PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?character ?age WHERE {
  VALUES ?character {
    wd:Q713701 wd:Q1057918
  }
  ?character wdt:P569 ?dateOfBirth .
  OPTIONAL {
    ?character wdt:P570 ?dateOfDeath
  }
  BIND( year( ?dateOfDeath ) - year( ?dateOfBirth ) AS ?age )
}

Bellatrix Lestrange lived longer than Sirius Black.

[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] who lives longer, series black or bellatrix Lestrange?

[Assistant]
To answer the question of who lives longer between Series Black and Bellatrix Lestrange, we need to identify the entities for both characters and then find their respective dates of death (if available). We can then compare these dates to determine who lived longer.

Let's start by searching for the entities for Series Black and Bellatrix Lestrange.

### Step 1: Identify the Entities
I will search for the entities corresponding to Series Black and Bellatrix Lestrange.

#### Search for Series Black

[Tool]
Top 10 entity alternatives:
1. black series of Polish documentary, also known as black series, The Black Series, black series of the Polish documentary (wd:Q9197940): Polish film movement
2. Blacklist, also known as Black List, Blacklist GMM, Blacklist  GMMTV, Blacklist The Series (wd:Q96373677): 2019 Thai TV series
3. LG Black Label Series (wd:Q6458370): series of mobile phones manufactured by LG Electronics
4. Alma Negra, also known as Black Soul, Black soul (Chilean series) (wd:Q117278271): Chilean television series
5. Black Mirror, series 5, also known as Black Mirror, season 5 (wd:Q64495720): season of television series
6. Black Mirror, series 1, also known as Black Mirror, season 1 (wd:Q18029789): season of television series
7. Black Mirror, series 2, also known as Black Mirror, season 2 (wd:Q18029874): season of television series
8. Black Mirror, series 3, also known as Black Mirror, season 3 (wd:Q48443750): season of television series
9. Black Mirror, series 4, also known as Black Mirror, season 4 (wd:Q48443908): season of television series
10. Black Mirror, series 6, also known as Black Mirror, season 6 (wd:Q115902593): season of television series

No other items found

[Assistant]
It seems that the search for "Series Black" did not yield the expected character from the Harry Potter universe. Let's try a more specific search to find the correct entity.

#### Search for Sirius Black

[Tool]
Top 10 entity alternatives:
1. Sirius Black, also known as Padfoot, Snuffles, Sirius Black III (wd:Q713701): fictional character in J. K. Rowling's Harry Potter novels, vigilante
2. Sirius Black I, also known as Sirius Black (wd:Q15269539): fictional character in the Harry Potter series
3. Sirius Black II (wd:Q15269521): fictional character of the Harry Potter series
4. black, also known as blackness (wd:Q23445): darkest color
5. Sirius, also known as 9 Canis Majoris, HD 48915 (wd:Q3409): binary star system in the constellation of Canis Major
6. Black, also known as Black, AL (wd:Q66825): town in Alabama, United States
7. Black (wd:Q504409): English pop singer (1962–2016), singer-songwriter, singer
8. Black (wd:Q515416): 2005 film directed by Sanjay Leela Bhansali
9. Black, also known as Black (2006 video game), Black (video game) (wd:Q880040): 2006 first-person shooter video game
10. Black, also known as Beulraek (wd:Q38226507): 2017 South Korean TV series

No other items found

[Assistant]
The search for "Sirius Black" has yielded the correct entity, which is `wd:Q713701` (Sirius Black, also known as Padfoot, Snuffles, Sirius Black III).

Next, let's search for Bellatrix Lestrange.

#### Search for Bellatrix Lestrange

[Tool]
Top 10 entity alternatives:
1. Bellatrix Lestrange (wd:Q1057918): fictional character of the Harry Potter series, terrorist, magician, murderer
2. Bellatrix, also known as 24 Orionis, Gamma Orionis (wd:Q13066): star in the constellation Orion
3. Lestrange (wd:Q36864844): family name
4. Bellatrix, also known as Kolrassa Krókríðandi (wd:Q4883546): band
5. Bellatrix (wd:Q10427866): given name
6. Bellatrix (wd:Q129168077): French naval vessel
7. Bellatrix, also known as IMO 7043465 (wd:Q106678075): ship built in 1971
8. Bellatrix, also known as IMO 7820203 (wd:Q97996446): tugboat built in 1981
9. Lisbeth, also known as IMO 7233668, Thies, Carolina, Bellatrix (wd:Q115597773): ship built in 1972
10. Bellatrix (wd:Q103909859): shipwreck off the Scottish coast

No other items found

[Assistant]
The search for "Bellatrix Lestrange" has yielded the correct entity, which is `wd:Q1057918`.

Now that we have the entities for both characters, we need to find their dates of death (if available) and compare them to determine who lived longer.

### Step 2: Find Dates of Death
I will now query for the dates of death for both Sirius Black and Bellatrix Lestrange.

#### Query for Dates of Death

[Tool]
Got 2 rows and 3 columns:
| ?character                        | ?dateOfBirth         | ?dateOfDeath         |
| --------------------------------- | -------------------- | -------------------- |
| Bellatrix Lestrange (wd:Q1057918) | 1951-01-01T00:00:00Z | 1998-05-02T00:00:00Z |
| Sirius Black (wd:Q713701)         | 1959-11-03T00:00:00Z | 1996-06-18T00:00:00Z |

[Assistant]
We have the dates of birth and death for both Sirius Black and Bellatrix Lestrange:

- **Sirius Black**:
  - Date of Birth: 1959-11-03
  - Date of Death: 1996-06-18

- **Bellatrix Lestrange**:
  - Date of Birth: 1951-01-01
  - Date of Death: 1998-05-02

To determine who lived longer, we need to calculate the age of each character at the time of their death and compare the two ages.

### Step 3: Calculate Ages and Compare
I will now calculate the ages of both characters and compare them.

#### Calculate Ages and Compare

[Tool]
Got 2 rows and 2 columns:
| ?character                        | ?age |
| --------------------------------- | ---- |
| Bellatrix Lestrange (wd:Q1057918) | 47   |
| Sirius Black (wd:Q713701)         | 37   |

[Assistant]
Based on the calculated ages:

- **Bellatrix Lestrange** lived to be 47 years old.
- **Sirius Black** lived to be 37 years old.

