PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?item ?population ?area WHERE {
  VALUES ?item {
    wd:Q1781 wd:Q43296 wd:Q183
  }
  .
  {
    SELECT ?item ( MAX( ?popDate ) AS ?maxPopDate ) WHERE {
      ?item p:P1082 ?popStmt .
      ?popStmt ps:P1082 ?population ; pq:P585 ?popDate .
    }
    GROUP BY ?item
  }
  .
  ?item p:P1082 ?popStmt .
  ?popStmt ps:P1082 ?population ; pq:P585 ?popDate .
  FILTER ( ?popDate = ?maxPopDate ) .
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ; pq:P518 ?part .
    FILTER ( ?part != wd:Q187223 )
  }
  .
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 .
      ?areaStmt2 pq:P518 wd:Q187223
    }
  }
}
ORDER BY ?item

Using entities:
- Budapest (wd:Q1781), also known as Alt-Ofen, Buda, Buda Pest, Buda-Pest, Budapešť, etc.: is instance of town in Hungary / is instance of largest city / is instance of enclave / is instance of national capital / capital and largest city of Hungary
- Germany (wd:Q183), also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany: is instance of sovereign state / is instance of country / country in central Europe / country in central Europe / country in Central Europe / country in Central Europe
- lagoon (wd:Q187223), also known as albufera, haff, liman: is subclass of lake / is subclass of bay / shallow body of water / shallow body of water / shallow body of water separated from larger body of water by a barrier
- Wake Island (wd:Q43296), also known as Halcyon Island, Isla San Francisco, Isle de Saint Francois, Isle de St Francois, Isle of Saint Francis, etc.: is instance of unincorporated territory of the United States / is instance of Unincorporated territories of the United States / is instance of administrative territorial entity / is instance of insular area of the United States / is instance of atoll / coral atoll in the North Pacific Ocean administered by the US and also claimed by the Marshall Islands

Using properties:
- population (wd:P1082 as p/ps), also known as human population, inhabitants, P1082: number of people inhabiting the place; number of people of subject / number of people inhabiting the place; number of people of subject / number of people inhabiting the place; number of people of subject
- area (wd:P2046 as p/ps/wdt), also known as acreage, camera resolution, die area, die size, image resolution, etc.: area occupied by an object / area occupied by an object
- applies to part (wd:P518 as pq), also known as applied to, applies to aspect, applies to form, applies to phase, apply to, etc.: part, aspect, or form of the item to which the claim applies / part of the item for which the claim is valid / part of the item for which the claim is valid
- 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

Answer:
The item IDs, populations, and land areas (excluding lagoon measurements) are:
- Budapest (wd:Q1781): Population = 1,686,222; Land area = 52,514 km²
- Germany (wd:Q183): Population = 84,358,845; Land area = 349,390 km²
- Wake Island (wd:Q43296): Population = 100; Land area = 7.1 km²

The item IDs, populations, and land areas (excluding lagoon measurements) are:
- Budapest (wd:Q1781): Population = 1,686,222; Land area = 52,514 km²
- Germany (wd:Q183): Population = 84,358,845; Land area = 349,390 km²
- Wake Island (wd:Q43296): Population = 100; Land area = 7.1 km²

SPARQL query over wikidata:
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?item ?population ?area WHERE {
  VALUES ?item {
    wd:Q1781 wd:Q43296 wd:Q183
  }
  .
  {
    SELECT ?item ( MAX( ?popDate ) AS ?maxPopDate ) WHERE {
      ?item p:P1082 ?popStmt .
      ?popStmt ps:P1082 ?population ; pq:P585 ?popDate .
    }
    GROUP BY ?item
  }
  .
  ?item p:P1082 ?popStmt .
  ?popStmt ps:P1082 ?population ; pq:P585 ?popDate .
  FILTER ( ?popDate = ?maxPopDate ) .
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ; pq:P518 ?part .
    FILTER ( ?part != wd:Q187223 )
  }
  .
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 .
      ?areaStmt2 pq:P518 wd:Q187223
    }
  }
}
ORDER BY ?item

Using entities:
- Budapest (wd:Q1781), also known as Alt-Ofen, Buda, Buda Pest, Buda-Pest, Budapešť, etc.: is instance of town in Hungary / is instance of largest city / is instance of enclave / is instance of national capital / capital and largest city of Hungary
- Germany (wd:Q183), also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany: is instance of sovereign state / is instance of country / country in central Europe / country in central Europe / country in Central Europe / country in Central Europe
- lagoon (wd:Q187223), also known as albufera, haff, liman: is subclass of lake / is subclass of bay / shallow body of water / shallow body of water / shallow body of water separated from larger body of water by a barrier
- Wake Island (wd:Q43296), also known as Halcyon Island, Isla San Francisco, Isle de Saint Francois, Isle de St Francois, Isle of Saint Francis, etc.: is instance of unincorporated territory of the United States / is instance of Unincorporated territories of the United States / is instance of administrative territorial entity / is instance of insular area of the United States / is instance of atoll / coral atoll in the North Pacific Ocean administered by the US and also claimed by the Marshall Islands

Using properties:
- population (wd:P1082 as p/ps), also known as human population, inhabitants, P1082: number of people inhabiting the place; number of people of subject / number of people inhabiting the place; number of people of subject / number of people inhabiting the place; number of people of subject
- area (wd:P2046 as p/ps/wdt), also known as acreage, camera resolution, die area, die size, image resolution, etc.: area occupied by an object / area occupied by an object
- applies to part (wd:P518 as pq), also known as applied to, applies to aspect, applies to form, applies to phase, apply to, etc.: part, aspect, or form of the item to which the claim applies / part of the item for which the claim is valid / part of the item for which the claim is valid
- 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

Execution result:
Got 3 rows and 3 columns:
| item                    | population             | area                 |
| ----------------------- | ---------------------- | -------------------- |
| Budapest (wd:Q1781)     | 1686222 (xsd:decimal)  | 52514 (xsd:decimal)  |
| Germany (wd:Q183)       | 84358845 (xsd:decimal) | 349390 (xsd:decimal) |
| Wake Island (wd:Q43296) | 100 (xsd:decimal)      | 7.1 (xsd:decimal)    |

[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] What are the item IDs, populations (if available), and land areas in square kilometers (if available) for Budapest, Wake Island, and Germany, excluding any area measurements that apply to lagoons?

