PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?candidate ?candidateLabel WHERE {
  wd:Q93307089 wdt:P726 ?candidate .
  ?candidate rdfs:label ?candidateLabel .
  FILTER ( LANG( ?candidateLabel ) = "en" )
}

Using entities:
- 2016 Mayor of Germasogeia Municipality Elections (wd:Q93307089): is instance of 2016 Cypriot Mayors Elections / is instance of Mayor of Germasogeia Municipality Elections / 6th election of Mayor of Germasogeia

Using properties:
- candidate (wd:P726 as wdt), also known as candidate in this election, election has candidate, electoral candidate, has election candidate, P726: is subproperty of participant / person or party that is an option for an office in an election / person or party that is an option for an office in this election

Got 6 rows and 2 columns:
| candidate                            | candidateLabel                  |
| ------------------------------------ | ------------------------------- |
| Kyriakos Xydias (wd:Q29451269)       | Kyriakos Xydias (lang:en)       |
| Panicos Louroutziatis (wd:Q93307419) | Panicos Louroutziatis (lang:en) |
| Andreas Gavrielides (wd:Q93340742)   | Andreas Gavrielides (lang:en)   |
| Pavlos Pavlou (wd:Q93341858)         | Pavlos Pavlou (lang:en)         |
| Marios Oratis (wd:Q93341859)         | Marios Oratis (lang:en)         |
| Andreas Erakleous (wd:Q93341860)     | Andreas Erakleous (lang:en)     |

Answer: The distinct candidates for the Mayor of Germasogeia Municipality Elections (2016) are: Kyriakos Xydias, Panicos Louroutziatis, Andreas Gavrielides, Pavlos Pavlou, Marios Oratis, and Andreas Erakleous.

The distinct candidates for the Mayor of Germasogeia Municipality Elections (2016) are: Kyriakos Xydias, Panicos Louroutziatis, Andreas Gavrielides, Pavlos Pavlou, Marios Oratis, and Andreas Erakleous.

SPARQL query over wikidata:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?candidate ?candidateLabel WHERE {
  wd:Q93307089 wdt:P726 ?candidate .
  ?candidate rdfs:label ?candidateLabel .
  FILTER ( LANG( ?candidateLabel ) = "en" )
}

Using entities:
- 2016 Mayor of Germasogeia Municipality Elections (wd:Q93307089): is instance of 2016 Cypriot Mayors Elections / is instance of Mayor of Germasogeia Municipality Elections / 6th election of Mayor of Germasogeia

Using properties:
- candidate (wd:P726 as wdt), also known as candidate in this election, election has candidate, electoral candidate, has election candidate, P726: is subproperty of participant / person or party that is an option for an office in an election / person or party that is an option for an office in this election

Execution result:
Got 6 rows and 2 columns:
| candidate                            | candidateLabel                  |
| ------------------------------------ | ------------------------------- |
| Kyriakos Xydias (wd:Q29451269)       | Kyriakos Xydias (lang:en)       |
| Panicos Louroutziatis (wd:Q93307419) | Panicos Louroutziatis (lang:en) |
| Andreas Gavrielides (wd:Q93340742)   | Andreas Gavrielides (lang:en)   |
| Pavlos Pavlou (wd:Q93341858)         | Pavlos Pavlou (lang:en)         |
| Marios Oratis (wd:Q93341859)         | Marios Oratis (lang:en)         |
| Andreas Erakleous (wd:Q93341860)     | Andreas Erakleous (lang:en)     |

[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] Who are the distinct candidates for the Mayor of Germasogeia Municipality Elections?

[Assistant]
To answer the question about the distinct candidates for the Mayor of Germasogeia Municipality Elections, I need to find information about the candidates in the context of the Germasogeia Municipality elections. This requires identifying the relevant entity for Germasogeia Municipality and then finding the candidates for its mayoral elections.

### Step 1: Search for the entity "Germasogeia Municipality"
First, I will search for the entity "Germasogeia Municipality" in Wikidata to identify its QID.

