PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?year ?country ?countryLabel ( COUNT( DISTINCT ?stmt ) AS ?count ) WHERE {
  ?person wdt:P31 wd:Q5 .
  ?person p:P166 ?stmt .
  ?stmt ps:P166 ?award .
  ?award rdfs:label ?awardLabel .
  FILTER ( LANG( ?awardLabel ) = "en" ) .
  FILTER ( ( CONTAINS( LCASE( ?awardLabel ) , "nobel prize" ) || CONTAINS( LCASE( ?awardLabel ) , "prize in economic sciences in memory of alfred nobel" ) ) && ! CONTAINS( LCASE( ?awardLabel ) , "ig nobel" ) ) .
  OPTIONAL {
    ?stmt pq:P585 ?awardDate .
  }
  FILTER ( BOUND( ?awardDate ) ) .
  BIND( SUBSTR( STR( ?awardDate ) , 1 , 4 ) AS ?year ) .
  ?person wdt:P27 ?country .
  ?country rdfs:label ?countryLabel .
  FILTER ( LANG( ?countryLabel ) = "en" ) .
}
GROUP BY ?year ?country ?countryLabel
ORDER BY DESC ( ?year ) ?countryLabel

Using entities:
- human (wd:Q5), also known as human being, humans, individual Homo sapiens, individual human, modern humans, etc.: is instance of class of organisms known by one particular common name / is instance of organisms known by a 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

Using properties:
- award received (wd:P166 as p/ps), also known as award, awarded, awards, awards received, award won, etc.: award or recognition received by a person, organisation or creative work / award or recognition received by a person, organization or creative work / award or recognition received by a person, organization or creative work / awards received by a person, organisation or creative work
- country of citizenship (wd:P27 as wdt), also known as citizen of, citizenship, country, (legal) nationality, nation, etc.: is subproperty of country / the object is a country that recognizes the subject as its citizen / the object is a country that recognizes the subject as its citizen
- 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
- 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

Got 770 rows and 4 columns, showing the first 5 and last 5 rows below:
| year | country                                | countryLabel                         | count       |
| ---- | -------------------------------------- | ------------------------------------ | ----------- |
| 2024 | Canada (wd:Q16)                        | Canada (lang:en)                     | 1 (xsd:int) |
| 2024 | South Korea (wd:Q884)                  | South Korea (lang:en)                | 1 (xsd:int) |
| 2024 | Turkey (wd:Q43)                        | Turkey (lang:en)                     | 1 (xsd:int) |
| 2024 | United Kingdom (wd:Q145)               | United Kingdom (lang:en)             | 3 (xsd:int) |
| 2024 | United States (wd:Q30)                 | United States (lang:en)              | 7 (xsd:int) |
| ...  | ...                                    | ...                                  | ...         |
| 1901 | France (wd:Q142)                       | France (lang:en)                     | 1 (xsd:int) |
| 1901 | German Empire (wd:Q43287)              | German Empire (lang:en)              | 2 (xsd:int) |
| 1901 | Kingdom of Prussia (wd:Q27306)         | Kingdom of Prussia (lang:en)         | 2 (xsd:int) |
| 1901 | Kingdom of the Netherlands (wd:Q29999) | Kingdom of the Netherlands (lang:en) | 1 (xsd:int) |
| 1901 | Weimar Republic (wd:Q41304)            | Weimar Republic (lang:en)            | 1 (xsd:int) |

Answer:
I ran a SPARQL query that counts Nobel Prize awards received by human individuals, grouped by the award year and the individual’s country of citizenship. Key details and assumptions:
- I identify Nobel awards by the English label of the award containing "Nobel Prize" or the official economics prize name, and explicitly exclude "Ig Nobel". This captures the standard Nobel Prize categories (Physics, Chemistry, Medicine/Physiology, Literature, Peace, and the Prize in Economic Sciences in Memory of Alfred Nobel) including year-specific prize items.
- I count award statements (p:P166 statement nodes) that have a point-in-time qualifier (pq:P585) and extract the year from that qualifier.
- I restrict to human recipients (wdt:P31 wd:Q5) and use country of citizenship (wdt:P27) to attribute recipients to countries. If a person has multiple citizenships, they are counted once for each country.
- Results include historical or predecessor states as recorded in Wikidata (e.g., German Empire, Kingdom of Prussia) when those appear as the person’s country of citizenship.