[Assistant]
To answer the question, I need to find the item IDs, populations, and land areas in square kilometers for Budapest, Wake Island, and Germany, while excluding any area measurements that apply to lagoons.

### Step 1: Identify the entities for Budapest, Wake Island, and Germany
I will first search for the Wikidata item IDs (QIDs) for these three locations.

#### Search for Budapest

Question: Budapest

Top 10 entity alternatives:
1. Budapest (wd:Q1781), also known as Alt-Ofen, Buda, Buda Pest, Buda-Pest, Budapešť, etc.: capital and largest city of Hungary / is instance of national capital / is instance of town in Hungary / is instance of largest city / is instance of enclave
2. Budapest (wd:Q16467392): 2013 single by George Ezra / is instance of single
3. Budapest (wd:Q55420238): 2018 film directed by Xavier Gens / is instance of film
4. Budapest (wd:Q603551), also known as Budapest, GA, Budapest, Georgia: human settlement in Haralson County, Georgia, United States of America / is instance of unincorporated community in the United States
5. Budapest (wd:Q825871), also known as Budapest, MO: is instance of unincorporated community in the United States / unincorporated community in Missouri
6. Budapest (wd:Q109017715), also known as AKENA VERANDAS, ELA, Fondation Océan Vital, FRA 77, Livie, Livie, CND 77, etc.: is instance of watercraft / is instance of Watercraft / IMOCA 60 (1996 Fa)
7. .budapest (wd:Q20571386), also known as dot budapest: is instance of top-level domain / top-level domain
8. Budapest (wd:Q4984056), also known as Budapest Live: 1984 live album by Manfred Mann's Earth Band / is instance of album
9. Budapest (wd:Q221191): is instance of musical group / band
10. Budapest (wd:Q3646355): is instance of musical work/composition / song performed by Jethro Tull

Question: Wake Island

Top 10 entity alternatives:
1. Wake Island (wd:Q43296), also known as Halcyon Island, Isla San Francisco, Isle de Saint Francois, Isle de St Francois, Isle of Saint Francis, etc.: coral atoll in the North Pacific Ocean administered by the US and also claimed by the Marshall Islands / is instance of Unincorporated territories of the United States / is instance of unincorporated territory of the United States / is instance of administrative territorial entity / is instance of insular area of the United States / is instance of atoll
2. Wake Island (wd:Q1581633): 1942 film directed by John Farrow / is instance of film
3. Wake Island (wd:Q32040976), also known as Wake Islet: island in the territory Wake Island, United States of America / is instance of island
4. Wake Island (wd:Q32040970): is instance of Wikimedia duplicated page
5. Wake Island Rail (wd:Q541492), also known as Gallirallus wakensis, Hypotaenidia wakensis: is instance of taxon / species of bird
6. Wake Island Airfield (wd:Q7960943), also known as AWK, PWAK: airport in United States Minor Outlying Islands, United States of America / is instance of airport
7. USS Wake Island (wd:Q7875218): 1943 Casablanca-class escort carrier / is instance of escort carrier
8. Wake Island Conference (wd:Q7960939): 1950 conference between US President Harry S. Truman and ... MacArthur to confer about the progress of the Korean War
9. Marine Fighter Attack Squadron 211 (wd:Q7907175, matched via Wake Island Avengers), also known as Bastion Defenders, VMFA-211, Wake Island Avengers: United States Marine Corps fighter attack squadron / is instance of Military organization / is instance of military unit
10. Wake Island Device (wd:Q7960940): is instance of United States military award devices

Question: Germany

Top 10 entity alternatives:
1. Germany (wd:Q183), also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany: is instance of sovereign state / country in central Europe / country in central Europe / country in Central Europe / country in Central Europe / is instance of country
2. Weimar Republic (wd:Q41304, matched via Germany), also known as German People's State, German Reich, German Republic, Germany, Republic of Germany, etc.: Germany in the years 1918/1919–1933 / Germany in the years 1918/1919–1933 / is instance of historical country / is instance of historical period
3. German Reich (wd:Q1206012, matched via Germany), also known as Deutsches Reich, German Empire, German Realm, Germany, Imperial Germany: official name for the German nation state from 1871 to 1945, and name of Germany until 1949 / official name for the German nation state from 1871 to 1945, and name of Germany until 1949 / is instance of historical country
4. Burg Drachenfels (wd:Q1011486, matched via Germany), also known as Germany: is instance of castle / is instance of Castle / castle
5. Germany (wd:Q5552078): is instance of constituency of the European Parliament / European Parliament constituency
6. Geilenkirchen-Heinsberg (wd:Q2268218, matched via Germany), also known as Germany: is instance of district of Germany
7. Germany (wd:Q18151573), also known as Germany, IN, Germany, Indiana: is instance of unincorporated community in the United States / unincorporated community in Clark County, Indiana
8. Germany (wd:Q19147844): is instance of literary work / poem by Vladimir Mayakovsky
9. Germany (wd:Q3839160), also known as Ludwig, West: is instance of animated character / Hetalia character
10. Germany (wd:Q22006587): is instance of horse / is instance of Horse / race horse

[Assistant]
From the search results, I have identified the correct Wikidata item IDs for the three locations:

- **Budapest**: `wd:Q1781` (capital city of Hungary)
- **Wake Island**: `wd:Q43296` (coral atoll administered by the US)
- **Germany**: `wd:Q183` (sovereign state)

Now, I need to retrieve:
1. The **item ID** (already identified).
2. The **population** (property `P1082`).
3. The **land area in square kilometers** (property `P2046`), excluding any area measurements that apply to lagoons.

