PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q388973 wdt:P176 wd:Q157062
}

No, After Eight is not manufactured by Unilever.

[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] Is After Eight manufactured by Unilever?

[Assistant] Let's start by looking at some examples.

[Tool]
Example 1:
Question:
Which companies produce hovercrafts?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?hovercraft wdt:P31 / wdt:P279 * wd:Q185757 ; wdt:P176 ?uri .
}

Using entities:
hovercraft, also known as ACV, air-cushion vehicle (wd:Q185757): vehicle capable of movement within ground effect at speed or stationary over all surfaces without contact

Using properties:
manufacturer, also known as manufactured by, provider, provided by, product of, producer (wd:P176 as wdt): (main or final) manufacturer or producer of this product
subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

Example 2:
Question:
What is the timezone in San Pedro de Atacama?

SPARQL:
SELECT DISTINCT ?o1 WHERE {
  wd:Q187893 wdt:P421 ?o1 .
}

Using entities:
San Pedro de Atacama (wd:Q187893): town in Chile

Using properties:
located in time zone, also known as TZ, time zone, timezone (wd:P421 as wdt): time zone for this item

Example 3:
Question:
List the seven kings of Rome.

SPARQL:
SELECT * WHERE {
  ?res wdt:P39 wd:Q55375123 .
}

Using entities:
King of Rome, also known as Rex, rex Romae (wd:Q55375123): the chief magistrate of the ancient Roman Kingdom (for the medieval German title, use Q782985)

Using properties:
position held, also known as function, held position, holds position, job, office held (wd:P39 as wdt): subject currently or formerly holds the object position or public office

[Assistant] Based on the examples, we need to identify the entities and properties relevant to our question. We need to find out if "After Eight" is manufactured by "Unilever". Let's start by searching for these entities in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. After Eight, also known as After Eight mint chocolate (wd:Q388973): chocolate brand of Nestle
2. Polar Bear, also known as After Eight, Peppermint Paddy (wd:Q7209010): mint chocolate cocktail
3. After Eight (wd:Q60786577): 1982 debut studio album by Taco
4. Dahlia 'Twyning's After Eight', also known as Twyning's After Eight (wd:Q83544826): Dahlia cultivar
5. 8 Man After, also known as Eightman After (wd:Q115948406): 1993 original video animation series
6. DeltaD, also known as after eight protein, delta-like protein D, dld, aei (wd:Q29825141): protein found in Danio rerio
7. Quarter After Eight (wd:Q15762559): journal
8. Twelve after Eight (wd:Q131628059): journal article from 'Journal of Medical Humanities' published in 2015
9. Chernobyl: eight years after (wd:Q56608399): scientific article published on 01 October 1994
10. After Eighty Study (wd:Q63818230): clinical trial

[Assistant] The first result, "After Eight" (wd:Q388973), seems to be the correct entity for the chocolate brand. Now, let's search for "Unilever" to confirm it is the correct entity.

[Tool]
Top 10 entity alternatives:
1. Unilever (wd:Q157062): British multinational consumer goods company
2. Unilever, also known as UNVR, Unilever Tbk, Lever Zeepfabrieken N.V., PT Unilever  Tbk (wd:Q7885208): British company in
3. Unilever Headquarters, also known as Unilever, Weena 455 (wd:Q125679337): office building at Weena 455, Centrum, Rotterdam, the Netherlands
4. T.H. Estabrooks Co. Ltd., also known as Brooke Bond, Unilever (wd:Q116708935): Canadian business.
5. Hindustan Unilever, also known as Unilever (India), HUL, Hindustan Unilever Limited (wd:Q1619376): Indian consumer goods company
6. Unilever House (wd:Q2494434): grade II listed office building in City of London, United kingdom
7. Unilever Philippines, also known as Unilever (Philippines) (wd:Q18159894): Philippine subsidiary of Unilever
8. Unilever Pakistan, also known as Unilever Pakistan Limited (wd:Q7885211): Pakistani subsidiary of a foreign company
9. Unilever Gloucester, also known as Walls Ice Cream Factory (wd:Q96412295): food manufacturing site in Gloucester, England, UK
10. Unilever (Bydgoszcz) (wd:Q25415517): business enterprise

[Assistant]
The first result, "Unilever" (wd:Q157062), is the correct entity for the British multinational consumer goods company. Now, we need to check if "After Eight" (wd:Q388973) is manufactured by "Unilever" (wd:Q157062). We can use the `manufacturer` property (wdt:P176) to check this relationship.

Let's construct a simple SPARQL query to check if "After Eight" is manufactured by "Unilever".

[Tool] False

[Assistant]
The result of the ASK query is `False`, which means that "After Eight" is not manufactured by "Unilever".

Therefore, the answer to the question "Is After Eight manufactured by Unilever?" is:

**No, After Eight is not manufactured by Unilever.**

I will now provide the final SPARQL query and answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correctly formulated as an ASK query to check if After Eight is manufactured by Unilever. The entities and property used are appropriate, and the execution result is False, which correctly answers the question. No further refinement is needed.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?business ?businessLabel WHERE {
  {
    ?business wdt:P112 wd:Q317521 .
  } UNION {
    ?business wdt:P169 wd:Q317521 .
  } UNION {
    ?business wdt:P2860 wd:Q317521 .
  }
  OPTIONAL {
    ?business rdfs:label ?businessLabel .
    FILTER ( LANG( ?businessLabel ) = "en" )
  }
}

Got 16 rows and 2 columns, showing the first 5 and last 5 rows below:
| business                                                | businessLabel                                |
| ------------------------------------------------------- | -------------------------------------------- |
| Texas Institute of Technology & Science (wd:Q109332244) | Texas Institute of Technology & Science (en) |
| Thud (wd:Q115815194)                                    | Thud (en)                                    |
| X Corp. (wd:Q117617480)                                 | X Corp. (en)                                 |
| X Holdings Corp (wd:Q117705742)                         | X Holdings Corp (en)                         |
| xAI (wd:Q120599684)                                     | xAI (en)                                     |
| ...                                                     | ...                                          |
| The Boring Company (wd:Q28874479)                       | The Boring Company (en)                      |
| Neuralink (wd:Q29043471)                                | Neuralink (en)                               |
| X.com (wd:Q30686532)                                    | X.com (en)                                   |
| Tesla, Inc. (wd:Q478214)                                | Tesla, Inc. (en)                             |
| PayPal (wd:Q483959)                                     | PayPal (en)                                  |

The businesses where Elon Musk, the person in charge of Tesla, is involved as a founder, holds a position, or is an investor are: Texas Institute of Technology & Science, Thud, X Corp., X Holdings Corp, xAI, Zip2, The Boring Company, Neuralink, X.com, Tesla, Inc., PayPal.

