PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?country ?countryLabel ?pandemic ?pandemicLabel ?tabularData WHERE {
  VALUES ?country {
    wd:Q38
  }
  VALUES ?pandemic {
    wd:Q84104992
  }
  ?pandemic wdt:P17 ?country ; wdt:P8204 ?tabularData .
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
  OPTIONAL {
    ?pandemic rdfs:label ?pandemicLabel FILTER ( LANG( ?pandemicLabel ) = "en" )
  }
}

Wikidata does not store the full time series of COVID-19 deaths in Italy as structured statements, but provides a link to a tabular data file containing this information. The country is Italy, the pandemic is the COVID-19 pandemic in Italy, and the time series (including cumulative deaths and dates) can be found in the linked tabular case data file: http://commons.wikimedia.org/data/main/Data:COVID-19+(STCenter)/IT/Q38.tab

Get the country, the number of cumulative deaths, and the dates of those deaths related to the COVID-19 pandemic in Italy.

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

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

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

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

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

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

[User] Get the country, the number of cumulative deaths, and the dates of those deaths related to the COVID-19 pandemic in Italy.

[Assistant]
Let's break down the question:

1. We are interested in data related to the COVID-19 pandemic in Italy.
2. Specifically, we want:
   - The country (Italy)
   - The number of cumulative deaths
   - The dates of those deaths (likely, a time series: date and cumulative deaths per date)

Step 1: Identify the relevant entities and properties.
- COVID-19 pandemic: Let's search for its entity.
- Italy: wd:Q38 (well-known)
- Cumulative deaths: Let's search for a property related to "cumulative deaths" or "COVID-19 deaths".
- Date: Likely wdt:P585 (point in time).

Step 2: Search for the COVID-19 pandemic entity.

[Tool]
Top 10 entity alternatives:
1. COVID-19 pandemic, also known as COVID-19 outbreak, COVID-19 pandemic, People's Republic of China, COVID-19 pandemic, China, 2019nCov outbreak, COVID-19 epidemic (wd:Q81068910): instance of public health emergency of international concern, pandemic caused by SARS-CoV-2, instance of complex emergency, instance of disease outbreak, instance of pandemic
2. COVID-19 pandemic (wd:Q115703463): instance of preprint, Scholarly article
3. COVID-19 Pandemic (wd:Q120173222): scientific article published in 2021, instance of scholarly article
4. COVID-19 pandemic (wd:Q94659153): scientific article published on 01 March 2020, instance of scholarly article, instance of editorial
5. Covid-19 Pandemic (wd:Q119587442): scientific article published in 2021, instance of scholarly article
6. COVID-19 pandemic in Italy, also known as COVID pandemic in Italy, Italy COVID-19 pandemic, Italian COVID-19 outbreak, Italian 2020 coronavirus outbreak, COVID-19 outbreak in Italy (wd:Q84104992): coronavirus pandemic in Italy, instance of disease outbreak
7. COVID-19 pandemic in India, also known as 2019-20 coronavirus pandemic in India, Indian COVID-19 outbreak, Indian coronavirus pandemic, Indian coronavirus outbreak, India COVID-19 pandemic (wd:Q84055514): ongoing COVID-19 viral pandemic in India, instance of disease outbreak
8. COVID-19 pandemic in Spain, also known as Spanish COVID-19 outbreak, Spanish 2019–20 coronavirus outbreak, Spain COVID-19 pandemic, COVID pandemic in Spain, COVID-19 outbreak in Spain (wd:Q84166704): instance of disease outbreak, viral outbreak in Spain
9. COVID-19 pandemic in Germany, also known as Germany COVID-19 pandemic, German COVID-19 outbreak, 2020 Germany COVID-19 pandemic, German 2019–20 coronavirus outbreak, COVID pandemic in Germany (wd:Q83889294): ongoing coronavirus pandemic in Germany, instance of disease outbreak
10. COVID-19 pandemic in Brazil, also known as Brazil COVID-19 pandemic, COVID pandemic in Brazil, coronavirus pandemic in Brazil, 2020 COVID-19 outbreak in Brazil, 2020 Brazil COVID-19 pandemic (wd:Q86597695): ongoing coronavirus pandemic in Brazil, instance of disease outbreak

[Assistant]
The most relevant entity for "COVID-19 pandemic in Italy" is wd:Q84104992.

Next, we need to find properties related to cumulative deaths and time series data for this entity. Let's search for properties related to "deaths" for this entity.

