PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wikibase: <http://wikiba.se/ontology#>
SELECT ?property ?propertyLabel ?value WHERE {
  wd:Q9696 ?prop ?value .
  ?property wikibase:directClaim ?prop .
  OPTIONAL {
    ?property rdfs:label ?propertyLabel FILTER ( LANG( ?propertyLabel ) = "en" )
  }
}

Got 412 rows and 3 columns, showing the first 5 and last 5 rows below:
| property                                            | propertyLabel                                 | value                                                                                |
| --------------------------------------------------- | --------------------------------------------- | ----------------------------------------------------------------------------------------- |
| video (wd:P10)                                      | video (en)                                    | <http://commons.wikimedia.org/wiki/Special:FilePath/Kennedy%20inauguration%20footage.ogv> |
| George Eastman Museum people ID (wd:P10018)         | George Eastman Museum people ID (en)          | 90819                                                                                |
| Portuguese National Library author ID (wd:P1005)    | Portuguese National Library author ID (en)    | 11414                                                                                |
| Nationale Thesaurus voor Auteursnamen ID (wd:P1006) | Nationale Thesaurus voor Auteursnamen ID (en) | 068645325                                                                                |
| Academy of Athens authority ID (wd:P10141)          | Academy of Athens authority ID (en)           | 40796                                                                                |
| ...                                                 | ...                                           | ...                                                                                |
| Kallías ID (wd:P9918)                               | Kallías ID (en)                               | PE00084182                                                                                |
| Kalliope-Verbund (GND) ID (wd:P9964)                | Kalliope-Verbund (GND) ID (en)                | 118561383                                                                                |
| Musik-Sammler.de artist ID (wd:P9965)               | Musik-Sammler.de artist ID (en)               | 107100                                                                                |
| CANTIC ID (wd:P9984)                                | CANTIC ID (en)                                | 981058523360406706                                                                        |
| MYmovies person ID (wd:P9995)                       | MYmovies person ID (en)                       | 118973                                                                                |

The properties and their values associated with John F. Kennedy (wd:Q9696) include all direct Wikidata properties (such as instance of, occupation, sex or gender, date of birth, image, family name, country of citizenship, place of birth, etc.) and their corresponding values. The full list can be obtained from the query results.

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

[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 with notes:
1. For P39 legislative memberships, filter by pq:P2937 (legislative term) on the p:P39 statement; don’t use P793 for term filtering. For UK MPs, avoid a generic Q16707842-only scan; rely on term qualifiers and proper date overlap on pq:P580/pq:P582.
2. For residence (P551), time bounds are qualifiers on the statement: p:P551/ps:P551 place, pq:P580 start, pq:P582 end. To include containing areas (e.g., NYC boroughs), expand via wdt:P551/wdt:P131*. Durations usually need client-side calc on QLever.
3. Ticker symbols are pq:P249 on p:P414 statements; query via the statement node (?org p:P414 ?st . ?st ps:P414 ?ex ; pq:P249 ?ticker). Use VALUES to pre-limit orgs/exchanges. Don’t expect wdt:P249 on the org. Prefer qualifiers for time-bound tickers.
4. Archives: use p:P485/ps:P485 for the holding institution. Inventory numbers are qualifiers pq:P217 on that P485 statement. Don’t expect wdt:P217 directly on the person/work. “Archive descriptions” may be separate collection items (P195).
5. When listing contained municipalities or similar, prefer wdt:P150* (has parts) from the parent to enumerate children, then join other relations (e.g., twin towns via wdt:P190). Avoid broad wdt:P131+ scans from children; they are slow and can timeout.
6. On QLever, avoid SERVICE wikibase:label (frequent 301). Use OPTIONAL rdfs:label with LANG filters or fetch QIDs then labels separately. For property mapping, (?prop wikibase:directClaim ?p). Pre-limit with subselect/VALUES to avoid timeouts.
7. Employer constraints: to find people employed by UN or international orgs, use wdt:P108 directly for UN (wd:Q1065), or traverse employer to part of (wdt:P361*) UN, or employer wdt:P31 wd:Q484652 (international organization). Combine with other occupations.
8. For sports/events/editions, prefer explicit edition items (e.g., Q47345468) with wdt:P361 linking to the parent series/tour and filter by wdt:P585/P571 for year; avoid P641/P2139 that model sports played or participants, not the edition metadata.
9. For author/creator queries, use occupation wdt:P106 (e.g., author/writer) rather than P31 typing. To list works, prefer the reverse path ^wdt:P50 ?work; P800 (notable work) is incomplete and curated, not a full bibliography.
10. Awards: use p:P166/ps:P166 and constrain award via wdt:P31/wdt:P279* to a target class (e.g., Cannes awards Q28444913). Recipients can be films or people; don’t assume film-specific properties exist. Count/group by recipient type if needed.
11. To select current/active codes or memberships on QLever, use statement nodes (p:/ps:) and MINUS { ?st pq:P582 [] }. For time series counts (e.g., daily members), generate a date series (VALUES/UNNEST), then test overlap with pq:P580/pq:P582 per date.
12. For people’s native names, use wdt:P1559 (name in native language) on the person, not rdfs:label. Return multiple if multiple native languages exist; pick a preferred language client-side if needed.
13. For multi-valued props (e.g., P1082), GROUP BY entity and aggregate (MAX/MIN/SAMPLE) to avoid duplicates; DISTINCT won’t collapse multiple statements. For category/gender counts, prefer indicator sums: SUM(IF(cond,1,0)) over COUNT on OPTIONAL vars.
14. Avoid SERVICE wikibase:label on QLever; it often 301s. Use OPTIONAL rdfs:label with LANG filters, or fetch QIDs first and labels in a follow-up. Pre-limit with subselect/VALUES to avoid timeouts when joining many labels.
15. For P551 duration comparisons, avoid unsupported date arithmetic. If needed, compute overlaps client-side. On-endpoint, you can only compare presence/counts or use simple MIN/MAX of dates (casting and arithmetic are limited in QLever).
16. When excluding specific entities from a relation (e.g., siblings), use FILTER to drop explicit QIDs or FILTER NOT EXISTS with a VALUES list. Ensure you use the exact sibling QIDs returned by the KG, not assumed ones.

General notes for all knowledge graphs:
1. When checking date interval overlaps, handle open intervals (missing start/end) and use xsd:dateTime. Use OR conditions to cover start within, end within, or spanning the whole range.
2. For P39 queries, qualifiers (e.g., P2937 term, P768 electoral district, P4100 parliamentary group) live on the p:P39 statement node. Do not expect them as wdt: properties on the person or use ps: for non-main values.
3. To get sitelink counts, use wikibase:sitelinks on the entity; it returns a single numeric value, so GROUP BY the entity and avoid COUNT over ?sitelink.
4. Use subclass expansion for types: wdt:P31/wdt:P279* target (e.g., dams, educational institutions). This captures items modeled with subclasses.
5. For locations, prefer hierarchies via wdt:P131+ to reach a containing region/state, and wdt:P17 for country. If HQ is a building, P159 ps points to a city; specific site is qualifier pq:P276 on the P159 statement.
6. When excluding or selecting by a legislative term on P39, check pq:P2937 on the relevant statement. Use FILTER NOT EXISTS over p:P39 with ps:P39 target and pq:P2937 value to exclude a term.
7. QLever endpoints may not support Wikidata’s “WITH {…} AS %name” and “INCLUDE %name” syntax. Avoid named subqueries; use nested SELECTs or VALUES instead.
8. The wikibase:label SERVICE often fails on QLever. Prefer rdfs:label with FILTER(LANG(..)="en") or get QIDs only and resolve labels client-side.
9. Avoid COUNT over OPTIONAL variables that may be unbound for all rows; prefer EXISTS/BOUND checks or aggregate over DISTINCT entities to prevent misleading zeros.
10. On QLever, heavy OPTIONAL label/property joins often time out. Pre-limit with a subselect/VALUES over IDs (ORDER BY + LIMIT), then join labels/properties. Or fetch QIDs first and labels in a second, smaller query.
11. For precise date filtering on Wikidata (birth/death), prefer p/psv time nodes and wikibase:timeValue plus timePrecision checks over wdt:P569/P570 to include values with year/month precision and avoid silent drops.
12. When filtering by exact day of birth or event, avoid STRSTARTS on wdt:P569; instead bound an interval [day, next day) with xsd:dateTime to include all precisions. On QLever, add hint:Prior hint:rangeSafe true to improve performance on time ranges.

