PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?type ( COUNT( DISTINCT ?organization ) AS ?count ) WHERE {
  ?organization wdt:P131 | wdt:P17 | wdt:P159 | wdt:P276 wd:Q5765 ; wdt:P31 ?type .
}
GROUP BY ?type
ORDER BY DESC ( ?count )

Got 19 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?type                                        | ?count |
| -------------------------------------------- | ------ |
| raid (wd:Q476807)                            | 3      |
| military expedition (wd:Q1384277)            | 2      |
| war (wd:Q198)                                | 1      |
| tourism in a region (wd:Q98374854)           | 1      |
| aspect in a geographic region (wd:Q74817647) | 1      |
| ...                                          | ...    |
| conquest (wd:Q1361229)                       | 1      |
| astronomical observatory (wd:Q1254933)       | 1      |
| historical period (wd:Q11514315)             | 1      |
| annual event (wd:Q11483816)                  | 1      |
| regional economy (wd:Q100773131)             | 1      |

The types of organizations located in the Balearic Islands, along with the count of distinct organizations for each type, ordered by the count in descending order, are as follows:

| Type of Organization                          | Count |
| --------------------------------------------- | ----- |
| raid (wd:Q476807)                             | 3     |
| military expedition (wd:Q1384277)             | 2     |
| war (wd:Q198)                                 | 1     |
| tourism in a region (wd:Q98374854)            | 1     |
| aspect in a geographic region (wd:Q74817647)  | 1     |
| ...                                           | ...   |
| conquest (wd:Q1361229)                        | 1     |
| astronomical observatory (wd:Q1254933)        | 1     |
| historical period (wd:Q11514315)              | 1     |
| annual event (wd:Q11483816)                   | 1     |
| regional economy (wd:Q100773131)              | 1     |

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

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

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

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

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

[User] What are the types of organizations located in the Balearic Islands, along with the count of distinct organizations for each type, ordered by the count in descending order?

[Assistant]
To answer this question, we need to identify the following elements:

1. **Balearic Islands**: We need to find the entity representing the Balearic Islands.
2. **Organizations**: We need to find entities that represent organizations.
3. **Types of Organizations**: We need to find the types (classes) of these organizations.
4. **Count of Distinct Organizations**: We need to count the number of distinct organizations for each type.
5. **Ordering**: We need to order the results by the count in descending order.

Let's start by finding the entity for the Balearic Islands.

### Step 1: Find the entity for the Balearic Islands
I will use the `search_entity` function to find the entity for the Balearic Islands.

[Tool]
Top 10 entity alternatives:
1. Balearic Islands, also known as Balearic Isles, Islas baleares (wd:Q5765): archipelago in the Mediterranean Sea
2. Balearic Islands (wd:Q4850673): Spanish Congress Electoral District
3. Balearic Islands (wd:Q107356469): province
4. Balearic Islands (wd:Q107356467): autonomous community of Spain
5. Balearic Islands (wd:Q107356470): region (1833–1982)
6. Balearic Islands (wd:Q103891547): encyclopedia article
7. flag of the Balearic Islands, also known as Balearic Islands flag, the Balearic Islands flag (wd:Q435103): flag of the Spanish autonomous community of the Balearic Islands
8. history of Balearic Islands, also known as Balearic Islands history (wd:Q11925943)
9. geography of the Balearic Islands, also known as the Balearic Islands geography, Balearic Islands geography (wd:Q11924065)
10. Balearic Islands Award (wd:Q11953178): Spanish soccer award

[Assistant]
The most relevant entity for the Balearic Islands is `wd:Q5765`, which represents the archipelago in the Mediterranean Sea.

### Step 2: Identify organizations located in the Balearic Islands
We need to find organizations that are located in the Balearic Islands. The property for location is typically `P131` (located in the administrative territorial entity).

### Step 3: Identify types of organizations
We need to find the types (classes) of these organizations. The type of an organization can be found using the `P31` (instance of) property.

### Step 4: Count distinct organizations for each type
We will group the organizations by their types and count the distinct organizations for each type.

### Step 5: Order the results by the count in descending order
We will order the results by the count in descending order.

Let's start by writing a SPARQL query to find organizations located in the Balearic Islands and their types.

```sparql
SELECT ?type (COUNT(DISTINCT ?organization) AS ?count)
WHERE {
  ?organization wdt:P131 wd:Q5765 ;
                wdt:P31 ?type .
}
GROUP BY ?type
ORDER BY DESC(?count)
```

I will now execute this query to get the results.

[Tool]
Got 2 rows and 2 columns:
| ?type                                  | ?count |
| -------------------------------------- | ------ |
| astronomical observatory (wd:Q1254933) | 1      |
| Catalan dialect (wd:Q3025977)          | 1      |

[Assistant]
The initial query returned only two types of organizations located in the Balearic Islands, which seems limited. Let's refine our approach to ensure we are capturing all relevant organizations.

### Refinement:
1. **Check for alternative properties**: Sometimes, organizations might be linked to the Balearic Islands through other properties like `P17` (country), `P159` (headquarters location), or `P276` (location).
2. **Include more specific types**: We can include more specific types of organizations by using broader classes or subclasses.

Let's modify the query to include these additional properties and check for more specific types of organizations.

```sparql
SELECT ?type (COUNT(DISTINCT ?organization) AS ?count)
WHERE {
  ?organization wdt:P131|wdt:P17|wdt:P159|wdt:P276 wd:Q5765 ;
                wdt:P31 ?type .
}
GROUP BY ?type
ORDER BY DESC(?count)
```

I will now execute this refined query to get a more comprehensive result.

[Tool]
Got 19 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?type                                        | ?count |
| -------------------------------------------- | ------ |
| raid (wd:Q476807)                            | 3      |
| military expedition (wd:Q1384277)            | 2      |
| war (wd:Q198)                                | 1      |
| tourism in a region (wd:Q98374854)           | 1      |
| aspect in a geographic region (wd:Q74817647) | 1      |
| ...                                          | ...    |
| conquest (wd:Q1361229)                       | 1      |
| astronomical observatory (wd:Q1254933)       | 1      |
| historical period (wd:Q11514315)             | 1      |
| annual event (wd:Q11483816)                  | 1      |
| regional economy (wd:Q100773131)             | 1      |

[Assistant]
The refined query has returned a more comprehensive list of types of organizations located in the Balearic Islands, along with the count of distinct organizations for each type, ordered by the count in descending order.

Here are the top 5 and bottom 5 results:

| Type of Organization                          | Count |
| --------------------------------------------- | ----- |
| raid (wd:Q476807)                             | 3     |
| military expedition (wd:Q1384277)             | 2     |
| war (wd:Q198)                                 | 1     |
| tourism in a region (wd:Q98374854)            | 1     |
| aspect in a geographic region (wd:Q74817647)  | 1     |
| ...                                           | ...   |
| conquest (wd:Q1361229)                        | 1     |
| astronomical observatory (wd:Q1254933)        | 1     |
| historical period (wd:Q11514315)              | 1     |
| annual event (wd:Q11483816)                   | 1     |
| regional economy (wd:Q100773131)              | 1     |

This query provides a detailed breakdown of the types of organizations located in the Balearic Islands, along with the count of distinct organizations for each type, ordered by the count in descending order.

