PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX schema: <http://schema.org/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?entity ?label ?description ?type ?alias ?location WHERE {
  VALUES ?entity {
    wd:Q18032040
  }
  ?entity rdfs:label ?label .
  OPTIONAL {
    ?entity schema:description ?description .
    FILTER ( LANG( ?description ) = 'en' )
  }
  OPTIONAL {
    ?entity wdt:P31 ?type
  }
  OPTIONAL {
    ?entity skos:altLabel ?alias .
    FILTER ( LANG( ?alias ) = 'en' )
  }
  OPTIONAL {
    ?entity wdt:P637 ?location
  }
}

Got 96 rows and 6 columns, showing the first 5 and last 5 rows below:
| ?entity                | ?label              | ?description                                    | ?type           | ?alias                      | ?location |
| ---------------------- | ------------------- | ----------------------------------------------- | --------------- | --------------------------- | --------- |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | B12                         |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | B61                         |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | BTBD34                      |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | EDP1                        |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | hBACURD2                    |           |
| ...                    | ...                 | ...                                             | ...             | ...                         | ...       |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | B61                         |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | BTBD34                      |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | EDP1                        |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | hBACURD2                    |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | TNF alpha induced protein 1 |           |

TNFAIP1 (wd:Q18032040) is a protein-coding gene in the species Homo sapiens. It is also known by several aliases, including B12, B61, BTBD34, EDP1, hBACURD2, and TNF alpha induced protein 1.

[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 TNFAIP1 ?

[Assistant]
To answer the question about what TNFAIP1 is, we need to identify the entity corresponding to TNFAIP1 in the knowledge graph and then gather relevant information about it, such as its description or type.

### Step 1: Identify the Entity for TNFAIP1
First, let's search for the entity corresponding to TNFAIP1 in the Wikidata knowledge graph.

I will use a simple SPARQL query to find the entity with the label "TNFAIP1".

[Tool]
Got 2 rows and 1 column:
| ?entity                |
| ---------------------- |
| TNFAIP1 (wd:Q18032040) |
| TNFAIP1 (wd:Q24590424) |

[Assistant]
The search returned two entities with the label "TNFAIP1":

1. **wd:Q18032040**
2. **wd:Q24590424**

### Step 2: Determine the Correct Entity
To determine which of these entities is the correct one, we need to gather more information about each entity, such as their descriptions or types.

I will execute a SPARQL query to get the descriptions and types of both entities.

[Tool]
SPARQL execution failed:
failed to parse input: parse error at position 244 for token EOF with content '' (the input most likely does not follow the grammar)

[Assistant]
It seems there was a syntax error in the SPARQL query. Let me correct the query and try again.

I will ensure the query is properly formatted and re-execute it to get the descriptions and types of the entities.

### Corrected Query
```sparql
SELECT ?entity ?description ?type WHERE {
  VALUES ?entity { wd:Q18032040 wd:Q24590424 }
  ?entity rdfs:label 'TNFAIP1'@en .
  OPTIONAL { ?entity schema:description ?description . FILTER(LANG(?description) = 'en') }
  OPTIONAL { ?entity wdt:P31 ?type }
}
```

Let's re-execute the query.

[Tool]
Got 2 rows and 3 columns:
| ?entity                | ?description                                    | ?type           |
| ---------------------- | ----------------------------------------------- | --------------- |
| TNFAIP1 (wd:Q18032040) | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) |
| TNFAIP1 (wd:Q24590424) | gene of the species Macaca nemestrina           |                 |

[Assistant]
The query results provide the following information:

1. **wd:Q18032040**:
   - Description: "protein-coding gene in the species Homo sapiens"
   - Type: "gene" (wd:Q7187)

2. **wd:Q24590424**:
   - Description: "gene of the species Macaca nemestrina"
   - Type: Not specified

Given that the user is likely asking about the human gene, the correct entity is **wd:Q18032040**.

### Step 3: Finalize the Answer
Now that we have identified the correct entity, we can provide a more detailed answer about TNFAIP1.

I will execute a final SPARQL query to get more details about the entity, such as its aliases, location, and any other relevant information.

### Final SPARQL Query
```sparql
SELECT ?entity ?label ?description ?type ?alias ?location WHERE {
  VALUES ?entity { wd:Q18032040 }
  ?entity rdfs:label ?label .
  OPTIONAL { ?entity schema:description ?description . FILTER(LANG(?description) = 'en') }
  OPTIONAL { ?entity wdt:P31 ?type }
  OPTIONAL { ?entity skos:altLabel ?alias . FILTER(LANG(?alias) = 'en') }
  OPTIONAL { ?entity wdt:P637 ?location }
}
```

Let's execute this query.

[Tool]
Got 96 rows and 6 columns, showing the first 5 and last 5 rows below:
| ?entity                | ?label              | ?description                                    | ?type           | ?alias                      | ?location |
| ---------------------- | ------------------- | ----------------------------------------------- | --------------- | --------------------------- | --------- |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | B12                         |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | B61                         |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | BTBD34                      |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | EDP1                        |           |
| TNFAIP1 (wd:Q18032040) | "TNFAIP1"@ar        | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | hBACURD2                    |           |
| ...                    | ...                 | ...                                             | ...             | ...                         | ...       |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | B61                         |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | BTBD34                      |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | EDP1                        |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | hBACURD2                    |           |
| TNFAIP1 (wd:Q18032040) | "ടിഎന്‍എഫ്എഐപി1"@ml | protein-coding gene in the species Homo sapiens | gene (wd:Q7187) | TNF alpha induced protein 1 |           |

[Assistant]
The query results provide detailed information about the entity **wd:Q18032040** (TNFAIP1):

- **Label**: TNFAIP1
- **Description**: Protein-coding gene in the species Homo sapiens
- **Type**: Gene (wd:Q7187)
- **Aliases**: B12, B61, BTBD34, EDP1, hBACURD2, TNF alpha induced protein 1

There is no specific location information provided for this gene in the results.

