PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?author WHERE {
  wd:Q44649835 wdt:P50 ?author
}

Richard S. Zemel

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

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

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

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

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

[User] Who wrote "Prototypical Networks for Few-shot Learning"?

[Assistant]
To answer the question about who wrote "Prototypical Networks for Few-shot Learning," I need to identify the paper and then find its authors. I'll start by searching for the paper in the knowledge graph.

### Step 1: Search for the Paper
I will use the `search` function to look for the paper titled "Prototypical Networks for Few-shot Learning."

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT * WHERE { ?search wdt:P1476 ?title }",
    "query": "Prototypical Networks for Few-shot Learning"
  }
}
```

This query will help me find the item representing the paper in the knowledge graph. Once I have the paper's IRI, I can proceed to find its authors.

[Tool]
Falling back to an unconstrained search on the full precomputed search indices:
Got more than the maximum supported number of subject candidates (131,072)

Top 10 entity alternatives:
1. Prototypical Networks for Few-shot Learning (wd:Q44649835): scientific article published in January 2017
2. Hybrid Graph Neural Networks for Few-Shot Learning (wd:Q114887023): scientific article published in 2022
3. Convolutional Shrinkage Neural N...a-Learning for Few-Shot Learning (wd:Q114955057): scientific article published in 2022
4. Facial Skin Type Analysis Using ...rning with Prototypical Networks (wd:Q129793741): scholarly article published on 31 December 2023
5. Few-Shot Ensemble Learning for V...on with SlowFast Memory Networks (wd:Q130987343): scientific article published on 12 October 2020
6. Co-Learning for Few-Shot Learning (wd:Q114955104): scientific article published in 2022
7. Differential learning for learning networks (wd:Q123992464): US patent 11468327
8. Bayesian Learning for Neural Networks (wd:Q60016544): scientific article published in 1996
9. Information Maximization for Few-Shot Learning (wd:Q104090143): scientific article published in November 2020
10. Ensemble Learning for Multi-Layer Networks (wd:Q77697315): scholarly article by David Barber & Christopher M. Bishop published 1998 in Advances in Neural Information Processing Systems 10

No other items found

[Assistant]
The search has returned the correct paper "Prototypical Networks for Few-shot Learning" with the IRI `wd:Q44649835`. Now, I need to find the authors of this paper.

### Step 2: Find the Authors of the Paper
I will use the `list` function to find the authors of the paper by looking for the property `P50` (author) associated with the paper.

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "subject": "wd:Q44649835",
    "property": "wdt:P50"
  }
}
```

This query will list the authors of the paper "Prototypical Networks for Few-shot Learning."

[Tool]
Got 1 row and 3 columns:
| subject                                                    | property         | object                          |
| ---------------------------------------------------------- | ---------------- | ------------------------------- |
| Prototypical Networks for Few-shot Learning (wd:Q44649835) | author (wdt:P50) | Richard S. Zemel (wd:Q26702597) |

[Assistant]
The list function has returned the author of the paper "Prototypical Networks for Few-shot Learning" as Richard S. Zemel. However, it seems that only one author is listed. To ensure completeness, I will run a SPARQL query to retrieve all authors of the paper.

### Step 3: Execute a SPARQL Query to Retrieve All Authors
I will execute a SPARQL query to retrieve all authors of the paper "Prototypical Networks for Few-shot Learning."

```json
{
  "name": "execute",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?author WHERE { wd:Q44649835 wdt:P50 ?author }"
  }
}
```

This query will return all authors of the paper.

[Tool]
Got 1 row and 1 column:
| ?author                         |
| ------------------------------- |
| Richard S. Zemel (wd:Q26702597) |

[Assistant]
The SPARQL query has confirmed that Richard S. Zemel is the only author listed for the paper "Prototypical Networks for Few-shot Learning."

### Final Answer
The author of "Prototypical Networks for Few-shot Learning" is Richard S. Zemel.

I will now provide the final SPARQL query and answer.

```json
{
  "name": "answer",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?author WHERE { wd:Q44649835 wdt:P50 ?author }",
    "answer": "Richard S. Zemel"
  }
}
```

This query and answer confirm that Richard S. Zemel is the author of the paper "Prototypical Networks for Few-shot Learning."

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?university ?universityLabel ?studentCount WHERE {
  ?university wdt:P2196 ?studentCount .
  ?university wdt:P31 wd:Q3918 .
  FILTER ( ?studentCount > 200000 ) .
  OPTIONAL {
    ?university rdfs:label ?universityLabel .
    FILTER ( LANG( ?universityLabel ) = "en" ) .
  }
}

Got 7 rows and 3 columns:
| ?university                                                | ?universityLabel                           | ?studentCount |
| ---------------------------------------------------------- | ------------------------------------------ | ------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | National Open University of Nigeria, Lafia | 500000        |
| University of Phoenix (wd:Q1889100)                        | University of Phoenix                      | 420000        |
| Cairo University (wd:Q194445)                              | Cairo University                           | 257200        |
| National Autonomous University of Mexico (wd:Q222738)      | National Autonomous University of Mexico   | 360883        |
| Ain Shams University (wd:Q2723670)                         | Ain Shams University                       | 220000        |
| Allama Iqbal Open University (wd:Q3694529)                 | Allama Iqbal Open University               | 1326266       |
| Universitas Terbuka (wd:Q7894421)                          | Universitas Terbuka                        | 525360        |

The universities with more than 200,000 students are: National Open University of Nigeria, Lafia (500,000 students), University of Phoenix (420,000 students), Cairo University (257,200 students), National Autonomous University of Mexico (360,883 students), Ain Shams University (220,000 students), Allama Iqbal Open University (1,326,266 students), and Universitas Terbuka (525,360 students).

[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 universities have more than 200000 students?

[Assistant] To answer this question, we need to identify the universities and their student counts. We'll start by searching for the property that represents the number of students at a university.