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

```sparql
SELECT ?type (COUNT(DISTINCT ?organization) AS ?count)
WHERE {
  ?organization wdt:P131|wdt:P17|wdt:P159|wdt:P276 wd:Q5765 ;
                wdt:P31 ?type .
}
GROUP BY ?type
ORDER BY DESC(?count)
```

The answer to the user's question is the list of types of organizations located in the Balearic Islands, along with the count of distinct organizations for each type, ordered by the count in descending order, as shown above.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT DISTINCT ?subclass WHERE {
  ?subclass wdt:P279 * wd:Q87167 .
}

Got 195 rows and 1 column, showing the first 5 and last 5 rows below:
| ?subclass                              |
| -------------------------------------- |
| Lelampahan (wd:Q104079854)             |
| Tantri (wd:Q104079920)                 |
| Itihasa (wd:Q104080182)                |
| Wariga (wd:Q104080231)                 |
| agama (wd:Q104080293)                  |
| ...                                    |
| Gĕguritan Bañuwurung (wd:Q99430861)    |
| Gaguritan Rusak Gianyar (wd:Q99471867) |
| Gaguritan Pakang Raras (wd:Q99471961)  |
| Gaguritan Japatuwan (wd:Q99472047)     |
| Gaguritan Ni Sumala (wd:Q99472115)     |

The distinct items that are subclasses, at any level, of a manuscript (a document written by hand) include 195 items such as Lelampahan, Tantri, Itihasa, Wariga, agama, Gĕguritan Bañuwurung, Gaguritan Rusak Gianyar, Gaguritan Pakang Raras, Gaguritan Japatuwan, and Gaguritan Ni Sumala.

[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 distinct items are subclasses, at any level, of a manuscript (a document written by hand)?

[Assistant]
To answer this question, we need to identify the entity for "manuscript" and then find all the items that are subclasses of it, potentially at multiple levels. We'll start by searching for the entity representing "manuscript" in Wikidata.

Let's search for the entity "manuscript".

[Tool]
Top 10 entity alternatives:
1. manuscript, also known as MSS, MS, manuscript book, manuscripts (wd:Q87167): document written by hand
2. block lettering, also known as ball and stick, block letter, block letters, manuscript, printscript (wd:Q1261015): sans-serif (or "gothic") style of writing in many scripts in whi... "please print"; may also be used as a synonym of block capitals
3. manuscript, also known as manuscript format, manuscript (publishing) (wd:Q2376293): work that an author submits to a publisher or editor for publication
4. Genkou, also known as manuscript (wd:Q127698834): Writings and other works intended for publication or presentation.
5. Manuscript (wd:Q16411389): album
6. Manuscript (wd:Q60851530): studio album by Manizha
7. Manuscript (wd:Q103955283): encyclopedia article
8. Manuscript (wd:Q122933956): sculpture by Seymour Lipton
9. Manuscript (wd:Q125094390): decorative artwork (glass) by Robert Kehlmann
10. Manuscript (wd:Q20797240): painting by Silja Rantanen

[Assistant]
The most relevant entity for "manuscript" is `wd:Q87167`, which is described as "document written by hand." Next, we need to find all the items that are subclasses of this entity, potentially at multiple levels. We can achieve this by using the `rdfs:subClassOf` property recursively.

Let's construct a SPARQL query to find all distinct items that are subclasses of `wd:Q87167`.

```sparql
SELECT DISTINCT ?subclass
WHERE {
  ?subclass wdt:P279* wd:Q87167 .
}
```

This query uses the `wdt:P279*` property path to find all subclasses of `wd:Q87167` at any level. Let's execute this query.

[Tool]
Got 195 rows and 1 column, showing the first 5 and last 5 rows below:
| ?subclass                              |
| -------------------------------------- |
| Lelampahan (wd:Q104079854)             |
| Tantri (wd:Q104079920)                 |
| Itihasa (wd:Q104080182)                |
| Wariga (wd:Q104080231)                 |
| agama (wd:Q104080293)                  |
| ...                                    |
| Gĕguritan Bañuwurung (wd:Q99430861)    |
| Gaguritan Rusak Gianyar (wd:Q99471867) |
| Gaguritan Pakang Raras (wd:Q99471961)  |
| Gaguritan Japatuwan (wd:Q99472047)     |
| Gaguritan Ni Sumala (wd:Q99472115)     |

[Assistant]
The SPARQL query has returned 195 distinct items that are subclasses of `wd:Q87167` (manuscript). These items represent various types of manuscripts or documents written by hand.

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

```sparql
SELECT DISTINCT ?subclass
WHERE {
  ?subclass wdt:P279* wd:Q87167 .
}
```

The answer to the question "Which distinct items are subclasses, at any level, of a manuscript (a document written by hand)?" is the list of 195 distinct items returned by the query.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?property ?value WHERE {
  wd:Q9696 ?property ?value .
  FILTER ( STRSTARTS( STR( ?property ) , STR( wdt: ) ) )
}
LIMIT 100

Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property                                            | ?value                                                                                |
| ---------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| video (wdt:P10)                                      | <http://commons.wikimedia.org/wiki/Special:FilePath/Kennedy%20inauguration%20footage.ogv> |
| George Eastman Museum people ID (wdt:P10018)         | 90819                                                                                |
| Portuguese National Library author ID (wdt:P1005)    | 11414                                                                                |
| Nationale Thesaurus voor Auteursnamen ID (wdt:P1006) | 068645325                                                                                |
| Academy of Athens authority ID (wdt:P10141)          | 40796                                                                                |
| ...                                                  | ...                                                                                |
| image of grave (wdt:P1442)                           | <http://commons.wikimedia.org/wiki/Special:FilePath/JFK%20grave.jpg>                      |
| nickname (wdt:P1449)                                 | Jack                                                                                |
| birth name (wdt:P1477)                               | "John Fitzgerald Kennedy"@mul                                                             |
| name in native language (wdt:P1559)                  | John F. Kennedy                                                                           |
| killed by (wdt:P157)                                 | Lee Harvey Oswald (wd:Q48745)                                                             |

The properties and their values associated with John F. Kennedy include various details such as his occupation, date of birth, place of birth, and more. The query retrieves the most relevant properties and their values, excluding any annotation properties or labels.

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

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

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

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

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

[User] What are the properties and their values associated with John F. Kennedy?

[Assistant]
To answer the question about the properties and their values associated with John F. Kennedy, we need to first identify the entity for John F. Kennedy in the knowledge graph. Once we have the correct entity, we can list all the properties and their corresponding values for that entity.

Let's start by searching for the entity representing John F. Kennedy.

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

[Assistant]
The top result for "John F. Kennedy" is the person who was the President of the United States from 1961 to 1963. The corresponding entity in Wikidata is `wd:Q9696`.

Next, we will list all the properties and their values associated with this entity.

[Tool]
Got 1,663 rows and 3 columns, showing the first 10 rows below:
| subject                    | property                         | object                                                                                |
| -------------------------- | -------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| John F. Kennedy (wd:Q9696) | instance of (wdt:P31)            | human (wd:Q5)                                                                                |
| John F. Kennedy (wd:Q9696) | occupation (wdt:P106)            | writer (wd:Q36180)                                                                                |
| John F. Kennedy (wd:Q9696) | sex or gender (wdt:P21)          | male (wd:Q6581097)                                                                                |
| John F. Kennedy (wd:Q9696) | given name (wdt:P735)            | John (wd:Q4925477)                                                                                |
| John F. Kennedy (wd:Q9696) | date of birth (wdt:P569)         | 1917-05-29T00:00:00Z                                                                                |
| John F. Kennedy (wd:Q9696) | Commons category (wdt:P373)      | John F. Kennedy                                                                                |
| John F. Kennedy (wd:Q9696) | image (wdt:P18)                  | <http://commons.wikimedia.org/wiki/Special:FilePath/John%20F.%20Kennedy%2C%20White%20House%20color%20photo%20portrait.jpg> |
| John F. Kennedy (wd:Q9696) | country of citizenship (wdt:P27) | United States (wd:Q30)                                                                                |
| John F. Kennedy (wd:Q9696) | family name (wdt:P734)           | Kennedy (wd:Q120401)                                                                                |
| John F. Kennedy (wd:Q9696) | place of birth (wdt:P19)         | Brookline (wd:Q49142)                                                                                |
| ...                        | ...                              | ...                                                                                |

[Assistant]
The list function has returned a large number of properties and their values associated with John F. Kennedy. To provide a more structured and readable response, we can construct a SPARQL query to retrieve these properties and their values in a more organized manner.

