PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?country WHERE {
  wd:Q78707 wdt:P17 ?country .
}

The Yenisei river flows through Russia.

[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] Through which countries does the Yenisei river flow?

[Assistant] To answer the question about the countries through which the Yenisei river flows, I need to identify the Yenisei river and the property that indicates the countries it flows through. Let's start by searching for the Yenisei river in Wikidata.

[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. Yenisey, also known as River Yenisei, Yenisei River, Yenisei R., Yenisei, R. Yenisei (wd:Q78707): river in Asia; the 5th-longest in the world
2. Yenisei River (wd:Q34969643): river in Russia
3. Yenisei River, also known as IMO 9629586 (wd:Q113553726): ship built in 2013
4. Little Yenisey, also known as Little Yenisei River, Shishged River, Shishged Gol, Maly Yenisey, Malyj Enisej (wd:Q1511505): river in Russia and Mongolia
5. A new ferrobacterium from Yenisei River (wd:Q82038486): scientific article published on October 29, 2010
6. river, also known as rivers (wd:Q4022): larger natural watercourse
7. Club Atlético River Plate, also known as CA River Plate, C.A.R.P., CARP, River, River Plate (wd:Q15799): Argentine professional sports club based in Buenos Aires
8. Khakas people, also known as Yenisei Kirgiz, Yennisej Tatars, Yenisei Turks, Yenisei Tatars, Yenisei Kyrgyz (wd:Q240293): ethnic group
9. main stream, also known as main stem, mainstem, river, trunk, veletok (wd:Q573344): final large channel of a riverine system
10. Kaduna River, also known as River (wd:Q2338605): river in Nigeria

No other items found

[Assistant]
The search results show that the Yenisei river is represented by the entity `wd:Q78707`. Now, I need to find the property that indicates the countries through which the river flows. I will look for properties related to the Yenisei river that might indicate the countries it flows through.

Let's list the properties of the Yenisei river to find the relevant one.

[Tool]
Got 879 rows and 3 columns, showing the first 10 rows below:
| subject             | property                                                    | object                                                                                |
| ------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------ |
| Yenisey (wd:Q78707) | instance of (wdt:P31)                                       | river (wd:Q4022)                                                                                |
| Yenisey (wd:Q78707) | country (wdt:P17)                                           | Russia (wd:Q159)                                                                                |
| Yenisey (wd:Q78707) | located in the administrative territorial entity (wdt:P131) | Tuva (wd:Q960)                                                                                |
| Yenisey (wd:Q78707) | coordinate location (wdt:P625)                              | POINT(94.451800 51.727900)                                                                       |
| Yenisey (wd:Q78707) | Commons category (wdt:P373)                                 | Yenisei River                                                                                |
| Yenisey (wd:Q78707) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Yenisey%20from%20the%20Railway%20Bridge.JPG> |
| Yenisey (wd:Q78707) | described by source (wdt:P1343)                             | Encyclopædia Britannica 11th edition (wd:Q867541)                                                |
| Yenisey (wd:Q78707) | on focus list of Wikimedia project (wdt:P5008)              | wd:Q6173448                                                                                |
| Yenisey (wd:Q78707) | native label (wdt:P1705)                                    | "Ионесси"@mis                                                                                |
| Yenisey (wd:Q78707) | topic's main category (wdt:P910)                            | wd:Q9461636                                                                                |
| ...                 | ...                                                         | ...                                                                                |

[Assistant]
From the list of properties, we can see that the Yenisei river has a `country` property (`wdt:P17`) that links to Russia (`wd:Q159`). However, this only shows one country. To ensure we capture all countries through which the Yenisei river flows, I will construct a SPARQL query to retrieve all countries associated with the Yenisei river.

Let's execute the SPARQL query to find the countries through which the Yenisei river flows.

[Tool]
Got 1 row and 1 column:
| ?country         |
| ---------------- |
| Russia (wd:Q159) |

[Assistant]
The SPARQL query returned only one country, Russia, through which the Yenisei river flows. Since the Yenisei river is a major river in Siberia, it is reasonable to conclude that it primarily flows through Russia. However, to ensure completeness, I will check if there are any other properties or statements that might indicate additional countries.

Let's check if there are any other properties or statements that might provide more information about the countries through which the Yenisei river flows.

[Tool]
Got 3 rows and 3 columns:
| subject             | property                                                    | object                      |
| ------------------- | ----------------------------------------------------------- | --------------------------- |
| Yenisey (wd:Q78707) | located in the administrative territorial entity (wdt:P131) | Tuva (wd:Q960)              |
| Yenisey (wd:Q78707) | located in the administrative territorial entity (wdt:P131) | Khakassia (wd:Q6543)        |
| Yenisey (wd:Q78707) | located in the administrative territorial entity (wdt:P131) | Krasnoyarsk Krai (wd:Q6563) |

[Assistant]
The `located in the administrative territorial entity` property (`wdt:P131`) shows that the Yenisei river flows through several regions within Russia, specifically Tuva (`wd:Q960`), Khakassia (`wd:Q6543`), and Krasnoyarsk Krai (`wd:Q6563`). These regions are all part of Russia, which confirms that the Yenisei river flows through Russia.

Since the initial query and the additional checks both confirm that the Yenisei river flows through Russia and no other countries were mentioned, we can conclude that the Yenisei river flows through Russia.

Here is the final SPARQL query and answer:

```sparql
SELECT ?country WHERE {
  wd:Q78707 wdt:P17 ?country .
}
```

Answer: The Yenisei river flows through Russia.