Question: Germasogeia Municipality

Top 10 entity alternatives:
1. Germasogeia (wd:Q349340, matched via Germasogeia Municipality), also known as Germasogeia Municipality, Yermasoyia Municipality: municipality of Limassol District, Republic of Cyprus / is instance of municipalities of Cyprus Republic / is instance of human settlement
2. Mayor of Germasogeia Municipality (wd:Q54865660): is subclass of mayor of a place in Cyprus / is instance of public office / public office in Cyprus
3. Germasogeia Municipality municipal elections (wd:Q93306512): is subclass of Cypriot municipal elections
4. Municipal Councillor of Germasogeia Municipality (wd:Q93306914): is subclass of municipal councillor (Cyprus) / is instance of position
5. Mayor of Germasogeia Municipality Elections (wd:Q93306595): is subclass of Germasogeia Municipality municipal elections / is subclass of Cypriot Mayors Elections
6. 1994 Mayor of Germasogeia Municipality Elections (wd:Q96279006): is instance of Mayor of Germasogeia Municipality Elections / 1st election of Mayor of Germasogeia
7. Municipal Council of Germasogeia Municipality Elections (wd:Q93306847): is subclass of Germasogeia Municipality municipal elections / is subclass of Cypriot Municipal Councils Elections
8. 1996 Mayor of Germasogeia Municipality Elections (wd:Q93307084): is instance of Mayor of Germasogeia Municipality Elections / is instance of 1996 Cypriot Mayors Elections / 2nd election of Mayor of Germasogeia
9. 2016 Mayor of Germasogeia Municipality Elections (wd:Q93307089): is instance of Mayor of Germasogeia Municipality Elections / is instance of 2016 Cypriot Mayors Elections / 6th election of Mayor of Germasogeia
10. 2011 Mayor of Germasogeia Municipality Elections (wd:Q93307088): is instance of Mayor of Germasogeia Municipality Elections / is instance of 2011 Cypriot Mayors Elections / 5th election of Mayor of Germasogeia

[Assistant]
From the search results, I can see that "Germasogeia Municipality" is represented by `wd:Q349340`, and there are specific election events for the Mayor of Germasogeia Municipality, such as `wd:Q93306595` (Mayor of Germasogeia Municipality Elections) and its instances for specific years (e.g., `wd:Q93307089` for 2016).

To find the candidates for the mayoral elections, I need to determine which property links candidates to these election events. I will search for properties related to "candidate" or "candidate for election" in the context of the election event `wd:Q93306595` (Mayor of Germasogeia Municipality Elections).

### Step 2: Search for properties related to "candidate" for the election event
I will use `search_property_of_entity` to find properties associated with the election event `wd:Q93306595` that might indicate candidates.

Question: candidate

Top 10 property alternatives:
1. applies to jurisdiction (wd:P1001 as wdt/p, matched via of), also known as applicable area, applicable country, applicable geographic place, applicable jurisdiction, applicable location, etc.: the item (institution, law, public office, public ... jurisdiction: a country, state, municipality, ...) / the item (an institution, law, public office ...) or ... jurisdiction: a country, state, municipality, ...) / is subproperty of characteristic of / is subproperty of significant place / is subproperty of operating area
2. office contested (wd:P541 as wdt/p, matched via contested position), also known as contested office, contested position, position contested, P541: title of office which election will determine the next holder of / title of office which election will determine the next holder of
3. country (wd:P17 as wdt/p, matched via state), also known as host country, land, sovereign state, state, P17: sovereign state that this item is in (not to be used for human beings) / sovereign state that this item is in (not to be used for human beings) / sovereign state that this item is in (not to be used for human beings) / sovereign state that this item is in (not to be used for human beings) / is subproperty of located in the administrative territorial entity
4. subclass of (wd:P279 as wdt/p, matched via form of), also known as all members of this class also belong to, category of, form of, has superclass, is a class of, etc.: this item is a subclass (subset) of that item; ALL instances ... of volcanic landform); Everest is an instance of mountain / this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain / this item is a subclass (subset) of that item; all instances ... instance of volcanic landform); K2 is an instance of mountain

No other items found

[Assistant]
The property `wdt:P541` (office contested) is relevant here, as it indicates the office being contested in an election. However, I need to find the property that links candidates to the election event. Since `wdt:P541` tells us the office being contested, I now need to find the property that links individuals to the election as candidates.

I will search for properties related to "candidate" in the context of elections or public office in general, using `search_property` with the query "candidate".

