Go, Dog. Go! (wd:Q104750296)                | production company (wdt:P272) | WildBrain Studios (wd:Q60749767)     |
| ...                                         | ...                           | ...                                  |

[Assistant] The property wd:P272 is used to link items (such as films or shows) to their production companies. Now I will write a SPARQL query to count the number of items produced by each production company, filter those with more than 100 items, and order the results by the number of items produced in descending order. I will also include the label of the production company for better readability.

[Tool]
Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| productionCompany                       | productionCompanyLabel         | itemCount      |
| --------------------------------------- | ------------------------------ | -------------- |
| Theater Basel (wd:Q391991)              | Theater Basel (en)             | 7865 (xsd:int) |
| Schauspielhaus Zürich (wd:Q40313234)    | Schauspielhaus Zürich (en)     | 3203 (xsd:int) |
| Vitagraph Studios (wd:Q1418400)         | Vitagraph Studios (en)         | 2551 (xsd:int) |
| Essanay Studios (wd:Q1368540)           | Essanay Studios (en)           | 1974 (xsd:int) |
| Universal Pictures (wd:Q168383)         | Universal Pictures (en)        | 1625 (xsd:int) |
| ...                                     | ...                            | ...            |
| Muziektheater Transparant (wd:Q2747185) | Muziektheater Transparant (en) | 197 (xsd:int)  |
| Troma Entertainment (wd:Q640786)        | Troma Entertainment (en)       | 194 (xsd:int)  |
| Mitteldeutscher Rundfunk (wd:Q158948)   | Mitteldeutscher Rundfunk (en)  | 193 (xsd:int)  |
| Bavaria Film (wd:Q648034)               | Bavaria Film (en)              | 190 (xsd:int)  |
| Mongolkino (wd:Q21663353)               | Mongolkino (en)                | 189 (xsd:int)  |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves production companies that have produced more than 100 items, counts the number of items per company, filters by count > 100, and orders the results in descending order by the number of items produced. The query also retrieves English labels for the companies. The answer provides a clear summary with example companies and their production counts, matching the query results. The output is complete, accurate, and follows the guidelines for SPARQL queries and answers.


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 ?item ?itemLabel ?partOf ?partOfLabel WHERE {
  {
    ?item wdt:P31 wd:Q11660 .
  } UNION {
    ?item wdt:P279 wd:Q11660 .
  }
  OPTIONAL {
    ?item wdt:P361 ?partOf .
  }
  OPTIONAL {
    ?item rdfs:label ?itemLabel FILTER ( LANG( ?itemLabel ) = "en" )
  }
  OPTIONAL {
    ?partOf rdfs:label ?partOfLabel FILTER ( LANG( ?partOfLabel ) = "en" )
  }
}
LIMIT 10

SPARQL execution failed:
Tried to allocate 6.6 GB, but only 5.4 GB were available. Clear the cache or allow more memory for QLever during startup

Here are some items that are instances or subclasses of artificial intelligence, along with the objects they are part of if available. The items include automated music production, autonomous navigation, Green AI, Red AI, artificial intelligence in hiring, responsible AI, Vision and Language, You.com, and artificial intelligence in fraud detection. However, none of these items have 'part of' relations populated 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] 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, I need to identify the entity for "artificial intelligence" in the knowledge graph. Then, I will find items that are instances or subclasses of this entity. Additionally, I will find the objects these items are part of.

Step 1: Search for the entity "artificial intelligence" in Wikidata.

[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

[Assistant]
The entity "artificial intelligence" is identified as wd:Q11660, which is the field of computer science that develops and studies intelligent machines.

Step 2: Find items that are instances (P31) or subclasses (P279) of artificial intelligence (wd:Q11660).

Step 3: For these items, find the objects they are part of (P361).

I will now construct and execute a SPARQL query to retrieve these items and the objects they are part of.

[Tool]
SPARQL execution failed:
Tried to allocate 6.6 GB, but only 5.4 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
The query I executed was too large and caused a memory issue. I will try to reduce the complexity by splitting the query into two parts: first, get items that are instances of artificial intelligence and their parts; second, get items that are subclasses of artificial intelligence and their parts. Then I will combine the results.

Step 1: Get items that are instances of artificial intelligence and their parts.

[Tool]
SPARQL execution failed:
Tried to allocate 6.6 GB, but only 5.4 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant] The query for instances of artificial intelligence still causes a memory issue. I will try to limit the number of results further to 10 and run the query again.

[Tool]
SPARQL execution failed:
Tried to allocate 6.6 GB, but only 5.4 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
The query for instances of artificial intelligence still causes a memory issue even with a limit of 10. This suggests that there might be very few or no direct instances of artificial intelligence in the data, or the data structure is complex.

I will now try to find subclasses of artificial intelligence (wd:Q11660) and their parts with a limit of 10.

[Tool]
SPARQL execution failed:
Tried to allocate 6.6 GB, but only 5.4 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
The query for subclasses of artificial intelligence also causes a memory issue. This suggests that the direct approach of querying instances or subclasses with their parts is too memory-intensive.

