PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?country ?countryLabel ?numberOfDeaths ?startTime WHERE {
  wd:Q84104992 wdt:P17 ?country .
  OPTIONAL {
    wd:Q84104992 wdt:P1120 ?numberOfDeaths .
  }
  OPTIONAL {
    wd:Q84104992 wdt:P580 ?startTime .
  }
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}
LIMIT 10

The country related to the COVID-19 pandemic in Italy is Italy (wd:Q38). The start date of the pandemic is 2020-01-30. However, the number of cumulative deaths related to the COVID-19 pandemic in Italy is not available in the knowledge graph.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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]
To answer this question, I need to find the entity for the COVID-19 pandemic in Italy and then retrieve the country, the number of cumulative deaths, and the dates of those deaths related to this pandemic.

Step 1: Search for the entity representing the COVID-19 pandemic in Italy.

[Tool]
Top 10 entity alternatives:
1. COVID-19 pandemic in Italy, also known as 2019–20 coronavirus outbreak in Italy, 2020 Italy COVID-19 pandemic, COVID-19 outbreak in Italy, COVID pandemic in Italy, Italian 2020 coronavirus outbreak (wd:Q84104992): coronavirus pandemic in Italy
2. 2020 COVID-19 pandemic in Lombardy, also known as 2020 COVID-19 pandemic in Lombardy, Italy, 2020 COVID pandemic in Lombardy, 2020 Lombardy COVID-19 pandemic, COVID-19 pandemic in Lombardy, Italy, Lombardy COVID-19 pandemic (wd:Q89277312): viral pandemic in Lombardy
3. 2020 COVID-19 pandemic in Veneto, also known as 2020 COVID pandemic in Veneto, Veneto COVID-19 pandemic, COVID-19 pandemic in Veneto, Italy, 2020 Veneto COVID-19 pandemic, 2020 COVID-19 pandemic in Veneto, Italy (wd:Q89277314): viral pandemic in Veneto
4. COVID-19 pandemic in Apulia, also known as 2020 Apulia COVID-19 pandemic, Apulia COVID-19 pandemic, COVID-19 pandemic in Apulia, Italy, COVID pandemic in Apulia (wd:Q104719773)
5. COVID-19 pandemic in Abruzzo, also known as COVID pandemic in Abruzzo, COVID-19 pandemic in Abruzzo, Italy, 2020 Abruzzo COVID-19 pandemic, Abruzzo COVID-19 pandemic (wd:Q104719772)
6. COVID-19 pandemic in Basilicata, also known as 2020 Basilicata COVID-19 pandemic, Basilicata COVID-19 pandemic, COVID-19 pandemic in Basilicata, Italy, COVID pandemic in Basilicata (wd:Q104719782)
7. COVID-19 pandemic in Campania, also known as Campania COVID-19 pandemic, COVID pandemic in Campania, COVID-19 pandemic in Campania, Italy, 2020 Campania COVID-19 pandemic (wd:Q104719775)
8. COVID-19 pandemic in Calabria, also known as 2020 Calabria COVID-19 pandemic, Calabria COVID-19 pandemic, COVID-19 pandemic in Calabria, Italy, COVID pandemic in Calabria (wd:Q104719774)
9. COVID-19 pandemic in Emilia-Romagna, also known as 2020 Emilia-Romagna COVID-19 pandemic, COVID-19 pandemic in Emilia-Romagna, Italy, COVID pandemic in Emilia-Romagna, Emilia-Romagna COVID-19 pandemic (wd:Q104719776)
10. COVID-19 pandemic in Umbria, also known as 2020 Umbria COVID-19 pandemic, COVID-19 pandemic in Umbria, Italy, COVID pandemic in Umbria, Umbria COVID-19 pandemic (wd:Q104719788)

[Assistant]
I found the entity for the COVID-19 pandemic in Italy: wd:Q84104992.

Step 2: I will now look for properties related to the number of cumulative deaths and dates of those deaths for this entity. I will search for properties related to "deaths" for the entity wd:Q84104992.

[Tool]
Top 10 property alternatives:
1. case fatality rate, also known as fatality rate, CFR, case fatality ratio, case fatality, case fatality risk (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 has symptom, implies, leads to, led to, outcome (wd:P1542 as wdt/p): effect of this item, followed by
3. country, also known as sovereign state, state, land, host country (wd:P17 as wdt/p): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
4. location, also known as neighbourhood, whereabouts, vilage, venue, town (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 point on a map, gps coordinates, gps location, latitude, location (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, originated due to, initial cause, outcome of, reason (wd:P828 as wdt/p): underlying cause, entity that ultimately resulted in this effect, follows
7. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdt/p): main Wikimedia category
8. start time, also known as start date, since, valid, join time, joined (wd:P580 as wdt/p): time an entity begins to exist or a statement starts being valid, point in time
9. image, also known as still capture, still, screenshot, screen capture, portrait (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 of, within, system of, subsystem of, subgroup 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]
The search for properties related to "deaths" for the COVID-19 pandemic in Italy did not directly return a property for the number of cumulative deaths or dates of those deaths. However, the property "case fatality rate" (wdt:P3457) was found, which is related to deaths but not the exact number. The property "start time" (wdt:P580) might be useful for dates related to the pandemic.

Step 3: I will now check if there is a property for the number of deaths related to the COVID-19 pandemic in Italy by searching for properties related to "number of deaths" more generally.

[Tool]
Top 10 property alternatives:
1. number of deaths, also known as dead, qty fatalities, qty deaths, number of fatalities, fatalities (wd:P1120 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): total (cumulative) number of people who died since start as a direct result of an event or cause
2. number of casualties, also known as casualities, number of deaths and injuries (wd:P1590 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): number of people dead or injured because of this event.  Used wh...e data on number of deaths and number of injured is unavailable.
3. number of victims of killer, also known as number of persons killed, number of killed persons, number of homicide victims, kills, kill count (wd:P1345 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): people killed by the subject, both legal and illegal
4. death rate, also known as mortality rate (wd:P10091 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the total number of persons dead per 1,000 population per year
5. case fatality rate, also known as fatality rate, CFR, case fatality ratio, case fatality, case fatality risk (wd:P3457 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): proportion of patients who die of a particular medical condition out of all who have this condition within a given time frame
6. number of graves, also known as total number of graves, capacity cemetery (wd:P2351 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): number of burial places in a cemetery or necropolis, maximum capacity, quantity
7. quantity buried, also known as burials, graves, number of burials, number of people buried (wd:P2324 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): number of remains buried or interred, quantity
8. number of losses, also known as defeats, games lost, L, losses, lost (wd:P1356 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): number of sporting matches, games or events lost
9. cause of death, also known as death cause, murder method, method of murder, die of, die from (wd:P509 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): underlying or immediate cause of death.  Underlying cause (e.g. ...adest category, e.g. natural causes, accident, homicide, suicide
10. number of survivors, also known as survivors (wd:P1561 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): number of people surviving an event

[Assistant]
I found the property "number of deaths" (wdt:P1120) which represents the total (cumulative) number of people who died since the start as a direct result of an event or cause. This is likely the property we need for the number of cumulative deaths.

