PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?city ?cityLabel ?population WHERE {
  ?city wdt:P31 wd:Q515 ; wdt:P131 wd:Q1408 ; wdt:P1082 ?population .
  FILTER ( ?population > 100000 ) OPTIONAL {
    ?city rdfs:label ?cityLabel FILTER ( LANG( ?cityLabel ) = "en" )
  }
}
ORDER BY DESC ( ?population )

According to the data available in Wikidata, there are no cities in New Jersey with a population greater than 100,000 that are directly linked with the property 'located in the administrative territorial entity' (wdt:P131) to New Jersey (wd:Q1408). The data might be incomplete or structured differently, so no such cities could be retrieved with the given criteria.

[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] Give me all cities in New Jersey with more than 100000 inhabitants.

[Assistant]
To answer this question, I need to find entities that are cities located in New Jersey and have a population greater than 100,000 inhabitants.

Step 1: Identify the entity for "New Jersey."
Step 2: Identify the property for "located in" or "located in the administrative territorial entity."
Step 3: Identify the property for "population."
Step 4: Construct a SPARQL query to find cities in New Jersey with a population greater than 100,000.

I will start by searching for the entity "New Jersey."

[Tool]
Top 10 entity alternatives:
1. New Jersey, also known as Garden State, US-NJ, the State of New Jersey, the Garden State, State of New Jersey (wd:Q1408): state of the United States of America
2. Newark, also known as Brick City, City of Newark, Newark, New Jersey, Newark, NJ (wd:Q25395): city in Essex County, New Jersey, United States
3. Newton, also known as Newton, New Jersey, Newton, NJ (wd:Q1089234): town in Sussex County, New Jersey, United States
4. Newfield, also known as Newfield, NJ, Newfield, New Jersey (wd:Q931145): borough in New Jersey
5. New Jersey (wd:Q779923): 1988 studio album by Bon Jovi
6. Province of New Jersey, also known as New Jersey, New Jersey Colony, Colony of New Jersey (wd:Q2335128): former colony in North America, part of Great Britain
7. Newfoundland, also known as Newfoundland, New Jersey (wd:Q7017818): unincorporated community in New Jersey, United States
8. Newtonville, also known as Newtonville, New Jersey, Newtonville, NJ (wd:Q14705810): unincorporated community in Atlantic County, New Jersey, United States
9. Newport, also known as Newport, New Jersey (wd:Q7018698): human settlement in Downe Township, New Jersey, United States of America
10. Newtown, also known as Newtown, New Jersey (wd:Q20657441): human settlement in Piscataway, New Jersey, United States of America

[Assistant]
I found the entity for New Jersey as wd:Q1408, which is a state of the United States of America.

Next, I need to identify the property for "located in the administrative territorial entity" and the property for "population."

I will search for properties related to "located in" or "located in the administrative territorial entity" first.

[Tool]
Top 10 property alternatives:
1. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): person or organization occupying property, used by
4. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): 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 ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): geocoordinates of the subject. For Earth, please note that only WGS84 coordinating system is supported at the moment

[Assistant]
The property "located in the administrative territorial entity" (wdt:P131) is the appropriate property to use for the location of cities within New Jersey.

Next, I will search for the property related to "population."

[Tool]
Top 10 property alternatives:
1. tabular population, also known as population (wd:P4179 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): same as population (P1082); used when there is data for many different points in time
2. population, also known as inhabitants, human population (wd:P1082 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): number of people inhabiting the place; number of people of subject
3. has census, also known as has human population census (wd:P8701 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): general population census of the country or region
4. urban population (wd:P6343 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): number of people living within the territorial entity who live in its urban parts, population
5. birth rate (wd:P8763 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): the total number of live births per 1,000 population
6. category populated by, also known as category filled by, category has template, cat populated by, component adding into the category, populated by (wd:P4329 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): software component (template, module, extension, MediaWiki core) populating the category
7. rural population (wd:P6344 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): number of people living within the territorial entity who live in its rural parts, population
8. female population (wd:P1539 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): number of female people inhabiting the place; number of female people of subject, population
9. male population, also known as male inhabitants, number of males (wd:P1540 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): number of male people inhabiting the place; number of male people of subject, population
10. number of households, also known as dwellings, households (wd:P1538 as ps/p/psn/pqn/wdtn/wdt/pq/psv/prn/prv/pqv/pr): total number of households in this place, includes dwellings of all types

[Assistant]
The property "population" (wdt:P1082) is the appropriate property to use for the number of inhabitants.

Now I will construct a SPARQL query to find all cities in New Jersey (wd:Q1408) with a population greater than 100,000. I will also filter for instances that are cities. I will search for the entity for "city" to use it in the query.

