PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?property ( COUNT( ?property ) AS ?count ) WHERE {
  wd:Q40231 ?property ?object .
  FILTER ( STRSTARTS( STR( ?property ) , STR( wdt: ) ) && ?property != wdt:P373 && ?property != wdt:P18 && ?property != wdt:P646 && ?property != wdt:P227 && ?property != wdt:P244 && ?property != wdt:P1343 && ?property != wdt:P5008 )
}
GROUP BY ?property
ORDER BY DESC ( ?count )

Using entities:
- public election (wd:Q40231), also known as election, elections: is subclass of voting / is subclass of competition / process by which a population chooses the holder of a public office

Got 71 rows and 2 columns, showing the first 5 and last 5 rows below:
| property                                      | count       |
| --------------------------------------------- | ----------- |
| properties for this type (wdt:P1963)          | 6 (xsd:int) |
| part of (wdt:P361)                            | 3 (xsd:int) |
| Vikidia article ID (wdt:P12800)               | 3 (xsd:int) |
| topic's main template (wdt:P1424)             | 3 (xsd:int) |
| Encyclopedia of Modern Ukraine ID (wdt:P4613) | 2 (xsd:int) |
| ...                                           | ...         |
| pronunciation audio (wdt:P443)                | 1 (xsd:int) |
| UK Parliament thesaurus ID (wdt:P4527)        | 1 (xsd:int) |
| Art & Architecture Thesaurus ID (wdt:P1014)   | 1 (xsd:int) |
| National Library of Korea ID (wdt:P5034)      | 1 (xsd:int) |
| BNCF Thesaurus ID (wdt:P508)                  | 1 (xsd:int) |

Answer:
The properties used in claims related to public elections (wd:Q40231) and their usage counts are:

- wdt:P1963 (properties for this type): 6 times
- wdt:P361 (part of): 3 times
- wdt:P12800 (Vikidia article ID): 3 times
- wdt:P1424 (topic's main template): 3 times
- wdt:P4613 (Encyclopedia of Modern Ukraine ID): 2 times
- 67 other properties used once or twice (e.g., wdt:P443, wdt:P4527, wdt:P1014, etc.)

Note: Metadata properties (like rdfs:label, schema:name, etc.) and statement-level properties (p:Pxxx) have been excluded to focus on direct semantic claims.

The properties used in claims related to public elections (wd:Q40231) and their usage counts are:

- wdt:P1963 (properties for this type): 6 times
- wdt:P361 (part of): 3 times
- wdt:P12800 (Vikidia article ID): 3 times
- wdt:P1424 (topic's main template): 3 times
- wdt:P4613 (Encyclopedia of Modern Ukraine ID): 2 times
- 67 other properties used once or twice (e.g., wdt:P443, wdt:P4527, wdt:P1014, etc.)

Note: Metadata properties (like rdfs:label, schema:name, etc.) and statement-level properties (p:Pxxx) have been excluded to focus on direct semantic claims.

SPARQL query over wikidata:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?property ( COUNT( ?property ) AS ?count ) WHERE {
  wd:Q40231 ?property ?object .
  FILTER ( STRSTARTS( STR( ?property ) , STR( wdt: ) ) && ?property != wdt:P373 && ?property != wdt:P18 && ?property != wdt:P646 && ?property != wdt:P227 && ?property != wdt:P244 && ?property != wdt:P1343 && ?property != wdt:P5008 )
}
GROUP BY ?property
ORDER BY DESC ( ?count )

Using entities:
- public election (wd:Q40231), also known as election, elections: is subclass of voting / is subclass of competition / process by which a population chooses the holder of a public office

Execution result:
Got 71 rows and 2 columns, showing the first 5 and last 5 rows below:
| property                                      | count       |
| --------------------------------------------- | ----------- |
| properties for this type (wdt:P1963)          | 6 (xsd:int) |
| part of (wdt:P361)                            | 3 (xsd:int) |
| Vikidia article ID (wdt:P12800)               | 3 (xsd:int) |
| topic's main template (wdt:P1424)             | 3 (xsd:int) |
| Encyclopedia of Modern Ukraine ID (wdt:P4613) | 2 (xsd:int) |
| ...                                           | ...         |
| pronunciation audio (wdt:P443)                | 1 (xsd:int) |
| UK Parliament thesaurus ID (wdt:P4527)        | 1 (xsd:int) |
| Art & Architecture Thesaurus ID (wdt:P1014)   | 1 (xsd:int) |
| National Library of Korea ID (wdt:P5034)      | 1 (xsd:int) |
| BNCF Thesaurus ID (wdt:P508)                  | 1 (xsd:int) |

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

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.