Now, I will provide the final answer using 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 ?actor ?actorLabel WHERE {
  ?actor wdt:P106 wd:Q33999 .
  ?actor wdt:P19 wd:Q183 .
  OPTIONAL {
    ?actor rdfs:label ?actorLabel .
    FILTER ( LANG( ?actorLabel ) = "en" )
  }
}
LIMIT 100

Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?actor                                   | ?actorLabel              |
| ---------------------------------------- | ------------------------ |
| Betty Astor (wd:Q102635)                 | Betty Astor              |
| Béatrice Jean-Philippe (wd:Q104699210)   | Béatrice Jean-Philippe   |
| Michael Bornhütter (wd:Q105062031)       | Michael Bornhütter       |
| Phong Giang (wd:Q105062089)              | Phong Giang              |
| Patrick Nattawat Finkler (wd:Q105654184) | Patrick Nattawat Finkler |
| ...                                      | ...                      |
| Carl Winterhoff (wd:Q5040978)            | Carl Winterhoff          |
| Anja Sommavilla (wd:Q551286)             | Anja Sommavilla          |
| Christoph Banken (wd:Q55439603)          | Christoph Banken         |
| Veit Bethke (wd:Q5574838)                | Veit Bethke              |
| Heidemarie Fuentes (wd:Q5698755)         | Heidemarie Fuentes       |

The actors born in Germany include: Betty Astor, Béatrice Jean-Philippe, Michael Bornhütter, Phong Giang, Patrick Nattawat Finkler, 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] Which actors were born in Germany?

[Assistant]
To answer the question "Which actors were born in Germany?", we need to identify the relevant entities and properties:

1. **Actors**: We need to find people who are actors.
2. **Born in Germany**: We need to find the place of birth and ensure it is Germany.

Let's start by identifying the property for "place of birth" and the entity for "Germany".

### Step 1: Identify the property for "place of birth"
We can use the `list` function to find examples of how the property "place of birth" is used in Wikidata.

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "property": "wdt:P19"
  }
}
```

### Step 2: Identify the entity for "Germany"
We can use the `search` function to find the entity for "Germany".

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT * WHERE { ?s ?p ?search }",
    "query": "Germany"
  }
}
```

### Step 3: Identify the property for "actor"
We can use the `search` function to find the property for "actor".

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT * WHERE { ?s ?search ?o }",
    "query": "actor"
  }
}
```

Let's start with the first step to identify the property for "place of birth".

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

[Assistant]
From the results, we can see that the property for "place of birth" is `wdt:P19`. Now, let's identify the entity for "Germany".

### Step 2: Identify the entity for "Germany"
We will use the `search` function to find the entity for "Germany".

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT * WHERE { ?s ?p ?search }",
    "query": "Germany"
  }
}
```

Let's proceed with this step.

[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. Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
2. Weimar Republic, also known as German Republic, Weimar Germany, Republic of Germany, Germany, German Reich (wd:Q41304): Germany in the years 1918/1919–1933
3. German Reich, also known as Deutsches Reich, German Empire, German Realm, Germany, Imperial Germany (wd:Q1206012): official name for the German nation state from 1871 to 1945, and name of Germany until 1949
4. Burg Drachenfels, also known as Germany (wd:Q1011486): castle
5. Germany (wd:Q5552078): European Parliament constituency
6. Geilenkirchen-Heinsberg, also known as Germany (wd:Q2268218)
7. Germany, also known as Germany, IN, Germany, Indiana (wd:Q18151573): unincorporated community in Clark County, Indiana
8. Germany (wd:Q19147844): poem by Vladimir Mayakovsky
9. Germany, also known as Ludwig, West (wd:Q3839160): Hetalia character
10. Germany, also known as Germany, Texas, Germany, TX (wd:Q15221573): unincorporated community in Houston County, Texas, United States

No other items found

No literal items found

[Assistant]
From the search results, the entity for "Germany" is `wd:Q183`.

### Step 3: Identify the property for "actor"
We will use the `search` function to find the property for "actor".

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT * WHERE { ?s ?search ?o }",
    "query": "actor"
  }
}
```

Let's proceed with this step.

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

Top 10 property alternatives:
1. agent of action, also known as being done by, initiator, initiated by, done by, doer (wd:P12995 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): particular item that initiates this action or class of actions
2. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
3. cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
4. agents of action have role, also known as actors have role, agent has role, agent of action has role, agents have role, done by agent(s) with role (wd:P12993 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): role that agents of this action take on in the context of this action
5. describes actor of (wd:P12027 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): predicate sense whose actor (an agent or a cause—basically the instigator of an action) is denoted by this sense
6. character role, also known as role, plays, performer of, as, acting role (wd:P453 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)
7. class of agent(s) of action, also known as agent class of action, done by agent(s) of class, class of doer(s), agent selectional restriction, agent selectional preference (wd:P12994 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): class of items that may initiate this action or class of actions
8. depicted by, also known as acted by, shown in, seen in, portrayed by, played by (wd:P1299 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): object depicting this subject (object being creative works such as books, films, paintings)
9. used by, also known as armiger, user, played by, of (wd:P1535 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): item or concept that makes use of the subject (use sub-properties when appropriate)
10. notable role, also known as notable character role, notable for character role, notable for role (wd:P10606 as wdtn/prn/psv/pqv/prv/pr/pq/wdt/psn/ps/p/pqn): role in a theatrical, motion picture, television, or other work that a performer is particularly known for, notable work

No other items found

[Assistant]
From the search results, the property for "actor" is `wdt:P31` with the value `wd:Q33999` (instance of actor).

### Step 4: Build the SPARQL Query
Now that we have identified the necessary entities and properties, we can build the SPARQL query to find actors born in Germany.

```sparql
SELECT ?actor ?actorLabel
WHERE {
  ?actor wdt:P31 wd:Q33999 .  # ?actor is an instance of actor
  ?actor wdt:P19 wd:Q183 .    # ?actor was born in Germany
  OPTIONAL { ?actor rdfs:label ?actorLabel . FILTER(LANG(?actorLabel) = "en") }
}
LIMIT 100
```

Let's execute this query to get the results.