### Final Answer
TNFAIP1 (wd:Q18032040) is a protein-coding gene in the species Homo sapiens. It is also known by several aliases, including B12, B61, BTBD34, EDP1, hBACURD2, and TNF alpha induced protein 1.

Here is the final SPARQL query and answer:

```sparql
SELECT ?entity ?label ?description ?type ?alias ?location WHERE {
  VALUES ?entity { wd:Q18032040 }
  ?entity rdfs:label ?label .
  OPTIONAL { ?entity schema:description ?description . FILTER(LANG(?description) = 'en') }
  OPTIONAL { ?entity wdt:P31 ?type }
  OPTIONAL { ?entity skos:altLabel ?alias . FILTER(LANG(?alias) = 'en') }
  OPTIONAL { ?entity wdt:P637 ?location }
}
```

Answer: TNFAIP1 (wd:Q18032040) is a protein-coding gene in the species Homo sapiens. It is also known by several aliases, including B12, B61, BTBD34, EDP1, hBACURD2, and TNF alpha induced protein 1.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?borderingCountry ) AS ?count ) WHERE {
  wd:Q408 wdt:P47 ?borderingCountry .
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 7      |

Australia shares a border with 7 countries.

[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] With how many countries does Australia share a border?

[Assistant] To answer the question about how many countries Australia shares a border with, we need to identify the entity for Australia and then look for properties that indicate borders or neighboring countries. Let's start by finding the entity for Australia.

[Tool]
Got 110 rows and 1 column, showing the first 5 and last 5 rows below:
| ?australia                |
| ------------------------- |
| Australia (wd:Q100270480) |
| Australia (wd:Q100270726) |
| Australia (wd:Q103777627) |
| Australia (wd:Q103891323) |
| Australia (wd:Q104269190) |
| ...                       |
| Australia (wd:Q94379622)  |
| Australia (wd:Q95377977)  |
| Australia (wd:Q95463439)  |
| Australia (wd:Q95463464)  |
| Australia (wd:Q99292881)  |

[Assistant] It seems there are multiple entities with the label "Australia". We need to filter these to find the correct one, which is likely the country. Let's try to filter by the type of entity, specifically looking for the country.

[Tool]
Got 1 row and 1 column:
| ?australia          |
| ------------------- |
| Australia (wd:Q408) |

[Assistant] We have identified the correct entity for Australia, which is `wd:Q408`. Next, we need to find the property that indicates bordering countries. The property `wdt:P47` (borders) is commonly used for this purpose. Let's check if Australia has any bordering countries using this property.

[Tool]
Got 7 rows and 1 column:
| ?borderingCountry          |
| -------------------------- |
| Indonesia (wd:Q252)        |
| New Caledonia (wd:Q33788)  |
| Timor-Leste (wd:Q574)      |
| New Zealand (wd:Q664)      |
| Solomon Islands (wd:Q685)  |
| Vanuatu (wd:Q686)          |
| Papua New Guinea (wd:Q691) |

[Assistant]
The query returned seven bordering countries for Australia. However, it's important to note that these are maritime borders rather than land borders, as Australia is an island continent. To confirm the number of countries Australia shares a border with, we can count the results.

Let's write a final SPARQL query to count the number of bordering countries.

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 7      |

[Assistant]
The final SPARQL query confirms that Australia shares a border with 7 countries. These are maritime borders, as Australia is an island continent.

Here is the final SPARQL query and answer:

```sparql
SELECT (COUNT(?borderingCountry) AS ?count) WHERE {
  wd:Q408 wdt:P47 ?borderingCountry .
}
```

Answer: Australia shares a border with 7 countries.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?deathDate WHERE {
  wd:Q43347 wdt:P570 ?deathDate .
}

Rumi died on December 17, 1273.

[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] When did Rumi die?

[Assistant] To answer the question "When did Rumi die?", we need to identify the entity for Rumi and then find the property that corresponds to the date of death. Let's start by searching for Rumi in the knowledge graph.

[Tool]
Got 11 rows and 1 column, showing the first 5 and last 5 rows below:
| ?rumi               |
| ------------------- |
| Rumi (wd:Q11241326) |
| Rumi (wd:Q11578550) |
| Rumi (wd:Q19949377) |
| Rumi (wd:Q35510087) |
| wd:Q365547          |
| ...                 |
| Rumi (wd:Q43347)    |
| Rumi (wd:Q55238528) |
| Rumi (wd:Q56275708) |
| Rumi (wd:Q94749493) |
| Rumi (wd:Q97655647) |

[Assistant]
The search for Rumi returned multiple results. We need to identify the correct Rumi, which is likely to be the famous Persian poet. Let's look at the descriptions or labels of these entities to narrow it down.

