PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?language ?languageLabel WHERE {
  wd:Q45 wdt:P37 ?language .
  OPTIONAL {
    ?language rdfs:label ?languageLabel .
    FILTER ( LANG( ?languageLabel ) = "en" )
  }
}

Got 2 rows and 2 columns:
| ?language             | ?languageLabel |
| --------------------- | -------------- |
| Mirandese (wd:Q13330) | Mirandese      |
| Portuguese (wd:Q5146) | Portuguese     |

Portuguese people primarily speak Portuguese and some also speak Mirandese.

[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 do portuguese people speak?

[Assistant]
To answer the question "What do Portuguese people speak?", we need to identify the language(s) spoken by people in Portugal. We can start by finding the entity for Portugal and then look for properties related to languages spoken in the country.

### Step 1: Identify the entity for Portugal
Let's search for the entity representing Portugal.

```sparql
SELECT * WHERE { ?search ?p ?o }
```
Query: "Portugal"

If we find the correct entity, we can proceed to find the language(s) spoken there.

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

Top 10 entity alternatives:
1. Portugal, also known as POR, Portuguese Republic, PRT (wd:Q45): country in Southwestern Europe
2. Portugal (wd:Q7232531): constituency of the European Parliament
3. Portugal (wd:Q14110517): family name
4. Portugal (wd:Q21087356): comic book Cyril Pedrosa
5. Portugal, also known as Portugal, Oca, Coristanco (wd:Q31283096)
6. Portugal, also known as IMO 9147083 (wd:Q83609413): ship built in 1998
7. Portugal (wd:Q10638037): single
8. Portugal (wd:Q66987006): Populated place in Alforja (Baix Camp)
9. Portugal (wd:Q61400880): 2018 film directed by Lauri Lagle
10. Portugal (wd:Q35267157): stream in Sofala Province, Mozambique

No other items found

[Assistant]
The top result for "Portugal" is `wd:Q45`, which represents the country Portugal. Now, let's find the language(s) spoken in Portugal.

### Step 2: Find the language(s) spoken in Portugal
We need to look for properties that indicate the official or commonly spoken languages in Portugal. A common property for this is `P37` (official language).

```sparql
SELECT ?language WHERE {
  wd:Q45 wdt:P37 ?language .
}
```

This query will return the language(s) that are officially spoken in Portugal.

Let's execute this query.

[Tool]
Got 2 rows and 1 column:
| ?language             |
| --------------------- |
| Mirandese (wd:Q13330) |
| Portuguese (wd:Q5146) |

[Assistant]
The query returned two languages spoken in Portugal: Mirandese (`wd:Q13330`) and Portuguese (`wd:Q5146`).

To provide a more complete answer, we can also include the labels of these languages. Let's modify the query to include the labels:

```sparql
SELECT ?language ?languageLabel WHERE {
  wd:Q45 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?languageLabel . FILTER(LANG(?languageLabel) = "en") }
}
```

This query will return the IRIs and English labels of the languages spoken in Portugal.

Let's execute this query.

[Tool]
Got 2 rows and 2 columns:
| ?language             | ?languageLabel |
| --------------------- | -------------- |
| Mirandese (wd:Q13330) | Mirandese      |
| Portuguese (wd:Q5146) | Portuguese     |

[Assistant]
The query returned the languages spoken in Portugal along with their English labels:

- Mirandese (`wd:Q13330`)
- Portuguese (`wd:Q5146`)

Therefore, the answer to the question "What do Portuguese people speak?" is that they primarily speak Portuguese, and some also speak Mirandese.

Here is the final SPARQL query and answer:

```sparql
SELECT ?language ?languageLabel WHERE {
  wd:Q45 wdt:P37 ?language .
  OPTIONAL { ?language rdfs:label ?languageLabel . FILTER(LANG(?languageLabel) = "en") }
}
```

Answer: Portuguese people primarily speak Portuguese and some also speak Mirandese.

Let's finalize this with the answer function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?player ?playerLabel WHERE {
  ?player wdt:P647 wd:Q162990 .
  OPTIONAL {
    ?player rdfs:label ?playerLabel .
    FILTER ( LANG( ?playerLabel ) = "en" )
  }
}

Got 117 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?player                       | ?playerLabel     |
| ----------------------------- | ---------------- |
| Larry Sanders (wd:Q1034012)   | Larry Sanders    |
| LeBron James (wd:Q108870659)  | LeBron James     |
| Eddie Jordan (wd:Q1134403)    | Eddie Jordan     |
| Reggie Geary (wd:Q115509)     | Reggie Geary     |
| DeSagana Diop (wd:Q1179612)   | DeSagana Diop    |
| ...                           | ...              |
| Austin Carr (wd:Q933952)      | Austin Carr      |
| Winston Bennett (wd:Q943368)  | Winston Bennett  |
| Mickey Dillard (wd:Q962929)   | Mickey Dillard   |
| Ben McDonald (wd:Q967072)     | Ben McDonald     |
| Tristan Thompson (wd:Q973212) | Tristan Thompson |

The Cleveland Cavaliers have drafted the following players: Larry Sanders, LeBron James, Eddie Jordan, Reggie Geary, DeSagana Diop, Austin Carr, Winston Bennett, Mickey Dillard, Ben McDonald, Tristan Thompson, and many 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] who did the cleveland cavaliers draft?

[Assistant] To answer the question about who the Cleveland Cavaliers drafted, we need to identify the entity for the Cleveland Cavaliers and the property for the draft picks. Let's start by searching for the Cleveland Cavaliers in the knowledge graph.

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

Top 10 entity alternatives:
1. Cleveland Cavaliers, also known as Cavaliers, Cleveland Cavs, Cavs (wd:Q162990): National Basketball Association team in Cleveland, Ohio
2. history of the Cleveland Cavaliers, also known as Cleveland Cavaliers history, the Cleveland Cavaliers history (wd:Q20710370): aspect of history
3. 2022–23 Cleveland Cavaliers season (wd:Q112133040): NBA professional basketball team season
4. 2023–24 Cleveland Cavaliers season (wd:Q118908694): 2023–24 NBA season by team
5. 2015–16 Cleveland Cavaliers season (wd:Q20020792): NBA professional basketball team season (won championship)
6. 2018–19 Cleveland Cavaliers season (wd:Q54958273): season of National Basketball Association team the Cleveland Cavaliers
7. 2020–21 Cleveland Cavaliers season (wd:Q98553687): NBA professional basketball team season
8. 2021–22 Cleveland Cavaliers season (wd:Q107570641): NBA season for the Cleveland Cavaliers (2021-2022)
9. 2017–18 Cleveland Cavaliers season (wd:Q30641924): NBA professional basketball team season
10. 2019–20 Cleveland Cavaliers season (wd:Q63859185): NBA professional basketball team season