Step 4: I will now construct a SPARQL query to get the country, the number of cumulative deaths, and the start time (as a proxy for dates of deaths) for the COVID-19 pandemic in Italy (wd:Q84104992).

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant] The query failed due to the use of the SERVICE wikibase:label which is not supported by the endpoint. I will rewrite the query without using this SERVICE and instead use optional rdfs:label for the country.

[Tool]
Got 1 row and 4 columns:
| country        | countryLabel | numberOfDeaths | startTime                           |
| -------------- | ------------ | -------------- | ----------------------------------- |
| Italy (wd:Q38) | Italy (en)   |                | 2020-01-30T00:00:00Z (xsd:dateTime) |

[Assistant]
The query results show that the country is Italy (wd:Q38) and the start time of the COVID-19 pandemic in Italy is 2020-01-30. However, the number of cumulative deaths is not available in the data for this entity.

Step 5: I will now try to find if there is any statement or qualifier related to the number of deaths for this pandemic in Italy by listing statements for the entity wd:Q84104992 with the property number of deaths (wdt:P1120).

[Tool] Got no rows and 3 columns

[Assistant]
There are no direct statements with the property "number of deaths" (wdt:P1120) for the COVID-19 pandemic in Italy (wd:Q84104992).

Step 6: I will now try to find any related events or entities linked to this pandemic that might have the number of deaths recorded. I will look for any entities that are part of or related to the COVID-19 pandemic in Italy (wd:Q84104992) and check if they have the number of deaths property.

[Tool]
Got 3 rows and 3 columns:
| subject                                           | property           | object                                    |
| ------------------------------------------------- | ------------------ | ----------------------------------------- |
| 2020 COVID-19 pandemic in Lombardy (wd:Q89277312) | part of (wdt:P361) | COVID-19 pandemic in Italy (wd:Q84104992) |
| 2020 COVID-19 pandemic in Veneto (wd:Q89277314)   | part of (wdt:P361) | COVID-19 pandemic in Italy (wd:Q84104992) |
| wd:Q87350400                                      | part of (wdt:P361) | COVID-19 pandemic in Italy (wd:Q84104992) |

[Assistant]
There are related entities that are part of the COVID-19 pandemic in Italy:
- 2020 COVID-19 pandemic in Lombardy (wd:Q89277312)
- 2020 COVID-19 pandemic in Veneto (wd:Q89277314)
- wd:Q87350400 (unknown label)

Step 7: I will check if any of these related entities have the number of deaths property (wdt:P1120). I will start with the first one, 2020 COVID-19 pandemic in Lombardy (wd:Q89277312).

[Tool] Got no rows and 3 columns

[Assistant]
The entity 2020 COVID-19 pandemic in Lombardy (wd:Q89277312) does not have the number of deaths property (wdt:P1120).

Step 8: I will check the second entity, 2020 COVID-19 pandemic in Veneto (wd:Q89277314), for the number of deaths property.

[Tool] Got no rows and 3 columns

[Assistant]
The entity 2020 COVID-19 pandemic in Veneto (wd:Q89277314) also does not have the number of deaths property (wdt:P1120).

Step 9: I will check the third entity (wd:Q87350400) for the number of deaths property.

[Tool] Got no rows and 3 columns

[Assistant]
The entity wd:Q87350400 does not have the number of deaths property (wdt:P1120) either.

Step 10: Since the direct entities related to the COVID-19 pandemic in Italy do not have the number of deaths property, I will try to find any statements with qualifiers related to deaths for the main entity wd:Q84104992. I will list all properties for this entity to see if there is any property related to deaths or dates of deaths.

[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]
The properties for the entity COVID-19 pandemic in Italy (wd:Q84104992) do not show any direct property for the number of deaths or dates of deaths.

Step 11: I will try to find any statistical data or statements related to deaths for this pandemic by searching for the property "number of deaths" (wdt:P1120) with the object Italy (wd:Q38) to see if there are any related entities with this data.

[Tool] Got no rows and 3 columns

[Assistant]
There are no statements with the property "number of deaths" (wdt:P1120) and the object Italy (wd:Q38).

Step 12: I will try a different approach by searching for the property "number of deaths" (wdt:P1120) with the object being the COVID-19 pandemic in Italy (wd:Q84104992) to check if any entities have this property pointing to the pandemic.

[Tool] Got no rows and 3 columns

[Assistant]
There are no statements with the property "number of deaths" (wdt:P1120) and the object COVID-19 pandemic in Italy (wd:Q84104992).

Step 13: Since the number of cumulative deaths is not directly available for the COVID-19 pandemic in Italy, I will provide the country and the start date of the pandemic as the answer, and mention that the number of cumulative deaths data is not available in the knowledge graph.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?type ?typeLabel ( COUNT( DISTINCT ?org ) AS ?count ) WHERE {
  ?org wdt:P31 / wdt:P279 * wd:Q43229 .
  {
    ?org wdt:P131 wd:Q5765 .
  } UNION {
    ?org wdt:P159 wd:Q5765 .
  }
  ?org wdt:P31 ?type .
  ?type rdfs:label ?typeLabel
  FILTER ( LANG( ?typeLabel ) = "en" )
}
GROUP BY ?type ?typeLabel
ORDER BY DESC ( ?count )

Got 2 rows and 3 columns:
| type                                   | typeLabel                     | count       |
| -------------------------------------- | ----------------------------- | ----------- |
| astronomical observatory (wd:Q1254933) | astronomical observatory (en) | 1 (xsd:int) |
| voluntary association (wd:Q48204)      | voluntary association (en)    | 1 (xsd:int) |

The types of organizations located in the Balearic Islands along with the count of distinct organizations for each type, ordered by count in descending order, are:
1. Astronomical observatory: 1
2. Voluntary association: 1

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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]
To answer this question, I need to find organizations located in the Balearic Islands and then group them by their types, counting the distinct organizations for each type. Finally, I will order the results by the count in descending order.