I will check the labels and descriptions of the top few results to find the correct Rumi.

[Tool]
Got 1 row and 3 columns:
| ?rumi               | ?label | ?description                  |
| ------------------- | ------ | ----------------------------- |
| Rumi (wd:Q11241326) | Rumi   | Japanese master of ceremonies |

[Assistant]
It seems that the only result with a description in English is `wd:Q11241326`, which describes Rumi as a "Japanese master of ceremonies." This is not the Rumi we are looking for.

Let's try another approach by searching for "Jalaluddin Rumi," which is the full name of the famous Persian poet.

[Tool] Got no rows and 1 columns

[Assistant] It appears that searching for "Jalaluddin Rumi" did not yield any results. Let's try a different approach by searching for "Mevlana Jalaluddin Rumi," which is another common name for the poet.

[Tool] Got no rows and 1 columns

[Assistant]
Searching for "Mevlana Jalaluddin Rumi" also did not yield any results. Let's try a more general search for "Jalaluddin" and filter the results to find the correct Rumi.

I will search for entities with the label "Jalaluddin" and then check their descriptions to identify the correct Rumi.

[Tool]
Got 4 rows and 3 columns:
| ?rumi                      | ?label | ?description                                             |
| -------------------------- | ------ | -------------------------------------------------------- |
| Jalaluddin (wd:Q123866032) |        | Indonesian politician (born 1983 in Ie Rhob Barat)       |
| Jalaluddin (wd:Q123866190) |        | Indonesian politician (born 1974 in Aceh Utara)          |
| Jalaluddin (wd:Q123884968) |        | Indonesian politician (born 1975 in Tembeng Putik Baret) |
| Jalaluddin (wd:Q123894453) |        | Indonesian politician (born 1974 in Matakali)            |

[Assistant]
None of the results for "Jalaluddin" seem to match the famous Persian poet Rumi. Let's try a different approach by searching for "Rumi" and filtering the results based on the description to find the correct entity.

I will search for entities with the label "Rumi" and filter the results to find the one described as a poet or related to Persian literature.

[Tool]
Got 9 rows and 3 columns:
| ?rumi               | ?label | ?description                    |
| ------------------- | ------ | ------------------------------- |
| Rumi (wd:Q11241326) |        | Japanese master of ceremonies   |
| Rumi (wd:Q11578550) |        | Japanese actress                |
| Rumi (wd:Q35510087) |        | spring in Sindh, Pakistan       |
| wd:Q365547          |        | Wikimedia disambiguation page   |
| Rumi (wd:Q37151610) |        | family name                     |
| Rumi (wd:Q43347)    |        | Sufi and Persian poet           |
| Rumi (wd:Q55238528) |        | given name                      |
| Rumi (wd:Q56275708) |        | opera by Behzad Abdi            |
| Rumi (wd:Q97655647) |        | Japanese female given name (るみ) |

[Assistant]
From the results, the entity `wd:Q43347` is described as "Sufi and Persian poet," which matches the famous Rumi we are looking for.

Now that we have identified the correct Rumi, let's find the date of his death. We will use the property `wdt:P570` which represents the date of death.

Let's construct the SPARQL query to find the date of death for Rumi.

[Tool]
Got 1 row and 1 column:
| ?deathDate           |
| -------------------- |
| 1273-12-17T00:00:00Z |

[Assistant]
The SPARQL query returned the date of death for Rumi as `1273-12-17T00:00:00Z`.

Therefore, Rumi died on December 17, 1273.

Here is the final SPARQL query and answer:

```sparql
SELECT ?deathDate WHERE {
  wd:Q43347 wdt:P570 ?deathDate .
}
```

Answer: Rumi died on December 17, 1273.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?animal WHERE {
  ?operation wdt:P710 wd:Q625657 .
  ?animal wdt:P1344 ?operation .
  ?animal wdt:P31 wd:Q729 .
}

There are no animals recorded as participating in military operations involving the Australian Defence Force in the Wikidata knowledge graph.

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

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

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

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

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

[User] Which animal participated in a military operation with the Australian Defence Force?

[Assistant]
To answer this question, we need to identify the animal that participated in a military operation with the Australian Defence Force. We can break this down into the following steps:

1. Identify the entity for the Australian Defence Force.
2. Find military operations involving the Australian Defence Force.
3. Identify animals that participated in those operations.

Let's start by finding the entity for the Australian Defence Force.

[Tool]
Got 1 row and 1 column:
| ?adf                                  |
| ------------------------------------- |
| Australian Defence Force (wd:Q625657) |

[Assistant]
We have identified the entity for the Australian Defence Force as `wd:Q625657`. Next, we need to find military operations involving the Australian Defence Force. We will look for operations where the Australian Defence Force was involved.

Let's execute a query to find these operations.

[Tool]
Got 53 rows and 1 column, showing the first 5 and last 5 rows below:
| ?operation                                      |
| ----------------------------------------------- |
| Emu War (wd:Q14665)                             |
| Cope North (wd:Q29325071)                       |
| Exercise Talisman Sabre 2005 (wd:Q41061291)     |
| Exercise Talisman Sabre 2007 (wd:Q41061297)     |
| Exercise Talisman Sabre 2009 (wd:Q41061301)     |
| ...                                             |
| Exercise Cooperation Spirit 2017 (wd:Q45388821) |
| Exercise Phoenix Spirit 2013 (wd:Q45389964)     |
| Exercise Cooperation Spirit 2012 (wd:Q45390541) |
| Exercise Cooperation Spirit 2011 (wd:Q45390638) |
| Exercise Talisman Sabre (wd:Q5420046)           |

[Assistant]
We have found several military operations involving the Australian Defence Force. Now, we need to identify animals that participated in these operations. We will look for animals that were involved in any of these operations.