No other items found

[Assistant] The top result for "Cleveland Cavaliers" is `wd:Q162990`, which represents the National Basketball Association team in Cleveland, Ohio. Next, we need to find the property that indicates the players drafted by the team. Let's search for properties related to drafting in the context of the Cleveland Cavaliers.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
2. sport, also known as play, plays, sport played, sports (wd:P641 as p/wdt): sport that the subject participates or participated in or is associated with
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. league or competition, also known as tournament, sports league, division, cup, competition (wd:P118 as wdt/p): league or competition in which team or player has played, or in which an event occurs, participant in
5. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as p/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
6. home venue, also known as home water, venue, stadium, home ground, home field (wd:P115 as p/wdt): home stadium or venue of a sports team or applicable performing arts organization, location
7. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
8. category for members of a team, also known as category for players, category for team members (wd:P6112 as wdt/p): Wikimedia category for players playing for a sports team, topic's main category
9. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
10. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as p/wdt): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person

No other items found

[Assistant] The search for properties related to drafting in the context of the Cleveland Cavaliers did not yield any specific results. However, we can try a different approach by looking for the property that indicates the team a player was drafted by. Let's search for properties related to drafting in the context of NBA players.

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

[Assistant] It looks like I made a mistake in the SPARQL query. The variable `?search` should be included in the WHERE clause. Let's correct this and try again.

[Tool]
Top 10 property alternatives:
1. drafted by (wd:P647 as wdt/pq/ps): which team the player was drafted by
2. member of sports team, also known as plays for, played for, sport team, team, team played for (wd:P54 as wdt/ps/pq): sports teams or clubs that the subject represents or represented, member of
3. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as wdt/ps): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
4. employer, also known as working at, working for, working place, works at, works for (wd:P108 as wdt/ps): person or organization for which the subject works or worked, affiliation
5. participating team, also known as teams (wd:P1923 as wdt/ps): like 'Participant' (P710) but for teams. For an event like a cyc...member of sports team' (P54) as a qualifier for the individuals), participant
6. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt/ps): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
7. head coach, also known as team manager, trainer, senior coach, manager, coached by (wd:P286 as wdt/ps): on-field manager or head coach of a sports club (not to be confu...eneral manager P505, which is not a coaching position) or person
8. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as wdt/ps): person or organization occupying property, used by
9. supported sports team, also known as Fan of sports team, team supported, Supports sports team, Sports team supported, is a fan of team (wd:P6758 as wdt/ps): sports team for which a person or a group is a supporter
10. coach of sports team, also known as trainer of sports team, club manager of sports team, trainer of, team manager of sports team, team manager of (wd:P6087 as wdt/ps): sports club or team for which this person is or was on-field manager or coach

No other items found

[Assistant]
The search for properties related to drafting in the context of NBA players yielded the property `wd:P647`, which indicates the team a player was drafted by. Now, we can construct a SPARQL query to find the players drafted by the Cleveland Cavaliers.

Let's execute the following SPARQL query:

```sparql
SELECT ?player ?playerLabel
WHERE {
  ?player wdt:P647 wd:Q162990 .
  OPTIONAL { ?player rdfs:label ?playerLabel . FILTER(LANG(?playerLabel) = "en") }
}
```

This query will return the players drafted by the Cleveland Cavaliers along with their English labels.

[Tool]
Got 117 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?player                       | ?playerLabel     |
| ----------------------------- | ---------------- |
| Larry Sanders (wd:Q1034012)   | Larry Sanders    |
| LeBron James (wd:Q108870659)  | LeBron James     |
| Eddie Jordan (wd:Q1134403)    | Eddie Jordan     |
| Reggie Geary (wd:Q115509)     | Reggie Geary     |
| DeSagana Diop (wd:Q1179612)   | DeSagana Diop    |
| ...                           | ...              |
| Austin Carr (wd:Q933952)      | Austin Carr      |
| Winston Bennett (wd:Q943368)  | Winston Bennett  |
| Mickey Dillard (wd:Q962929)   | Mickey Dillard   |
| Ben McDonald (wd:Q967072)     | Ben McDonald     |
| Tristan Thompson (wd:Q973212) | Tristan Thompson |

[Assistant]
The SPARQL query returned a list of players drafted by the Cleveland Cavaliers. Here are some of the players:

- Larry Sanders
- LeBron James
- Eddie Jordan
- Reggie Geary
- DeSagana Diop
- Austin Carr
- Winston Bennett
- Mickey Dillard
- Ben McDonald
- Tristan Thompson