[Tool]
Top 10 property alternatives:
1. case fatality rate, also known as case fatality, case fatality ratio, case fatality risk, CFR, fatality rate (wd:P3457 as wdt/p): proportion of patients who die of a particular medical condition out of all who have this condition within a given time frame
2. has effect, also known as results in, result, outcome, led to, leads to (wd:P1542 as wdt/p): subproperty of followed by, effect of this item
3. country, also known as state, sovereign state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), subproperty of located in the administrative territorial entity
4. location, also known as neighborhood, locale, neighbourhood, place, place held (wd:P276 as wdt/p): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
5. coordinate location, also known as gps co-ordinates, wgs 84, gps coordinates, gps location, latitude (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
6. has cause, also known as implied by, effect of, initial cause, originated due to, outcome of (wd:P828 as wdt/p): underlying cause, entity that ultimately resulted in this effect, subproperty of follows
7. topic's main category, also known as category for this topic, Wikipedia category, Wikimedia category, Wiki category, subject category (wd:P910 as wdt/p): main Wikimedia category
8. start time, also known as start date, since, join time, joined, join date (wd:P580 as wdt/p): time an entity begins to exist or a statement starts being valid, subproperty of point in time
9. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
10. part of, also known as meronym of, contained within, of, part of-property, section of (wd:P361 as wdt/p): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))

No other items found

[Assistant]
None of the top properties directly refer to a time series of cumulative deaths. However, Wikidata often models time series data using "quantity" statements with qualifiers for "point in time" (P585). Let's check what statements exist for wd:Q84104992 to see if there are any time series or death count properties used.

I'll list all triples where wd:Q84104992 is the subject to look for relevant properties.

[Tool]
Got 341 rows and 3 columns, showing the first 10 rows below:
| subject                                   | property                       | object                                                                                |
| ----------------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------- |
| COVID-19 pandemic in Italy (wd:Q84104992) | instance of (wdt:P31)          | disease outbreak (wd:Q3241045)                                                                                |
| COVID-19 pandemic in Italy (wd:Q84104992) | country (wdt:P17)              | Italy (wd:Q38)                                                                                |
| COVID-19 pandemic in Italy (wd:Q84104992) | coordinate location (wdt:P625) | POINT(12.500000 42.500000) (geo:wktLiteral)                                                                       |
| COVID-19 pandemic in Italy (wd:Q84104992) | Commons category (wdt:P373)    | COVID-19 pandemic in Italy                                                                                |
| COVID-19 pandemic in Italy (wd:Q84104992) | image (wdt:P18)                | <http://commons.wikimedia.org/wiki/Special:FilePath/COVID-19%20Outbreak%20Cases%20in%20Italy%20%28Density%29.svg> |
| COVID-19 pandemic in Italy (wd:Q84104992) | part of (wdt:P361)             | COVID-19 pandemic by country and territory (wd:Q83741704)                                                         |
| COVID-19 pandemic in Italy (wd:Q84104992) | location (wdt:P276)            | Italy (wd:Q38)                                                                                |
| COVID-19 pandemic in Italy (wd:Q84104992) | described at URL (wdt:P973)    | <http://www.salute.gov.it/nuovocoronavirus>                                                                       |
| COVID-19 pandemic in Italy (wd:Q84104992) | has part(s) (wdt:P527)         | 2020 COVID-19 pandemic in Lombardy (wd:Q89277312)                                                                 |
| COVID-19 pandemic in Italy (wd:Q84104992) | start time (wdt:P580)          | 2020-01-30T00:00:00Z (xsd:dateTime)                                                                               |
| ...                                       | ...                            | ...                                                                                |

[Assistant]
From the listed properties, none directly indicate a time series of cumulative deaths. However, Wikidata often uses "number of deaths" (P1120) for such statistics, sometimes with qualifiers for "point in time" (P585).

Let's check if P1120 (number of deaths) is used for wd:Q84104992, and if so, whether it has qualifiers for dates.

[Tool] Got no rows and 3 columns

[Assistant]
There are no direct "number of deaths" (P1120) statements on wd:Q84104992. However, sometimes such statistics are attached to related entities, such as "COVID-19 pandemic by country and territory" or even to the country entity (Italy) itself, with qualifiers for the event (COVID-19 pandemic).

Let's check if Italy (wd:Q38) has any P1120 (number of deaths) statements, possibly qualified by event or point in time.

[Tool]
Got 1 row and 3 columns:
| subject        | property                   | object                                     |
| -------------- | -------------------------- | ------------------------------------------ |
| Italy (wd:Q38) | number of deaths (p:P1120) | s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 |

[Tool]
Got 8 rows and 3 columns:
| subject                                    | property                     | object                                       |
| ------------------------------------------ | ---------------------------- | -------------------------------------------- |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | point in time (pq:P585)      | 2022-01-01T00:00:00Z (xsd:dateTime)          |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | number of deaths (ps:P1120)  | 713499 (xsd:decimal)                         |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | wikibase:rank                | wikibase:NormalRank                          |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | rdf:type                     | wikibase:BestRank                            |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | prov:wasDerivedFrom          | ref:db6e62651cf01ee463d129bbc917df15cd524068 |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | point in time (pqv:P585)     | v:b2d5337aae66cfc2d138cc6fa7c4e368           |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | number of deaths (psv:P1120) | v:1e789c082ec46e9de2d549e5192e129b           |
| s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259 | rdf:type                     | wikibase:Statement                           |

[Tool] Got no rows and 3 columns

[Assistant]
There is a "number of deaths" statement for Italy (wd:Q38) with a "point in time" qualifier, but it is not explicitly linked to the COVID-19 pandemic in Italy (wd:Q84104992). The value is 713,499 as of 2022-01-01, which seems too high for COVID-19 deaths alone and likely refers to total deaths in Italy for that year.