Available knowledge graphs:
- wikidata at http://localhost:7001 with prefix index for entities and similarity index for properties, and notes:
  - P106 = occupation. Use wdt:P106 to get occupations and fetch English labels with rdfs:label + FILTER(LANG(?label)="en"). When returning human occupations confirm the subject is human (wdt:P31 wd:Q5) to avoid non-human items.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for quick/direct values; for time-series/point-in-time use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect statement qualifiers (e.g., 'applies to part') and prov:wasDerivedFrom references (pr:P854 etc.) to detect city-proper vs metro/prefecture scopes before using values.
  - Common person properties: P31 (instance of human Q5), P106 (occupation), P569 (date of birth), P19 (place of birth), P27 (country of citizenship), P21 (sex/gender), P166 (award received). Use wdt: for direct values when possible.
  - P17 (country) can be incomplete/misleading for places (historical/overseas tags). To get modern cities in a country combine: instance-of city (P31=Q515) + wdt:P17 OR use wdt:P131* to find an admin parent whose wdt:P17 = the country. Also cross-check coordinates (P625) or admin hierarchy to spot outliers.
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Be aware many items have year-only or imprecise dates and coverage is biased toward notable/public figures (sportspeople, politicians, artists).
  - To retrieve qualifiers (e.g., award year) use the statement/qualifier pattern: p:Pxxx / ps:Pxxx / pq:P585. Use wdt:Pxxx for normalized/direct values when you don't need qualifiers. Example: p:P166 + pq:P585 gives award year.
  - To get a person's occupations: use wdt:P106 for direct/normalized values and rdfs:label for human-readable names (FILTER(LANG(...))). Example: wd:Q... wdt:P106 ?occ . ?occ rdfs:label ?occLabel . FILTER(LANG(?occLabel)="en").
  - P166 = award received. Use wdt:P166 for normalized/direct values when you only need award items. To get award years or qualifiers use p:P166 / ps:P166 / pq:P585. Many statements lack pq:P585 — use OPTIONAL { ?stmt pq:P585 ?date } and ORDER BY DESC(?date). When listing award winners, filter ?person wdt:P31 wd:Q5 to ensure humans.
  - Population time-series pattern: wd:Q... p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . ORDER BY DESC(?date). pq:P585 is the point-in-time qualifier. IMPORTANT: time-series may mix scopes (city proper vs metro) — e.g., Tokyo has a 2016 entry ~38,140,000 likely metro vs smaller city figures. Inspect qualifiers/references for scope before use.
  - Use wdt:P131* to traverse the admin hierarchy and compare wdt:P17 on the place vs an admin parent to detect historical/colonial country assignments. When mismatches appear, inspect the admin parent's instance-of (P31), labels, qualifiers and references to interpret the historical scope.
  - wdt:P106 often yields multiple occupations for a person. Use GROUP_CONCAT(DISTINCT ?occLabel; separator=", ") or return multiple rows. Always fetch labels with rdfs:label + FILTER(LANG(?label)="en") to get readable occupation names.
  - When a search_entity returns multiple hits (person vs sculpture vs memorial), confirm you selected the human/statements by checking wdt:P31 (instance of) and relevant wdt: properties (e.g., wdt:P106). This avoids using a non-human QID by mistake.
  - search_autocomplete can be constrained with a SPARQL pattern containing ?search (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }). Use constraints to focus results (e.g., humans) and avoid ambiguous fallbacks; if it returns no results, fallback to search_entity and verify wdt:P31.
  - Use search_property_of_entity to find relevant properties for an entity (e.g., 'award' -> P166) and search_object_of_property to discover candidate objects (e.g., 'physicist' for P106). This reduces guessing P/Q ids and surfaces qualifiers.
  - Inspect statement references and qualifiers: use prov:wasDerivedFrom on p: statements to find sources, and check qualifiers (e.g., 'applies to part' or other scope qualifiers) to disambiguate city vs metro vs admin area. Reference labels can be empty; inspect reference node properties for URLs or publication dates.

Index types used for entities and properties:
- Prefix index: Retrieves items by overlap between query keywords and item words. The query keywords can match item words exactly or as prefixes.
- Similarity index: Retrieves items by cosine similarity between their embeddings and the query embedding.

General notes across knowledge graphs:
- Use rdfs:label + FILTER(LANG(?label)="en") for labels; avoid SERVICE wikibase:label. Use wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access statement qualifiers (e.g., award year via pq:P585). When dates matter, prefer xsd:dateTime and watch for year-only precision.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct/normalized values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (dates via pq:P585). For dates use xsd:dateTime and expect year-only precision. For places: combine P17 with P131 or coordinate (P625) filters to avoid historical/overseas mismatches.
- Use OPTIONAL for pq: qualifiers (e.g., pq:P585), sort by qualifier (ORDER BY pq:P585 DESC) to prefer recent values. Use GROUP_CONCAT to combine multiple labels/values in one row. Watch for typed literals (xsd:dateTime, xsd:decimal) when filtering or sorting.
- Before querying, use search_entity to resolve human-friendly names to wd:Q identifiers and verify instance-of (e.g., check wdt:P31 = wd:Q5 for humans) to avoid ambiguous matches. Prefer the top match but programmatically confirm P31 before using its data.
- Use search_property to confirm property identifiers (e.g., "country" -> P17) before embedding them in queries. This prevents mistakes from assuming an ID and helps pick the best property if multiple choices exist.
- search_autocomplete can fall back to an unconstrained search or error when there are too many candidates. If this happens (e.g., "more than the maximum supported number of subject candidates"), retry with narrower constraints such as instance-of (wdt:P31), additional type filters, or short prefixes to reduce the candidate set; otherwise fallback to search_entity and verify wdt:P31.
- Use list(...) to inspect all triples on an entity for exploration, but expect large results for notable items. Prefer wdt: properties or targeted p:/ps:/pq: patterns in SPARQL when you need normalized values or qualifiers, respectively.
- Before embedding property or item IDs in SPARQL, use search_property, search_property_of_entity, and search_object_of_property to discover the appropriate P/PID or object QIDs. This reduces errors from guessing IDs and surfaces better property alternatives and qualifiers.
- search_constrained and search_autocomplete can return no results for constrained queries or when the index is large. If they fail, fallback to search_entity + verify P31 (or use search_property_of_entity). Constrain with instance-of (wdt:P31) or other properties in the SPARQL to improve matches.
- When selecting awards by year, prefer xsd:dateTime range filters (e.g., >= "2001-01-01T00:00:00Z"^^xsd:dateTime && < "2002-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for portability and precision.
- When selecting award winners via wdt:P166, filter ?person wdt:P31 wd:Q5 to ensure humans — fictional characters or non-humans may have award claims. If you need award years use p:P166 / ps:P166 / pq:P585 and OPTIONAL { ?stmt pq:P585 ?year } to retrieve qualifier years.
- Use wdt:P625 (coordinates) together with wdt:P131* and wdt:P17 checks to validate a place's modern country or to filter by geography. When country labels mismatch historically, inspect admin parents and coordinates to resolve whether the item refers to historical/colonial scope or current boundaries.
- Prefer wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (e.g., pq:P585 for award year). Always fetch labels with rdfs:label + FILTER(LANG(?label) = "en").
- If search_autocomplete returns too many candidates or errors, retry with constraints (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }) or narrower query text. If still failing, fallback to search_entity and verify wdt:P31 for type checks.
- For award-year queries use p:P166 / ps:P166 / pq:P585 to access award statements and year qualifiers. Filter by xsd:dateTime ranges (e.g., >= "2020-01-01T00:00:00Z"^^xsd:dateTime && < "2021-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for robustness.
- Use search_property_of_entity to surface relevant properties for an entity (e.g., 'birth' -> P569/P19). list(...) can inspect triples but may return large results for notable items—prefer targeted wdt: patterns (wdt:Pxxx) or p:/ps:/pq: patterns when you need qualifiers.