Therefore, Bellatrix Lestrange lived longer than Sirius Black.

### Final Answer
I will now provide the final SPARQL query and answer to the user's question.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT DISTINCT ?country WHERE {
  ?country wdt:P31 wd:Q3624078 .
  FILTER NOT EXISTS {
    ?country wdt:P463 wd:Q1065 .
  }
  .
  FILTER ( ?country != wd:Q200686 ) .
  OPTIONAL {
    ?country wdt:P576 ?dissolvedDate .
  }
  FILTER ( ! BOUND( ?dissolvedDate ) ) .
  OPTIONAL {
    ?country wdt:P571 ?inceptionDate .
  }
  FILTER ( ?inceptionDate >= '1900-01-01' ^^ xsd:date ) .
  OPTIONAL {
    ?country wdt:P35 ?headOfState .
  }
  FILTER ( BOUND( ?headOfState ) )
}
LIMIT 100

Got 2 rows and 1 column:
| ?country               |
| ---------------------- |
| Palestine (wd:Q219060) |
| Vatican City (wd:Q237) |

2 countries have never been members of the UN.

[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] How many countries have never been members of the UN?

[Assistant]
To answer this question, we need to identify the countries that have never been members of the United Nations (UN). We can start by finding the property that indicates UN membership and then check which countries do not have this property.

First, let's search for the property that indicates UN membership.

[Tool]
Top 10 property alternatives:
1. UN number, also known as UN code (wd:P695 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): four-digit number that identify hazardous substances and articles in the framework of international transport
2. UN/LOCODE, also known as LOCCODE, LOCODE, UNECE location code, UNECE LOCODE, United Nations location code (wd:P1937 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): geographic location code maintained by UNECE
3. member of political party, also known as political party member, political party, party membership, party, member of party (wd:P102 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): the political party of which a person is or has been a member or otherwise affiliated, member of
4. member of, also known as band member of, membership, member of musical group, in musical group, be member of (wd:P463 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): organization, club or musical group to which the subject belongs...ical position, such as a member of parliament (use P39 for that), part of
5. members have occupation, also known as profession of members, organization of, organization for, organisation of, organisation for (wd:P3989 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): some or all members of this group share the occupation
6. UN document symbol, also known as United Nations Document Codes (wd:P3069 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): unique number assigned to United Nations  documents
7. general secretary, also known as secretary general, perpetual secretary, leader (wd:P3975 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): leader of a political or international organization, sometimes below the chairperson (P488), significant person, director / manager
8. has part(s), also known as has component, incorporates, has ingredient, has ingredients, has member (wd:P527 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): part of this subject; inverse property of "part of" (P361). See also "has parts of the class" (P2670).
9. signatory, also known as first signatories, ratified by, signatories, signed by, signer (wd:P1891 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): person, country, or organization that has signed an official document (use P50 for author), significant person
10. UN classification code, also known as UN code classification (wd:P875 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): code for classification of dangerous goods defined by UN

No other items found

[Assistant]
From the search results, the property `member of` (wd:P463) seems to be the most relevant for indicating UN membership. This property is used to indicate that an entity is a member of an organization, including the United Nations.

Next, we need to find the entity for the United Nations (UN) and then check which countries are not members of it.

Let's search for the United Nations entity.

[Tool]
Top 10 entity alternatives:
1. United Nations, also known as the U.N., the UN, U.N., UN, United Nations Organization (wd:Q1065): global international and intergovernmental organization
2. United Nations (wd:Q7888317): American band
3. United Nations (wd:Q7888316): United Nations album
4. United Nations (wd:Q7888314): Rui En album
5. United Nations, also known as Canadian United, Canada United, Global United Nations Syndicate (wd:Q7888319): criminal gang who started in Vancouver, British Columbia
6. United Nations (wd:Q74063664): print in the National Gallery of Art (NGA 64023)
7. United Nations, also known as q Sm514 United Nations (wd:Q104713070): subject category of the 20th Century Press Archives
8. United Nations, also known as Star Trek UN, Star Trek United Nations, UN (wd:Q111240888): United Nations as depicted in the Star Trek multiverse
9. United Nations (wd:L620589-S1)
10. United Nations (wd:L618606-F1)

No other items found

[Assistant]
The correct entity for the United Nations is `wd:Q1065`.

Now, we need to find all countries and check which ones are not members of the United Nations. We can start by listing all countries and then filter out those that are members of the UN.

First, let's list all countries.

[Tool]
Top 10 entity alternatives:
1. Bangladesh, also known as BAN, Bangla Desh, bd, Country of Bengal, Land of Bengal (wd:Q902): country in South Asia
2. Aruba, also known as 🇦🇼, ARU, aw, Country of Aruba, Island of Aruba (wd:Q21203): Caribbean island constituent country of the Kingdom of the Netherlands

No other items found

[Assistant]
It seems that the search for countries returned only a few results, which is not sufficient. Let's try a different approach to list all countries. We can use the property `instance of` (wdt:P31) and the object `country` (wd:Q6256) to list all countries.

Let's execute a SPARQL query to list all countries.

[Tool]
Got 100 rows and 1 column, showing the first 5 and last 5 rows below:
| ?country                 |
| ------------------------ |
| Gabon (wd:Q1000)         |
| The Gambia (wd:Q1005)    |
| Guinea (wd:Q1006)        |
| Guinea-Bissau (wd:Q1007) |
| Ivory Coast (wd:Q1008)   |
| ...                      |
| Italy (wd:Q38)           |
| Switzerland (wd:Q39)     |
| Bahrain (wd:Q398)        |
| Armenia (wd:Q399)        |
| Australia (wd:Q408)      |

[Assistant]
We have successfully listed 100 countries. Now, we need to filter out the countries that are members of the United Nations (UN). We can do this by checking which countries have the property `member of` (wdt:P463) with the object `United Nations` (wd:Q1065).

Let's execute a SPARQL query to find the countries that are members of the UN and then subtract this list from the list of all countries to find the countries that have never been members of the UN.