[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 businesses are founded by the person in charge of Tesla?

[Assistant] Let's start by looking at some examples.

[Tool]
Example 1:
Question:
Who is the president of Eritrea?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q986 wdt:P35 ?uri
}

Using entities:
Eritrea, also known as er, ERI, Ertrā, IRI, Iritriyā (wd:Q986): country in the Horn of Africa

Using properties:
head of state, also known as leader, state headed by, monarch, president, queen (wd:P35 as wdt): official with the highest formal authority in a country/state, director / manager, chairperson

Example 2:
Question:
When was the last episode of the TV series Friends aired?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q79784 wdt:P582 ?uri .
}

Using entities:
Friends, also known as F.R.I.E.N.D.S, F.R.I.E.N.D.S. (wd:Q79784): American sitcom (1994–2004)

Using properties:
end time, also known as ends, stop time, endtime, fall date, ineffective (wd:P582 as wdt): moment when an entity ceases to exist and a statement stops being entirely valid or no longer be true, point in time

Example 3:
Question:
Give me all soccer clubs in Spain.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 wd:Q476028 .
  ?uri wdt:P17 wd:Q29 .
}

Using entities:
Spain, also known as Kingdom of Spain (wd:Q29): country in southwestern Europe with territories in Africa
association football club, also known as association soccer club, F.C., FC, football club, soccer club (wd:Q476028): sports club devoted to association football (soccer)

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)

[Assistant] From the examples, we can see how specific entities and properties are used to form SPARQL queries. To answer the current question, we need to identify the person in charge of Tesla and then find the businesses they have founded. Let's first search for the entity representing Tesla.

[Tool]
Top 10 entity alternatives:
1. Nikola Tesla, also known as Tesla, Nicola Tesla (wd:Q9036): Serbian-American inventor (1856–1943), mechanical engineer, electrical engineer, electrician, physicist, futurist, inventor
2. Tesla, Inc., also known as Tesla, Tesla Inc. (wd:Q478214): American automotive, energy storage and solar power company
3. tesla, also known as T (wd:Q163343): SI unit of magnetic flux density
4. Tesla (wd:Q1428953): American hard rock band
5. Nvidia Tesla, also known as NVidia Tesla, NVIDIA Tesla, Tesla (wd:Q16258100): Nvidia's brand name for their products targeting stream processing and/or general purpose GPU
6. Tesla (wd:Q88053410): 2020 film directed by Michael Almereyda
7. Tesla (wd:Q2406220): lunar crater
8. Tesla, also known as TESLA, Tesla (Czech Republic) (wd:Q1548225): Czech manufacturer of electronical and electrical equipment (est.1991)
9. Tesla, also known as NVIDIA Tesla, Nvidia Tesla, NVidia Tesla (wd:Q1050485): GPU microarchitecture designed by NVIDIA
10. Tesla, also known as Tesla, West Virginia, Tesla, WV (wd:Q7705502): unincorporated community in Braxton County, West Virginia

[Assistant]
The correct entity for Tesla, the company, is Tesla, Inc. (wd:Q478214). Next, let's find out who is in charge of Tesla, Inc. We can look for the CEO or founder of the company.

Let's search for the CEO of Tesla, Inc.

[Tool]
Top 10 property alternatives:
1. chief executive officer, also known as executive director, CEO, chief executive (wd:P169 as wdt/p): highest-ranking corporate officer appointed as the CEO within an organization, director / manager, corporate officer
2. chairperson, also known as dean, headed by, leader, president, chairwoman (wd:P488 as wdt/p): presiding member of an organization, group or body, director / manager, significant person
3. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as wdt/p): founder or co-founder of this organization, religion, place or entity, creator
4. board member, also known as member of the board, member of the board of directors (wd:P3320 as wdt/p): member(s) of the board for the organization
5. headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt/p): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
6. owned by, also known as belongs to, stockholders, stockholder, shareholders, shareholder (wd:P127 as wdt/p): owner of the subject
7. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt/p): time when an entity begins to exist; for date of official opening use P1619, start time
8. owner of, also known as owns, owns property, shareholder of (wd:P1830 as wdt/p): entities owned by the subject
9. country of origin, also known as origin country, place of origin, originates from, country, CoO (wd:P495 as wdt/p): country of origin of this item (creative work, food, phrase, product, etc.), significant place, country
10. has subsidiary, also known as subordinate body, subsidiary body, subordinate unit, subsidiary, subsidiary company (wd:P355 as wdt/p): subsidiary of a company or organization; generally a fully owned...siness division" (P199). Opposite of parent organization (P749)., has part(s), owner of

No other items found

[Assistant]
The property for the CEO of Tesla, Inc. is `chief executive officer` (wdt:P169). Let's execute a SPARQL query to find the CEO of Tesla, Inc.

```sparql
SELECT DISTINCT ?ceo WHERE {
  wd:Q478214 wdt:P169 ?ceo .
}
```

Let's run this query.

[Tool]
Got 1 row and 1 column:
| ceo                    |
| ---------------------- |
| Elon Musk (wd:Q317521) |

[Assistant]
The CEO of Tesla, Inc. is Elon Musk (wd:Q317521). Now, we need to find the businesses that Elon Musk has founded. We can use the property `founded by` (wdt:P112) to find these businesses.

Let's execute a SPARQL query to find the businesses founded by Elon Musk.

```sparql
SELECT DISTINCT ?business WHERE {
  ?business wdt:P112 wd:Q317521 .
}
```

Let's run this query.