### Step 2: Understand the properties and constraints
- **Population** (`P1082`): This is a numeric value. For cities and countries, it's often recorded with qualifiers like `P585` (point in time) and `P518` (applies to part) to specify whether it's city proper, metro area, etc. We want the most recent population value.
- **Land area** (`P2046`): This is the total land area in square kilometers. We must exclude measurements that apply to lagoons. To do this, we need to check the qualifiers on the area statements. The qualifier `P518` (applies to part) might indicate if the area refers to a lagoon or other feature. We can filter out any area statements where `P518` is a lagoon (e.g., `wd:Q217547` for "lagoon").

We will construct a SPARQL query that:
- Selects the item ID, population, and land area for each of the three entities.
- Filters out any land area statements where `P518` (applies to part) is a lagoon.
- Uses the most recent population value (by ordering by `P585` descending).
- Uses direct `wdt:P1082` and `wdt:P2046` where possible, but falls back to statement-based queries if qualifiers are needed.

### Step 3: Construct the SPARQL query
We will query for each of the three entities (`wd:Q1781`, `wd:Q43296`, `wd:Q183`) and retrieve:
- `?item` (the item ID)
- `?population` (from `p:P1082` with `ps:P1082` and `pq:P585` for time)
- `?area` (from `p:P2046` with `ps:P2046` and filter out if `pq:P518` is a lagoon)

We will use `OPTIONAL` for both population and area to handle cases where one might be missing.

Let me write the SPARQL query:

```sparql
SELECT ?item ?population ?area WHERE {
  VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 }

  # Population: get the most recent value
  OPTIONAL {
    ?item p:P1082 ?popStmt .
    ?popStmt ps:P1082 ?population ;
             pq:P585 ?popDate .
    FILTER(LANG(?population) = "")
  }
  # Order by date descending to get the most recent
  # We'll use ORDER BY and LIMIT 1 per item later, but we'll handle it in the query structure

  # Land area: get value and exclude lagoons
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ;
              pq:P518 ?part .
    FILTER(LANG(?area) = "")
    # Exclude if the area applies to a lagoon
    FILTER(?part != wd:Q217547)
  }

  # Also allow direct values without qualifiers (for cases where no qualifiers exist)
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER(LANG(?area) = "")
    # Check if there is a qualifier that is a lagoon; if not, include
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 ;
            ?areaStmt2 pq:P518 wd:Q217547
    }
  }

  # For population, if no statement with qualifier, try direct
  OPTIONAL {
    ?item wdt:P1082 ?population .
    FILTER(LANG(?population) = "")
  }

  # We need to ensure we get the most recent population if multiple exist
  # We'll use a subquery to get the most recent population per item
}
ORDER BY ?item
```

Wait — this is getting complex. Let me simplify and use a better structure.

Actually, let me use a **subquery** to get the most recent population per item, and then join with land area.

### Step 4: Refine the SPARQL query with subquery for population

```sparql
SELECT ?item ?population ?area WHERE {
  VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 }

  # Subquery to get the most recent population for each item
  {
    SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE {
      ?item p:P1082 ?popStmt .
      ?popStmt ps:P1082 ?population ;
               pq:P585 ?popDate .
      FILTER(LANG(?population) = "")
    }
    GROUP BY ?item
  }

  # Get the population with the most recent date
  ?item p:P1082 ?popStmt .
  ?popStmt ps:P1082 ?population ;
           pq:P585 ?popDate .
  FILTER(?popDate = ?maxPopDate)
  FILTER(LANG(?population) = "")

  # Land area: get value and exclude lagoons
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ;
              pq:P518 ?part .
    FILTER(LANG(?area) = "")
    FILTER(?part != wd:Q217547)
  }

  # Also allow direct values without qualifiers (for cases where no qualifiers exist)
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER(LANG(?area) = "")
    # Exclude if any qualifier on area is a lagoon
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 ;
            ?areaStmt2 pq:P518 wd:Q217547
    }
  }
}
ORDER BY ?item
```

This query is still complex. Let me test a simpler version first, using direct properties (`wdt:P1082`, `wdt:P2046`) and then check if qualifiers are needed.

### Step 5: Try a simpler query first — direct properties

Let me execute a simple query to see if direct properties return useful data:

execute

SELECT ?item ?population ?area WHERE { VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 } . ?item wdt:P1082 ?population . ?item wdt:P2046 ?area . FILTER(LANG(?population) = "") . FILTER(LANG(?area) = "") }

[Assistant]
The direct properties (`wdt:P1082` and `wdt:P2046`) did not return any results. This means we must use the statement-based approach with qualifiers (`p:P1082`, `ps:P1082`, `pq:P585`, etc.).

### Step 6: Reconstruct the SPARQL query with full statement handling

We need to:
- For population: get the most recent value using `p:P1082` + `ps:P1082` + `pq:P585`
- For area: get `p:P2046` + `ps:P2046` and exclude any with `pq:P518` = `wd:Q217547` (lagoon)
- Use `OPTIONAL` for both, and ensure we get the most recent population per item

Let me write a corrected version:

```sparql
SELECT ?item ?population ?area WHERE {
  VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 }

  # Get the most recent population
  {
    SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE {
      ?item p:P1082 ?popStmt .
      ?popStmt ps:P1082 ?population ;
               pq:P585 ?popDate .
      FILTER(LANG(?population) = "")
    }
    GROUP BY ?item
  }

  ?item p:P1082 ?popStmt .
  ?popStmt ps:P1082 ?population ;
           pq:P585 ?popDate .
  FILTER(?popDate = ?maxPopDate)
  FILTER(LANG(?population) = "")

  # Land area: get value, exclude if applies to lagoon
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ;
              pq:P518 ?part .
    FILTER(LANG(?area) = "")
    FILTER(?part != wd:Q217547)
  }

  # Also include direct area if no qualifier or qualifier is not lagoon
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER(LANG(?area) = "")
    # Only include if no qualifier on area is a lagoon
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 ;
            ?areaStmt2 pq:P518 wd:Q217547
    }
  }
}
ORDER BY ?item
```

This query should work, but let me test it.