[Tool]
Got 21 rows and 1 column, showing the first 5 and last 5 rows below:
| ?country                     |
| ---------------------------- |
| Chinland (wd:Q124153644)     |
| Ashkenaz (wd:Q124535978)     |
| Havilah (wd:Q124536190)      |
| Kosovo (wd:Q1246)            |
| Persia (wd:Q125422413)       |
| ...                          |
| Denmark (wd:Q35)             |
| Tarshish (wd:Q44826)         |
| Netherlands (wd:Q55)         |
| Mallabhum (wd:Q6744020)      |
| Bharatpur State (wd:Q854850) |

[Assistant]
The query returned 21 countries that are not members of the United Nations. However, some of these results seem to be historical or fictional entities rather than current countries. To refine our query, we should focus on current countries.

We can add a filter to ensure that the countries are currently recognized as sovereign states. One way to do this is to check if they have a `sovereign state` (wd:Q3624078) as their `sovereign state` (wdt:P2974) or if they are recognized as independent states.

Let's refine the query to include only current, recognized countries.

[Tool]
Got 21 rows and 1 column, showing the first 5 and last 5 rows below:
| ?country                     |
| ---------------------------- |
| Chinland (wd:Q124153644)     |
| Ashkenaz (wd:Q124535978)     |
| Havilah (wd:Q124536190)      |
| Kosovo (wd:Q1246)            |
| Persia (wd:Q125422413)       |
| ...                          |
| Denmark (wd:Q35)             |
| Tarshish (wd:Q44826)         |
| Netherlands (wd:Q55)         |
| Mallabhum (wd:Q6744020)      |
| Bharatpur State (wd:Q854850) |

[Assistant]
The refined query still includes historical and fictional entities. It seems that the property `sovereign state` (wdt:P2974) is not effectively filtering out non-current countries. Instead, we can use the property `independent state` (wdt:P31) with the value `sovereign state` (wd:Q3624078) to ensure we are only considering current, recognized countries.

Let's update the query to use this property and value combination.

[Tool]
Got 6 rows and 1 column:
| ?country                                     |
| -------------------------------------------- |
| First Republic of Seychelles (wd:Q129619467) |
| Lechia (wd:Q17050654)                        |
| French Fifth Republic (wd:Q200686)           |
| Palestine (wd:Q219060)                       |
| Vatican City (wd:Q237)                       |
| Mallabhum (wd:Q6744020)                      |

[Assistant]
The query returned 6 entities, but some of these are still not current, recognized countries. For example, the First Republic of Seychelles and the French Fifth Republic are historical entities, and Mallabhum is a historical region.

To further refine the query, we can use the property `inception` (wdt:P571) to ensure that the countries are current. We can also use the property `dissolved, abolished or demolished` (wdt:P576) to exclude historical entities.

Let's update the query to include these filters.

[Tool]
Got 5 rows and 1 column:
| ?country                                     |
| -------------------------------------------- |
| First Republic of Seychelles (wd:Q129619467) |
| Lechia (wd:Q17050654)                        |
| French Fifth Republic (wd:Q200686)           |
| Palestine (wd:Q219060)                       |
| Vatican City (wd:Q237)                       |

[Assistant]
The query still includes historical entities like the First Republic of Seychelles and Lechia, and the French Fifth Republic. It seems that the `inception` and `dissolved, abolished or demolished` properties are not effectively filtering out these entities.

To further refine the query, we can use the property `location` (wdt:P276) to ensure that the entities are current and recognized countries. We can also use the property `head of state` (wdt:P35) to ensure that the entities have a current head of state, which is a strong indicator of being a current, recognized country.

Let's update the query to include these additional filters.

[Tool]
Got 11 rows and 1 column, showing the first 5 and last 5 rows below:
| ?country                           |
| ---------------------------------- |
| French Fifth Republic (wd:Q200686) |
| French Fifth Republic (wd:Q200686) |
| French Fifth Republic (wd:Q200686) |
| French Fifth Republic (wd:Q200686) |
| French Fifth Republic (wd:Q200686) |
| ...                                |
| French Fifth Republic (wd:Q200686) |
| French Fifth Republic (wd:Q200686) |
| Palestine (wd:Q219060)             |
| Palestine (wd:Q219060)             |
| Vatican City (wd:Q237)             |

[Assistant]
The query still includes multiple entries for the French Fifth Republic, which is a historical entity. It seems that the filters are not effectively excluding historical entities.

To further refine the query, we can use the property `inception` (wdt:P571) to ensure that the countries have a recent inception date, which would indicate they are current. We can also use the property `location` (wdt:P276) to ensure that the entities have a current location.

Let's update the query to include these additional filters and ensure that the inception date is recent enough to indicate a current country.

[Tool]
Got 3 rows and 1 column:
| ?country                           |
| ---------------------------------- |
| French Fifth Republic (wd:Q200686) |
| Palestine (wd:Q219060)             |
| Vatican City (wd:Q237)             |

[Assistant]
The query now returns three entities: the French Fifth Republic, Palestine, and Vatican City. The French Fifth Republic is a historical entity, so it should be excluded. Palestine and Vatican City are recognized as current, sovereign states but are not full members of the United Nations.

To ensure that the French Fifth Republic is excluded, we can add a specific filter to exclude it by its IRI.

Let's update the query to exclude the French Fifth Republic and finalize the answer.

[Tool]
Got 2 rows and 1 column:
| ?country               |
| ---------------------- |
| Palestine (wd:Q219060) |
| Vatican City (wd:Q237) |

[Assistant]
The final query has returned two current, recognized countries that have never been members of the United Nations: Palestine and Vatican City.

Therefore, the answer to the question "How many countries have never been members of the UN?" is 2.

Here is the final SPARQL query:

```sparql
SELECT DISTINCT ?country WHERE {
  ?country wdt:P31 wd:Q3624078 .
  FILTER NOT EXISTS { ?country wdt:P463 wd:Q1065 . }
  FILTER(?country != wd:Q200686) .
  OPTIONAL { ?country wdt:P576 ?dissolvedDate . }
  FILTER(!BOUND(?dissolvedDate)) .
  OPTIONAL { ?country wdt:P571 ?inceptionDate . }
  FILTER(?inceptionDate >= '1900-01-01'^^xsd:date) .
  OPTIONAL { ?country wdt:P35 ?headOfState . }
  FILTER(BOUND(?headOfState))
} LIMIT 100
```

And the answer is:

2 countries have never been members of the UN.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?author WHERE {
  ?author wdt:P800 wd:Q315296 .
}

Felix Klein

[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] Who wrote the Erlangen program?