[Tool]
Got 14 rows and 1 column, showing the first 5 and last 5 rows below:
| business                                                |
| ------------------------------------------------------- |
| Texas Institute of Technology & Science (wd:Q109332244) |
| Thud (wd:Q115815194)                                    |
| X Corp. (wd:Q117617480)                                 |
| X Holdings Corp (wd:Q117705742)                         |
| xAI (wd:Q120599684)                                     |
| ...                                                     |
| Zip2 (wd:Q28222602)                                     |
| The Boring Company (wd:Q28874479)                       |
| Neuralink (wd:Q29043471)                                |
| X.com (wd:Q30686532)                                    |
| PayPal (wd:Q483959)                                     |

[Assistant]
The businesses founded by Elon Musk include:

- Texas Institute of Technology & Science (wd:Q109332244)
- Thud (wd:Q115815194)
- X Corp. (wd:Q117617480)
- X Holdings Corp (wd:Q117705742)
- xAI (wd:Q120599684)
- Zip2 (wd:Q28222602)
- The Boring Company (wd:Q28874479)
- Neuralink (wd:Q29043471)
- X.com (wd:Q30686532)
- PayPal (wd:Q483959)

Here is the final SPARQL query and the answer to the question:

```sparql
SELECT DISTINCT ?business WHERE {
  ?business wdt:P112 wd:Q317521 .
}
```

The businesses founded by Elon Musk, the person in charge of Tesla, are:
- Texas Institute of Technology & Science
- Thud
- X Corp.
- X Holdings Corp
- xAI
- Zip2
- The Boring Company
- Neuralink
- X.com
- PayPal

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is on the right track, but it only considers businesses where Elon Musk is listed as a founder (P112). However, he might have been involved in founding these businesses in other capacities, such as being a key member or a major investor. To ensure we capture all relevant businesses, we should also consider other properties that indicate his involvement, such as P169 (position held) and P2860 (investor). Additionally, the query should include optional clauses to fetch labels for better readability.

[Assistant]
Thank you for the feedback. To ensure we capture all relevant businesses where Elon Musk has been involved, we should consider additional properties such as `position held` (P169) and `investor` (P2860). We will also include optional clauses to fetch labels for better readability.

Let's update the SPARQL query accordingly:

```sparql
SELECT DISTINCT ?business ?businessLabel WHERE {
  {
    ?business wdt:P112 wd:Q317521 .
  } UNION {
    ?business wdt:P169 wd:Q317521 .
  } UNION {
    ?business wdt:P2860 wd:Q317521 .
  }
  OPTIONAL { ?business rdfs:label ?businessLabel . FILTER(LANG(?businessLabel) = "en") }
}
```

This query will return businesses where Elon Musk is listed as a founder, holds a position, or is an investor. It will also include the English labels for these businesses for better readability.

Let's execute this updated query.

[Tool]
Got 16 rows and 2 columns, showing the first 5 and last 5 rows below:
| business                                                | businessLabel                                |
| ------------------------------------------------------- | -------------------------------------------- |
| Texas Institute of Technology & Science (wd:Q109332244) | Texas Institute of Technology & Science (en) |
| Thud (wd:Q115815194)                                    | Thud (en)                                    |
| X Corp. (wd:Q117617480)                                 | X Corp. (en)                                 |
| X Holdings Corp (wd:Q117705742)                         | X Holdings Corp (en)                         |
| xAI (wd:Q120599684)                                     | xAI (en)                                     |
| ...                                                     | ...                                          |
| The Boring Company (wd:Q28874479)                       | The Boring Company (en)                      |
| Neuralink (wd:Q29043471)                                | Neuralink (en)                               |
| X.com (wd:Q30686532)                                    | X.com (en)                                   |
| Tesla, Inc. (wd:Q478214)                                | Tesla, Inc. (en)                             |
| PayPal (wd:Q483959)                                     | PayPal (en)                                  |

[Assistant]
The updated SPARQL query has returned a list of businesses where Elon Musk is involved as a founder, holds a position, or is an investor. Here are the businesses along with their labels:

- Texas Institute of Technology & Science (wd:Q109332244)
- Thud (wd:Q115815194)
- X Corp. (wd:Q117617480)
- X Holdings Corp (wd:Q117705742)
- xAI (wd:Q120599684)
- Zip2 (wd:Q28222602)
- The Boring Company (wd:Q28874479)
- Neuralink (wd:Q29043471)
- X.com (wd:Q30686532)
- Tesla, Inc. (wd:Q478214)
- PayPal (wd:Q483959)

Here is the final SPARQL query and the answer to the question:

```sparql
SELECT DISTINCT ?business ?businessLabel WHERE {
  {
    ?business wdt:P112 wd:Q317521 .
  } UNION {
    ?business wdt:P169 wd:Q317521 .
  } UNION {
    ?business wdt:P2860 wd:Q317521 .
  }
  OPTIONAL { ?business rdfs:label ?businessLabel . FILTER(LANG(?businessLabel) = "en") }
}
```

The businesses where Elon Musk, the person in charge of Tesla, is involved as a founder, holds a position, or is an investor are:
- Texas Institute of Technology & Science
- Thud
- X Corp.
- X Holdings Corp
- xAI
- Zip2
- The Boring Company
- Neuralink
- X.com
- Tesla, Inc.
- PayPal

[Tool] Stopped generation process

[User]
Feedback (status=done):
The provided SPARQL query correctly identifies the businesses founded, held positions in, or invested in by Elon Musk, who is the person in charge of Tesla. The query uses the appropriate properties to find the relevant businesses and includes optional labels for readability. The results are accurate and comprehensive.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?genre ( STRLEN( ?label ) AS ?length ) WHERE {
  wd:Q68061 wdt:P136 ?genre .
  ?genre rdfs:label ?label .
  FILTER ( LANG( ?label ) = "en" )
}
ORDER BY ASC ( ?length )
LIMIT 1

The genre with the fewest English characters that Cage the Elephant belongs to is 'blues' (wd:Q9759).

[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 genre that Cage the Elephant belongs to has the fewest english characters?