### Step 7: Execute the SPARQL query

SELECT ?item ?population ?area WHERE { VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 } . { SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE { ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . FILTER(LANG(?population) = "") } GROUP BY ?item } . ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . FILTER(?popDate = ?maxPopDate) . FILTER(LANG(?population) = "") . OPTIONAL { ?item p:P2046 ?areaStmt . ?areaStmt ps:P2046 ?area ; pq:P518 ?part . FILTER(LANG(?area) = "") . FILTER(?part != wd:Q217547) } . OPTIONAL { ?item wdt:P2046 ?area . FILTER(LANG(?area) = "") . FILTER NOT EXISTS { ?item p:P2046 ?areaStmt2 ; ?areaStmt2 pq:P518 wd:Q217547 } } } ORDER BY ?item

Call to function execute returned an error:
failed to parse input: parse error at position 676 for token PNAME_LN with content 'wd:Q217547' (the input most likely does not follow the grammar)

[Assistant]
I see there was a syntax error in the SPARQL query. The issue is with the `FILTER NOT EXISTS` clause — I incorrectly used a semicolon (`;`) instead of a period (`.`) to separate triples. Let me fix that.

### Step 8: Fix the SPARQL syntax error

The problematic part:
```sparql
FILTER NOT EXISTS {
  ?item p:P2046 ?areaStmt2 ;
        ?areaStmt2 pq:P518 wd:Q217547
}
```

This is invalid. It should be:
```sparql
FILTER NOT EXISTS {
  ?item p:P2046 ?areaStmt2 .
  ?areaStmt2 pq:P518 wd:Q217547
}
```

Let me correct the full query:

```sparql
SELECT ?item ?population ?area WHERE {
  VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 }

  # Subquery to get the most recent population date per item
  {
    SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE {
      ?item p:P1082 ?popStmt .
      ?popStmt ps:P1082 ?population ;
               pq:P585 ?popDate .
      FILTER(LANG(?population) = "")
    }
    GROUP BY ?item
  }

  # Get the population with the most recent date
  ?item p:P1082 ?popStmt .
  ?popStmt ps:P1082 ?population ;
           pq:P585 ?popDate .
  FILTER(?popDate = ?maxPopDate)
  FILTER(LANG(?population) = "")

  # Land area: get value and exclude if applies to lagoon (via qualifier)
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ;
              pq:P518 ?part .
    FILTER(LANG(?area) = "")
    FILTER(?part != wd:Q217547)
  }

  # Also include direct area if no qualifier or if no qualifier is a lagoon
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER(LANG(?area) = "")
    # Only include if there is no qualifier on area that is a lagoon
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 .
      ?areaStmt2 pq:P518 wd:Q217547
    }
  }
}
ORDER BY ?item
```

Now, let me execute this corrected version.

SELECT ?item ?population ?area WHERE { VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 } . { SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE { ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . FILTER(LANG(?population) = "") } GROUP BY ?item } . ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . FILTER(?popDate = ?maxPopDate) . FILTER(LANG(?population) = "") . OPTIONAL { ?item p:P2046 ?areaStmt . ?areaStmt ps:P2046 ?area ; pq:P518 ?part . FILTER(LANG(?area) = "") . FILTER(?part != wd:Q217547) } . OPTIONAL { ?item wdt:P2046 ?area . FILTER(LANG(?area) = "") . FILTER NOT EXISTS { ?item p:P2046 ?areaStmt2 . ?areaStmt2 pq:P518 wd:Q217547 } } } ORDER BY ?item

[Assistant]
The query returned no results, which suggests that either:
1. The population or area data is not structured as expected, or
2. The `FILTER(LANG(?population) = "")` and `FILTER(LANG(?area) = "")` are too restrictive — perhaps the values are typed literals (e.g., `xsd:decimal`) and not language-tagged strings.

In Wikidata, numeric values like population and area are typically stored as **typed literals** (e.g., `13000000^^xsd:decimal`), not as language-tagged strings. So `LANG()` will return an empty string for typed literals, but we should not filter by `LANG()` for numeric values — we should only use `LANG()` for string literals.

Let me remove the `LANG()` filters, since population and area are numeric and should not have language tags.

### Step 9: Remove `LANG()` filters and retry

```sparql
SELECT ?item ?population ?area WHERE {
  VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 }

  # Subquery to get the most recent population date per item
  {
    SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE {
      ?item p:P1082 ?popStmt .
      ?popStmt ps:P1082 ?population ;
               pq:P585 ?popDate .
    }
    GROUP BY ?item
  }

  # Get the population with the most recent date
  ?item p:P1082 ?popStmt .
  ?popStmt ps:P1082 ?population ;
           pq:P585 ?popDate .
  FILTER(?popDate = ?maxPopDate)

  # Land area: get value and exclude if applies to lagoon (via qualifier)
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ;
              pq:P518 ?part .
    FILTER(?part != wd:Q217547)
  }

  # Also include direct area if no qualifier or if no qualifier is a lagoon
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 .
      ?areaStmt2 pq:P518 wd:Q217547
    }
  }
}
ORDER BY ?item
```

Now execute:

SELECT ?item ?population ?area WHERE { VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 } . { SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE { ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . } GROUP BY ?item } . ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . FILTER(?popDate = ?maxPopDate) . OPTIONAL { ?item p:P2046 ?areaStmt . ?areaStmt ps:P2046 ?area ; pq:P518 ?part . FILTER(?part != wd:Q217547) } . OPTIONAL { ?item wdt:P2046 ?area . FILTER NOT EXISTS { ?item p:P2046 ?areaStmt2 . ?areaStmt2 pq:P518 wd:Q217547 } } } ORDER BY ?item

[Assistant]
The query returned results, but there is a duplication for **Wake Island** with two different area values: `6` and `7.1` square kilometers. This suggests there are two different area statements — one possibly including lagoon or other features, and one excluding them.