To find COVID-19-specific deaths, let's check if there are any "number of deaths" (P1120) statements for Italy (wd:Q38) with a qualifier for "cause of death" or "event" related to COVID-19. Let's search for properties related to "cause of death" or "event" as qualifiers for P1120.

[Tool]
Top 10 property alternatives:
1. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
2. manner of death, also known as circumstance of death, death manner, death type, nature of death, type of death (wd:P1196 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): general circumstances of a person's death; e.g. natural causes, ...ysiological mechanism, e.g. heart attack, trauma, pneumonia etc.
3. place of death, also known as POD, location of death, killed in, died in, died (wd:P20 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, subproperty of location
4. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): total (cumulative) number of people who died since start as a direct result of an event or cause
5. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): subproperty of dissolved, abolished or demolished date, date on which the subject died, subproperty of end time
6. case fatality rate, also known as case fatality, case fatality ratio, case fatality risk, CFR, fatality rate (wd:P3457 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): proportion of patients who die of a particular medical condition out of all who have this condition within a given time frame
7. death rate, also known as mortality rate (wd:P10091 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the total number of persons dead per 1,000 population per year
8. killed by, also known as slaughtered by, mortally wounded by, murdered by, murderer, shot dead by (wd:P157 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): person or organization who killed the subject, subproperty of significant person
9. has effect, also known as results in, result, outcome, led to, leads to (wd:P1542 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): subproperty of followed by, effect of this item
10. category for deaths in this time period (wd:P12687 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): category item for people who died in this time period, subproperty of point in time

[Tool]
Top 10 property alternatives:
1. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): significant or notable events associated with the subject
2. point in time, also known as year, when, time of event, time, during (wd:P585 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): date something took place, existed or a statement was true; for providing time use the "refine date" property (P4241)
3. Commons gallery, also known as gallery (wd:P935 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): name of the Wikimedia Commons gallery page(s) related to this item (is suitable to allow multiple links to more gallery pages)
4. publication date, also known as published, publication time, publication, pubdate, launched (wd:P577 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): date or point in time when a work was first published or released, subproperty of point in time
5. review score by, also known as reviewer, publication, scorer, score issuer (wd:P447 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): issuer of a review score
6. house publication, also known as official media, publishes, publication, organization publication, organisation publication (wd:P2813 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): media that speaks for an organization or a movement, and that is usually edited by its members
7. is proceedings from, also known as is conference proceedings from, documents event (wd:P4745 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): this work contains articles presented at the given event
8. final event (wd:P3967 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): final event of a competition
9. phonographic copyright, also known as ℗, (P) (wd:P12456 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): person or organization that holds the phonographic copyright
10. pH value, also known as pH, ㏗ (wd:P9440 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): acidity and alkalinity of an aqueous solution

[Tool]
Got 21,929 rows and 3 columns, showing the first 10 rows below:
| subject                                     | property                   | object                                            |
| ------------------------------------------- | -------------------------- | ------------------------------------------------- |
| Italy (wd:Q38)                              | number of deaths (p:P1120) | s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259        |
| France (wd:Q142)                            | number of deaths (p:P1120) | s:Q142-77325b4e-492a-125f-56ed-8811d855400d       |
| World War II (wd:Q362)                      | number of deaths (p:P1120) | s:Q362-aaf80984-4027-b96a-bc5e-cebdab2a9151       |
| World War I (wd:Q361)                       | number of deaths (p:P1120) | s:Q361-eeba8120-4ee1-e171-9d4b-04c396bd0449       |
| COVID-19 (wd:Q84263196)                     | number of deaths (p:P1120) | s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53  |
| AIDS (wd:Q12199)                            | number of deaths (p:P1120) | s:Q12199-197c215e-42bd-6351-eb3f-99105e2847d1     |
| The Holocaust (wd:Q2763)                    | number of deaths (p:P1120) | s:Q2763-88818ab9-4c33-9f6d-521e-711da379de71      |
| malaria (wd:Q12156)                         | number of deaths (p:P1120) | s:Q12156-c8da0948-4410-a959-1176-9ea0898f49f8     |
| tuberculosis (wd:Q12204)                    | number of deaths (p:P1120) | s:Q12204-0237e563-4925-fff2-2f3d-d04e96c1ca65     |
| Russian invasion of Ukraine (wd:Q110999040) | number of deaths (p:P1120) | s:Q110999040-1bb885b9-49ee-d5b2-7711-87e93defd149 |
| ...                                         | ...                        | ...                                               |