To provide a complete and accurate answer, I will include the SPARQL query and the list of players in the final response.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?player ?playerLabel
WHERE {
  ?player wdt:P647 wd:Q162990 .
  OPTIONAL { ?player rdfs:label ?playerLabel . FILTER(LANG(?playerLabel) = "en") }
}
```

The Cleveland Cavaliers have drafted the following players:

- Larry Sanders
- LeBron James
- Eddie Jordan
- Reggie Geary
- DeSagana Diop
- Austin Carr
- Winston Bennett
- Mickey Dillard
- Ben McDonald
- Tristan Thompson

And many others.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?movement WHERE {
  wd:Q156386 wdt:P135 ?movement .
}

Henri Rousseau worked in the artistic styles of Post-impressionism, Naïve art, and Primitivism.

[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 artistic style did henri rousseau work in?

[Assistant]
To answer the question about the artistic style Henri Rousseau worked in, I'll start by identifying Henri Rousseau in the knowledge graph and then look for properties related to his artistic style.

First, I will search for Henri Rousseau in the knowledge graph.

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

Top 10 entity alternatives:
1. Henri Rousseau, also known as le Douanier Rousseau, Henri rousseau, Le Douanier, le Douanier, h. rousseau (wd:Q156386): French painter (1844–1910), printmaker, painter, artist
2. Henri Rousseau (wd:Q106847442): French illustrator, illustrator
3. Henri Rousseau (wd:Q110034910): Righteous Among the Nations, farmer
4. Henri Rousseau (wd:Q110890688): 1942 exhibition at Museum of Modern Art
5. Henri Rousseau (wd:Q110893138): 1985 exhibition at Museum of Modern Art
6. Henri Emilien Rousseau, also known as Henri Émilien Rousseau, Henri E. Rousseau (wd:Q3132420): French painter (1875-1933), painter
7. Henri-Paul Rousseau (wd:Q3130451): Canadian businessperson, university teacher, businessperson
8. Rousseau, Henri(-Julien-Félix) (wd:Q103964446): encyclopedia article
9. Portrait of Henri Rousseau (wd:Q52083876): underglass painting by Franz Marc
10. Charles Henri Joseph Rousseau (wd:Q125622073): artisan (1732-1820), artisan

No other items found

[Assistant]
The top result for "Henri Rousseau" is the French painter Henri Rousseau (wd:Q156386). Now, I will look for properties related to his artistic style. I will search for properties where Henri Rousseau is the subject and the object is related to an artistic style.

I will use the `list` function to find relevant triples.

[Tool]
Got 1,003 rows and 3 columns, showing the first 10 rows below:
| subject                     | property                         | object                                                                                |
| --------------------------- | -------------------------------- | ----------------------------------------------------------------------------------------------------- |
| Henri Rousseau (wd:Q156386) | instance of (wdt:P31)            | human (wd:Q5)                                                                                |
| Henri Rousseau (wd:Q156386) | occupation (wdt:P106)            | artist (wd:Q483501)                                                                                |
| Henri Rousseau (wd:Q156386) | sex or gender (wdt:P21)          | male (wd:Q6581097)                                                                                |
| Henri Rousseau (wd:Q156386) | given name (wdt:P735)            | Henri (wd:Q1605665)                                                                                |
| Henri Rousseau (wd:Q156386) | date of birth (wdt:P569)         | 1844-05-21T00:00:00Z                                                                                |
| Henri Rousseau (wd:Q156386) | Commons category (wdt:P373)      | Henri Rousseau                                                                                |
| Henri Rousseau (wd:Q156386) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/Rousseau%20Joyeux%20Farceurs%20Dornac%20crop.jpg> |
| Henri Rousseau (wd:Q156386) | country of citizenship (wdt:P27) | France (wd:Q142)                                                                                |
| Henri Rousseau (wd:Q156386) | family name (wdt:P734)           | Rousseau (wd:Q16036532)                                                                               |
| Henri Rousseau (wd:Q156386) | place of birth (wdt:P19)         | Laval (wd:Q184154)                                                                                |
| ...                         | ...                              | ...                                                                                |

[Assistant]
From the list of triples, I don't see any direct property that indicates the artistic style of Henri Rousseau. However, I can try searching for properties that might be related to his artistic style, such as "movement" or "artistic movement."

I will use the `search` function to find properties related to artistic styles or movements associated with Henri Rousseau.

[Tool]
Top 10 property alternatives:
1. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as wdt/p): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
2. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as p/wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
3. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as p/wdt): notable scientific, artistic or literary work, or other work of significance among subject's works
4. pseudonym, also known as nom de plume, nom-de-plume, nome de guerre, pen name, ring name (wd:P742 as p/wdt): alias used by someone (for nickname use P1449), name
5. Artsy artist ID, also known as Artsy artist identifier, Artsy id (wd:P2042 as p/wdt): identifier at the Artsy website
6. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
7. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as p/wdt): language(s) that a person or a people speaks, writes or signs, including the native language(s)
8. artist files at, also known as has artist files in, has artist files at, has artist file in, has artist file at, artist files in (wd:P9493 as wdt/p): institution or collection that holds artist files about the subject, documentation files at
9. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
10. has works in the collection, also known as has items in the collection, works in collection, work in collection, museum housing this person's work, items in the collection (wd:P6379 as wdt/p): collection that has works of this person or organisation (use archives at [P485] for archives)

No other items found

[Assistant]
The top result for properties related to artistic styles or movements is "movement" (wdt:P135). I will now construct a SPARQL query to find the artistic movements associated with Henri Rousseau.

Let's execute the SPARQL query to find the artistic movements Henri Rousseau was part of.

[Tool]
Got 3 rows and 1 column:
| ?movement                       |
| ------------------------------- |
| post-impressionism (wd:Q166713) |
| naïve art (wd:Q281108)          |
| primitivism (wd:Q848365)        |

[Assistant]
The SPARQL query returned the following artistic movements associated with Henri Rousseau:

1. Post-impressionism (wd:Q166713)
2. Naïve art (wd:Q281108)
3. Primitivism (wd:Q848365)

These are the artistic styles in which Henri Rousseau worked.

Now, I will provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q234801 wdt:P31 wd:Q12160552 .
  wd:Q234801 wdt:P31 wd:Q5576009 .
}

True, Islamic people pray to Allah.

[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 do islamic people pray to?

[Assistant] To answer the question "Who do Islamic people pray to?", we need to identify the entity that represents the deity in Islam. In Islam, the central figure worshipped is Allah. Let's start by searching for the entity representing Allah in the knowledge graph.

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

Top 10 entity alternatives:
1. God, also known as All-Knowing, The One, The Lord, The All, supreme being (wd:Q190): principal object of faith in monotheistic religions, a divine entity that created and typically supervises all existence
2. Allah, also known as Allāh, ﷲ, الله, Allaah, ʾAllāh (wd:Q234801): Arabic word for God
3. Allah, also known as ﷲ, الله, ʾAllāh, ʾallāh, God in Arabic (wd:Q234801): Arabic word for God
4. God in Islam, also known as Allah, God (Islam), God (wd:Q2095353): Islamic conception of God
5. Clarence 13X, also known as Father Allah, Clarence Edward Smith, Allah the Father, Allah (wd:Q1095354): founder of The Nation of Gods and Earths (1928–1969), black supremacist, religious leader
6. Allah, also known as Allah, Arizona, Allah, AZ (wd:Q22058587): populated place located in Maricopa County, Arizona
7. Allah Allah / Hülya, also known as Allah Allah, Hülya (wd:Q6043103): 1987 studio album by İbrahim Tatlıses
8. Allah (wd:Q37516973): family name
9. Allah (wd:Q30064506): book
10. Allah (wd:Q126126270): painting by Isma'il Gulgee

No other items found

[Assistant]
From the search results, the most relevant entity for Allah in the context of Islam is `wd:Q234801`. This entity represents the Arabic word for God, which is the deity that Islamic people pray to.

Now, let's construct a simple SPARQL query to confirm that this entity is indeed the one that Islamic people pray to. We can check if there are any statements linking this entity to Islam or Islamic practices.

Let's start with a basic query to see if there are any relevant statements about `wd:Q234801`:

```sparql
SELECT ?statement ?object
WHERE {
  wd:Q234801 ?statement ?object .
}
LIMIT 10
```

This will give us an idea of the properties and objects associated with Allah in the knowledge graph.

[Tool]
Got 10 rows and 2 columns:
| ?statement         | ?object                                      |
| ------------------ | -------------------------------------------- |
| schema:description | "Akuc g tutlayt taεṛabt"@shi                 |
| schema:description | Arabic word for God                          |
| schema:description | "arabisches Wort für Gott"@de                |
| schema:description | "Arabisch woord voor God"@nl                 |
| schema:description | "arabiskt ord för gud"@sv                    |
| schema:description | "arabska beseda za Boga"@sl                  |
| schema:description | "Arapça "Tanrı""@tr                          |
| schema:description | "arapski naziv za Boga"@hr                   |
| schema:description | "biziň dünýämizi döreden, Yslamyň Hudaýy"@tk |
| schema:description | "Bōg podle muzułmanōw"@szl                   |

[Assistant]
The results show that `wd:Q234801` (Allah) has multiple descriptions in different languages, all confirming that it is the Arabic word for God. However, these descriptions alone do not directly link Allah to Islamic practices or prayers.

To further confirm that Allah is the deity that Islamic people pray to, we can look for more specific statements that link Allah to Islam or Islamic practices. For example, we can check if there are any statements that indicate Allah is the central figure in Islam or if there are any properties that link Allah to Islamic prayers.

Let's try a more targeted query to find such statements:

```sparql
SELECT ?property ?object
WHERE {
  wd:Q234801 ?property ?object .
  FILTER(?property IN (wdt:P31, wdt:P279, wdt:P106, wdt:P138))
}
LIMIT 10
```

This query will look for specific properties like `P31` (instance of), `P279` (subclass of), `P106` (occupation), and `P138` (named after) to see if they provide more context.

Let's execute this query.

[Tool]
Got 2 rows and 2 columns:
| ?property             | ?object                                  |
| --------------------- | ---------------------------------------- |
| instance of (wdt:P31) | theonym (wd:Q12160552)                   |
| instance of (wdt:P31) | God in Abrahamic religions (wd:Q5576009) |

[Assistant]
The results show that `wd:Q234801` (Allah) is an instance of a theonym (a name of a god) and a God in Abrahamic religions. This confirms that Allah is indeed a deity, specifically in the context of Abrahamic religions, which includes Islam.

To further solidify that Allah is the deity that Islamic people pray to, we can look for statements that directly link Allah to Islam. For example, we can check if there are any statements that indicate Allah is the central figure in Islam or if there are any properties that link Allah to Islamic practices or prayers.

Let's try a more specific query to find such statements:

```sparql
SELECT ?property ?object
WHERE {
  wd:Q234801 ?property ?object .
  FILTER(?property IN (wdt:P101, wdt:P127, wdt:P138, wdt:P141, wdt:P178))
}
LIMIT 10
```

This query will look for properties like `P101` (field of work), `P127` (owned by), `P138` (named after), `P141` (based on), and `P178` (developer) to see if they provide more context.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
The query did not return any results, which suggests that there are no direct statements linking Allah to specific properties like field of work, owned by, named after, based on, or developer. However, we can infer from the previous results that Allah is a theonym and a God in Abrahamic religions, which includes Islam.

To provide a clear and concise answer to the question "Who do Islamic people pray to?", we can use the information we have gathered so far. The most relevant and direct answer is that Islamic people pray to Allah, represented by the entity `wd:Q234801`.

Let's finalize the answer with a simple SPARQL query that confirms this:

```sparql
ASK WHERE {
  wd:Q234801 wdt:P31 wd:Q12160552 .
  wd:Q234801 wdt:P31 wd:Q5576009 .
}
```

This ASK query will confirm that Allah is indeed a theonym and a God in Abrahamic religions, which includes Islam.

Let's execute this query.

[Tool] True

[Assistant]
The ASK query returned `True`, confirming that `wd:Q234801` (Allah) is indeed a theonym and a God in Abrahamic religions, which includes Islam.

Therefore, the answer to the question "Who do Islamic people pray to?" is:

Islamic people pray to Allah, represented by the entity `wd:Q234801`.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  wd:Q234801 wdt:P31 wd:Q12160552 .
  wd:Q234801 wdt:P31 wd:Q5576009 .
}
```

Answer: True, Islamic people pray to Allah.

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?poi ?poiLabel WHERE {
  ?poi wdt:P131 wd:Q60 .
  ?poi wdt:P31 wd:Q570116 .
  OPTIONAL {
    ?poi rdfs:label ?poiLabel .
    FILTER ( LANG( ?poiLabel ) = "en" )
  }
}
LIMIT 10

Some popular tourist attractions in New York City include the National September 11 Memorial & Museum and the Statue of Liberty.

[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 can do in new york?

[Assistant]
To provide a meaningful answer to the question "What can you do in New York?", we need to identify activities, attractions, or events that are associated with New York. We can start by searching for entities related to New York and then look for properties that describe activities or attractions.

### Step 1: Identify New York
First, let's find the entity for New York City in Wikidata.

### Step 2: Search for Activities or Attractions in New York
Once we have the entity for New York City, we can search for properties that describe activities or attractions in New York.

Let's start by finding the entity for New York City.

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

Top 10 entity alternatives:
1. New York City, also known as Capital of the World, Big Apple, the five boroughs, The City So Nice They Named It Twice, NY, NY (wd:Q60): most populous city in the United States
2. New City, also known as New City, New York, New City, NY (wd:Q3053698): census designated place in the town of Clarkstown, Rockland County, New York, United States
3. Yorkville, also known as Yorkville, New York, Yorkville, Manhattan, Yorkville, New York City (wd:Q1189975): neighborhood of Manhattan, New York City, New York, United States
4. New York City, also known as New York City I (wd:Q18710663): painting by Piet Mondrian at the Musée national d’Art moderne, Paris, France
5. New York City (wd:Q7013127): band
6. New York City (wd:Q63438423): 2019  song by Kylie Minogue
7. New York City (wd:Q7013124): John Lennon and Yoko Ono song
8. New York City (wd:Q3875477): 2007 debut single by Emigrate
9. New York City (wd:Q16998793): song by the British glam rock band T. Rex
10. New York City (wd:Q3339042): 2003 studio album by the Peter Malick Group featuring Norah Jones

No other items found

[Assistant]
The top result for "New York City" is `wd:Q60`, which is the entity for New York City, the most populous city in the United States. This is the correct entity we need.

### Step 3: Find Activities or Attractions in New York City
Next, we need to find properties that describe activities or attractions in New York City. We can look for properties like "point of interest" or "landmark" to find relevant entities.

Let's search for properties that describe points of interest or landmarks in New York City.

[Tool]
Top 10 property alternatives:
1. located in/on physical feature, also known as located on the terrain feature, location (terrain feature), loc (terr), on, on geographical feature (wd:P706 as wdt/p): located on the specified (geo)physical feature. Should not be us...nly political/administrative (P131) or a mountain range (P4552)., location, part of
2. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as p/wdt): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
3. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as p/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
4. located in or next to body of water, also known as located on body of water, ocean, on bay, next to lake, on coast of (wd:P206 as wdt/p): body of water on or next to which a place is located, location
5. location map (wd:P1943 as p/wdt): blank geographic map image suitable for overlaying coordinate points, as defined at c:Category:Location maps, image
6. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as wdt/p): significant or notable events associated with the subject
7. museum-digital place ID (wd:P9957 as p/wdt): identifier for a place in museum-digital's md:term vocabulary
8. territory overlaps, also known as partially overlaps, partially coincident with, partly coincident with, partly located in territorial entity, partly overlaps (wd:P3179 as p/wdt): part or all of the area associated with (this) entity overlaps part or all of the area associated with that entity, partially coincident with
9. locator map image, also known as locator map, map image, map (wd:P242 as p/wdt): geographic map image which highlights the location of the subject within some larger entity, image
10. shares border with, also known as shares borders with, next to, bordered by, border, borders clockwise (wd:P47 as p/wdt): countries or administrative subdivisions, of equal level, that t...rders, either by land or water. A single common point is enough.