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

Top 10 property alternatives:
1. count of students, also known as count of pupils, count of students, enrollment, number of pupils, number of pupils or students (wd:P2196 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): number of pupils or students of any type in an educational organ...evious years normal rank (not deprecated rank). Don't overwrite.
2. student, also known as teacher to, teacher of, had student, students, pupils (wd:P802 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): notable student(s) of the subject individual, significant person
3. number of out-of-school children (wd:P2573 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): number of out-of-school children reported for a place
4. number of teachers, also known as faculty count (wd:P10610 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): number of teachers at an educational facility
5. school class, also known as class, cohort, grade, school cohort, stage (wd:P7174 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): (qualifier) class of a given year/time period to which the person belonged in an educational institution
6. number of children, also known as children, children (number), how many children, issue, kids (wd:P1971 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): number of children of the person, animal, or character
7. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): educational institution attended by subject, affiliation
8. student retention rate (wd:P11698 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): proportion of first-year students at the educational institution who return for their second year
9. number of participants, also known as number of competitors, number of teams, participant count, participants, qty participants (wd:P1132 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): number of participants of an event, e.g. people or groups of people that take part in the event (NO units), quantity
10. educational stage, also known as student classification (wd:P7374 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): educational stage of a specific school; qualifier of "educated at" (P69): the education stage of someone's attendance of school

No other items found

[Assistant]
From the search results, the property `wd:P2196` (number of pupils or students) seems to be the most relevant for our query. Now, let's construct a SPARQL query to find universities that have more than 200,000 students.

We will start by selecting the universities and their student counts, and then filter the results to only include those with more than 200,000 students.

Let's try the following query:

```sparql
SELECT ?university ?universityLabel ?studentCount
WHERE {
  ?university wdt:P2196 ?studentCount.
  FILTER(?studentCount > 200000).
  OPTIONAL { ?university rdfs:label ?universityLabel. }
}
```

This query selects the universities (`?university`) and their student counts (`?studentCount`), filters to include only those with more than 200,000 students, and optionally retrieves the labels of the universities.

Let's execute this query.

[Tool]
Got 884 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?university                                                | ?universityLabel                           | ?studentCount |
| ---------------------------------------------------------- | ------------------------------------------ | ------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | National Open University of Nigeria, Lafia | 500000        |
| Damascus University (wd:Q1351317)                          | "Đại học Damascus"@vi                      | 210929        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| ...                                                        | ...                                        | ...           |
| national school (wd:Q96386155)                             | "سکوله کبڠساءن"@ms-arab                    | 2044299       |
| national school (wd:Q96386155)                             | "École nationale"@fr                       | 2071890       |
| national school (wd:Q96386155)                             | national school                            | 2071890       |
| national school (wd:Q96386155)                             | "sekolah kebangsaan"@ms                    | 2071890       |
| national school (wd:Q96386155)                             | "سکوله کبڠساءن"@ms-arab                    | 2071890       |

[Assistant]
The query has returned several universities with more than 200,000 students. However, there are some duplicates in the results, which might be due to multiple labels for the same university. To clean up the results, we can use the `DISTINCT` keyword to ensure each university is listed only once.

Let's modify the query to include `DISTINCT`:

```sparql
SELECT DISTINCT ?university ?universityLabel ?studentCount
WHERE {
  ?university wdt:P2196 ?studentCount.
  FILTER(?studentCount > 200000).
  OPTIONAL { ?university rdfs:label ?universityLabel. }
}
```

This should give us a cleaner list of universities. Let's execute this updated query.

[Tool]
Got 884 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?university                                                | ?universityLabel                           | ?studentCount |
| ---------------------------------------------------------- | ------------------------------------------ | ------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | National Open University of Nigeria, Lafia | 500000        |
| Damascus University (wd:Q1351317)                          | "Đại học Damascus"@vi                      | 210929        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| ...                                                        | ...                                        | ...           |
| national school (wd:Q96386155)                             | "sekolah kebangsaan"@ms                    | 2071890       |
| national school (wd:Q96386155)                             | "سکوله کبڠساءن"@ms-arab                    | 511320        |
| national school (wd:Q96386155)                             | "سکوله کبڠساءن"@ms-arab                    | 2039229       |
| national school (wd:Q96386155)                             | "سکوله کبڠساءن"@ms-arab                    | 2044299       |
| national school (wd:Q96386155)                             | "سکوله کبڠساءن"@ms-arab                    | 2071890       |

[Assistant]
It appears that even with the `DISTINCT` keyword, there are still multiple entries for the same university with different labels. This is likely because the `rdfs:label` property can have multiple values in different languages. To further refine the results, we can limit the labels to a specific language, such as English, to avoid duplicates.

Let's update the query to filter the labels to English:

```sparql
SELECT DISTINCT ?university ?universityLabel ?studentCount
WHERE {
  ?university wdt:P2196 ?studentCount.
  FILTER(?studentCount > 200000).
  OPTIONAL { ?university rdfs:label ?universityLabel. FILTER(LANG(?universityLabel) = "en"). }
}
```

This should give us a cleaner list of universities with their English labels. Let's execute this updated query.

[Tool]
Got 27 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?university                                                | ?universityLabel                           | ?studentCount |
| ---------------------------------------------------------- | ------------------------------------------ | ------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | National Open University of Nigeria, Lafia | 500000        |
| Damascus University (wd:Q1351317)                          | Damascus University                        | 210929        |
| University of California (wd:Q184478)                      | University of California                   | 295573        |
| University of Phoenix (wd:Q1889100)                        | University of Phoenix                      | 420000        |
| University of Buenos Aires (wd:Q194223)                    | University of Buenos Aires                 | 262932        |
| ...                                                        | ...                                        | ...           |
| Universitas Terbuka (wd:Q7894421)                          | Universitas Terbuka                        | 525360        |
| national school (wd:Q96386155)                             | national school                            | 511320        |
| national school (wd:Q96386155)                             | national school                            | 2039229       |
| national school (wd:Q96386155)                             | national school                            | 2044299       |
| national school (wd:Q96386155)                             | national school                            | 2071890       |

[Assistant]
The query has returned a cleaner list of universities with more than 200,000 students, but there are still some duplicates for the "national school" entry. This is likely because the "national school" entry is not a single university but a broader category. To further refine the results, we can filter out entries that are not specific universities.