[Tool]
Got 2,456 rows and 3 columns, showing the first 10 rows below:
| subject                 | property                                       | object                                                                       |
| ----------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------- |
| COVID-19 (wd:Q84263196) | instance of (wdt:P31)                          | class of disease (wd:Q112193867)                                             |
| COVID-19 (wd:Q84263196) | Commons category (wdt:P373)                    | COVID-19                                                                     |
| COVID-19 (wd:Q84263196) | image (wdt:P18)                                | <http://commons.wikimedia.org/wiki/Special:FilePath/Fphar-11-00937-g001.jpg> |
| COVID-19 (wd:Q84263196) | subclass of (wdt:P279)                         | Virus diseases of plants (wd:Q107295072)                                     |
| COVID-19 (wd:Q84263196) | described at URL (wdt:P973)                    | <https://covid19bilgi.saglik.gov.tr/tr/>                                     |
| COVID-19 (wd:Q84263196) | short name (wdt:P1813)                         | COVID-19 (cy)                                                                |
| COVID-19 (wd:Q84263196) | official name (wdt:P1448)                      | coronavirus disease (en)                                                     |
| COVID-19 (wd:Q84263196) | on focus list of Wikimedia project (wdt:P5008) | wd:Q4099686                                                                  |
| COVID-19 (wd:Q84263196) | significant event (wdt:P793)                   | COVID-19 pandemic (wd:Q81068910)                                             |
| COVID-19 (wd:Q84263196) | different from (wdt:P1889)                     | severe acute respiratory syndrome (wd:Q103177)                               |
| ...                     | ...                                            | ...                                                                          |

[Tool]
Got 8 rows and 3 columns:
| subject                                          | property                     | object                                       |
| ------------------------------------------------ | ---------------------------- | -------------------------------------------- |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | point in time (pq:P585)      | 2020-03-25T00:00:00Z (xsd:dateTime)          |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | number of deaths (ps:P1120)  | 18700 (xsd:decimal)                          |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | wikibase:rank                | wikibase:NormalRank                          |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | rdf:type                     | wikibase:BestRank                            |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | prov:wasDerivedFrom          | ref:3b1685c9b7535bd3a8f19a9cf222f4fbe5055119 |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | point in time (pqv:P585)     | v:496819088d75a8fc70e52a8af951a0b1           |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | number of deaths (psv:P1120) | v:3954ac2cb7a207183750e435ec724bb1           |
| s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53 | rdf:type                     | wikibase:Statement                           |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Got 21,929 rows and 3 columns, showing the first 10 rows below:
| subject                                     | property                   | object                                            |
| ------------------------------------------- | -------------------------- | ------------------------------------------------- |
| Italy (wd:Q38)                              | number of deaths (p:P1120) | s:Q38-fb81a32f-4f8d-2897-6dc4-e5b14d542259        |
| France (wd:Q142)                            | number of deaths (p:P1120) | s:Q142-77325b4e-492a-125f-56ed-8811d855400d       |
| World War II (wd:Q362)                      | number of deaths (p:P1120) | s:Q362-aaf80984-4027-b96a-bc5e-cebdab2a9151       |
| World War I (wd:Q361)                       | number of deaths (p:P1120) | s:Q361-eeba8120-4ee1-e171-9d4b-04c396bd0449       |
| COVID-19 (wd:Q84263196)                     | number of deaths (p:P1120) | s:Q84263196-00f8d855-44fd-72d9-4e34-4c160861ee53  |
| AIDS (wd:Q12199)                            | number of deaths (p:P1120) | s:Q12199-197c215e-42bd-6351-eb3f-99105e2847d1     |
| The Holocaust (wd:Q2763)                    | number of deaths (p:P1120) | s:Q2763-88818ab9-4c33-9f6d-521e-711da379de71      |
| malaria (wd:Q12156)                         | number of deaths (p:P1120) | s:Q12156-c8da0948-4410-a959-1176-9ea0898f49f8     |
| tuberculosis (wd:Q12204)                    | number of deaths (p:P1120) | s:Q12204-0237e563-4925-fff2-2f3d-d04e96c1ca65     |
| Russian invasion of Ukraine (wd:Q110999040) | number of deaths (p:P1120) | s:Q110999040-1bb885b9-49ee-d5b2-7711-87e93defd149 |
| ...                                         | ...                        | ...                                               |

[Tool]
Top 10 entity alternatives:
1. COVID-19 deaths in Lombardy, Italy: data in context (wd:Q93257290): scientific article published on 24 April 2020, instance of scholarly article
2. Increased Risk of COVID-19-Relat...g General Practitioners in Italy (wd:Q96168829): scientific article published on 03 June 2020, instance of scholarly article
3. COVID-19 pandemic in Italy, also known as COVID pandemic in Italy, Italy COVID-19 pandemic, Italian COVID-19 outbreak, Italian 2020 coronavirus outbreak, COVID-19 outbreak in Italy (wd:Q84104992): coronavirus pandemic in Italy, instance of disease outbreak
4. COVID-19 protests in Australia, also known as COVID-19 protests in Italy, Impact of the COVID-19 pandemic in the Protestantism in Australia (wd:Q108456892)
5. Comparison of Reported Deaths Fr...ease in Total Mortality in Italy (wd:Q97678128): scientific article published on 20 July 2020, instance of scholarly article
6. COVID-19 vaccination programme in Italy, also known as COVID-19 vaccination campaign in Italy (wd:Q104776834): plan to immunize against COVID-19, instance of mass vaccination
7. 2020 COVID-19 pandemic in Veneto, also known as 2020 COVID-19 pandemic in Veneto, Italy, 2020 COVID pandemic in Veneto, 2020 Veneto COVID-19 pandemic, COVID-19 pandemic in Veneto, Italy, Veneto COVID-19 pandemic (wd:Q89277314): instance of disease outbreak, viral pandemic in Veneto
8. 2020 COVID-19 pandemic in Lombardy, also known as Lombardy COVID-19 pandemic, COVID-19 pandemic in Lombardy, Italy, 2020 Lombardy COVID-19 pandemic, 2020 COVID pandemic in Lombardy, 2020 COVID-19 pandemic in Lombardy, Italy (wd:Q89277312): instance of disease outbreak, viral pandemic in Lombardy
9. Deaths from COVID-19 in healthca...rs in Italy - what can we learn? (wd:Q94682562): scientific article published on 15 May 2020, instance of scholarly article
10. COVID-19 experience in Bergamo, Italy (wd:Q91627899): scientific article published on 07 April 2020, instance of scholarly article