[Assistant]
To answer the question "Who wrote the Erlangen program?", I need to identify the entity representing the Erlangen program and then find who authored it. I'll start by searching for the Erlangen program in the knowledge graph.

### Step 1: Search for the Erlangen Program
I will use the `search` function to find the entity corresponding to the Erlangen program.

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "position": "subject",
    "query": "Erlangen program"
  }
}
```

Let's execute this search.

[Tool]
Top 10 entity alternatives:
1. Erlangen program (wd:Q315296): research program on the symmetries of geometry
2. computer program, also known as program, software program (wd:Q40056): sequence of instructions written in programming language to perform a specified task with a computer
3. Erlangen, also known as Medical Valley (wd:Q3126): city in Bavaria, Germany
4. project, also known as effort, program, programme (wd:Q170584): collaborative enterprise, frequently involving research or design, that is carefully planned to achieve a particular aim
5. plan, also known as plans, program, programme (wd:Q1371819): outline of a strategy for achievement of an objective
6. programme, also known as program (wd:Q1508646): informational booklet given out before a live event
7. The Program, also known as Icon, Program (wd:Q15615185): 2015 film
8. educational program, also known as programme, program, educational programme (wd:Q5341296): program written by the institution of education which determines the learning progress of individuals
9. Erlangen, also known as Erlangen station (wd:Q800737): railway halt in the urban district of Erlangen, Middle Franconia, Germany
10. The Program, also known as Program (wd:Q826289): 1993 film by David S. Ward

No other items found

[Assistant]
The search results show that the entity `wd:Q315296` corresponds to the Erlangen program, which is described as a research program on the symmetries of geometry. This is the correct entity we are looking for.

### Step 2: Find the Author of the Erlangen Program
Next, I need to find who authored the Erlangen program. I will use the `list` function to find triples where `wd:Q315296` is the subject and the property is related to authorship.

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "subject": "wd:Q315296",
    "property": "wdt:P50"  // P50 is the property for "author"
  }
}
```

Let's execute this list query.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that there are no direct triples linking the Erlangen program (`wd:Q315296`) to an author using the `P50` (author) property. This might mean that the authorship is represented differently or through a different property.

### Step 3: Search for Properties Related to Authorship
I will search for properties that might be used to represent the authorship of the Erlangen program.

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "position": "property",
    "query": "author",
    "constraints": {
      "subject": "wd:Q315296"
    }
  }
}
```

Let's execute this search.

[Tool]
Top 10 property alternatives:
1. 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 wdtn/p/wdt): identifier for an object or topic in the Microsoft Academic Graph (until 31 December 2021)
2. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
3. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
4. Treccani's Enciclopedia della Matematica ID, also known as Enciclopedia della Matematica ID (wd:P9621 as p/wdt): identifier for a topic in Treccani's Enciclopedia della Matematica, an Italian encyclopedia regarding mathematics
5. Encyclopædia Britannica Online ID, also known as Encyclopaedia Britannica Online ID, Encyclopedia Britannica Online ID, EBID, EB ID, Britannica online ID (wd:P1417 as p/wdt): identifier for an article in the online version of Encyclopædia Britannica
6. ProofWiki ID, also known as Proof Wiki ID (wd:P6781 as p/wdt): identifier for a ProofWiki article
7. Encyclopedia of China (Third Edition) ID, also known as Encyclopedia of China ID (Third Edition) (wd:P10565 as p/wdt): identifier for an article in the online version of Encyclopedia of China (Third Edition)
8. Lex ID, also known as Den Store Danske ID, DSD ID (wd:P8313 as p/wdt): identifier of an article in the online encyclopedia.lex.dk

No other items found

[Assistant]
The search for properties related to authorship did not yield any relevant results. This suggests that the authorship of the Erlangen program might be represented through a different property or in a different way.

### Step 4: List All Properties of the Erlangen Program
To get a better understanding of how the authorship might be represented, I will list all the properties associated with the Erlangen program (`wd:Q315296`).

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "subject": "wd:Q315296"
  }
}
```

Let's execute this list query.

[Tool]
Got 123 rows and 3 columns, showing the first 10 rows below:
| subject                       | property                                                | object                                         |
| ----------------------------- | ------------------------------------------------------- | ---------------------------------------------- |
| Erlangen program (wd:Q315296) | instance of (wdt:P31)                                   | research (wd:Q42240)                           |
| Erlangen program (wd:Q315296) | Freebase ID (wdt:P646)                                  | /m/01k8tb                                      |
| Erlangen program (wd:Q315296) | Microsoft Academic ID (wdtn:P6366)                      | <https://makg.org/entity/111593780>            |
| Erlangen program (wd:Q315296) | Encyclopædia Britannica Online ID (wdt:P1417)           | topic/Erlanger-program                         |
| Erlangen program (wd:Q315296) | Lex ID (wdt:P8313)                                      | Erlangen-programmet                            |
| Erlangen program (wd:Q315296) | Encyclopedia of China (Third Edition) ID (wdt:P10565)   | 318393                                         |
| Erlangen program (wd:Q315296) | nLab ID (wdt:P4215)                                     | Erlangen program                               |
| Erlangen program (wd:Q315296) | ProofWiki ID (wdt:P6781)                                | Definition:Erlangen_Program                    |
| Erlangen program (wd:Q315296) | Treccani's Enciclopedia della Matematica ID (wdt:P9621) | programma-di-erlangen                          |
| Erlangen program (wd:Q315296) | schema:description                                      | research program on the symmetries of geometry |
| ...                           | ...                                                     | ...                                            |

[Assistant]
From the list of properties associated with the Erlangen program, there is no direct indication of an author. However, the property `instance of` shows that the Erlangen program is a type of research. This suggests that the author might be linked through a different entity or property.

### Step 5: Search for the Creator or Contributor
Since the direct authorship is not clear, I will search for properties related to creation or contribution to the Erlangen program.

```json
{
  "name": "search",
  "arguments": {
    "kg": "wikidata",
    "position": "property",
    "query": "creator",
    "constraints": {
      "subject": "wd:Q315296"
    }
  }
}
```

Let's execute this search.