SPARQL prefixes for use in function calls:
- bd: <http://www.bigdata.com/rdf#
- cc: <http://creativecommons.org/ns#
- data: <https://www.wikidata.org/wiki/Special:EntityData/
- dc: <http://purl.org/dc/elements/1.1/
- dct: <http://purl.org/dc/terms/
- foaf: <http://xmlns.com/foaf/0.1/
- geo: <http://www.opengis.net/ont/geosparql#
- geof: <http://www.opengis.net/def/function/geosparql/
- gn: <http://www.geonames.org/ontology#
- hint: <http://www.bigdata.com/queryHints#
- 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/
- qb: <http://purl.org/linked-data/cube#
- 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/
- void: <http://rdfs.org/ns/void#
- 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#

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not ask the user for clarifications without first attempting to complete the task. If the task input is incomplete or ambiguous, try to make reasonable assumptions and document them.
- 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.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- 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.
- 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.
- 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.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] Which properties are used in claims related to items that are public elections? Include a count of the number of times it was used.

[Assistant]
To answer the question, I need to find properties that are used in claims related to items that are public elections, along with a count of how many times each property was used.

### Step 1: Identify the entity for "public elections"
First, I need to find the Wikidata item that represents "public elections". I'll use `search_entity` to look for this.

Question: public elections

Top 10 entity alternatives:
1. Department of Public Elections (wd:Q124322778): government department of Tamil Nadu state, India / is instance of government department / is instance of government agency
2. Elections and public opinion (wd:Q57572641): is instance of scholarly article / is instance of academic article
3. Elections and public health (wd:Q73073516): scientific article published on 01 October 2000 / is instance of scholarly article / is instance of academic article / is instance of editorial
4. The Public Administration of Elections (wd:Q57258220): is instance of scholarly article / is instance of academic article / article
5. public election (wd:Q40231), also known as election, elections: process by which a population chooses the holder of a public office / is subclass of competition / is subclass of voting
6. corruption (wd:Q366, matched via public corruption), also known as public corruption: form of dishonesty or criminal offense undertaken by a person ... acquire illicit benefit or abuse power for one's private gain / is subclass of wrongdoing
7. marketing (wd:Q39809, matched via public relations), also known as communications, mktg, PR, promotion, publicity, etc.: activities to gain and keep customers, providing value to them through offerings and communication about offerings / study and process of promoting, distributing, and selling products or services to customers / is subclass of public communication / is instance of academic discipline / is subclass of economic activity / is instance of field of study / is instance of specialism / is instance of discipline / is instance of specialty / is instance of function
8. public domain (wd:Q19652), also known as 🅮, DP, out of copyright, PD: no exclusive intellectual property rights apply / no exclusive intellectual property rights apply / is instance of copyright status / is instance of legal concept / is subclass of public good
9. infrastructure (wd:Q121359, matched via public infrastructure), also known as public infrastructure, social infrastructure: fundamental facilities and systems serving a country, city, or other areas / is instance of type of structure / is subclass of structure / is subclass of Structure
10. hobby (wd:Q47728, matched via public sport), also known as hobbies, passtime, pastime, public sport: regular activity that is done for enjoyment / regular activity that is done for enjoyment / is subclass of recreation

[Assistant]
The most relevant entity for "public elections" is **wd:Q40231**, which is labeled as "public election" and described as "process by which a population chooses the holder of a public office". This matches our intent.

### Step 2: Find claims (statements) related to this item
Now, I need to find all the properties (claims) used in statements about this item (wd:Q40231). I'll use `list` to retrieve all triples where wd:Q40231 is the subject.

list

wd:Q40231

Got 799 rows and 3 columns, showing the first 10 rows below:
| subject                     | property                                       | object                                                                        |
| --------------------------- | ---------------------------------------------- | ----------------------------------------------------------------------------- |
| public election (wd:Q40231) | Commons category (wdt:P373)                    | Elections                                                                     |
| public election (wd:Q40231) | image (wdt:P18)                                | <http://commons.wikimedia.org/wiki/Special:FilePath/Election%20MG%203455.JPG> |
| public election (wd:Q40231) | part of (wdt:P361)                             | politics (wd:Q7163)                                                           |
| public election (wd:Q40231) | subclass of (wdt:P279)                         | voting (wd:Q189760)                                                           |
| public election (wd:Q40231) | Freebase ID (wdt:P646)                         | /m/02l3h                                                                      |
| public election (wd:Q40231) | GND ID (wdtn:P227)                             | <https://d-nb.info/gnd/4064286-0>                                             |
| public election (wd:Q40231) | described by source (wdt:P1343)                | Metropolitan Museum of Art Tagging Vocabulary (wd:Q106727050)                 |
| public election (wd:Q40231) | Library of Congress authority ID (wdtn:P244)   | <http://id.loc.gov/authorities/names/sh85041557>                              |
| public election (wd:Q40231) | on focus list of Wikimedia project (wdt:P5008) | wd:Q6173448                                                                   |
| public election (wd:Q40231) | different from (wdt:P1889)                     | corporate election (wd:Q5172455)                                              |
| ...                         | ...                                            | ...                                                                           |

[Assistant]
I now have a list of properties used in claims related to "public election" (wd:Q40231). The next step is to count how many times each property is used.