[Tool]
Top 10 property alternatives:
1. image, also known as img, infobox image, photo, photograph, picture (wd:P18 as wdt/p): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
2. start time, also known as start date, since, join time, joined, join date (wd:P580 as wdt/p): time an entity begins to exist or a statement starts being valid, subproperty of point in time
3. has effect, also known as results in, result, outcome, led to, leads to (wd:P1542 as wdt/p): subproperty of followed by, effect of this item
4. tabular case data, also known as Commons Data, data on Common, .tab (wd:P8204 as wdt/p): tabular data on Wikimedia Commons of confirmed cases, recoveries...603, P8049, P8010, P1120. Try to avoid deleting such statements.
5. coordinate location, also known as gps co-ordinates, wgs 84, gps coordinates, gps location, latitude (wd:P625 as wdt/p): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment
6. instance of, also known as example of, has type, is a, is an, is of type (wd:P31 as wdt/p): type to which this subject corresponds/belongs. Different from P...tains, and the volcano is a type (instance) of volcanic landform
7. topic's main category, also known as category for this topic, Wikipedia category, Wikimedia category, Wiki category, subject category (wd:P910 as wdt/p): main Wikimedia category
8. has cause, also known as implied by, effect of, initial cause, originated due to, outcome of (wd:P828 as wdt/p): underlying cause, entity that ultimately resulted in this effect, subproperty of follows
9. has part(s), also known as has ingredient, has ingredients, had part, has member, has part (wd:P527 as wdt/p): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
10. country, also known as state, sovereign state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), subproperty of located in the administrative territorial entity

No other items found

[Tool]
Got 1 row and 3 columns:
| subject                                   | property                      | object                                                                       |
| ----------------------------------------- | ----------------------------- | ---------------------------------------------------------------------------- |
| COVID-19 pandemic in Italy (wd:Q84104992) | tabular case data (wdt:P8204) | <http://commons.wikimedia.org/data/main/Data:COVID-19+(STCenter)/IT/Q38.tab> |

[Assistant]
I have found that the entity "COVID-19 pandemic in Italy" (wd:Q84104992) has a property "tabular case data" (wdt:P8204) pointing to a Wikimedia Commons data file: http://commons.wikimedia.org/data/main/Data:COVID-19+(STCenter)/IT/Q38.tab

This file likely contains the time series data for COVID-19 cases and deaths in Italy. However, this is an external data file, not directly queryable via SPARQL on Wikidata. Wikidata itself does not store the full time series of cumulative deaths as structured statements, but rather links to external tabular data.

Therefore, within Wikidata's SPARQL endpoint, the best we can do is to return the country (Italy), the link to the tabular case data, and the fact that this data contains the time series of deaths (as per the file's documentation).

I will now write a SPARQL query that returns:
- The country (Italy)
- The tabular case data file URL
- The entity for the COVID-19 pandemic in Italy

This is the closest possible answer using Wikidata's structured data.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?type ( COUNT( DISTINCT ?org ) AS ?orgCount ) WHERE {
  ?org wdt:P31 / wdt:P279 * wd:Q43229 .
  {
    ?org wdt:P131 + wd:Q5765 .
  } UNION {
    ?org wdt:P159 wd:Q5765 .
  }
  ?org wdt:P31 ?type .
}
GROUP BY ?type
ORDER BY DESC ( ?orgCount )

Got 2 rows and 2 columns:
| type                                   | orgCount    |
| -------------------------------------- | ----------- |
| astronomical observatory (wd:Q1254933) | 1 (xsd:int) |
| voluntary association (wd:Q48204)      | 1 (xsd:int) |

There are two types of organizations located in the Balearic Islands according to Wikidata: 'astronomical observatory' (1 organization) and 'voluntary association' (1 organization). Each type has 1 distinct organization.

What are the types of organizations located in the Balearic Islands, along with the count of distinct organizations for each type, ordered by the count in descending order?

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

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

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

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

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

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

[User] What are the types of organizations located in the Balearic Islands, along with the count of distinct organizations for each type, ordered by the count in descending order?

[Assistant]
Let's break down the question:

1. We need to find organizations located in the Balearic Islands.
2. For each organization, we need its "type" (likely P31: instance of).
3. We need to count the number of distinct organizations for each type.
4. The results should be ordered by the count, descending.