```json
{
  "name": "execute",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?actor ?actorLabel WHERE { ?actor wdt:P31 wd:Q33999 . ?actor wdt:P19 wd:Q183 . OPTIONAL { ?actor rdfs:label ?actorLabel . FILTER(LANG(?actorLabel) = \"en\") } } LIMIT 100"
  }
}
```

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query did not return any results. This could be due to the fact that the property `wdt:P31` with the value `wd:Q33999` might not be the most appropriate way to identify actors. Let's try another approach by using the property `wdt:P106` (occupation) with the value `wd:Q33999` (actor).

### Step 5: Refine the SPARQL Query
We will use `wdt:P106` to identify actors and `wdt:P19` to filter by place of birth.

```sparql
SELECT ?actor ?actorLabel
WHERE {
  ?actor wdt:P106 wd:Q33999 .  # ?actor has occupation actor
  ?actor wdt:P19 wd:Q183 .     # ?actor was born in Germany
  OPTIONAL { ?actor rdfs:label ?actorLabel . FILTER(LANG(?actorLabel) = "en") }
}
LIMIT 100
```

Let's execute this refined query to get the results.

```json
{
  "name": "execute",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?actor ?actorLabel WHERE { ?actor wdt:P106 wd:Q33999 . ?actor wdt:P19 wd:Q183 . OPTIONAL { ?actor rdfs:label ?actorLabel . FILTER(LANG(?actorLabel) = \"en\") } } LIMIT 100"
  }
}
```

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?actor                                   | ?actorLabel              |
| ---------------------------------------- | ------------------------ |
| Betty Astor (wd:Q102635)                 | Betty Astor              |
| Béatrice Jean-Philippe (wd:Q104699210)   | Béatrice Jean-Philippe   |
| Michael Bornhütter (wd:Q105062031)       | Michael Bornhütter       |
| Phong Giang (wd:Q105062089)              | Phong Giang              |
| Patrick Nattawat Finkler (wd:Q105654184) | Patrick Nattawat Finkler |
| ...                                      | ...                      |
| Carl Winterhoff (wd:Q5040978)            | Carl Winterhoff          |
| Anja Sommavilla (wd:Q551286)             | Anja Sommavilla          |
| Christoph Banken (wd:Q55439603)          | Christoph Banken         |
| Veit Bethke (wd:Q5574838)                | Veit Bethke              |
| Heidemarie Fuentes (wd:Q5698755)         | Heidemarie Fuentes       |

[Assistant]
The refined SPARQL query has returned the expected results, listing actors who were born in Germany. Here are the first few and last few results:

| Actor IRI                                 | Actor Label              |
| ----------------------------------------- | ------------------------ |
| wd:Q102635                                | Betty Astor              |
| wd:Q104699210                             | Béatrice Jean-Philippe   |
| wd:Q105062031                             | Michael Bornhütter       |
| wd:Q105062089                             | Phong Giang              |
| wd:Q105654184                             | Patrick Nattawat Finkler |
| ...                                       | ...                      |
| wd:Q5040978                               | Carl Winterhoff          |
| wd:Q551286                                | Anja Sommavilla          |
| wd:Q55439603                              | Christoph Banken         |
| wd:Q5574838                               | Veit Bethke              |
| wd:Q5698755                               | Heidemarie Fuentes       |

The SPARQL query is correct and provides the desired results. Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT ?actor ?actorLabel
WHERE {
  ?actor wdt:P106 wd:Q33999 .  # ?actor has occupation actor
  ?actor wdt:P19 wd:Q183 .     # ?actor was born in Germany
  OPTIONAL { ?actor rdfs:label ?actorLabel . FILTER(LANG(?actorLabel) = "en") }
}
LIMIT 100
```

The answer to the question "Which actors were born in Germany?" is the list of actors returned by the query.

```json
{
  "name": "answer",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?actor ?actorLabel WHERE { ?actor wdt:P106 wd:Q33999 . ?actor wdt:P19 wd:Q183 . OPTIONAL { ?actor rdfs:label ?actorLabel . FILTER(LANG(?actorLabel) = \"en\") } } LIMIT 100",
    "answer": "The actors born in Germany include: Betty Astor, Béatrice Jean-Philippe, Michael Bornhütter, Phong Giang, Patrick Nattawat Finkler, and many others."
  }
}
```

[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 ?country ?countryLabel WHERE {
  ?country wdt:P31 wd:Q849866 .
  FILTER NOT EXISTS {
    ?country wdt:P576 []
  }
  FILTER NOT EXISTS {
    ?country wdt:P582 []
  }
  OPTIONAL {
    ?country rdfs:label ?countryLabel .
    FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 4 rows and 2 columns:
| ?country                             | ?countryLabel              |
| ------------------------------------ | -------------------------- |
| People's Republic of China (wd:Q148) | People's Republic of China |
| Cuba (wd:Q241)                       | Cuba                       |
| Laos (wd:Q819)                       | Laos                       |
| Vietnam (wd:Q881)                    | Vietnam                    |

The current communist countries are: People's Republic of China, Cuba, Laos, and Vietnam.

[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] Give me all communist countries.

[Assistant]
To find all communist countries, we need to identify the relevant properties and entities in the knowledge graph. We can start by searching for the term "communist country" to see if there is a specific property or entity that represents this concept.

Let's perform a search for "communist country" to see what we can find.

[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. communist state, also known as Marxist-Leninist state, communist country (wd:Q849866): state that is administered and governed by a single communist party
2. fictional communist state, also known as fictional communist country (wd:Q82519323): communist state which only exists in a work of fiction
3. Communist Party of the Basque Country (wd:Q3750882): political party in Spain
4. Communist Party of the Valencian Country (wd:Q3326408): political party in Spain
5. Communist Movement of the Valencian Country (wd:Q11693816): political party
6. country, also known as countries (wd:Q6256): distinct territorial body or political entity
7. country music, also known as country, country and western, country & western (wd:Q83440): genre of American popular music
8. sovereign state, also known as country, nation, sovereign country, sovereign nation, Westphalian state (wd:Q3624078): state that has the highest authority over a territory
9. Dennis Rodman, also known as Rodzilla, Psycho, The Worm, Worm, Country (wd:Q201608): American basketball player, professional wrestler, basketball player, amateur wrestler, basketball coach, voice actor, film actor, actor
10. Country (wd:Q1754454): 1984 film directed by Richard Pearce

No other items found

No literal items found

[Assistant]
The search results indicate that the most relevant entity for "communist country" is "communist state" (wd:Q849866). However, this entity is a class or type rather than a specific country. To find countries that are classified as communist states, we need to look for countries that have this property.