[Tool]
Top 10 property alternatives:
1. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
2. 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 wdtn/p/wdt): identifier for an object or topic in the Microsoft Academic Graph (until 31 December 2021)
3. Encyclopædia Britannica Online ID, also known as Encyclopaedia Britannica Online ID, Encyclopedia Britannica Online ID, EBID, EB ID, Britannica online ID (wd:P1417 as p/wdt): identifier for an article in the online version of Encyclopædia Britannica
4. Freebase ID, also known as Freebase, MID, Freebase identifier, FID (wd:P646 as p/wdt): identifier for a page in the Freebase database. Format: "/m/0" f...r IDs starting with "/g/", use Google Knowledge Graph ID (P2671)
5. ProofWiki ID, also known as Proof Wiki ID (wd:P6781 as p/wdt): identifier for a ProofWiki article
6. Lex ID, also known as Den Store Danske ID, DSD ID (wd:P8313 as p/wdt): identifier of an article in the online encyclopedia.lex.dk

No other items found

[Assistant]
The search for properties related to "creator" did not yield any relevant results either. This suggests that the authorship might be represented through a different property or indirectly.

### Step 6: Search for the Author Using a Different Approach
Given that the direct properties for authorship are not yielding results, I will try a different approach. I will search for the author of the Erlangen program by looking for statements where the Erlangen program is the object and the subject is a person.

```json
{
  "name": "list",
  "arguments": {
    "kg": "wikidata",
    "object": "wd:Q315296"
  }
}
```

Let's execute this list query.

[Tool]
Got 24 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                | object                        |
| --------------------------------------------------------------------------------------------------------- | ----------------------- | ----------------------------- |
| Felix Klein (wd:Q76641)                                                                                | notable work (wdt:P800) | Erlangen program (wd:Q315296) |
| <https://ar.wikipedia.org/wiki/%D8%A8%D8%B1%D9%86%D8%A7%D9%85%D8%AC_%D8%A5%D8%B1%D9%84%D9%86%D8%BA%D9%86> | schema:about            | Erlangen program (wd:Q315296) |
| s:Q76641-c7654d54-453c-61b8-9b40-c66612d55e8a                                                             | notable work (ps:P800)  | Erlangen program (wd:Q315296) |
| <https://ca.wikipedia.org/wiki/Programa_d%27Erlangen>                                                     | schema:about            | Erlangen program (wd:Q315296) |
| <https://cs.wikipedia.org/wiki/Erlangensk%C3%BD_program>                                                  | schema:about            | Erlangen program (wd:Q315296) |
| <https://de.wikipedia.org/wiki/Erlanger_Programm>                                                         | schema:about            | Erlangen program (wd:Q315296) |
| <https://en.wikipedia.org/wiki/Erlangen_program>                                                          | schema:about            | Erlangen program (wd:Q315296) |
| <https://es.wikipedia.org/wiki/Programa_de_Erlangen>                                                      | schema:about            | Erlangen program (wd:Q315296) |
| <https://fr.wikipedia.org/wiki/Programme_d%27Erlangen>                                                    | schema:about            | Erlangen program (wd:Q315296) |
| <https://it.wikipedia.org/wiki/Programma_di_Erlangen>                                                     | schema:about            | Erlangen program (wd:Q315296) |
| ...                                                                                | ...                     | ...                           |

[Assistant]
The list query shows that Felix Klein (`wd:Q76641`) is listed as a notable work of the Erlangen program. This indicates that Felix Klein is the author of the Erlangen program.

### Step 7: Construct the Final SPARQL Query
Now that I have identified the author, I will construct the final SPARQL query to confirm this and provide the answer.

```sparql
SELECT ?author
WHERE {
  wd:Q315296 wdt:P800 ?author .
}
```

Let's execute this SPARQL query.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that the SPARQL query did not return any results, which suggests that the relationship might be represented differently. Let's try another approach by directly querying for the author using the `notable work` property in the reverse direction.

### Step 8: Query for the Author Using the Reverse Property
I will use the `notable work` property in the reverse direction to find the author of the Erlangen program.

```sparql
SELECT ?author
WHERE {
  ?author wdt:P800 wd:Q315296 .
}
```

Let's execute this SPARQL query.

[Tool]
Got 1 row and 1 column:
| ?author                 |
| ----------------------- |
| Felix Klein (wd:Q76641) |

[Assistant]
The SPARQL query successfully returned the author of the Erlangen program, which is Felix Klein (`wd:Q76641`).

### Final Answer
The author of the Erlangen program is Felix Klein.

```json
{
  "name": "answer",
  "arguments": {
    "kg": "wikidata",
    "sparql": "SELECT ?author WHERE { ?author wdt:P800 wd:Q315296 . }",
    "answer": "Felix Klein"
  }
}
```

This concludes the process.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( DISTINCT ?character ) AS ?count ) WHERE {
  ?character wdt:P1080 ?narrativeUniverse ; wdt:P21 wd:Q658107 ; wdt:P520 wd:Q11292 .
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 0      |

Despite trying various approaches, the SPARQL queries consistently returned 0 results. This suggests that the data in Wikidata might not be structured in a way that allows us to directly answer the question 'How many fictional female swordfighters are there?' using the properties and entities we have tried.

[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] How many fictional female swordfighters are there?

[Assistant]
To answer the question "How many fictional female swordfighters are there?", we need to identify the relevant entities and properties in the knowledge graph. We will start by searching for entities that represent fictional characters, specifically female characters who are swordfighters.

First, let's search for the property that indicates a character is fictional. We can use the term "fictional" to find this property.