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
Let's break down the question:

1. The entity is "John F. Kennedy". I need to find his Wikidata identifier.
2. The user wants all properties and their values associated with him. This means I should retrieve all triples where John F. Kennedy is the subject, and return the property and object.

First, I'll search for the entity "John F. Kennedy" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. John F. Kennedy, also known as John Kennedy, John Fitzgerald Kennedy, John F Kennedy, President Kennedy, Kennedy (wd:Q9696): President of the United States from 1961 to 1963, instance of human, 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, instance of pre-commissioning unit, instance of supercarrier
3. MV John F. Kennedy, also known as IMO 8643195, John F. Kennedy (wd:Q29378914): Staten Island Ferry vessel., instance of ferry
4. John F. Kennedy (wd:Q130973914): sculpture by Evangelos Frudakis, instance of visual artwork, instance of sculpture
5. John F. Kennedy, also known as John Flanders Kennedy (wd:Q28836957): American politician and member of the Georgia State Senate, instance of human, politician
6. John F Kennedy (wd:Q22006430): instance of horse, horse
7. John F Kennedy (wd:Q42586306): cultural heritage monument in Aruba, instance of cultural property, instance of building
8. Rosa 'John F. Kennedy', also known as John F. Kennedy (wd:Q83668459): instance of rose cultivar, rose cultivar
9. John F. Kennedy (wd:Q80912685): scientific article published on 01 November 1963, instance of scholarly article
10. John F. Kennedy (wd:Q78526936): instance of version, edition or translation, edition; published in 1997

[Assistant]
The main entity for John F. Kennedy, the U.S. President, is wd:Q9696.

Next, I will retrieve all properties and their values where John F. Kennedy (wd:Q9696) is the subject. This will include both direct (wdt:) and statement (p:) properties to cover both direct values and those with qualifiers/references.

I will start by listing all direct (wdt:) properties and their values for wd:Q9696.

[Tool]
Got 1,679 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)            | naval officer (wd:Q10669499)                                                                                |
| 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 (xsd:dateTime)                                                                                |
| 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) | family name (wdt:P734)           | Kennedy (wd:Q120401)                                                                                |
| John F. Kennedy (wd:Q9696) | country of citizenship (wdt:P27) | United States (wd:Q30)                                                                                |
| John F. Kennedy (wd:Q9696) | place of birth (wdt:P19)         | Brookline (wd:Q49142)                                                                                |
| ...                        | ...                              | ...                                                                                |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?category ?label ( SUM( ?unitCount ) AS ?totalUnits ) WHERE {
  {
    SELECT ( IF( ?unitCount > 499 , STR( ?legalForm ) , "OTHER" ) AS ?category ) ( IF( ?unitCount > 499 , ?frLabel , "AUTRES" @fr ) AS ?label ) ?unitCount WHERE {
      {
        SELECT ?legalForm ( COUNT( ?unit ) AS ?unitCount ) ( SAMPLE( ?labelInner ) AS ?frLabel ) WHERE {
          ?legalForm wdt:P31 wd:Q56427813 .
          ?unit wdt:P1454 ?legalForm ; wdt:P17 wd:Q142 .
          OPTIONAL {
            ?legalForm rdfs:label ?labelInner .
            FILTER ( LANG( ?labelInner ) = "fr" )
          }
        }
        GROUP BY ?legalForm
      }
    }
  }
}
GROUP BY ?category ?label
ORDER BY DESC ( ?totalUnits )

Got 25 rows and 3 columns, showing the first 5 and last 5 rows below:
| category                                  | label                                                                    | totalUnits       |
| ----------------------------------------- | ------------------------------------------------------------------------ | ---------------- |
| http://www.wikidata.org/entity/Q98889979  | société à responsabilité limitée (sans autre indication) (fr)            | 145518 (xsd:int) |
| http://www.wikidata.org/entity/Q112176862 | société par actions simplifiée (fr)                                      | 132557 (xsd:int) |
| http://www.wikidata.org/entity/Q112226256 | commune et commune nouvelle (fr)                                         | 36663 (xsd:int)  |
| http://www.wikidata.org/entity/Q2944984   | centre communal d'action sociale (fr)                                    | 17152 (xsd:int)  |
| http://www.wikidata.org/entity/Q112176662 | société étrangère non immatriculée au RCS (fr)                           | 16573 (xsd:int)  |
| ...                                       | ...                                                                      | ...              |
| http://www.wikidata.org/entity/Q110567707 | SA à directoire (s.a.i.) (fr)                                            | 778 (xsd:int)    |
| http://www.wikidata.org/entity/Q87713520  | service déconcentré de l'état à compétence (inter) départementale (fr)   | 745 (xsd:int)    |
| http://www.wikidata.org/entity/Q112176968 | commune associée et commune déléguée (fr)                                | 639 (xsd:int)    |
| http://www.wikidata.org/entity/Q3118017   | groupement d'intérêt économique (fr)                                     | 571 (xsd:int)    |
| http://www.wikidata.org/entity/Q112176670 | régie d'une collectivité locale ...ractère industriel ou commercial (fr) | 516 (xsd:int)    |

For each level 3 legal category (legal form) in France, the query returns the number of legal units (with the category name in French) where the count is greater than 499. All other categories (with counts of 499 or less) are summed up in a summary line labeled "AUTRES" (OTHER). The results include the Wikidata IRI for each category, the French label, and the total number of legal units.

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.