Let's create a SPARQL query to fetch the properties and their values for John F. Kennedy (`wd:Q9696`).

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property          | ?value                                                   |
| ------------------ | -------------------------------------------------------- |
| schema:description | "1961年至1963年美国总统"@zh-cn                                  |
| schema:description | "1961年至1963年美國第35任總統"@zh                                 |
| schema:description | "34ma prezidanto di Usa"@io                              |
| schema:description | "35ain arlywydd Unol Daleithiau America"@cy              |
| schema:description | "35. Amerika Birleşik Devletleri başkanı (1961–1963)"@tr |
| ...                | ...                                                      |
| schema:name        | "John Fitzgerald Kennedy"@lij                            |
| schema:name        | "John Fitzgerald Kennedy"@mg                             |
| schema:name        | "John Fitzgerald Kennedy"@nds                            |
| schema:name        | "John Fitzgerald Kennedy"@oc                             |
| schema:name        | "John Fitzgerald Kennedy"@pl                             |

[Assistant]
The SPARQL query has returned a large number of properties and their values, including many descriptions and names in different languages. To make the results more useful and relevant, we can filter out the language-specific properties and focus on the core properties that provide meaningful information about John F. Kennedy.

Let's refine the SPARQL query to exclude language-specific properties and focus on the most relevant ones.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property          | ?value                                                   |
| ------------------ | -------------------------------------------------------- |
| schema:description | "1961年至1963年美国总统"@zh-cn                                  |
| schema:description | "1961年至1963年美國第35任總統"@zh                                 |
| schema:description | "34ma prezidanto di Usa"@io                              |
| schema:description | "35ain arlywydd Unol Daleithiau America"@cy              |
| schema:description | "35. Amerika Birleşik Devletleri başkanı (1961–1963)"@tr |
| ...                | ...                                                      |
| schema:name        | "John Fitzgerald Kennedy"@lij                            |
| schema:name        | "John Fitzgerald Kennedy"@mg                             |
| schema:name        | "John Fitzgerald Kennedy"@nds                            |
| schema:name        | "John Fitzgerald Kennedy"@oc                             |
| schema:name        | "John Fitzgerald Kennedy"@pl                             |

[Assistant]
It seems that the filter to exclude annotation properties did not work as expected. Let's try a different approach to filter out the language-specific properties and focus on the core properties.

We can explicitly exclude properties that are known to be language-specific, such as `schema:description` and `schema:name`. We can also limit the results to the most commonly used properties in Wikidata.

Let's refine the SPARQL query again.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property  | ?value                          |
| ---------- | ------------------------------- |
| rdf:type   | wikibase:Item                   |
| rdfs:label | "Con Kennedi"@az                |
| rdfs:label | "Čon Kennedi"@tly               |
| rdfs:label | "Džons Kenedijs"@lv             |
| rdfs:label | "Ioannes Fitzgerald Kennedy"@la |
| ...        | ...                             |
| rdfs:label | "John F. Kennedy"@pt-br         |
| rdfs:label | "John F. Kennedy"@rmf           |
| rdfs:label | "John F. Kennedy"@rw            |
| rdfs:label | "John F. Kennedy"@sco           |
| rdfs:label | "John F. Kennedy"@se            |

[Assistant]
It appears that the query is still returning many language-specific labels. To further refine the query, we can explicitly exclude `rdfs:label` and focus on properties that are more likely to contain meaningful information about John F. Kennedy.

Let's try a more targeted approach by listing only the properties that are commonly used in Wikidata and are not language-specific.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property     | ?value                  |
| ------------- | ----------------------- |
| rdf:type      | wikibase:Item           |
| skos:altLabel | "Başkan Kennedy"@tr     |
| skos:altLabel | "Ioannes F. Kennedy"@la |
| skos:altLabel | "Jack Kennedy"@ace      |
| skos:altLabel | "Jack Kennedy"@ami      |
| ...           | ...                     |
| skos:altLabel | "John F. Kennedy"@fr    |
| skos:altLabel | "John F. Kennedy"@hu    |
| skos:altLabel | "John F. Kennedy"@it    |
| skos:altLabel | "John F. Kennedy"@pl    |
| skos:altLabel | "John F. Kennedy"@ru    |

[Assistant]
The query is still returning many alternative labels (`skos:altLabel`), which are not the core properties we are interested in. To get the most relevant properties, we should focus on the direct properties (those prefixed with `wdt:`) that are commonly used in Wikidata.

Let's refine the query to include only the direct properties (`wdt:`) and exclude any annotation properties or labels.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property                                            | ?value                                                                                |
| ---------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| video (wdt:P10)                                      | <http://commons.wikimedia.org/wiki/Special:FilePath/Kennedy%20inauguration%20footage.ogv> |
| George Eastman Museum people ID (wdt:P10018)         | 90819                                                                                |
| Portuguese National Library author ID (wdt:P1005)    | 11414                                                                                |
| Nationale Thesaurus voor Auteursnamen ID (wdt:P1006) | 068645325                                                                                |
| Academy of Athens authority ID (wdt:P10141)          | 40796                                                                                |
| ...                                                  | ...                                                                                |
| image of grave (wdt:P1442)                           | <http://commons.wikimedia.org/wiki/Special:FilePath/JFK%20grave.jpg>                      |
| nickname (wdt:P1449)                                 | Jack                                                                                |
| birth name (wdt:P1477)                               | "John Fitzgerald Kennedy"@mul                                                             |
| name in native language (wdt:P1559)                  | John F. Kennedy                                                                           |
| killed by (wdt:P157)                                 | Lee Harvey Oswald (wd:Q48745)                                                             |

[Assistant]
The refined SPARQL query has successfully returned the most relevant properties and their values associated with John F. Kennedy. These properties are direct properties (`wdt:`) and provide meaningful information about him.

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

```sparql
SELECT ?property ?value WHERE {
  wd:Q9696 ?property ?value .
  FILTER (STRSTARTS(STR(?property), STR(wdt:)))
} LIMIT 100
```

This query retrieves the direct properties and their values for John F. Kennedy, excluding any annotation properties or labels.