No other items found

[Assistant]
The search did not return properties directly related to "point of interest" or "landmark" for New York City. However, we can use the property `located in the administrative territorial entity` (P131) to find entities that are located in New York City.

### Step 4: Find Points of Interest in New York City
We will use the property `located in the administrative territorial entity` (P131) to find entities that are located in New York City. We can then filter these entities to find points of interest or landmarks.

Let's execute a SPARQL query to find points of interest in New York City.

```sparql
SELECT ?poi ?poiLabel
WHERE {
  ?poi wdt:P131 wd:Q60 .
  OPTIONAL { ?poi rdfs:label ?poiLabel . FILTER(LANG(?poiLabel) = "en") }
}
LIMIT 10
```

This query will return the top 10 points of interest in New York City along with their labels.

Let's execute this query.

[Tool]
Got 10 rows and 2 columns:
| ?poi                                                | ?poiLabel                           |
| --------------------------------------------------- | ----------------------------------- |
| Freedom's Journal (wd:Q1003760)                     | Freedom's Journal                   |
| New Yorkers (wd:Q100400533)                         | New Yorkers                         |
| Savage School of Physical Education (wd:Q100513885) | Savage School of Physical Education |
| YIVO Archives (wd:Q101093848)                       | YIVO Archives                       |
| Decoda (wd:Q101114098)                              | Decoda                              |
| George M. Cohan's Theatre (wd:Q101252358)           | George M. Cohan's Theatre           |
| Human Worldwide (wd:Q101579920)                     | Human Worldwide                     |
| The Magic Shop (wd:Q101579921)                      | The Magic Shop                      |
| Buttermilk Channel (wd:Q1018123)                    | Buttermilk Channel                  |
| NYC Media Lab (wd:Q101812777)                       | NYC Media Lab                       |

[Assistant]
The initial query returned a list of entities located in New York City, but many of these entities are not typical points of interest or landmarks. To refine the results, we can add a filter to include only entities that are classified as points of interest or landmarks.

### Step 5: Refine the Query to Include Only Points of Interest
We can use the property `instance of` (P31) to filter for entities that are instances of a point of interest or landmark. Let's search for the entity for "point of interest" and "landmark" to use in our query.

#### Search for "point of interest" and "landmark"
Let's search for the entities for "point of interest" and "landmark".

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

Top 10 entity alternatives:
1. point of interest, also known as Point of Interest, POI (wd:Q960648): specific location that someone may find useful or interesting
2. architectural landmark, also known as landmark, landmark building, POI, POI: architecture, point of interest (wd:Q2319498): geographic feature or building, that is easily recognizable and/or well known
3. California Point of Historical Interest, also known as California Points of Historical Interest, California POI, point of interest (wd:Q16152996): site, building, feature, or event designated as of historical or cultural significance in the state of California, United States
4. point of interest (wd:L1132194-S2)
5. point of interest (wd:L413798-S2)
6. POI data, also known as POI dataset, point of interest data (wd:Q125840751): Type of point data with a special interest for people
7. Points of interest (wd:Q114257023): scientific article published in 1998
8. Points of Interest (wd:Q119862272): podcast from KETR
9. Windy Point Vista Point of Interest (wd:Q49582053): park in the United States of America
10. Configurable point of interest alerts (wd:Q119104449): US patent 11265678

No other items found

[Assistant]
The top result for "point of interest" is `wd:Q960648`, and the top result for "landmark" is `wd:Q2319498`. These are the entities we need to filter our results.

### Step 6: Refine the Query to Include Only Points of Interest and Landmarks
We will now refine our query to include only entities that are instances of a point of interest or a landmark.

Here is the refined SPARQL query:

```sparql
SELECT ?poi ?poiLabel
WHERE {
  ?poi wdt:P131 wd:Q60 .
  ?poi wdt:P31 ?instanceOf .
  VALUES ?instanceOf { wd:Q960648 wd:Q2319498 }
  OPTIONAL { ?poi rdfs:label ?poiLabel . FILTER(LANG(?poiLabel) = "en") }
}
LIMIT 10
```

This query will return the top 10 points of interest and landmarks in New York City along with their labels.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query did not return any results, which suggests that the entities we are looking for might not be directly classified as points of interest or landmarks using the `instance of` (P31) property.

### Step 7: Broaden the Search to Include Other Relevant Entities
We can broaden our search to include other relevant entities that might be considered points of interest or landmarks. For example, we can include entities that are classified as buildings, monuments, parks, or tourist attractions.

Let's search for the entities for "building," "monument," "park," and "tourist attraction."

#### Search for "building," "monument," "park," and "tourist attraction"
Let's search for these entities.

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

Top 10 entity alternatives:
1. Monument Lake Park Building and Hatchery Complex (wd:Q106808139)
2. tourist attraction, also known as attraction for tourists, POI for tourists, tourism attraction, touristic attraction, tourist place (wd:Q570116): place of interest where tourists visit
3. Monument Park (wd:Q6906829): museum in center field at Yankee Stadium honoring the greatest Yankees players
4. Park Building (wd:Q7137655): building in Ohio, United States
5. Monument Park (wd:Q3006929): place in Gauteng, South Africa
6. Tourist Attraction (wd:Q4167290): episode of The Outer Limits
7. Park Building (wd:Q7137656): building in Massachusetts, United States of America
8. Monument Park (wd:Q31818374): park in Madison County, Iowa, United States of America
9. Monument Park (wd:Q49527948): park in Cook County, Illinois, United States of America
10. Monument Park (wd:Q49527950): park in the United States of America

No other items found

[Assistant]
The search results show that the entity for "tourist attraction" is `wd:Q570116`. We can use this entity to filter our results to include tourist attractions in New York City.

### Step 8: Refine the Query to Include Tourist Attractions
We will now refine our query to include only entities that are instances of a tourist attraction.

Here is the refined SPARQL query:

```sparql
SELECT ?poi ?poiLabel
WHERE {
  ?poi wdt:P131 wd:Q60 .
  ?poi wdt:P31 wd:Q570116 .
  OPTIONAL { ?poi rdfs:label ?poiLabel . FILTER(LANG(?poiLabel) = "en") }
}
LIMIT 10
```

This query will return the top 10 tourist attractions in New York City along with their labels.