[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 with notes:
1. For P39 legislative memberships, filter by pq:P2937 (legislative term) on the p:P39 statement; don’t use P793 for term filtering. For UK MPs, avoid a generic Q16707842-only scan; rely on term qualifiers and proper date overlap on pq:P580/pq:P582.
2. For residence (P551), time bounds are qualifiers on the statement: p:P551/ps:P551 place, pq:P580 start, pq:P582 end. To include containing areas (e.g., NYC boroughs), expand via wdt:P551/wdt:P131*. Durations usually need client-side calc on QLever.
3. Ticker symbols are pq:P249 on p:P414 statements; query via the statement node (?org p:P414 ?st . ?st ps:P414 ?ex ; pq:P249 ?ticker). Use VALUES to pre-limit orgs/exchanges. Don’t expect wdt:P249 on the org. Prefer qualifiers for time-bound tickers.
4. Archives: use p:P485/ps:P485 for the holding institution. Inventory numbers are qualifiers pq:P217 on that P485 statement. Don’t expect wdt:P217 directly on the person/work. “Archive descriptions” may be separate collection items (P195).
5. When listing contained municipalities or similar, prefer wdt:P150* (has parts) from the parent to enumerate children, then join other relations (e.g., twin towns via wdt:P190). Avoid broad wdt:P131+ scans from children; they are slow and can timeout.
6. On QLever, avoid SERVICE wikibase:label (frequent 301). Use OPTIONAL rdfs:label with LANG filters or fetch QIDs then labels separately. For property mapping, (?prop wikibase:directClaim ?p). Pre-limit with subselect/VALUES to avoid timeouts.
7. Employer constraints: to find people employed by UN or international orgs, use wdt:P108 directly for UN (wd:Q1065), or traverse employer to part of (wdt:P361*) UN, or employer wdt:P31 wd:Q484652 (international organization). Combine with other occupations.
8. For sports/events/editions, prefer explicit edition items (e.g., Q47345468) with wdt:P361 linking to the parent series/tour and filter by wdt:P585/P571 for year; avoid P641/P2139 that model sports played or participants, not the edition metadata.
9. For author/creator queries, use occupation wdt:P106 (e.g., author/writer) rather than P31 typing. To list works, prefer the reverse path ^wdt:P50 ?work; P800 (notable work) is incomplete and curated, not a full bibliography.
10. Awards: use p:P166/ps:P166 and constrain award via wdt:P31/wdt:P279* to a target class (e.g., Cannes awards Q28444913). Recipients can be films or people; don’t assume film-specific properties exist. Count/group by recipient type if needed.
11. To select current/active codes or memberships on QLever, use statement nodes (p:/ps:) and MINUS { ?st pq:P582 [] }. For time series counts (e.g., daily members), generate a date series (VALUES/UNNEST), then test overlap with pq:P580/pq:P582 per date.
12. For people’s native names, use wdt:P1559 (name in native language) on the person, not rdfs:label. Return multiple if multiple native languages exist; pick a preferred language client-side if needed.
13. For multi-valued props (e.g., P1082), GROUP BY entity and aggregate (MAX/MIN/SAMPLE) to avoid duplicates; DISTINCT won’t collapse multiple statements. For category/gender counts, prefer indicator sums: SUM(IF(cond,1,0)) over COUNT on OPTIONAL vars.
14. Avoid SERVICE wikibase:label on QLever; it often 301s. Use OPTIONAL rdfs:label with LANG filters, or fetch QIDs first and labels in a follow-up. Pre-limit with subselect/VALUES to avoid timeouts when joining many labels.
15. For P551 duration comparisons, avoid unsupported date arithmetic. If needed, compute overlaps client-side. On-endpoint, you can only compare presence/counts or use simple MIN/MAX of dates (casting and arithmetic are limited in QLever).
16. When excluding specific entities from a relation (e.g., siblings), use FILTER to drop explicit QIDs or FILTER NOT EXISTS with a VALUES list. Ensure you use the exact sibling QIDs returned by the KG, not assumed ones.

General notes for all knowledge graphs:
1. When checking date interval overlaps, handle open intervals (missing start/end) and use xsd:dateTime. Use OR conditions to cover start within, end within, or spanning the whole range.
2. For P39 queries, qualifiers (e.g., P2937 term, P768 electoral district, P4100 parliamentary group) live on the p:P39 statement node. Do not expect them as wdt: properties on the person or use ps: for non-main values.
3. To get sitelink counts, use wikibase:sitelinks on the entity; it returns a single numeric value, so GROUP BY the entity and avoid COUNT over ?sitelink.
4. Use subclass expansion for types: wdt:P31/wdt:P279* target (e.g., dams, educational institutions). This captures items modeled with subclasses.
5. For locations, prefer hierarchies via wdt:P131+ to reach a containing region/state, and wdt:P17 for country. If HQ is a building, P159 ps points to a city; specific site is qualifier pq:P276 on the P159 statement.
6. When excluding or selecting by a legislative term on P39, check pq:P2937 on the relevant statement. Use FILTER NOT EXISTS over p:P39 with ps:P39 target and pq:P2937 value to exclude a term.
7. QLever endpoints may not support Wikidata’s “WITH {…} AS %name” and “INCLUDE %name” syntax. Avoid named subqueries; use nested SELECTs or VALUES instead.
8. The wikibase:label SERVICE often fails on QLever. Prefer rdfs:label with FILTER(LANG(..)="en") or get QIDs only and resolve labels client-side.
9. Avoid COUNT over OPTIONAL variables that may be unbound for all rows; prefer EXISTS/BOUND checks or aggregate over DISTINCT entities to prevent misleading zeros.
10. On QLever, heavy OPTIONAL label/property joins often time out. Pre-limit with a subselect/VALUES over IDs (ORDER BY + LIMIT), then join labels/properties. Or fetch QIDs first and labels in a second, smaller query.
11. For precise date filtering on Wikidata (birth/death), prefer p/psv time nodes and wikibase:timeValue plus timePrecision checks over wdt:P569/P570 to include values with year/month precision and avoid silent drops.
12. When filtering by exact day of birth or event, avoid STRSTARTS on wdt:P569; instead bound an interval [day, next day) with xsd:dateTime to include all precisions. On QLever, add hint:Prior hint:rangeSafe true to improve performance on time ranges.

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
Let's break down the question:

1. **Entities and Properties:**
   - "Legal units" in France: We need to identify the entity for "legal unit" and the property that links a legal unit to its legal category.
   - "Level 3 legal category": We need to find the entity for "legal category" and how levels are modeled (likely a subclass or a property indicating the level).
   - "Category name in French": We'll need the French label for each category.
   - "Number of legal units": We need to count legal units per category.
   - "France": Restrict to legal units in France.
   - "OTHER" summary: Sum the counts for categories with 499 or fewer units and label as "OTHER".

2. **Stepwise Plan:**
   - Find the entity for "legal unit" and "legal category" in France.
   - Find the property linking a legal unit to its legal category.
   - Find how "level 3" is represented (property or subclass).
   - Find the property for "country" to restrict to France.
   - Build a query to count legal units per level 3 legal category, get the French label, filter for counts > 499.
   - Add a summary line for "OTHER" (sum of counts for categories with ≤ 499 units).

Let's start by searching for the relevant entities and properties.

---

**Step 1: Search for "legal unit" entity in Wikidata.**

[Tool]
Top 10 entity alternatives:
1. legal unit of measure (wd:Q22282168)
2. United States legal system, also known as court system of the United States, legal system of the United States (wd:Q4445375): instance of judiciary
3. [New legal measure units] (wd:Q66870079): scientific article published on 01 September 1976, instance of scholarly article
4. type of legal entity in the UK, also known as type of business entity in the UK, type of business entity in the United Kingdom, type of business entity (UK), type of business entity (United Kingdom), type of legal entity in the United Kingdom (wd:Q57655433)
5. United Arab Emirates Legal Process (wd:Q18356599)
6. Coronary care units: legal considerations (wd:Q68900361): scientific article published on 01 January 1974, instance of scholarly article
7. Coronary care units: legal considerations (wd:Q39223479): scientific article published on January 1978, instance of scholarly article
8. Legal Affairs Unit Files (NAID 7408582) (wd:Q63912670): series in the National Archives and Records Administration's holdings, instance of collection
9. LEGAL UNITS OF ELECTRIC MEASURE (wd:Q81196971): scientific article published on 01 January 1895, instance of scholarly article
10. United Nations Office of Legal Affairs (wd:Q638357): instance of organization established by the United Nations, United Nations office

[Assistant]
The search for "legal unit" did not directly return an entity for "legal unit" as used in French administrative/statistical context. In France and Eurostat, "legal unit" often refers to a registered legal entity (business, association, etc.), which may be modeled as "legal entity" or "business entity" in Wikidata.

Let's try searching for "legal entity" and "business entity" to find the correct entity.