Now, let's provide the final answer to the user.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?legalFormLabel ?finalCount WHERE {
  {
    SELECT ?legalForm ( COUNT( ?entity ) AS ?count ) WHERE {
      ?entity wdt:P17 wd:Q142 .
      ?entity wdt:P1454 ?legalForm .
    }
    GROUP BY ?legalForm
    HAVING ( ?count > 499 )
  } UNION {
    SELECT ( strdt( "OTHER" , xsd:string ) AS ?legalFormLabel ) ( SUM( ?otherCount ) AS ?otherSum ) WHERE {
      {
        SELECT ?legalForm ( COUNT( ?entity ) AS ?otherCount ) WHERE {
          ?entity wdt:P17 wd:Q142 .
          ?entity wdt:P1454 ?legalForm .
        }
        GROUP BY ?legalForm
        HAVING ( ?otherCount <= 499 )
      }
    }
  }
  OPTIONAL {
    ?legalForm rdfs:label ?legalFormLabel .
    FILTER ( LANG( ?legalFormLabel ) = "fr" )
  }
  BIND( IF( BOUND( ?count ) , ?count , ?otherSum ) AS ?finalCount )
}

Got 25 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?legalFormLabel                                                     | ?finalCount |
| ------------------------------------------------------------------- | ----------- |
| OTHER                                                               | 8499        |
| "SA à directoire (s.a.i.)"@fr                                       | 668         |
| "association déclarée"@fr                                           | 4288        |
| "société coopérative agricole"@fr                                   | 779         |
| "établissement public local à ca...ère industriel ou commercial"@fr | 1283        |
| ...                                                                 | ...         |
| "établissement d'hospitalisation"@fr                                | 868         |
| "syndicat intercommunal à vocation unique"@fr                       | 12447       |
| "société à responsabilité limitée (sans autre indication)"@fr       | 69069       |
| "SA d'économie mixte à conseil d'administration"@fr                 | 1130        |
| "société anonyme à conseil d'administration (s.a.i.)"@fr            | 2820        |

The number of legal units for each level 3 legal category in France, with the category name in French, where the count of legal units is greater than 499, and a summary line labeled 'OTHER' that sums up the number of legal units for all other categories with counts of 499 or less, is as follows:

| Legal Form Label                                                    | Final Count |
|---------------------------------------------------------------------|-------------|
| OTHER                                                               | 8499        |
| 'SA à directoire (s.a.i.)'@fr                                       | 668         |
| 'association déclarée'@fr                                           | 4288        |
| 'société coopérative agricole'@fr                                   | 779         |
| 'établissement public local à caractère industriel ou commercial'@fr | 1283        |
| ...                                                                 | ...         |
| 'établissement d'hospitalisation'@fr                                | 868         |
| 'syndicat intercommunal à vocation unique'@fr                       | 12447       |
| 'société à responsabilité limitée (sans autre indication)'@fr       | 69069       |
| 'SA d'économie mixte à conseil d'administration'@fr                 | 1130        |
| 'société anonyme à conseil d'administration (s.a.i.)'@fr            | 2820        |

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

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

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

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

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

[User] What is the number of legal units for each level 3 legal category in France, with the category name in French, where the count of legal units is greater than 499? Additionally, provide a summary line labeled "OTHER" that sums up the number of legal units for all other categories with counts of 499 or less.

[Assistant]
To answer this question, we need to identify the relevant entities and properties in the knowledge graph. We are looking for legal units in France, categorized by their legal status, and we want to count these units per category. We also need to filter out categories with fewer than 500 units and sum up the remaining ones under a label "OTHER".

Let's start by identifying the entity for France and the property for legal units or legal status. We'll use the `search_entity` function to find the entity for France and the `search_property` function to find the property for legal units or legal status.

[Tool]
Top 10 entity alternatives:
1. France, also known as FR, FRA, French Republic (wd:Q142): country in Western Europe
2. France men's national association football team, also known as France, France men's national football team, France national association football team, France national football team, France national soccer team (wd:Q47774): national association football team representing France
3. French Fifth Republic, also known as 5th French Republic, French Republic, French 5th Republic, France, Fifth French Republic (wd:Q200686): current system of government of France (1958–present)
4. Bourbon Restoration in France, also known as France, Kingdom of France (wd:Q207162): period of French history, 1814–1830
5. French Community, also known as France (wd:Q1120363): International political association
6. France at the 2008 Summer Olympics, also known as France (wd:Q146845): sporting event delegation
7. France, also known as French battleship France (wd:Q49644): 1912 Courbet-class battleship
8. France, also known as On a Half Clear Morning (wd:Q76488728): 2021 film directed by Bruno Dumont
9. La France, also known as France (wd:Q3209212): 2007 film by Serge Bozon
10. France (wd:Q5478259): constituency of the European Parliament

[Assistant] The correct entity for France is `wd:Q142`. Next, let's search for the property that represents legal units or legal status. We will use the `search_property` function to find this property.

[Tool]
Top 10 property alternatives:
1. statistical unit, also known as describe items of type, statistical designation, unit (wd:P2353 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): member of a dataset; type of statistical designation
2. military unit, also known as member of military unit, unit (wd:P7779 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): smallest military unit that a person is/was in, member of
3. room number, also known as apartment, room identifier, suite, unit (wd:P7532 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): number or identifier of a room, which can qualify the address (P6375) of a place to complete it
4. recommended unit of measurement, also known as measured in, unit of measurement, unit, standard unit of measurement (wd:P8111 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): unit in which a quantity is measured as recommended by a standard (SI, ISO, IEC, etc.)
5. laws applied, also known as applied law, law applied, legal base (wd:P3014 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): the constitutional and statutory provisions that were the primary basis for a decision or law
6. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of
7. legal form, also known as legal structure, structured as (wd:P1454 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): legal form of an entity, instance of
8. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
9. law identifier, also known as law number, regulation number, regulation id, regulation i.d., law or regulation identifying number (wd:P8550 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): a sequence of numbers assigned to laws/acts that were passed by countries or local governments
10. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of

[Assistant]
The property that seems most relevant to our query is `legal form` (wd:P1454). This property describes the legal form of an entity, which could be used to categorize legal units.

Next, we need to find the property that links entities to their country, which in this case is France. The property `located in the administrative territorial entity` (wd:P131) might be useful, but it is more specific to administrative divisions. A more general property for country is `country` (wd:P17).

Let's confirm this by listing some triples where the property is `wd:P17` and the object is `wd:Q142` (France).

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                                          | property          | object           |
| ---------------------------------------------------------------- | ----------------- | ---------------- |
| Charlie Hebdo (wd:Q1043104)                                      | country (wdt:P17) | France (wd:Q142) |
| Provence (wd:Q101081)                                            | country (wdt:P17) | France (wd:Q142) |
| 1998 FIFA World Cup (wd:Q101730)                                 | country (wdt:P17) | France (wd:Q142) |
| Channel Tunnel (wd:Q10257)                                       | country (wdt:P17) | France (wd:Q142) |
| Occitania (wd:Q104285)                                           | country (wdt:P17) | France (wd:Q142) |
| Paris-Saclay University (wd:Q109409389)                          | country (wdt:P17) | France (wd:Q142) |
| Sephardi Jews (wd:Q102251)                                       | country (wdt:P17) | France (wd:Q142) |
| European Charter for Regional or Minority Languages (wd:Q106308) | country (wdt:P17) | France (wd:Q142) |
| Forcalquier (wd:Q104431)                                         | country (wdt:P17) | France (wd:Q142) |
| Voulpaix (wd:Q104295)                                            | country (wdt:P17) | France (wd:Q142) |
| ...                                                              | ...               | ...              |

[Assistant]
The property `country` (wdt:P17) is indeed used to link entities to their country, and it confirms that many entities are linked to France (wd:Q142).

Now, we need to find the property that links entities to their legal form. We already identified `legal form` (wd:P1454) as a potential candidate. Let's list some triples where the property is `wd:P1454` to see if it provides the legal forms we are interested in.