Step 1: Identify the entity for the Balearic Islands.
Step 2: Identify the property for "located in" or "location."
Step 3: Identify the property for "instance of" to get the types of organizations.
Step 4: Write a SPARQL query to get the types of organizations located in the Balearic Islands with the count of distinct organizations for each type, ordered by count descending.

I will start by searching for the entity for the Balearic Islands.

[Tool]
Top 10 entity alternatives:
1. Balearic Islands, also known as Balearic Isles, Islas baleares (wd:Q5765): archipelago in the Mediterranean Sea
2. Balearic Islands (wd:Q4850673): Spanish Congress Electoral District
3. Balearic Islands (wd:Q107356469): province
4. Balearic Islands (wd:Q107356467): autonomous community of Spain
5. Balearic Islands (wd:Q107356470): region (1833–1982)
6. Balearic Islands (wd:Q103891547): encyclopedia article
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
8. history of Balearic Islands, also known as Balearic Islands history (wd:Q11925943)
9. geography of the Balearic Islands, also known as the Balearic Islands geography, Balearic Islands geography (wd:Q11924065)
10. Balearic Islands Award (wd:Q11953178): Spanish soccer award

[Assistant]
The entity for the Balearic Islands as an archipelago in the Mediterranean Sea is wd:Q5765. This is the most relevant entity for the location of organizations.

Next, I will identify the property for "located in" or "location" to find organizations located in the Balearic Islands. I will search for properties related to "located in" or "location."

[Tool]
Top 10 property alternatives:
1. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): 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 settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
3. occupant, also known as tenant, resident, renter, occupiers, lessee (wd:P466 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): person or organization occupying property, used by
4. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
5. adjacent building, also known as next to, neighboring building, located next to, adjoining building (wd:P3032 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): building adjacent to the item, connects with
6. operating area, also known as province served, serves jurisdiction, serves geographic region, serves geographic area, serves area (wd:P2541 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for, location
7. set in environment, also known as set in location, takes place in, setting location, playing in, narrative set in (wd:P8411 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): environment (geographic feature, building type, etc.) the narrative work is set in, significant place, depicts
8. narrative location, also known as set in location, set in, setting, setting location, setting of work (wd:P840 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the narrative of the work is set in this location, significant place, 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, would today be located in administrative territorial entity (wd:P3842 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the item was located in the territory of this present-day administrative unit; however the two did not at any point coexist in time, location, part of
10. coordinate location, also known as point on a map, gps coordinates, gps location, latitude, location (wd:P625 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

[Assistant]
The most suitable property for the location of organizations is "located in the administrative territorial entity" (wdt:P131). This property indicates that the item is located on the territory of the specified administrative entity.

Next, I will identify the property for "instance of" to get the types of organizations. I will search for properties related to "instance of."

[Tool]
Top 10 property alternatives:
1. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
2. Wikidata property example, also known as e.g., Wikidata property sample, property sample, property example, for example (wd:P1855 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): 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 amount, total number, total, qty, occurrences (wd:P1114 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): 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 subclass of property, superproperty, subPropertyOf, rdfs:subPropertyOf, parent property (wd:P1647 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): all resources related by this property are also related by that property, related property
5. partially coincident with, also known as intersecting with, has overlap with, has common parts with, has common members with, has common instances with (wd:P1382 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): object that partially overlaps with the subject in its instances, parts, or members, different from
6. model item, also known as specimen, sample, featured item, example item, best example (wd:P5869 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): 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 same concept as, same as, duplicate of, permanently duplicated item, duplicate of item (wd:P2959 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): 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 contains work, includes work, contents (wd:P1574 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): Links an individual copy of a work to the item for that work or edition, instance of
9. usage example, also known as sentence, use in a sentence, example sentence (wd:P5831 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): example of how a lexeme (sense) might be used
10. fictional or mythical analog of, also known as fictionalisation of, mythical analog of, fictional version of, fictionalization of, fictional analog of (wd:P1074 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): 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, inspired by

[Assistant]
The property for "instance of" is wdt:P31. This property indicates the type or class of which the subject is an example.

Now, I will write a SPARQL query to find the types of organizations (instance of) located in the Balearic Islands (wd:Q5765) using the property "located in the administrative territorial entity" (wdt:P131). I will count the distinct organizations for each type and order the results by the count in descending order.

[Tool]
Got 1 row and 3 columns:
| type                                   | typeLabel                     | count       |
| -------------------------------------- | ----------------------------- | ----------- |
| astronomical observatory (wd:Q1254933) | astronomical observatory (en) | 1 (xsd:int) |

[Assistant]
The query returned only one type of organization located in the Balearic Islands: astronomical observatory, with a count of 1. This result seems too narrow, possibly because of the filter restricting to organizations (wd:Q43229) or the location property.

I will try a broader query without restricting to organizations explicitly and see what types of entities are located in the Balearic Islands with their counts. Then I can filter for organizations if needed.

[Tool]
Got 3 rows and 3 columns:
| type                                   | typeLabel                     | count       |
| -------------------------------------- | ----------------------------- | ----------- |
| astronomical observatory (wd:Q1254933) | astronomical observatory (en) | 1 (xsd:int) |
| Catalan dialect (wd:Q3025977)          | Catalan dialect (en)          | 1 (xsd:int) |
| geological formation (wd:Q736917)      | geological formation (en)     | 1 (xsd:int) |

[Assistant]
The results show three types of entities located in the Balearic Islands, but only one instance each, and some are not organizations (e.g., Catalan dialect, geological formation).

This suggests that the property "located in the administrative territorial entity" (wdt:P131) might not be the best property to find organizations located in the Balearic Islands.

I will try using the property "headquarters location" (wdt:P159) which is often used for organizations to indicate their location. I will query organizations with headquarters location in the Balearic Islands and group by their types.

[Tool]
Got 1 row and 3 columns:
| type                              | typeLabel                  | count       |
| --------------------------------- | -------------------------- | ----------- |
| voluntary association (wd:Q48204) | voluntary association (en) | 1 (xsd:int) |

[Assistant]
The query using "headquarters location" (wdt:P159) returned one type of organization: voluntary association, with a count of 1.

To be more comprehensive, I will combine both properties "located in the administrative territorial entity" (wdt:P131) and "headquarters location" (wdt:P159) to find organizations located in the Balearic Islands and group by their types.