Step 1: Find the entity for "Balearic Islands".
Step 2: Find the property for "located in" or "location".
Step 3: Find the property for "instance of" (type).
Step 4: Build a query to count organizations by type.

Let's start by searching for the entity "Balearic Islands".

[Tool]
Top 10 entity alternatives:
1. Balearic Islands, also known as Balearic Isles, Islas baleares (wd:Q5765): archipelago in the Mediterranean Sea, instance of archipelago
2. Balearic Islands (wd:Q4850673): instance of electoral district of the Spanish Congress, Spanish Congress Electoral District
3. Balearic Islands (wd:Q107356469): instance of historical province of Spain, province
4. Balearic Islands (wd:Q107356467): instance of autonomous community of Spain, instance of historical nationality, autonomous community of Spain
5. Balearic Islands (wd:Q103891547): instance of encyclopedia article, encyclopedia article
6. Balearic Islands (wd:Q107356470): instance of region of Spain, region (1833–1982)
7. flag of the Balearic Islands, also known as Balearic Islands flag, the Balearic Islands flag (wd:Q435103): flag of the Spanish autonomous community of the Balearic Islands, instance of flag of a country subdivision
8. history of Balearic Islands, also known as Balearic Islands history (wd:Q11925943): instance of aspect of history
9. geography of the Balearic Islands, also known as Balearic Islands geography, the Balearic Islands geography (wd:Q11924065): instance of geography of geographic location
10. Balearic Islands Ombudsman (wd:Q11950661)

[Assistant]
The main entity for the Balearic Islands as a geographic region is wd:Q5765.

Next, let's find the property for "located in" or "location" to identify organizations located in the Balearic Islands.

[Tool]
Top 10 property alternatives:
1. location, also known as neighborhood, locale, neighbourhood, place, place held (wd:P276 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
2. located in the administrative territorial entity, also known as is in the shire of, is in the voivodeship of, is in the state of, is in the territory of, is in the town of (wd:P131 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., subproperty of location, subproperty of part of
3. occupant, also known as location of, tenant, resident, renter, occupiers (wd:P466 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): person or organization occupying property, subproperty of used by
4. headquarters location, also known as headquarters locality, headquarters town, HQ, HQ location, location of headquarters (wd:P159 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, subproperty of significant place
5. adjacent building, also known as adjoining building, next to, neighboring building, located next to (wd:P3032 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): building adjacent to the item, subproperty of connects with
6. operating area, also known as operating region, serves geographic region, serves geographic area, serves area, region served (wd:P2541 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, subproperty of location
7. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): environment (geographic feature, building type, etc.) the narrative work is set in, subproperty of significant place, subproperty of depicts
8. narrative location, also known as set in location, setting of work, setting, setting location, setting place (wd:P840 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the narrative of the work is set in this location, subproperty of significant place, subproperty of depicts
9. located in the present-day administrative territorial entity, also known as present-day administrative territorial entity, located in the current administrative territorial entity, located in the modern-day administrative territorial entity, located in the present-day administrative territorial entity, located in present day administrative territorial entity (wd:P3842 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, subproperty of location, subproperty of part of
10. work location, also known as worked from, work place, working from, working at, worked at (wd:P937 as pqv/prv/psn/pqn/wdt/pr/p/psv/wdtn/prn/ps/pq): location where persons or organisations were actively participating in employment, business or other work, subproperty of significant place, subproperty of location

[Assistant]
The most relevant properties for organizations located in a region are:
- "located in the administrative territorial entity" (wdt:P131)
- "headquarters location" (wdt:P159)

To capture organizations based in the Balearic Islands, we should use both properties, as some organizations may use one or the other.

Next, let's confirm the property for "instance of" (type).

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

[Assistant]
The property for "instance of" (type) is wdt:P31.

Now, let's check what types of organizations are present in Wikidata and how they are modeled. Usually, organizations are instances of "organization" (Q43229) or its subclasses.

Let's confirm the entity for "organization".

[Tool]
Top 10 entity alternatives:
1. organization, also known as organisations, organization in sociology, organisation, organizations, institution (wd:Q43229)
2. .org, also known as dot-org, .org domain, .organization (wd:Q32131): instance of generic top-level domain, generic top-level domain
3. The Organization, also known as Organization (wd:Q1986084): 1971 film by Don Medford, instance of film
4. Organization (wd:Q105755428): Turkish intelligence series broadcast on TRT 1, instance of television series
5. organization, also known as auxiliary organization (wd:Q4827314)
6. Organization (wd:Q7101933): instance of scientific journal, peer-reviewed academic journal
7. Samaritans, also known as Organization (wd:Q2256668): Swiss first aid organization, instance of organization, instance of aid agency
8. organization (wd:Q2204053)
9. organization, also known as organisation, to organize, organizing (wd:Q2029841)
10. Organization (wd:Q33771005): instance of written work, essay by Parke Godwin

[Assistant]
The main entity for "organization" is wd:Q43229.