I will try a different approach: first, find items that are instances or subclasses of artificial intelligence without retrieving their parts. Then, for a smaller set of these items, I will query their parts separately.

Step 1: Get items that are instances or subclasses of artificial intelligence with a limit of 20.

[Tool]
Got 20 rows and 2 columns, showing the first 5 and last 5 rows below:
| item                                               | itemLabel                               |
| -------------------------------------------------- | --------------------------------------- |
| automated music production (wd:Q100148106)         | automated music production (en)         |
| autonomous navigation (wd:Q100712173)              | autonomous navigation (en)              |
| Green AI (wd:Q103845039)                           | Green AI (en)                           |
| Red AI (wd:Q103873243)                             | Red AI (en)                             |
| artificial intelligence in hiring (wd:Q104878955)  | artificial intelligence in hiring (en)  |
| ...                                                | ...                                     |
| generative artificial intelligence (wd:Q117246174) | generative artificial intelligence (en) |
| applied artificial intelligence (wd:Q117289639)    | applied artificial intelligence (en)    |
| affective computing (wd:Q1185804)                  | affective computing (en)                |
| machine unlearning (wd:Q118970497)                 | machine unlearning (en)                 |
| evolutionary computation (wd:Q1197129)             | evolutionary computation (en)           |

[Assistant]
I have retrieved 20 items that are instances or subclasses of artificial intelligence. Now, I will query the objects these items are part of (P361) for these specific items.

Step 2: For the retrieved items, find the objects they are part of.

[Tool]
SPARQL execution failed:
Tried to allocate 6.6 GB, but only 5.4 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant] The query to find the objects these items are part of still causes a memory issue. I will reduce the number of items in the VALUES clause to 5 and try again.

[Tool]
SPARQL execution failed:
Tried to allocate 6.6 GB, but only 5.4 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
The query still causes a memory issue even with only 5 items in the VALUES clause. This suggests that the property P361 (part of) might be very complex or large for these items.

I will try a different approach: I will first check if these items have the property P361 at all by querying the count of such relations for one item. This will help me understand if the property is heavily used or not.

Step 3: Check the count of P361 relations for one item (e.g., wd:Q100148106).

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The item wd:Q100148106 (automated music production) has no P361 (part of) relations. I will check another item from the list to see if it has any P361 relations.

Step 4: Check the count of P361 relations for another item (e.g., wd:Q100712173).

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 0 (xsd:int) |

[Assistant]
The item wd:Q100712173 (autonomous navigation) also has no P361 (part of) relations.

Given that the items I checked do not have "part of" relations, it is likely that many items related to artificial intelligence do not have this property populated.

I will now provide the list of items that are instances or subclasses of artificial intelligence without the "part of" information, as the memory constraints and data availability limit further exploration.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves items that are instances or subclasses of artificial intelligence and their labels. However, the question also asks for the objects these items are part of, which the current query does not address. The query should be refined to include an OPTIONAL clause to retrieve the 'part of' relations (wdt:P361) for each item, along with their labels if available. This will provide a more complete answer to the user's question. Additionally, the query should ensure it returns the IRIs of the items as required.

[Assistant]
Thank you for the feedback. I will refine the SPARQL query to include an OPTIONAL clause to retrieve the 'part of' relations (wdt:P361) for each item, along with their labels if available. This will provide a more complete answer to the user's question while ensuring the IRIs of the items are returned.

I will limit the number of results to avoid memory issues and include the 'part of' information.

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query is logically correct in terms of structure and the properties used to find items that are instances or subclasses of artificial intelligence and their part-of relations. However, the query failed to execute due to memory allocation issues on the QLever endpoint. To fix this, the query should be refined to reduce memory usage, for example by limiting the number of results earlier or optimizing the query pattern. Also, consider removing one of the OPTIONAL clauses for labels or limiting the number of optional label retrievals. Once the query can run successfully, it should return the expected results. Therefore, the system should retry with a more memory-efficient query.

[Assistant]
To address the memory allocation issues, I will refine the query to reduce memory usage by limiting the number of results earlier and simplifying the query pattern. I will remove one of the OPTIONAL clauses for labels and limit the number of optional label retrievals.

I will first retrieve items that are instances or subclasses of artificial intelligence with their IRIs and labels, limiting to 10 results. Then, I will separately retrieve the 'part of' relations for these items.

Step 1: Retrieve items that are instances or subclasses of artificial intelligence with labels, limited to 10.

[Tool]
Got 10 rows and 2 columns:
| item                                                       | itemLabel                                       |
| ---------------------------------------------------------- | ----------------------------------------------- |
| automated music production (wd:Q100148106)                 | automated music production (en)                 |
| autonomous navigation (wd:Q100712173)                      | autonomous navigation (en)                      |
| Green AI (wd:Q103845039)                                   | Green AI (en)                                   |
| Red AI (wd:Q103873243)                                     | Red AI (en)                                     |
| artificial intelligence in hiring (wd:Q104878955)          | artificial intelligence in hiring (en)          |
| responsible AI (wd:Q105606338)                             | responsible AI (en)                             |
| wd:Q106548707                                              |                                                 |
| Vision and Language (wd:Q111467776)                        | Vision and Language (en)                        |
| You.com (wd:Q111598768)                                    | You.com (en)                                    |
| artificial intelligence in fraud detection (wd:Q111982800) | artificial intelligence in fraud detection (en) |

[Assistant]
I have retrieved 10 items that are instances or subclasses of artificial intelligence along with their labels.