[Tool]
Top 10 entity alternatives:
1. city, also known as cities (wd:Q515): large human settlement
2. Manchester City F.C., also known as The Sky Blues, City, The Citizens, The Blues, Sky Blues (wd:Q50602): association football club in Manchester, England
3. Leicester City F.C., also known as The Foxes, Leicester Fosse Football Club, Leicester Fosse FC, Leicester Fosse, Leicester City (wd:Q19481): association football club in Leicester, England
4. Swansea City A.F.C., also known as Swansea City Association Football Club, The Swans, The Jacks, Swansea City AFC, Swansea City (wd:Q18659): association football club in Swansea, Wales
5. Stoke City F.C., also known as City, SCFC, Stoke, Stoke City, Stoke City FC (wd:Q18736): association football club in Stoke-on-Trent, England
6. Birmingham City F.C., also known as BCFC, City, Blues, Birmingham City Football Club, Birmingham City (wd:Q19444): association football club in Birmingham, England
7. Norwich City F.C., also known as Norwich City FC, The Canaries, Norwich City Football Club, Norwich City, Norwich (wd:Q18721): association football club in Norwich, England
8. Cardiff City F.C., also known as Cardiff, The Bluebirds, City, CCFC, Cardiff City Football Club (wd:Q18662): association football club in Cardiff, Wales
9. Hull City A.F.C., also known as City, HCAFC, Hull, Hull City, Hull City AFC (wd:Q19477): association football club in Hull, England
10. Coventry City F.C., also known as Coventry City FC, Sky Blues, Coventry City Football Club, Coventry City, Coventry (wd:Q19580): association football club in Coventry, England

[Assistant]
The entity for "city" is wd:Q515, which represents a large human settlement.

Now I will construct the SPARQL query to find all cities (wd:Q515) located in New Jersey (wd:Q1408) with a population (wdt:P1082) greater than 100,000.

[Tool] Got no rows and 3 columns

[Assistant]
The query returned no results, which might be because some cities in New Jersey are not directly linked with the property "located in the administrative territorial entity" (wdt:P131) to New Jersey, but rather through counties or other administrative divisions.

I will modify the query to include cities that are located in any administrative territorial entity that is itself located in New Jersey. This means I will check for cities where the location chain goes through an intermediate administrative entity that is located in New Jersey.

[Tool] Got no rows and 3 columns

[Assistant]
The modified query still returned no results. It is possible that some cities in New Jersey are directly located in New Jersey without an intermediate administrative entity, or the data might be structured differently.

I will try a more inclusive approach by checking if the city is located in New Jersey or any administrative territorial entity located in New Jersey using a UNION clause.

[Tool] Got no rows and 3 columns

[Assistant]
The query with the UNION clause also returned no results. It seems that the cities in New Jersey might not be classified strictly as instances of "city" (wd:Q515) or the population data might be missing or structured differently.

I will try to relax the condition on the instance type and only check for entities located in New Jersey with population greater than 100,000, and then filter for those that have a label containing "city" or similar in English. This might help to capture more entities that are cities but not explicitly typed as wd:Q515.

[Tool]
Got 3 rows and 3 columns:
| place                                                                   | placeLabel                                                     | population           |
| ----------------------------------------------------------------------- | -------------------------------------------------------------- | -------------------- |
| Allentown-Bethlehem-Easton metropolitan area (wd:Q113137311)            | Allentown-Bethlehem-Easton metropolitan area (en)              | 861889 (xsd:decimal) |
| Atlantic City–Ocean City–Villas urban area (wd:Q125801657)              | Atlantic City–Ocean City–Villas urban area (en)                | 294921 (xsd:decimal) |
| Atlantic City-Hammonton, NJ Metropolitan Statistical Area (wd:Q6173874) | Atlantic City-Hammonton, NJ Metropolitan Statistical Area (en) | 274534 (xsd:decimal) |

[Assistant]
The query returned some metropolitan and urban areas related to New Jersey but not individual cities.

I will try a different approach: I will look for entities that are instances of "human settlement" (wd:Q486972) located in New Jersey with a population greater than 100,000. This might include cities and towns. I will also retrieve their labels.

[Tool] Got no rows and 3 columns

[Assistant]
The query for human settlements in New Jersey with a population greater than 100,000 returned no results. It seems that the direct location property might not be used consistently for cities in New Jersey.

I will try to find all cities in New Jersey by searching for entities that are instances of city (wd:Q515) and located in any administrative territorial entity that is part of New Jersey (wd:Q1408) using a recursive approach with the property "located in the administrative territorial entity" (wdt:P131)* to traverse the location hierarchy. Then filter by population greater than 100,000. However, since QLever does not support property paths, I will try to find cities that are located in New Jersey or in any entity that is located in New Jersey (one level of indirection).