We can add a filter to ensure that the entities are indeed universities. We can use the property `wdt:P31` (instance of) to check if the entity is an instance of a university (`wd:Q3918`).

Let's update the query to include this filter:

```sparql
SELECT DISTINCT ?university ?universityLabel ?studentCount
WHERE {
  ?university wdt:P2196 ?studentCount.
  ?university wdt:P31 wd:Q3918.
  FILTER(?studentCount > 200000).
  OPTIONAL { ?university rdfs:label ?universityLabel. FILTER(LANG(?universityLabel) = "en"). }
}
```

This should give us a list of specific universities with more than 200,000 students. Let's execute this updated query.

[Tool]
Got 7 rows and 3 columns:
| ?university                                                | ?universityLabel                           | ?studentCount |
| ---------------------------------------------------------- | ------------------------------------------ | ------------- |
| National Open University of Nigeria, Lafia (wd:Q131290725) | National Open University of Nigeria, Lafia | 500000        |
| University of Phoenix (wd:Q1889100)                        | University of Phoenix                      | 420000        |
| Cairo University (wd:Q194445)                              | Cairo University                           | 257200        |
| National Autonomous University of Mexico (wd:Q222738)      | National Autonomous University of Mexico   | 360883        |
| Ain Shams University (wd:Q2723670)                         | Ain Shams University                       | 220000        |
| Allama Iqbal Open University (wd:Q3694529)                 | Allama Iqbal Open University               | 1326266       |
| Universitas Terbuka (wd:Q7894421)                          | Universitas Terbuka                        | 525360        |

[Assistant]
The query has now returned a clean and accurate list of specific universities with more than 200,000 students. Here are the universities:

1. **National Open University of Nigeria, Lafia** - 500,000 students
2. **University of Phoenix** - 420,000 students
3. **Cairo University** - 257,200 students
4. **National Autonomous University of Mexico** - 360,883 students
5. **Ain Shams University** - 220,000 students
6. **Allama Iqbal Open University** - 1,326,266 students
7. **Universitas Terbuka** - 525,360 students

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

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?holiday ) AS ?count ) WHERE {
  wd:Q980 wdt:P832 ?holiday .
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 13     |

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

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

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

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

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

[User] How many public holidays does the state Bavaria have in a year?

[Assistant] To answer this question, we need to identify the entity for the state Bavaria and the property for public holidays. Let's start by searching for the entity representing Bavaria.

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

Top 10 entity alternatives:
1. state election in Bavaria, also known as Bavarian state election, state elections in Bavaria, Bavaria state election (wd:Q1845840): type of elections in Germany
2. Bavaria, also known as Bayern, Free State of Bavaria, Freistaat Bayern, Freistoot Bayern (wd:Q980): federated state in the south of Germany
3. Bavarian State Library, also known as Bayerische Staatsbibliothek, Bibliotheca Regia Monacensis, BSB, bsb-muenchen.de (wd:Q256507): state library of the Free State of Bavaria
4. Bavarian State Opera (wd:Q681931): opera company in Munich, Germany
5. 2018 Bavarian state election, also known as 2018 Bavaria state election (wd:Q19833920): state election in Bavaria, Germany
6. Bavarian State Orchestra, also known as Bavarian State Opera Orchestra (wd:Q683117): orchestra
7. 2013 Bavarian state election, also known as 2013 Bavaria state election (wd:Q1250713): state election in Bavaria, Germany
8. 2023 Bavarian state election, also known as 2023 Bavaria state election (wd:Q60540258): state election in Bavaria, Germany
9. 2008 Bavarian state election, also known as 2008 Bavaria state election (wd:Q258560): state election in Bavaria, Germany
10. Bavarian State Chancellery (wd:Q812286): Bavarian state agency

No other items found

[Assistant] From the search results, the entity for the state Bavaria is `wd:Q980`. Next, let's find the property that represents public holidays. We can search for this property by looking at the properties associated with Bavaria.

[Tool]
Got 1,381 rows and 3 columns, showing the first 10 rows below:
| subject           | property                                                    | object                                                   |
| ----------------- | ----------------------------------------------------------- | -------------------------------------------------------- |
| Bavaria (wd:Q980) | instance of (wdt:P31)                                       | federated state of Germany (wd:Q1221156)                 |
| Bavaria (wd:Q980) | country (wdt:P17)                                           | Germany (wd:Q183)                                        |
| Bavaria (wd:Q980) | located in the administrative territorial entity (wdt:P131) | Germany (wd:Q183)                                        |
| Bavaria (wd:Q980) | coordinate location (wdt:P625)                              | POINT(11.385556 49.078611)                               |
| Bavaria (wd:Q980) | Commons category (wdt:P373)                                 | Bavaria                                                  |
| Bavaria (wd:Q980) | part of (wdt:P361)                                          | Southern Germany (wd:Q46519)                             |
| Bavaria (wd:Q980) | population (wdt:P1082)                                      | 13124737                                                 |
| Bavaria (wd:Q980) | inception (wdt:P571)                                        | 1919-01-12T00:00:00Z                                     |
| Bavaria (wd:Q980) | short name (wdt:P1813)                                      | Bavaria                                                  |
| Bavaria (wd:Q980) | described by source (wdt:P1343)                             | Brockhaus and Efron Encyclopedic Dictionary (wd:Q602358) |
| ...               | ...                                                         | ...                                                      |

[Assistant] From the list of properties associated with Bavaria, there is no direct property indicating public holidays. However, we can try to search for the property that might represent public holidays in general. Let's search for the term "public holiday" to see if we can find a relevant property.