Let's execute a query to find such animals.

[Tool]
Got 1 row and 1 column:
| ?animal                    |
| -------------------------- |
| George Pearce (wd:Q364568) |

[Assistant]
We have found one entity, `George Pearce (wd:Q364568)`, that participated in a military operation involving the Australian Defence Force. However, we need to verify if this entity is indeed an animal. Let's check the type of `George Pearce` to confirm if it is an animal.

Let's execute a query to check the type of `George Pearce`.

[Tool]
Got 1 row and 1 column:
| ?type         |
| ------------- |
| human (wd:Q5) |

[Assistant]
It appears that `George Pearce` is a human, not an animal. This means we need to refine our query to specifically look for animals that participated in military operations involving the Australian Defence Force.

Let's modify our query to filter for animals. We will use the property `wdt:P31` to check if the entity is an instance of an animal (`wd:Q729`).

Let's execute the refined query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there are no animals that have been recorded as participating in military operations involving the Australian Defence Force in the Wikidata knowledge graph. Given this result, we cannot provide a definitive answer to the question.

I will now provide a cancellation response explaining the situation and including the best attempt at a SPARQL query.

[Tool] Stopped generation process


Failed to generate SPARQL query

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

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

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

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

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

[User] What was the main subject addressed in the Deep Purple song “Smoke on the water”?

[Assistant]
To answer this question, we need to identify the song "Smoke on the Water" by Deep Purple and then find out what the main subject of the song is. We'll start by searching for the song and the band in Wikidata.

Let's first find the entity for "Smoke on the Water" and ensure it is associated with Deep Purple.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the initial query did not return any results. Let's try a different approach by directly searching for the song "Smoke on the Water" and then verifying that it is indeed by Deep Purple.

I will search for the song "Smoke on the Water" and check its performer.

[Tool]
Got 6 rows and 2 columns:
| ?song                             | ?performer               |
| --------------------------------- | ------------------------ |
| Smoke on the Water (wd:Q3435148)  | Deep Purple (wd:Q101505) |
| Smoke on the Water (wd:Q636399)   | Deep Purple (wd:Q101505) |
| Smoke on the Water (wd:Q94003891) | Deep Purple (wd:Q101505) |
| Smoke on the Water (wd:Q94008225) | Deep Purple (wd:Q101505) |
| Smoke on the Water (wd:Q7545910)  | Red Foley (wd:Q2135973)  |
| Smoke on the Water (wd:Q93971258) | Pat Boone (wd:Q342778)   |

[Assistant]
We have multiple results for "Smoke on the Water," but only the ones performed by Deep Purple are relevant. The most likely candidate is `wd:Q3435148`, which is the most commonly referenced version of the song by Deep Purple.

Next, we need to find the main subject of the song. We can look for properties related to the song's content or lyrics. One common property for this is `P856` (official website), but for the main subject, we might need to look at descriptions or other metadata.

Let's check if there is any description or subject information available for the song `wd:Q3435148`.


Failed to generate SPARQL query

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

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

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

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

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

[User] What are the titles of the Star Wars series movies?

[Assistant]
To answer the user's question about the titles of the Star Wars series movies, I need to identify the entity representing the Star Wars franchise and then find the movies associated with it. I will start by searching for the Star Wars franchise entity.

### Step 1: Identify the Star Wars franchise entity
I will search for the entity representing the Star Wars franchise.

### Step 2: Find the movies associated with the Star Wars franchise
Once I have the entity for the Star Wars franchise, I will look for the movies that are part of this franchise.

### Step 3: Retrieve the titles of these movies
Finally, I will retrieve the titles of these movies.

Let's start by identifying the Star Wars franchise entity.

[Tool]
Got 29 rows and 1 column, showing the first 5 and last 5 rows below:
| ?starWars                 |
| ------------------------- |
| Star Wars (wd:Q107079530) |
| Star Wars (wd:Q109284196) |
| Star Wars (wd:Q114499694) |
| Star Wars (wd:Q114545379) |
| wd:Q1163494               |
| ...                       |
| Star Wars (wd:Q63570640)  |
| Star Wars (wd:Q73836451)  |
| Star Wars (wd:Q7601114)   |
| Star Wars (wd:Q7601115)   |
| Star Wars (wd:Q7601116)   |

[Assistant]
The query returned multiple entities with the label "Star Wars." Among these, `wd:Q7601114` is likely the main entity representing the Star Wars franchise. I will verify this by checking its properties and ensuring it is indeed the correct entity.

### Step 2: Verify the Star Wars franchise entity
I will check the properties of `wd:Q7601114` to confirm it is the main Star Wars franchise entity.

### Step 3: Find the movies associated with the Star Wars franchise
Once confirmed, I will find the movies associated with this entity.

### Step 4: Retrieve the titles of these movies
Finally, I will retrieve the titles of these movies.

Let's verify the entity `wd:Q7601114`.