[Tool]
Got 2,065 rows and 3 columns, showing the first 5 and last 5 rows below:
| city                       | cityLabel        | population             |
| -------------------------- | ---------------- | ---------------------- |
| Chengdu (wd:Q30002)        | Chengdu (en)     | 20937757 (xsd:decimal) |
| Shenzhen (wd:Q15174)       | Shenzhen (en)    | 17681600 (xsd:decimal) |
| Shenzhen (wd:Q15174)       | Shenzhen (en)    | 17494398 (xsd:decimal) |
| Dhaka (wd:Q1354)           | Dhaka (en)       | 16800000 (xsd:decimal) |
| Mumbai (wd:Q1156)          | Mumbai (en)      | 15414288 (xsd:decimal) |
| ...                        | ...              | ...                    |
| Shahrisabz (wd:Q569766)    | Shahrisabz (en)  | 100300 (xsd:decimal)   |
| Gangtok (wd:Q186141)       | Gangtok (en)     | 100286 (xsd:decimal)   |
| Boconó (wd:Q889344)        | Boconó (en)      | 100240 (xsd:decimal)   |
| Girona City (wd:Q24001084) | Girona City (en) | 100231 (xsd:decimal)   |
| Cottbus (wd:Q3214)         | Cottbus (en)     | 100010 (xsd:decimal)   |

[Assistant]
The query returned many cities worldwide, not just in New Jersey. It seems the filter for location in New Jersey is not working as expected.

I will refine the query to explicitly check that the location or its parent location is New Jersey (wd:Q1408) and that the city is an instance of city (wd:Q515) and located in New Jersey or its administrative subdivisions. I will also limit the results to distinct cities to avoid duplicates.

[Tool]
Got 1,789 rows and 3 columns, showing the first 5 and last 5 rows below:
| city                       | cityLabel        | population             |
| -------------------------- | ---------------- | ---------------------- |
| Chengdu (wd:Q30002)        | Chengdu (en)     | 20937757 (xsd:decimal) |
| Shenzhen (wd:Q15174)       | Shenzhen (en)    | 17681600 (xsd:decimal) |
| Shenzhen (wd:Q15174)       | Shenzhen (en)    | 17494398 (xsd:decimal) |
| Dhaka (wd:Q1354)           | Dhaka (en)       | 16800000 (xsd:decimal) |
| Mumbai (wd:Q1156)          | Mumbai (en)      | 15414288 (xsd:decimal) |
| ...                        | ...              | ...                    |
| Shahrisabz (wd:Q569766)    | Shahrisabz (en)  | 100300 (xsd:decimal)   |
| Gangtok (wd:Q186141)       | Gangtok (en)     | 100286 (xsd:decimal)   |
| Boconó (wd:Q889344)        | Boconó (en)      | 100240 (xsd:decimal)   |
| Girona City (wd:Q24001084) | Girona City (en) | 100231 (xsd:decimal)   |
| Cottbus (wd:Q3214)         | Cottbus (en)     | 100010 (xsd:decimal)   |

[Assistant]
The query still returns many cities worldwide, which means the filter for location in New Jersey is not effective.

I will try a different approach: I will find all cities that have New Jersey (wd:Q1408) as their "located in the administrative territorial entity" (wdt:P131) directly, without considering intermediate locations. I will also filter for population greater than 100,000.

[Tool] Got no rows and 3 columns

[Assistant]
There are no cities directly located in New Jersey with a population greater than 100,000 according to the direct property wdt:P131.

I will now try to find all cities that are located in any county or municipality that is part of New Jersey, and then filter for population greater than 100,000. I will first find the counties or municipalities in New Jersey.

[Tool] Got no rows and 2 columns

[Assistant] I tried to find counties in New Jersey using the class "county of the United States" (wd:Q10864048) but got no results. I will try a more general approach to find administrative territorial entities located in New Jersey.

[Tool]
Got 10 rows and 2 columns:
| adminEntity                                            | adminEntityLabel                            |
| ------------------------------------------------------ | ------------------------------------------- |
| Middletown-Point (wd:Q100251767)                       | Middletown-Point (en)                       |
| May's Landing (wd:Q100252342)                          | May's Landing (en)                          |
| New Jerseyans (wd:Q100744785)                          | New Jerseyans (en)                          |
| New Jersey Class 1 senate seat (wd:Q101498869)         | New Jersey Class 1 senate seat (en)         |
| New Jersey Class 2 senate seat (wd:Q101498980)         | New Jersey Class 2 senate seat (en)         |
| Metuchen (wd:Q1021527)                                 | Metuchen (en)                               |
| Capital State Park (wd:Q102896736)                     | Capital State Park (en)                     |
| New Jersey General Assembly district 1 (wd:Q104235495) | New Jersey General Assembly district 1 (en) |
| New Jersey General Assembly district 2 (wd:Q104235509) | New Jersey General Assembly district 2 (en) |
| New Jersey General Assembly district 3 (wd:Q104235510) | New Jersey General Assembly district 3 (en) |

[Assistant]
The query returned some entities located in New Jersey, but they are not necessarily counties or municipalities.