[Tool]
Top 10 entity alternatives:
1. legal person, also known as legal subject, subject of law, persona iuris, legal personality, legal entity (wd:Q3778211)
2. corporate personhood, also known as Corporation person, Judicial person, Legal entity, corporate juridical person, Artificial person (wd:Q3376073)
3. legal entity (wd:L5236-S3)
4. Legal Entity Identifier, also known as LEI, LEI number, LEI code, Global Legal Entity Identifier (wd:Q6517388): 20-digit, alpha-numeric reference string to uniquely identify a legally distinct entity, instance of organization identifier, instance of unique identifier
5. separate legal entity, also known as SLE (wd:Q7451779)
6. ISO 20275, also known as ELF, entity legal forms (wd:Q106152815): international standard for legal forms of entities, instance of ISO standard, instance of nomenclature
7. International Legal Entity (wd:Q126891722): instance of legal form, legal form in Portugal
8. other legal person, also known as other legal entity, ANNA (wd:Q99528763)
9. school legal entity (wd:Q56447357)
10. Legal Entity Identifier, also known as ISO 17442 code, ISO 17442 ID, Legal Entity ID, LEI, LEI Code (wd:P1278): instance of Wikidata property for an international standard identifier, instance of Wikidata property for an identifier having a checksum, instance of Wikidata property for items about corporations, instance of Wikidata property to identify organizations, identifier for a legally distinct entity per ISO 17442

[Assistant]
The most relevant entity for "legal unit" as a registered entity is likely "legal person" (wd:Q3778211), which is also known as "legal entity." This should cover companies, associations, etc.

Now, let's search for "legal category" or "legal form" to find the entity for the legal classification of these units.

[Tool]
Top 10 entity alternatives:
1. legal form (wd:Q10541491)
2. legal form, also known as legal structure, structured as (wd:P1454): instance of Wikidata property for items about corporations, instance of Wikidata property related to law and justice, legal form of an entity
3. legal formalism (wd:Q1779729)
4. form book, also known as form books, legal formularies, legal forms, formularies, formbooks (wd:Q5469925)
5. Legal Formants (wd:Q3748131)
6. type of legal entity in Germany, also known as type of business entity in Germany, type of legal entity (Germany), type of legal form in Germany, type of business entity (Germany), legal form in Germany (wd:Q19335303)
7. historical business entity type, also known as former business entity, historical legal form, defunct business entity (wd:Q130499319)
8. discontinued legal form (wd:Q123991206)
9. type of legal entity in Namibia, also known as type of business entity in Namibia, type of legal form in Namibia, legal form (Namibia), legal form in Namibia, legal business in Namibia (wd:Q123686137)
10. type of legal entity in Lithuania, also known as legal entity type in Lithuania, legal form in Lithuania, legal form (Lithuania), type of legal form in Lithuania (wd:Q130448283)

[Assistant]
The entity "legal form" (wd:Q10541491) is the general concept for the legal classification of an entity (such as SARL, SA, association, etc.). There is also a property "legal form" (wd:P1454) used to indicate the legal form of an entity.

Now, let's check how "level 3" is represented. In the French legal form classification (NAF, NACE, etc.), levels are often modeled as part of a hierarchy (e.g., NAF level 3). Let's search for "legal form level" or similar to see if there's a property or subclass for this.

[Tool]
Top 10 property alternatives:
1. Levels.fyi company ID, also known as ID of Levels.fyi, Levels.fyi id, levels fyi id, Levels.fyi company identifier (wd:P10146 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): unique identifier of a company listed on the Levels.fyi salary comparison website
2. next higher rank, also known as higher level, next higher level, next (higher), higher rank (wd:P3730 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): higher rank or level in a ranked hierarchy like sport league, mi...fiers. For sports leagues/taxa, use specific properties instead., subproperty of followed by
3. level of description (wd:P6224 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): position of the unit of description in the hierarchy of the fonds
4. sports league level, also known as sport league level, level on sport league pyramid, level on league pyramid, league level, level on pyramid (wd:P3983 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the level of the sport league in the sport league system
5. school class, also known as stage, school cohort, grade, class, cohort (wd:P7174 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): (qualifier) class of a given year/time period to which the person belonged in an educational institution
6. degree of difficulty, also known as level of difficulty, difficulty level, difficulty, difficulty degree (wd:P7252 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): difficulty level of an item
7. next lower rank, also known as lower level, next lower level, next (lower), lower rank (wd:P3729 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): lower rank or level in a ranked hierarchy like sport league, mil...fiers. For sports leagues/taxa, use specific properties instead., subproperty of follows
8. league level above, also known as level above (league), promotion to (wd:P2499 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the league above this sports league, subproperty of next higher rank
9. floors above ground, also known as floor count, levels above ground, number of floors, number of stories, storeys (wd:P1101 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): above ground floor count of a building (ground floor and attic included)
10. league level below, also known as relegation to, level below (league) (wd:P2500 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the league below this sports league, subproperty of next lower rank

[Assistant]
None of the properties found directly relate to a "level" of legal form. In practice, legal form hierarchies (such as NAF/NACE) are often modeled as subclasses (P279) or as part of a classification system.

Let's check if "legal form" (wd:Q10541491) has subclasses for the French system, and if these subclasses are organized by level.

[Tool]
Got 82 rows and 3 columns, showing the first 10 rows below:
| subject                                   | property               | object                    |
| ----------------------------------------- | ---------------------- | ------------------------- |
| consortium (wd:Q194166)                   | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| small and medium enterprises (wd:Q622439) | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| syndicate (wd:Q681615)                    | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| commandite company (wd:Q165758)           | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| family business (wd:Q1395324)             | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| local self-government (wd:Q6621940)       | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| municipality of Finland (wd:Q856076)      | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| financial endowment (wd:Q2620430)         | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| agricultural cooperative (wd:Q2996615)    | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| type of business entity (wd:Q1269299)     | subclass of (wdt:P279) | legal form (wd:Q10541491) |
| ...                                       | ...                    | ...                       |

[Assistant]
There are many subclasses of "legal form" (wd:Q10541491), including "type of business entity" (wd:Q1269299), "commandite company" (wd:Q165758), etc. The French legal forms (e.g., SARL, SA, association loi 1901) are likely among these or their subclasses.

To focus on France, let's check if there are subclasses specifically for French legal forms, or if there's a property indicating the country or jurisdiction for a legal form.

[Tool]
Top 10 property alternatives:
1. Wikidata property, also known as has property, main property, property, property for this item (wd:P1687 as wdt/p): main Wikidata property for this item
2. topic's main category, also known as category for this topic, Wikipedia category, Wikimedia category, Wiki category, subject category (wd:P910 as wdt/p): main Wikimedia category
3. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
4. subclass of, also known as subcategory of, subgenre of, sort of, subtype of, type of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
5. facet of, also known as subject in, topic of, subtopic of, subitem of, section in (wd:P1269 as wdt/p): topic of which this item is an aspect; item that offers a broader perspective on the same topic
6. different from, also known as not to be confused with, to be distinguished from, similar to, see also, rejected match (wd:P1889 as wdt/p): item that is different from another item, with which it may be confused
7. GND ID, also known as GND identifier, Universal Authority File, Integrated Authority File, IndividualisedGnd, GND-IDN (wd:P227 as wdt/p/wdtn): identifier from an international authority file of names, subjects, and organizations
8. exact match, also known as direct match, the same as, skos:exactMatch, same meaning, sameas (wd:P2888 as wdt/p): URL used to link two items or properties, indicating a high degree of confidence that the concepts can be used interchangeably
9. Google Knowledge Graph ID, also known as Google knowledge panel, Google Knowledge Panel ID, kgmid, MREID (wd:P2671 as wdt/p/wdtn): identifier for Google Knowledge Graph API, starting with "/g/". For IDs starting with "/m/", use Freebase ID (P646)

No other items found

[Assistant]
There is no direct "country" property for "legal form." However, many legal forms are jurisdiction-specific and may have "country of origin" or similar properties, or their labels/descriptions may specify "France."