[Tool]
Got more than 131,072 rows and 3 columns, showing the first 10 rows below:
| subject                                                                  | property               | object                                      |
| ------------------------------------------------------------------------ | ---------------------- | ------------------------------------------- |
| Sky News Arabia Channel (wd:Q116058286)                                  | legal form (wdt:P1454) | United States (wd:Q30)                      |
| wd:Q117706302                                                            | legal form (wdt:P1454) | Greece (wd:Q41)                             |
| Porsche center Iran (wd:Q117071330)                                      | legal form (wdt:P1454) | Iran (wd:Q794)                              |
| United Nations (wd:Q1065)                                                | legal form (wdt:P1454) | intergovernmental organization (wd:Q245065) |
| Limak Records (wd:Q111827527)                                            | legal form (wdt:P1454) | music (wd:Q638)                             |
| Vivace Hub (wd:Q113133663)                                               | legal form (wdt:P1454) | Washington, D.C. (wd:Q61)                   |
| Prague (wd:Q1085)                                                        | legal form (wdt:P1454) | regions of the Czech Republic (wd:Q38911)   |
| Institute of Geology for the Development of Fossil Fuels (wd:Q113407999) | legal form (wdt:P1454) | 2010 (wd:Q1995)                             |
| Good Lord Hardware Lira City (wd:Q119964423)                             | legal form (wdt:P1454) | law (wd:Q7748)                              |
| wd:Q117360040                                                            | legal form (wdt:P1454) | state (wd:Q7275)                            |
| ...                                                                      | ...                    | ...                                         |

[Assistant]
The property `legal form` (wdt:P1454) is used to link entities to their legal forms, but the results show a variety of different types of legal forms, including countries and other entities. We need to focus on legal forms that are specific to organizations or entities in France.

To refine our search, let's look for specific legal forms that are commonly used in France. We can use the `search_object_of_property` function to find objects (legal forms) for the property `wdt:P1454` that are associated with entities in France.

Let's search for legal forms that are commonly used in France.

[Tool]
Top 10 entity alternatives:
1. société anonyme, also known as S.A., Société anonyme (France), Société anonyme, SA (France) (wd:Q18195767): type of company in French law
2. European economic interest group, also known as EEIG (France) (wd:Q112176872): legal category of EEIG in France
3. department of France, also known as department, French department, departments of France, département, departement (wd:Q6465): administrative subdivision in France
4. prefecture, also known as préfectures in France, prefectures in France, préfectures, French préfectures, prefectures (wd:Q179831): governmental subdivision in France
5. Région de France (wd:Q17561724): legal category of French territorial authority
6. declared association, also known as association déclarée, incorporated association (France) (wd:Q111166550): type of legal entity in France
7. undeclared association, also known as association non déclarée, unincorporated association (France) (wd:Q111191469): type of legal entity in France
8. agglomeration community, also known as agglomeration community of France, communauté d'agglomération (wd:Q159321): France intercommunal subdivision combining a commune and its suburbs
9. commune community, also known as community of communes of France, communautés de communes, community of communes in France, communites of communes, commune community in France (wd:Q423785): type of French public institution for intermunicipal cooperation, bringing together neighboring municipalities
10. urban community, also known as urban community of France (wd:Q423790): France intercommunal subdivision integrating a city and its suburbs

No other items found

No literal items found

[Assistant]
The search results provide several legal forms that are commonly used in France. These include:

1. `société anonyme` (wd:Q18195767)
2. `European economic interest group` (wd:Q112176872)
3. `department of France` (wd:Q6465)
4. `prefecture` (wd:Q179831)
5. `Région de France` (wd:Q17561724)
6. `declared association` (wd:Q111166550)
7. `undeclared association` (wd:Q111191469)
8. `agglomeration community` (wd:Q159321)
9. `commune community` (wd:Q423785)
10. `urban community` (wd:Q423790)

We will use these legal forms to construct our SPARQL query. The query will count the number of legal units for each legal form in France, filter out those with fewer than 500 units, and sum up the remaining ones under a label "OTHER".

Let's start by constructing a basic SPARQL query to count the number of legal units for each legal form in France.

```sparql
SELECT ?legalForm ?count
WHERE {
  ?entity wdt:P17 wd:Q142 .
  ?entity wdt:P1454 ?legalForm .
  SERVICE wikibase:label { bd:serviceParam wikibase:language "fr". }
}
GROUP BY ?legalForm
HAVING (?count > 499)
```

However, since we cannot use `SERVICE wikibase:label`, we will use `rdfs:label` to get the labels in French. We will also add the logic to sum up the counts for categories with 499 or fewer units under the label "OTHER".

```sparql
WITH {
  SELECT ?legalForm (COUNT(?entity) AS ?count)
  WHERE {
    ?entity wdt:P17 wd:Q142 .
    ?entity wdt:P1454 ?legalForm .
  }
  GROUP BY ?legalForm
} AS %results
SELECT ?legalFormLabel ?count
WHERE {
  VALUES ?legalForm { wd:Q18195767 wd:Q112176872 wd:Q6465 wd:Q179831 wd:Q17561724 wd:Q111166550 wd:Q111191469 wd:Q159321 wd:Q423785 wd:Q423790 }
  ?legalForm rdfs:label ?legalFormLabel .
  FILTER(LANG(?legalFormLabel) = "fr")
  {
    SELECT ?legalForm (SUM(?count) AS ?totalCount)
    FROM %results
    WHERE {
      ?legalForm ?count .
      FILTER(?count > 499)
    }
    GROUP BY ?legalForm
  } UNION {
    SELECT (strdt("OTHER", xsd:string) AS ?legalFormLabel) (SUM(?count) AS ?totalCount)
    FROM %results
    WHERE {
      ?legalForm ?count .
      FILTER(?count <= 499)
    }
  }
}
```

Let's execute this query to see the results.

[Tool]
SPARQL execution failed:
failed to parse input: no matching token found from position 160: '%results
SELECT ?legalFormLabel ?count
WHERE {
  VALUES ?legalForm { wd:Q18195767 wd:Q112176872 wd:Q6465 wd:Q179831 wd:Q17561724 wd:Q111166550 wd:Q111191469 wd:Q159321 wd:Q423785 wd:Q423790 }
  ?legalForm rdfs:label ?legalFormLabel .
  FILTER(LANG(?legalFormLabel) = "fr")
  {
    SELECT ?legalForm (SUM(?count) AS ?totalCount)
    FROM %results
    WHERE {
      ?legalForm ?count .
      FILTER(?count > 499)
    }
    GROUP BY ?legalForm
  } UNION {
    SELECT (strdt("OTHER", xsd:string) AS ?legalFormLabel) (SUM(?count) AS ?totalCount)
    FROM %results
    WHERE {
      ?legalForm ?count .
      FILTER(?count <= 499)
    }
  }
}'

[Assistant]
It appears that the SPARQL query failed due to the use of the `%results` subquery, which is not supported in the QLever SPARQL endpoint. We need to restructure the query to avoid using this feature.

Instead, we can use a single query to count the legal units for each legal form and then filter and sum the results in a single step. Here's the revised query:

```sparql
SELECT ?legalFormLabel ?count
WHERE {
  {
    SELECT ?legalForm (COUNT(?entity) AS ?count)
    WHERE {
      ?entity wdt:P17 wd:Q142 .
      ?entity wdt:P1454 ?legalForm .
    }
    GROUP BY ?legalForm
    HAVING (?count > 499)
  }
  UNION
  {
    SELECT (strdt("OTHER", xsd:string) AS ?legalFormLabel) (SUM(?count) AS ?count)
    WHERE {
      {
        SELECT ?legalForm (COUNT(?entity) AS ?count)
        WHERE {
          ?entity wdt:P17 wd:Q142 .
          ?entity wdt:P1454 ?legalForm .
        }
        GROUP BY ?legalForm
        HAVING (?count <= 499)
      }
    }
  }
  OPTIONAL {
    ?legalForm rdfs:label ?legalFormLabel .
    FILTER(LANG(?legalFormLabel) = "fr")
  }
}
```

This query will:
1. Count the number of legal units for each legal form in France.
2. Filter out legal forms with fewer than 500 units.
3. Sum up the counts for legal forms with 499 or fewer units under the label "OTHER".
4. Optionally retrieve the French labels for the legal forms.