[Tool]
Top 10 property alternatives:
1. fictional or mythical analog of, also known as fictionalisation of, mythical analog of, fictional version of, fictionalization of, fictional analog of (wd:P1074 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): used to link an entity or class of entities appearing in a creat...with the analogous entity or class of entities in the real world, inspired by
2. from narrative universe, also known as from narrative, in continuity, from universe, in cycle, in world of (wd:P1080 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): subject's fictional entity is in the object narrative. See also P1441 (present in work) and P1445 (fictional universe described in), part of
3. fictional universe described in (wd:P1445 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): to link a fictional universe with a work that describes it: <universe> "described in the work:" <work>
4. personality trait of fictional character, also known as personality trait, personality trait of fictitious character, personal quality of fictional character, fictitious character personality trait, personal quality of fictitious character (wd:P9652 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): particular personality trait which applies to this fictional character
5. takes place in fictional universe, also known as universe featured, described fictional universe, universe described, set in fictional universe, fictional universe described (wd:P1434 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): the subject is a work describing a fictional universe, i.e. whose plot occurs in this universe
6. Fantastic Fiction author ID, also known as Fantastic Fiction ID (wd:P5408 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): identifier for an author on the Fantastic Fiction website
7. simulates, also known as imitates, simulated, simulate, imitated, imitate (wd:P12328 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): item simulated, imitated, or made to appear real by this item
8. genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
9. form of creative work, also known as work type, work form, type of work, artform, type of reference work (wd:P7937 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): structure of a creative work
10. uses, also known as uses trope, used, using, wielded, wields (wd:P2283 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): item or concept used by the subject or in the operation (see also instrument [P1303] and armament [P520])

No other items found

[Assistant]
From the search results, the property `wd:P1080` (from narrative universe) seems to be the most relevant for identifying fictional characters. However, we also need to find properties that indicate a character is female and a swordfighter.

Let's search for the property that indicates a character is female.

[Tool]
Top 10 property alternatives:
1. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
2. grammatical gender, also known as gender (wd:P5185 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): grammatical gender of the word
3. female population (wd:P1539 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): number of female people inhabiting the place; number of female people of subject, population
4. female form of label, also known as female form of label (string), female gender sense, feminine form of label (wd:P2521 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): female form of name or title (for male use P3321)
5. unmarried partner, also known as romantic partner, partner of, partners, posslq, relationship (wd:P451 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples, significant person
6. performer, also known as actor, sung by, singer, recording by, recorded by (wd:P175 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): actor, musician, band or other performer associated with this role or musical work, contributor to the creative work or subject
7. cast member, also known as feat., starring (film/play), film starring, starring, actor (wd:P161 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): actor in the subject production [use "character role" (P453) and...use "recorded participant" (P11108) for non-fiction productions], significant person, performer
8. given name version for other gender, also known as personal name version for other gender, other genders given name, male version of this female given name, gender inflection of given name, female version of this male given name (wd:P1560 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): equivalent name (with respect to the meaning of the name) in the...n of a female first name. Add primarily the closest matching one
9. has grammatical gender, also known as grammatical genders, has genders (wd:P5109 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): specific form of noun class system of a language. Use qualifier ... subject of" (P805) to link to language specific item if present
10. number of points/goals/set scored, also known as goals scored, score for, points scored, number of points, number of goals (wd:P1351 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): goals / points scored in a match or an event used as qualifier to the participant. Use P1358 for league points.

No other items found

[Assistant]
The property `wd:P21` (sex or gender) is the most relevant for identifying female characters. Now, we need to find a property that indicates a character is a swordfighter.

Let's search for the property that indicates a character is a swordfighter.

[Tool]
Top 10 property alternatives:
1. International Fencing Federation fencer ID, also known as Fédération Internationale d'Escrime ID, FIE fencer ID, FIE ID, FIE identifier (wd:P2423 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): Identifier in the database of all fencers holding an internation...al Fencing Federation (FIE: Fédération Internationale d'Escrime)
2. Russian Fencing Federation fencer ID, also known as FFR fencer ID, Rusfencing.ru (wd:P7092 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): Russian Fencing Federation identifier
3. conflict, also known as war, theatre (military), theater (military), participated in conflict, military conflict (wd:P607 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): battles, wars or other military engagements in which the person or item participated, participant in
4. European Fencing Confederation fencer ID, also known as EFC fencer ID, Eurofencing fencer ID (wd:P4475 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): identifier on the European Fencing Confederation website
5. armament, also known as armed with, ship armament, weapon, wields (wd:P520 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): equippable weapon item for the subject, has facility
6. dan/kyu rank, also known as go rank, wushu rank, shogi rank, renju rank, kendo rank (wd:P468 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): rank system used in several board games (e.g. go, shogi, renju), martial arts (e.g. judo, kendo, wushu) and some other games
7. inverse property, also known as property opposite of, property inverse, opposite property, opponent, inverse of (wd:P1696 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): links a property to its inverse property (which relates the subject and object in reverse order)
8. enemy, also known as opponent, rival, archvillain, archrival, archnemesis (wd:P7047 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): opponent character or group of this fictive character or group, significant person
9. perpetrator, also known as robber, perpetrators, perpetrated by, perp, offender (wd:P8031 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): person or organization who carried out this harmful, illegal, or immoral act, participant
10. name of the character role, also known as character named as, role named as, role name, name of the role, name of the character (wd:P4633 as pqn/wdtn/psn/psv/prn/pq/ps/wdt/pqv/p/prv/pr): name by which a character role is recorded in a database or ment...ably a specific performance of a theatrical production of a play, name

No other items found

[Assistant]
The property `wd:P520` (armament) seems to be the most relevant for indicating that a character wields a sword. However, it is not specific to swordfighters. We can use this property and filter the results to include only those who wield swords.

Now, let's construct a SPARQL query to count the number of fictional female characters who wield swords. We will use the properties `wd:P1080` (from narrative universe) to ensure the character is fictional, `wd:P21` (sex or gender) to ensure the character is female, and `wd:P520` (armament) to ensure the character wields a sword.

We will start with a basic query and then refine it as needed.

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 0      |

[Assistant]
The initial query returned 0 results, which suggests that either the properties or the specific combination of properties we used might not be capturing the intended characters. Let's try a different approach by looking for characters who are specifically described as swordfighters or have a profession or occupation related to swordfighting.

We can search for the term "swordfighter" as a class or occupation to see if there are any relevant entities.

Let's search for the term "swordfighter" as a class or occupation.

[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

No other items found

No literal items found

[Assistant]
The search results show that `wd:Q11397897` (swordfighter) is the most relevant entity for our query. We can use this entity to filter characters who are described as swordfighters.

Now, let's update our SPARQL query to include this entity and count the number of fictional female characters who are swordfighters.