Let's execute this query.

[Tool]
Got 2 rows and 2 columns:
| ?poi                                                  | ?poiLabel                               |
| ----------------------------------------------------- | --------------------------------------- |
| National September 11 Memorial & Museum (wd:Q1136137) | National September 11 Memorial & Museum |
| Statue of Liberty (wd:Q9202)                          | Statue of Liberty                       |

[Assistant]
The query returned two well-known tourist attractions in New York City: the National September 11 Memorial & Museum and the Statue of Liberty. These are indeed popular places to visit in New York.

### Final Answer
To answer the question "What can you do in New York?", here are some popular tourist attractions in New York City:

1. **National September 11 Memorial & Museum** - A memorial and museum dedicated to the victims of the September 11 attacks.
2. **Statue of Liberty** - An iconic statue and a symbol of freedom, located on Liberty Island.

These are just a few of the many things you can do in New York City. There are numerous other attractions, museums, parks, and landmarks to explore.

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 wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?material WHERE {
  ?artwork wdt:P170 wd:Q165397 .
  ?artwork wdt:P186 ?material
}

Got 11 rows and 1 column, showing the first 5 and last 5 rows below:
| ?material                 |
| ------------------------- |
| steel (wd:Q11427)         |
| paper (wd:Q11472)         |
| ink (wd:Q127418)          |
| India ink (wd:Q12981547)  |
| white card (wd:Q15918049) |
| ...                       |
| limestone (wd:Q23757)     |
| cobblestone (wd:Q2996907) |
| bronze (wd:Q34095)        |
| glue (wd:Q500548)         |
| aluminum (wd:Q663)        |

Eduardo Paolozzi used materials such as steel, paper, ink, India ink, white card, limestone, cobblestone, bronze, glue, and aluminum in his artworks.

[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 materials did eduardo paolozzi use in his work?

[Assistant]
To answer the question about the materials Eduardo Paolozzi used in his work, we need to identify the relevant entities and properties in the knowledge graph. Let's start by finding the entity for Eduardo Paolozzi and then look for properties related to the materials he used in his artworks.

### Step 1: Identify Eduardo Paolozzi
First, let's search for Eduardo Paolozzi in the knowledge graph.

```sparql
SELECT * WHERE { ?search ?p ?o }
```

We will use the search function to find the entity for Eduardo Paolozzi.

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

Top 10 entity alternatives:
1. Eduardo Paolozzi, also known as O Paolozzi, Eduard Paolozzi, Sir Eduardo Paolozzi, Sir Eduardo Luigi Paolozzi, Eduardo Luigi Paolozzi (wd:Q165397): architectural draftsperson, British artist (1924-2005), university teacher, textile artist, graphic artist, visual artist, draftsperson, printmaker, ceramicist, filmmaker, collagist, muralist, designer, sculptor, painter, artist, actor
2. Eduardo Paolozzi (wd:Q110891355): 1964 exhibition at Museum of Modern Art
3. Eduardo Paolozzi (wd:Q55370707): exhibition
4. Paolozzi, Sir Eduardo (wd:Q103960948): encyclopedia article
5. Sir Eduardo Paolozzi (wd:Q28051888): painting by Cathleen Sabine Mann
6. Tentoonstelling seriewerken van Eduardo Paolozzi (wd:Q131420865): exhibition at the Stedelijk Museum Amsterdam in 1968
7. Eduardo Paolozzi - One Big Collage (wd:Q104634969): exhibition in 2014
8. Tentoonstelling Victor Pasmore en Eduardo Paolozzi (wd:Q131420611): exhibition at the Stedelijk Museum Amsterdam in 1960
9. Parallel Systems: Lawrence Alloway and Eduardo Paolozzi (wd:Q109792474): scholarly article published in Tate Papers (Autumn 2011)
10. Carlos Eduardo Bendini Giusti, also known as Eduardo, Dudu (wd:Q15402028): Brazilian association football player, association football player

No other items found

[Assistant]
The top result for Eduardo Paolozzi is `wd:Q165397`. We will use this entity to find the materials he used in his work.

### Step 2: Identify Properties Related to Materials
Next, we need to find properties that relate to the materials used in artworks. Common properties might include `P186` (material) or `P185` (medium).

Let's list the triples where Eduardo Paolozzi (`wd:Q165397`) is the subject to see if we can find any relevant properties.

```sparql
SELECT ?p ?o WHERE { wd:Q165397 ?p ?o }
```

We will use the list function to find these triples.

[Tool]
Got 661 rows and 3 columns, showing the first 10 rows below:
| subject                       | property                         | object                                                                        |
| ----------------------------- | -------------------------------- | ----------------------------------------------------------------------------- |
| Eduardo Paolozzi (wd:Q165397) | instance of (wdt:P31)            | human (wd:Q5)                                                                 |
| Eduardo Paolozzi (wd:Q165397) | occupation (wdt:P106)            | actor (wd:Q33999)                                                             |
| Eduardo Paolozzi (wd:Q165397) | sex or gender (wdt:P21)          | male (wd:Q6581097)                                                            |
| Eduardo Paolozzi (wd:Q165397) | given name (wdt:P735)            | Eduardo (wd:Q18130894)                                                        |
| Eduardo Paolozzi (wd:Q165397) | date of birth (wdt:P569)         | 1924-03-07T00:00:00Z                                                          |
| Eduardo Paolozzi (wd:Q165397) | Commons category (wdt:P373)      | Eduardo Paolozzi                                                              |
| Eduardo Paolozzi (wd:Q165397) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/NewtonBlakePaolozzi1.jpg> |
| Eduardo Paolozzi (wd:Q165397) | country of citizenship (wdt:P27) | United Kingdom (wd:Q145)                                                      |
| Eduardo Paolozzi (wd:Q165397) | family name (wdt:P734)           | Paolozzi (wd:Q37438324)                                                       |
| Eduardo Paolozzi (wd:Q165397) | place of birth (wdt:P19)         | Leith (wd:Q1018540)                                                           |
| ...                           | ...                              | ...                                                                           |

[Assistant]
From the list of triples, we can see that there are no direct properties related to the materials used in Eduardo Paolozzi's works. However, we can try to find the artworks created by Eduardo Paolozzi and then check the materials used in those artworks.

### Step 3: Find Artworks by Eduardo Paolozzi
We will search for artworks created by Eduardo Paolozzi using the `P170` (creator) property.

```sparql
SELECT ?artwork WHERE { ?artwork wdt:P170 wd:Q165397 }
```

We will use the execute function to run this query.