We need to **exclude the area value that applies to a lagoon**. Let’s inspect the qualifiers for Wake Island’s area statements.

### Step 10: Investigate why Wake Island has two area values

Let me check the qualifiers for `wd:Q43296` (Wake Island) on property `P2046` (area):

list

wd:Q43296

[Assistant] Now, let me inspect the statements for these two area values to see their qualifiers (especially `P518` — applies to part):

[Assistant]
This statement (`s:Q43296-b0a74143-...`) has `pq:P518` = `wd:Q187223` (lagoon). So this is the **lagoon area** — we must **exclude** it.

Now let’s check the other area statement:

[Assistant]
This second statement has `pq:P518` = `wd:Q11081619` (land), which is the correct land area — we should **include** this.

So in our SPARQL query, we must **exclude** any area statement where `pq:P518` is `wd:Q187223` (lagoon), but we **include** statements where `pq:P518` is `wd:Q11081619` (land) or no qualifier at all.

In our current query, we are filtering out only `wd:Q217547` (lagoon), but Wake Island uses `wd:Q187223` for lagoon. So we need to **include both lagoon QIDs** in the exclusion.

Let me check what `wd:Q217547` and `wd:Q187223` are:

wd:Q217547

Got 245 rows and 3 columns, showing the first 10 rows below:
| subject                    | property                    | object                              |
| -------------------------- | --------------------------- | ----------------------------------- |
| Antonio Urceo (wd:Q217547) | instance of (wdt:P31)       | human (wd:Q5)                       |
| Antonio Urceo (wd:Q217547) | occupation (wdt:P106)       | philologist (wd:Q13418253)          |
| Antonio Urceo (wd:Q217547) | sex or gender (wdt:P21)     | male (wd:Q6581097)                  |
| Antonio Urceo (wd:Q217547) | given name (wdt:P735)       | Antonio (wd:Q7141520)               |
| Antonio Urceo (wd:Q217547) | date of birth (wdt:P569)    | 1446-08-23T00:00:00Z (xsd:dateTime) |
| Antonio Urceo (wd:Q217547) | Freebase ID (wdt:P646)      | /m/0n460pt                          |
| Antonio Urceo (wd:Q217547) | VIAF cluster ID (wdtn:P214) | <http://viaf.org/viaf/66577430>     |
| Antonio Urceo (wd:Q217547) | place of birth (wdt:P19)    | Rubiera (wd:Q111275)                |
| Antonio Urceo (wd:Q217547) | date of death (wdt:P570)    | 1500-02-20T00:00:00Z (xsd:dateTime) |
| Antonio Urceo (wd:Q217547) | educated at (wdt:P69)       | University of Bologna (wd:Q131262)  |
| ...                        | ...                         | ...                                 |

[Assistant]
Wait — this is a person! `wd:Q217547` is **Antonio Urceo**, a human, not a lagoon. That means I made a mistake — I assumed `wd:Q217547` was a lagoon, but it's not. The correct lagoon item is `wd:Q187223`, as seen in Wake Island’s data.

So I need to **exclude only `wd:Q187223`** (lagoon), not `wd:Q217547`.

### Step 11: Final corrected SPARQL query

I will now update the query to:
- Exclude area statements where `pq:P518` = `wd:Q187223` (lagoon)
- Keep all other area values (including direct `wdt:P2046` if no lagoon qualifier exists)
- Keep the most recent population

```sparql
SELECT ?item ?population ?area WHERE {
  VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 }

  # Subquery to get the most recent population date per item
  {
    SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE {
      ?item p:P1082 ?popStmt .
      ?popStmt ps:P1082 ?population ;
               pq:P585 ?popDate .
    }
    GROUP BY ?item
  }

  # Get the population with the most recent date
  ?item p:P1082 ?popStmt .
  ?popStmt ps:P1082 ?population ;
           pq:P585 ?popDate .
  FILTER(?popDate = ?maxPopDate)

  # Land area: get value and exclude if applies to lagoon (wd:Q187223)
  OPTIONAL {
    ?item p:P2046 ?areaStmt .
    ?areaStmt ps:P2046 ?area ;
              pq:P518 ?part .
    FILTER(?part != wd:Q187223)
  }

  # Also include direct area if no qualifier or if no qualifier is a lagoon
  OPTIONAL {
    ?item wdt:P2046 ?area .
    FILTER NOT EXISTS {
      ?item p:P2046 ?areaStmt2 .
      ?areaStmt2 pq:P518 wd:Q187223
    }
  }
}
ORDER BY ?item
```

Now execute:

execute

SELECT ?item ?population ?area WHERE { VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 } . { SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE { ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . } GROUP BY ?item } . ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . FILTER(?popDate = ?maxPopDate) . OPTIONAL { ?item p:P2046 ?areaStmt . ?areaStmt ps:P2046 ?area ; pq:P518 ?part . FILTER(?part != wd:Q187223) } . OPTIONAL { ?item wdt:P2046 ?area . FILTER NOT EXISTS { ?item p:P2046 ?areaStmt2 . ?areaStmt2 pq:P518 wd:Q187223 } } } ORDER BY ?item

[Assistant]
Perfect! The query now returns the correct results:

- **Budapest** (`wd:Q1781`): Population = 1,686,222; Land area = 52,514 km²
- **Germany** (`wd:Q183`): Population = 84,358,845; Land area = 349,390 km²
- **Wake Island** (`wd:Q43296`): Population = 100; Land area = 7.1 km² (the lagoon-inclusive 6 km² value was excluded)

All lagoon-related area measurements have been successfully filtered out.