Step 2: For these items, I will now retrieve the 'part of' relations (wdt:P361) and their labels if available.

[Tool] Got no rows and 3 columns

[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 ?artwork ?artworkLabel ?creator ?creatorLabel WHERE {
  ?artwork wdt:P31 wd:Q860861 ; wdt:P170 ?creator ; wdt:P131 * wd:Q84 .
  ?creator wdt:P21 wd:Q6581072 .
  OPTIONAL {
    ?artwork rdfs:label ?artworkLabel FILTER ( LANG( ?artworkLabel ) = "en" )
  }
  OPTIONAL {
    ?creator rdfs:label ?creatorLabel FILTER ( LANG( ?creatorLabel ) = "en" )
  }
}
LIMIT 100

Got 3 rows and 4 columns:
| artwork                             | artworkLabel             | creator                                | creatorLabel                 |
| ----------------------------------- | ------------------------ | -------------------------------------- | ---------------------------- |
| The Tuckshop Tanner (wd:Q122678557) | The Tuckshop Tanner (en) | Jessica Wetherly (wd:Q122678392)       | Jessica Wetherly (en)        |
| The End (sculpture) (wd:Q118697934) | The End (sculpture) (en) | Heather Phillipson (wd:Q19517821)      | Heather Phillipson (en)      |
| Reach (wd:Q123049287)               | Reach (en)               | Pixie Mudge Massey Shaw (wd:Q90518856) | Pixie Mudge Massey Shaw (en) |

There are public artworks in London created by female artists. Examples include:
- The Tuckshop Tanner by Jessica Wetherly
- The End (sculpture) by Heather Phillipson
- Reach by Pixie Mudge Massey Shaw
These artworks are sculptures located in London and created by female artists.

[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 public artworks located in London created by female artists? For each artwork, include also the artwork's creator.

[Assistant]
To answer the question "What are the public artworks located in London created by female artists? For each artwork, include also the artwork's creator," I will proceed with the following steps:

1. Identify the entity for "London" to use as a location filter.
2. Identify the property for "public artwork" or similar to filter artworks.
3. Identify the property for "creator" or "artist" of the artwork.
4. Identify the property or qualifier to filter creators who are female.
5. Construct a SPARQL query to retrieve artworks located in London, created by female artists, including the artwork and the creator.

First, I will search for the entity "London" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. London, also known as Loniniensi, Trinovantum, the Big Smoke, Lundenwic, Lundenburh (wd:Q84): capital and largest city of England and the United Kingdom
2. City of London, also known as City of London (unparished area), City and County of the City of London, the City, London, Square Mile (wd:Q23311): city, ceremonial county and local government district that conta...ry central business district in the London Region in England, UK
3. London, also known as City of London, London, ON, London, Ontario (wd:Q92561): city in Southwestern Ontario, Canada
4. London, also known as London, KY, London, Kentucky (wd:Q3061911): city in Kentucky, United States
5. London, also known as London, OH, London, Ohio (wd:Q1001456): city in Madison County, Ohio, United States
6. London, also known as London, AR, London, Arkansas (wd:Q79348): city in Pope County, Arkansas, United States
7. London, also known as London, CA, London, California (wd:Q586353): census designated place in Tulare County, California, United States
8. London Recordings, also known as London, London Records (wd:Q935090): record label headquartered in the United Kingdom
9. London (wd:Q1749384): 2005 film directed by Hunter Richards
10. London, also known as Ronton (wd:Q2477346): settlement in Kiribati

[Assistant]
The entity for London as the capital and largest city of England and the United Kingdom is wd:Q84.

Next, I will search for properties related to "public artwork" or "public art" to identify how public artworks are represented in Wikidata.

[Tool]
Top 10 property alternatives:
1. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): notable scientific, artistic or literary work, or other work of significance among subject's works
2. Barcelona Public art ID, also known as Art Public ID (wd:P8601 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): identifier for public art in Barcelona
3. creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): maker of this creative work or other object (where no more specific property exists)
4. image with frame, also known as picture with frame, painting with surroundings, img with frame, image in frame, framed artwork (wd:P7420 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): media file of painting or other 2D artwork with its frame or more of its surroundings included
5. Art for the City inventory number, also known as Art for the City - inventory number, inventory number of a work of art in the Art for the City database, Umění pro město - inventory number (wd:P9471 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): alphanumeric inventory number of a work in the Art for the City database
6. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): primary topic of a work (see also P180: depicts), depicts
7. exhibited creator, also known as artist exhibited, exhibited artist (wd:P10661 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): intellectual or creative entity (person or organization) responsible for the elements exhibited, creator
8. cover art by, also known as jacket art by, sleeve art by, designer, cover design by, cover artist (wd:P736 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): name of person or team creating cover artwork for book, record album, single record etc.
9. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): structure of a creative work
10. collection, also known as editorial collection, series, repository, museum collection, held by (wd:P195 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): art, museum, archival, or bibliographic collection of which the subject is part, part of

[Assistant]
The property "creator" (wdt:P170) is used for the maker of a creative work, which is relevant for identifying the artist of an artwork.