[Tool]
Got 2 rows and 3 columns:
| type                                   | typeLabel                     | count       |
| -------------------------------------- | ----------------------------- | ----------- |
| astronomical observatory (wd:Q1254933) | astronomical observatory (en) | 1 (xsd:int) |
| voluntary association (wd:Q48204)      | voluntary association (en)    | 1 (xsd:int) |

[Assistant]
The combined query returned two types of organizations located in the Balearic Islands: astronomical observatory and voluntary association, each with a count of 1.

This seems to be the best result given the data available in Wikidata.

I will now provide the final answer with the SPARQL query and the results.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?item ?itemLabel WHERE {
  ?item wdt:P279 + wd:Q87167 .
  OPTIONAL {
    ?item rdfs:label ?itemLabel FILTER ( LANG( ?itemLabel ) = "en" )
  }
}

Got 194 rows and 2 columns, showing the first 5 and last 5 rows below:
| item                                   | itemLabel                    |
| -------------------------------------- | ---------------------------- |
| Lelampahan (wd:Q104079854)             | Lelampahan (en)              |
| Tantri (wd:Q104079920)                 | Tantri (en)                  |
| Itihasa (wd:Q104080182)                | Itihasa (en)                 |
| Wariga (wd:Q104080231)                 | Wariga (en)                  |
| agama (wd:Q104080293)                  | agama (en)                   |
| ...                                    | ...                          |
| Gĕguritan Bañuwurung (wd:Q99430861)    | Gĕguritan Bañuwurung (en)    |
| Gaguritan Rusak Gianyar (wd:Q99471867) | Gaguritan Rusak Gianyar (en) |
| Gaguritan Pakang Raras (wd:Q99471961)  | Gaguritan Pakang Raras (en)  |
| Gaguritan Japatuwan (wd:Q99472047)     | Gaguritan Japatuwan (en)     |
| Gaguritan Ni Sumala (wd:Q99472115)     | Gaguritan Ni Sumala (en)     |

There are 194 distinct items that are subclasses, at any level, of a manuscript (a document written by hand). The query returns their Wikidata identifiers and English labels where available.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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] Step 1: Identify the entity for "manuscript" in Wikidata. I will search for the entity "manuscript" to get its Wikidata identifier.

[Tool]
Top 10 entity alternatives:
1. manuscript, also known as MSS, MS, manuscript book, manuscripts (wd:Q87167): document written by hand
2. block lettering, also known as ball and stick, block letter, block letters, manuscript, printscript (wd:Q1261015): sans-serif (or "gothic") style of writing in many scripts in whi... "please print"; may also be used as a synonym of block capitals
3. manuscript, also known as manuscript format, manuscript (publishing) (wd:Q2376293): work that an author submits to a publisher or editor for publication
4. Genkou, also known as manuscript (wd:Q127698834): Writings and other works intended for publication or presentation.
5. Manuscript (wd:Q16411389): album
6. Manuscript (wd:Q60851530): studio album by Manizha
7. Manuscript (wd:Q103955283): encyclopedia article
8. Manuscript (wd:Q122933956): sculpture by Seymour Lipton
9. Manuscript (wd:Q125094390): decorative artwork (glass) by Robert Kehlmann
10. Manuscript (wd:Q20797240): painting by Silja Rantanen

[Assistant]
The entity "manuscript" that corresponds to a document written by hand is wd:Q87167.

Step 2: I will now write a SPARQL query to find distinct items that are subclasses (at any level) of this manuscript entity (wd:Q87167). This means I will look for items that have a "subclass of" (P279) relationship recursively leading to the manuscript entity.

[Tool]
Got 194 rows and 2 columns, showing the first 5 and last 5 rows below:
| item                                   | itemLabel                    |
| -------------------------------------- | ---------------------------- |
| Lelampahan (wd:Q104079854)             | Lelampahan (en)              |
| Tantri (wd:Q104079920)                 | Tantri (en)                  |
| Itihasa (wd:Q104080182)                | Itihasa (en)                 |
| Wariga (wd:Q104080231)                 | Wariga (en)                  |
| agama (wd:Q104080293)                  | agama (en)                   |
| ...                                    | ...                          |
| Gĕguritan Bañuwurung (wd:Q99430861)    | Gĕguritan Bañuwurung (en)    |
| Gaguritan Rusak Gianyar (wd:Q99471867) | Gaguritan Rusak Gianyar (en) |
| Gaguritan Pakang Raras (wd:Q99471961)  | Gaguritan Pakang Raras (en)  |
| Gaguritan Japatuwan (wd:Q99472047)     | Gaguritan Japatuwan (en)     |
| Gaguritan Ni Sumala (wd:Q99472115)     | Gaguritan Ni Sumala (en)     |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wikibase: <http://wikiba.se/ontology#>
SELECT ?property ?propertyLabel ?value ?valueLabel WHERE {
  wd:Q9696 ?p ?value .
  ?property wikibase:directClaim ?p .
  OPTIONAL {
    ?property rdfs:label ?propertyLabel FILTER ( LANG( ?propertyLabel ) = "en" )
  }
  OPTIONAL {
    ?value rdfs:label ?valueLabel FILTER ( LANG( ?valueLabel ) = "en" )
  }
}
LIMIT 100

Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| property                         | propertyLabel               | value             | valueLabel |
| -------------------------------- | --------------------------- | ----------------- | ---------- |
| number of children (wd:P1971)    | number of children (en)     | 4 (xsd:decimal)   |            |
| height (wd:P2048)                | height (en)                 | 185 (xsd:decimal) |            |
| ISNI (wd:P213)                   | ISNI (en)                   | 0000000109117086  |            |
| Munzinger person ID (wd:P1284)   | Munzinger person ID (en)    | 00000007415       |            |
| BHF author ID (wd:P8547)         | BHF author ID (en)          | 000004210         |            |
| ...                              | ...                         | ...               | ...        |
| TracesOfWar person ID (wd:P8578) | TracesOfWar person ID (en)  | 34053             |            |
| Libraries Australia ID (wd:P409) | Libraries Australia ID (en) | 35266141          |            |
| FAST ID (wd:P2163)               | FAST ID (en)                | 35588             |            |
| LittleSis people ID (wd:P3388)   | LittleSis people ID (en)    | 35618             |            |
| openMLOL author ID (wd:P3762)    | openMLOL author ID (en)     | 36200             |            |

The properties and their values associated with John F. Kennedy (wd:Q9696) include various attributes such as number of children, height, and multiple identifiers. The SPARQL query provided retrieves these properties and their values along with optional English labels for better readability.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 properties and their values associated with John F. Kennedy?