### Final Answer

The item IDs, populations, and land areas (excluding lagoon measurements) are:

- **Budapest (wd:Q1781)**: Population = 1,686,222; Land area = 52,514 km²
- **Germany (wd:Q183)**: Population = 84,358,845; Land area = 349,390 km²
- **Wake Island (wd:Q43296)**: Population = 100; Land area = 7.1 km²

I will now provide the final SPARQL query and answer.

answer

SELECT ?item ?population ?area WHERE { VALUES ?item { wd:Q1781 wd:Q43296 wd:Q183 } . { SELECT ?item (MAX(?popDate) AS ?maxPopDate) WHERE { ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . } GROUP BY ?item } . ?item p:P1082 ?popStmt . ?popStmt ps:P1082 ?population ; pq:P585 ?popDate . FILTER(?popDate = ?maxPopDate) . OPTIONAL { ?item p:P2046 ?areaStmt . ?areaStmt ps:P2046 ?area ; pq:P518 ?part . FILTER(?part != wd:Q187223) } . OPTIONAL { ?item wdt:P2046 ?area . FILTER NOT EXISTS { ?item p:P2046 ?areaStmt2 . ?areaStmt2 pq:P518 wd:Q187223 } } } ORDER BY ?item

Answer:
The item IDs, populations, and land areas (excluding lagoon measurements) are:
- Budapest (wd:Q1781): Population = 1,686,222; Land area = 52,514 km²
- Germany (wd:Q183): Population = 84,358,845; Land area = 349,390 km²
- Wake Island (wd:Q43296): Population = 100; Land area = 7.1 km²


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?item WHERE {
  {
    ?item wdt:P403 wd:Q2155719
  } UNION {
    ?item wdt:P1346 ?mid .
    ?mid wdt:P403 wd:Q2155719
  }
  .
  ?item wdt:P31 wd:Q4022
}

Using entities:
- Rivière Mégiscane (wd:Q2155719): is instance of river / watercourse in Quebec, Canada
- river (wd:Q4022), also known as rivers: is instance of ecosystem type / is instance of type of watercourse / is subclass of open water / is subclass of stream / larger natural watercourse / larger natural watercourse / natural watercourse

Using properties:
- winner (wd:P1346 as wdt), also known as 1st place medalist, awardee, champ, champion, has winner, etc.: is subproperty of participating team / is subproperty of participant / winner of a competition or similar event, not to be used for ... qualified with "for work" (P1686)) or for wars or battles / winner of an event - do not use for awards (use P166 instead), nor for wars or battles
- 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
- mouth of the watercourse (wd:P403 as wdt), also known as drain, flows into, outflow, outlet, river mouth, etc.: hydrological object / the body of water to which the watercourse drains / the body of water to which the watercourse drains

Got 8 rows and 1 column:
| item                                 |
| ------------------------------------ |
| Rivière Achepabanca (wd:Q22402791)   |
| Rivière Assup (wd:Q22403780)         |
| Rivière Attic (wd:Q22403929)         |
| Rivière Capousacataca (wd:Q22410355) |
| Rivière Collin (wd:Q22411694)        |
| Rivière Kekek (wd:Q22424844)         |
| Rivière Tavernier (wd:Q22438550)     |
| Rivière Whitegoose (wd:Q22440754)    |

Answer: The distinct items that either have the Rivière Mégiscane as their mouth of the watercourse or are tributaries of watercourses that have the Rivière Mégiscane as their mouth are: Rivière Achepabanca (wd:Q22402791), Rivière Assup (wd:Q22403780), Rivière Attic (wd:Q22403929), Rivière Capousacataca (wd:Q22410355), Rivière Collin (wd:Q22411694), Rivière Kekek (wd:Q22424844), Rivière Tavernier (wd:Q22438550), and Rivière Whitegoose (wd:Q22440754).

The distinct items that either have the Rivière Mégiscane as their mouth of the watercourse or are tributaries of watercourses that have the Rivière Mégiscane as their mouth are: Rivière Achepabanca (wd:Q22402791), Rivière Assup (wd:Q22403780), Rivière Attic (wd:Q22403929), Rivière Capousacataca (wd:Q22410355), Rivière Collin (wd:Q22411694), Rivière Kekek (wd:Q22424844), Rivière Tavernier (wd:Q22438550), and Rivière Whitegoose (wd:Q22440754).

SPARQL query over wikidata:
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?item WHERE {
  {
    ?item wdt:P403 wd:Q2155719
  } UNION {
    ?item wdt:P1346 ?mid .
    ?mid wdt:P403 wd:Q2155719
  }
  .
  ?item wdt:P31 wd:Q4022
}

Using entities:
- Rivière Mégiscane (wd:Q2155719): is instance of river / watercourse in Quebec, Canada
- river (wd:Q4022), also known as rivers: is instance of ecosystem type / is instance of type of watercourse / is subclass of open water / is subclass of stream / larger natural watercourse / larger natural watercourse / natural watercourse

Using properties:
- winner (wd:P1346 as wdt), also known as 1st place medalist, awardee, champ, champion, has winner, etc.: is subproperty of participating team / is subproperty of participant / winner of a competition or similar event, not to be used for ... qualified with "for work" (P1686)) or for wars or battles / winner of an event - do not use for awards (use P166 instead), nor for wars or battles
- 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
- mouth of the watercourse (wd:P403 as wdt), also known as drain, flows into, outflow, outlet, river mouth, etc.: hydrological object / the body of water to which the watercourse drains / the body of water to which the watercourse drains

Execution result:
Got 8 rows and 1 column:
| item                                 |
| ------------------------------------ |
| Rivière Achepabanca (wd:Q22402791)   |
| Rivière Assup (wd:Q22403780)         |
| Rivière Attic (wd:Q22403929)         |
| Rivière Capousacataca (wd:Q22410355) |
| Rivière Collin (wd:Q22411694)        |
| Rivière Kekek (wd:Q22424844)         |
| Rivière Tavernier (wd:Q22438550)     |
| Rivière Whitegoose (wd:Q22440754)    |

[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] What are the distinct items that either have the Rivière Mégiscane as their mouth of the watercourse or are tributaries of watercourses that have the Rivière Mégiscane as their mouth of the watercourse?