Let's try searching for subclasses of "legal form" with "France" in their label or description to identify French legal forms.

[Tool]
Top 10 entity alternatives:
1. type of legal entity in France, also known as legal form in France, type of legal form in France, type of business entity in France, legal business in France (wd:Q56427813)
2. legal form (wd:Q10541491)
3. legal form, also known as legal structure, structured as (wd:P1454): instance of Wikidata property for items about corporations, instance of Wikidata property related to law and justice, legal form of an entity
4. legal formalism (wd:Q1779729)
5. form book, also known as form books, legal formularies, legal forms, formularies, formbooks (wd:Q5469925)
6. Legal Formants (wd:Q3748131)
7. type of legal entity in Germany, also known as type of business entity in Germany, type of legal entity (Germany), type of legal form in Germany, type of business entity (Germany), legal form in Germany (wd:Q19335303)
8. historical business entity type, also known as former business entity, historical legal form, defunct business entity (wd:Q130499319)
9. discontinued legal form (wd:Q123991206)
10. type of legal entity in Namibia, also known as type of business entity in Namibia, type of legal form in Namibia, legal form (Namibia), legal form in Namibia, legal business in Namibia (wd:Q123686137)

[Assistant]
The entity "type of legal entity in France" (wd:Q56427813) is exactly what we need for French legal forms. This should have subclasses for the various French legal forms (SARL, SA, association, etc.).

Now, let's check its subclasses, which should correspond to the different legal forms in France.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that "type of legal entity in France" (wd:Q56427813) does not have any subclasses directly in Wikidata. However, legal forms like "SARL," "SA," etc., may still be linked to this entity via "instance of" or "legal form" properties.

Let's check which entities are instances of "type of legal entity in France" (wd:Q56427813).

[Tool]
Got 335 rows and 3 columns, showing the first 10 rows below:
| subject                                                        | property              | object                                        |
| -------------------------------------------------------------- | --------------------- | --------------------------------------------- |
| prefecture (wd:Q179831)                                        | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| agglomeration community (wd:Q159321)                           | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| commune community (wd:Q423785)                                 | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| urban community (wd:Q423790)                                   | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| métropole (wd:Q3333855)                                        | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| territorial collectivity of France (wd:Q583865)                | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| société à responsabilité limitée (wd:Q15091621)                | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| public establishment of an administrative nature (wd:Q3244038) | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| établissement public (wd:Q15343039)                            | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| ministry (wd:Q14037025)                                        | instance of (wdt:P31) | type of legal entity in France (wd:Q56427813) |
| ...                                                            | ...                   | ...                                           |

[Assistant]
We have found that entities such as "société à responsabilité limitée" (SARL, wd:Q15091621), "métropole" (wd:Q3333855), etc., are instances of "type of legal entity in France" (wd:Q56427813). These are the legal forms we need.

Now, we need to find the property that links a "legal unit" (legal person, wd:Q3778211) to its legal form. The property "legal form" (wd:P1454) is likely used for this purpose.

Let's confirm this by checking some examples of "legal person" (wd:Q3778211) and their properties.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                                                                  | property               | object                                              |
| ------------------------------------------------------------------------ | ---------------------- | --------------------------------------------------- |
| 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) | region of the Czech Republic (wd:Q38911)            |
| Institute of Geology for the Development of Fossil Fuels (wd:Q113407999) | legal form (wdt:P1454) | 2010 (wd:Q1995)                                     |
| Stuttgart (wd:Q1022)                                                     | legal form (wdt:P1454) | Körperschaft des öffentlichen Rechts (wd:Q15734684) |
| Komeito (wd:Q1061354)                                                    | legal form (wdt:P1454) | political party (wd:Q7278)                          |
| Saudi National Automotive Manufacturing Company (wd:Q110898049)          | legal form (wdt:P1454) | trade (wd:Q601401)                                  |
| Voices from Belarus (wd:Q109910885)                                      | legal form (wdt:P1454) | mass media (wd:Q11033)                              |
| wd:Q109458271                                                            | legal form (wdt:P1454) | software (wd:Q7397)                                 |
| ...                                                                      | ...                    | ...                                                 |

[Assistant]
The property "legal form" (wd:P1454) is indeed used to link entities (organizations, companies, etc.) to their legal form.

Now, to restrict to legal units in France, we should use the property "country" (wdt:P17) with value France (wd:Q142).

Next, we need to count, for each French legal form (instances of wd:Q56427813), the number of legal units (entities with wdt:P1454 pointing to that legal form and wdt:P17 France). We also need the French label for the legal form.

Finally, we need to filter for counts > 499, and sum the rest as "OTHER".

Let's build the SPARQL query step by step.

---

**Step 1: Count the number of legal units per French legal form (with French label), where the legal unit is in France.**

[Tool]
Got 223 rows and 2 columns, showing the first 5 and last 5 rows below:
| legalForm                                                          | unitCount        |
| ------------------------------------------------------------------ | ---------------- |
| limited liability company (with no further details) (wd:Q98889979) | 145518 (xsd:int) |
| wd:Q112176862                                                      | 132557 (xsd:int) |
| commune et commune nouvelle (wd:Q112226256)                        | 36663 (xsd:int)  |
| municipal social welfare centre (wd:Q2944984)                      | 17152 (xsd:int)  |
| Foreign company not registered with the RCS (wd:Q112176662)        | 16573 (xsd:int)  |
| ...                                                                | ...              |
| wd:Q87712827                                                       | 1 (xsd:int)      |
| communities of cities (wd:Q18706237)                               | 1 (xsd:int)      |
| wd:Q98889836                                                       | 1 (xsd:int)      |
| wd:Q112176896                                                      | 1 (xsd:int)      |
| wd:Q112176901                                                      | 1 (xsd:int)      |

[Assistant]
The query successfully returns the number of legal units per French legal form. Some results have QIDs without labels, so we need to add the French label for each legal form.

Next, let's:
- Add the French label for each legal form (OPTIONAL, with FILTER(LANG(?label)="fr")).
- Filter for counts > 499.
- Also, sum the counts for those with ≤ 499 and output as a single line labeled "OTHER".

Let's build the improved query.

[Tool]
Got 223 rows and 3 columns, showing the first 5 and last 5 rows below:
| legalForm                                                          | label                                                                    | unitCount        |
| ------------------------------------------------------------------ | ------------------------------------------------------------------------ | ---------------- |
| limited liability company (with no further details) (wd:Q98889979) | société à responsabilité limitée (sans autre indication) (fr)            | 145518 (xsd:int) |
| wd:Q112176862                                                      | société par actions simplifiée (fr)                                      | 132557 (xsd:int) |
| commune et commune nouvelle (wd:Q112226256)                        | commune et commune nouvelle (fr)                                         | 36663 (xsd:int)  |
| municipal social welfare centre (wd:Q2944984)                      | centre communal d'action sociale (fr)                                    | 17152 (xsd:int)  |
| Foreign company not registered with the RCS (wd:Q112176662)        | société étrangère non immatriculée au RCS (fr)                           | 16573 (xsd:int)  |
| ...                                                                | ...                                                                      | ...              |
| wd:Q98897531                                                       | société de participations financ...l sa à conseil d'administration) (fr) | 1 (xsd:int)      |
| wd:Q98889836                                                       | société de participations financ...ponsabilité limitée (spfpl sarl) (fr) | 1 (xsd:int)      |
| wd:Q112176472                                                      | indivision (fr)                                                          | 1 (xsd:int)      |
| wd:Q87715864                                                       | établissement public des cultes d'alsace-lorraine (fr)                   | 1 (xsd:int)      |
| wd:Q98886456                                                       | caisse de crédit maritime mutuel (fr)                                    | 1 (xsd:int)      |

[Assistant]
The query now returns the French label for each legal form and the count of legal units. Now, to meet the user's requirements:

- Return all legal forms with count > 499.
- Add a summary line labeled "OTHER" for the sum of all legal forms with count ≤ 499.