We can use the property `instance of` (P31) to find countries that are instances of the "communist state" class. Let's construct a SPARQL query to find these countries.

Here is the initial SPARQL query:

```sparql
SELECT ?country ?countryLabel
WHERE {
  ?country wdt:P31 wd:Q849866 .
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") }
}
```

This query selects countries that are instances of the "communist state" class and optionally retrieves their English labels.

Let's execute this query to see the results.

[Tool]
Got 7 rows and 2 columns:
| ?country                                      | ?countryLabel                   |
| --------------------------------------------- | ------------------------------- |
| People's Republic of China (wd:Q148)          | People's Republic of China      |
| Azerbaijan People's Government (wd:Q2305337)  | Azerbaijan People's Government  |
| Cuba (wd:Q241)                                | Cuba                            |
| Republic of Mahabad (wd:Q332232)              | Republic of Mahabad             |
| Laos (wd:Q819)                                | Laos                            |
| Gilan Socialist Soviet Republic (wd:Q8341881) | Gilan Socialist Soviet Republic |
| Vietnam (wd:Q881)                             | Vietnam                         |

[Assistant]
The SPARQL query returned a list of countries that are classified as communist states. However, some of the results, such as "Azerbaijan People's Government" and "Republic of Mahabad," are historical entities and not current countries. To refine the results, we should filter out historical entities and focus on current countries.

We can add a filter to ensure that the countries are current by checking if they have a dissolution date (P576) or an end time (P582). If they do not have these properties, they are likely current countries.

Here is the refined SPARQL query:

```sparql
SELECT ?country ?countryLabel
WHERE {
  ?country wdt:P31 wd:Q849866 .
  FILTER NOT EXISTS { ?country wdt:P576 [] }
  FILTER NOT EXISTS { ?country wdt:P582 [] }
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") }
}
```

This query selects countries that are instances of the "communist state" class and ensures they do not have a dissolution date or an end time, indicating they are current countries.

Let's execute this refined query to see the results.

[Tool]
Got 4 rows and 2 columns:
| ?country                             | ?countryLabel              |
| ------------------------------------ | -------------------------- |
| People's Republic of China (wd:Q148) | People's Republic of China |
| Cuba (wd:Q241)                       | Cuba                       |
| Laos (wd:Q819)                       | Laos                       |
| Vietnam (wd:Q881)                    | Vietnam                    |

[Assistant]
The refined SPARQL query has successfully filtered out historical entities and returned the current communist countries. The results include:

- People's Republic of China (wd:Q148)
- Cuba (wd:Q241)
- Laos (wd:Q819)
- Vietnam (wd:Q881)

These are the current countries that are classified as communist states.

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

```sparql
SELECT ?country ?countryLabel
WHERE {
  ?country wdt:P31 wd:Q849866 .
  FILTER NOT EXISTS { ?country wdt:P576 [] }
  FILTER NOT EXISTS { ?country wdt:P582 [] }
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") }
}
```

The current communist countries are:
- People's Republic of China
- Cuba
- Laos
- Vietnam

Let's provide this as the final answer.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?river WHERE {
  wd:Q125006 wdt:P177 ?river
}

The Brooklyn Bridge crosses the East River.

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

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

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

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

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

[User] Which river does the Brooklyn Bridge cross?

[Assistant] To answer the question about which river the Brooklyn Bridge crosses, I need to identify the entity for the Brooklyn Bridge and the property that indicates the river it crosses. Let's start by searching for the Brooklyn Bridge 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. Brooklyn Bridge, also known as East River Bridge, New York and Brooklyn Bridge (wd:Q125006): bridge in New York City, crossing the East River
2. Brooklyn Bridge (wd:Q929432): 1981 documentary film by Ken Burns
3. Brooklyn Bridge (wd:Q3881953): American television program in the 1990s
4. Brooklyn Bridge (wd:Q28224593): bridge in New South Wales, Australia
5. Brooklyn Bridge (wd:Q104850523): for transfering or transforming data from here to there
6. Brooklyn Bridge (wd:Q19133315): poem by Vladimir Mayakovsky
7. Brooklyn Bridge (wd:Q4974763): album by The Brooklyn Bridge
8. Brooklyn Bridge (wd:Q80080658): print by Frederick Mershimer (American, b. 1958); Printed by the artist (2013.449)
9. Brooklyn Bridge (wd:Q80084452): print by Ernest Born (American, 1898-1992); Charles Massin, Paris (2015.113)
10. Brooklyn Bridge (wd:Q90294662): item from the Cartões Postais Collection at Museu Paulista

No other items found

[Assistant] The top result for "Brooklyn Bridge" is the correct one, which is `wd:Q125006`. Now, I need to find the property that indicates the river the bridge crosses. I will list the triples where `wd:Q125006` is the subject to see if there is a relevant property.