### Step 3: Construct a SPARQL query to count property usage
I'll write a SPARQL query that:
- Selects all properties used in statements with subject wd:Q40231
- Counts the number of times each property is used
- Groups by property to get the count

I'll use `execute` to run this query.

execute

SELECT ?property (COUNT(?property) AS ?count) WHERE { wd:Q40231 ?property ?object . } GROUP BY ?property ORDER BY DESC(?count)

Got 183 rows and 2 columns, showing the first 5 and last 5 rows below:
| property                                 | count         |
| ---------------------------------------- | ------------- |
| schema:name                              | 142 (xsd:int) |
| rdfs:label                               | 142 (xsd:int) |
| skos:prefLabel                           | 142 (xsd:int) |
| skos:altLabel                            | 108 (xsd:int) |
| schema:description                       | 45 (xsd:int)  |
| ...                                      | ...           |
| National Library of Korea ID (wdt:P5034) | 1 (xsd:int)   |
| BNCF Thesaurus ID (wdt:P508)             | 1 (xsd:int)   |
| Yahoo Answers category ID (wdt:P5084)    | 1 (xsd:int)   |
| ASC Leiden Thesaurus ID (wdt:P5198)      | 1 (xsd:int)   |
| IAB code (wdt:P5250)                     | 1 (xsd:int)   |

[Assistant]
The query returned the properties used in claims related to "public election" (wd:Q40231) along with their usage counts. However, I notice that many of the top properties (like `schema:name`, `rdfs:label`, `skos:prefLabel`) are metadata labels rather than semantic claims about elections.

The question asks for properties used in "claims related to items that are public elections". In Wikidata, "claims" typically refer to statements with properties that describe real-world attributes (e.g., `wdt:P279` for subclass, `wdt:P361` for part of), not metadata labels.

### Step 4: Refine the query to exclude metadata properties
I'll refine the query to exclude common metadata properties (like `rdfs:label`, `schema:name`, `skos:prefLabel`, etc.) and focus on properties that represent semantic claims about elections.

Common metadata properties to exclude:
- `rdfs:label`
- `schema:name`
- `skos:prefLabel`
- `skos:altLabel`
- `schema:description`
- `wdt:P373` (Commons category)
- `wdt:P18` (image)
- `wdt:P646` (Freebase ID)
- `wdt:P227` (GND ID)
- `wdt:P244` (Library of Congress ID)
- `wdt:P1343` (described by source)
- `wdt:P5008` (on focus list)

I'll write a new SPARQL query that excludes these and counts only meaningful claim properties.

SELECT ?property (COUNT(?property) AS ?count) WHERE { wd:Q40231 ?property ?object . FILTER(?property != rdfs:label && ?property != schema:name && ?property != skos:prefLabel && ?property != skos:altLabel && ?property != schema:description && ?property != wdt:P373 && ?property != wdt:P18 && ?property != wdt:P646 && ?property != wdt:P227 && ?property != wdt:P244 && ?property != wdt:P1343 && ?property != wdt:P5008) } GROUP BY ?property ORDER BY DESC(?count)

Got 171 rows and 2 columns, showing the first 5 and last 5 rows below:
| property                                      | count       |
| --------------------------------------------- | ----------- |
| described by source (p:P1343)                 | 7 (xsd:int) |
| properties for this type (p:P1963)            | 6 (xsd:int) |
| properties for this type (wdt:P1963)          | 6 (xsd:int) |
| part of (wdt:P361)                            | 3 (xsd:int) |
| Vikidia article ID (wdt:P12800)               | 3 (xsd:int) |
| ...                                           | ...         |
| ASC Leiden Thesaurus ID (wdt:P5198)           | 1 (xsd:int) |
| IAB code (wdt:P5250)                          | 1 (xsd:int) |
| IPTC NewsCode (wdt:P5429)                     | 1 (xsd:int) |
| EuroVoc ID (wdt:P5437)                        | 1 (xsd:int) |
| category for eponymous categories (wdt:P6186) | 1 (xsd:int) |

[Assistant]
I notice that some properties are still appearing in the `p:` namespace (statement-level properties) rather than `wdt:` (direct properties). In Wikidata, `p:Pxxx` represents the statement node, while `wdt:Pxxx` is the direct property. Since we're interested in the actual claim properties (not the statement structure), I should use `wdt:Pxxx` properties only.

Also, I see that `described by source (p:P1343)` and `properties for this type (p:P1963)` are appearing in the `p:` namespace. These are not direct properties but statement-level properties. I should filter them out and only consider direct properties (`wdt:Pxxx`).

### Step 5: Final refined query - only direct properties (`wdt:Pxxx`)
I'll modify the query to only include direct properties (`wdt:Pxxx`) and exclude metadata properties.

I'll execute the final query:

SELECT ?property (COUNT(?property) AS ?count) WHERE { wd:Q40231 ?property ?object . FILTER(STRSTARTS(STR(?property), STR(wdt:)) && ?property != wdt:P373 && ?property != wdt:P18 && ?property != wdt:P646 && ?property != wdt:P227 && ?property != wdt:P244 && ?property != wdt:P1343 && ?property != wdt:P5008) } GROUP BY ?property ORDER BY DESC(?count)

Got 71 rows and 2 columns, showing the first 5 and last 5 rows below:
| property                                      | count       |
| --------------------------------------------- | ----------- |
| properties for this type (wdt:P1963)          | 6 (xsd:int) |
| part of (wdt:P361)                            | 3 (xsd:int) |
| Vikidia article ID (wdt:P12800)               | 3 (xsd:int) |
| topic's main template (wdt:P1424)             | 3 (xsd:int) |
| Encyclopedia of Modern Ukraine ID (wdt:P4613) | 2 (xsd:int) |
| ...                                           | ...         |
| pronunciation audio (wdt:P443)                | 1 (xsd:int) |
| UK Parliament thesaurus ID (wdt:P4527)        | 1 (xsd:int) |
| Art & Architecture Thesaurus ID (wdt:P1014)   | 1 (xsd:int) |
| National Library of Korea ID (wdt:P5034)      | 1 (xsd:int) |
| BNCF Thesaurus ID (wdt:P508)                  | 1 (xsd:int) |

[Assistant]
The query now returns only direct properties (`wdt:Pxxx`) used in claims related to "public election" (wd:Q40231), excluding metadata properties. The results show the count of how many times each property is used.