I will try to find all cities that are located in any entity that is located in New Jersey (one level of indirection) and have a population greater than 100,000.

[Tool] Got no rows and 3 columns

[Assistant]
The query for cities located in any entity that is located in New Jersey with a population greater than 100,000 returned no results.

It seems that the data for cities in New Jersey with population might not be consistently structured or available in the expected way.

I will try to find all cities in New Jersey regardless of population to see if any cities are listed at all.

[Tool] Got no rows and 2 columns

[Assistant]
There are no cities directly located in New Jersey according to the property wdt:P131.

I will try to find cities that have New Jersey as their "state" (wdt:P131 might not be used, but maybe wdt:P131 or other properties like wdt:P131 or wdt:P131 are used). I will search for properties related to "state" for the entity New Jersey.

[Tool]
Top 10 property alternatives:
1. 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 wdt/p): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
2. 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
3. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as wdt/p): (list of) direct subdivisions of an administrative territorial entity, has part(s)
4. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as wdt/p): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson
5. official symbol, also known as symbol, state tartan, official tartan, floral emblem, official emblem (wd:P2238 as wdt/p): official symbol of an organisation
6. NE.se ID, also known as Nationalencyklopedin ID, Nationalencyklopedin Online ID, NE ID, NE.se (wd:P3222 as wdt/p): ID of article on the Swedish Nationalencyklopedin (NE.se) site
7. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as wdt/p): seat of government of a country, province, state or other type of administrative territorial entity
8. flag, also known as flag description (wd:P163 as wdt/p): subject's flag
9. 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))
10. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as wdt/p): label for the items in their official language  or their original language, name

No other items found

[Assistant]
The property "located in the administrative territorial entity" (wdt:P131) is the main property used to indicate that an item is located in a state like New Jersey.

Since direct queries for cities located in New Jersey with population greater than 100,000 returned no results, it might be that the cities are classified differently or the population data is missing.

I will try to find all cities in the United States (wd:Q30) with population greater than 100,000 and then filter those that have New Jersey as their state or located in New Jersey. This might help to find cities that are linked to New Jersey differently.

[Tool] Got no rows and 5 columns

[Assistant]
The query for cities in the United States with population greater than 100,000 and located in New Jersey returned no results.

It seems that the data for cities in New Jersey with population greater than 100,000 is not available or not structured as expected in Wikidata.

I will now provide the final answer based on the queries and findings.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
SELECT ?date WHERE {
  wd:Q211 p:P463 ?statement .
  ?statement ps:P463 wd:Q458 ; pq:P580 ?date .
}

Latvia joined the European Union on May 1, 2004.

[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] When did Latvia join the EU?

[Assistant] To answer the question "When did Latvia join the EU?", I need to find the entity for Latvia and the property that indicates the date it joined the European Union. I will start by searching for the entity "Latvia" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Latvia, also known as Latvija, Republic of Latvia, LVA, lv, Leţmо̄ Vabāmо̄ (wd:Q211): sovereign state in northeastern Europe
2. 1284 Latvia, also known as Latvia (wd:Q137583): asteroid
3. RAF-977, also known as Latvia, RAF-977D (wd:Q2425377): soviet bustype
4. RAF-2203, also known as Latvia (wd:Q1065645): van and minibus manufactured by Rīgas Autobusu Fabrika in Latvia
5. Latvia (wd:Q6497226): constituency of the European Parliament
6. Latvia (wd:Q104537040)
7. Latvia (wd:Q104892776): scientific article published in December 2012
8. Latvia (wd:Q58040021)
9. Latvia (wd:Q103948319): encyclopedia article
10. Latvia (wd:L254271)

[Assistant] The entity for Latvia as a sovereign state in northeastern Europe is wd:Q211. Next, I will search for properties related to the European Union membership or joining date for this entity. I will search for properties of Latvia related to "European Union" or "membership".

[Tool]
Top 10 property alternatives:
1. Encyclopedia of the History of Ukraine ID, also known as EHU, EIY, history.org.ua, ЕІУ (wd:P8935 as wdt/p): ID for an entry on the official website of the Encyclopedia of the History of Ukraine
2. legislative body, also known as aboriginal council, Zila, Tehsil, rural council, representative body (wd:P194 as wdt/p): legislative body governing this entity; political institution wi...ted representatives, such as a parliament/legislature or council
3. subreddit, also known as r/, reddit, Reddit community ID, Reddit forum, Reddit sub ID (wd:P3984 as wdt/p): name of the Reddit forum associated to this subject
4. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as wdt/p): URL of the official page of an item (current or former). Usage: ...but see the "Hijacked or dead websites" section of the Talk page, URL
5. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as wdt/p): work where this item is described, attested in
6. Enzyklopädie des europäischen Ostens ID (wd:P9498 as wdt/p): identifier for an article in the Enzyklopädie des europäischen Ostens
7. EU Knowledge Graph item ID, also known as EU Knowledge Graph item identifier (wd:P11012 as wdtn/wdt/p): identifier for the corresponding entity in the EU Knowledge Graph
8. contains the administrative territorial entity, also known as has shires, has rural cities, has regions, has municipalities, has members (wd:P150 as wdt/p): (list of) direct subdivisions of an administrative territorial entity, has part(s)
9. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as wdt/p): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
10. basic form of government, also known as form of government, government, type of government (wd:P122 as wdt/p): subject's government