To summarize, our plan is:
- Find all entities that are instances (wdt:P31) of "organization" (wd:Q43229) or its subclasses.
- That are located in the Balearic Islands (wd:Q5765) via either "located in the administrative territorial entity" (wdt:P131) or "headquarters location" (wdt:P159), including containment via wdt:P131+.
- For each, get their type (wdt:P31).
- Count the number of distinct organizations per type.
- Order by count descending.

Now, let's check how "located in the administrative territorial entity" (wdt:P131) works for contained areas, as the Balearic Islands contain multiple municipalities and cities. To ensure we include organizations in all contained areas, we should use wdt:P131+ (transitive closure).

Let's build a draft query to get organization types and counts for those located in the Balearic Islands or any of its contained areas, using both wdt:P131+ and wdt:P159.

First, let's check if organizations in the Balearic Islands use wdt:P131 or wdt:P159 more commonly.

[Tool]
Got 4 rows and 3 columns:
| subject                                         | property                                                    | object                      |
| ----------------------------------------------- | ----------------------------------------------------------- | --------------------------- |
| El Cielo de Consell observatory (wd:Q115521876) | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |
| wd:Q125745042                                   | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |
| Port des Canonge Formation (wd:Q131542892)      | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |
| Balearic (wd:Q804899)                           | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |

[Tool]
Got 1 row and 3 columns:
| subject       | property                         | object                      |
| ------------- | -------------------------------- | --------------------------- |
| wd:Q114497915 | headquarters location (wdt:P159) | Balearic Islands (wd:Q5765) |

[Tool]
Got 4 rows and 3 columns:
| subject                                         | property                                                    | object                      |
| ----------------------------------------------- | ----------------------------------------------------------- | --------------------------- |
| El Cielo de Consell observatory (wd:Q115521876) | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |
| wd:Q125745042                                   | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |
| Port des Canonge Formation (wd:Q131542892)      | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |
| Balearic (wd:Q804899)                           | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q5765) |

[Tool]
Got 51 rows and 3 columns, showing the first 10 rows below:
| subject                                                         | property                                                    | object                           |
| --------------------------------------------------------------- | ----------------------------------------------------------- | -------------------------------- |
| Balearic Islands (wd:Q5765)                                     | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Mallorca (wd:Q8828)                                             | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Formentera (wd:Q52626)                                          | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Gymnesian Islands (wd:Q1248991)                                 | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Statute of Autonomy of the Balearic Islands (wd:Q3497643)       | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Ara Més (wd:Q114697636)                                         | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Estatut d'Autonomia de les Illes Balears de 2007 (wd:Q20108232) | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Day of the Balearic Islands (wd:Q2837870)                       | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Unity for the Isles (wd:Q9091499)                               | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| Podemos Balearic Islands (wd:Q105645935)                        | located in the administrative territorial entity (wdt:P131) | Balearic Islands (wd:Q107356467) |
| ...                                                             | ...                                                         | ...                              |

[Tool]
Got 162 rows and 3 columns, showing the first 10 rows below:
| subject                                                           | property                                                    | object              |
| ----------------------------------------------------------------- | ----------------------------------------------------------- | ------------------- |
| Ma-2141 (wd:Q107192267)                                           | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| Tamarit Line (wd:Q107560905)                                      | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| wd:Q108454714                                                     | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| Former railway line Santa Maria del Camí-Felanitx (wd:Q114212310) | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| Former railway line Palma-Santanyí (wd:Q114212569)                | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| House of Trullol (wd:Q114621622)                                  | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| Rubiols (wd:Q11703349)                                            | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| Torrent de na Borges (wd:Q11704658)                               | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| wd:Q11704720                                                      | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| Alcúdia bay (wd:Q11907519)                                        | located in the administrative territorial entity (wdt:P131) | Mallorca (wd:Q8828) |
| ...                                                               | ...                                                         | ...                 |

[Tool]
Falling back to an unconstrained search on the full search indices due to an error:
Got more than the maximum supported number of object candidates (65,536)

Top 10 entity alternatives:
1. organization, also known as organisations, organization in sociology, organisation, organizations, institution (wd:Q43229)
2. .org, also known as dot-org, .org domain, .organization (wd:Q32131): instance of generic top-level domain, generic top-level domain
3. The Organization, also known as Organization (wd:Q1986084): 1971 film by Don Medford, instance of film
4. Organization (wd:Q105755428): Turkish intelligence series broadcast on TRT 1, instance of television series
5. organization, also known as auxiliary organization (wd:Q4827314)
6. Organization (wd:Q7101933): instance of scientific journal, peer-reviewed academic journal
7. Samaritans, also known as Organization (wd:Q2256668): Swiss first aid organization, instance of organization, instance of aid agency
8. organization (wd:Q2204053)
9. organization, also known as organisation, to organize, organizing (wd:Q2029841)
10. Organization (wd:Q33771005): instance of written work, essay by Parke Godwin

No other items found

No literal items found

[Assistant]
We have confirmed that organizations are instances of wd:Q43229 ("organization") or its subclasses.

To ensure we capture all relevant organizations, we should:
- Find all entities where wdt:P31/wdt:P279* wd:Q43229 (instance of organization or subclass).
- That are located in the Balearic Islands (wd:Q5765) or any of its contained areas, via wdt:P131+ or wdt:P159.