Let's execute this query to see the results.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: The target ?count of an AS clause was already used in the query body.

[Assistant]
It looks like the issue is with the reuse of the variable `?count` in the `AS` clause. To resolve this, we need to use different variables for the counts in the two parts of the union. Let's modify the query to use distinct variables for the counts in each part of the union.

Here's the revised query:

```sparql
SELECT ?legalFormLabel ?finalCount
WHERE {
  {
    SELECT ?legalForm (COUNT(?entity) AS ?count)
    WHERE {
      ?entity wdt:P17 wd:Q142 .
      ?entity wdt:P1454 ?legalForm .
    }
    GROUP BY ?legalForm
    HAVING (?count > 499)
  }
  UNION
  {
    SELECT (strdt("OTHER", xsd:string) AS ?legalFormLabel) (SUM(?otherCount) AS ?finalCount)
    WHERE {
      {
        SELECT ?legalForm (COUNT(?entity) AS ?otherCount)
        WHERE {
          ?entity wdt:P17 wd:Q142 .
          ?entity wdt:P1454 ?legalForm .
        }
        GROUP BY ?legalForm
        HAVING (?otherCount <= 499)
      }
    }
  }
  OPTIONAL {
    ?legalForm rdfs:label ?legalFormLabel .
    FILTER(LANG(?legalFormLabel) = "fr")
  }
  BIND(IF(BOUND(?count), ?count, ?finalCount) AS ?finalCount)
}
```

This query will:
1. Count the number of legal units for each legal form in France.
2. Filter out legal forms with fewer than 500 units.
3. Sum up the counts for legal forms with 499 or fewer units under the label "OTHER".
4. Optionally retrieve the French labels for the legal forms.
5. Use a `BIND` clause to ensure the correct count is returned.

Let's execute this query to see the results.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: The target variable ?finalCount of an AS clause was already used before in the query body.

[Assistant]
It seems that the issue persists because the variable `?finalCount` is being used in both the `SELECT` clause and the `AS` clause. To resolve this, we need to use a different variable for the `AS` clause in the second part of the union.

Let's modify the query to use a different variable for the sum in the second part of the union and then bind the final count correctly.

Here's the revised query:

```sparql
SELECT ?legalFormLabel ?finalCount
WHERE {
  {
    SELECT ?legalForm (COUNT(?entity) AS ?count)
    WHERE {
      ?entity wdt:P17 wd:Q142 .
      ?entity wdt:P1454 ?legalForm .
    }
    GROUP BY ?legalForm
    HAVING (?count > 499)
  }
  UNION
  {
    SELECT (strdt("OTHER", xsd:string) AS ?legalFormLabel) (SUM(?otherCount) AS ?otherSum)
    WHERE {
      {
        SELECT ?legalForm (COUNT(?entity) AS ?otherCount)
        WHERE {
          ?entity wdt:P17 wd:Q142 .
          ?entity wdt:P1454 ?legalForm .
        }
        GROUP BY ?legalForm
        HAVING (?otherCount <= 499)
      }
    }
  }
  OPTIONAL {
    ?legalForm rdfs:label ?legalFormLabel .
    FILTER(LANG(?legalFormLabel) = "fr")
  }
  BIND(IF(BOUND(?count), ?count, ?otherSum) AS ?finalCount)
}
```

This query will:
1. Count the number of legal units for each legal form in France.
2. Filter out legal forms with fewer than 500 units.
3. Sum up the counts for legal forms with 499 or fewer units under the label "OTHER".
4. Optionally retrieve the French labels for the legal forms.
5. Use a `BIND` clause to ensure the correct count is returned.

Let's execute this query to see the results.

[Tool]
Got 25 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?legalFormLabel                                                     | ?finalCount |
| ------------------------------------------------------------------- | ----------- |
| OTHER                                                               | 8499        |
| "SA à directoire (s.a.i.)"@fr                                       | 668         |
| "association déclarée"@fr                                           | 4288        |
| "société coopérative agricole"@fr                                   | 779         |
| "établissement public local à ca...ère industriel ou commercial"@fr | 1283        |
| ...                                                                 | ...         |
| "établissement d'hospitalisation"@fr                                | 868         |
| "syndicat intercommunal à vocation unique"@fr                       | 12447       |
| "société à responsabilité limitée (sans autre indication)"@fr       | 69069       |
| "SA d'économie mixte à conseil d'administration"@fr                 | 1130        |
| "société anonyme à conseil d'administration (s.a.i.)"@fr            | 2820        |

[Assistant]
The query has successfully returned the number of legal units for each legal form in France, with the counts filtered to show only those greater than 499. Additionally, it includes a summary line labeled "OTHER" that sums up the number of legal units for all other categories with counts of 499 or less.

Here are the results:

| Legal Form Label                                                    | Final Count |
|---------------------------------------------------------------------|-------------|
| OTHER                                                               | 8499        |
| "SA à directoire (s.a.i.)"@fr                                       | 668         |
| "association déclarée"@fr                                           | 4288        |
| "société coopérative agricole"@fr                                   | 779         |
| "établissement public local à caractère industriel ou commercial"@fr | 1283        |
| ...                                                                 | ...         |
| "établissement d'hospitalisation"@fr                                | 868         |
| "syndicat intercommunal à vocation unique"@fr                       | 12447       |
| "société à responsabilité limitée (sans autre indication)"@fr       | 69069       |
| "SA d'économie mixte à conseil d'administration"@fr                 | 1130        |
| "société anonyme à conseil d'administration (s.a.i.)"@fr            | 2820        |

The final SPARQL query is:

```sparql
SELECT ?legalFormLabel ?finalCount
WHERE {
  {
    SELECT ?legalForm (COUNT(?entity) AS ?count)
    WHERE {
      ?entity wdt:P17 wd:Q142 .
      ?entity wdt:P1454 ?legalForm .
    }
    GROUP BY ?legalForm
    HAVING (?count > 499)
  }
  UNION
  {
    SELECT (strdt("OTHER", xsd:string) AS ?legalFormLabel) (SUM(?otherCount) AS ?otherSum)
    WHERE {
      {
        SELECT ?legalForm (COUNT(?entity) AS ?otherCount)
        WHERE {
          ?entity wdt:P17 wd:Q142 .
          ?entity wdt:P1454 ?legalForm .
        }
        GROUP BY ?legalForm
        HAVING (?otherCount <= 499)
      }
    }
  }
  OPTIONAL {
    ?legalForm rdfs:label ?legalFormLabel .
    FILTER(LANG(?legalFormLabel) = "fr")
  }
  BIND(IF(BOUND(?count), ?count, ?otherSum) AS ?finalCount)
}
```

I will now provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
SELECT ?settlement ?settlementLabel ?settlementType ?settlementTypeLabel ?settlementCoords ?centerCoords ?distance WHERE {
  wd:Q2180733 wdt:P625 ?centerCoords .
  ?settlement wdt:P17 wd:Q213 ; wdt:P625 ?settlementCoords ; wdt:P31 ?settlementType .
  FILTER ( ?settlementType IN ( wd:Q515 , wd:Q3957 , wd:Q486972 ) ) BIND( geof:distance ( ?settlementCoords , ?centerCoords ) AS ?distance ) FILTER ( ?distance <= 35 ) OPTIONAL {
    ?settlement rdfs:label ?settlementLabel .
    FILTER ( LANG( ?settlementLabel ) = "en" )
  }
  OPTIONAL {
    ?settlementType rdfs:label ?settlementTypeLabel .
    FILTER ( LANG( ?settlementTypeLabel ) = "en" )
  }
}
ORDER BY ASC ( ?distance )

The settlements within a 35 km radius from the village of Mezno in the Czech Republic are:

1. **Kříženec** - Human Settlement (16.7266 km)
2. **Bozejny** - Human Settlement (17.7845 km)
3. **Kobylí** - Human Settlement (18.5617 km)
4. **Benešov** - Town (27.6431 km)
5. **Žlíbek** - Human Settlement (28.1408 km)
6. **Svatý Ján u Květova** - Human Settlement (28.8679 km)
7. **Vidrholec** - Human Settlement (32.0025 km)
8. **Unnamed Settlement** - Human Settlement (32.7267 km)
9. **Loutkov** - Human Settlement (33.9777 km)
10. **Přestavlky** - Human Settlement (34.9303 km)

Each settlement's coordinates and the distance from the center point (Mezno) are included in the results.