No other items found

[Assistant] The property "member of" (wdt:P463) seems relevant as it can indicate membership in an organization like the European Union. I will now query the date when Latvia (wd:Q211) became a member of the European Union (wd:Q458). The property for the date of joining an organization is often "start time" (wdt:P580). I will construct a query to find the start time of Latvia's membership in the European Union.

[Tool]
Got 1 row and 1 column:
| date                                |
| ----------------------------------- |
| 2004-05-01T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?chancellor ?chancellorLabel WHERE {
  ?chancellor wdt:P39 wd:Q4970706 .
  OPTIONAL {
    ?chancellor rdfs:label ?chancellorLabel FILTER ( LANG( ?chancellorLabel ) = "en" )
  }
}

Got 10 rows and 2 columns:
| chancellor                      | chancellorLabel           |
| ------------------------------- | ------------------------- |
| Konrad Adenauer (wd:Q2492)      | Konrad Adenauer (en)      |
| Ludwig Erhard (wd:Q2496)        | Ludwig Erhard (en)        |
| Kurt Georg Kiesinger (wd:Q2512) | Kurt Georg Kiesinger (en) |
| Willy Brandt (wd:Q2514)         | Willy Brandt (en)         |
| Helmut Schmidt (wd:Q2516)       | Helmut Schmidt (en)       |
| Helmut Kohl (wd:Q2518)          | Helmut Kohl (en)          |
| Gerhard Schröder (wd:Q2530)     | Gerhard Schröder (en)     |
| Walter Scheel (wd:Q2571)        | Walter Scheel (en)        |
| Angela Merkel (wd:Q567)         | Angela Merkel (en)        |
| Olaf Scholz (wd:Q61053)         | Olaf Scholz (en)          |

The federal chancellors of Germany are Konrad Adenauer, Ludwig Erhard, Kurt Georg Kiesinger, Willy Brandt, Helmut Schmidt, Helmut Kohl, Gerhard Schröder, Walter Scheel, Angela Merkel, and Olaf Scholz.

[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] Give me all federal chancellors of Germany.

[Assistant]
To answer the question "Give me all federal chancellors of Germany," I need to identify the entity for "Germany" and the property or entity representing "federal chancellor" in the knowledge graph. Then I will query for all individuals who held the position of federal chancellor of Germany.

Step 1: Search for the entity representing "Germany."
Step 2: Search for the property or entity representing "federal chancellor" or "chancellor of Germany."
Step 3: Construct a SPARQL query to retrieve all federal chancellors of Germany.

I will start with step 1 and search for the entity "Germany" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Germany, also known as BR Deutschland, Bundesrepublik Deutschland, Deutschland, Federal Republic of Germany (wd:Q183): country in Central Europe
2. Weimar Republic, also known as German Republic, Weimar Germany, Republic of Germany, Germany, German Reich (wd:Q41304): Germany in the years 1918/1919–1933
3. German Reich, also known as Deutsches Reich, German Empire, German Realm, Germany, Imperial Germany (wd:Q1206012): official name for the German nation state from 1871 to 1945, and name of Germany until 1949
4. Burg Drachenfels, also known as Germany (wd:Q1011486): castle
5. Germany (wd:Q5552078): European Parliament constituency
6. Geilenkirchen-Heinsberg, also known as Germany (wd:Q2268218)
7. Germany, also known as Germany, IN, Germany, Indiana (wd:Q18151573): unincorporated community in Clark County, Indiana
8. Germany (wd:Q19147844): poem by Vladimir Mayakovsky
9. Germany, also known as Ludwig, West (wd:Q3839160): Hetalia character
10. Germany, also known as Germany, Texas, Germany, TX (wd:Q15221573): unincorporated community in Houston County, Texas, United States

[Assistant] The entity for Germany as a country is wd:Q183. Next, I will search for the property or entity representing "federal chancellor" or "chancellor of Germany" to identify the position held by the federal chancellors.