The most frequently used direct properties are:
1. **wdt:P1963** (properties for this type) - 6 times
2. **wdt:P361** (part of) - 3 times
3. **wdt:P12800** (Vikidia article ID), **wdt:P1424** (topic's main template) - 3 times each
4. Several properties with 2 or 1 usage

This is the final answer.

### Final SPARQL Query and Answer:

answer

SELECT ?property (COUNT(?property) AS ?count) WHERE { wd:Q40231 ?property ?object . FILTER(STRSTARTS(STR(?property), STR(wdt:)) && ?property != wdt:P373 && ?property != wdt:P18 && ?property != wdt:P646 && ?property != wdt:P227 && ?property != wdt:P244 && ?property != wdt:P1343 && ?property != wdt:P5008) } GROUP BY ?property ORDER BY DESC(?count)

Answer:
The properties used in claims related to public elections (wd:Q40231) and their usage counts are:

- wdt:P1963 (properties for this type): 6 times
- wdt:P361 (part of): 3 times
- wdt:P12800 (Vikidia article ID): 3 times
- wdt:P1424 (topic's main template): 3 times
- wdt:P4613 (Encyclopedia of Modern Ukraine ID): 2 times
- 67 other properties used once or twice (e.g., wdt:P443, wdt:P4527, wdt:P1014, etc.)

Note: Metadata properties (like rdfs:label, schema:name, etc.) and statement-level properties (p:Pxxx) have been excluded to focus on direct semantic claims.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT ?year ?studentCount WHERE {
  ?org wdt:P6782 "024z2rq82" .
  ?org p:P1753 ?stmt .
  ?stmt ps:P1753 ?studentCount .
  OPTIONAL {
    ?stmt pq:P585 ?year
  }
}
ORDER BY ?year

Using properties:
- list related to category (wd:P1753 as p/ps), also known as associated list with category, category has list, category's list, list equivalent to category, P1753: is subproperty of has list / Wikimedia list equivalent to a Wikimedia category / Wikimedia list equivalent to a Wikimedia category
- point in time (wd:P585 as pq), also known as as of, at time, by date, date, during, etc.: date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241) / time and date something took place, existed or a statement was true / time and date something took place, existed or a statement was true / time and date something took place, existed or a statement was true
- ROR ID (wd:P6782 as wdt), also known as Research Organization Registry ID, ror.org ID, P6782: identifier for the Research Organization Registry / identifier for the Research Organization Registry

Answer: There is no available data in Wikidata about the number of students at the University of Düsseldorf (ROR ID: 024z2rq82) broken down by year. The property for student count (P1753 or P1174) is not used for this institution, and no yearly enrollment statistics are recorded in the knowledge base.

There is no available data in Wikidata about the number of students at the University of Düsseldorf (ROR ID: 024z2rq82) broken down by year. The property for student count (P1753 or P1174) is not used for this institution, and no yearly enrollment statistics are recorded in the knowledge base.

SPARQL query over wikidata:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX p: <http://www.wikidata.org/prop/>
SELECT ?year ?studentCount WHERE {
  ?org wdt:P6782 "024z2rq82" .
  ?org p:P1753 ?stmt .
  ?stmt ps:P1753 ?studentCount .
  OPTIONAL {
    ?stmt pq:P585 ?year
  }
}
ORDER BY ?year

Using properties:
- list related to category (wd:P1753 as p/ps), also known as associated list with category, category has list, category's list, list equivalent to category, P1753: is subproperty of has list / Wikimedia list equivalent to a Wikimedia category / Wikimedia list equivalent to a Wikimedia category
- point in time (wd:P585 as pq), also known as as of, at time, by date, date, during, etc.: date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241) / time and date something took place, existed or a statement was true / time and date something took place, existed or a statement was true / time and date something took place, existed or a statement was true
- ROR ID (wd:P6782 as wdt), also known as Research Organization Registry ID, ror.org ID, P6782: identifier for the Research Organization Registry / identifier for the Research Organization Registry

Execution result:
Got no rows and 2 columns

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

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.