The query returns rows with: year (YYYY), country IRI, countryLabel (English), and count (number of Nobel Prize award statements for individuals with that country in that year). Example rows (from the query run):
- 2024 | wd:Q30 | United States | 7
- 2024 | wd:Q145 | United Kingdom | 3
- 1901 | wd:Q142 | France | 1
- 1901 | wd:Q43287 | German Empire | 2

If you want a version that:
- attributes recipients by place of birth instead of citizenship, or
- collapses historical states into modern countries, or
- counts unique individuals (so a multi-prize winner in one year counts once),
I can adjust the query accordingly. The full SPARQL query used is provided above and returns the detailed per-year, per-country counts.

I ran a SPARQL query that counts Nobel Prize awards received by human individuals, grouped by the award year and the individual’s country of citizenship. Key details and assumptions:
- I identify Nobel awards by the English label of the award containing "Nobel Prize" or the official economics prize name, and explicitly exclude "Ig Nobel". This captures the standard Nobel Prize categories (Physics, Chemistry, Medicine/Physiology, Literature, Peace, and the Prize in Economic Sciences in Memory of Alfred Nobel) including year-specific prize items.
- I count award statements (p:P166 statement nodes) that have a point-in-time qualifier (pq:P585) and extract the year from that qualifier.
- I restrict to human recipients (wdt:P31 wd:Q5) and use country of citizenship (wdt:P27) to attribute recipients to countries. If a person has multiple citizenships, they are counted once for each country.
- Results include historical or predecessor states as recorded in Wikidata (e.g., German Empire, Kingdom of Prussia) when those appear as the person’s country of citizenship.

The query returns rows with: year (YYYY), country IRI, countryLabel (English), and count (number of Nobel Prize award statements for individuals with that country in that year). Example rows (from the query run):
- 2024 | wd:Q30 | United States | 7
- 2024 | wd:Q145 | United Kingdom | 3
- 1901 | wd:Q142 | France | 1
- 1901 | wd:Q43287 | German Empire | 2

If you want a version that:
- attributes recipients by place of birth instead of citizenship, or
- collapses historical states into modern countries, or
- counts unique individuals (so a multi-prize winner in one year counts once),
I can adjust the query accordingly. The full SPARQL query used is provided above and returns the detailed per-year, per-country counts.

SPARQL query over wikidata:
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?year ?country ?countryLabel ( COUNT( DISTINCT ?stmt ) AS ?count ) WHERE {
  ?person wdt:P31 wd:Q5 .
  ?person p:P166 ?stmt .
  ?stmt ps:P166 ?award .
  ?award rdfs:label ?awardLabel .
  FILTER ( LANG( ?awardLabel ) = "en" ) .
  FILTER ( ( CONTAINS( LCASE( ?awardLabel ) , "nobel prize" ) || CONTAINS( LCASE( ?awardLabel ) , "prize in economic sciences in memory of alfred nobel" ) ) && ! CONTAINS( LCASE( ?awardLabel ) , "ig nobel" ) ) .
  OPTIONAL {
    ?stmt pq:P585 ?awardDate .
  }
  FILTER ( BOUND( ?awardDate ) ) .
  BIND( SUBSTR( STR( ?awardDate ) , 1 , 4 ) AS ?year ) .
  ?person wdt:P27 ?country .
  ?country rdfs:label ?countryLabel .
  FILTER ( LANG( ?countryLabel ) = "en" ) .
}
GROUP BY ?year ?country ?countryLabel
ORDER BY DESC ( ?year ) ?countryLabel

Using entities:
- human (wd:Q5), also known as human being, humans, individual Homo sapiens, individual human, modern humans, etc.: is instance of class of organisms known by one particular common name / is instance of organisms known by a 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

Using properties:
- award received (wd:P166 as p/ps), also known as award, awarded, awards, awards received, award won, etc.: award or recognition received by a person, organisation or creative work / award or recognition received by a person, organization or creative work / award or recognition received by a person, organization or creative work / awards received by a person, organisation or creative work
- country of citizenship (wd:P27 as wdt), also known as citizen of, citizenship, country, (legal) nationality, nation, etc.: is subproperty of country / the object is a country that recognizes the subject as its citizen / the object is a country that recognizes the subject as its citizen
- 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
- 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 770 rows and 4 columns, showing the first 5 and last 5 rows below:
| year | country                                | countryLabel                         | count       |
| ---- | -------------------------------------- | ------------------------------------ | ----------- |
| 2024 | Canada (wd:Q16)                        | Canada (lang:en)                     | 1 (xsd:int) |
| 2024 | South Korea (wd:Q884)                  | South Korea (lang:en)                | 1 (xsd:int) |
| 2024 | Turkey (wd:Q43)                        | Turkey (lang:en)                     | 1 (xsd:int) |
| 2024 | United Kingdom (wd:Q145)               | United Kingdom (lang:en)             | 3 (xsd:int) |
| 2024 | United States (wd:Q30)                 | United States (lang:en)              | 7 (xsd:int) |
| ...  | ...                                    | ...                                  | ...         |
| 1901 | France (wd:Q142)                       | France (lang:en)                     | 1 (xsd:int) |
| 1901 | German Empire (wd:Q43287)              | German Empire (lang:en)              | 2 (xsd:int) |
| 1901 | Kingdom of Prussia (wd:Q27306)         | Kingdom of Prussia (lang:en)         | 2 (xsd:int) |
| 1901 | Kingdom of the Netherlands (wd:Q29999) | Kingdom of the Netherlands (lang:en) | 1 (xsd:int) |
| 1901 | Weimar Republic (wd:Q41304)            | Weimar Republic (lang:en)            | 1 (xsd:int) |