[Tool]
Got 10 rows and 2 columns:
| ?property          | ?value                 |
| ------------------ | ---------------------- |
| schema:description | 1992 pinball           |
| schema:description | "videogioco"@it        |
| schema:description | "компьютерная игра"@ru |
| schema:description | "لعبة فيديو"@ar        |
| schema:name        | Star Wars              |
| schema:name        | "Star Wars"@ru         |
| schema:name        | "スター・ウォーズ (ピンボール)"@ja  |
| schema:name        | "星際大戰"@zh              |
| rdf:type           | wikibase:Item          |
| rdfs:label         | Star Wars              |

[Assistant]
The entity `wd:Q7601114` seems to be related to a video game or pinball machine, not the main Star Wars franchise. I will try another entity from the list, such as `wd:Q1163494`, which is also labeled "Star Wars."

### Step 2: Verify the Star Wars franchise entity
I will check the properties of `wd:Q1163494` to confirm it is the main Star Wars franchise entity.

### Step 3: Find the movies associated with the Star Wars franchise
Once confirmed, I will find the movies associated with this entity.

### Step 4: Retrieve the titles of these movies
Finally, I will retrieve the titles of these movies.

Let's verify the entity `wd:Q1163494`.

[Tool]
Got 10 rows and 2 columns:
| ?property          | ?value                                        |
| ------------------ | --------------------------------------------- |
| schema:description | "aðgreiningarsíða á Wikipediu"@is             |
| schema:description | "deurverwiesziede"@nds-nl                     |
| schema:description | "faqe kthjelluese e Wikimedias"@sq            |
| schema:description | "halaman disambiguasi Wikimedia"@id           |
| schema:description | "laman disambiguasi"@min                      |
| schema:description | "Laman nyahkekaburan"@ms                      |
| schema:description | "Mäğnälär bite Wikimedia proyektında"@tt-latn |
| schema:description | "Muardüüdag artiikel"@frr                     |
| schema:description | "ojúewé ìṣojútùú Wikimedia"@yo                |
| schema:description | "pachina de desambigación"@an                 |

[Assistant]
The entity `wd:Q1163494` appears to be a disambiguation page, not the main Star Wars franchise entity. Let's try another entity from the list, such as `wd:Q20043`, which is also labeled "Star Wars."

### Step 2: Verify the Star Wars franchise entity
I will check the properties of `wd:Q20043` to confirm it is the main Star Wars franchise entity.

### Step 3: Find the movies associated with the Star Wars franchise
Once confirmed, I will find the movies associated with this entity.

### Step 4: Retrieve the titles of these movies
Finally, I will retrieve the titles of these movies.

Let's verify the entity `wd:Q20043`.

[Tool]
Got 10 rows and 2 columns:
| ?property          | ?value                |
| ------------------ | --------------------- |
| schema:description | "Nudelart"@de         |
| schema:description | "tipo de pasta"@es    |
| schema:description | "tipo di pasta"@it    |
| schema:description | type of pasta         |
| schema:description | "نوع من المعكرونة"@ar |
| schema:name        | "strangozzi"@it       |
| schema:name        | "Strangozzi"@fr       |
| schema:name        | stringozzi            |
| schema:name        | "stringozzi"@es       |
| schema:name        | "Stringozzi"@de       |

[Assistant]
It seems there was a mistake in the previous query. The entity `wd:Q20043` is actually related to a type of pasta, not the Star Wars franchise. Let's try another entity from the list, such as `wd:Q1163494`, which is labeled "Star Wars" and is more likely to be the correct entity.

### Step 2: Verify the Star Wars franchise entity
I will check the properties of `wd:Q1163494` again to confirm it is the main Star Wars franchise entity.

### Step 3: Find the movies associated with the Star Wars franchise
Once confirmed, I will find the movies associated with this entity.

### Step 4: Retrieve the titles of these movies
Finally, I will retrieve the titles of these movies.

Let's verify the entity `wd:Q1163494` again.


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?releaseDate WHERE {
  wd:Q58815001 wdt:P577 ?releaseDate
}

The film 'Red Notice' was released on 2021-11-12.

[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] When the film Red Notice was released?

[Assistant] To find out when the film "Red Notice" was released, I need to identify the entity for the film "Red Notice" and then look for the property that indicates its release date. Let's start by searching for the entity "Red Notice" in Wikidata.

[Tool]
Got 1 row and 1 column:
| ?film                     |
| ------------------------- |
| Red Notice (wd:Q58815001) |

[Assistant] I found the entity for the film "Red Notice" in Wikidata, which is `wd:Q58815001`. Now, I will look for the property that indicates the release date of the film. The property for release date is typically `P577` (publication date). Let's construct a query to retrieve the release date of "Red Notice".

[Tool]
Got 1 row and 1 column:
| ?releaseDate         |
| -------------------- |
| 2021-11-12T00:00:00Z |

[Assistant] The release date of the film "Red Notice" is 2021-11-12. I will now provide the final SPARQL query and the answer to the user's question.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?deathDate WHERE {
  wd:Q1768 wdt:P570 ?deathDate .
}

Ella Fitzgerald died on June 15, 1996.

[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] When did Ella Fitzgerald die?

[Assistant] To answer the question "When did Ella Fitzgerald die?", we need to identify the entity for Ella Fitzgerald and then find the property that corresponds to the date of death. Let's start by searching for the entity representing Ella Fitzgerald in the knowledge graph.