There is no direct property for "public artwork," but we can filter artworks by location (London) and by the type of artwork. I will search for the entity "public artwork" or "public art" to see if there is a specific class or type for such artworks.

[Tool]
Top 10 entity alternatives:
1. Color artwork for medical publications (wd:Q78904504): scientific article published on 01 January 1960
2. audience, also known as public (wd:Q211198): group of people who participate in a show or encounter a work of art
3. work of art, also known as art, artistic work, artistic works, art object, art piece (wd:Q838948): aesthetic item or artistic creation
4. public, also known as general public, publics, the general public, the public (wd:Q2388316): grouping of individual people
5. citizen journalism, also known as citizen reporter, democratic journalism, guerrilla journalism, participatory journalism, public (wd:Q848979): journalism genre
6. creative work, also known as artwork, creative works, work, work of art, works (wd:Q17537576): artistic creation
7. work, also known as artwork, creation, creative work (wd:Q386724): intellectual or artistic creation
8. work, also known as artwork, creation, creative work (wd:Q386724): intellectual or artistic creation
9. Artwork (wd:Q2667810): 2009 album by The Used
10. Public (wd:Q7257279): quartier of Saint Barthélemy

[Assistant]
There is no specific entity for "public artwork" as a distinct class, but "work of art" (wd:Q838948) or "creative work" (wd:Q17537576) could be used as a general class for artworks.

Next, I will identify the property for "location" or "located in" to filter artworks located in London. I will search for properties related to "location."

[Tool]
Top 10 property alternatives:
1. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): location where persons or organisations were actively participating in employment, business or other work, significant place, location
2. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
3. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
4. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): person or organization occupying property, used by
5. recorded at studio or venue, also known as venue, studio, recording venue, recording studio, recorded at event (wd:P483 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): studio or location where a musical composition/release was recorded, recording location
6. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): home stadium or venue of a sports team or applicable performing arts organization, location
7. published in, also known as song on album, tome of the book, tome of, track of, track on (wd:P1433 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): larger work that a given work was published in, like a book, journal or music album, part of
8. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, would today be located in administrative territorial entity (wd:P3842 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
9. directions, also known as locality or place (wd:P2795 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): describe how to find the subject - directions, objects along way, comments
10. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): subject's ordinal position as qualitatively evaluated relative to other members of a group

[Assistant]
The property "location" (wdt:P276) is used for the location of an object, structure, or event. This is suitable for filtering artworks located in London.

Next, I will identify the property or qualifier to filter creators who are female. I will search for properties related to "gender" for the creator.

[Tool]
Top 10 property alternatives:
1. grammatical gender, also known as gender (wd:P5185 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): grammatical gender of the word
2. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
3. requires grammatical feature, also known as grammatical feature required, required grammatical feature, requires case, requires mood, uses case (wd:P5713 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): grammatical features required or generally used with lexeme or form of lexeme
4. has grammatical gender, also known as grammatical genders, has genders (wd:P5109 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): specific form of noun class system of a language. Use qualifier ... subject of" (P805) to link to language specific item if present
5. personal pronoun, also known as gender pronoun, personal gender pronoun, personal pronouns, PGP, PGPs (wd:P6553 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): personal pronoun(s) this person goes by
6. semantic gender, also known as natural gender (wd:P10339 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): used when a word is only used to refer to people of this gender or to animals of this sex, independent of the grammatical gender
7. female form of label, also known as female form of label (string), female gender sense, feminine form of label (wd:P2521 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): female form of name or title (for male use P3321)
8. gender educated (wd:P7419 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): genders educated at this educational institution
9. male form of label, also known as male form of label (string), male gender sense, masculine form of label (wd:P3321 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): male form of name or title (for female use P2521)
10. given name version for other gender, also known as personal name version for other gender, other genders given name, male version of this female given name, gender inflection of given name, female version of this male given name (wd:P1560 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): equivalent name (with respect to the meaning of the name) in the...n of a female first name. Add primarily the closest matching one

[Assistant]
The property "sex or gender" (wdt:P21) is used for the sex or gender identity of a human or animal. We can use this property to filter creators who are female (wd:Q6581072).

Now, I will construct a SPARQL query to find artworks (instances of work of art) located in London (wd:Q84) created by female artists. The query will return the artwork and its creator.

[Tool] Got no rows and 4 columns

[Assistant]
The query returned no results, which might be due to the strict filtering on the location property (wdt:P276) directly as London (wd:Q84). Public artworks might be located in specific places within London, not directly linked to London itself.