We will use the properties `wd:P1080` (from narrative universe) to ensure the character is fictional, `wd:P21` (sex or gender) to ensure the character is female, and `wd:P106` (occupation) to ensure the character is a swordfighter.

Here is the updated SPARQL query:

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 0      |

[Assistant]
The updated query still returned 0 results, which suggests that the combination of properties and the specific entity for "swordfighter" might not be capturing the intended characters.

Let's try another approach by looking for characters who are described as having a profession or occupation related to swordfighting, such as "warrior" or "knight," and then filter those who wield swords.

We will search for the terms "warrior" and "knight" as occupations to see if there are any relevant entities.

Let's search for the term "warrior" as an occupation.

[Tool]
Top 10 entity alternatives:
1. warrior, also known as fighter (wd:Q1250916): person specializing in combat or warfare
2. Warrior, also known as Warrior, AL, Warrior, Alabama (wd:Q79521): city in Jefferson and Blount counties in the State of Alabama, USA
3. Warrior (wd:Q1415964): 2011 film by Gavin O'Connor
4. The Ultimate Warrior, also known as Warrior, Ultimate Warrior, Dingo Warrior, James Brian Hellwig (wd:Q333421): American professional wrestler and bodybuilder (1959-2014), professional wrestler, bodybuilder, orator
5. The Ultimate Warrior, also known as Dingo Warrior, Ultimate Warrior, Warrior, James Brian Hellwig (wd:Q333421): American professional wrestler and bodybuilder (1959-2014), professional wrestler, bodybuilder, orator
6. Warrior, also known as Warrior tracked armoured vehicle (wd:Q589212): British infantry fighting vehicle family
7. Warrior, also known as Warrior (Kesha album) (wd:Q210905): second studio album by Kesha
8. Warrior (wd:Q46992827): American television series
9. Warrior (wd:Q18786468): 2015 single by Nina Sublati
10. The Warrior, also known as Warrior (wd:Q1646836): 2001 film directed by Asif Kapadia

No other items found

No literal items found

[Assistant]
The search results show that `wd:Q1250916` (warrior) is the most relevant entity for our query. We can use this entity to filter characters who are described as warriors.

Next, let's search for the term "knight" as an occupation to see if there are any relevant entities.

Let's search for the term "knight" as an occupation.

[Tool]
Top 10 entity alternatives:
1. knight (wd:Q102083): person granted an honorary title by a monarch or other political leader
2. knight, also known as ♘, ♞, horse, horsey, jumper (wd:Q136): piece in the board game of chess
3. Daniel Ridgway Knight, also known as knight r., D. R. Knight, knight d.r., Knight, d.r. knight paris (wd:Q481259): American artist (1839-1924), painter
4. 29391 Knight, also known as Knight (wd:Q582422): asteroid
5. Lebadea martha, also known as Knight (wd:Q1765110): species of insect
6. Joseph Knight, also known as Knight, J. Knight (wd:Q6284642): English horticulturist (1778-1855), horticulturist, botanist
7. Knight, also known as Knight, WI (wd:Q1659277): town in Wisconsin, USA
8. knight (wd:Q2943226): playing card
9. Knight (wd:Q1777139): family name
10. Louis Aston Knight, also known as L.A. Knight, l. aston knight, L. Aston Knight, Luis Aston Knight, knight l.a. (wd:Q3261116): American artist (1873–1948), painter, artist

No other items found

No literal items found

[Assistant]
The search results show that `wd:Q102083` (knight) is the most relevant entity for our query. We can use this entity to filter characters who are described as knights.

Now, let's update our SPARQL query to include both "warrior" and "knight" as possible occupations, and count the number of fictional female characters who are either warriors or knights and wield swords.

Here is the updated SPARQL query:

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 0      |

[Assistant]
The updated query still returned 0 results, which suggests that even with the broader occupations "warrior" and "knight," combined with the property for wielding a sword, we are not capturing any fictional female characters.

Given that the results are still 0, it's possible that the specific combination of properties and entities we are using might not be sufficient to capture the intended characters. Let's try a more flexible approach by looking for characters who are described as having a profession or occupation related to combat or fighting, and then filter those who wield swords.

We will search for the term "combat" and "fighting" as occupations to see if there are any relevant entities.

Let's search for the term "combat" as an occupation.

[Tool]
Top 10 entity alternatives:
1. violence, also known as combat, fighting, violence (traits) (wd:Q124490): destructive action characterized by lack of empathy and involving communication, manipulation, coercion and/or punishment
2. battle, also known as ⚔, combat (wd:Q178561): part of a war which is well defined in duration, area and force commitment
3. combat, also known as brawl, fight, fighting, scuffle (wd:Q650711): purposeful violent conflict
4. Colonel Fabien, also known as Combat (wd:Q1110656): Paris Métro station
5. Kombat, also known as Combat (wd:Q3496518): photograph by Max Alpert
6. Combat! (wd:Q1247850): television series
7. Combat (wd:Q2588960): 1977 video game
8. Combat (wd:Q1113445): French newspaper
9. Combat (wd:Q573162): movement in the French Resistance in unoccupied France
10. The Combat, also known as Combat (wd:Q19867693): 1916 film by Ralph Ince

No other items found

No literal items found

[Assistant]
The search results for "combat" did not yield any relevant occupations. Let's try searching for the term "fighting" as an occupation to see if there are any relevant entities.

Let's search for the term "fighting" as an occupation.

[Tool]
Top 10 entity alternatives:
1. violence, also known as combat, fighting, violence (traits) (wd:Q124490): destructive action characterized by lack of empathy and involving communication, manipulation, coercion and/or punishment
2. combat, also known as brawl, fight, fighting, scuffle (wd:Q650711): purposeful violent conflict
3. Fighting (wd:Q1027387): 2009 film directed by Dito Montiel
4. brawling, also known as fight, fighting, brawl (wd:Q1709241): confrontation between two or more people without weapons, or wit...tings and (or) injuries of varying degrees of severity to health
5. Fighting (wd:Q644352): 1975 studio album by Thin Lizzy
6. close combat, also known as fighting, physical confrontation (wd:Q15041854): violent physical confrontation between two or more opponents at short range
7. Fighting (wd:Q116987078): 2023 single by BSS featuring Lee Young-ji
8. Fighting (wd:Q5447867): Tank album
9. fighting (wd:Q25587250): Pokémon type
10. Fighting (wd:Q106417827): 2020 single by Tyler Joe Miller

No other items found

No literal items found

[Assistant]
The search results for "fighting" also did not yield any relevant occupations. Given that the specific combinations of properties and entities we have tried so far have not produced any results, it's possible that the data in Wikidata does not have a straightforward way to identify fictional female swordfighters.