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

Top 10 property alternatives:
1. public holiday, also known as bank holiday, legal holiday, national holiday (wd:P832 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): official public holiday that occurs in this place in its honor, usually a non-working day, significant event
2. closed on, also known as closed days, days not open, except (wd:P3026 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): exceptions to open days (P3025), usually special dates
3. open days, also known as open on days, days open, opening days (wd:P3025 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): days in the week subject item is open within the opening season
4. day in year for periodic occurrence, also known as date, day, day in year, day of periodic occurrence, day of year (wd:P837 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): when a specific holiday or periodic event occurs. Can be used as property or qualifier
5. day of week, also known as day of the week, days of week (wd:P2894 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): day of the week on which this item occurs, applies to or is valid
6. date of official closure, also known as closed, official closure, date of closure, closure date, closing date (wd:P3999 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): date of official closure of a building or event, end time
7. set during recurring event, also known as narrative event, narrative holiday, set during event, set during holiday (wd:P9215 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): recurring event (e.g. season, month, holiday) during which this fictional work is set
8. opening time, also known as hour opens, opens at, opening hour, open hour (wd:P8626 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): time when an establishment or attraction opens, qualifier for P3025
9. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): date or point in time a place, organization, or event officially opened, start time, inception
10. created for, also known as cause of creation, commissioned for, made for, occasion, on the occasion of (wd:P9883 as psn/pqn/prv/pr/ps/p/prn/psv/wdt/pq/wdtn/pqv): a work, event, etc. for which the subject work was created (e.g.... play was created for; a film that a screenplay was written for)

No other items found

[Assistant]
The property `wd:P832` (public holiday) seems to be the most relevant for our query. Now, let's check if this property is used with Bavaria to find out the number of public holidays.

We will construct a SPARQL query to count the number of public holidays in Bavaria.

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 13     |

[Assistant]
The state of Bavaria has 13 public holidays in a year.

Here is the final SPARQL query:

```sparql
SELECT (COUNT(?holiday) AS ?count)
WHERE {
  wd:Q980 wdt:P832 ?holiday .
}
```

And the answer is 13 public holidays.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  ?place wdt:P625 ?coordinate .
  ?place wdt:P17 wd:Q39 .
  BIND( strafter( str( ?coordinate ) , "Point(" ) AS ?coords ) BIND( strbefore( ?coords , " " ) AS ?longitude ) BIND( strafter( ?coords , " " ) AS ?latitude ) FILTER ( xsd:float ( ?latitude ) > 47.8 )
}

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] Does Switzerland have a place north of the Point (8.57 47.8)?

[Assistant]
To answer this question, we need to check if there is any place in Switzerland that has a latitude greater than 47.8 and a longitude around 8.57. We can use the `geof:distance` function to calculate the distance between the point (8.57, 47.8) and the places in Switzerland. However, since we only care about the relative position (north), we can directly compare the latitude values.

Let's start by finding the IRI for Switzerland and the property for the geographic location (coordinate) of a place.

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

Top 10 entity alternatives:
1. Switzerland, also known as Confoederatio Helvetica, Swiss, Swiss Confederation (wd:Q39): country in Central Europe
2. Old Swiss Confederacy, also known as Swiss Confederation, Switzerland (wd:Q435583): confederation of cantons (1291-1798)
3. Old Swiss Confederacy, also known as Swiss Confederation, Switzerland (wd:Q435583): confederation of cantons (1291-1798)
4. Switzerland at the 1900 Summer Olympics, also known as Switzerland (wd:Q141744): sporting event delegation
5. federation of eight cantons, also known as Confederacy of eight cantons, Switzerland, Swiss Confederation, federation of 8 cantons, Old Swiss Confederacy (wd:Q340787)
6. Federation of thirteen cantons, also known as Federation of 13 cantons, Swiss Confederacy, Swiss Confederation, Switzerland (wd:Q351537)
7. Switzerland, also known as Switzerland, SC (wd:Q2373348): community in South Carolina, USA
8. Switzerland (wd:Q80282): community in Florida, United States
9. Switzerland in the Eurovision Song Contest 2006, also known as Switzerland (wd:Q7659174)
10. Switzerland (wd:Q420049): album by rock band Electric Six

No other items found

[Assistant] From the search results, the correct IRI for Switzerland is `wd:Q39`. Now let's find the property for the geographic location (coordinate) of a place.

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

Top 10 property alternatives:
1. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
2. this zoological name is coordinate with, also known as coordinate name (wd:P2743 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): links coordinate zoological names, different from
3. time index, also known as time coordinate, timestamp, temporal coordinate, relative time (wd:P4895 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): indicate when something happens relative to its beginning
4. genomic start, also known as start coordinate, genomic from, Genloc From, from, Genloc Start (wd:P644 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): genomic starting coordinate of the biological sequence (e.g. a gene)
5. spatial reference system, also known as coordinate reference system, CRS, SRS (wd:P3037 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): frame of reference used to produce a map
6. y-offset, also known as ordinate, y-axis (wd:P8685 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): offset on the y-axis or secondary axis
7. genomic end, also known as stop coordinate, stop, genomic stop, Genloc End, Genloc Stop (wd:P645 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): genomic ending coordinate of the biological sequence (e.g. a gene)
8. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): subject's ordinal position as qualitatively evaluated relative to other members of a group
9. political alignment, also known as political position, alignment (wd:P1387 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): political position within the left–right political spectrum
10. orientation, also known as alignment, direction, face, faces, facing (wd:P7469 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): axis of alignment of an item, or direction perpendicular to its primary face (specify with P518 if necessary)

No other items found

[Assistant]
The correct property for the geographic location (coordinate) of a place is `wd:P625`.

Now, we can construct a SPARQL query to check if there is any place in Switzerland that has a latitude greater than 47.8. We will use the `geof:lat` and `geof:long` functions to extract the latitude and longitude from the coordinate.

Let's write and execute the SPARQL query:

```sparql
ASK WHERE {
  ?place wdt:P625 ?coordinate .
  ?place wdt:P17 wd:Q39 .
  FILTER(geof:lat(?coordinate) > 47.8)
}
```

This query checks if there is any place in Switzerland (`wd:Q39`) with a latitude greater than 47.8.

Let's execute this query.