Now, let's build the SPARQL query:
- Select the type (?type) of each organization.
- Count the number of distinct organizations (?org) per type.
- Order by count descending.
- Optionally, get the type label.

Here is the query plan:
```
SELECT ?type (COUNT(DISTINCT ?org) AS ?orgCount)
WHERE {
  ?org wdt:P31/wdt:P279* wd:Q43229 .
  {
    ?org wdt:P131+ wd:Q5765 .
  }
  UNION
  {
    ?org wdt:P159 wd:Q5765 .
  }
  ?org wdt:P31 ?type .
}
GROUP BY ?type
ORDER BY DESC(?orgCount)
```
Optionally, we can add the type label with OPTIONAL.

Let's execute this query.

[Tool]
Got 2 rows and 2 columns:
| type                                   | orgCount    |
| -------------------------------------- | ----------- |
| astronomical observatory (wd:Q1254933) | 1 (xsd:int) |
| voluntary association (wd:Q48204)      | 1 (xsd:int) |

[Tool]
Got 2 rows and 2 columns:
| type                                   | orgCount    |
| -------------------------------------- | ----------- |
| astronomical observatory (wd:Q1254933) | 1 (xsd:int) |
| voluntary association (wd:Q48204)      | 1 (xsd:int) |

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?item WHERE {
  ?item wdt:P279 * wd:Q87167 .
  FILTER ( ?item != wd:Q87167 )
}

Got 194 rows and 1 column, showing the first 5 and last 5 rows below:
| item                                   |
| -------------------------------------- |
| Lelampahan (wd:Q104079854)             |
| Tantri (wd:Q104079920)                 |
| Itihasa (wd:Q104080182)                |
| Wariga (wd:Q104080231)                 |
| agama (wd:Q104080293)                  |
| ...                                    |
| Gĕguritan Bañuwurung (wd:Q99430861)    |
| Gaguritan Rusak Gianyar (wd:Q99471867) |
| Gaguritan Pakang Raras (wd:Q99471961)  |
| Gaguritan Japatuwan (wd:Q99472047)     |
| Gaguritan Ni Sumala (wd:Q99472115)     |

There are 194 distinct items that are subclasses, at any level, of a manuscript (wd:Q87167) in Wikidata. The query returns their identifiers (QIDs).

Which distinct items are subclasses, at any level, of a manuscript (a document written by hand)?

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

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

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

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

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

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

[User] Which distinct items are subclasses, at any level, of a manuscript (a document written by hand)?

[Assistant]
Let's break down the question:

1. The user wants all distinct items that are subclasses (at any level) of "manuscript".
2. "Manuscript" is described as "a document written by hand".
3. In Wikidata, "subclass of" is property P279.
4. We need to find the entity for "manuscript" and then query all items that are subclasses (direct or indirect) of it.

Step 1: Find the entity for "manuscript".

I'll search for "manuscript" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. manuscript, also known as MSS, manuscript book, MS, manuscripts (wd:Q87167)
2. block lettering, also known as manuscript, printscript, print writing, ball and stick, block letters (wd:Q1261015)
3. manuscript, also known as manuscript format, manuscript (publishing) (wd:Q2376293)
4. Genkou, also known as manuscript (wd:Q127698834): Writings and other works intended for publication or presentation.
5. Manuscript (wd:Q16411389): instance of album, album
6. Manuscript (wd:Q60851530): studio album by Manizha, instance of album
7. Manuscript (wd:Q77898882): painting by Robert Rauschenberg, instance of painting
8. MANuscript (wd:Q118823871): instance of literary work
9. Manuscript (wd:Q75594785): photograph by Percy Benzie Abery, instance of photograph
10. Manuscript (wd:Q103955283): instance of encyclopedia article, encyclopedia article

[Assistant]
The most relevant entity for "manuscript" as a document written by hand is wd:Q87167.

Step 2: Find all items that are subclasses (at any level) of wd:Q87167.

In Wikidata, this is done by following the "subclass of" (P279) property recursively.

Step 3: Build the SPARQL query.

We want all items ?item such that ?item wdt:P279* wd:Q87167, and ?item != wd:Q87167 (to avoid including "manuscript" itself unless the user wants it).

Optionally, we can include labels for readability.

I'll now construct and execute the query.

[Tool]
Got 194 rows and 1 column, showing the first 5 and last 5 rows below:
| item                                   |
| -------------------------------------- |
| Lelampahan (wd:Q104079854)             |
| Tantri (wd:Q104079920)                 |
| Itihasa (wd:Q104080182)                |
| Wariga (wd:Q104080231)                 |
| agama (wd:Q104080293)                  |
| ...                                    |
| Gĕguritan Bañuwurung (wd:Q99430861)    |
| Gaguritan Rusak Gianyar (wd:Q99471867) |
| Gaguritan Pakang Raras (wd:Q99471961)  |
| Gaguritan Japatuwan (wd:Q99472047)     |
| Gaguritan Ni Sumala (wd:Q99472115)     |

[Tool] Stopped generation process