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

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Search for the entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Execute intermediate queries to get feedback and to verify your assumptions. You may need to refine or rethink your current plan based on the query results and go back to step 2 if needed, possibly multiple times.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

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

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

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

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

Additional rules to follow:
- Explain your thought process before and after each step and function call.
- Do not just use or make up entity or property identifiers without verifying their existence in the knowledge graphs first.
- Do not ask the user for clarifications without first attempting to complete the task. If the task input is incomplete or ambiguous, try to make reasonable assumptions and document them.
- Do not use "SERVICE wikibase:label { bd:serviceParam wikibase:language ..." in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.
- Always execute your final SPARQL query before giving an answer to make sure it returns the expected results.
- The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
- Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
- Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
- For questions with a "True" or "False" answer the SPARQL query should be an ASK query.

[User] What is the total number of Nobel Prizes received by individuals from each country for each year, grouped by year and country?

[Assistant]
I'll first search Wikidata for the item representing "Nobel Prize" and related Nobel Prize awards so I can restrict awards to the proper set. I'll explain each step and run queries to verify IDs.

Question: Nobel Prize

Top 10 entity alternatives:
1. Nobel Prize (wd:Q7191), also known as Nobel: set of annual international awards, primarily 5 established in 1895 by Alfred Nobel / is instance of science award / is instance of award / is subclass of prize
2. Nobel Prize (wd:Q56550587): scientific article published in the British Medical Journal / is instance of scholarly article / is instance of academic article
3. Nobel Prize (wd:Q84949135): scientific article published on 01 October 1964 / is instance of scholarly article / is instance of academic article / is instance of editorial
4. Nobel Prize (wd:Q93856116): is instance of scholarly article / is instance of academic article
5. Nobel Prize (wd:Q93924922): scientific article published in October 1960 / scientific article published in October 1960 / scientific article published in October 1960 / is instance of scholarly article / is instance of academic article
6. Nobel prize (wd:Q68617247): scientific article published on 01 October 1970 / is instance of scholarly article / is instance of academic article
7. Nobel prize (wd:Q68632222): scientific article published on 01 October 1971 / is instance of scholarly article / is instance of academic article
8. Nobel Prize (wd:Q68562111): scientific article published on 01 October 1966 / is instance of scholarly article / is instance of academic article
9. NOBEL PRIZE (wd:Q81850411): scientific article published on 01 December 1912 / is instance of scholarly article / is instance of academic article / is instance of editorial
10. Nobel Prize in Literature (wd:Q37922, matched via Nobel Literature Prize), also known as Nobel Literature Prize: one of five prizes established by Alfred Nobel / is instance of literary award / is subclass of Nobel Prize

[Assistant]
I'll run a SPARQL query to list award items used in P166 for humans whose English label contains "nobel" — this will find the specific Nobel prize items used in award statements so I can reliably restrict to Nobel prizes. I'll now execute that query.

execute

SELECT DISTINCT ?award ?awardLabel WHERE {
  ?person wdt:P31 wd:Q5 .
  ?person p:P166 ?stmt .
  ?stmt ps:P166 ?award .
  ?award rdfs:label ?awardLabel .
  FILTER(LANG(?awardLabel)="en") .
  FILTER(CONTAINS(LCASE(?awardLabel), "nobel"))
} LIMIT 200