[Tool]
Got 493 rows and 3 columns, showing the first 10 rows below:
| subject                      | property                                                    | object                                                                                |
| ---------------------------- | ----------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| Brooklyn Bridge (wd:Q125006) | instance of (wdt:P31)                                       | suspension bridge (wd:Q12570)                                                                                |
| Brooklyn Bridge (wd:Q125006) | country (wdt:P17)                                           | United States (wd:Q30)                                                                                |
| Brooklyn Bridge (wd:Q125006) | located in the administrative territorial entity (wdt:P131) | Manhattan (wd:Q11299)                                                                                |
| Brooklyn Bridge (wd:Q125006) | coordinate location (wdt:P625)                              | POINT(-73.996333 40.705667)                                                                                |
| Brooklyn Bridge (wd:Q125006) | Commons category (wdt:P373)                                 | Brooklyn Bridge                                                                                |
| Brooklyn Bridge (wd:Q125006) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/New%20York%20City%20%28New%20York%2C%20USA%29%2C%20Brooklyn%20Bridge%20--%202012%20--%206630.jpg> |
| Brooklyn Bridge (wd:Q125006) | location (wdt:P276)                                         | New York City (wd:Q60)                                                                                |
| Brooklyn Bridge (wd:Q125006) | heritage designation (wdt:P1435)                            | National Historic Landmark (wd:Q624232)                                                                                |
| Brooklyn Bridge (wd:Q125006) | made from material (wdt:P186)                               | steel (wd:Q11427)                                                                                |
| Brooklyn Bridge (wd:Q125006) | height (wdt:P2048)                                          | 84                                                                                |
| ...                          | ...                                                         | ...                                                                                |

[Assistant]
From the list of triples, I don't see a direct property indicating the river the Brooklyn Bridge crosses. However, the property `located in the administrative territorial entity` (`wdt:P131`) and `location` (`wdt:P276`) might provide some context. Since the bridge is located in New York City, the river it crosses is likely the East River, which is a well-known waterway in that area.

To confirm this, I will search for the East River and check if it is indeed the river that the Brooklyn Bridge crosses.

[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. East River, also known as Sound River, The East River (wd:Q212862): tidal strait in New York City
2. Hillsborough River, also known as East RIver (wd:Q3433700): river in Prince Edward Island, Canada
3. East River (wd:Q5329267): river in Colorado, United States
4. East River (wd:Q5329266): river in Florida, United States
5. East River Sheet Harbour, also known as East River (wd:Q22353131): human settlement and watercourse in Nova Scotia, Canada
6. Slate River, also known as East River (wd:Q7538852): river in Gunnison County, Colorado, United States
7. East River (wd:Q49900434): tributary of the Fox River in Wisconsin, United States
8. East River (wd:Q5329268): river in Minnesota, United States
9. East River (wd:Q5329269): river in West Virginia and Virginia, United States
10. East River, also known as East River, Nova Scotia (wd:Q5329265): community in Nova Scotia, Canada

No other items found

[Assistant]
The top result for "East River" is the correct one, which is `wd:Q212862`. Now, I need to verify if the Brooklyn Bridge (`wd:Q125006`) crosses this river. I will check if there is a direct relationship between the Brooklyn Bridge and the East River.

Let's execute a SPARQL query to find out if the Brooklyn Bridge crosses the East River.

[Tool] Got no rows and 1 columns

[Assistant]
The initial query did not return any results, which suggests that there might not be a direct `part of` (`wdt:P361`) relationship between the Brooklyn Bridge and the East River. Instead, let's try a different approach by looking for a property that directly indicates the river a bridge crosses.

I will search for a property that might indicate the river a bridge crosses.

[Tool]
Top 10 property alternatives:
1. crosses, also known as under, tunnel under, through, spanning, over (wd:P177 as wdt/p): obstacle (body of water, road, railway...) which this bridge (ferry, ford) crosses over or this tunnel goes under
2. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
3. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
4. 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)
5. plaque image, also known as memorial plaque image, inscription image, image of plaque, image of inscription, commemorative plaque image (wd:P1801 as wdt/p): image of plaque or inscription commemorating or mentioning the subject. For graves, use grave picture (P1442), image
6. width, also known as breadth, bus width, size (wd:P2049 as wdt/p): width of an object, length
7. 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
8. carries (wd:P2505 as wdt/p): item (e.g. road, railway, canal) carried by a bridge, a tunnel or a mountain pass, part of
9. different from, also known as not to be confused with, to be distinguished from, similar to, see also, rejected match (wd:P1889 as wdt/p): item that is different from another item, with which it may be confused
10. made from material, also known as material used, manufactured out of, manufactured from, made of material, made of (wd:P186 as wdt/p): material the subject or the object is made of or derived from (do not confuse with P10672 which is used for processes), uses

No other items found

[Assistant] The property `crosses` (`wdt:P177`) is the correct one to indicate the river a bridge crosses. Now, I will execute a SPARQL query to find out which river the Brooklyn Bridge crosses using this property.

[Tool]
Got 1 row and 1 column:
| ?river                  |
| ----------------------- |
| East River (wd:Q212862) |

[Assistant]
The SPARQL query confirms that the Brooklyn Bridge crosses the East River (`wd:Q212862`).

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

```sparql
SELECT ?river WHERE { wd:Q125006 wdt:P177 ?river }
```

The Brooklyn Bridge crosses the East River.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?mountain ?height WHERE {
  ?mountain wdt:P31 wd:Q8502 ; wdt:P2044 ?height ; wdt:P17 wd:Q837 .
  FILTER ( ?mountain != wd:Q159621 ) .
  FILTER ( ?mountain != wd:Q513 ) .
  FILTER ( ?height < 9000 )
}
ORDER BY DESC ( ?height )
LIMIT 1

The highest mountain after Annapurna is Kanchenjunga.

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

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

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

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

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

[User] Which mountain is the highest after the Annapurna?

[Assistant]
To answer the question about which mountain is the highest after Annapurna, we need to identify the height of Annapurna and then find the next highest mountain. Here's the step-by-step approach:

1. Identify the entity for Annapurna.
2. Find the height of Annapurna.
3. Query for mountains and their heights, excluding Annapurna.
4. Sort the results by height in descending order and select the top one.

Let's start by identifying the entity for Annapurna.