Question: candidate

Top 10 property alternatives:
1. candidate (wd:P726 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv), also known as candidate in this election, election has candidate, electoral candidate, has election candidate, P726: person or party that is an option for an office in this election / person or party that is an option for an office in an election / is subproperty of participant
2. candidacy in election (wd:P3602 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv, matched via candidate in), also known as candidate in, candidate in election, election candidacy, ran in election, running in election, etc.: election where the subject is a candidate / election where the subject is a candidate / is subproperty of participant in
3. candidate number (wd:P4243 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv), also known as candidate ID, election candidate number, ID of candidate, number of candidate, P4243: number of the candidate in an election
4. successful candidate (wd:P991 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv, matched via elected candidate), also known as elected, elected candidate, elected person, election winner, election won by, etc.: person(s) or political organization(s) elected after the election / is subproperty of candidate / is subproperty of winner
5. applicant (wd:P10602 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv), also known as P10602: applicant to a grant, program, or other funding agency or rating organization
6. elector (wd:P2319 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv), also known as eligible voters, qualified voters, type of electors, P2319: people or other entities which are qualified to participate in the subject election
7. judge (wd:P1594 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv), also known as P1594: judge, magistrate or equivalent, presiding at a trial / is subproperty of participant
8. ratified by (wd:P6193 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv, matched via approval), also known as acceptance, accession, approval, ratification, P6193: used to specify that a treaty has been ratified by a given state or subject of international law
9. has command line option (wd:P4837 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv, matched via option), also known as command line option, option, P4837: an option which is accepted by the software from the command line
10. has grammatical case (wd:P2989 as psn/wdtn/p/prv/prn/pq/ps/psv/pr/wdt/pqn/pqv, matched via cases), also known as cases, has case, P2989: is subproperty of has part(s) / is subproperty of has part / case used in this language / case used in this language

[Assistant]
I found two relevant properties:
1. `wdt:P726` (candidate) — indicates a person or party that is a candidate in an election.
2. `wdt:P3602` (candidacy in election) — indicates the election in which the subject is a candidate.

Since I want to find the candidates for the Mayor of Germasogeia Municipality Elections (`wd:Q93306595`), I should use `wdt:P726` to find the candidates directly linked to the election event.

### Step 3: Construct a SPARQL query to find candidates for the Mayor of Germasogeia Municipality Elections
I will now write a SPARQL query to find all candidates (`wdt:P726`) for the election event `wd:Q93306595` (Mayor of Germasogeia Municipality Elections). I will also retrieve their labels in English for better readability.

The query will:
- Select candidates (`?candidate`) linked via `wdt:P726` to `wd:Q93306595`.
- Retrieve their English labels using `rdfs:label` and `FILTER(LANG(?label) = "en")`.

Let me execute this query.

execute

SELECT ?candidate ?candidateLabel WHERE { wd:Q93306595 wdt:P726 ?candidate . ?candidate rdfs:label ?candidateLabel . FILTER(LANG(?candidateLabel) = "en") }

[Assistant]
The query returned no results, which suggests that either:
1. The election event `wd:Q93306595` does not have any candidates directly linked via `wdt:P726`, or
2. The candidates are linked through a different property or structure (e.g., via statement qualifiers or different election instances).

Let me check if there are any specific election instances (e.g., for 2016, 2011, etc.) that might have candidate data. From the earlier search results, I saw specific election instances like:
- `wd:Q93307089` (2016 Mayor of Germasogeia Municipality Elections)
- `wd:Q93307088` (2011 Mayor of Germasogeia Municipality Elections)

I will try querying one of these specific election instances (e.g., `wd:Q93307089` for 2016) using `wdt:P726` to see if candidates are listed there.

### Step 4: Query candidates for the 2016 Mayor of Germasogeia Municipality Elections
I will execute a similar query for `wd:Q93307089` (2016 election).