[Assistant] Let's start by looking at some examples.

[Tool]
Example 1:
Question:
When did the Ming dynasty dissolve?

SPARQL:
SELECT DISTINCT ?date WHERE {
  wd:Q9903 wdt:P576 ?date .
}

Using entities:
Ming dynasty, also known as Ming period, Ming imperium, Ming China, Ming-era, Ming Empire (wd:Q9903): imperial dynasty of China from 1368 to 1644

Using properties:
dissolved, abolished or demolished date, also known as ended, publication end date, final issue, date repealed, final year (wd:P576 as wdt): point in time at which the subject (organisation, building) ceas...g equipment,  "discontinued date" (P2669) for stopping a product, end time

Example 2:
Question:
What is the total population of Melbourne, Florida?

SPARQL:
SELECT DISTINCT ?val WHERE {
  wd:Q949779 wdt:P1082 ?val .
}

Using entities:
Melbourne, also known as Melbourne, FL, Melbourne, Florida (wd:Q949779): city in Brevard County, Florida, United States of America

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject

Example 3:
Question:
What country is Mount Everest in?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q513 wdt:P17 ?uri .
}

Using entities:
Mount Everest, also known as Chomolungma, Chomolangma, Zhumulangma, World highest peak, Sagarmāthā (wd:Q513): Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas

Using properties:
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity

[Assistant]
Based on the examples, we need to identify the entity for "Cage the Elephant" and then find the genre associated with it. After that, we can filter the genres based on the length of their English labels.

First, let's search for the entity "Cage the Elephant".

[Tool]
Top 10 entity alternatives:
1. Cage the Elephant (wd:Q68061): American rock band from Bowling Green, Kentucky
2. Cage the Elephant (wd:Q1950243): debut album by the American rock band Cage the Elephant
3. The Cage, also known as Cage, Cage, The, Pilot: The Cage (wd:Q20909): 1986 episode of Star Trek: The Original Series directed by Robert Butler
4. The Cage, also known as La Cage (wd:Q3206978): 1975 film by Pierre Granier-Deferre
5. The Cage, also known as Cage (wd:Q41016691): 2017 film by Akram Khan
6. The Cage, also known as Cage, La Cage (wd:Q7720935): 1963 film by Robert Darène
7. West Fourth Street Courts, also known as West 4th Street Courts, The Cage (wd:Q7985184): athletic venue in Manhattan, New York
8. The Cage (wd:Q3520180): album by Tygers of Pan Tang
9. The Elephant (wd:Q31645050): hill in New Zealand
10. The Cage, also known as Colivia (wd:Q29581150): Romanian film

[Assistant]
The correct entity for "Cage the Elephant" is `wd:Q68061`, which represents the American rock band.

Next, we need to find the genre associated with this band. We can use the property `genre` (P136) to find the genres.

Let's list the genres associated with `wd:Q68061`.

[Tool]
Got 6 rows and 3 columns:
| subject                       | property         | object                        |
| ----------------------------- | ---------------- | ----------------------------- |
| Cage the Elephant (wd:Q68061) | genre (wdt:P136) | blues (wd:Q9759)              |
| Cage the Elephant (wd:Q68061) | genre (wdt:P136) | punk rock (wd:Q3071)          |
| Cage the Elephant (wd:Q68061) | genre (wdt:P136) | alternative rock (wd:Q11366)  |
| Cage the Elephant (wd:Q68061) | genre (wdt:P136) | indie rock (wd:Q183504)       |
| Cage the Elephant (wd:Q68061) | genre (wdt:P136) | psychedelic rock (wd:Q206159) |
| Cage the Elephant (wd:Q68061) | genre (wdt:P136) | garage rock (wd:Q211573)      |

[Assistant]
The genres associated with Cage the Elephant are:
- Blues (wd:Q9759)
- Punk rock (wd:Q3071)
- Alternative rock (wd:Q11366)
- Indie rock (wd:Q183504)
- Psychedelic rock (wd:Q206159)
- Garage rock (wd:Q211573)

Now, we need to find the genre with the fewest English characters. To do this, we will write a SPARQL query that retrieves the labels of these genres and sorts them by the length of their labels.

Let's execute the SPARQL query to find the genre with the fewest English characters.

```sparql
SELECT ?genre (STRLEN(?label) AS ?length)
WHERE {
  wd:Q68061 wdt:P136 ?genre .
  ?genre rdfs:label ?label .
  FILTER(LANG(?label) = "en")
}
ORDER BY ASC(?length)
LIMIT 1
```

This query will return the genre with the shortest label in English.

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| genre            | length      |
| ---------------- | ----------- |
| blues (wd:Q9759) | 5 (xsd:int) |

[Assistant]
The genre with the fewest English characters associated with Cage the Elephant is "blues" (wd:Q9759).

Here is the final SPARQL query and answer:

```sparql
SELECT ?genre (STRLEN(?label) AS ?length)
WHERE {
  wd:Q68061 wdt:P136 ?genre .
  ?genre rdfs:label ?label .
  FILTER(LANG(?label) = "en")
}
ORDER BY ASC(?length)
LIMIT 1
```

Answer: The genre with the fewest English characters that Cage the Elephant belongs to is "blues" (wd:Q9759).

Let's finalize this with the answer function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. It finds the genre with the fewest English characters that Cage the Elephant belongs to, which is 'blues'. The query correctly uses the `STRLEN` function to calculate the length of the label and orders the results accordingly. The answer provided is accurate and complete.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?time WHERE {
  wd:Q81214 wdt:P580 ?time .
}

The first sperm whales existed approximately 3.6 million years ago.

[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 the first sperm whales exist?

[Assistant] Let's start by looking at some examples.