[Tool]
Got 242 rows and 1 column, showing the first 5 and last 5 rows below:
| ?artwork                                                  |
| --------------------------------------------------------- |
| Turkish Musik (wd:Q103310694)                             |
| Bunk ! no. 12: "Refreshing and Delicious" (wd:Q104786819) |
| Bunk ! no. 31: "Hi-Ho" (wd:Q104786820)                    |
| Bunk ! no. 17: "Has Jazz a Future ?" (wd:Q104786821)      |
| Untitled (wd:Q105087275)                                  |
| ...                                                       |
| Untitled (wd:Q74892324)                                   |
| Rhine Garden Sculpture (wd:Q76396487)                     |
| I was a Rich Man's Plaything (wd:Q85394510)               |
| Medea (wd:Q99203759)                                      |
| St. Sebastian III (wd:Q99203762)                          |

[Assistant]
We have found several artworks created by Eduardo Paolozzi. Now, we need to find the materials used in these artworks. We will use the `P186` (material) property to find the materials.

### Step 4: Find Materials Used in Artworks
We will query the materials used in the artworks created by Eduardo Paolozzi.

```sparql
SELECT ?artwork ?material WHERE {
  ?artwork wdt:P170 wd:Q165397 .
  ?artwork wdt:P186 ?material
}
```

We will use the execute function to run this query.

[Tool]
Got 53 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?artwork                                                  | ?material                 |
| --------------------------------------------------------- | ------------------------- |
| Introduction to "General Dynamic F. U. N" (wd:Q105093664) | paper (wd:Q11472)         |
| Introduction to "General Dynamic F. U. N" (wd:Q105093664) | ink (wd:Q127418)          |
| Introduction to "General Dynamic F. U. N" (wd:Q105093665) | paper (wd:Q11472)         |
| Introduction to "General Dynamic F. U. N" (wd:Q105093665) | ink (wd:Q127418)          |
| Introduction to "General Dynamic F. U. N" (wd:Q105093666) | paper (wd:Q11472)         |
| ...                                                       | ...                       |
| Untitled (wd:Q74892309)                                   | wove paper (wd:Q21279007) |
| Untitled (wd:Q74892324)                                   | wove paper (wd:Q21279007) |
| Rhine Garden Sculpture (wd:Q76396487)                     | limestone (wd:Q23757)     |
| Rhine Garden Sculpture (wd:Q76396487)                     | cobblestone (wd:Q2996907) |
| Rhine Garden Sculpture (wd:Q76396487)                     | bronze (wd:Q34095)        |

[Assistant]
We have successfully retrieved the materials used in the artworks created by Eduardo Paolozzi. The materials include paper, ink, wove paper, limestone, cobblestone, and bronze.

### Final SPARQL Query
To provide a clear and concise answer, we will list the distinct materials used in Eduardo Paolozzi's artworks.

```sparql
SELECT DISTINCT ?material WHERE {
  ?artwork wdt:P170 wd:Q165397 .
  ?artwork wdt:P186 ?material
}
```

We will use the execute function to run this final query.

[Tool]
Got 11 rows and 1 column, showing the first 5 and last 5 rows below:
| ?material                 |
| ------------------------- |
| steel (wd:Q11427)         |
| paper (wd:Q11472)         |
| ink (wd:Q127418)          |
| India ink (wd:Q12981547)  |
| white card (wd:Q15918049) |
| ...                       |
| limestone (wd:Q23757)     |
| cobblestone (wd:Q2996907) |
| bronze (wd:Q34095)        |
| glue (wd:Q500548)         |
| aluminum (wd:Q663)        |

[Assistant]
The materials used in Eduardo Paolozzi's artworks include:

- Steel (wd:Q11427)
- Paper (wd:Q11472)
- Ink (wd:Q127418)
- India ink (wd:Q12981547)
- White card (wd:Q15918049)
- Limestone (wd:Q23757)
- Cobblestone (wd:Q2996907)
- Bronze (wd:Q34095)
- Glue (wd:Q500548)
- Aluminum (wd:Q663)

### Final Answer
Eduardo Paolozzi used a variety of materials in his artworks, including steel, paper, ink, India ink, white card, limestone, cobblestone, bronze, glue, and aluminum.

Here is the final SPARQL query and answer:

```sparql
SELECT DISTINCT ?material WHERE {
  ?artwork wdt:P170 wd:Q165397 .
  ?artwork wdt:P186 ?material
}
```

Answer: Eduardo Paolozzi used materials such as steel, paper, ink, India ink, white card, limestone, cobblestone, bronze, glue, and aluminum in his artworks.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?work ?workLabel WHERE {
  wd:Q310379 wdt:P800 ?work .
  OPTIONAL {
    ?work rdfs:label ?workLabel .
    FILTER ( LANG( ?workLabel ) = "en" )
  }
}

Got 4 rows and 2 columns:
| ?work                                     | ?workLabel                  |
| ----------------------------------------- | --------------------------- |
| A Prayer for Owen Meany (wd:Q13911)       | A Prayer for Owen Meany     |
| The Cider House Rules (wd:Q1538984)       | The Cider House Rules       |
| A Widow for One Year (wd:Q1755628)        | A Widow for One Year        |
| The World According to Garp (wd:Q2507282) | The World According to Garp |

John Irving wrote the following works:
- A Prayer for Owen Meany
- The Cider House Rules
- A Widow for One Year
- The World According to Garp

[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 did john irving wrote?

[Assistant]
To answer the question about what John Irving wrote, I need to first identify John Irving as an entity in the knowledge graph and then find the works he authored. Let's start by searching for John Irving in the knowledge graph.

I will use the `search` function to find the entity corresponding to John Irving.

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

Top 10 entity alternatives:
1. John Irving, also known as John Wallace Blunt, Jr., John Winslow Irving (wd:Q310379): American novelist and screenwriter, screenwriter, novelist, writer
2. John Irving, also known as John Goodison Irving (wd:Q16300810): association football player, Footballer (born 1988)
3. John Irving (wd:Q14324464): American basketball player, basketball player
4. John Irving (wd:Q108284934): Scottish Royal Navy officer and explorer, naval officer
5. John Irving (wd:Q6240869): association football player, Footballer 1867-1942
6. John Beaufain Irving, also known as Irving, john irving, J.B. Irving, J. Beaufain Irving (wd:Q26196600): American artist 1825-1877, painter
7. John Irving (wd:Q6240863): British MP, slave holder and West Indies merchant (1766-1845), politician
8. John Irving (wd:Q16025218): Canadian politician
9. John Irving (wd:Q6240871): American Navy sailor
10. John Irving (wd:Q59772748): food writer

No other items found

[Assistant]
The search results show multiple entities named John Irving. The first result, `wd:Q310379`, corresponds to the American novelist and screenwriter John Irving, which is the person we are interested in.

Next, I will look for the works that John Irving has written. To do this, I will use the `list` function to find the properties and objects associated with `wd:Q310379`.

Specifically, I will look for the property `wdt:P800` (notable work) to find the notable works of John Irving.

Let's proceed with the `list` function.