[Tool]
SPARQL execution failed:
Not supported: Function "<http://www.opengis.net/def/function/geosparql/lat>" is currently not supported by QLever.

[Assistant]
It appears that the `geof:lat` function is not supported by the QLever SPARQL endpoint. Instead, we can extract the latitude and longitude from the coordinate using string manipulation functions.

The coordinate is typically stored as a string in the format `Point(longitude latitude)`. We can use the `strbefore` and `strafter` functions to extract the latitude and longitude.

Let's modify the query to use these functions:

```sparql
ASK WHERE {
  ?place wdt:P625 ?coordinate .
  ?place wdt:P17 wd:Q39 .
  BIND(strafter(str(?coordinate), "Point(") AS ?coords)
  BIND(strbefore(?coords, " ") AS ?longitude)
  BIND(strafter(?coords, " ") AS ?latitude)
  FILTER(xsd:float(?latitude) > 47.8)
}
```

This query extracts the latitude and longitude from the coordinate string and checks if the latitude is greater than 47.8.

Let's execute this query.

[Tool] False

[Assistant]
The SPARQL query returned `False`, indicating that there is no place in Switzerland with a latitude greater than 47.8.

Therefore, the answer to the question "Does Switzerland have a place north of the Point (8.57 47.8)?" is **False**.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  ?place wdt:P625 ?coordinate .
  ?place wdt:P17 wd:Q39 .
  BIND(strafter(str(?coordinate), "Point(") AS ?coords)
  BIND(strbefore(?coords, " ") AS ?longitude)
  BIND(strafter(?coords, " ") AS ?latitude)
  FILTER(xsd:float(?latitude) > 47.8)
}
```

Answer: **False**

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

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?person ?netWorth WHERE {
  ?person wdt:P106 wd:Q131524 ; wdt:P2218 ?netWorth .
}
ORDER BY DESC ( ?netWorth )
LIMIT 1

The most wealthy entrepreneur is Elon Musk, with a net worth of approximately 270.1 billion USD.

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

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

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

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

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

[User] Who is the most wealthy entrepreneur?

[Assistant]
To answer the question about the most wealthy entrepreneur, we need to identify the relevant entities and properties in the knowledge graph. We will start by searching for the property that represents wealth or net worth and then look for individuals who are entrepreneurs.

First, let's search for the property that represents net worth.

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

Top 10 property alternatives:
1. net worth, also known as declared patrimony, estimated net worth, net wealth, net worth estimate, personal assets (wd:P2218 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): totality of wealth possessed by a person
2. net profit, also known as profit, net return, net income, clear profit, clear gain (wd:P2295 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): private entity profit after accounting for all costs
3. estimated value, also known as valuation, traded value, traded cap, price estimation, estimated worth (wd:P8340 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): estimated value of a house, company, patent, etc
4. total revenue, also known as turnover, sales, revenue, income, gross revenue (wd:P2139 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): income gained by an organization during a given time frame. Not to be confused with fiscal revenue
5. item of property constraint, also known as claim value, value, qid, property value, item qualifier (wd:P2305 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): qualifier to define a property constraint in combination with "property constraint" (P2302)
6. prize money, also known as money, purse, value (wd:P2121 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): amount in a specific currency
7. price, also known as cost, exchange rate, list price, MSRP, published price (wd:P2284 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): published price listed or paid for a product (use with unit of currency)
8. numeric value, also known as numerical value, value (wd:P1181 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): numerical value of a number, a mathematical constant, or a physical constant
9. total assets, also known as assets, balance sheet total (wd:P2403 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): value of assets held by a private or public entity
10. box office, also known as earnings (wd:P2142 as wdtn/pqn/ps/psv/prn/pqv/pq/psn/p/prv/pr/wdt): box office takings accumulated by a film, total revenue

No other items found

[Assistant]
The top result for the search query "net worth" is the property `wd:P2218`. This property seems to represent the net worth of a person. Next, we need to find individuals who are entrepreneurs. We will search for the occupation "entrepreneur" and then combine it with the net worth property to find the most wealthy entrepreneur.

Let's search for the occupation "entrepreneur".

[Tool]
Top 10 entity alternatives:
1. entrepreneur, also known as undertaking (wd:Q131524): person who owns and operates a business
2. Entrepreneur (wd:Q5380740): business magazine for entrepreneurs
3. entrepreneurship, also known as entrepreneurial approach, entrepreneurship approach (wd:Q3908516): creation or extraction of economic value
4. talent manager, also known as artist manager, manager, pop entrepreneur (wd:Q1320883): person who guides the career of an artist
5. female entrepreneur, also known as entrepreneure, female entrepreneurs (wd:Q5442755): women who organize and manage an enterprise
6. theatre entrepreneur, also known as impresario (wd:Q17994313): profession
7. social entrepreneur (wd:Q12336252): occupation
8. serial entrepreneur (wd:Q2273346): entrepreneur who continually starts new businesses
9. fashion entrepreneur (wd:Q5436768)
10. Internet entrepreneur (wd:Q52228055): entrepreneur who applies innovation to create new businesses on the Internet

No other items found

No literal items found

[Assistant]
The top result for the search query "entrepreneur" is the occupation `wd:Q131524`. This property seems to represent the occupation of being an entrepreneur. Now, we can construct a SPARQL query to find the most wealthy entrepreneur by combining the net worth property `wd:P2218` with the occupation `wd:Q131524`.

Let's write and execute the SPARQL query to find the most wealthy entrepreneur.

[Tool]
Got 1 row and 2 columns:
| ?person                | ?netWorth    |
| ---------------------- | ------------ |
| Elon Musk (wd:Q317521) | 270100000000 |

[Assistant]
The most wealthy entrepreneur according to the data in Wikidata is Elon Musk, with a net worth of approximately 270.1 billion USD.

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:Q283 wdt:P462 wd:Q1396399 .
}

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] Is water colorless?

[Assistant] To determine if water is colorless, we need to find the property that describes the color of water in the knowledge graph. We can start by searching for the entity representing water and then look for properties related to its color.

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

Top 10 entity alternatives:
1. water, also known as dihydrogen oxide, hydrogen hydroxide, dihydrogen monoxide, hydrogen oxide, oxidane (wd:Q283): chemical compound whose molecules are formed by two hydrogen atoms and one oxygen atom
2. water, also known as dihydridooxygen, pure water, oxidane, hydrogen oxide, hydrogen hydroxide (wd:Q283): chemical compound whose molecules are formed by two hydrogen atoms and one oxygen atom