[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. Annapurna, also known as Annapurna Himal, Annapurna (Himalayas), Annapurna Massif (wd:Q159621): mountain range in the Himalayas
2. Annapurna I, also known as Annapurna, Annapurna I Main (wd:Q16466024): mountain in the Himalayas
3. Annapoorna, also known as Umamaheswari, Annapurna (wd:Q4767988): Indian Odia-language author, actor
4. Annapurna (wd:Q4767995): village development committee in Bhojpur District in the Kosi Zone of eastern Nepal
5. Annapurna (wd:Q4768000): 1960 film by V. Madhusudhan Rao
6. Annapurna (wd:Q4767994): book by Maurice Herzog
7. Annapurna (wd:Q88543102): female given name
8. Annapurna (wd:Q70575435): Kannada book by Niranjan
9. Annapurna (wd:Q12301785): Danish singer, singer
10. Annapurna, also known as Category:IMO 9014793, IMO 9014793 (wd:Q83601304): ship built in 1992

No other items found

[Assistant] From the search results, the most relevant entity for Annapurna is `wd:Q159621`, which refers to the Annapurna massif in the Himalayas. Let's confirm that this is the correct entity by checking its properties.

[Tool]
Got 476 rows and 3 columns, showing the first 10 rows below:
| subject                | property                                                    | object                                                                 |
| ---------------------- | ----------------------------------------------------------- | ---------------------------------------------------------------------- |
| Annapurna (wd:Q159621) | instance of (wdt:P31)                                       | mountain system (wd:Q46831)                                            |
| Annapurna (wd:Q159621) | country (wdt:P17)                                           | Nepal (wd:Q837)                                                        |
| Annapurna (wd:Q159621) | located in the administrative territorial entity (wdt:P131) | Gandaki Province (wd:Q25104016)                                        |
| Annapurna (wd:Q159621) | coordinate location (wdt:P625)                              | POINT(83.820278 28.596111)                                             |
| Annapurna (wd:Q159621) | Commons category (wdt:P373)                                 | Annapurna Himal                                                        |
| Annapurna (wd:Q159621) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Annapurna%20I.jpg> |
| Annapurna (wd:Q159621) | has part(s) (wdt:P527)                                      | Annapurna II (wd:Q449534)                                              |
| Annapurna (wd:Q159621) | elevation above sea level (wdt:P2044)                       | 7756                                                                   |
| Annapurna (wd:Q159621) | native label (wdt:P1705)                                    | "अन्नपुर्ण"@ne                                                         |
| Annapurna (wd:Q159621) | significant event (wdt:P793)                                | first ascent (wd:Q1194369)                                             |
| ...                    | ...                                                         | ...                                                                    |

[Assistant]
The entity `wd:Q159621` is indeed the Annapurna massif, and it has a height (elevation above sea level) of 7756 meters. Now, let's find the next highest mountain after Annapurna.

We will query for mountains and their heights, excluding Annapurna, and sort the results by height in descending order to find the next highest mountain.

Here is the SPARQL query to achieve this:

```sparql
SELECT ?mountain ?height
WHERE {
  ?mountain wdt:P31 wd:Q8502 ;  # Instance of mountain
          wdt:P2044 ?height .   # Elevation above sea level
  FILTER(?mountain != wd:Q159621)  # Exclude Annapurna
}
ORDER BY DESC(?height)
LIMIT 1
```

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| ?mountain                  | ?height |
| -------------------------- | ------- |
| Peak 17400 (wd:Q123374100) | 17450   |

[Assistant]
The query returned `Peak 17400` (wd:Q123374100) as the highest mountain after Annapurna, with a height of 17450 meters. However, this result seems unusual because it is significantly higher than Annapurna, which has a height of 7756 meters. This suggests that there might be an issue with the data or the query.

Let's refine the query to ensure we are only considering well-known and verified mountains. We can add a filter to exclude peaks that are not well-documented or have heights that seem unrealistic.

Here is the refined SPARQL query:

```sparql
SELECT ?mountain ?height
WHERE {
  ?mountain wdt:P31 wd:Q8502 ;  # Instance of mountain
          wdt:P2044 ?height .   # Elevation above sea level
  FILTER(?mountain != wd:Q159621)  # Exclude Annapurna
  FILTER(?height < 9000)  # Exclude unrealistic heights
}
ORDER BY DESC(?height)
LIMIT 1
```

Let's execute this refined query.

[Tool]
Got 1 row and 2 columns:
| ?mountain                    | ?height |
| ---------------------------- | ------- |
| Telegraph Peak (wd:Q7696249) | 8989    |

[Assistant]
The refined query returned `Telegraph Peak` (wd:Q7696249) as the highest mountain after Annapurna, with a height of 8989 meters. However, this result still seems unusual because it is higher than many well-known peaks in the Himalayas, including Mount Everest.

Let's further refine the query to ensure we are only considering well-known and verified mountains in the Himalayas. We can add a filter to include only mountains that are part of the Himalayas.

Here is the further refined SPARQL query:

```sparql
SELECT ?mountain ?height
WHERE {
  ?mountain wdt:P31 wd:Q8502 ;  # Instance of mountain
          wdt:P2044 ?height ;   # Elevation above sea level
          wdt:P17 wd:Q837 .     # Located in Nepal
  FILTER(?mountain != wd:Q159621)  # Exclude Annapurna
  FILTER(?height < 9000)  # Exclude unrealistic heights
}
ORDER BY DESC(?height)
LIMIT 1
```

Let's execute this further refined query.

[Tool]
Got 1 row and 2 columns:
| ?mountain               | ?height |
| ----------------------- | ------- |
| Mount Everest (wd:Q513) | 8848.86 |

[Assistant]
The further refined query returned `Mount Everest` (wd:Q513) as the highest mountain after Annapurna, with a height of 8848.86 meters. This result is more reasonable and aligns with the well-known fact that Mount Everest is the highest peak in the world.

However, since the question asks for the highest mountain after Annapurna, we need to ensure that we are not including Mount Everest, which is higher than Annapurna. Let's adjust the query to exclude both Annapurna and Mount Everest and find the next highest mountain.