[Tool]
Example 1:
Question:
Where did Abraham Lincoln die?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q91 wdt:P20 ?uri .
}

Using entities:
Abraham Lincoln, also known as President Lincoln, Uncle Abe, Honest Abe, Abe Lincoln (wd:Q91): president of the United States from 1861 to 1865, military officer, statesperson, postmaster, politician, farmer, writer, lawyer

Using properties:
place of death, also known as POD, location of death, killed in, deathplace, died in (wd:P20 as wdt): most specific known (e.g. city instead of country, or hospital i... city) death location of a person, animal or fictional character, location

Example 2:
Question:
Where does Piccadilly start?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1124023 wdt:P559 ?uri
}

Using entities:
Piccadilly (wd:Q1124023): road in the City of Westminster, London, England

Using properties:
terminus, also known as end point, terminating connection, termini, terminuses, trailhead (wd:P559 as wdt): the feature (intersecting road, train station, etc.) at the end of a linear feature

Example 3:
Question:
Which museum exhibits The Scream by Munch?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q471379 wdt:P527 / wdt:P276 ?uri .
  ?uri wdt:P31 wd:Q207694
}

Using entities:
art museum, also known as museums of art, museum of art, art museums (wd:Q207694): building or space for the exhibition of art (for institution, use Q3196771)
The Scream, also known as Scream (wd:Q471379): group of paintings by Edvard Munch

Using properties:
location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as wdt): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).

[Assistant] Based on the examples, we need to identify the entity for sperm whales and the property for the time of their first existence. Let's start by searching for the entity "sperm whale".

[Tool]
Top 10 entity alternatives:
1. sperm whale, also known as cachalot, Physeter macrocephalus (wd:Q81214): largest species of toothed whale
2. Sperm Whale, also known as Nahang-e-Anbar (wd:Q20111251): 2015 film directed by Saman Moghadam
3. Sperm whale (wd:Q115479916): water colour painting of a sperm whale
4. sperm whale, also known as cachalot (wd:Q113680749): fictional monster from Dungeons & Dragons
5. sperm whale (wd:L591643-S1)
6. pygmy sperm whale, also known as Kogia breviceps (wd:Q244300): species of mammal
7. dwarf sperm whale, also known as Kogia sima (wd:Q42669): species of mammal
8. macroraptorial sperm whale, also known as raptorial sperm whale (wd:Q56274847): highly predatory whales of the sperm whale superfamily (Physeteroidea) of the Miocene epoch
9. Sperm Whale Head (wd:Q21903224): point in Australia
10. High-finned sperm whale, also known as high-finned cachalot (wd:Q99565665): species of whale (Cryptid)

[Assistant] The correct entity for sperm whale is `wd:Q81214`. Next, we need to find out the property that indicates when the first sperm whales existed. We can search for properties related to "first appearance" or "origin" for the sperm whale entity.

[Tool]
Top 10 property alternatives:
1. 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
2. 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
3. short name, also known as short title, shortname, abbreviation, shortened name, initialism (wd:P1813 as wdt/p): short name of a place, organisation, person, journal, wikidata property, etc. Used by some Wikipedia templates., name
4. 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)
5. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt/p): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
6. named after, also known as toponym, namesake, named in honor of, named for, etymology (wd:P138 as wdt/p): entity or event that inspired the subject's name, or namesake (i...fier "applies to name" (P5168) can be used to indicate which one
7. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as wdt/p): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
8. taxon rank, also known as taxonomic rank, type of taxon, rank (wd:P105 as wdt/p): level in a taxonomic hierarchy, instance of
9. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as wdt/p): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
10. Microsoft Academic ID, also known as MS Academic ID, Microsoft ID, Microsoft Academic Work ID, Microsoft Academic topic ID, Microsoft Academic Source ID (wd:P6366 as p/wdt/wdtn): identifier for an object or topic in the Microsoft Academic Graph (until 31 December 2021)

No other items found

[Assistant]
It seems that the property "first appearance" or "origin" is not directly listed among the top properties for the sperm whale entity. However, the property "start time" (`wd:P580`) might be relevant, as it can indicate when something begins to exist. Let's check if this property is used for the sperm whale entity.

I will run a SPARQL query to see if there is a value for `wd:P580` for the sperm whale entity `wd:Q81214`.

```sparql
SELECT ?time WHERE {
  wd:Q81214 wdt:P580 ?time .
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| time                                   |
| -------------------------------------- |
| -3600000-01-01T00:00:00 (xsd:dateTime) |

[Assistant]
The result shows that the start time for the sperm whale is `-3600000-01-01T00:00:00`, which corresponds to approximately 3.6 million years ago. This is likely the time when the first sperm whales existed.

To provide a more human-readable format, we can convert this datetime to a more understandable form. However, since the SPARQL endpoint does not support the `xsd:dateTime` format directly, we can extract the year part and present it.

Here is the final SPARQL query:

```sparql
SELECT ?time WHERE {
  wd:Q81214 wdt:P580 ?time .
}
```

And the answer is that the first sperm whales existed approximately 3.6 million years ago.

Let's finalize this with the answer function.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is correct and returns the expected result. The answer provided is accurate based on the data from Wikidata.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT DISTINCT ?character WHERE {
  ?character wdt:P639 wd:Q15228 .
}

Thank you for the feedback. Let's try a more direct approach by using `wdt:P639` (character appears in) and adding the necessary filters to find the character who is a swordfighter, marries a half-elf, and belongs to the Rangers of the North.

### Step 1: Find characters in "The Lord of the Rings"
We will use the property `wdt:P639` to find characters in "The Lord of the Rings."

### Step 2: Filter characters who are swordfighters
We will use the property `wdt:P106` (occupation) to check if they are swordfighters.

### Step 3: Filter characters who have married a half-elf
We will use the property `wdt:P26` (spouse) to find characters who have married a half-elf.

### Step 4: Filter characters who belong to the Rangers of the North
We will use the property `wdt:P108` (member of) to find characters who belong to the Rangers of the North.

Let's start by finding characters in "The Lord of the Rings" and then gradually add the filters.

### Step 1: Find characters in "The Lord of the Rings"
```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P639 wd:Q15228 .
}
```

Let's execute this query to find characters in "The Lord of the Rings."

[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 swordfighter in the lord of the rings marry a half-elven and belong to rangers of the north?

[Assistant] Let's start by looking at some examples.

[Tool]
Example 1:
Question:
What movies does Jesse Eisenberg play in?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P161 wd:Q219512 .
}

Using entities:
Jesse Eisenberg, also known as Jesse Adam Eisenberg (wd:Q219512): television actor, American actor, film director, screenwriter, voice actor, stage actor, film actor, actor

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer

Example 2:
Question:
What was the last movie with Alec Guinness?

SPARQL:
SELECT DISTINCT ?s1 WHERE {
  ?s1 wdt:P31 wd:Q11424 .
  ?s1 wdt:P161 wd:Q103894 .
  ?s1 wdt:P577 ?o1 .
}
order by DESC ( ?o1 )
limit 1

Using entities:
Alec Guinness, also known as Alec Guinness de Cuffe, Sir Alec Guinness (wd:Q103894): British actor (1914–2000), television actor, character actor, autobiographer, screenwriter, stage actor, film actor, actor
film, also known as cinematic work, moving picture, movie, motion pictures, motion picture (wd:Q11424): sequence of images that give the impression of movement, stored on film stock

Using properties:
cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as wdt): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
publication date, also known as launched, published, publication time, publication, pubdate (wd:P577 as wdt): date or point in time when a work was first published or released, point in time

Example 3:
Question:
Who created Family Guy?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q5930 wdt:P170 ?uri
}

Using entities:
Family Guy (wd:Q5930): American adult animated sitcom

Using properties:
creator, also known as created by, visual artist, sculptor, sculpted by, realized by (wd:P170 as wdt): maker of this creative work or other object (where no more specific property exists)

[Assistant]
From the examples, we can see how to structure basic SPARQL queries and use properties and entities. Now, let's break down the user's question:

1. Identify the swordfighter in "The Lord of the Rings."
2. Check if this swordfighter marries a half-elven.
3. Verify if this swordfighter belongs to the Rangers of the North.

First, let's search for entities related to "The Lord of the Rings" and "swordfighter."

[Tool]
Top 10 entity alternatives:
1. The Lord of the Rings, also known as Lord of the Rings, LotR, TLotR (wd:Q15228): 1954–1955 fantasy novel trilogy by J. R. R. Tolkien
2. The Lord of the Rings trilogy, also known as Lord of the Rings, LotR, The Lord of the Rings (wd:Q190214): 2001-2003 three films directed by Peter Jackson
3. Sauron, also known as Necromancer, Mairon, Lord of the Rings, Aulendil, Gorthaur (wd:Q2281): fictional character from J.R.R. Tolkien's legendarium, fictional tyrant, necromancer
4. The Lord of the Rings, also known as Lord of the Rings (wd:Q378913): 1978 animated film by Ralph Bakshi
5. Lord of the Rings (wd:Q905691): stage musical
6. Lord of the Rings (wd:Q1194258): board game
7. Symphony No. 1 "The Lord of the Rings", also known as The Lord of the Rings (wd:Q268133): musical work; symphony in five movements composed by Johan de Meij
8. Themes of The Lord of the Rings (wd:Q7781266): Academic analyses of The Lord of the Rings
9. The Lord of the Rings (wd:Q3227050): 1981 British radio drama series
10. The Lord of the Rings (wd:Q3227049): 1979 radio series

[Tool]
Top 10 entity alternatives:
1. swordfighter, also known as sword-fighter, swordsman, swordsmen, swordswoman (wd:Q11397897): person who fights with a sword
2. The tale of the sword - swords a...ordfighters in Bronze Age Europe (wd:Q56826244): scholarly article by Kristian Kristiansen published in November 2002
3. Tales of Hoards and Swordfighter...ia: The Brand New and the Broken (wd:Q58587763): scholarly article by Lene Melheim published in January 2014

[Assistant]
Based on the search results, we can use `wd:Q15228` for "The Lord of the Rings" and `wd:Q11397897` for "swordfighter."

Next, let's find characters in "The Lord of the Rings" who are swordfighters. We will use the property `wdt:P690` (occupation) to check if they are swordfighters.

We also need to identify the half-elven character and the Rangers of the North. Let's search for these entities as well.

### Step 1: Find swordfighters in "The Lord of the Rings"
We will start by finding characters in "The Lord of the Rings" who are swordfighters.

### Step 2: Identify the half-elven character
We will search for the half-elven character.

### Step 3: Identify the Rangers of the North
We will search for the Rangers of the North.

Let's start with the first step.

### Step 1: Find swordfighters in "The Lord of the Rings"
We will use the property `wdt:P690` to find characters who are swordfighters.

```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P690 wd:Q11397897 .
  ?character wdt:P449 wd:Q15228 .
}
```

Let's execute this query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems there are no results for characters in "The Lord of the Rings" who are swordfighters using the property `wdt:P690`. Let's try another approach by searching for characters in "The Lord of the Rings" and then checking if they are swordfighters using a different property.

### Step 1: Find characters in "The Lord of the Rings"
We will use the property `wdt:P800` (notable work) to find characters in "The Lord of the Rings."

```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P800 wd:Q15228 .
}
```

Let's execute this query to find characters in "The Lord of the Rings."

[Tool]
Got 2 rows and 1 column:
| character                         |
| --------------------------------- |
| Brothers Hildebrandt (wd:Q102043) |
| J. R. R. Tolkien (wd:Q892)        |

[Assistant]
The query returned "Brothers Hildebrandt" and "J. R. R. Tolkien," which are not characters in "The Lord of the Rings." It seems we need a different approach to find the characters.

Let's try using the property `wdt:P639` (depicted in) to find characters depicted in "The Lord of the Rings."

### Step 1: Find characters in "The Lord of the Rings"
We will use the property `wdt:P639` to find characters depicted in "The Lord of the Rings."

```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P639 wd:Q15228 .
}
```

Let's execute this query to find characters in "The Lord of the Rings."

[Tool] Got no rows and 1 columns

[Assistant]
It seems the property `wdt:P639` did not yield any results either. Let's try another approach by searching for characters in "The Lord of the Rings" using the property `wdt:P800` (notable work) but with a different method to filter out non-character entities.

### Step 1: Find characters in "The Lord of the Rings"
We will use the property `wdt:P800` and filter out non-character entities by checking if they are instances of fictional characters (`wd:Q95074`).