3. ice, also known as crystalline water, frozen ice, frozen water, solid water, water (wd:Q23392): water frozen into the solid state
4. drinking water, also known as drinkable water, improved drinking water, potable water, water (wd:Q7892): water safe for consumption
5. Water, also known as Water (2005), Water (2005 movie) (wd:Q617395): 2005 film by Deepa Mehta
6. water (wd:Q2637515): classical element in philosophy and cosmology
7. Water (wd:Q123169436): 2023 single by Tyla
8. ☵, also known as water, U+2635, TRIGRAM FOR WATER, kan3, k’an (wd:Q2785811): trigram for water (U+2635): kan3, kǎn, k’an
9. liquid water, also known as water (wd:Q29053744): form of water
10. Water, also known as Water (Elitsa & Stoyan song) (wd:Q2735297): song by Elitsa Todorova and Stoyan Yankoulov

No other items found

[Assistant] The top result for "water" is the chemical compound water (wd:Q283). Now, let's find out if there is a property that describes the color of water. We can search for properties related to color and see if they apply to water.

[Tool]
Top 10 property alternatives:
1. color, also known as with the colour, with the color, pigment, of the colour, of the color (wd:P462 as p/wdt): color of subject
2. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
3. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
4. has characteristic, also known as has quality, inherent property, object has characteristic, parameter, quality (wd:P1552 as p/wdt): inherent or distinguishing quality or feature of the entity. Use a more specific property when possible
5. has use, also known as utility, used in, used for, used as, use (wd:P366 as p/wdt): main use of the subject (includes current and former usage)
6. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as p/wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
7. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as p/wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
8. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
9. mass, also known as displacement, molecular mass, molecular weight, weight (wd:P2067 as p/wdt): mass (in colloquial usage also known as weight) of the item
10. name (image), also known as name as image, label image, label as image, image of name, image as label (wd:P7407 as p/wdt): name of something, when it's not fully supported by Unicode or contains characters that are not widely supported, icon

No other items found

[Assistant]
The property `color` (wd:P462) seems to be the most relevant for describing the color of water. Let's check if this property is associated with water (wd:Q283) and what value it has.

I will execute a SPARQL query to find the color of water.

[Tool]
Got 5 rows and 1 column:
| ?color                       |
| ---------------------------- |
| colorless (wd:Q1396399)      |
| white (wd:Q23444)            |
| green (wd:Q3133)             |
| color of water (wd:Q5148636) |
| turquoise (wd:Q5960345)      |

[Assistant]
The query returned multiple colors associated with water, including "colorless" (wd:Q1396399), "white" (wd:Q23444), "green" (wd:Q3133), "color of water" (wd:Q5148636), and "turquoise" (wd:Q5960345). However, the most relevant and accurate description for pure water is "colorless."

To confirm this, we can use an ASK query to check if "colorless" is one of the colors associated with water.

Let's execute the ASK query.

[Tool] True

[Assistant]
The ASK query returned `True`, confirming that water is indeed colorless.

Therefore, the answer to the question "Is water colorless?" is **True**.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?turkishLabel WHERE {
  wd:Q30 rdfs:label ?turkishLabel FILTER ( LANG( ?turkishLabel ) = 'tr' )
}

Got 1 row and 1 column:
| ?turkishLabel                    |
| -------------------------------- |
| "Amerika Birleşik Devletleri"@tr |

People from Turkey call the official name of the United States of America 'Amerika Birleşik Devletleri'.

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

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

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

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

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

[User] How do people from Turkey call the official name of the united states of america?

[Assistant]
To answer this question, we need to find the official name of the United States of America and then check how it is referred to in Turkish. We'll start by identifying the entity for the United States of America and then look for its official name. After that, we will check if there is a specific label for this name in Turkish.

First, let's identify the entity for the United States of America.

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

Top 10 entity alternatives:
1. United States, also known as the U.S. of A, United States of America, the US of America, United States of North America, U. S. (wd:Q30): country primarily located in North America
2. Istadus Unidus the America, also known as United States of America (wd:Q29045433): Wikimedia permanent duplicate item
3. United States of America (wd:Q100270731): scholarly article
4. United States of America (wd:Q103971355): encyclopedic article; Grove Art Online
5. United States of America (wd:Q105870683): vocal track by Kikki Danielsson; 1980 studio recording
6. United States of America (wd:Q105870692): song written and composed by Donna Fargo, originally recorded by Donna Fargo and released in 1974
7. United States of America (wd:Q106315054): vocal track by Donna Fargo; 1974 studio recording
8. United States of America, also known as Star Trek USA, USA (wd:Q99281400): country of the United States of America as depicted in Star Trek
9. United States of America (wd:L350484-S1)
10. United States (of America) (wd:L399000-S1)

No other items found

[Assistant]
The top result for "United States of America" is `wd:Q30`. This is the correct entity for the United States of America. Next, we need to find the official name of the United States of America and then check if there is a specific label for this name in Turkish.

Let's first find the official name of the United States of America.

[Tool]
Got 1 row and 3 columns:
| subject                | property                  | object                       |
| ---------------------- | ------------------------- | ---------------------------- |
| United States (wd:Q30) | official name (wdt:P1448) | the United States of America |

[Assistant]
The official name of the United States of America is "the United States of America". Now, we need to find out how this name is referred to in Turkish. We will look for the Turkish label of the official name.

Let's check if there is a Turkish label for the official name of the United States of America.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there is no direct Turkish label for the official name of the United States of America. However, we can try to find the Turkish label for the entity `wd:Q30` itself, which represents the United States of America, and see if it provides us with the information we need.

Let's check the Turkish label for the United States of America (`wd:Q30`).

[Tool]
Got 1 row and 1 column:
| ?turkishLabel                    |
| -------------------------------- |
| "Amerika Birleşik Devletleri"@tr |

[Assistant]
The Turkish label for the United States of America (`wd:Q30`) is "Amerika Birleşik Devletleri". This is how people from Turkey refer to the official name of the United States of America.

Therefore, the answer to the question is:

People from Turkey call the official name of the United States of America "Amerika Birleşik Devletleri".

Here is the final SPARQL query:

```sparql
SELECT ?turkishLabel WHERE { wd:Q30 rdfs:label ?turkishLabel FILTER(LANG(?turkishLabel) = 'tr') }
```

And the answer:

People from Turkey call the official name of the United States of America "Amerika Birleşik Devletleri".

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?award ) AS ?count ) WHERE {
  wd:Q242643 wdt:P166 wd:Q194285 .
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 0      |

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

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

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

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

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

[User] How often did Naomi Novik win the nebula award?

[Assistant]
To answer the question about how often Naomi Novik won the Nebula Award, we need to identify the following:
1. The entity representing Naomi Novik.
2. The entity representing the Nebula Award.
3. The property that indicates winning an award.

Let's start by searching for Naomi Novik and the Nebula Award.

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

Top 10 entity alternatives:
1. Naomi Novik (wd:Q242643): science fiction writer, American writer, fantasy author, programmer, novelist, writer
2. Naomi (wd:Q2596433): Ruth's mother-in-law in the Book of Ruth
3. Naomi, also known as Trinity Starr, Trinity Fatu, Trinity, Nalgomi, Naomi Knight (wd:Q3119295): American professional wrestler, professional wrestler, celebrity, dancer, model
4. 6139 Naomi, also known as Naomi (wd:Q553669): asteroid
5. Naomi, also known as Naomi, PA, Naomi, Pennsylvania (wd:Q6964608): settlement in Pennsylvania, US
6. Naomi (wd:Q108444706): American television series
7. Novik (wd:Q1547078): 1911 Russian destroyer
8. Naomi, also known as Naomi (first name), Naomi (given name) (wd:Q18508449): female given name
9. Naomi (wd:Q6964620): novel by Junichiro Tanizaki
10. Naomi, also known as Florin Moldovan (wd:Q22957923): Romanian recording artist, songwriter and actress, songwriter, singer, actor

No other items found

[Assistant]
The top result for "Naomi Novik" is `wd:Q242643`, which seems to be the correct entity for the science fiction writer Naomi Novik.

Next, let's search for the Nebula Award.

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

Top 10 entity alternatives:
1. Nebula Award (wd:Q194285): literature prize for science fiction and fantasy works from the United States
2. Xingyun Award, also known as Chinese Nebula Award, World Chinese Science Fiction Association (wd:Q10891657): Chinese literary award
3. Nebula Awards 22, also known as Nebula Award Showcase (wd:Q30078063): 1988 anthology edited by George Zebrowski
4. Nebula Awards Showcase, also known as Nebula Winners, Nebula Award Stories (wd:Q28419278): series of annual science fiction and fantasy anthologies
5. Nebula Anthologies, also known as Nebula Award anthologies (wd:Q81185197): anthologies of Nebula-winning short fiction, along with a select...nners-up, published roughly annually since the award's inception
6. Nebula Award Weekend (wd:Q130712827): science fiction convention
7. Nebula Awards Weekend (wd:Q6985281)
8. Nebula Awards 20 (wd:Q30078081): 1985 anthology edited by George Zebrowski
9. Nebula Awards 21 (wd:Q30078078): 1986 anthology edited by George Zebrowski
10. Nebula Awards 23 (wd:Q30078049): 1989 anthology edited by Michael Bishop

No other items found

[Assistant]
The top result for "Nebula Award" is `wd:Q194285`, which is the correct entity for the Nebula Award.

Now, we need to find the property that indicates winning an award. A common property for this is `wdt:P166` (award received).

Let's construct a SPARQL query to count how many times Naomi Novik (`wd:Q242643`) has won the Nebula Award (`wd:Q194285`).

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 0      |

[Assistant]
The result shows that Naomi Novik has won the Nebula Award 0 times.

Therefore, the answer to the question "How often did Naomi Novik win the Nebula Award?" is 0.

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/>
SELECT ?creator WHERE {
  wd:Q1211977 wdt:P170 ?creator
}

The creators of the 'Dinosaurs' TV show are Michael Jacobs and Bob Young.

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

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

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

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

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

[User] Who is the creator of the Dinosaurs tv show ?

[Assistant] To find the creator of the "Dinosaurs" TV show, I need to identify the entity for the TV show and then look for the property that indicates its creator. Let's start by searching for the "Dinosaurs" TV show in Wikidata.

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

Top 10 entity alternatives:
1. television program, also known as TV show, television broadcast, T.V. show, T. V. show, TV programme (wd:Q15416): segment of audiovisual content intended for broadcast on television
2. television series, also known as TV show, TV-series, television show, TV series (wd:Q5398426): connected set of television program episodes under the same title
3. Show TV (wd:Q2006738): Turkish television station
4. TV Show (wd:Q3281229): 2007 album by Sergey Lazarev
5. reality television, also known as reality TV program, TV reality show, TV reality program, television reality show, television reality program (wd:Q182415): genre of television programming that documents unscripted situations and actual occurrences
6. Showtime, also known as Showtime (TV network), Showtime TV, Showtime Networks, Showtime Entertainment, SHO (wd:Q23589): American premium cable TV channel
7. television presenter, also known as television anchor, TV show host, TV presenter, TV host, TV anchor (wd:Q947873): person who introduces or hosts television programs
8. variety show, also known as television variety program, television variety show, variety, variety entertainment, variety program (wd:Q336181): television genre characterized by a series of specialty acts such as music, dance, comedy, magic, acrobatics and ventriloquism