[Tool]
Got 3 rows and 1 column:
| ?entity                         |
| ------------------------------- |
| Ella Fitzgerald (wd:Q100786004) |
| Ella Fitzgerald (wd:Q1768)      |
| Ella Fitzgerald (wd:Q28732978)  |

[Assistant] We found three entities with the label "Ella Fitzgerald". To determine which one is the correct person, we need to look at their descriptions or other properties. Let's check the descriptions of these entities to see which one refers to the famous jazz singer.

[Tool]
Got 3 rows and 2 columns:
| ?entity                         | ?description                                          |
| ------------------------------- | ----------------------------------------------------- |
| Ella Fitzgerald (wd:Q100786004) | American drag queen                                   |
| Ella Fitzgerald (wd:Q1768)      | American jazz singer (1917–1996)                      |
| Ella Fitzgerald (wd:Q28732978)  | tram stop in the 19th arrondissement of Paris, France |

[Assistant]
The entity `wd:Q1768` is described as "American jazz singer (1917–1996)", which matches the famous jazz singer Ella Fitzgerald. Now that we have identified the correct entity, we can find the date of her death. The property for the date of death in Wikidata is `wdt:P570`.

Let's construct a SPARQL query to retrieve the date of death for Ella Fitzgerald.

[Tool]
Got 1 row and 1 column:
| ?deathDate           |
| -------------------- |
| 1996-06-15T00:00:00Z |

[Assistant]
The SPARQL query returned the date of death for Ella Fitzgerald as `1996-06-15T00:00:00Z`. This is the date when she passed away.

Now, let's format the answer and provide the final SPARQL query.

[Tool] Stopped generation process


PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  wd:Q362 wdt:P582 '1945-09-02T00:00:00Z' ^^ xsd:dateTime .
}

True

[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] When did World War II officially end?

[Assistant] To answer when World War II officially ended, I need to identify the entity for World War II and then find the property that indicates its end date. Let's start by searching for the entity representing World War II.

[Tool]
Got 7 rows and 1 column:
| ?ww2                         |
| ---------------------------- |
| World War II (wd:Q104869378) |
| World War II (wd:Q105750294) |
| wd:Q107394187                |
| World War II (wd:Q110874375) |
| World War II (wd:Q15053456)  |
| World War II (wd:Q15053459)  |
| World War II (wd:Q362)       |

[Assistant]
There are multiple entities with the label "World War II". The most commonly used entity for World War II in Wikidata is `wd:Q362`. I will use this entity to find the end date of World War II.

Next, I will look for the property that indicates the end date of World War II.

[Tool]
Got 1 row and 1 column:
| ?endDate             |
| -------------------- |
| 1945-09-02T00:00:00Z |

[Assistant]
The end date of World War II is recorded as `1945-09-02T00:00:00Z` in Wikidata. This means World War II officially ended on September 2, 1945.

I will now construct the final SPARQL query to confirm this information.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?material WHERE {
  wd:Q39231 wdt:P186 ?material .
}

Mount Fuji is made of basalt.

[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 Fujiyama made of?

[Assistant] To answer the question "What is the Fujiyama made of?", we need to identify the entity representing Mount Fuji (Fujiyama) and then look for properties that describe its composition or material. Let's start by finding the entity for Mount Fuji.

[Tool]
Got 16 rows and 1 column, showing the first 5 and last 5 rows below:
| ?mountfuji                 |
| -------------------------- |
| Mount Fuji (wd:Q102968773) |
| Mount Fuji (wd:Q105770291) |
| Mount Fuji (wd:Q106767913) |
| Mount Fuji (wd:Q106851327) |
| Mount Fuji (wd:Q112200960) |
| ...                        |
| Mount Fuji (wd:Q57654621)  |
| Mount Fuji (wd:Q78620185)  |
| Mount Fuji (wd:Q78754492)  |
| Mount Fuji (wd:Q79412063)  |
| Mount Fuji (wd:Q83608290)  |

[Assistant]
It seems there are multiple entities with the label "Mount Fuji". The most common and well-known entity for Mount Fuji is likely to be the one with the identifier `wd:Q102968773`. However, to be sure, let's check the properties of these entities to see which one is the correct Mount Fuji in Japan.

I will query the properties of these entities to find the one that is located in Japan and is a volcano, as Mount Fuji is known to be a stratovolcano.

Let's try to filter the entities based on their location and type.

[Tool]
Got 9 rows and 3 columns:
| ?mountfuji                 | ?location      | ?type                           |
| -------------------------- | -------------- | ------------------------------- |
| Mount Fuji (wd:Q102968773) | Japan (wd:Q17) | print (wd:Q11060274)            |
| Mount Fuji (wd:Q11455978)  | Japan (wd:Q17) | mountain (wd:Q8502)             |
| Mount Fuji (wd:Q39231)     | Japan (wd:Q17) | isolated peak (wd:Q11571545)    |
| Mount Fuji (wd:Q39231)     | Japan (wd:Q17) | shintaisan (wd:Q11588709)       |
| Mount Fuji (wd:Q39231)     | Japan (wd:Q17) | active volcano (wd:Q1330974)    |
| Mount Fuji (wd:Q39231)     | Japan (wd:Q17) | stratovolcano (wd:Q169358)      |
| Mount Fuji (wd:Q39231)     | Japan (wd:Q17) | highest point (wd:Q3393392)     |
| Mount Fuji (wd:Q39231)     | Japan (wd:Q17) | tourist attraction (wd:Q570116) |
| Mount Fuji (wd:Q39231)     | Japan (wd:Q17) | mountain (wd:Q8502)             |

[Assistant]
From the results, it is clear that the entity `wd:Q39231` represents Mount Fuji in Japan, as it has the properties of being a stratovolcano, an active volcano, and a mountain located in Japan.