I will try to relax the location condition by using the property "located in the administrative territorial entity" (wdt:P131) instead of "location" (wdt:P276) to capture artworks located in any administrative area within London. I will also keep the other conditions the same.

[Tool] Got no rows and 4 columns

[Assistant]
The query still returned no results. It is possible that the artworks are not directly linked to London (wd:Q84) via the property "located in the administrative territorial entity" (wdt:P131) either.

I will try a different approach: I will look for artworks that are instances of "sculpture" (a common form of public artwork) and located in London or its boroughs. I will also keep the filter for female creators.

First, I will search for the entity "sculpture" to use as a class for artworks.

[Tool]
Top 10 entity alternatives:
1. art of sculpture, also known as sculpting, sculpture (wd:Q11634): branch of the visual arts that operates in three dimensions
2. sculpture, also known as piece of sculpture, sculptural work, sculptures (wd:Q860861): three-dimensional work of art
3. Sculpture (wd:Q7439454): feature on the shells of mollusks
4. Sculpture (wd:Q2924134): 2005 orchestral composition by Magnus Lindberg
5. Sculpture (wd:Q18352932): sculpture by Philolaos at Lyon
6. sculpture (wd:Q17310537): manufacture of sculptures in arts and crafts
7. Balance, also known as Moenen and Mariken, Sculpture (wd:Q83740338): public art sculpture by Jan Samsom at the Daalseweg roundabout in Nijmegen, the Netherlands
8. sculpture, also known as Escultura (wd:Q131820184): sculpture by Juan Antonio Giraldo in Las Palmas de Gran Canaria
9. sculpture (wd:Q14325265): concept in malacology
10. sculpture (wd:Q110551761): sculpture by Dietrich Ebert in Berlin, Germany

[Assistant]
The entity for sculpture as a three-dimensional work of art is wd:Q860861.

I will now modify the query to find artworks that are instances of sculpture (wd:Q860861), located in London (wd:Q84) or its administrative territorial entities, created by female artists. I will use a property path for location to include artworks located in London or its subdivisions.

[Tool]
Got 3 rows and 4 columns:
| artwork                             | artworkLabel             | creator                                | creatorLabel                 |
| ----------------------------------- | ------------------------ | -------------------------------------- | ---------------------------- |
| The Tuckshop Tanner (wd:Q122678557) | The Tuckshop Tanner (en) | Jessica Wetherly (wd:Q122678392)       | Jessica Wetherly (en)        |
| The End (sculpture) (wd:Q118697934) | The End (sculpture) (en) | Heather Phillipson (wd:Q19517821)      | Heather Phillipson (en)      |
| Reach (wd:Q123049287)               | Reach (en)               | Pixie Mudge Massey Shaw (wd:Q90518856) | Pixie Mudge Massey Shaw (en) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves public artworks that are sculptures located in London and created by female artists. It returns both the artwork and creator identifiers along with their English labels, which aligns well with the user's request. The example answers provided are relevant and accurate based on the query results. The query uses appropriate properties and filters, and the results are concise and informative. No further refinement is necessary.


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 ?catBreed ?catBreedLabel ?cat ?catLabel WHERE {
  ?catBreed wdt:P31 wd:Q43577 .
  ?catBreed rdfs:label ?catBreedLabel
  FILTER ( LANG( ?catBreedLabel ) = "en" ) .
  OPTIONAL {
    ?cat wdt:P31 ?catBreed .
    OPTIONAL {
      ?cat rdfs:label ?catLabel FILTER ( LANG( ?catLabel ) = "en" )
    }
  }
}
ORDER BY ?catBreedLabel ?catLabel
LIMIT 100

Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| catBreed                       | catBreedLabel         | cat | catLabel |
| ------------------------------ | --------------------- | --- | -------- |
| Abyssinian (wd:Q7955)          | Abyssinian (en)       |     |          |
| Aegean cat (wd:Q7957)          | Aegean cat (en)       |     |          |
| American Bobtail (wd:Q7874)    | American Bobtail (en) |     |          |
| American burmese (wd:Q2928526) | American burmese (en) |     |          |
| American Curl (wd:Q7960)       | American Curl (en)    |     |          |
| ...                            | ...                   | ... | ...      |
| Pixie-bob (wd:Q42693)          | Pixie-bob (en)        |     |          |
| Raas (wd:Q17517549)            | Raas (en)             |     |          |
| Ragamuffin (wd:Q42637)         | Ragamuffin (en)       |     |          |
| Ragdoll (wd:Q42688)            | Ragdoll (en)          |     |          |
| Rex mutation (wd:Q42949)       | Rex mutation (en)     |     |          |

I have improved the query to ensure that cat breeds always have an English label, improving readability. The query returns up to 100 cat breeds (instances of 'cat breed' wd:Q43577) along with cats that are instances of those breeds if available. For example, Abyssinian and American Bobtail are cat breeds returned with their English labels. Cats that are instances of these breeds are also included if available.

[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 cat breeds? For each cat breed, also return the cats that are instances of it if available.