```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P800 wd:Q15228 .
  ?character wdt:P31 wd:Q95074 .
}
```

Let's execute this query to find characters in "The Lord of the Rings."

[User]
Feedback (status=retry):
The current SPARQL query did not return any results, which suggests that the approach might need to be refined. Instead of using `wdt:P800` (notable work), we should use `wdt:P639` (character appears in) to find characters in 'The Lord of the Rings'. Additionally, we need to ensure that the character is a member of the Rangers of the North and has married a half-elf. Let's construct a new query that includes these criteria.

[Assistant]
Thank you for the feedback. Let's refine our approach by using `wdt:P639` (character appears in) to find characters in "The Lord of the Rings" and then filter those characters to find the ones who are members of the Rangers of the North and have married a half-elf.

### Step 1: Find characters in "The Lord of the Rings"
We will use the property `wdt:P639` to find characters in "The Lord of the Rings."

### Step 2: Filter characters who are members of the Rangers of the North
We will use the property `wdt:P108` (member of) to find characters who are members of the Rangers of the North.

### Step 3: Filter characters who have married a half-elf
We will use the property `wdt:P26` (spouse) to find characters who have married a half-elf.

Let's start by finding characters in "The Lord of the Rings" and then gradually add the filters.

### Step 1: Find characters in "The Lord of the Rings"
```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P639 wd:Q15228 .
}
```

Let's execute this query to find characters in "The Lord of the Rings."

[Tool] Got no rows and 1 columns

[Assistant]
It seems that using `wdt:P639` (character appears in) did not yield any results. Let's try another approach by using the property `wdt:P800` (notable work) and filtering out non-character entities by checking if they are instances of fictional characters (`wd:Q95074`).

### Step 1: Find characters in "The Lord of the Rings"
We will use the property `wdt:P800` and filter out non-character entities by checking if they are instances of fictional characters.

```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P800 wd:Q15228 .
  ?character wdt:P31 wd:Q95074 .
}
```

Let's execute this query to find characters in "The Lord of the Rings."

[User]
Feedback (status=retry):
The initial approach did not yield any results. Instead of using `wdt:P800` (notable work), let's try using `wdt:P639` (character appears in) again, but this time with a different filter to ensure we are looking at characters from 'The Lord of the Rings'. Additionally, we will add filters to check if the character is a swordfighter, marries a half-elven, and belongs to the Rangers of the North.

[Assistant]
Thank you for the feedback. Let's try a more direct approach by using `wdt:P639` (character appears in) and adding the necessary filters to find the character who is a swordfighter, marries a half-elf, and belongs to the Rangers of the North.

### Step 1: Find characters in "The Lord of the Rings"
We will use the property `wdt:P639` to find characters in "The Lord of the Rings."

### Step 2: Filter characters who are swordfighters
We will use the property `wdt:P106` (occupation) to check if they are swordfighters.

### Step 3: Filter characters who have married a half-elf
We will use the property `wdt:P26` (spouse) to find characters who have married a half-elf.

### Step 4: Filter characters who belong to the Rangers of the North
We will use the property `wdt:P108` (member of) to find characters who belong to the Rangers of the North.

Let's start by finding characters in "The Lord of the Rings" and then gradually add the filters.

### Step 1: Find characters in "The Lord of the Rings"
```sparql
SELECT DISTINCT ?character WHERE {
  ?character wdt:P639 wd:Q15228 .
}
```

Let's execute this query to find characters in "The Lord of the Rings."


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?company ?foundingDate WHERE {
  VALUES ?company {
    wd:Q2905359 wd:Q3821214
  }
  ?company wdt:P571 ?foundingDate
}
ORDER BY ASC ( ?foundingDate )
LIMIT 1

La Sportiva started earlier.

[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 company started earlier, Black Diamond Equipment or la sportiva?

[Assistant] Let's start by looking at some examples.

[Tool]
Example 1:
Question:
How deep is Lake Chiemsee?

SPARQL:
SELECT DISTINCT ?n WHERE {
  wd:Q4138 wdt:P4511 ?n
}

Using entities:
Chiemsee, also known as Chiem-see (wd:Q4138): freshwater lake in Bavaria, Germany

Using properties:
vertical depth, also known as depth, oceandepth, lakedepth (wd:P4511 as wdt): vertical distance from a horizontal area to a point below. Compare with "horizontal depth" (P5524), length

Example 2:
Question:
Where is Sungkyunkwan University?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  VALUES ?p {
    wdt:P17 wdt:P276 wdt:P131
  }
  wd:Q41085 ?p ?uri .
}

Using entities:
Sungkyunkwan University, also known as 성균관대학교 (wd:Q41085): private university in Seoul, South Korea

Using properties:
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): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
country, also known as sovereign state, state, land, host country (wd:P17 as wdt): sovereign state that this item is in (not to be used for human beings), located in the administrative territorial entity
location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as wdt): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object

Example 3:
Question:
How many politicians graduated from Columbia University?

SPARQL:
SELECT DISTINCT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P106 wd:Q82955 ; wdt:P69 wd:Q49088 .
}

Using entities:
politician, also known as pol, political leader, polit., political figure (wd:Q82955): person who holds or seeks positions in government

Using properties:
occupation, also known as job, work, vocation, profession, employment (wd:P106 as wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as wdt): educational institution attended by subject, affiliation

[Assistant] Based on the examples, we need to identify the entities for the companies and the property for their founding dates. Let's start by searching for the entities.

[Tool]
Top 10 entity alternatives:
1. Black Diamond Equipment (wd:Q2905359): Manufacturer of equipment for climbing, skiing, and mountain sports
2. anthracite, also known as black diamond, stone coal, Kilkenny coal, hard coal, crow coal (wd:Q182458): hard coal with a volatile component (gas) of less than 10 %
3. Black Diamond, also known as Black Diamond, Washington, Black Diamond, WA (wd:Q1191152): city in King County, Washington, United States
4. Black Diamond, also known as Black Diamond, FL, Black Diamond, Florida (wd:Q570406): census designated place in Citrus County, Florida, United States
5. carbonado, also known as black diamond (wd:Q429950): toughest form of natural diamond; it is an impure form of polycr...ne diamond consisting of diamond, graphite, and amorphous carbon
6. Black Diamond (wd:Q880216): former town in Alberta, Canada
7. Black Diamond, also known as Black Diamond (library), The Black Diamond (wd:Q1142174): modern waterfront extension to the Royal Danish Library's old building
8. The Black Diamond, also known as Black Diamond, Le Diamant noir (wd:Q20802174): 1922 film directed by André Hugon
9. Black Diamond (wd:Q784842): 2008 sophomore studio album by Buraka Som Sistema
10. Black Diamond (wd:Q3398877): Kiss song

[Assistant] The correct entity for Black Diamond Equipment is `wd:Q2905359`. Now let's search for La Sportiva.

[Tool]
Top 10 entity alternatives:
1. La Sportiva (wd:Q3821214): Outdoor Sporting Goods Company
2. Sociedad Sportiva Argentina, also known as La Sportiva (wd:Q39081671): defunct sports club in Buenos Aires
3. La Domenica Sportiva (wd:Q3208328): Italian TV series or program
4. SS Lazio, also known as S.S. Lazio, SSLazio, S.S.Lazio, SSL, S.S.L. (wd:Q2609): football section of the sports club based in Rome, Lazio, Italy
5. Fiat Sportiva Latina (wd:Q50397322)
6. La Domenica Sportiva Estate (wd:Q114560708): Italian television program
7. La Sportiva Competition Award (wd:Q66761549)
8. Unione sportiva La Piemonte (wd:Q124297461): Italian institution in Torino
9. S.S. Lazio Marines, also known as Lazio Marines, Marines Lazio, Società Sportiva Lazio Marines, S.S. Lazio Marines AFT (wd:Q3963703): American football team based in Roma, Italy
10. Associazione Sportiva La Fratellanza 1874 (wd:Q100527922)

[Assistant] The correct entity for La Sportiva is `wd:Q3821214`. Next, we need to find the property for the founding date of these companies. Let's search for the property.

[Tool]
Top 10 property alternatives:
1. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): time when an entity begins to exist; for date of official opening use P1619, start time
2. location of formation, also known as place of incorporation, originates from, place of establishment, place of formation, place of foundation (wd:P740 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): location where a group or organization was formed, location of creation
3. date of incorporation, also known as date incorporated, incorporated, incorporated on date, incorporation date, year incorporated (wd:P10786 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date when company, organization or city was incorporated
4. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): notable scientific, artistic or literary work, or other work of significance among subject's works
5. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): founder or co-founder of this organization, religion, place or entity, creator
6. foundational text, also known as main text, treaty, organic law, founding document, establishing document (wd:P457 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): text through which an institution or object has been created or established
7. time of discovery or invention, also known as invented in, time of invention, time of discovery, invention time, invented on (wd:P575 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date or point in time when the item was discovered or invented
8. introduced on, also known as bill introduced on, introduction date, introduced date (wd:P9448 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): date when a bill was introduced into a legislature, inception
9. start time, also known as start date, since, valid, join time, joined (wd:P580 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): time an entity begins to exist or a statement starts being valid, point in time
10. earliest date, also known as no earlier than, terminus post quem, start date (earliest), on or after this date, minimum date (wd:P1319 as pqn/prv/pq/wdt/ps/prn/psv/p/wdtn/pqv/psn/pr): earliest date at which an event could have happened. Use as qualifier for other date properties

[Assistant]
The property for the founding date is `wd:P571`. Now, let's write a SPARQL query to compare the founding dates of Black Diamond Equipment and La Sportiva.

We will select the founding dates of both companies and then compare them to determine which one started earlier.

Here is the initial SPARQL query:

```sparql
SELECT ?company ?foundingDate
WHERE {
  VALUES ?company { wd:Q2905359 wd:Q3821214 }
  ?company wdt:P571 ?foundingDate
}
```

Let's execute this query to get the founding dates.

[Tool]
Got 2 rows and 2 columns:
| company                               | foundingDate                        |
| ------------------------------------- | ----------------------------------- |
| Black Diamond Equipment (wd:Q2905359) | 1989-12-01T00:00:00Z (xsd:dateTime) |
| La Sportiva (wd:Q3821214)             | 1928-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
We have the founding dates for both companies:
- Black Diamond Equipment: 1989-12-01
- La Sportiva: 1928-01-01

Now, let's modify the SPARQL query to determine which company started earlier. We can use a `FILTER` clause to compare the dates.

Here is the updated SPARQL query:

```sparql
ASK WHERE {
  VALUES ?company { wd:Q2905359 wd:Q3821214 }
  ?company wdt:P571 ?foundingDate .
  FILTER(?foundingDate = "1928-01-01T00:00:00Z"^^xsd:dateTime)
}
```

This query will return `true` if La Sportiva started earlier, and `false` otherwise. However, a more straightforward approach would be to directly compare the dates and return the company that started earlier.

Let's execute the following SPARQL query:

```sparql
SELECT ?company ?foundingDate
WHERE {
  VALUES ?company { wd:Q2905359 wd:Q3821214 }
  ?company wdt:P571 ?foundingDate
}
ORDER BY ASC(?foundingDate)
LIMIT 1
```

This query will return the company that started earlier along with its founding date.

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| company                   | foundingDate                        |
| ------------------------- | ----------------------------------- |
| La Sportiva (wd:Q3821214) | 1928-01-01T00:00:00Z (xsd:dateTime) |

[Assistant]
The query results show that La Sportiva started earlier than Black Diamond Equipment.

Therefore, the answer to the question is:

La Sportiva started earlier.

Here is the final SPARQL query and answer:

```sparql
SELECT ?company ?foundingDate
WHERE {
  VALUES ?company { wd:Q2905359 wd:Q3821214 }
  ?company wdt:P571 ?foundingDate
}
ORDER BY ASC(?foundingDate)
LIMIT 1
```

Answer: La Sportiva started earlier.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The output is correct and complete. The SPARQL query successfully retrieves the founding dates of both companies and correctly identifies La Sportiva as the company that started earlier.