9. reality show, also known as reality television program, TV reality show, TV reality program, television reality show, television reality program (wd:Q3421644): television genre
10. television series season, also known as TV show season, TV series season, TV season, season of a TV show, television show season (wd:Q3464665): set of episodes produced for a television series

No other items found

[Assistant]
The search results did not directly provide the specific "Dinosaurs" TV show. However, the second result, "television series" (wd:Q5398426), seems relevant. Let's try to find the specific "Dinosaurs" TV show by looking for instances of this class.

I will use the `list` function to find instances of "television series" and filter for those with the label "Dinosaurs".

[Tool]
Got 75,111 rows and 3 columns, showing the first 10 rows below:
| subject                            | property              | object                          |
| ---------------------------------- | --------------------- | ------------------------------- |
| Game of Thrones (wd:Q23572)        | instance of (wdt:P31) | television series (wd:Q5398426) |
| Liv and Maddie (wd:Q8172269)       | instance of (wdt:P31) | television series (wd:Q5398426) |
| The Big Bang Theory (wd:Q8539)     | instance of (wdt:P31) | television series (wd:Q5398426) |
| Friends (wd:Q79784)                | instance of (wdt:P31) | television series (wd:Q5398426) |
| Squid Game (wd:Q106582931)         | instance of (wdt:P31) | television series (wd:Q5398426) |
| Teletubbies (wd:Q41403)            | instance of (wdt:P31) | television series (wd:Q5398426) |
| Breaking Bad (wd:Q1079)            | instance of (wdt:P31) | television series (wd:Q5398426) |
| Doctor Who (wd:Q34316)             | instance of (wdt:P31) | television series (wd:Q5398426) |
| Lost (wd:Q23567)                   | instance of (wdt:P31) | television series (wd:Q5398426) |
| How I Met Your Mother (wd:Q147235) | instance of (wdt:P31) | television series (wd:Q5398426) |
| ...                                | ...                   | ...                             |

[Assistant]
The list of instances of "television series" is quite large, and it does not include "Dinosaurs" in the first few entries. To narrow down the search, I will use the `search` function again, but this time I will constrain the search to instances of "television series" and look for the label "Dinosaurs".

Let's try that:

[Tool]
Top 10 entity alternatives:
1. Dinosaurs (wd:Q1211977): television program
2. Walking with Dinosaurs (wd:Q743334): documentary television miniseries
3. Chased by Dinosaurs (wd:Q3132869): 2002 film by Tim Haines
4. Clash of the Dinosaurs (wd:Q678457): television series
5. The World After Dinosaurs (wd:Q21161171): television series
6. The Truth About Killer Dinosaurs (wd:Q3495678): television series
7. Harry and His Bucket Full of Dinosaurs (wd:Q3280044): television series

No other items found

[Assistant]
The search has identified "Dinosaurs" (wd:Q1211977) as the correct television program. Now, I need to find the creator of this TV show. The property for "creator" in Wikidata is typically `P178` (creator).

Let's execute a SPARQL query to find the creator of the "Dinosaurs" TV show.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the property `P178` (creator) is not used for the "Dinosaurs" TV show. Let's try another property that might be used to indicate the creator, such as `P57` (director) or `P58` (screenwriter). These properties are often used for creators of TV shows.

Let's execute a SPARQL query to check these properties.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that neither `P57` (director) nor `P58` (screenwriter) are used for the "Dinosaurs" TV show either. Let's try a more general approach by listing all properties and their values for the "Dinosaurs" TV show to see if there is any other property that might indicate the creator.

I will use the `list` function to get all properties and values for the "Dinosaurs" TV show (wd:Q1211977).

[Tool]
Got 239 rows and 3 columns, showing the first 10 rows below:
| subject                 | property                     | object                                |
| ----------------------- | ---------------------------- | ------------------------------------- |
| Dinosaurs (wd:Q1211977) | instance of (wdt:P31)        | television series (wd:Q5398426)       |
| Dinosaurs (wd:Q1211977) | title (wdt:P1476)            | Dinosaurs                             |
| Dinosaurs (wd:Q1211977) | has part(s) (wdt:P527)       | Dinosaurs, season 1 (wd:Q63522500)    |
| Dinosaurs (wd:Q1211977) | country of origin (wdt:P495) | United States (wd:Q30)                |
| Dinosaurs (wd:Q1211977) | official website (wdt:P856)  | <https://movies.disney.com/dinosaurs> |
| Dinosaurs (wd:Q1211977) | genre (wdt:P136)             | sitcom (wd:Q170238)                   |
| Dinosaurs (wd:Q1211977) | award received (wdt:P166)    | Genesis Award (wd:Q135498)            |
| Dinosaurs (wd:Q1211977) | cast member (wdt:P161)       | Christopher Meloni (wd:Q380095)       |
| Dinosaurs (wd:Q1211977) | creator (wdt:P170)           | Michael Jacobs (wd:Q3308269)          |
| Dinosaurs (wd:Q1211977) | start time (wdt:P580)        | 1991-04-26T00:00:00Z                  |
| ...                     | ...                          | ...                                   |

[Assistant]
The list of properties for the "Dinosaurs" TV show includes the property `P170` (creator), which is used to indicate the creator of the show. The creator is Michael Jacobs (wd:Q3308269).

Let's confirm this by executing a SPARQL query to retrieve the creator of the "Dinosaurs" TV show using the `P170` property.

[Tool]
Got 2 rows and 1 column:
| ?creator                     |
| ---------------------------- |
| Michael Jacobs (wd:Q3308269) |
| Bob Young (wd:Q4934478)      |

[Assistant]
The creators of the "Dinosaurs" TV show are Michael Jacobs (wd:Q3308269) and Bob Young (wd:Q4934478).

Here is the final SPARQL query and the answer:

```sparql
SELECT ?creator WHERE { wd:Q1211977 wdt:P170 ?creator }
```

The creators of the "Dinosaurs" TV show are Michael Jacobs and Bob Young.

[Tool] Stopped generation process