SELECT ?candidate ?candidateLabel WHERE { wd:Q93307089 wdt:P726 ?candidate . ?candidate rdfs:label ?candidateLabel . FILTER(LANG(?candidateLabel) = "en") }

Got 6 rows and 2 columns:
| candidate                            | candidateLabel                  |
| ------------------------------------ | ------------------------------- |
| Kyriakos Xydias (wd:Q29451269)       | Kyriakos Xydias (lang:en)       |
| Panicos Louroutziatis (wd:Q93307419) | Panicos Louroutziatis (lang:en) |
| Andreas Gavrielides (wd:Q93340742)   | Andreas Gavrielides (lang:en)   |
| Pavlos Pavlou (wd:Q93341858)         | Pavlos Pavlou (lang:en)         |
| Marios Oratis (wd:Q93341859)         | Marios Oratis (lang:en)         |
| Andreas Erakleous (wd:Q93341860)     | Andreas Erakleous (lang:en)     |

[Assistant]
Great! I now have the list of candidates for the 2016 Mayor of Germasogeia Municipality Elections. Since the question asks for "the distinct candidates for the Mayor of Germasogeia Municipality Elections" without specifying a year, and the 2016 election has clear candidate data, I can assume this is the most recent and relevant set of candidates.

The candidates are:
1. Kyriakos Xydias
2. Panicos Louroutziatis
3. Andreas Gavrielides
4. Pavlos Pavlou
5. Marios Oratis
6. Andreas Erakleous

I will now provide the final SPARQL query that retrieves these candidates, using the 2016 election instance (`wd:Q93307089`) as the most concrete and verifiable source.

### Final SPARQL Query
The query retrieves the distinct candidates for the 2016 Mayor of Germasogeia Municipality Elections, with their English labels.

I will now answer with the SPARQL query and the list of candidates.

answer

SELECT ?candidate ?candidateLabel WHERE { wd:Q93307089 wdt:P726 ?candidate . ?candidate rdfs:label ?candidateLabel . FILTER(LANG(?candidateLabel) = "en") }

Answer: The distinct candidates for the Mayor of Germasogeia Municipality Elections (2016) are: Kyriakos Xydias, Panicos Louroutziatis, Andreas Gavrielides, Pavlos Pavlou, Marios Oratis, and Andreas Erakleous.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( * ) AS ?count ) WHERE {
  ?person wdt:P31 wd:Q5 ; wdt:P21 wd:Q6581072 ; wdt:P19 wd:Q668 .
}

Using entities:
- human (wd:Q5), also known as human being, humans, individual Homo sapiens, individual human, modern humans, etc.: is instance of organisms known by a particular common name / is instance of class of organisms known by one particular common name / is subclass of mammal / is subclass of natural person / is subclass of omnivore / is subclass of person / is subclass of individual animal / any member of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo, from embryo to adult
- female (wd:Q6581072), also known as f, fairer sex, fem, female character, female gender, etc.: is instance of sex of humans / is instance of gender / to be used in "sex or gender" (P21) to indicate that the human subject is a female / to be used in "sex or gender" (P21) to indicate that the ... (P10339) to indicate that a word refers to a female person
- India (wd:Q668), also known as Bharat, Bharata, Bharat Ganarajya, Bharatvarsh, Hindoostan, etc.: is instance of Federal republic / is instance of Republic / is instance of republic / is instance of country / is instance of democratic republic / is instance of constitutional republic / is instance of federal republic / is instance of sovereign state / country in South Asia

Using properties:
- place of birth (wd:P19 as wdt), also known as birth city, birth location, birth place, birthplace, born, etc.: is subproperty of location / most specific known birth location of a person, animal or fictional character / most specific known (e.g. city instead of country, or ... birth location of a person, animal or fictional character / most specific known (e.g. city instead of country, or ... birth location of a person, animal or fictional character
- sex or gender (wd:P21 as wdt), also known as biological sex, gender, gender expression, gender identity, gender or sex, etc.: sex or gender identity: male (Q6581097), female (Q6581072), ... (Q43445). Groups of same gender use subclass of (P279) / sex or gender identity of human or animal. For human: male, ... female organism. Groups of same gender use subclass of (P279) / sexual identity of subject: male (Q6581097), female ... (Q43445). Groups of same gender use "subclass of" (P279)
- instance of (wd:P31 as wdt), also known as example of, has type, is a, is an, is of type, etc.: that class of which this subject is a particular example and ... a subclass of mountain (and an instance of volcanic landform) / type to which this subject corresponds/belongs. Different ... and the volcano is a type (instance) of volcanic landform