[Tool]
Got 4 rows and 3 columns:
| subject                  | property                | object                                    |
| ------------------------ | ----------------------- | ----------------------------------------- |
| John Irving (wd:Q310379) | notable work (wdt:P800) | The World According to Garp (wd:Q2507282) |
| John Irving (wd:Q310379) | notable work (wdt:P800) | The Cider House Rules (wd:Q1538984)       |
| John Irving (wd:Q310379) | notable work (wdt:P800) | A Prayer for Owen Meany (wd:Q13911)       |
| John Irving (wd:Q310379) | notable work (wdt:P800) | A Widow for One Year (wd:Q1755628)        |

[Assistant]
The list function has returned the notable works of John Irving. These include:

- The World According to Garp (wd:Q2507282)
- The Cider House Rules (wd:Q1538984)
- A Prayer for Owen Meany (wd:Q13911)
- A Widow for One Year (wd:Q1755628)

Now, I will construct a SPARQL query to retrieve these works and their labels. Since the user might be interested in the human-readable titles, I will include an optional clause to fetch the labels of the works.

Let's execute the SPARQL query.

```sparql
SELECT ?work ?workLabel
WHERE {
  wd:Q310379 wdt:P800 ?work .
  OPTIONAL { ?work rdfs:label ?workLabel . FILTER(LANG(?workLabel) = "en") }
}
```

This query will return the works and their English labels.

Let's execute this query.

[Tool]
Got 4 rows and 2 columns:
| ?work                                     | ?workLabel                  |
| ----------------------------------------- | --------------------------- |
| A Prayer for Owen Meany (wd:Q13911)       | A Prayer for Owen Meany     |
| The Cider House Rules (wd:Q1538984)       | The Cider House Rules       |
| A Widow for One Year (wd:Q1755628)        | A Widow for One Year        |
| The World According to Garp (wd:Q2507282) | The World According to Garp |

[Assistant]
The SPARQL query has successfully retrieved the notable works of John Irving along with their English labels. Here are the results:

- **A Prayer for Owen Meany** (wd:Q13911)
- **The Cider House Rules** (wd:Q1538984)
- **A Widow for One Year** (wd:Q1755628)
- **The World According to Garp** (wd:Q2507282)

These are the works that John Irving has written.

I will now provide the final SPARQL query and the answer to the user's question.

```sparql
SELECT ?work ?workLabel
WHERE {
  wd:Q310379 wdt:P800 ?work .
  OPTIONAL { ?work rdfs:label ?workLabel . FILTER(LANG(?workLabel) = "en") }
}
```

Answer: John Irving wrote the following works:
- A Prayer for Owen Meany
- The Cider House Rules
- A Widow for One Year
- The World According to Garp

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?city WHERE {
  wd:Q18708 wdt:P131 | wdt:P159 ?city .
}

London

[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 city does fulham play in?

[Assistant]
To answer the question about which city Fulham plays in, I need to identify the entity for Fulham FC and then find the city where the club is located.

### Step 1: Identify the entity for Fulham FC
I will start by searching for the entity representing Fulham FC in the knowledge graph.

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

Top 10 entity alternatives:
1. Fulham F.C., also known as FFC, Fulham, Fulham FC, Fulham Football Club, The Cottagers (wd:Q18708): association football club in London, England
2. Fulham United FC (wd:Q19873570): football club
3. Fulham FC reserve and academy teams (wd:Q23662057): UK association football club
4. Arsenal FC v Fulham FC, 12 August 2007 (wd:Q123737318)
5. Arsenal FC v Fulham FC, 29 Apr 2007 (wd:Q123737319)
6. Fulham FC v Arsenal FC, 20 April 2013 (wd:Q123744439)
7. Football: Fulham FC sacks coach Chris Coleman (wd:Q17671273): Wikinews article
8. Fulham (wd:Q3367868): area in the London Borough of Hammersmith and Fulham
9. Fulham, also known as Fulham, South Australia, Australia, Fulham, South Australia (wd:Q5507907): suburb of Adelaide, South Australia
10. Fulham (wd:Q5507914): Parliamentary constituency in the United Kingdom, 1955-1997

No other items found

[Assistant]
The top result for "Fulham FC" is `wd:Q18708`, which represents Fulham Football Club. Now, I need to find the city where this club is located.

### Step 2: Find the city where Fulham FC is located
I will look for the property that indicates the location or city of the club. A common property for this is `P131` (located in the administrative territorial entity).

Let's check the triples for `wd:Q18708` to see if we can find the city.

[Tool]
Got 436 rows and 3 columns, showing the first 10 rows below:
| subject                 | property                    | object                                                                    |
| ----------------------- | --------------------------- | ------------------------------------------------------------------------- |
| Fulham F.C. (wd:Q18708) | instance of (wdt:P31)       | association football club (wd:Q476028)                                    |
| Fulham F.C. (wd:Q18708) | country (wdt:P17)           | United Kingdom (wd:Q145)                                                  |
| Fulham F.C. (wd:Q18708) | Commons category (wdt:P373) | Fulham FC                                                                 |
| Fulham F.C. (wd:Q18708) | image (wdt:P18)             | <http://commons.wikimedia.org/wiki/Special:FilePath/Craven%20Cottage.JPG> |
| Fulham F.C. (wd:Q18708) | inception (wdt:P571)        | 1879-01-01T00:00:00Z                                                      |
| Fulham F.C. (wd:Q18708) | sport (wdt:P641)            | association football (wd:Q2736)                                           |
| Fulham F.C. (wd:Q18708) | official name (wdt:P1448)   | Fulham Football Club                                                      |
| Fulham F.C. (wd:Q18708) | official website (wdt:P856) | <https://www.fulhamfc.com/>                                               |
| Fulham F.C. (wd:Q18708) | native label (wdt:P1705)    | Fulham F.C.                                                               |
| Fulham F.C. (wd:Q18708) | owned by (wdt:P127)         | Shahid Khan (wd:Q2276408)                                                 |
| ...                     | ...                         | ...                                                                       |

[Assistant]
From the list of triples, I don't see a direct property indicating the city where Fulham FC is located. However, the property `P17` (country) shows that Fulham FC is in the United Kingdom.

To find the city, I will look for the property `P131` (located in the administrative territorial entity) or `P159` (headquarters location). Let's try these properties specifically.

### Step 3: Query for the city where Fulham FC is located
I will execute a SPARQL query to find the city where Fulham FC is located using the properties `P131` and `P159`.

[Tool]
Got 1 row and 1 column:
| ?city           |
| --------------- |
| London (wd:Q84) |

[Assistant]
The SPARQL query returned that Fulham FC is located in London (wd:Q84).

### Final Answer
Fulham FC plays in the city of London.

Now, I will provide the final SPARQL query and answer.

[Tool] Stopped generation process