[Assistant]
To answer the question "What are the cat breeds? For each cat breed, also return the cats that are instances of it if available," I need to:

1. Identify the entity for "cat breed" in Wikidata.
2. Find all instances of this entity (i.e., all cat breeds).
3. For each cat breed, find cats that are instances of that breed.

Step 1: Search for the entity "cat breed" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. cat breed, also known as breed of cat, pedigree cat, pedigreed cat (wd:Q43577): type of pet breed
2. cat breed (wd:L456414-S1)
3. cat breed (wd:L456395-S1)
4. animal breed, also known as dog breed, sheep breed, horse breed, goat breed, duck breed (wd:P4743): subject item belongs to a specific group of domestic animals, generally given by association
5. cat registry, also known as cat breed registry (wd:Q5051107): organization that registers domestic, usually purebred, cats
6. cat breeding (wd:Q3587680): breeding of house cats
7. cattle breed, also known as cow breed (wd:Q12045585): selectively bred form of the domesticated cattle
8. Catherine Breed (wd:Q130086352): American swimmer, swimmer
9. mixed breed cat (wd:Q116193823): cat with mixed breeds
10. Heck cattle, also known as Munich-Berlin (cattle breed) (wd:Q475329): cattle breed, look-alike bred-back of the extinct aurochs

[Assistant]
The entity "cat breed" is wd:Q43577. This represents the type of pet breed specifically for cats.

Next, I will find all instances of this entity (all cat breeds) and for each cat breed, find cats that are instances of that breed. I will write a SPARQL query to get this information.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant]
The query failed because the SERVICE wikibase:label is not supported by the QLever SPARQL endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label for labels.

I will now write a query to get cat breeds and cats that are instances of those breeds, including their labels using rdfs:label.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| catBreed                        | catBreedLabel           | cat                    | catLabel     |
| ------------------------------- | ----------------------- | ---------------------- | ------------ |
| wd:Q2933986                     |                         |                        |              |
| wd:Q25456858                    |                         |                        |              |
| wd:Q125904120                   |                         |                        |              |
| wd:Q18021887                    |                         |                        |              |
| wd:Q131315636                   |                         |                        |              |
| ...                             | ...                     | ...                    | ...          |
| Oriental Longhair (wd:Q2099338) | Oriental Longhair (en)  |                        |              |
| Oriental Shorthair (wd:Q42696)  | Oriental Shorthair (en) |                        |              |
| Owyhee Bob (wd:Q25456868)       | Owyhee Bob (en)         |                        |              |
| Persian cat (wd:Q42610)         | Persian cat (en)        | Pumpkin (wd:Q51846260) | Pumpkin (en) |
| Peterbald (wd:Q42663)           | Peterbald (en)          |                        |              |

[Assistant]
I have retrieved cat breeds and cats that are instances of those breeds. Some cat breeds have cats listed as instances, for example, the Persian cat breed has a cat named Pumpkin as an instance.

I will now finalize the answer with the SPARQL query and a summary of the findings.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query correctly retrieves cat breeds and their instances (cats) if available, including labels in English. However, there are some cat breeds without labels in the result, which reduces readability. The query should ensure that the cat breed label is always retrieved and preferably filter out or handle cases where the label is missing. Also, the query currently returns up to 100 results, which is fine, but it might be useful to clarify this limit in the answer. Additionally, the OPTIONAL pattern for cat labels is nested inside the OPTIONAL for cats, which is good, but the cat breed label retrieval is also optional, which may cause missing labels. It would be better to make the cat breed label retrieval mandatory or at least ensure it is present for all returned breeds. Overall, the answer is good but can be improved by ensuring labels are always present for cat breeds and clarifying the limit in the answer.

[Assistant]
Thank you for the feedback. To improve the query, I will:

1. Make the retrieval of cat breed labels mandatory by removing the OPTIONAL clause around it.
2. Filter out cat breeds that do not have an English label to ensure readability.
3. Keep the OPTIONAL pattern for cats and their labels as it is, since cats may or may not be available for each breed.
4. Clarify in the answer that the query returns up to 100 cat breeds with their cats if available.

I will now update the SPARQL query accordingly.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| catBreed                       | catBreedLabel         | cat | catLabel |
| ------------------------------ | --------------------- | --- | -------- |
| Abyssinian (wd:Q7955)          | Abyssinian (en)       |     |          |
| Aegean cat (wd:Q7957)          | Aegean cat (en)       |     |          |
| American Bobtail (wd:Q7874)    | American Bobtail (en) |     |          |
| American burmese (wd:Q2928526) | American burmese (en) |     |          |
| American Curl (wd:Q7960)       | American Curl (en)    |     |          |
| ...                            | ...                   | ... | ...      |
| Pixie-bob (wd:Q42693)          | Pixie-bob (en)        |     |          |
| Raas (wd:Q17517549)            | Raas (en)             |     |          |
| Ragamuffin (wd:Q42637)         | Ragamuffin (en)       |     |          |
| Ragdoll (wd:Q42688)            | Ragdoll (en)          |     |          |
| Rex mutation (wd:Q42949)       | Rex mutation (en)     |     |          |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves cat breeds (instances of wd:Q43577) along with their English labels and optionally the cats that are instances of those breeds with their English labels. The use of OPTIONAL clauses ensures that cat breeds without known individual cats are still included. The query respects the requirement to return identifiers and labels, and the result is ordered and limited appropriately. Overall, the output is correct and complete for the user's question.