Available knowledge graphs:
- wikidata at http://localhost:7001 with prefix index for entities and similarity index for properties, and notes:
  - P106 = occupation. Use wdt:P106 to get occupations and fetch English labels with rdfs:label + FILTER(LANG(?label)="en"). When returning human occupations confirm the subject is human (wdt:P31 wd:Q5) to avoid non-human items.
  - Population (P1082) and tabular (P4179): use wdt:P1082 for quick/direct values; for time-series/point-in-time use p:P1082 + ps:P1082 + pq:P585 and ORDER BY pq:P585 DESC. Inspect statement qualifiers (e.g., 'applies to part') and prov:wasDerivedFrom references (pr:P854 etc.) to detect city-proper vs metro/prefecture scopes before using values.
  - Common person properties: P31 (instance of human Q5), P106 (occupation), P569 (date of birth), P19 (place of birth), P27 (country of citizenship), P21 (sex/gender), P166 (award received). Use wdt: for direct values when possible.
  - P17 (country) can be incomplete/misleading for places (historical/overseas tags). To get modern cities in a country combine: instance-of city (P31=Q515) + wdt:P17 OR use wdt:P131* to find an admin parent whose wdt:P17 = the country. Also cross-check coordinates (P625) or admin hierarchy to spot outliers.
  - When filtering by date of birth (P569) use xsd:dateTime comparisons (e.g. >= "1900-01-01T00:00:00Z"^^xsd:dateTime). Be aware many items have year-only or imprecise dates and coverage is biased toward notable/public figures (sportspeople, politicians, artists).
  - To retrieve qualifiers (e.g., award year) use the statement/qualifier pattern: p:Pxxx / ps:Pxxx / pq:P585. Use wdt:Pxxx for normalized/direct values when you don't need qualifiers. Example: p:P166 + pq:P585 gives award year.
  - To get a person's occupations: use wdt:P106 for direct/normalized values and rdfs:label for human-readable names (FILTER(LANG(...))). Example: wd:Q... wdt:P106 ?occ . ?occ rdfs:label ?occLabel . FILTER(LANG(?occLabel)="en").
  - P166 = award received. Use wdt:P166 for normalized/direct values when you only need award items. To get award years or qualifiers use p:P166 / ps:P166 / pq:P585. Many statements lack pq:P585 — use OPTIONAL { ?stmt pq:P585 ?date } and ORDER BY DESC(?date). When listing award winners, filter ?person wdt:P31 wd:Q5 to ensure humans.
  - Population time-series pattern: wd:Q... p:P1082 ?stmt . ?stmt ps:P1082 ?pop . OPTIONAL { ?stmt pq:P585 ?date } . ORDER BY DESC(?date). pq:P585 is the point-in-time qualifier. IMPORTANT: time-series may mix scopes (city proper vs metro) — e.g., Tokyo has a 2016 entry ~38,140,000 likely metro vs smaller city figures. Inspect qualifiers/references for scope before use.
  - Use wdt:P131* to traverse the admin hierarchy and compare wdt:P17 on the place vs an admin parent to detect historical/colonial country assignments. When mismatches appear, inspect the admin parent's instance-of (P31), labels, qualifiers and references to interpret the historical scope.
  - wdt:P106 often yields multiple occupations for a person. Use GROUP_CONCAT(DISTINCT ?occLabel; separator=", ") or return multiple rows. Always fetch labels with rdfs:label + FILTER(LANG(?label)="en") to get readable occupation names.
  - When a search_entity returns multiple hits (person vs sculpture vs memorial), confirm you selected the human/statements by checking wdt:P31 (instance of) and relevant wdt: properties (e.g., wdt:P106). This avoids using a non-human QID by mistake.
  - search_autocomplete can be constrained with a SPARQL pattern containing ?search (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }). Use constraints to focus results (e.g., humans) and avoid ambiguous fallbacks; if it returns no results, fallback to search_entity and verify wdt:P31.
  - Use search_property_of_entity to find relevant properties for an entity (e.g., 'award' -> P166) and search_object_of_property to discover candidate objects (e.g., 'physicist' for P106). This reduces guessing P/Q ids and surfaces qualifiers.
  - Inspect statement references and qualifiers: use prov:wasDerivedFrom on p: statements to find sources, and check qualifiers (e.g., 'applies to part' or other scope qualifiers) to disambiguate city vs metro vs admin area. Reference labels can be empty; inspect reference node properties for URLs or publication dates.

Index types used for entities and properties:
- Prefix index: Retrieves items by overlap between query keywords and item words. The query keywords can match item words exactly or as prefixes.
- Similarity index: Retrieves items by cosine similarity between their embeddings and the query embedding.

General notes across knowledge graphs:
- Use rdfs:label + FILTER(LANG(?label)="en") for labels; avoid SERVICE wikibase:label. Use wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access statement qualifiers (e.g., award year via pq:P585). When dates matter, prefer xsd:dateTime and watch for year-only precision.
- Practical query patterns: use rdfs:label + FILTER(LANG(...)) for labels. Use wdt:Pxxx for direct/normalized values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (dates via pq:P585). For dates use xsd:dateTime and expect year-only precision. For places: combine P17 with P131 or coordinate (P625) filters to avoid historical/overseas mismatches.
- Use OPTIONAL for pq: qualifiers (e.g., pq:P585), sort by qualifier (ORDER BY pq:P585 DESC) to prefer recent values. Use GROUP_CONCAT to combine multiple labels/values in one row. Watch for typed literals (xsd:dateTime, xsd:decimal) when filtering or sorting.
- Before querying, use search_entity to resolve human-friendly names to wd:Q identifiers and verify instance-of (e.g., check wdt:P31 = wd:Q5 for humans) to avoid ambiguous matches. Prefer the top match but programmatically confirm P31 before using its data.
- Use search_property to confirm property identifiers (e.g., "country" -> P17) before embedding them in queries. This prevents mistakes from assuming an ID and helps pick the best property if multiple choices exist.
- search_autocomplete can fall back to an unconstrained search or error when there are too many candidates. If this happens (e.g., "more than the maximum supported number of subject candidates"), retry with narrower constraints such as instance-of (wdt:P31), additional type filters, or short prefixes to reduce the candidate set; otherwise fallback to search_entity and verify wdt:P31.
- Use list(...) to inspect all triples on an entity for exploration, but expect large results for notable items. Prefer wdt: properties or targeted p:/ps:/pq: patterns in SPARQL when you need normalized values or qualifiers, respectively.
- Before embedding property or item IDs in SPARQL, use search_property, search_property_of_entity, and search_object_of_property to discover the appropriate P/PID or object QIDs. This reduces errors from guessing IDs and surfaces better property alternatives and qualifiers.
- search_constrained and search_autocomplete can return no results for constrained queries or when the index is large. If they fail, fallback to search_entity + verify P31 (or use search_property_of_entity). Constrain with instance-of (wdt:P31) or other properties in the SPARQL to improve matches.
- When selecting awards by year, prefer xsd:dateTime range filters (e.g., >= "2001-01-01T00:00:00Z"^^xsd:dateTime && < "2002-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for portability and precision.
- When selecting award winners via wdt:P166, filter ?person wdt:P31 wd:Q5 to ensure humans — fictional characters or non-humans may have award claims. If you need award years use p:P166 / ps:P166 / pq:P585 and OPTIONAL { ?stmt pq:P585 ?year } to retrieve qualifier years.
- Use wdt:P625 (coordinates) together with wdt:P131* and wdt:P17 checks to validate a place's modern country or to filter by geography. When country labels mismatch historically, inspect admin parents and coordinates to resolve whether the item refers to historical/colonial scope or current boundaries.
- Prefer wdt:Pxxx for normalized/direct values when qualifiers aren't needed. Use p:Pxxx / ps:Pxxx / pq:Pxxx to access qualifiers (e.g., pq:P585 for award year). Always fetch labels with rdfs:label + FILTER(LANG(?label) = "en").
- If search_autocomplete returns too many candidates or errors, retry with constraints (e.g., SELECT * WHERE { ?search wdt:P31 wd:Q5 }) or narrower query text. If still failing, fallback to search_entity and verify wdt:P31 for type checks.
- For award-year queries use p:P166 / ps:P166 / pq:P585 to access award statements and year qualifiers. Filter by xsd:dateTime ranges (e.g., >= "2020-01-01T00:00:00Z"^^xsd:dateTime && < "2021-01-01T00:00:00Z"^^xsd:dateTime) instead of YEAR() for robustness.
- Use search_property_of_entity to surface relevant properties for an entity (e.g., 'birth' -> P569/P19). list(...) can inspect triples but may return large results for notable items—prefer targeted wdt: patterns (wdt:Pxxx) or p:/ps:/pq: patterns when you need qualifiers.