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

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

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

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

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

[User] What are the settlements (villages and towns) in the Czech Republic within a 35 km radius from the village of Mezno? Please include their coordinates, the coordinates of the center point, and the distance from the center point.

[Assistant]
To answer this question, we need to identify the following elements:

1. The village of Mezno in the Czech Republic.
2. The property for geographic coordinates.
3. The property for the country of a settlement.
4. The property for the type of settlement (village, town).

Let's start by searching for the village of Mezno in the Czech Republic.

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

[Assistant] It seems that the search did not return the specific village of Mezno in the Czech Republic. Let's try a more targeted search by including "village" in the query to narrow down the results.

[Tool]
Top 10 entity alternatives:
1. Village restoration in the Czech Republic (wd:Q58549909): scholarly article by Alena Salašová published June 1996 in International Journal of Heritage Studies
2. a village in the Czech Republic (wd:L1031233-S1)
3. Czech Republic, also known as CZR, CZE, Czechia, CZ (wd:Q213): country in Central Europe
4. Czech Republic, also known as CZ, CZE, CZR, Czechia (wd:Q213): country in Central Europe
5. Czech Republic (wd:Q3496079): member state of the Czech and Slovak Federal Republic (1990–1992)
6. Czech Republic (wd:Q5201764): constituency of the European Parliament
7. Czech Republic, also known as Star Trek Czech Republic (wd:Q99288382): the country of the Czech Republic as depicted in Star Trek
8. CZECH REPUBLIC (wd:Q60164570)
9. Czech Republic (wd:Q59760445): scholarly article by LUKAS LINEK & ZDENKA MANSFELDOVA published December 2004 in European Journal of Political Research
10. Czech Republic (wd:Q59212257): scholarly article by Aleš Kudrnáč et al published December 2016 in European Journal of Political Research Political Data Yearbook

[Assistant] The search still did not return the specific village of Mezno in the Czech Republic. Let's try another approach by searching for "Mezno" and then filtering the results to find the correct village.

[Tool]
Top 10 entity alternatives:
1. Mezno (wd:Q2180733): village in Benešov District of Central Bohemian region
2. Mezno (wd:Q30917394): former railway stop in Mezno, Czech Republic
3. Mezno (wd:Q111685698): railway stop in Czechia
4. Mezno (wd:Q96153716): pond in Mezno, Czech Republic
5. Mitrovice (Mezno) (wd:Q12038239): village in Benešov District of Central Bohemian region
6. Mezno Tunnel (wd:Q108507418)
7. Meznokto metropola (wd:Q12352510)
8. Structural isomerism and thermod...ed by B3LYP and PCM calculations (wd:Q43063359): scientific article published in June 2010

[Assistant]
From the search results, the entity `wd:Q2180733` appears to be the village of Mezno in the Czech Republic. Let's confirm this by checking the details of this entity.

### Step 1: Confirm the Entity for Mezno
We will list the triples for `wd:Q2180733` to verify that it is indeed the village of Mezno in the Czech Republic.

[Tool]
Got 233 rows and 3 columns, showing the first 10 rows below:
| subject             | property                                                    | object                                                                        |
| ------------------- | ----------------------------------------------------------- | ----------------------------------------------------------------------------- |
| Mezno (wd:Q2180733) | instance of (wdt:P31)                                       | municipality of the Czech Republic (wd:Q5153359)                              |
| Mezno (wd:Q2180733) | country (wdt:P17)                                           | Czech Republic (wd:Q213)                                                      |
| Mezno (wd:Q2180733) | located in the administrative territorial entity (wdt:P131) | Benešov District (wd:Q792883)                                                 |
| Mezno (wd:Q2180733) | coordinate location (wdt:P625)                              | POINT(14.644975 49.534590)                                                    |
| Mezno (wd:Q2180733) | Commons category (wdt:P373)                                 | Mezno                                                                         |
| Mezno (wd:Q2180733) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Mezno%2C%20domy%20II.jpg> |
| Mezno (wd:Q2180733) | population (wdt:P1082)                                      | 383                                                                           |
| Mezno (wd:Q2180733) | official name (wdt:P1448)                                   | "Mezno"@cs                                                                    |
| Mezno (wd:Q2180733) | official website (wdt:P856)                                 | <https://www.mezno.cz/>                                                       |
| Mezno (wd:Q2180733) | postal code (wdt:P281)                                      | 257 86                                                                        |
| ...                 | ...                                                         | ...                                                                           |

[Assistant]
The entity `wd:Q2180733` is indeed the village of Mezno in the Czech Republic. It has the following relevant properties:
- Country: Czech Republic (`wd:Q213`)
- Coordinate location: `POINT(14.644975 49.534590)`

### Step 2: Identify Properties for Settlements and Coordinates
Next, we need to identify the properties for settlements (villages and towns) and their coordinates.

- **Settlement Type**: We need to find the properties that indicate whether a place is a village or a town.
- **Coordinates**: We already know the property for coordinates is `wdt:P625`.

Let's search for properties related to the type of settlement.

### Step 3: Search for Properties Related to Settlement Type
We will search for properties related to the type of settlement (village, town).

[Tool]
Top 10 property alternatives:
1. contains settlement, also known as populated places within, settlement (wd:P1383 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): settlement which an administrative division contains, has part(s)
2. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
3. number of households, also known as dwellings, households (wd:P1538 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): total number of households in this place, includes dwellings of all types
4. Or Movement settlement ID (wd:P5335 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): the ID of a settlement in the Or Movement database for the Negev and the Galilee
5. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
6. number of housing units, also known as dwelling-places, housing units, number of dwelling-places (wd:P10814 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): number of housing units (dwellings) in a specific place (such as city, county, state, country etc.)
7. associated cadastral district, also known as cadastral community, cadastral district, cadastral districts, cadastral municipality, Katastralgemeinde (wd:P10254 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): cadastral district in which a place is located or is part of. Us...inct from administrative entities (P131) in predefined countries
8. location of creation, also known as manufactured in, manufacturing location, mint, minted at, photographed at (wd:P1071 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): place where the item was conceived or made; where applicable, location of final assembly, significant place
9. architectural style, also known as architecture, style of architecture (wd:P149 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): architectural style of a structure, movement
10. SSB urban settlement number (wd:P3992 as prn/psn/pq/pr/wdt/prv/psv/pqv/pqn/p/ps/wdtn): Numeric ID for urban settlements in Norway assigned by Statistics Norway (SSB) since 1980

[Assistant]
The property `wdt:P1383` (contains settlement) seems to be the most relevant for identifying the type of settlement. However, it might not directly give us the type of settlement (village, town). Instead, we can use the property `wdt:P31` (instance of) to check if a place is a village or a town.