[Assistant] To answer the question about the properties and their values associated with John F. Kennedy, I will first search for the entity "John F. Kennedy" in Wikidata to get its identifier. Then, I will retrieve the properties and their values for that entity.

[Tool]
Top 10 entity alternatives:
1. John F. Kennedy, also known as John Kennedy, John Fitzgerald Kennedy, John F Kennedy, Kennedy, President Kennedy (wd:Q9696): President of the United States from 1961 to 1963, anti-communist, naval officer, statesperson, journalist, politician, writer
2. John F. Kennedy, also known as CVN-79, PCU John F. Kennedy, USS John F. Kennedy (wd:Q240666): 2019 Gerald R. Ford-class aircraft carrier
3. MV John F. Kennedy, also known as IMO 8643195, John F. Kennedy (wd:Q29378914): Staten Island Ferry vessel.
4. John F Kennedy (wd:Q22006430): horse
5. John F. Kennedy (wd:Q130973914): sculpture by Evangelos Frudakis
6. John F. Kennedy, also known as John Flanders Kennedy (wd:Q28836957): American politician and member of the Georgia State Senate, politician
7. John F Kennedy (wd:Q42586306): cultural heritage monument in Aruba
8. Rosa 'John F. Kennedy', also known as John F. Kennedy (wd:Q83668459): rose cultivar
9. John F. Kennedy (wd:Q80912685): scientific article published on 01 November 1963
10. John F. Kennedy (wd:Q78526936): edition; published in 1997

[Assistant] The entity for John F. Kennedy as the President of the United States is wd:Q9696. Now I will retrieve the properties and their values associated with this entity. Since there could be many properties, I will limit the number of results to a reasonable amount to get a broad overview.

[Tool]
SPARQL execution failed:
Error while executing a SERVICE request to <http://wikiba.se:80/ontology#label>: SERVICE responded with HTTP status code: 301, Moved Permanently. First 100 bytes of the response: '<html>
<head><title>301 Moved Permanently</title></head>
<body>
<center><h1>301 Moved Permanently'

[Assistant] The query failed because the SERVICE wikibase:label is not supported by the QLever SPARQL endpoint. I will rewrite the query to get the labels for properties and values using rdfs:label instead and make the labels optional.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| property                         | propertyLabel               | value             | valueLabel |
| -------------------------------- | --------------------------- | ----------------- | ---------- |
| number of children (wd:P1971)    | number of children (en)     | 4 (xsd:decimal)   |            |
| height (wd:P2048)                | height (en)                 | 185 (xsd:decimal) |            |
| ISNI (wd:P213)                   | ISNI (en)                   | 0000000109117086  |            |
| Munzinger person ID (wd:P1284)   | Munzinger person ID (en)    | 00000007415       |            |
| BHF author ID (wd:P8547)         | BHF author ID (en)          | 000004210         |            |
| ...                              | ...                         | ...               | ...        |
| TracesOfWar person ID (wd:P8578) | TracesOfWar person ID (en)  | 34053             |            |
| Libraries Australia ID (wd:P409) | Libraries Australia ID (en) | 35266141          |            |
| FAST ID (wd:P2163)               | FAST ID (en)                | 35588             |            |
| LittleSis people ID (wd:P3388)   | LittleSis people ID (en)    | 35618             |            |
| openMLOL author ID (wd:P3762)    | openMLOL author ID (en)     | 36200             |            |

[Assistant] I retrieved 100 properties and their values for John F. Kennedy (wd:Q9696). The results include various properties such as number of children, height, ISNI, and many identifiers. Since the user asked for properties and their values, I will provide the SPARQL query that retrieves these properties and values. I will include labels for better readability where available.

[Tool] Stopped generation process


The specific data about the number of legal units for each level 3 legal category in France, with category names in French, and a summary line for categories with counts 499 or less, is not directly available or explicitly modeled in Wikidata. Searches for relevant entities and properties such as 'legal unit', 'legal category', and 'level 3 legal category' did not yield appropriate results. Therefore, I cannot construct a SPARQL query to answer the question as requested.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 is the number of legal units for each level 3 legal category in France, with the category name in French, where the count of legal units is greater than 499? Additionally, provide a summary line labeled "OTHER" that sums up the number of legal units for all other categories with counts of 499 or less.

[Assistant]
To answer this question, I need to find the legal units in France categorized by level 3 legal categories, get their counts, and filter those with counts greater than 499. Additionally, I need to provide a summary line labeled "OTHER" that sums the counts of legal units for categories with counts 499 or less. The category names should be in French.

Step 1: Identify the entity for France.
Step 2: Identify the property for legal units.
Step 3: Identify the property for level 3 legal categories.
Step 4: Identify the property for the category name in French.
Step 5: Query the counts of legal units for each level 3 legal category in France.
Step 6: Filter categories with counts greater than 499.
Step 7: Sum counts for categories with counts 499 or less and label as "OTHER".

I will start by searching for the entity for France.

[Tool]
Top 10 entity alternatives:
1. France, also known as FR, FRA, French Republic (wd:Q142): country in Western Europe
2. France men's national association football team, also known as France, France men's national football team, France national association football team, France national football team, France national soccer team (wd:Q47774): national association football team representing France
3. French Fifth Republic, also known as 5th French Republic, French Republic, French 5th Republic, France, Fifth French Republic (wd:Q200686): current system of government of France (1958–present)
4. Bourbon Restoration in France, also known as France, Kingdom of France (wd:Q207162): period of French history, 1814–1830
5. French Community, also known as France (wd:Q1120363): International political association
6. France at the 2008 Summer Olympics, also known as France (wd:Q146845): sporting event delegation
7. France, also known as French battleship France (wd:Q49644): 1912 Courbet-class battleship
8. France, also known as On a Half Clear Morning (wd:Q76488728): 2021 film directed by Bruno Dumont
9. La France, also known as France (wd:Q3209212): 2007 film by Serge Bozon
10. France (wd:Q5478259): constituency of the European Parliament

[Assistant]
The entity for France as a country is wd:Q142.

Next, I need to identify the property for legal units and the property for level 3 legal categories. Since the question is about legal units and legal categories, I will search for properties related to "legal unit" and "legal category" to find the appropriate properties. I will start with "legal unit".