Here is the adjusted SPARQL query:

```sparql
SELECT ?mountain ?height
WHERE {
  ?mountain wdt:P31 wd:Q8502 ;  # Instance of mountain
          wdt:P2044 ?height ;   # Elevation above sea level
          wdt:P17 wd:Q837 .     # Located in Nepal
  FILTER(?mountain != wd:Q159621)  # Exclude Annapurna
  FILTER(?mountain != wd:Q513)  # Exclude Mount Everest
  FILTER(?height < 9000)  # Exclude unrealistic heights
}
ORDER BY DESC(?height)
LIMIT 1
```

Let's execute this adjusted query.

[Tool]
Got 1 row and 2 columns:
| ?mountain                | ?height |
| ------------------------ | ------- |
| Kanchenjunga (wd:Q82019) | 8586    |

[Assistant]
The adjusted query returned `Kanchenjunga` (wd:Q82019) as the highest mountain after Annapurna, with a height of 8586 meters. This result is consistent with the well-known fact that Kanchenjunga is the third highest mountain in the world and is located in the Himalayas.

Therefore, the highest mountain after Annapurna is Kanchenjunga.

Here is the final SPARQL query and answer:

```sparql
SELECT ?mountain ?height
WHERE {
  ?mountain wdt:P31 wd:Q8502 ;  # Instance of mountain
          wdt:P2044 ?height ;   # Elevation above sea level
          wdt:P17 wd:Q837 .     # Located in Nepal
  FILTER(?mountain != wd:Q159621)  # Exclude Annapurna
  FILTER(?mountain != wd:Q513)  # Exclude Mount Everest
  FILTER(?height < 9000)  # Exclude unrealistic heights
}
ORDER BY DESC(?height)
LIMIT 1
```

The highest mountain after Annapurna is Kanchenjunga.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?population WHERE {
  wd:Q3114 wdt:P1082 ?population
}

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

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

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

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

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

[User] How many people live in the capital of Australia?

[Assistant] To answer the question "How many people live in the capital of Australia?", we need to identify the capital of Australia and then find the population of that city. Let's start by identifying the capital of Australia.

[Tool]
Top 10 entity alternatives:
1. Establishment of the capital in Valladolid, also known as Capital of Valladolid (wd:Q8262638)
2. capital of the Netherlands (wd:Q834162): capital
3. Saigon, also known as Sài Gòn, Saigon Capital City, Đô thành Sài Gòn (wd:Q105076255): capital of South Vietnam from 1956 to 1975 including the French-era cities of Saigon and Cholon

No other items found

[Assistant] It seems that the search did not return the expected result for the capital of Australia. Let me try a different approach by directly searching for "Canberra" since it is known to be the capital of Australia.

[Tool]
No entity items found

No other items found

[Assistant]
It appears that the direct search for "Canberra" as the capital of Australia did not yield any results either. Let's try a more specific search by looking for the capital of Australia using the country and the property for capital.

I will search for the capital of Australia using the country and the property for capital.

[Tool]
Top 10 entity alternatives:
1. Canberra, also known as Canberra, Australia, Canberra, Australian Capital Territory (wd:Q3114): capital city of Australia

No other items found

No literal items found

[Assistant]
The search has correctly identified Canberra as the capital of Australia. Now, let's find the population of Canberra.

I will look for the population of Canberra using the property for population.

[Tool]
No entity items found

No other items found

No literal items found

[Assistant]
It seems that the direct search for the population of Canberra did not return any results. Let's try a different approach by executing a SPARQL query to directly retrieve the population of Canberra.

I will execute a SPARQL query to get the population of Canberra.

[Tool]
Got 1 row and 1 column:
| ?population |
| ----------- |
| 381488      |

[Assistant]
The population of Canberra, the capital of Australia, is 381,488.

Here is the final SPARQL query and answer:

```sparql
SELECT ?population WHERE { wd:Q3114 wdt:P1082 ?population }
```

The answer to the question "How many people live in the capital of Australia?" is 381,488.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?city WHERE {
  wd:Q9696 wdt:P20 ?hospital .
  ?hospital wdt:P131 ?city .
}

John F. Kennedy died in Dallas.

[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] In which city did John F. Kennedy die?

[Assistant] To answer the question about the city where John F. Kennedy died, I need to identify the entity for John F. Kennedy and the property that indicates the place of death. Let's start by searching for John F. Kennedy 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. John F. Kennedy, also known as John Kennedy, John Fitzgerald Kennedy, John F Kennedy, Kennedy, President Kennedy (wd:Q9696): President of the United States from 1961 to 1963, anti-communist, naval officer, statesperson, journalist, politician, writer
2. John F. Kennedy, also known as CVN-79, PCU John F. Kennedy, USS John F. Kennedy (wd:Q240666): 2019 Gerald R. Ford-class aircraft carrier
3. MV John F. Kennedy, also known as IMO 8643195, John F. Kennedy (wd:Q29378914): Staten Island Ferry vessel.
4. John F Kennedy (wd:Q22006430): horse
5. John F. Kennedy (wd:Q130973914): sculpture by Evangelos Frudakis
6. John F. Kennedy, also known as John Flanders Kennedy (wd:Q28836957): American politician and member of the Georgia State Senate, politician
7. John F Kennedy (wd:Q42586306): cultural heritage monument in Aruba
8. Rosa 'John F. Kennedy', also known as John F. Kennedy (wd:Q83668459): rose cultivar
9. John F. Kennedy (wd:Q80912685): scientific article published on 01 November 1963
10. John F. Kennedy (wd:Q78526936): edition; published in 1997

No other items found

[Assistant] The top result for "John F. Kennedy" is the correct entity, which is `wd:Q9696`. Now, I need to find the property that indicates the place of death. I will search for properties related to "death" or "place of death" for this entity.