This requires a UNION query:
1. One part returns legal forms with count > 499.
2. The other part sums the counts for legal forms with count ≤ 499 and outputs "OTHER" as the label.

Let's construct this final query.

[Tool]
Call to function execute returned an error:
failed to parse input: parse error at position 343 for token UNION with content 'UNION' (the input most likely does not follow the grammar)

[Assistant]
QLever does not support UNION in this context. Instead, we can run two subqueries and combine their results using BIND and GROUP BY logic.

Here's the plan:
- In one query, for each legal form, compute the count and the French label.
- Use a CASE-like approach: For forms with count > 499, output their QID and label; for others, output "OTHER" and "AUTRES".
- GROUP BY the output label/QID, and SUM the counts for "OTHER".

Let's implement this logic using IF and GROUP BY.

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

[Assistant]
The error is due to reusing ?label in the SELECT and AS clauses. To fix this, we need to use a different variable for the sampled label in the subquery and only assign ?label in the outer SELECT.

Let's adjust the query accordingly.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: The expression "IF ( ?unitCount > 499 , STR ( ?legalForm ) , "OTHER" )" does not aggregate ?unitCount, ?legalForm. All non-aggregated variables must be part of the GROUP BY clause.

[Assistant]
The error is because QLever requires all non-aggregated variables in the SELECT to be in the GROUP BY clause. To work around this, let's use a subquery to assign the category and label, then aggregate in the outer query.

Here's the revised plan:
1. In the inner subquery, for each legal form, get the count and French label.
2. In the outer query, assign "OTHER"/"AUTRES" to those with count ≤ 499, and group/sum accordingly.

Let's implement this.

[Tool]
Got 25 rows and 3 columns, showing the first 5 and last 5 rows below:
| category                                  | label                                                                    | totalUnits       |
| ----------------------------------------- | ------------------------------------------------------------------------ | ---------------- |
| http://www.wikidata.org/entity/Q98889979  | société à responsabilité limitée (sans autre indication) (fr)            | 145518 (xsd:int) |
| http://www.wikidata.org/entity/Q112176862 | société par actions simplifiée (fr)                                      | 132557 (xsd:int) |
| http://www.wikidata.org/entity/Q112226256 | commune et commune nouvelle (fr)                                         | 36663 (xsd:int)  |
| http://www.wikidata.org/entity/Q2944984   | centre communal d'action sociale (fr)                                    | 17152 (xsd:int)  |
| http://www.wikidata.org/entity/Q112176662 | société étrangère non immatriculée au RCS (fr)                           | 16573 (xsd:int)  |
| ...                                       | ...                                                                      | ...              |
| http://www.wikidata.org/entity/Q110567707 | SA à directoire (s.a.i.) (fr)                                            | 778 (xsd:int)    |
| http://www.wikidata.org/entity/Q87713520  | service déconcentré de l'état à compétence (inter) départementale (fr)   | 745 (xsd:int)    |
| http://www.wikidata.org/entity/Q112176968 | commune associée et commune déléguée (fr)                                | 639 (xsd:int)    |
| http://www.wikidata.org/entity/Q3118017   | groupement d'intérêt économique (fr)                                     | 571 (xsd:int)    |
| http://www.wikidata.org/entity/Q112176670 | régie d'une collectivité locale ...ractère industriel ou commercial (fr) | 516 (xsd:int)    |

[Tool] Stopped generation process


PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?settlement ?settlementCoord ?distance ?centerCoord WHERE {
  VALUES ?type {
    wd:Q532 wd:Q3957
  }
  ?settlement wdt:P31 ?type ; wdt:P17 wd:Q213 ; wdt:P625 ?settlementCoord .
  BIND( "Point(14.644975 49.534590)" ^^ geo:wktLiteral AS ?centerCoord ) BIND( geof:distance ( ?settlementCoord , ?centerCoord ) AS ?distance ) FILTER ( ?distance <= 35 )
}
ORDER BY ?distance
LIMIT 100

The settlements (villages and towns) in the Czech Republic within a 35 km radius from the village of Mezno (center coordinates: POINT(14.644975 49.534590)) include, for example: Dědice, Alenina Lhota, Makov, Všechov, Oltyně, Týnčany, Hostín (Kovářov), Benešov, Smrdov, and Třebějice. For each, the query provides their Wikidata IRI, coordinates, the center point coordinates, and the distance from Mezno in kilometers.

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.

[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 with notes:
1. For P39 legislative memberships, filter by pq:P2937 (legislative term) on the p:P39 statement; don’t use P793 for term filtering. For UK MPs, avoid a generic Q16707842-only scan; rely on term qualifiers and proper date overlap on pq:P580/pq:P582.
2. For residence (P551), time bounds are qualifiers on the statement: p:P551/ps:P551 place, pq:P580 start, pq:P582 end. To include containing areas (e.g., NYC boroughs), expand via wdt:P551/wdt:P131*. Durations usually need client-side calc on QLever.
3. Ticker symbols are pq:P249 on p:P414 statements; query via the statement node (?org p:P414 ?st . ?st ps:P414 ?ex ; pq:P249 ?ticker). Use VALUES to pre-limit orgs/exchanges. Don’t expect wdt:P249 on the org. Prefer qualifiers for time-bound tickers.
4. Archives: use p:P485/ps:P485 for the holding institution. Inventory numbers are qualifiers pq:P217 on that P485 statement. Don’t expect wdt:P217 directly on the person/work. “Archive descriptions” may be separate collection items (P195).
5. When listing contained municipalities or similar, prefer wdt:P150* (has parts) from the parent to enumerate children, then join other relations (e.g., twin towns via wdt:P190). Avoid broad wdt:P131+ scans from children; they are slow and can timeout.
6. On QLever, avoid SERVICE wikibase:label (frequent 301). Use OPTIONAL rdfs:label with LANG filters or fetch QIDs then labels separately. For property mapping, (?prop wikibase:directClaim ?p). Pre-limit with subselect/VALUES to avoid timeouts.
7. Employer constraints: to find people employed by UN or international orgs, use wdt:P108 directly for UN (wd:Q1065), or traverse employer to part of (wdt:P361*) UN, or employer wdt:P31 wd:Q484652 (international organization). Combine with other occupations.
8. For sports/events/editions, prefer explicit edition items (e.g., Q47345468) with wdt:P361 linking to the parent series/tour and filter by wdt:P585/P571 for year; avoid P641/P2139 that model sports played or participants, not the edition metadata.
9. For author/creator queries, use occupation wdt:P106 (e.g., author/writer) rather than P31 typing. To list works, prefer the reverse path ^wdt:P50 ?work; P800 (notable work) is incomplete and curated, not a full bibliography.
10. Awards: use p:P166/ps:P166 and constrain award via wdt:P31/wdt:P279* to a target class (e.g., Cannes awards Q28444913). Recipients can be films or people; don’t assume film-specific properties exist. Count/group by recipient type if needed.
11. To select current/active codes or memberships on QLever, use statement nodes (p:/ps:) and MINUS { ?st pq:P582 [] }. For time series counts (e.g., daily members), generate a date series (VALUES/UNNEST), then test overlap with pq:P580/pq:P582 per date.
12. For people’s native names, use wdt:P1559 (name in native language) on the person, not rdfs:label. Return multiple if multiple native languages exist; pick a preferred language client-side if needed.
13. For multi-valued props (e.g., P1082), GROUP BY entity and aggregate (MAX/MIN/SAMPLE) to avoid duplicates; DISTINCT won’t collapse multiple statements. For category/gender counts, prefer indicator sums: SUM(IF(cond,1,0)) over COUNT on OPTIONAL vars.
14. Avoid SERVICE wikibase:label on QLever; it often 301s. Use OPTIONAL rdfs:label with LANG filters, or fetch QIDs first and labels in a follow-up. Pre-limit with subselect/VALUES to avoid timeouts when joining many labels.
15. For P551 duration comparisons, avoid unsupported date arithmetic. If needed, compute overlaps client-side. On-endpoint, you can only compare presence/counts or use simple MIN/MAX of dates (casting and arithmetic are limited in QLever).
16. When excluding specific entities from a relation (e.g., siblings), use FILTER to drop explicit QIDs or FILTER NOT EXISTS with a VALUES list. Ensure you use the exact sibling QIDs returned by the KG, not assumed ones.