Now, let's find out what Mount Fuji is made of. We will look for properties related to the composition or material of the mountain.

I will query for properties such as `P186` (material), `P1542` (has part), and `P2670` (consists of) to find the composition of Mount Fuji.

[Tool]
Got 1 row and 3 columns:
| ?material          | ?part | ?composition |
| ------------------ | ----- | ------------ |
| basalt (wd:Q43338) |       |              |

[Assistant]
The query results show that Mount Fuji is primarily made of basalt. There are no additional parts or compositions listed in the data.

Therefore, the answer to the question "What is the Fujiyama made of?" is that Mount Fuji is made of basalt.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  wd:Q1650 wdt:P2043 ?weserLength .
  wd:Q584 wdt:P2043 ?rhineLength .
  FILTER ( ?weserLength > ?rhineLength )
}

False

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

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

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

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

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

[User] Is the Weser longer than the Rhine?

[Assistant]
To answer this question, we need to find the lengths of the rivers Weser and Rhine and compare them. Let's start by identifying the entities for these rivers and their respective lengths.

First, I will search for the entities representing the Weser and Rhine rivers.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant]
It seems there was a timeout issue with the initial query. Let me try again with a simpler query to just identify the entities for the Weser and Rhine rivers.

I will directly look up the entities for the Weser and Rhine rivers using their names.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that the query did not return any results, which suggests that the labels might not be exact matches or there might be some other issue. Let me try a different approach by using the `wd:` prefix to directly reference the entities for the Weser and Rhine rivers, assuming they are known.

I will use the following Wikidata IDs:
- Weser: Q1587
- Rhine: Q2309

Let's retrieve the lengths of these rivers using these IDs.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query did not return any results, which suggests that the property `P2043` (length) might not be the correct property for the length of the rivers, or the IDs might be incorrect. Let me verify the IDs and the property.

I will first check the labels and properties of the entities to ensure they are correct.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that the entities `wd:Q1587` and `wd:Q2309` are not returning any properties or labels, which suggests that these IDs might be incorrect. Let me try searching for the correct IDs using the labels "Weser" and "Rhine" again, but this time with a more relaxed filter to ensure we get the correct entities.

I will use a more flexible query to find the correct entities for the Weser and Rhine rivers.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant]
It seems that the query is timing out again. Let me try a different approach by using a more direct method to find the correct entities for the Weser and Rhine rivers.

I will use the `wd:` prefix with the known IDs for the Weser and Rhine rivers and directly query for their lengths using the `wdt:P2043` property, which is the standard property for the length of a river in Wikidata.

Let's try this approach:

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the IDs `wd:Q1587` and `wd:Q2309` are not correct for the Weser and Rhine rivers, or the property `P2043` is not being used for their lengths. Let me try a different approach by searching for the correct entities using a more flexible query and then retrieving their lengths.

I will first find the correct entities for the Weser and Rhine rivers and then retrieve their lengths.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant]
Given the repeated timeouts, it seems that the SPARQL endpoint is having issues with the current queries. Let me try a different approach by breaking down the query into smaller parts and ensuring that we get the correct entities first.

I will start by finding the entity for the Weser river and then separately for the Rhine river. Once we have the correct entities, we can retrieve their lengths.

Let's start with the Weser river:

[Tool]
Got 12 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?river                | ?riverLabel |
| --------------------- | ----------- |
| Weser (wd:Q106677892) |             |
| Weser (wd:Q1650)      |             |
| Weser (wd:Q19649410)  |             |
| Weser (wd:Q19801877)  |             |
| wd:Q200492            |             |
| ...                   | ...         |
| Weser (wd:Q34857185)  |             |
| Weser (wd:Q37225304)  |             |
| Weser (wd:Q56515182)  |             |
| Weser (wd:Q64584496)  |             |
| Weser (wd:Q84711860)  |             |

[Assistant]
It looks like there are multiple entities with the label "Weser". We need to identify the correct one, which is likely the main river. Let's filter the results to find the most relevant entity by checking for additional properties such as the type of entity (e.g., river).

I will add a filter to check if the entity is a river (using the property `P31` for instance of and the value `Q4022` for river).

[Tool]
Got 2 rows and 2 columns:
| ?river               | ?riverLabel |
| -------------------- | ----------- |
| Weser (wd:Q1650)     |             |
| Weser (wd:Q34857185) |             |

[Assistant]
We have two potential entities for the Weser river. To further narrow it down, let's check the lengths of these entities to see which one is the main river. We will use the property `P2043` for length.

Let's retrieve the lengths of these entities.

[Tool]
Got 1 row and 2 columns:
| ?river           | ?length |
| ---------------- | ------- |
| Weser (wd:Q1650) | 451     |

[Assistant]
The entity `wd:Q1650` has a length of 451 kilometers, which is consistent with the known length of the Weser river. Therefore, `wd:Q1650` is the correct entity for the Weser river.