[Tool]
Top 10 entity alternatives:
1. chancellor, also known as federal chancellor (wd:Q373085): executive position in a government
2. Federal Chancellor of Germany, also known as German Federal Chancellor, prime minister of Germany, German Chancellor, Federal Chancellor of the Federal Republic of Germany, Federal Chancellor (wd:Q4970706): head of government of the Federal Republic of Germany (1949–present)
3. Chancellor of Switzerland, also known as Federal Chancellor, Federal Chancellor of Switzerland (wd:Q687810): position
4. federal chancellor (wd:Q110714556): head of cabinet in Germany, Austria, Switzerland
5. Federal Chancellor of Austria, also known as Chancellor of Austria (wd:Q1006398): Austrian office
6. Federal Chancellor of Austria, also known as Chancellor of Austria (wd:Q1006398): Austrian office
7. Vice-Chancellor of Germany, also known as Vice Chancellor of Germany, Deputy to the Federal Chancellor (wd:Q504272): position
8. Federal Chancellor Willy Brandt Foundation (wd:Q1006253)
9. Federal Chancellor Helmut Schmidt Foundation (wd:Q27926502)
10. resignation of a federal chancellor (wd:Q114389267): resignation of a member of the Swiss Federal Council

[Assistant]
The entity for the Federal Chancellor of Germany is wd:Q4970706. Now I will construct a SPARQL query to find all individuals who held the position of Federal Chancellor of Germany (wd:Q4970706). The property for "position held" is usually wdt:P39 in Wikidata.

I will query for all persons who have held the position wd:Q4970706.

[Tool]
Got 10 rows and 2 columns:
| chancellor                      | chancellorLabel           |
| ------------------------------- | ------------------------- |
| Konrad Adenauer (wd:Q2492)      | Konrad Adenauer (en)      |
| Ludwig Erhard (wd:Q2496)        | Ludwig Erhard (en)        |
| Kurt Georg Kiesinger (wd:Q2512) | Kurt Georg Kiesinger (en) |
| Willy Brandt (wd:Q2514)         | Willy Brandt (en)         |
| Helmut Schmidt (wd:Q2516)       | Helmut Schmidt (en)       |
| Helmut Kohl (wd:Q2518)          | Helmut Kohl (en)          |
| Gerhard Schröder (wd:Q2530)     | Gerhard Schröder (en)     |
| Walter Scheel (wd:Q2571)        | Walter Scheel (en)        |
| Angela Merkel (wd:Q567)         | Angela Merkel (en)        |
| Olaf Scholz (wd:Q61053)         | Olaf Scholz (en)          |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?actor ?actorLabel ?birthday WHERE {
  wd:Q162371 wdt:P161 ?actor .
  ?actor wdt:P569 ?birthday .
  OPTIONAL {
    ?actor rdfs:label ?actorLabel FILTER ( LANG( ?actorLabel ) = "en" )
  }
}

The birthdays of the actors of the television show Charmed are:
- Holly Marie Combs: December 3, 1973
- Alyssa Milano: December 19, 1972
- Shannen Doherty: April 12, 1971
- Rose McGowan: September 5, 1973

[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] Give me the birthdays of all actors of the television show Charmed.

[Assistant]
To answer this question, I need to identify the entity for the television show "Charmed" and the property that links actors to this show. Then I will find the actors of "Charmed" and retrieve their birthdays.

Step 1: Search for the entity "Charmed" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Charmed (wd:Q162371): American supernatural drama / fantasy television series (1998–2006)
2. Charmed (wd:Q53162693): American television series, reboot of Charmed (1998–2006)
3. Charmed (wd:Q1067576): Norwegian musical group
4. Charmed, also known as Charmed (comics) (wd:Q2564039): comic book that is an officially licensed continuation of the popular The WB television series of the same name
5. Charmed (wd:Q11963476): 2000 self-titled debut studio album by Charmed
6. Charmed (wd:Q5086449): video game based on the fourth season of the television series of the same name
7. Charmed (wd:Q133201824)
8. Charmed (wd:Q128008863): episode of The Little Mermaid (S1 E7)
9. charmed (wd:L8016-F3)
10. charmed (wd:L8016-F5)

[Assistant] The entity "Charmed" that refers to the American supernatural drama/fantasy television series (1998–2006) is wd:Q162371. Next, I need to find the property that links actors to this television show. Typically, the property "cast member" (wdt:P161) is used for this purpose. I will verify this by checking the cast members of "Charmed" (wd:Q162371).

[Tool]
Got 4 rows and 3 columns:
| subject              | property               | object                         |
| -------------------- | ---------------------- | ------------------------------ |
| Charmed (wd:Q162371) | cast member (wdt:P161) | Alyssa Milano (wd:Q189067)     |
| Charmed (wd:Q162371) | cast member (wdt:P161) | Shannen Doherty (wd:Q207598)   |
| Charmed (wd:Q162371) | cast member (wdt:P161) | Rose McGowan (wd:Q211040)      |
| Charmed (wd:Q162371) | cast member (wdt:P161) | Holly Marie Combs (wd:Q162578) |

[Assistant] The property "cast member" (wdt:P161) is indeed used to link actors to the television show "Charmed" (wd:Q162371). I have found some actors: Alyssa Milano, Shannen Doherty, Rose McGowan, and Holly Marie Combs. Now I will retrieve the birthdays (property wdt:P569) of all actors who are cast members of "Charmed."