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 ?item ?itemLabel ?connectingLine ?connectingLineLabel WHERE {
  ?item wdt:P81 ?connectingLine .
  ?connectingLine wdt:P31 wd:Q15079663 .
  FILTER NOT EXISTS {
    ?item wdt:P31 wd:Q928830
  }
  OPTIONAL {
    ?item rdfs:label ?itemLabel FILTER ( LANG( ?itemLabel ) = "en" )
  }
  OPTIONAL {
    ?connectingLine rdfs:label ?connectingLineLabel FILTER ( LANG( ?connectingLineLabel ) = "en" )
  }
}
LIMIT 100

Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| item                                               | itemLabel                               | connectingLine                               | connectingLineLabel                |
| -------------------------------------------------- | --------------------------------------- | -------------------------------------------- | ---------------------------------- |
| wd:Q111738574                                      |                                         | Line 1 of the Salvador subway (wd:Q10318644) | Line 1 of the Salvador subway (en) |
| wd:Q19614659                                       |                                         | Line 1 (Yerevan Metro) (wd:Q10318696)        | Line 1 (Yerevan Metro) (en)        |
| Nazarbekian (wd:Q2060834)                          | Nazarbekian (en)                        | Line 1 (Yerevan Metro) (wd:Q10318696)        | Line 1 (Yerevan Metro) (en)        |
| Fukuoka City Subway Hakata Station (wd:Q124368779) | Fukuoka City Subway Hakata Station (en) | Nanakuma Line (wd:Q1039166)                  | Nanakuma Line (en)                 |
| Tuqiao depot (wd:Q10927801)                        | Tuqiao depot (en)                       | Batong line (wd:Q1039452)                    | Batong line (en)                   |
| ...                                                | ...                                     | ...                                          | ...                                |
| Plaza Rakyat LRT station (wd:Q6150701)             | Plaza Rakyat LRT station (en)           | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Pudu LRT station (wd:Q6150815)                     | Pudu LRT station (en)                   | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Hang Tuah station (wd:Q27142)                      | Hang Tuah station (en)                  | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Sultan Ismail LRT station (wd:Q6150771)            | Sultan Ismail LRT station (en)          | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Sentul LRT station (wd:Q27140)                     | Sentul LRT station (en)                 | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |

The query returns items that are not metro stations but have a connecting line which is a rapid transit railway line. For each such item, the connecting line is also returned. Examples include Nazarbekian connected to Line 1 (Yerevan Metro), Fukuoka City Subway Hakata Station connected to Nanakuma Line, and Tuqiao depot connected to Batong line, among others.

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

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

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

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

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

[User] What are the items that are not metro stations, but have a connecting line which is a rapid transit railway line? For each such item, return also the connecting line in your results.

[Assistant]
To answer this question, I need to identify items that have a connecting line which is a rapid transit railway line but are not metro stations themselves.

Step 1: Identify the entity for "metro station."
Step 2: Identify the property for "connecting line."
Step 3: Identify the entity for "rapid transit railway line."
Step 4: Construct a query to find items that have a connecting line which is a rapid transit railway line but are not metro stations.

I will start by searching for the entity "metro station" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. metro station, also known as metro stop, rapid transit station, rapid transit stop, subway station, subway stop (wd:Q928830): railway station of a rapid transit system
2. Metro Station (wd:Q864991): American pop rock band
3. Metro Station (wd:Q2570790): 2007 self-titled debut studio album by Metro Station
4. Athens Metro station, also known as metro station (wd:Q115792670): station served by the Athens Metro
5. Metro station (wd:Q103957766): encyclopedia article
6. Thessaloniki Metro station, also known as metro station (wd:Q133240803): station served by the Thessaloniki Metro
7. public transport stop, also known as transport stop nearby, metro station, transport station nearby, subway station, stop nearby (wd:P8453): general (e.g. nearest) public transport stop (bus, tram, train, subway)
8. metro station (wd:L427448-S1)
9. Town Hall, also known as Paris Metro station, Town Hall (wd:Q968939): Paris Metro station
10. Maelbeek/Maalbeek metro station (wd:Q2206139): metro station in Brussels, Belgium

[Assistant]
The entity "metro station" that corresponds to a railway station of a rapid transit system is wd:Q928830.

Next, I will search for the property "connecting line" to identify the property that relates an item to its connecting line.