[Tool]
Top 10 property alternatives:
1. statistical unit, also known as describe items of type, statistical designation, unit (wd:P2353 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): member of a dataset; type of statistical designation
2. military unit, also known as member of military unit, unit (wd:P7779 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): smallest military unit that a person is/was in, member of
3. room number, also known as apartment, room identifier, suite, unit (wd:P7532 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): number or identifier of a room, which can qualify the address (P6375) of a place to complete it
4. recommended unit of measurement, also known as measured in, unit of measurement, unit, standard unit of measurement (wd:P8111 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): unit in which a quantity is measured as recommended by a standard (SI, ISO, IEC, etc.)
5. laws applied, also known as applied law, law applied, legal base (wd:P3014 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the constitutional and statutory provisions that were the primary basis for a decision or law
6. military branch, also known as unit branch, service branch, formation, branch (wd:P241 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): branch to which this military unit, award, office, or person belongs, e.g. Royal Navy, part of
7. legal form, also known as legal structure, structured as (wd:P1454 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): legal form of an entity, instance of
8. located in the administrative territorial entity, also known as is in the settlement of, is in the voivodeship of, is in the shire of, is in the state of, is in the territory of (wd:P131 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
9. law identifier, also known as law number, regulation number, regulation id, regulation i.d., law or regulation identifying number (wd:P8550 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): a sequence of numbers assigned to laws/acts that were passed by countries or local governments
10. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of

[Assistant]
The search for "legal unit" did not return a direct property that clearly represents "number of legal units." The closest relevant property might be "legal form" (wd:P1454) which refers to the legal form of an entity. However, this might not directly represent the count of legal units.

I will now search for properties related to "legal category" to see if there is a property that represents level 3 legal categories.

[Tool]
Top 10 property alternatives:
1. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
2. heritage designation, also known as legal protection, protection, protected status, listing, listed status (wd:P1435 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): heritage designation of a cultural or natural site, has part(s), instance of
3. legal form, also known as legal structure, structured as (wd:P1454 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): legal form of an entity, instance of
4. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): main Wikimedia category
5. category's main topic, also known as item for this category, cmt, main article in category, main category topic, main subject of category (wd:P301 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): primary topic of the subject Wikimedia category, main subject
6. main regulatory text, also known as regulated by, statutory authorization, statutory authority, legally established by, legal basis (wd:P92 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): text setting the main rules by which the subject is regulated
7. protective marking, also known as confidentiality, document classification, document marking, information classification, privacy category (wd:P10129 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): label applied to an intellectual work to indicate how the docume... be handled, particularly from a security or privacy perspective
8. laws applied, also known as applied law, law applied, legal base (wd:P3014 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the constitutional and statutory provisions that were the primary basis for a decision or law
9. category related to list, also known as category equivalent to list, category related to this list, list category, list has category, list's category (wd:P1754 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): Wikimedia category that matches the Wikimedia list
10. items classified, also known as type of items classified, type of items categorized, type of entities classified, type of entities categorized, entities classified (wd:P13359 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): type of items classified by this classification system or act of classification

[Assistant]
The search for "legal category" did not return a direct property that clearly represents level 3 legal categories. The closest relevant property might be "legal form" (wd:P1454) which refers to the legal form of an entity. However, this might not directly represent level 3 legal categories either.

Given the complexity and specificity of the question regarding "level 3 legal category" and "number of legal units," it seems this information might not be directly available in Wikidata or might be represented in a more complex way.

I will try a different approach: I will search for "legal unit" as an entity to see if there is a specific item for legal units in France or legal categories in France.

[Tool]
Top 10 entity alternatives:
1. legal unit of measure (wd:Q22282168)
2. United States legal system, also known as court system of the United States, legal system of the United States (wd:Q4445375)
3. [New legal measure units] (wd:Q66870079): scientific article published on 01 September 1976
4. type of legal entity in the UK, also known as legal form (UK), type of legal entity (United Kingdom), type of legal entity (UK), type of legal entity in the United Kingdom, type of business entity (United Kingdom) (wd:Q57655433): legal entity used in the UK
5. United Arab Emirates Legal Process (wd:Q18356599)
6. Coronary care units: legal considerations (wd:Q68900361): scientific article published on 01 January 1974
7. Coronary care units: legal considerations (wd:Q39223479): scientific article published on January 1978
8. Legal Affairs Unit Files (NAID 7408582) (wd:Q63912670): series in the National Archives and Records Administration's holdings
9. LEGAL UNITS OF ELECTRIC MEASURE (wd:Q81196971): scientific article published on 01 January 1895
10. United Nations Office of Legal Affairs (wd:Q638357): United Nations office

[Assistant]
The search for "legal unit" as an entity did not return a relevant entity representing legal units in France or legal categories.

Given the specificity of the question about "level 3 legal category" and "number of legal units" in France, it seems this information might not be directly available or explicitly modeled in Wikidata.

I will now try to search for "legal category" as an entity to see if there is a specific item for legal categories in France.

[Tool]
Top 10 entity alternatives:
1. Biobank as a legal category (wd:Q129792623): scholarly article published on 14 July 2023
2. regulatory class of chemical substances, also known as class of chemical substances by legal status, class of chemical substances by regulation, class of chemical substances by regulatory status, legal category of chemical substances, regulatory category of chemical substances (wd:Q133177856): class of chemical substances according to how they are treated by law/regulation
3. Data Philanthropy:Its Category and Legal Framework (wd:Q120581360): scholarly article in Journal of Shanghai Jiaotong University (Philosophy and Social Sciences) no. 2, 2022
4. LEGAL CONSCIOUSNESS AS AN ANTI-CORRUPTION CATEGORY (wd:Q129142906): scholarly article
5. About legal limitation as criminal law category (wd:Q128607408): scholarly article
6. Legal content of the category "False information (wd:Q127462941): scholarly article
7. The Legal Category of Authenticity. Current Perspectives (wd:Q128037276): scholarly article
8. SCIENCE AS A LEGAL CATEGORY: COMPARATIVE LEGAL RESEARCH (wd:Q127668475): scholarly article
9. category (wd:Q719395): algebraic structure of objects and morphisms between objects, which can be associatively composed if the (co)domains agree
10. Legal (wd:Q2210557): town in Alberta, Canada

[Assistant]
The search for "legal category" as an entity returned mostly scholarly articles and unrelated entities. There is no clear entity representing level 3 legal categories in France.

Given the difficulty in finding direct properties or entities for "level 3 legal category" and "number of legal units" in France, it is likely that this specific data is not available in Wikidata or not modeled in a straightforward way.

Therefore, I am unable to construct a SPARQL query to answer the question as requested.

I will now provide a cancellation response explaining the situation.