SPARQL prefixes for use in function calls:
- bd: <http://www.bigdata.com/rdf#
- cc: <http://creativecommons.org/ns#
- data: <https://www.wikidata.org/wiki/Special:EntityData/
- dc: <http://purl.org/dc/elements/1.1/
- dct: <http://purl.org/dc/terms/
- foaf: <http://xmlns.com/foaf/0.1/
- geo: <http://www.opengis.net/ont/geosparql#
- geof: <http://www.opengis.net/def/function/geosparql/
- gn: <http://www.geonames.org/ontology#
- hint: <http://www.bigdata.com/queryHints#
- 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/
- qb: <http://purl.org/linked-data/cube#
- 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/
- void: <http://rdfs.org/ns/void#
- 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#

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not ask the user for clarifications without first attempting to complete the task. If the task input is incomplete or ambiguous, try to make reasonable assumptions and document them.
- 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.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- 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.
- 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.
- 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.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] How many students did the educational organization with the Research Organization Registry (ROR) ID "024z2rq82" have each year? Please include a count for each year (if available), and order results by year.

[Assistant]
To answer this question, I need to find the number of students associated with the educational organization that has the Research Organization Registry (ROR) ID "024z2rq82", grouped by year.

### Step 1: Understand the ROR ID and its relevance
The Research Organization Registry (ROR) is a global, community-led registry of open identifiers for research organizations. The ROR ID "024z2rq82" uniquely identifies an organization. I need to find this organization in Wikidata and determine if there is any property or statement that tracks the number of students over time.

### Step 2: Search for the organization using the ROR ID
First, I will search for the entity in Wikidata using the ROR ID. In Wikidata, ROR IDs are stored under the property "Research Organization Registry ID" (P6782). I will use this property to locate the organization.

I will construct a SPARQL query to find the entity with P6782 = "024z2rq82".

### Step 3: Check if the organization has student count data
Once I identify the organization, I need to check if there is a property that tracks the number of students over time. The most likely property for this is "number of students" (P1753). I will also check for qualifiers like "point in time" (P585) to get yearly data.

### Step 4: Construct the SPARQL query
I will write a SPARQL query to:
1. Find the organization with ROR ID "024z2rq82".
2. Retrieve the number of students (P1753) and the year (P585) for each record.
3. Order the results by year.

Let me execute this query.

execute

SELECT ?year ?studentCount WHERE { ?org wdt:P6782 "024z2rq82" . ?org p:P1753 ?stmt . ?stmt ps:P1753 ?studentCount . OPTIONAL { ?stmt pq:P585 ?year } . FILTER(isLiteral(?year)) . ORDER BY ?year}

Call to function execute returned an error:
failed to parse input: parse error at position 178 for token ORDER with content 'ORDER' (the input most likely does not follow the grammar)

[Assistant]
I see that there was a syntax error in the SPARQL query. The issue is likely due to the use of `FILTER(isLiteral(?year))` and the way the `ORDER BY` clause is structured. Let me fix the query.

### Step 5: Correct the SPARQL query
The problem is that `?year` is expected to be an `xsd:dateTime` value (as per Wikidata's data model for qualifiers like `pq:P585`). I should not use `FILTER(isLiteral(?year))` because `?year` is not a literal in the traditional sense but a typed literal (e.g., `xsd:dateTime`). Instead, I should directly use `ORDER BY ?year` and ensure the query structure is correct.

I will rewrite the query to:
1. Find the organization with ROR ID "024z2rq82".
2. Retrieve the number of students (`ps:P1753`) and the year (`pq:P585`).
3. Order the results by year.

Let me execute the corrected query.

SELECT ?year ?studentCount WHERE { ?org wdt:P6782 "024z2rq82" . ?org p:P1753 ?stmt . ?stmt ps:P1753 ?studentCount . OPTIONAL { ?stmt pq:P585 ?year } . ORDER BY ?year}

Call to function execute returned an error:
failed to parse input: parse error at position 151 for token ORDER with content 'ORDER' (the input most likely does not follow the grammar)

[Assistant]
It seems there is still an issue with the SPARQL query syntax. The problem might be related to how the `OPTIONAL` clause interacts with the `ORDER BY` or the structure of the query. Let me simplify and ensure the query follows the correct SPARQL syntax.

### Step 6: Simplify and verify the query structure
I will:
1. Ensure that the `SELECT` clause includes only the variables I need: `?year` and `?studentCount`.
2. Use `OPTIONAL` for the year (`pq:P585`) because not all statements may have a year qualifier.
3. Use `ORDER BY ?year` at the end, ensuring it is placed correctly after the `WHERE` clause.

I will also check if the ROR ID is correctly matched and if the property `P1753` (number of students) is used for this organization.

Let me execute the corrected and simplified query.

SELECT ?year ?studentCount WHERE { ?org wdt:P6782 "024z2rq82" . ?org p:P1753 ?stmt . ?stmt ps:P1753 ?studentCount . OPTIONAL { ?stmt pq:P585 ?year } } ORDER BY ?year

[Assistant]
The query returned no results, which means that either:
1. The organization with ROR ID "024z2rq82" does not have any "number of students" (P1753) statements in Wikidata, or
2. The ROR ID "024z2rq82" is not linked to any entity in Wikidata.

### Step 7: Verify the existence of the ROR ID in Wikidata
I will first check if the ROR ID "024z2rq82" is associated with any entity in Wikidata by querying for the entity directly using the ROR ID, without filtering for student counts.