Got 11 rows and 2 columns, showing the first 5 and last 5 rows below:
| award                                                            | awardLabel                                                     |
| ---------------------------------------------------------------- | -------------------------------------------------------------- |
| Nobel Prize winner (wd:Q115784320)                               | Nobel Prize winner (lang:en)                                   |
| Ig Nobel Prize (wd:Q184253)                                      | Ig Nobel Prize (lang:en)                                       |
| Nobel Peace Prize (wd:Q35637)                                    | Nobel Peace Prize (lang:en)                                    |
| Nobel Prize in Literature (wd:Q37922)                            | Nobel Prize in Literature (lang:en)                            |
| Nobel Prize in Physics (wd:Q38104)                               | Nobel Prize in Physics (lang:en)                               |
| ...                                                              | ...                                                            |
| 2005 Nobel Peace Prize (wd:Q4604682)                             | 2005 Nobel Peace Prize (lang:en)                               |
| 2007 Nobel Peace Prize (wd:Q4609021)                             | 2007 Nobel Peace Prize (lang:en)                               |
| Prize in Economic Sciences in Memory of Alfred Nobel (wd:Q47170) | Prize in Economic Sciences in Memory of Alfred Nobel (lang:en) |
| Nobel Prize in Physiology or Medicine (wd:Q80061)                | Nobel Prize in Physiology or Medicine (lang:en)                |
| Nobel Laureate Meetings at Lindau (wd:Q835775)                   | Nobel Laureate Meetings at Lindau (lang:en)                    |

[Assistant]
I'll run a SPARQL query that checks, for award items whose label contains "nobel", whether they are subclass/instance of the central Nobel Prize item wd:Q7191. This helps exclude unrelated items like the Ig Nobel Prize.

SELECT DISTINCT ?award ?awardLabel (IF(EXISTS{ ?award wdt:P279* wd:Q7191 } || EXISTS{ ?award wdt:P31 ?t . ?t wdt:P279* wd:Q7191 }, "yes","no") AS ?isNobelClass) WHERE {
  ?award rdfs:label ?awardLabel .
  FILTER(LANG(?awardLabel) = "en") .
  FILTER(CONTAINS(LCASE(?awardLabel), "nobel"))
} ORDER BY ?awardLabel LIMIT 200

SPARQL execution failed:
HTTPConnectionPool(host='localhost', port=7001): Read timed out. (read timeout=30)

[Assistant]
Planned SPARQL query (explained above). I'll now execute it to get the counts. Assumptions: using country of citizenship (P27) to attribute individuals to countries; counting each award statement (p:P166) with a pq:P585 point-in-time date; excluding Ig Nobel and other non-Alfred Nobel items by filtering award labels to contain "Nobel Prize" or the official economics prize name. If a person has multiple P27 values they will be counted once for each country. I'll run the query now.

SELECT (SUBSTR(STR(?awardDate),1,4) AS ?year) ?country ?countryLabel (COUNT(DISTINCT ?stmt) AS ?count) WHERE {
  ?person wdt:P31 wd:Q5 .
  ?person p:P166 ?stmt .
  ?stmt ps:P166 ?award .
  ?award rdfs:label ?awardLabel . FILTER(LANG(?awardLabel) = "en") .
  FILTER(
    (CONTAINS(LCASE(?awardLabel), "nobel prize") || CONTAINS(LCASE(?awardLabel), "prize in economic sciences in memory of alfred nobel"))
    && !CONTAINS(LCASE(?awardLabel), "ig nobel")
  ) .
  OPTIONAL { ?stmt pq:P585 ?awardDate . }
  FILTER(BOUND(?awardDate)) .
  ?person wdt:P27 ?country .
  ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") .
}
GROUP BY ?year ?country ?countryLabel
ORDER BY DESC(?year) ?countryLabel

SPARQL execution failed:
Invalid SPARQL query: The expression "SUBSTR ( STR ( ?awardDate ) , 1 , 4 )" does not aggregate ?awardDate. All non-aggregated variables must be part of the GROUP BY clause.

[Assistant]
I'll re-run the query binding the year string first and grouping by the bound ?year to satisfy grouping rules.

SELECT ?year ?country ?countryLabel (COUNT(DISTINCT ?stmt) AS ?count) WHERE {
  ?person wdt:P31 wd:Q5 .
  ?person p:P166 ?stmt .
  ?stmt ps:P166 ?award .
  ?award rdfs:label ?awardLabel . FILTER(LANG(?awardLabel) = "en") .
  FILTER(
    (CONTAINS(LCASE(?awardLabel), "nobel prize") || CONTAINS(LCASE(?awardLabel), "prize in economic sciences in memory of alfred nobel"))
    && !CONTAINS(LCASE(?awardLabel), "ig nobel")
  ) .
  OPTIONAL { ?stmt pq:P585 ?awardDate . }
  FILTER(BOUND(?awardDate)) .
  BIND(SUBSTR(STR(?awardDate), 1, 4) AS ?year) .
  ?person wdt:P27 ?country .
  ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") .
}
GROUP BY ?year ?country ?countryLabel
ORDER BY DESC(?year) ?countryLabel