[Assistant]
To answer this question, we need to find distinct items that either:

1. Have **Rivière Mégiscane** as their *mouth of the watercourse* (i.e., they are rivers or watercourses that flow into Rivière Mégiscane), or
2. Are *tributaries* of watercourses that have Rivière Mégiscane as their mouth.

This requires understanding the hydrological relationships in Wikidata:

- The property for "mouth of the watercourse" is **P1313**.
- The property for "tributary of" is **P1346**.
- We need to find:
  - Watercourses with `wdt:P1313` = Rivière Mégiscane (direct mouths)
  - Watercourses that are tributaries (`wdt:P1346`) of any watercourse that itself has Rivière Mégiscane as its mouth.

### Step 1: Find the Wikidata item for "Rivière Mégiscane"

Let’s search for the entity "Rivière Mégiscane".

Question: Rivière Mégiscane

Top 10 entity alternatives:
1. Rivière Mégiscane (wd:Q2155719): watercourse in Quebec, Canada / is instance of river
2. Detroit River (wd:Q318435, matched via Riviere Detroit), also known as Riviere Detroit, Rivière Détroit: river along the border between Michigan and Ontario / is instance of river
3. Lys (wd:Q208493, matched via Goudne Riviere), also known as Golden River, Goudne Riviere, Leie: river in France and Belgium, and a left tributary of the Scheldt / is instance of river
4. Rivière (wd:Q1023491): is instance of commune of France / commune in Pas-de-Calais, France
5. Sankuru (wd:Q209829, matched via Sankuru (rivière)), also known as Sankuru (rivière): is instance of province of the Democratic Republic of the Congo / province of the Democratic Republic of the Congo
6. Chalais (wd:Q1152948, matched via La Rivière), also known as La Rivière, Saint-Christophe (réunie à Chalais): is instance of commune of France / commune in Charente, France / commune in Charente, France
7. La Rivière (wd:Q501962), also known as La Ribièra: is instance of commune of France / commune in Gironde, France
8. La Rivière (wd:Q1068802): is instance of commune of France / commune in Isère, France
9. St. Clair River (wd:Q1054325, matched via Riviere Sainte-Claire), also known as Riviere Sainte-Claire, Rivière Sainte-Claire, Saint Clair River, St. Clair: river in North America on the boundary of Michigan and Ontario / is instance of river
10. Rivière (wd:Q1170903): commune in Indre-et-Loire, France / is instance of commune of France

[Assistant]
The correct entity for **Rivière Mégiscane** is **wd:Q2155719**, which is a river in Quebec, Canada.

### Step 2: Understand the relationships

We need to find:

1. Watercourses that have **Rivière Mégiscane (wd:Q2155719)** as their mouth → `wdt:P1313 wd:Q2155719`
2. Watercourses that are tributaries (`wdt:P1346`) of any watercourse that has Rivière Mégiscane as its mouth.

So we need to:
- Find all watercourses `?watercourse1` such that `?watercourse1 wdt:P1313 wd:Q2155719`
- Find all watercourses `?watercourse2` such that `?watercourse2 wdt:P1346 ?tributaryOf`, and `?tributaryOf wdt:P1313 wd:Q2155719`

Then combine both sets with `UNION`.

We also want to ensure we are only selecting watercourses (rivers or streams), so we should filter by `wdt:P31/wdt:P279* wd:Q201678` (instance of or subclass of "river").

### Step 3: Construct the SPARQL query

We will:
- Use `wdt:P1313` to find direct mouths
- Use `wdt:P1346` to find tributaries of those rivers
- Use `UNION` to combine both sets
- Filter for items that are rivers (via `wdt:P31/wdt:P279* wd:Q201678`)
- Return distinct items

Let’s write the SPARQL query and execute it.

execute

SELECT DISTINCT ?item WHERE { { ?item wdt:P1313 wd:Q2155719 } UNION { ?item wdt:P1346 ?mid . ?mid wdt:P1313 wd:Q2155719 } . ?item wdt:P31/wdt:P279* wd:Q201678 }

[Assistant]
The query returned no results, which suggests that either:

1. There are no rivers in Wikidata that directly have Rivière Mégiscane as their mouth, or
2. The tributary relationships are not modeled using `wdt:P1346`, or
3. The item `wd:Q2155719` (Rivière Mégiscane) is not properly linked in the graph.

Let’s investigate the entity `wd:Q2155719` to see what properties it has, especially regarding `P1313` (mouth of watercourse) and `P1346` (tributary of).

We’ll list its triples.

list

wd:Q2155719

Got 128 rows and 3 columns, showing the first 10 rows below:
| subject                         | property                                                    | object                                            |
| ------------------------------- | ----------------------------------------------------------- | ------------------------------------------------- |
| Rivière Mégiscane (wd:Q2155719) | instance of (wdt:P31)                                       | river (wd:Q4022)                                  |
| Rivière Mégiscane (wd:Q2155719) | country (wdt:P17)                                           | Canada (wd:Q16)                                   |
| Rivière Mégiscane (wd:Q2155719) | located in the administrative territorial entity (wdt:P131) | La Vallée-de-l'Or (wd:Q575254)                    |
| Rivière Mégiscane (wd:Q2155719) | coordinate location (wdt:P625)                              | POINT(-75.663889 48.120833) (geo:wktLiteral)      |
| Rivière Mégiscane (wd:Q2155719) | Google Knowledge Graph ID (wdtn:P2671)                      | <http://g.co/kg/g/1213j2s4>                       |
| Rivière Mégiscane (wd:Q2155719) | GeoNames ID (wdtn:P1566)                                    | <https://sws.geonames.org/6124509/>               |
| Rivière Mégiscane (wd:Q2155719) | OpenStreetMap relation ID (wdtn:P402)                       | <https://www.openstreetmap.org/relation/11077229> |
| Rivière Mégiscane (wd:Q2155719) | length (wdt:P2043)                                          | 230 (xsd:decimal)                                 |
| Rivière Mégiscane (wd:Q2155719) | CGNDB unique ID (wdt:P821)                                  | EHFLV                                             |
| Rivière Mégiscane (wd:Q2155719) | mouth of the watercourse (wdt:P403)                         | Lac Parent (wd:Q1799605)                          |
| ...                             | ...                                                         | ...                                               |

[Assistant]
Ah! We found the issue.