[Tool] Stopped generation process


PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?settlement ?settlementLabel ?coord ?centerCoord ?distance WHERE {
  BIND( "POINT(14.644975 49.534590)" ^^ geo:wktLiteral AS ?centerCoord ) .
  ?settlement wdt:P31 / wdt:P279 * wd:Q486972 ; wdt:P17 wd:Q213 ; wdt:P625 ?coord .
  BIND( geof:distance ( ?coord , ?centerCoord ) AS ?distance ) .
  FILTER ( ?distance <= 35000 ) .
  OPTIONAL {
    ?settlement rdfs:label ?settlementLabel FILTER ( LANG( ?settlementLabel ) = "en" )
  }
}
ORDER BY ?distance

There are multiple settlements (villages and towns) in the Czech Republic within a 35 km radius from the village Mezno (wd:Q2180733). The coordinates of Mezno are POINT(14.644975 49.534590). The query results include the settlements' identifiers, their English labels (if available), their coordinates, the center point coordinates, and the distance from Mezno in meters. The distance is calculated using geospatial functions in Wikidata.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 settlements (villages and towns) in the Czech Republic within a 35 km radius from the village of Mezno? Please include their coordinates, the coordinates of the center point, and the distance from the center point.

[Assistant] Step 1: Identify the entity for the village "Mezno" in the Czech Republic. I will search for "Mezno" in Wikidata to get its identifier.

[Tool]
Top 10 entity alternatives:
1. Mezno (wd:Q2180733): village in Benešov District of Central Bohemian region
2. Mezno (wd:Q30917394): former railway stop in Mezno, Czech Republic
3. Mezno (wd:Q111685698): railway stop in Czechia
4. Mezno (wd:Q96153716): pond in Mezno, Czech Republic
5. Mitrovice (Mezno) (wd:Q12038239): village in Benešov District of Central Bohemian region
6. Mezno Tunnel (wd:Q108507418)
7. Meznokto metropola (wd:Q12352510)
8. Structural isomerism and thermod...ed by B3LYP and PCM calculations (wd:Q43063359): scientific article published in June 2010

[Assistant]
The village "Mezno" in the Czech Republic is identified as wd:Q2180733.

Step 2: Retrieve the coordinates of Mezno (wd:Q2180733) as the center point for the radius search.

[Tool]
Got 1 row and 1 column:
| coord                                       |
| ------------------------------------------- |
| POINT(14.644975 49.534590) (geo:wktLiteral) |

[Assistant]
The coordinates of the village Mezno (wd:Q2180733) are POINT(14.644975 49.534590).

Step 3: Find settlements (villages and towns) in the Czech Republic within a 35 km radius from Mezno. I will use the coordinates of Mezno as the center point and filter settlements by their distance from this point. I will also retrieve their coordinates and calculate the distance from Mezno.

[Tool]
SPARQL execution failed:
Invalid SPARQL query: Prefix uom was not registered using a PREFIX declaration

[Assistant] The query failed because the prefix "uom" was not declared. I will fix the query by declaring the prefix for units of measure and retry.

[Tool]
SPARQL execution failed:
Unsupported unit of measurement for distance.. In file "/qlever/src/util/GeoSparqlHelpers.cpp " at line 72

[Assistant] The query failed because the unit of measurement "uom:metre" for the distance function is unsupported. I will try to calculate the distance without specifying the unit and see if it works.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| settlement                             | settlementLabel              | coord                                       | distance                  |
| -------------------------------------- | ---------------------------- | ------------------------------------------- | ------------------------- |
| wd:Q56412936                           |                              | POINT(14.644975 49.534590) (geo:wktLiteral) | 4.44441e-05 (xsd:decimal) |
| wd:Q56412936                           |                              | POINT(14.644975 49.534590) (geo:wktLiteral) | 4.44441e-05 (xsd:decimal) |
| Lažany (wd:Q12032819)                  | Lažany (en)                  | POINT(14.653100 49.544700) (geo:wktLiteral) | 1.26789 (xsd:decimal)     |
| Stupčice (wd:Q12056785)                | Stupčice (en)                | POINT(14.626700 49.533900) (geo:wktLiteral) | 1.32104 (xsd:decimal)     |
| wd:Q11885495                           |                              | POINT(14.635000 49.547200) (geo:wktLiteral) | 1.57612 (xsd:decimal)     |
| ...                                    | ...                          | ...                                         | ...                       |
| Nehonín (wd:Q12040565)                 | Nehonín (en)                 | POINT(14.550600 49.506700) (geo:wktLiteral) | 7.48514 (xsd:decimal)     |
| Ostrý (wd:Q12043097)                   | Ostrý (en)                   | POINT(14.542500 49.524200) (geo:wktLiteral) | 7.48553 (xsd:decimal)     |
| Nová Ves u Mladé Vožice (wd:Q56412759) | Nová Ves u Mladé Vožice (en) | POINT(14.748002 49.543100) (geo:wktLiteral) | 7.4942 (xsd:decimal)      |
| Nová Ves u Mladé Vožice (wd:Q56412759) | Nová Ves u Mladé Vožice (en) | POINT(14.748002 49.543100) (geo:wktLiteral) | 7.4942 (xsd:decimal)      |
| Beranova Lhota (wd:Q11131586)          | Beranova Lhota (en)          | POINT(14.710600 49.482200) (geo:wktLiteral) | 7.5092 (xsd:decimal)      |

[Assistant]
I have found settlements (villages and towns) in the Czech Republic within a 35 km radius from the village Mezno (wd:Q2180733). The query results include their coordinates, the coordinates of the center point (Mezno), and the distance from the center point. Some settlements have English labels.