[Tool]
Got 4 rows and 3 columns:
| actor                          | actorLabel             | birthday                            |
| ------------------------------ | ---------------------- | ----------------------------------- |
| Holly Marie Combs (wd:Q162578) | Holly Marie Combs (en) | 1973-12-03T00:00:00Z (xsd:dateTime) |
| Alyssa Milano (wd:Q189067)     | Alyssa Milano (en)     | 1972-12-19T00:00:00Z (xsd:dateTime) |
| Shannen Doherty (wd:Q207598)   | Shannen Doherty (en)   | 1971-04-12T00:00:00Z (xsd:dateTime) |
| Rose McGowan (wd:Q211040)      | Rose McGowan (en)      | 1973-09-05T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?lake ?lakeLabel ?country ?countryLabel WHERE {
  VALUES ?lake {
    wd:Q6386177 wd:Q22434937
  }
  ?lake wdt:P17 ?country .
  OPTIONAL {
    ?lake rdfs:label ?lakeLabel FILTER ( LANG( ?lakeLabel ) = "en" )
  }
  OPTIONAL {
    ?country rdfs:label ?countryLabel FILTER ( LANG( ?countryLabel ) = "en" )
  }
}

Got 2 rows and 4 columns:
| lake                         | lakeLabel          | country         | countryLabel |
| ---------------------------- | ------------------ | --------------- | ------------ |
| Limerick Lake (wd:Q22434937) | Limerick Lake (en) | Canada (wd:Q16) | Canada (en)  |
| Limerick Lake (wd:Q6386177)  | Limerick Lake (en) | Canada (wd:Q16) | Canada (en)  |

Both Limerick Lake entities (wd:Q6386177 and wd:Q22434937) are located in the country Canada.

[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] In which country is the Limerick Lake?

[Assistant] To answer the question "In which country is the Limerick Lake?" I will first search for the entity "Limerick Lake" in Wikidata to find its identifier. Then I will query the country in which this lake is located.

[Tool]
Top 10 entity alternatives:
1. Limerick Lake (wd:Q6386177): lake in Hastings Country, Ontario, Canada
2. Lake Limerick (wd:Q49303749): lake in Mason County, Washington state, United States of America
3. Limerick Lake (wd:Q22434937): lake in Algoma District, Ontario, Canada
4. Lake Limerick Dam (wd:Q48991830): dam impounding Cranberry Creek in Mason County, Washington state, United States of America
5. lake, also known as Lake, lakes (wd:Q23397): body of relatively still water, localized in a basin
6. Limerick, also known as Limerick City, Limrick, Limmerick (wd:Q133315): city in Ireland in the province of Munster
7. County Limerick, also known as Limerick, County Limerick, Ireland (wd:Q178283): county in Ireland
8. limerick, also known as Limerick (wd:Q204698): form of poetry
9. Lake, also known as Lake, Mississippi, Lake, MS (wd:Q2087707): town in Newton and Scott counties, Mississippi, United States
10. Limerick F.C., also known as LFC, Limerick, Limerick FC, Limerick Football Club (wd:Q1552212): association football club in Limerick, Ireland

[Assistant]
There are multiple entities named "Limerick Lake" in Wikidata. The relevant ones are:
- Limerick Lake (wd:Q6386177) in Hastings County, Ontario, Canada
- Limerick Lake (wd:Q22434937) in Algoma District, Ontario, Canada

I will now query the country for these two lakes to confirm their country.

[Tool]
Got 2 rows and 4 columns:
| lake                         | lakeLabel          | country         | countryLabel |
| ---------------------------- | ------------------ | --------------- | ------------ |
| Limerick Lake (wd:Q22434937) | Limerick Lake (en) | Canada (wd:Q16) | Canada (en)  |
| Limerick Lake (wd:Q6386177)  | Limerick Lake (en) | Canada (wd:Q16) | Canada (en)  |

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
ASK WHERE {
  wd:Q53696 wdt:P171 * wd:Q53636 .
  wd:Q53636 ( wdt:P31 | wdt:P279 ) * wd:Q10908
}

Yes, tree frogs (family Hylidae) are a type of amphibian, as they belong to the order Anura, which is a subclass of amphibians.

[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] Are tree frogs a type of amphibian?

[Assistant]
To answer the question "Are tree frogs a type of amphibian?" I need to check if the entity "tree frog" is classified as a subclass or instance of the entity "amphibian" in the knowledge graph.

Step 1: Search for the entity "tree frog" in Wikidata to get its identifier.
Step 2: Search for the entity "amphibian" in Wikidata to get its identifier.
Step 3: Check if "tree frog" is a subclass or instance of "amphibian" using the relevant properties.

I will start with Step 1 and search for the entity "tree frog".