### Step 4: Identify the Types of Settlements
We will search for the types of settlements (villages and towns) to use in our query.

### Step 5: Build the Initial SPARQL Query
We will start by building a query to find settlements in the Czech Republic within a 35 km radius from the village of Mezno. We will include their coordinates, the coordinates of the center point, and the distance from the center point.

Here is the initial SPARQL query:

```sparql
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>

SELECT ?settlement ?settlementLabel ?settlementType ?settlementCoords ?centerCoords ?distance
WHERE {
  wd:Q2180733 wdt:P625 ?centerCoords .
  ?settlement wdt:P17 wd:Q213 ;
              wdt:P625 ?settlementCoords ;
              wdt:P31 ?settlementType .
  FILTER(?settlementType = wd:Q515 || ?settlementType = wd:Q3957)
  BIND(geof:distance(?settlementCoords, ?centerCoords) AS ?distance)
  FILTER(?distance <= 35)
  OPTIONAL {
    ?settlement rdfs:label ?settlementLabel .
    FILTER(LANG(?settlementLabel) = "en")
  }
}
ORDER BY ASC(?distance)
```

Let's execute this query to see the results.

[Tool]
Got 1 row and 6 columns:
| ?settlement          | ?settlementLabel | ?settlementType | ?settlementCoords          | ?centerCoords              | ?distance |
| -------------------- | ---------------- | --------------- | -------------------------- | -------------------------- | --------- |
| Benešov (wd:Q470394) | Benešov          | town (wd:Q3957) | POINT(14.687021 49.781696) | POINT(14.644975 49.534590) | 27.6431   |

[Assistant]
The initial query returned one settlement, Benešov, which is a town in the Czech Republic within a 35 km radius from the village of Mezno. However, it seems that the query might be missing some settlements. Let's refine the query to ensure we capture all relevant settlements.

### Step 6: Refine the Query
We will expand the query to include more types of settlements and ensure that we are correctly filtering by distance. We will also include the labels for the settlement types for better readability.

Here is the refined SPARQL query:

```sparql
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>

SELECT ?settlement ?settlementLabel ?settlementType ?settlementTypeLabel ?settlementCoords ?centerCoords ?distance
WHERE {
  wd:Q2180733 wdt:P625 ?centerCoords .
  ?settlement wdt:P17 wd:Q213 ;
              wdt:P625 ?settlementCoords ;
              wdt:P31 ?settlementType .
  FILTER(?settlementType IN (wd:Q515, wd:Q3957, wd:Q486972))
  BIND(geof:distance(?settlementCoords, ?centerCoords) AS ?distance)
  FILTER(?distance <= 35)
  OPTIONAL {
    ?settlement rdfs:label ?settlementLabel .
    FILTER(LANG(?settlementLabel) = "en")
  }
  OPTIONAL {
    ?settlementType rdfs:label ?settlementTypeLabel .
    FILTER(LANG(?settlementTypeLabel) = "en")
  }
}
ORDER BY ASC(?distance)
```

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

[Tool]
Got 11 rows and 7 columns, showing the first 5 and last 5 rows below:
| ?settlement                        | ?settlementLabel    | ?settlementType               | ?settlementTypeLabel | ?settlementCoords          | ?centerCoords              | ?distance |
| ---------------------------------- | ------------------- | ----------------------------- | -------------------- | -------------------------- | -------------------------- | --------- |
| Kříženec (wd:Q131151545)           | Kříženec            | human settlement (wd:Q486972) | human settlement     | POINT(14.866409 49.579253) | POINT(14.644975 49.534590) | 16.7266   |
| Bozejny (wd:Q130751574)            | Bozejny             | human settlement (wd:Q486972) | human settlement     | POINT(14.885317 49.570214) | POINT(14.644975 49.534590) | 17.7845   |
| Kobylí (wd:Q131192266)             | Kobylí              | human settlement (wd:Q486972) | human settlement     | POINT(14.706352 49.696714) | POINT(14.644975 49.534590) | 18.5617   |
| Benešov (wd:Q470394)               | Benešov             | town (wd:Q3957)               | town                 | POINT(14.687021 49.781696) | POINT(14.644975 49.534590) | 27.6431   |
| Žlíbek (wd:Q131160197)             | Žlíbek              | human settlement (wd:Q486972) | human settlement     | POINT(14.414076 49.738761) | POINT(14.644975 49.534590) | 28.1408   |
| ...                                | ...                 | ...                           | ...                  | ...                        | ...                        | ...       |
| Svatý Ján u Květova (wd:Q15130889) | Svatý Ján u Květova | human settlement (wd:Q486972) | human settlement     | POINT(14.274167 49.437778) | POINT(14.644975 49.534590) | 28.8679   |
| Vidrholec (wd:Q130336928)          | Vidrholec           | human settlement (wd:Q486972) | human settlement     | POINT(14.535076 49.813469) | POINT(14.644975 49.534590) | 32.0025   |
| wd:Q2607163                        |                     | human settlement (wd:Q486972) | human settlement     | POINT(14.242222 49.400000) | POINT(14.644975 49.534590) | 32.7267   |
| Loutkov (wd:Q1872319)              | Loutkov             | human settlement (wd:Q486972) | human settlement     | POINT(15.114722 49.514722) | POINT(14.644975 49.534590) | 33.9777   |
| Přestavlky (wd:Q130373712)         | Přestavlky          | human settlement (wd:Q486972) | human settlement     | POINT(14.406174 49.808080) | POINT(14.644975 49.534590) | 34.9303   |

[Assistant]
The refined query has returned 11 settlements within a 35 km radius from the village of Mezno in the Czech Republic. The results include the settlement, its label, type, coordinates, the center point coordinates, and the distance from the center point.

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

```sparql
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>

SELECT ?settlement ?settlementLabel ?settlementType ?settlementTypeLabel ?settlementCoords ?centerCoords ?distance
WHERE {
  wd:Q2180733 wdt:P625 ?centerCoords .
  ?settlement wdt:P17 wd:Q213 ;
              wdt:P625 ?settlementCoords ;
              wdt:P31 ?settlementType .
  FILTER(?settlementType IN (wd:Q515, wd:Q3957, wd:Q486972))
  BIND(geof:distance(?settlementCoords, ?centerCoords) AS ?distance)
  FILTER(?distance <= 35)
  OPTIONAL {
    ?settlement rdfs:label ?settlementLabel .
    FILTER(LANG(?settlementLabel) = "en")
  }
  OPTIONAL {
    ?settlementType rdfs:label ?settlementTypeLabel .
    FILTER(LANG(?settlementTypeLabel) = "en")
  }
}
ORDER BY ASC(?distance)
```

The settlements within a 35 km radius from the village of Mezno in the Czech Republic are:

1. **Kříženec** - Human Settlement (16.7266 km)
2. **Bozejny** - Human Settlement (17.7845 km)
3. **Kobylí** - Human Settlement (18.5617 km)
4. **Benešov** - Town (27.6431 km)
5. **Žlíbek** - Human Settlement (28.1408 km)
6. **Svatý Ján u Květova** - Human Settlement (28.8679 km)
7. **Vidrholec** - Human Settlement (32.0025 km)
8. **Unnamed Settlement** - Human Settlement (32.7267 km)
9. **Loutkov** - Human Settlement (33.9777 km)
10. **Přestavlky** - Human Settlement (34.9303 km)

Each settlement's coordinates and the distance from the center point (Mezno) are included in the results.

[Tool] Stopped generation process