Next, let's find the correct entity for the Rhine river and its length. We will use a similar approach to identify the correct entity and then retrieve its length.

[Tool]
Got 1 row and 2 columns:
| ?river          | ?riverLabel |
| --------------- | ----------- |
| Rhine (wd:Q584) |             |

[Assistant] The entity `wd:Q584` is the correct entity for the Rhine river. Now, let's retrieve the length of the Rhine river using the property `P2043` for length.

[Tool]
Got 1 row and 2 columns:
| ?river          | ?length |
| --------------- | ------- |
| Rhine (wd:Q584) | 1232.7  |

[Assistant]
The length of the Rhine river is 1232.7 kilometers.

Now that we have the lengths of both the Weser and Rhine rivers, we can compare them to answer the question.

The length of the Weser river is 451 kilometers, and the length of the Rhine river is 1232.7 kilometers. Therefore, the Weser is not longer than the Rhine.

Let's confirm this with an ASK query to provide a boolean answer.

[Tool] False

[Assistant]
The ASK query confirms that the Weser is not longer than the Rhine.

Therefore, the answer to the question "Is the Weser longer than the Rhine?" is **False**.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  wd:Q1650 wdt:P2043 ?weserLength .
  wd:Q584 wdt:P2043 ?rhineLength .
  FILTER(?weserLength > ?rhineLength)
}
```

The answer is **False**.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?event ?eventLabel ?date ( xsd:integer ( ?deaths ) AS ?numDeaths ) WHERE {
  ?event wdt:P585 ?date ; wdt:P1120 ?deaths .
  FILTER ( ?date >= "1910-01-01T00:00:00Z" ^^ xsd:dateTime && ?date <= "1920-12-31T23:59:59Z" ^^ xsd:dateTime ) OPTIONAL {
    ?event rdfs:label ?eventLabel .
    FILTER ( LANG( ?eventLabel ) = "en" )
  }
}
ORDER BY DESC ( ?numDeaths )
LIMIT 1

The event that killed the most people between 1910 and 1920 is World War I, with approximately 17 million deaths.

[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 event killed the most people in the years 1910 to 1920?

[Assistant]
To answer this question, we need to identify events that occurred between 1910 and 1920 and determine which one resulted in the highest number of fatalities. We will start by searching for events that have a date property and a number of deaths property, and then filter those events to the specified time range.

Let's begin by identifying the relevant properties and entities.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the initial query did not return any results. Let's try a different approach by directly querying for events that have a date and a number of deaths, and then filter by the date range.

We will use `wdt:P585` (point in time) for the date and `wdt:P1120` (number of deaths) for the number of fatalities. We will also filter the results to only include events between 1910 and 1920.

Let's execute this query.

[Tool]
Got 1 row and 4 columns:
| ?event                                                             | ?eventLabel                                           | ?date                | ?deaths    |
| ------------------------------------------------------------------ | ----------------------------------------------------- | -------------------- | ---------- |
| massacre in front of the Reichstag on 13 January 1920 (wd:Q886480) | massacre in front of the Reichstag on 13 January 1920 | 1920-01-13T00:00:00Z | _:bn383028 |

[Assistant]
The query returned one event, which is the "massacre in front of the Reichstag on 13 January 1920." However, the number of deaths is represented as a blank node, which suggests that the value might be stored in a different format or property.

Let's modify the query to ensure we get the actual number of deaths and verify the result.

[Tool]
Got 1 row and 4 columns:
| ?event                                                             | ?eventLabel                                           | ?date                | ?deaths    |
| ------------------------------------------------------------------ | ----------------------------------------------------- | -------------------- | ---------- |
| massacre in front of the Reichstag on 13 January 1920 (wd:Q886480) | massacre in front of the Reichstag on 13 January 1920 | 1920-01-13T00:00:00Z | _:bn383028 |

[Assistant]
It appears that the number of deaths is still being returned as a blank node, which suggests that the value might be stored in a different format or property. Let's try to explicitly cast the number of deaths to an integer to see if that resolves the issue.

We will use the `xsd:integer` casting function to ensure the number of deaths is treated as an integer.

Let's execute this modified query.

[Tool]
Got 1 row and 4 columns:
| ?event                | ?eventLabel | ?date                | ?numDeaths |
| --------------------- | ----------- | -------------------- | ---------- |
| World War I (wd:Q361) | World War I | 1918-11-11T00:00:00Z | 17000000   |

[Assistant]
The query now correctly returns the event with the highest number of deaths between 1910 and 1920. The event is World War I, which resulted in approximately 17 million deaths.

Here is the final SPARQL query and the answer to the question:

```sparql
SELECT ?event ?eventLabel ?date (xsd:integer(?deaths) AS ?numDeaths)
WHERE {
  ?event wdt:P585 ?date ;
         wdt:P1120 ?deaths .
  FILTER(?date >= "1910-01-01T00:00:00Z"^^xsd:dateTime && ?date <= "1920-12-31T23:59:59Z"^^xsd:dateTime)
  OPTIONAL {
    ?event rdfs:label ?eventLabel .
    FILTER(LANG(?eventLabel) = "en")
  }
}
ORDER BY DESC(?numDeaths)
LIMIT 1
```

The event that killed the most people between 1910 and 1920 is World War I, with approximately 17 million deaths.

[Tool] Stopped generation process