[Tool]
Top 10 entity alternatives:
1. Hylidae, also known as Hylid, Tree frog (wd:Q53696): family of amphibians
2. tree frog (wd:Q1144302): any species of frog that spends a major portion of its lifespan in trees
3. Bokermannohyla izecksohni, also known as Bokermannohyla izeckshoni, Tree frog (wd:Q308753): species of amphibian
4. Rhacophorus lateralis, also known as Small Tree Frog, Tree frog (wd:Q2707407): species of amphibian
5. Leptopelis fiziensis, also known as Tree frog (wd:Q1946641): species of amphibian
6. Tree frog (wd:Q110818899)
7. Hyla arborea, also known as Common Tree Frog, European Tree Frog (wd:Q2142057): species of amphibian
8. red-eyed tree frog, also known as Agalychnis callidryas (wd:Q337616): species of amphibian
9. Australian green tree frog, also known as White's tree frog, Litoria caerulea, green tree frog, Ranoidea caerulea (wd:Q654682): species of amphibian
10. Common tree frog, also known as Polypedates leucomystax (wd:Q2277171): species of amphibian

[Assistant]
I found multiple entities related to "tree frog" in Wikidata. The most relevant ones are:
- wd:Q53696 (Hylidae, family of amphibians also known as tree frogs)
- wd:Q1144302 (tree frog, any species of frog that spends a major portion of its lifespan in trees)

I will now search for the entity "amphibian" in Wikidata to get its identifier.

[Tool]
Top 10 entity alternatives:
1. Amphibia, also known as amphibian, amphibians (wd:Q10908): class of tetrapods, whose living forms include frogs, toads, salamanders, newts and caecilians
2. amphibious vehicle, also known as amphibian (wd:Q474698): means of transport, viable on land as well as on (or under) water
3. amphibious aircraft, also known as amphibian (wd:Q474709): aircraft that can routinely both operate in water and in land
4. Amphibian (wd:Q15924195): 2005 studio album by Tanya Chua
5. Amphibian, also known as Kingsley Rice (wd:Q4748176): fictional character in Marvel Comics
6. Amphibian (wd:Q125685669): journal article from 'The Global South' published in 2022
7. amphibian (wd:L334419-F1)
8. amphibian (wd:L334419)
9. amphibian (wd:L229297-F1)
10. amphibian (wd:L229297)

[Assistant]
The entity for "amphibian" in Wikidata is wd:Q10908, which represents the class Amphibia.

Next, I will check if the entity "tree frog" (wd:Q1144302) is a subclass or instance of the entity "amphibian" (wd:Q10908). I will check the properties "instance of" (wdt:P31) and "subclass of" (wdt:P279) for this.

[Tool] False

[Assistant]
The entity "tree frog" (wd:Q1144302) is not directly or indirectly an instance or subclass of "amphibian" (wd:Q10908) according to the knowledge graph.

I will now check the other relevant entity "Hylidae" (wd:Q53696), which is a family of amphibians also known as tree frogs, to see if it is an instance or subclass of amphibian.

[Tool] False

[Assistant]
The entity "Hylidae" (wd:Q53696), which is a family of amphibians also known as tree frogs, is also not directly or indirectly an instance or subclass of "amphibian" (wd:Q10908) according to the knowledge graph.

To further verify, I will check if "Hylidae" has the property "taxon rank" (wdt:P105) with the value "family" (wd:Q35409) and if it is classified under amphibians in any other way.

[Tool] Got no rows and 1 columns

[Assistant]
The entity "Hylidae" (wd:Q53696) does not have a parent taxon that is an instance of "amphibian" (wd:Q10908) according to the knowledge graph.

I will now check the direct taxonomic classification of "Hylidae" to see if it is classified as an amphibian in any other way. I will query the taxon rank and parent taxon of "Hylidae".

[Tool]
Got 2 rows and 2 columns:
| taxonRank          | parentTaxon            |
| ------------------ | ---------------------- |
| family (wd:Q35409) | Hyloidea (wd:Q4896640) |
| family (wd:Q35409) | Anura (wd:Q53636)      |

[Assistant]
The entity "Hylidae" (wd:Q53696) has the taxon rank "family" (wd:Q35409) and has parent taxa "Hyloidea" (wd:Q4896640) and "Anura" (wd:Q53636).

I will now check if "Anura" (wd:Q53636) is an instance or subclass of "amphibian" (wd:Q10908) since Anura is the order of frogs.

[Tool] True

[Assistant]
The entity "Anura" (wd:Q53636), which is the order of frogs, is indeed an instance or subclass of "amphibian" (wd:Q10908).

Since "Hylidae" is a family within the order "Anura," which is a subclass of amphibians, it follows that tree frogs (Hylidae) are a type of amphibian.

I will now provide the final answer with a SPARQL query that confirms this classification.

[Tool] Stopped generation process