I will execute a simple query to find the entity with P6782 = "024z2rq82".

SELECT ?org WHERE { ?org wdt:P6782 "024z2rq82" }

[Assistant]
Great! The ROR ID "024z2rq82" is associated with the **University of Düsseldorf** (wd:Q317032). Now I know the entity I'm working with.

### Step 8: Check what properties are available for this organization
Next, I will investigate whether the University of Düsseldorf has any "number of students" (P1753) statements. I will list the triples for this entity to see if P1753 is used and if there are any qualifiers like P585 (point in time).

I will use the `list` function to inspect the triples for wd:Q317032.

list

wd:Q317032

Got 443 rows and 3 columns, showing the first 10 rows below:
| subject                               | property                                                    | object                                                                                |
| ------------------------------------- | ----------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| University of Düsseldorf (wd:Q317032) | instance of (wdt:P31)                                       | public university (wd:Q875538)                                                                                |
| University of Düsseldorf (wd:Q317032) | country (wdt:P17)                                           | Germany (wd:Q183)                                                                                |
| University of Düsseldorf (wd:Q317032) | located in the administrative territorial entity (wdt:P131) | North Rhine-Westphalia (wd:Q1198)                                                                                |
| University of Düsseldorf (wd:Q317032) | coordinate location (wdt:P625)                              | POINT(6.794169 51.190300) (geo:wktLiteral)                                                                             |
| University of Düsseldorf (wd:Q317032) | Commons category (wdt:P373)                                 | Heinrich-Heine-Universität                                                                                |
| University of Düsseldorf (wd:Q317032) | image (wdt:P18)                                             | <http://commons.wikimedia.org/wiki/Special:FilePath/Universit%C3%A4ts-%20und%20Landesbibliothek%20D%C3%BCsseldorf.jpg> |
| University of Düsseldorf (wd:Q317032) | part of (wdt:P361)                                          | DH.NRW (wd:Q105770388)                                                                                |
| University of Düsseldorf (wd:Q317032) | Freebase ID (wdt:P646)                                      | /m/03zz3_                                                                                |
| University of Düsseldorf (wd:Q317032) | GeoNames ID (wdtn:P1566)                                    | <https://sws.geonames.org/6947470/>                                                                                |
| University of Düsseldorf (wd:Q317032) | inception (wdt:P571)                                        | 1965-11-16T00:00:00Z (xsd:dateTime)                                                                                |
| ...                                   | ...                                                         | ...                                                                                |

[Assistant]
I see that the University of Düsseldorf (wd:Q317032) has many properties, but I don't see `P1753` (number of students) in the first 10 rows. To confirm whether `P1753` is used at all for this entity, I will search specifically for it using the `list` function with the property constraint.

wd:Q317032

[Assistant]
The query returned no results, which means that the **University of Düsseldorf** does not have any direct statements using the property "number of students" (P1753) in Wikidata.

### Step 9: Check for alternative ways to find student counts
Since P1753 is not used, I need to check if there are any other properties or statements that might indirectly provide student count data, such as:
- **P159** (headquarters) — not relevant.
- **P1128** (total enrollment) — this is a more commonly used property for student counts in educational institutions.
- **P1174** (total number of students) — another possible property.

I will check if `P1128` (total enrollment) is used for this university.

wd:Q317032

[Assistant]
I see that `wdt:P1128` is present, but it is labeled as "employees" in the triple, not "total enrollment." This suggests a mismatch in property usage — `P1128` is actually defined in Wikidata as **"employees"**, not "total enrollment." The correct property for "total enrollment" is **P1174**.

Let me check for `P1174` (total number of students).

wd:Q317032

[Assistant]
The property `P1174` (total number of students) is also not used for the University of Düsseldorf.

### Step 10: Check if student count data exists as statements with qualifiers
Even though direct `wdt:P1753`, `wdt:P1128`, or `wdt:P1174` are not present, it's possible that student count data exists as **statements** (using `p:Pxxx`) with qualifiers like `pq:P585` (point in time). I will check for any statements related to enrollment or student counts using `p:P1753` or `p:P1174`.

I will query for any statements (not direct values) using `p:P1753` or `p:P1174` for this university.

execute

SELECT ?stmt ?studentCount ?year WHERE { wd:Q317032 p:P1753 ?stmt . ?stmt ps:P1753 ?studentCount . OPTIONAL { ?stmt pq:P585 ?year } } ORDER BY ?year

[Assistant]
No results were returned for `p:P1753`. Let me now check for `p:P1174` (total number of students) as a statement.

SELECT ?stmt ?studentCount ?year WHERE { wd:Q317032 p:P1174 ?stmt . ?stmt ps:P1174 ?studentCount . OPTIONAL { ?stmt pq:P585 ?year } } ORDER BY ?year

[Assistant]
No results were returned for `p:P1174` either.

### Step 11: Conclusion
After thorough investigation:
- The ROR ID "024z2rq82" correctly identifies the **University of Düsseldorf** (wd:Q317032).
- The property for "number of students" (`P1753`) is **not used** for this institution in Wikidata.
- The property for "total enrollment" (`P1174`) is also **not used**.
- The only numeric property related to people is `P1128` (employees), which gives a single value (5923) but does not provide yearly data.
- No statements with qualifiers (`pq:P585`) for student counts exist in the knowledge base for this university.

### Final Assessment
There is **no available data** in Wikidata about the number of students at the University of Düsseldorf (ROR ID: 024z2rq82) broken down by year. While the institution is well-documented in Wikidata, student enrollment statistics over time are not included.

I will now provide the final answer.

answer

SELECT ?year ?studentCount WHERE { ?org wdt:P6782 "024z2rq82" . ?org p:P1753 ?stmt . ?stmt ps:P1753 ?studentCount . OPTIONAL { ?stmt pq:P585 ?year } } ORDER BY ?year

Answer: There is no available data in Wikidata about the number of students at the University of Düsseldorf (ROR ID: 024z2rq82) broken down by year. The property for student count (P1753 or P1174) is not used for this institution, and no yearly enrollment statistics are recorded in the knowledge base.