Got 770 rows and 4 columns, showing the first 5 and last 5 rows below:
| year | country                                | countryLabel                         | count       |
| ---- | -------------------------------------- | ------------------------------------ | ----------- |
| 2024 | Canada (wd:Q16)                        | Canada (lang:en)                     | 1 (xsd:int) |
| 2024 | South Korea (wd:Q884)                  | South Korea (lang:en)                | 1 (xsd:int) |
| 2024 | Turkey (wd:Q43)                        | Turkey (lang:en)                     | 1 (xsd:int) |
| 2024 | United Kingdom (wd:Q145)               | United Kingdom (lang:en)             | 3 (xsd:int) |
| 2024 | United States (wd:Q30)                 | United States (lang:en)              | 7 (xsd:int) |
| ...  | ...                                    | ...                                  | ...         |
| 1901 | France (wd:Q142)                       | France (lang:en)                     | 1 (xsd:int) |
| 1901 | German Empire (wd:Q43287)              | German Empire (lang:en)              | 2 (xsd:int) |
| 1901 | Kingdom of Prussia (wd:Q27306)         | Kingdom of Prussia (lang:en)         | 2 (xsd:int) |
| 1901 | Kingdom of the Netherlands (wd:Q29999) | Kingdom of the Netherlands (lang:en) | 1 (xsd:int) |
| 1901 | Weimar Republic (wd:Q41304)            | Weimar Republic (lang:en)            | 1 (xsd:int) |

[Assistant]
I'll now finalize by returning the SPARQL query I used and a short explanation of the results and assumptions. Then I'll call the answer function to stop and deliver the final output.

answer

SELECT ?year ?country ?countryLabel (COUNT(DISTINCT ?stmt) AS ?count) WHERE {
  ?person wdt:P31 wd:Q5 .
  ?person p:P166 ?stmt .
  ?stmt ps:P166 ?award .
  ?award rdfs:label ?awardLabel . FILTER(LANG(?awardLabel) = "en") .
  FILTER(
    (CONTAINS(LCASE(?awardLabel), "nobel prize") || CONTAINS(LCASE(?awardLabel), "prize in economic sciences in memory of alfred nobel"))
    && !CONTAINS(LCASE(?awardLabel), "ig nobel")
  ) .
  OPTIONAL { ?stmt pq:P585 ?awardDate . }
  FILTER(BOUND(?awardDate)) .
  BIND(SUBSTR(STR(?awardDate), 1, 4) AS ?year) .
  ?person wdt:P27 ?country .
  ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = "en") .
}
GROUP BY ?year ?country ?countryLabel
ORDER BY DESC(?year) ?countryLabel

Answer:
I ran a SPARQL query that counts Nobel Prize awards received by human individuals, grouped by the award year and the individual’s country of citizenship. Key details and assumptions:
- I identify Nobel awards by the English label of the award containing "Nobel Prize" or the official economics prize name, and explicitly exclude "Ig Nobel". This captures the standard Nobel Prize categories (Physics, Chemistry, Medicine/Physiology, Literature, Peace, and the Prize in Economic Sciences in Memory of Alfred Nobel) including year-specific prize items.
- I count award statements (p:P166 statement nodes) that have a point-in-time qualifier (pq:P585) and extract the year from that qualifier.
- I restrict to human recipients (wdt:P31 wd:Q5) and use country of citizenship (wdt:P27) to attribute recipients to countries. If a person has multiple citizenships, they are counted once for each country.
- Results include historical or predecessor states as recorded in Wikidata (e.g., German Empire, Kingdom of Prussia) when those appear as the person’s country of citizenship.

The query returns rows with: year (YYYY), country IRI, countryLabel (English), and count (number of Nobel Prize award statements for individuals with that country in that year). Example rows (from the query run):
- 2024 | wd:Q30 | United States | 7
- 2024 | wd:Q145 | United Kingdom | 3
- 1901 | wd:Q142 | France | 1
- 1901 | wd:Q43287 | German Empire | 2

If you want a version that:
- attributes recipients by place of birth instead of citizenship, or
- collapses historical states into modern countries, or
- counts unique individuals (so a multi-prize winner in one year counts once),
I can adjust the query accordingly. The full SPARQL query used is provided above and returns the detailed per-year, per-country counts.