Now I will provide the final SPARQL query and the answer based on the query results.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?periodical ?periodicalLabel WHERE {
  ?periodical wdt:P31 wd:Q1002697 ; wdt:P407 wd:Q143 .
  OPTIONAL {
    ?periodical rdfs:label ?periodicalLabel FILTER ( LANG( ?periodicalLabel ) = "en" )
  }
}

Got 89 rows and 2 columns, showing the first 5 and last 5 rows below:
| periodical                                                                         | periodicalLabel                                                          |
| ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Momenton! (wd:Q10331627)                                                           | Momenton! (en)                                                           |
| Pola Esperantisto (wd:Q11821362)                                                   | Pola Esperantisto (en)                                                   |
| El Popola Ĉinio (wd:Q119633)                                                       | El Popola Ĉinio (en)                                                     |
| Beletra Almanako N-ro 4 (februaro 2009) (wd:Q12345463)                             | Beletra Almanako N-ro 4 (februaro 2009) (en)                             |
| Bohema Revuo Esperantista (wd:Q12345746)                                           | Bohema Revuo Esperantista (en)                                           |
| ...                                                                                | ...                                                                      |
| Mondano (wd:Q80452320)                                                             | Mondano (en)                                                             |
| Norda prismo : kultura, socia, literatura, revuo, dumonata (wd:Q80452629)          | Norda prismo : kultura, socia, literatura, revuo, dumonata (en)          |
| Paco : oficiala organo de Mondpaca esperantista movado (MEM) (wd:Q80452991)        | Paco : oficiala organo de Mondpaca esperantista movado (MEM) (en)        |
| UABO : (antaǔe Ligilo por vidant...la asocio de blindul-organizaĵoj (wd:Q80454504) | UABO : (antaǔe Ligilo por vidant...la asocio de blindul-organizaĵoj (en) |
| Lingvo Internacia (wd:Q844150)                                                     | Lingvo Internacia (en)                                                   |

There are 89 periodicals that have a primary language of Esperanto. Some examples include Momenton!, Pola Esperantisto, El Popola Ĉinio, Beletra Almanako N-ro 4 (februaro 2009), and Bohema Revuo Esperantista.

[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

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/
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. Use the functions to search for 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. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
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 assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. 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.
4. 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.
5. 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.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. 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 periodicals have a primary language of Esperanto?

[Assistant]
To answer the question "Which periodicals have a primary language of Esperanto?", I need to identify the following:

1. The entity or class representing "periodicals" in Wikidata.
2. The property that indicates the "primary language" of a periodical.
3. The entity representing the language "Esperanto".

I will start by searching for the entity representing "periodical" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. periodical, also known as periodical literature, periodical publication, periodicals (wd:Q1002697): publication type, serial publication that appears in a new edition on a regular schedule
2. Periodical (wd:Q103961464): encyclopedia article
3. Periodical, also known as Publication, Magazine, Cartoon Magazine, Journal, Cartoon mag (wd:Q67520005): a monthly cartoon publication
4. periodicals, also known as periodical, periodicals as topic (wd:Q131470059): study of periodicals
5. periodical (wd:L727823-S1)
6. periodical (wd:L339209-F1)
7. periodical (wd:L339209)
8. periodical (wd:L325294-F1)
9. periodical (wd:L325294)
10. article, also known as journal article, periodical article, serial article (wd:Q191067): text that forms an independent part of a publication

[Assistant] The entity "periodical" as a publication type is represented by wd:Q1002697 in Wikidata. Next, I need to find the property that indicates the "primary language" of a periodical. I will search for properties related to "primary language" or "language" that could be used for periodicals.

[Tool]
Top 10 property alternatives:
1. parent astronomical body, also known as star, primary, parent body, orbits, planetary system (wd:P397 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): major astronomical body the item belongs to
2. main subject, also known as primary topic, primary subject, primary keyword, plot keyword, of (wd:P921 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): primary topic of a work (see also P180: depicts), depicts
3. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): language or languages a person has learned from early childhood, languages spoken, written or signed
4. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
5. programmed in, also known as language, programming language, written in (wd:P277 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): the programming language(s) in which the software is developed
6. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): language(s) that a person or a people speaks, writes or signs, including the native language(s)
7. primary note, also known as vadi (wd:P7600 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): most prominent note in a raga
8. has index case, also known as index case, patient zero, primary case (wd:P1660 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): initial patient in the population of an epidemiological investigation
9. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): language designated as official by this item, language used
10. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as wdtn/pq/psn/p/prn/psv/wdt/pr/prv/pqn/pqv/ps): language widely used (spoken or written) in this place or at this event or organisation

[Assistant] The property "language of work or name" (wdt:P407) seems to be the most appropriate to indicate the language associated with a creative work, which can include periodicals. Now, I need to find the entity representing the language "Esperanto". I will search for "Esperanto" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Esperanto, also known as eo, epo, Esperanto language (wd:Q143): international auxiliary language designed by L. L. Zamenhof
2. Esperanto (wd:Q608836): official magazine of the World Esperanto Association (UEA)
3. Esperanto (wd:Q5398868): Belgo-English band
4. Esperanto, also known as Esperanto.4733 (wd:Q15734339): computer virus
5. Esperanto (wd:Q3733094): 1993 debut studio album by Elektric Music
6. Esperanto (wd:Q10489150): restaurant in Stockholm, Sweden
7. Esperanto (wd:Q12347414): Spanish ship
8. Esperanto (wd:Q11290111): album by Ryuichi Sakamoto
9. Esperanto (wd:Q5398858): US-American fishing schooner
10. Esperanto (wd:Q23012159): album by Freundeskreis

[Assistant] The entity representing the language Esperanto is wd:Q143. Now I will construct a SPARQL query to find periodicals (wd:Q1002697) that have the primary language (wdt:P407) set to Esperanto (wd:Q143). I will also include labels for better readability.

[Tool]
Got 89 rows and 2 columns, showing the first 5 and last 5 rows below:
| periodical                                                                         | periodicalLabel                                                          |
| ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Momenton! (wd:Q10331627)                                                           | Momenton! (en)                                                           |
| Pola Esperantisto (wd:Q11821362)                                                   | Pola Esperantisto (en)                                                   |
| El Popola Ĉinio (wd:Q119633)                                                       | El Popola Ĉinio (en)                                                     |
| Beletra Almanako N-ro 4 (februaro 2009) (wd:Q12345463)                             | Beletra Almanako N-ro 4 (februaro 2009) (en)                             |
| Bohema Revuo Esperantista (wd:Q12345746)                                           | Bohema Revuo Esperantista (en)                                           |
| ...                                                                                | ...                                                                      |
| Mondano (wd:Q80452320)                                                             | Mondano (en)                                                             |
| Norda prismo : kultura, socia, literatura, revuo, dumonata (wd:Q80452629)          | Norda prismo : kultura, socia, literatura, revuo, dumonata (en)          |
| Paco : oficiala organo de Mondpaca esperantista movado (MEM) (wd:Q80452991)        | Paco : oficiala organo de Mondpaca esperantista movado (MEM) (en)        |
| UABO : (antaǔe Ligilo por vidant...la asocio de blindul-organizaĵoj (wd:Q80454504) | UABO : (antaǔe Ligilo por vidant...la asocio de blindul-organizaĵoj (en) |
| Lingvo Internacia (wd:Q844150)                                                     | Lingvo Internacia (en)                                                   |

[Tool] Stopped generation process