[Tool]
Top 10 property alternatives:
1. place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt/p): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location
2. place of burial, also known as interment, interment location, interment place, interred at, location of burial (wd:P119 as wdt/p): location of grave, resting place, place of ash-scattering, etc. ...veral places: e.g., re-burials, parts of body buried separately., location
3. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as wdt/p): dissolved, abolished or demolished date, date on which the subject died, end time
4. manner of death, also known as type of death, nature of death, death manner, death type, circumstance of death (wd:P1196 as wdt/p): general circumstances of a person's death; e.g. natural causes, ...ysiological mechanism, e.g. heart attack, trauma, pneumonia etc.
5. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdt/p): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
6. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
7. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as wdt/p): most specific known birth location of a person, animal or fictional character, location
8. image of grave, also known as headstone picture, image of gravestone, image of cenotaph, image of grave, image of shrine (wd:P1442 as wdt/p): picture of a person or animal's grave, gravestone or tomb, image
9. residence, also known as living in, place of residence, resided at, resided in, resident in (wd:P551 as wdt/p): the place where the person is or has been, resident, location
10. Find a Grave memorial ID, also known as FaG ID, FaGID, Find A Grave grave ID, Findagrave ID, Find A Grave memorial ID (wd:P535 as wdt/p): identifier of an individual's burial place in the Find a Grave database

No other items found

[Assistant]
The property `wd:P20` (place of death) is the correct property to use. Now, I will construct a SPARQL query to find the city where John F. Kennedy died.

Let's execute the SPARQL query:

```sparql
SELECT ?city WHERE {
  wd:Q9696 wdt:P20 ?city .
}
```

This query will return the city where John F. Kennedy died.

[Tool]
Got 1 row and 1 column:
| ?city                                    |
| ---------------------------------------- |
| Parkland Memorial Hospital (wd:Q3896131) |

[Assistant]
The result shows that John F. Kennedy died at Parkland Memorial Hospital. However, the question asks for the city, not the hospital. To get the city, I need to find the location of Parkland Memorial Hospital. Let's look for the city where Parkland Memorial Hospital is located.

I will use the property `wdt:P131` (located in the administrative territorial entity) to find the city.

Let's execute the SPARQL query:

```sparql
SELECT ?city WHERE {
  wd:Q9696 wdt:P20 ?hospital .
  ?hospital wdt:P131 ?city .
}
```

[Tool]
Got 1 row and 1 column:
| ?city              |
| ------------------ |
| Dallas (wd:Q16557) |

[Assistant]
The result shows that John F. Kennedy died in the city of Dallas.

Here is the final SPARQL query and answer:

```sparql
SELECT ?city WHERE {
  wd:Q9696 wdt:P20 ?hospital .
  ?hospital wdt:P131 ?city .
}
```

Answer: John F. Kennedy died in Dallas.

Now, I will provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?currency WHERE {
  wd:Q213 wdt:P38 ?currency .
}

The currency of the Czech Republic is the Czech koruna.

[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 is the currency of the Czech Republic?

[Assistant] To find the currency of the Czech Republic, I need to identify the entity for the Czech Republic and the property for currency. Let's start by searching for the Czech Republic entity.

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

Top 10 entity alternatives:
1. Czech Republic, also known as CZR, CZE, Czechia, CZ (wd:Q213): country in Central Europe
2. Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe
3. Czech Republic (wd:Q3496079): member state of the Czech and Slovak Federal Republic (1990–1992)
4. Czech Republic (wd:Q5201764): constituency of the European Parliament
5. Czech Republic, also known as Star Trek Czech Republic (wd:Q99288382): the country of the Czech Republic as depicted in Star Trek
6. CZECH REPUBLIC (wd:Q60164570)
7. Czech Republic (wd:Q59760445): scholarly article by LUKAS LINEK & ZDENKA MANSFELDOVA published December 2004 in European Journal of Political Research
8. Czech Republic (wd:Q59212257): scholarly article by Aleš Kudrnáč et al published December 2016 in European Journal of Political Research Political Data Yearbook
9. Czech Republic (wd:Q59212246): scholarly article by IVAN PETÚŠEK & ALEŠ KUDRNÁČ published 5 Jul...n European Journal of Political Research Political Data Yearbook
10. Czech Republic (wd:Q57542866): scholarly article

No other items found

[Assistant] The top result for the Czech Republic is wd:Q213, which seems to be the correct entity for the country. Next, I will search for the property that represents currency.

[Tool]
Top 10 property alternatives:
1. currency, also known as base currency, used money (wd:P38 as wdt/p): currency used by item, uses
2. nominal GDP, also known as GDP, GDP, nominal, gross domestic product, nominal GDP, nominal gross domestic product (wd:P2131 as wdt/p): market value of all officially recognized final goods and services produced within a country in a given period of time
3. central bank (wd:P1304 as wdt/p): country's central bank
4. flag, also known as flag description (wd:P163 as wdt/p): subject's flag
5. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
6. minimum wage (wd:P6794 as wdt/p): lowest wage which can be paid legally in a state for working
7. VAT rate, also known as goods and services tax rate, GST, sales tax, taxation rate for sales, tax rate for sales (wd:P2855 as wdt/p): percentage value-added tax in this country or region. Do not use for sales tax. For specialised rates, use qualifiers
8. religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as wdt/p): religion of a person, organization or religious building, or associated with this subject
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. continent (wd:P30 as wdt/p): continent of which the subject is a part, located in/on physical feature

No other items found

[Assistant]
The top result for the currency property is wd:P38, which seems to be the correct property for the currency used by a country. Now, I will construct a SPARQL query to find the currency of the Czech Republic using these entities and properties.

Let's execute the query:

```sparql
SELECT ?currency WHERE {
  wd:Q213 wdt:P38 ?currency .
}
```

This query will return the currency of the Czech Republic.

[Tool]
Got 1 row and 1 column:
| ?currency                 |
| ------------------------- |
| Czech koruna (wd:Q131016) |

[Assistant]
The currency of the Czech Republic is the Czech koruna (wd:Q131016).

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

[Tool] Stopped generation process