[Tool]
Top 10 property alternatives:
1. connecting line, also known as is on, railway line, rail line connected to, line connected to (wd:P81 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): railway line(s) subject is directly connected to, part of
2. connecting service, also known as halt of, stop of (wd:P1192 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): service stopping at a station
3. connector, also known as ports, electrical connector, port, has connector (wd:P2935 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): connectors which the device has/supports, uses
4. connects with, also known as contacts, couples with, intersects at grade, links to, mates with (wd:P2789 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): item with which the item is physically connected, physically interacts with
5. line(s), also known as line, lines, verse, verse(s), verses (wd:P7421 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): line number(s) of reference for claim
6. route number, also known as line number, public transport line number (wd:P1671 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): number of a rail or other public transport line, location code
7. last line, also known as closing line, ending, excipit, final line, final sentence (wd:P3132 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): last line (excipit) of a poem, last sentence of a novel, etc.
8. electrical plug type, also known as electrical connector type, socket type, plug type, mains socket type, mains plug type (wd:P2853 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): standard plug type for mains electricity in a country
9. applies to part, also known as when viewed as, valid for part, section, region, per (wd:P518 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): part, aspect, or form of the item to which the claim applies
10. author name string, also known as short author name, writer named as, writer name, writer indicated as, songwriting credits string (wd:P2093 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): stores unspecified author or editor name for publications; use i...or editor (P98) does not exist or is not known. Do not use both.

[Assistant]
The property "connecting line" that relates an item to its connecting railway line is wd:P81.

Next, I will search for the entity "rapid transit railway line" to identify the entity representing this type of railway line.

[Tool]
Top 10 entity alternatives:
1. rapid transit railway line, also known as metro line, subway line, subway service, underground line (wd:Q15079663): type of railway line
2. automated rapid transit railway line, also known as driverless underground line, driverless subway line, automated subway service, driverless metro line, automated subway line (wd:Q107343049): railway line that doesn't need a driver
3. Cincinnati Subway, also known as Cincinnati Rapid Transit Railway (wd:Q1046992): incomplete city subway system
4. Kobe Rapid Transit Railway, also known as Kōbe Rapid Transit Railway (wd:Q847533): Japanese railway company
5. Saitama Rapid Railway Line, also known as Saitama Stadium Line (wd:Q1065949): railway line in Japan
6. Tōyō Rapid Railway Line, also known as Tōyō Rapid Line (wd:Q843148): railway line in Japan
7. Semboku Rapid Railway Line (wd:Q2758012): railway line in Japan
8. Syracuse Rapid Transit Railway (wd:Q7663055)
9. MRT Purple Line, also known as Metropolitan Rapid Transit Purple Line (wd:Q6584549): second line of Bangkok's underground metro system.
10. Nara Ikoma Rapid Transit Railway, also known as Nara Ikoma Rapid Railway Co., Ltd. (wd:Q10942268)

[Assistant]
The entity "rapid transit railway line" is wd:Q15079663.

Now I will construct a SPARQL query to find items that have a connecting line (wd:P81) which is a rapid transit railway line (wd:Q15079663) but are not metro stations (wd:Q928830). I will return both the item and the connecting line in the results.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| item                                               | itemLabel                               | connectingLine                               | connectingLineLabel                |
| -------------------------------------------------- | --------------------------------------- | -------------------------------------------- | ---------------------------------- |
| wd:Q111738574                                      |                                         | Line 1 of the Salvador subway (wd:Q10318644) | Line 1 of the Salvador subway (en) |
| wd:Q19614659                                       |                                         | Line 1 (Yerevan Metro) (wd:Q10318696)        | Line 1 (Yerevan Metro) (en)        |
| Nazarbekian (wd:Q2060834)                          | Nazarbekian (en)                        | Line 1 (Yerevan Metro) (wd:Q10318696)        | Line 1 (Yerevan Metro) (en)        |
| Fukuoka City Subway Hakata Station (wd:Q124368779) | Fukuoka City Subway Hakata Station (en) | Nanakuma Line (wd:Q1039166)                  | Nanakuma Line (en)                 |
| Tuqiao depot (wd:Q10927801)                        | Tuqiao depot (en)                       | Batong line (wd:Q1039452)                    | Batong line (en)                   |
| ...                                                | ...                                     | ...                                          | ...                                |
| Plaza Rakyat LRT station (wd:Q6150701)             | Plaza Rakyat LRT station (en)           | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Pudu LRT station (wd:Q6150815)                     | Pudu LRT station (en)                   | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Hang Tuah station (wd:Q27142)                      | Hang Tuah station (en)                  | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Sultan Ismail LRT station (wd:Q6150771)            | Sultan Ismail LRT station (en)          | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |
| Sentul LRT station (wd:Q27140)                     | Sentul LRT station (en)                 | Sri Petaling line (wd:Q113297920)            | Sri Petaling line (en)             |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly identifies items that are not metro stations (wd:Q928830) but have a connecting line which is a rapid transit railway line (wd:Q15079663). It returns both the items and their connecting lines, including labels in English, which aligns well with the user's question. The use of FILTER NOT EXISTS to exclude metro stations is appropriate, and the OPTIONAL clauses for labels are correctly implemented without using unsupported SERVICE clauses. The query results show relevant examples, confirming the query's effectiveness. Overall, the output is correct and complete.