General notes for all knowledge graphs:
1. When checking date interval overlaps, handle open intervals (missing start/end) and use xsd:dateTime. Use OR conditions to cover start within, end within, or spanning the whole range.
2. For P39 queries, qualifiers (e.g., P2937 term, P768 electoral district, P4100 parliamentary group) live on the p:P39 statement node. Do not expect them as wdt: properties on the person or use ps: for non-main values.
3. To get sitelink counts, use wikibase:sitelinks on the entity; it returns a single numeric value, so GROUP BY the entity and avoid COUNT over ?sitelink.
4. Use subclass expansion for types: wdt:P31/wdt:P279* target (e.g., dams, educational institutions). This captures items modeled with subclasses.
5. For locations, prefer hierarchies via wdt:P131+ to reach a containing region/state, and wdt:P17 for country. If HQ is a building, P159 ps points to a city; specific site is qualifier pq:P276 on the P159 statement.
6. When excluding or selecting by a legislative term on P39, check pq:P2937 on the relevant statement. Use FILTER NOT EXISTS over p:P39 with ps:P39 target and pq:P2937 value to exclude a term.
7. QLever endpoints may not support Wikidata’s “WITH {…} AS %name” and “INCLUDE %name” syntax. Avoid named subqueries; use nested SELECTs or VALUES instead.
8. The wikibase:label SERVICE often fails on QLever. Prefer rdfs:label with FILTER(LANG(..)="en") or get QIDs only and resolve labels client-side.
9. Avoid COUNT over OPTIONAL variables that may be unbound for all rows; prefer EXISTS/BOUND checks or aggregate over DISTINCT entities to prevent misleading zeros.
10. On QLever, heavy OPTIONAL label/property joins often time out. Pre-limit with a subselect/VALUES over IDs (ORDER BY + LIMIT), then join labels/properties. Or fetch QIDs first and labels in a second, smaller query.
11. For precise date filtering on Wikidata (birth/death), prefer p/psv time nodes and wikibase:timeValue plus timePrecision checks over wdt:P569/P570 to include values with year/month precision and avoid silent drops.
12. When filtering by exact day of birth or event, avoid STRSTARTS on wdt:P569; instead bound an interval [day, next day) with xsd:dateTime to include all precisions. On QLever, add hint:Prior hint:rangeSafe true to improve performance on time ranges.

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/
qlss: <https://qlever.cs.uni-freiburg.de/spatialSearch/
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. Search for the 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. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
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 just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
3. Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
4. 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.
5. 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.
6. 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.
7. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
8. 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]
Let's break down the question:

1. Identify the entity for the village of Mezno in the Czech Republic.
2. Get its coordinates.
3. Find settlements (villages and towns) in the Czech Republic within a 35 km radius of Mezno.
   - Settlements: subclasses of "human settlement" (Q486972), but specifically "village" (Q532) and "town" (Q3957).
   - Must be located in the Czech Republic (Q213).
4. For each result, include:
   - The settlement's coordinates.
   - The coordinates of Mezno (center point).
   - The distance from Mezno.

Step 1: Search for the entity "Mezno" in Wikidata.

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

[Assistant]
The most relevant entity for the village of Mezno in the Czech Republic is wd:Q2180733.

Next, I will retrieve its coordinates to use as the center point for the radius search.

[Tool]
Got 234 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) (geo:wktLiteral)                                   |
| 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 (xsd:decimal)                                                             |
| 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]
Mezno (wd:Q2180733) has coordinates POINT(14.644975 49.534590).

Now, I need to find all settlements (villages and towns) in the Czech Republic within a 35 km radius of these coordinates. I will:
- Use wdt:P625 for coordinates.
- Filter for entities that are instance of "village" (wd:Q532) or "town" (wd:Q3957).
- Ensure they are located in the Czech Republic (wd:Q213).
- Calculate the distance from Mezno's coordinates and filter for <= 35 km.
- Return each settlement's IRI, coordinates, the center point coordinates, and the distance.

First, I will verify the property for "instance of" and the QIDs for "village" and "town".

[Tool]
Top 10 property alternatives:
1. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
2. Wikidata property example, also known as example of property, Wikidata property sample, property sample, property example, for example (wd:P1855 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): example where this Wikidata property is used; target item is one...ualifier the property being described given the associated value
3. quantity, also known as number of instances, total number, total, qty, occurrences (wd:P1114 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): number of instances of this subject in the universe of the subje...e actual number of instances in Wikidata may be lower or higher)
4. subproperty of, also known as parent property, superproperty, subPropertyOf, subclass of property, rdfs:subPropertyOf (wd:P1647 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): all resources related by this property are also related by that property, subproperty of related property
5. partially coincident with, also known as partially include, overlaps, partially in, partial concurrency, overlaps with (wd:P1382 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): object that partially overlaps with the subject in its instances, parts, or members, subproperty of different from
6. model item, also known as best example, example item, featured item, for example, sample (wd:P5869 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): defines which item is a best practice example of modelling a sub...lue of this property, usage instructions at Wikidata:Model items
7. permanent duplicated item, also known as identical to, same concept as, same as, permanently duplicated item, duplicate of item (wd:P2959 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): this item duplicates another item and the two can't be merged, a... cdowiki, gomwiki, nanwiki).  Use P31=Q17362920 for other wikis.
8. exemplar of, also known as includes work, contents, contains work (wd:P1574 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): Links an individual copy of a work to the item for that work or edition, subproperty of instance of
9. usage example, also known as example sentence, sentence, use in a sentence (wd:P5831 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): example of how a lexeme (sense) might be used
10. fictional or mythical analog of, also known as mythical analog of, fictional version of, fictionalization of, fictionalisation of, analog of (wd:P1074 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): used to link an entity or class of entities appearing in a creat...with the analogous entity or class of entities in the real world, subproperty of inspired by

[Tool]
Top 10 entity alternatives:
1. village, also known as small town (wd:Q532)
2. Edinburgh of the Seven Seas, also known as Village, Tristan Settlement, Tristan Island Settlement, The Village, The Settlement (wd:Q498153): main settlement of the island of Tristan da Cunha, instance of human settlement
3. The Village, also known as Village (wd:Q465227): 2004 film by M. Night Shyamalan, instance of film
4. village in Turkey, also known as köy, village (wd:Q1529096)
5. ri, also known as village (wd:Q483519)
6. village, also known as villages in China (Taiwan), villages in Taiwan, villages in the Republic of China, tsun, tsuns (wd:Q7930614)
7. The Village, also known as Village, Sie fanden eine Heimat (wd:Q3228143): 1953 Swiss drama film directed by Leopold Lindtberg, instance of film
8. Salari jalil, also known as village (wd:Q5798259): instance of village, village in Iran
9. Village (wd:Q2526032): instance of board game, 2011 board game
10. Küçüksöbeçimen, also known as Village (wd:Q6591308): Village in Sarız, Kayseri, Turkey, instance of village in Turkey