The property for "mouth of the watercourse" is **not P1313**, but **P403**.

In the data, we see:
```
Rivière Mégiscane (wd:Q2155719) | mouth of the watercourse (wdt:P403) | Lac Parent (wd:Q1799605)
```

So **Rivière Mégiscane** is the **mouth** of some other watercourse — meaning **other rivers flow into Rivière Mégiscane**.

Wait — this is the opposite of what we assumed.

Let’s clarify:

- **P403** = "mouth of the watercourse" — this means: *this river flows into [object]*.
- So if `Rivière Mégiscane wdt:P403 Lac Parent`, then **Rivière Mégiscane flows into Lac Parent**.
- Therefore, **Rivière Mégiscane is a tributary of Lac Parent**, not the other way around.

But the question asks:

> What are the distinct items that either have the Rivière Mégiscane as their mouth of the watercourse or are tributaries of watercourses that have the Rivière Mégiscane as their mouth of the watercourse?

So we need:

1. Watercourses that have **Rivière Mégiscane as their mouth** → i.e., they flow into Rivière Mégiscane → `?watercourse wdt:P403 wd:Q2155719`
2. Watercourses that are tributaries (`wdt:P1346`) of watercourses that have Rivière Mégiscane as their mouth → i.e., `?tributary wdt:P1346 ?mid . ?mid wdt:P403 wd:Q2155719`

So we need to use **P403**, not P1313.

Also, note: `P1346` = "tributary of" — meaning: *this river flows into that river*.

So if `A wdt:P1346 B`, then A flows into B.

So if B has Rivière Mégiscane as its mouth → `B wdt:P403 wd:Q2155719` → then A flows into B, which flows into Rivière Mégiscane → so A is a tributary of a river that flows into Rivière Mégiscane.

But the question says: “tributaries of watercourses that have the Rivière Mégiscane as their mouth”

→ So the watercourse (say, X) has Rivière Mégiscane as its mouth → X flows into Rivière Mégiscane → so X has `wdt:P403 wd:Q2155719`

Then, tributaries of X → those are watercourses that flow into X → `?Y wdt:P1346 X`

So we need:

- Direct: `?item wdt:P403 wd:Q2155719`
- Indirect: `?item wdt:P1346 ?mid . ?mid wdt:P403 wd:Q2155719`

And we want both sets.

Let’s write the corrected SPARQL query.

We also need to ensure we are selecting watercourses (rivers). From the data, we see `wd:Q4022` = river.

So we filter: `?item wdt:P31 wd:Q4022` (or subclass thereof).

Let’s execute the corrected query.

execute

SELECT DISTINCT ?item WHERE { { ?item wdt:P403 wd:Q2155719 } UNION { ?item wdt:P1346 ?mid . ?mid wdt:P403 wd:Q2155719 } . ?item wdt:P31 wd:Q4022 }

Got 8 rows and 1 column:
| item                                 |
| ------------------------------------ |
| Rivière Achepabanca (wd:Q22402791)   |
| Rivière Assup (wd:Q22403780)         |
| Rivière Attic (wd:Q22403929)         |
| Rivière Capousacataca (wd:Q22410355) |
| Rivière Collin (wd:Q22411694)        |
| Rivière Kekek (wd:Q22424844)         |
| Rivière Tavernier (wd:Q22438550)     |
| Rivière Whitegoose (wd:Q22440754)    |

[Assistant]
The query successfully returned 8 distinct watercourses that either:

1. Flow directly into **Rivière Mégiscane** (i.e., have it as their mouth via `P403`), or
2. Are tributaries of watercourses that flow into Rivière Mégiscane (via `P1346` → `P403`).

All results are rivers in Quebec, Canada, as expected.

### Final Answer:

The distinct items that either have Rivière Mégiscane as their mouth or are tributaries of watercourses that have Rivière Mégiscane as their mouth are:

- Rivière Achepabanca (wd:Q22402791)
- Rivière Assup (wd:Q22403780)
- Rivière Attic (wd:Q22403929)
- Rivière Capousacataca (wd:Q22410355)
- Rivière Collin (wd:Q22411694)
- Rivière Kekek (wd:Q22424844)
- Rivière Tavernier (wd:Q22438550)
- Rivière Whitegoose (wd:Q22440754)

These are all rivers that directly or indirectly feed into Rivière Mégiscane.

We’ll now provide the final SPARQL query and answer.

answer

SELECT DISTINCT ?item WHERE { { ?item wdt:P403 wd:Q2155719 } UNION { ?item wdt:P1346 ?mid . ?mid wdt:P403 wd:Q2155719 } . ?item wdt:P31 wd:Q4022 }

Answer: The distinct items that either have the Rivière Mégiscane as their mouth of the watercourse or are tributaries of watercourses that have the Rivière Mégiscane as their mouth are: Rivière Achepabanca (wd:Q22402791), Rivière Assup (wd:Q22403780), Rivière Attic (wd:Q22403929), Rivière Capousacataca (wd:Q22410355), Rivière Collin (wd:Q22411694), Rivière Kekek (wd:Q22424844), Rivière Tavernier (wd:Q22438550), and Rivière Whitegoose (wd:Q22440754).