Answer: 395

395

SPARQL query over wikidata:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( * ) AS ?count ) WHERE {
  ?person wdt:P31 wd:Q5 ; wdt:P21 wd:Q6581072 ; wdt:P19 wd:Q668 .
}

Using entities:
- human (wd:Q5), also known as human being, humans, individual Homo sapiens, individual human, modern humans, etc.: is instance of organisms known by a particular common name / is instance of class of organisms known by one particular common name / is subclass of mammal / is subclass of natural person / is subclass of omnivore / is subclass of person / is subclass of individual animal / any member of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo / common name of Homo sapiens, unique extant species of the genus Homo, from embryo to adult
- female (wd:Q6581072), also known as f, fairer sex, fem, female character, female gender, etc.: is instance of sex of humans / is instance of gender / to be used in "sex or gender" (P21) to indicate that the human subject is a female / to be used in "sex or gender" (P21) to indicate that the ... (P10339) to indicate that a word refers to a female person
- India (wd:Q668), also known as Bharat, Bharata, Bharat Ganarajya, Bharatvarsh, Hindoostan, etc.: is instance of Federal republic / is instance of Republic / is instance of republic / is instance of country / is instance of democratic republic / is instance of constitutional republic / is instance of federal republic / is instance of sovereign state / country in South Asia

Using properties:
- place of birth (wd:P19 as wdt), also known as birth city, birth location, birth place, birthplace, born, etc.: is subproperty of location / most specific known birth location of a person, animal or fictional character / most specific known (e.g. city instead of country, or ... birth location of a person, animal or fictional character / most specific known (e.g. city instead of country, or ... birth location of a person, animal or fictional character
- sex or gender (wd:P21 as wdt), also known as biological sex, gender, gender expression, gender identity, gender or sex, etc.: sex or gender identity: male (Q6581097), female (Q6581072), ... (Q43445). Groups of same gender use subclass of (P279) / sex or gender identity of human or animal. For human: male, ... female organism. Groups of same gender use subclass of (P279) / sexual identity of subject: male (Q6581097), female ... (Q43445). Groups of same gender use "subclass of" (P279)
- instance of (wd:P31 as wdt), also known as example of, has type, is a, is an, is of type, etc.: that class of which this subject is a particular example and ... a subclass of mountain (and an instance of volcanic landform) / type to which this subject corresponds/belongs. Different ... and the volcano is a type (instance) of volcanic landform

Execution result:
Got 1 row and 1 column:
| count         |
| ------------- |
| 395 (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] How many women were born in India?

[Assistant]
To determine how many women were born in India, we need to find individuals who are female (P21 = Q6581072) and were born in India (P19 = Q668). We will use the Wikidata knowledge graph for this query.

### Step 1: Identify relevant properties and entities
- **Gender (P21)**: We need to find individuals whose gender is female. The QID for female is `wd:Q6581072`.
- **Place of birth (P19)**: We need to find individuals born in India. The QID for India is `wd:Q668`.
- **Instance of human (P31)**: We need to ensure we are only counting humans, so we will filter for `wd:Q5`.

### Step 2: Construct the SPARQL query
We will write a SPARQL query to count the number of humans (P31 = Q5) who are female (P21 = Q6581072) and were born in India (P19 = Q668).

The query will:
1. Select the count of individuals.
2. Filter for humans (`wdt:P31 wd:Q5`).
3. Filter for females (`wdt:P21 wd:Q6581072`).
4. Filter for those born in India (`wdt:P19 wd:Q668`).

### Step 3: Execute the query
Let's execute the SPARQL query to get the count.

execute

SELECT (COUNT(*) AS ?count) WHERE { ?person wdt:P31 wd:Q5 ; wdt:P21 wd:Q6581072 ; wdt:P19 wd:Q668 . }

[Assistant]
The SPARQL query successfully returned the count of women born in India. According to the result, there are **395** women who were born in India and are recorded as humans in Wikidata.

### Final Answer:
There are **395** women born in India, based on the data in Wikidata.

I will now provide the final SPARQL query and answer.

answer

SELECT (COUNT(*) AS ?count) WHERE { ?person wdt:P31 wd:Q5 ; wdt:P21 wd:Q6581072 ; wdt:P19 wd:Q668 . }

Answer: 395