[Tool]
Top 10 entity alternatives:
1. town, also known as small city, small town (wd:Q3957)
2. Ipswich Town F.C., also known as Town, The Tractor Boys, The Blues, ITFC, Ipswich Town Football Club (wd:Q9653): association football club in Ipswich, England, instance of association football club
3. Huddersfield Town A.F.C., also known as Town, The Terriers, Huddersfield Town Football Club, Huddersfield Town FC, Huddersfield Town Association Football Club (wd:Q19473): association football club in Huddersfield, England, instance of association football club, instance of professional sports team
4. Luton Town F.C., also known as LTFC, Town, The Hatters, Luton Town Football Club, Luton Town FC (wd:Q18520): association football club in Luton, England, instance of association football club
5. Swindon Town F.C., also known as Town, The Robins, Swindon Town Football Club, Swindon Town FC, Swindon Town (wd:Q19634): association football club in Swindon, England, instance of association football club
6. The Town, also known as Town (wd:Q725539): 2010 film directed by Ben Affleck, instance of film
7. Grimsby Town F.C., also known as Grimsby Town FC, Town, The Mariners, GTFC, Grimsby Town Football Club (wd:Q18515): association football club in Cleethorpes, England, instance of association football club
8. Shrewsbury Town F.C., also known as Shrewsbury Town FC, Town, The Shrews, STFC, Shrewsbury Town Football Club (wd:Q19626): association football club in Shrewsbury, England, instance of association football club
9. Yeovil Town F.C., also known as Yeovil, YTFC, Yeovil Town Football Club, Yeovil Town FC, Yeovil Town (wd:Q19651): association football club in Yeovil, England, instance of association football club
10. Northampton Town F.C., also known as Town, Northampton, The Cobblers, NTFC, Northampton Town Football Club (wd:Q48945): association football club in Northampton, England, instance of association football club

[Tool]
Top 10 entity alternatives:
1. Czech Republic, also known as CZR, CZ, Czechia, CZE (wd:Q213): instance of landlocked country, instance of sovereign state, instance of successor state, country in Central Europe, instance of republic, instance of country
2. Czech Republic (wd:Q3496079): member state of the Czech and Slovak Federal Republic (1990–1992), instance of constituent republic within Czechoslovakia
3. Czech Republic (wd:Q5201764): instance of constituency of the European Parliament, constituency of the European Parliament
4. CZECH REPUBLIC (wd:Q60164570): instance of scholarly article
5. Czech Republic, also known as Star Trek Czech Republic (wd:Q99288382): the country of the Czech Republic as depicted in Star Trek, instance of real country in a fiction work, instance of Star Trek location
6. Czech Republic (wd:Q59760445): scholarly article by LUKAS LINEK & ZDENKA MANSFELDOVA published December 2004 in European Journal of Political Research, instance of scholarly article
7. Czech Republic (wd:Q59212257): scholarly article by Aleš Kudrnáč et al published December 2016 in European Journal of Political Research Political Data Yearbook, instance of scholarly article
8. Czech Republic (wd:Q59212246): scholarly article by IVAN PETÚŠEK & ALEŠ KUDRNÁČ published 5 Jul...n European Journal of Political Research Political Data Yearbook, instance of scholarly article
9. Czech Republic (wd:Q57542866): instance of scholarly article, scholarly article
10. Czech Republic (wd:Q56267408): scholarly article published 2001, instance of scholarly article

[Tool]
Top 10 property alternatives:
1. coordinate location, also known as gps co-ordinates, wgs 84, gps coordinates, gps location, latitude (wd:P625 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
2. this zoological name is coordinate with, also known as coordinate name (wd:P2743 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): links coordinate zoological names, subproperty of different from
3. coordinates of depicted place, also known as depicted item geolocation, geolocation of object, object geolocation (wd:P9149 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): coordinates of the place depicted in the work (or Commons image)
4. location map (wd:P1943 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): blank geographic map image suitable for overlaying coordinate points, as defined at c:Category:Location maps, subproperty of image
5. genomic start, also known as Genloc Start, start coordinate, genomic from, Genloc From, from (wd:P644 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): genomic starting coordinate of the biological sequence (e.g. a gene)
6. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): environment (geographic feature, building type, etc.) the narrative work is set in, subproperty of significant place, subproperty of depicts
7. narrative location, also known as set in location, setting of work, setting, setting location, setting place (wd:P840 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the narrative of the work is set in this location, subproperty of significant place, subproperty of depicts
8. location of discovery, also known as found in, location of discovered object, findspot, place of discovery, site of discovery (wd:P189 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): where the item was located when discovered, subproperty of significant place
9. spatial reference system, also known as coordinate reference system, CRS, SRS (wd:P3037 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): frame of reference used to produce a map
10. coordinates of geographic center, also known as geographic centre, geographic center, geographical midpoint, geographical centre, coordinates of geographic centre (wd:P5140 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): coordinates of the center of an area. Use qualifier "determination method" (P459) to indicate how, subproperty of coordinate location

[Assistant]
I have confirmed the following:
- "instance of" is wd:P31
- "village" is wd:Q532
- "town" is wd:Q3957
- "Czech Republic" is wd:Q213
- "coordinate location" is wd:P625

Now, I will construct a SPARQL query to:
- Select all entities that are instance of village or town
- Located in the Czech Republic
- Have coordinates
- Calculate the distance from Mezno (POINT(14.644975 49.534590))
- Filter for distance <= 35 km
- Return the settlement IRI, its coordinates, Mezno's coordinates, and the distance

I will use the Wikidata QLever endpoint's geof:distance function for the distance calculation.

Here is the query I will execute:

[Tool]
Got 20 rows and 4 columns, showing the first 5 and last 5 rows below:
| settlement                      | settlementCoord                             | distance              | centerCoord                                 |
| ------------------------------- | ------------------------------------------- | --------------------- | ------------------------------------------- |
| Dědice (wd:Q11862916)           | POINT(14.712500 49.513300) (geo:wktLiteral) | 5.41847 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Alenina Lhota (wd:Q10769866)    | POINT(14.521400 49.526400) (geo:wktLiteral) | 8.96484 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Makov (wd:Q12034954)            | POINT(14.548600 49.458900) (geo:wktLiteral) | 10.9215 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Všechov (wd:Q12006033)          | POINT(14.618100 49.431400) (geo:wktLiteral) | 11.6373 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Oltyně (wd:Q12042574)           | POINT(14.519400 49.423100) (geo:wktLiteral) | 15.3622 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| ...                             | ...                                         | ...                   | ...                                         |
| Týnčany (wd:Q12060188)          | POINT(14.331900 49.583100) (geo:wktLiteral) | 23.2169 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Hostín (Kovářov) (wd:Q12020388) | POINT(14.271300 49.531300) (geo:wktLiteral) | 26.9694 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Benešov (wd:Q470394)            | POINT(14.687021 49.781696) (geo:wktLiteral) | 27.6431 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Smrdov (wd:Q12055035)           | POINT(15.036700 49.565300) (geo:wktLiteral) | 28.4652 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |
| Třebějice (wd:Q2259116)         | POINT(14.819800 49.244492) (geo:wktLiteral) | 34.6505 (xsd:decimal) | POINT(14.644975 49.534590) (geo:wktLiteral) |

[Tool] Stopped generation process