However, we can try a more general approach by looking for characters who are described as having any kind of combat-related occupation and then filter those who wield swords. We can use the property `wd:P106` (occupation) and look for any combat-related occupations, and then use `wd:P520` (armament) to filter for those who wield swords.

Let's try a more general query to see if we can find any fictional female characters who wield swords, regardless of their specific occupation.

Here is the updated SPARQL query:

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 0      |

[Assistant]
Despite trying various approaches, the query still returns 0 results. This suggests that the specific combination of properties and entities we are using might not be capturing the intended characters in the Wikidata knowledge graph.

Given the constraints and the results, it appears that there might not be enough data in Wikidata to directly answer the question "How many fictional female swordfighters are there?" using the properties and entities we have tried.

Therefore, I will provide a detailed explanation of why I could not find a satisfactory SPARQL query and my best attempt so far.

### Explanation
1. **Initial Approach**: We started by looking for properties that indicate a character is fictional (`wd:P1080`), female (`wd:P21`), and a swordfighter (`wd:P520`).
2. **Refinement**: We refined the query by including specific occupations like "swordfighter" (`wd:Q11397897`), "warrior" (`wd:Q1250916`), and "knight" (`wd:Q102083`).
3. **Generalization**: We tried a more general approach by looking for any combat-related occupations and filtering by those who wield swords.
4. **Results**: Despite these efforts, the queries consistently returned 0 results, suggesting that the data in Wikidata might not be structured in a way that allows us to directly answer this question.

### Best Attempt
Here is the best SPARQL query I was able to construct:

```sparql
SELECT (COUNT(DISTINCT ?character) AS ?count) WHERE {
  ?character wdt:P1080 ?narrativeUniverse ;
             wdt:P21 wd:Q658107 ;
             wdt:P520 wd:Q11292 .
}
```

This query attempts to count the number of fictional female characters who wield swords, but it returns 0 results.

### Conclusion
Based on the available data and the properties we have tried, it appears that there are no fictional female swordfighters in the Wikidata knowledge graph that can be identified using the current approach.

I will now provide this explanation and the best attempt at the SPARQL query.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?openingDate WHERE {
  wd:Q71719 wdt:P1619 ?openingDate .
}

The Hollywood Walk of Fame opened on January 1, 1960.

[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 what year did the Hollywood Walk of Fame open?

[Assistant]
To find the year when the Hollywood Walk of Fame opened, I need to identify the entity for the Hollywood Walk of Fame and then find the property that indicates its opening date or establishment year.

First, I will search for the entity "Hollywood Walk of Fame" in the subject position.

[Tool]
Top 10 entity alternatives:
1. Hollywood Walk of Fame, also known as Walk of Fame (wd:Q71719): more than 2,700 five-pointed terrazzo and brass stars embedded i...ulevard and three blocks of Vine Street in Hollywood, California
2. star on Hollywood Walk of Fame, also known as Hollywood Walk of Fame star (wd:Q17985761): award in the Hollywood Walk of Fame, Hollywood, California, United States
3. Hollywood's Rock Walk of Fame (wd:Q3139400)
4. Walk of Fame (wd:Q48673901): 2017 film by Jesse Thomas
5. Walk of Fame (wd:Q51084447): street in Saint Petersburg, Russia
6. Starr gets star; Ringo added to Hollywood Walk of Fame (wd:Q18021457): Wikinews article
7. Walk of Fame (wd:Q7962075): memorial for famous dogs in Battersea Park, London, England, UK
8. walk of fame (wd:Q47502370): sidewalk or similar construction that commemorates outstanding individuals in a particular group
9. Walk of Fame (wd:Q114476628): episode of Cupcake Wars (S2 E11)
10. Walk of Fame, also known as Wigan's Walk of Fame, Believe Square (wd:Q121879112): public walkway in Wigan, England

No other items found

[Assistant] The top result is the entity for the Hollywood Walk of Fame (wd:Q71719). Now, I will look for the property that indicates the opening date or establishment year of the Hollywood Walk of Fame. I will search for properties related to "opening" or "establishment" with the subject constraint set to the Hollywood Walk of Fame.

[Tool]
Top 10 property alternatives:
1. date of official opening, also known as launch date, launched, officially opened on, official opening, official opening time (wd:P1619 as p/wdt): date or point in time a place, organization, or event officially opened, start time, inception
2. inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as p/wdt): time when an entity begins to exist; for date of official opening use P1619, start time
3. located on street, also known as on street, street address, street, square, road (wd:P669 as p/wdt): street, road, or square, where the item is located. To add the n...perty P6375 "street address", if there is no item for the street, location, part of
4. title, also known as name, titled, page name, original title, known as (wd:P1476 as p/wdt): published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work, name
5. image, also known as still capture, still, screenshot, screen capture, portrait (wd:P18 as p/wdt): image of relevant illustration of the subject; if available, als...tor map, flag image, signature image, logo image, collage image)
6. 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 p/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
7. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as p): label for the items in their official language  or their original language, name
8. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
9. ground level 360 degree view URL, also known as Mapillary, virtual tour URL, tour URL, street view URL, Mapy.CZ (wd:P5282 as p/wdt): URL to access a service that allows to visually navigate through images at ground level in the proximity of the item, URL
10. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): 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

No other items found

[Assistant]
The property "date of official opening" (wd:P1619) seems to be the most relevant for finding the opening date of the Hollywood Walk of Fame. I will now construct a SPARQL query to retrieve this information.

Let's execute the SPARQL query to find the opening date of the Hollywood Walk of Fame.

[Tool]
Got 1 row and 1 column:
| ?openingDate         |
| -------------------- |
| 1960-01-01T00:00:00Z |

[Assistant]
The Hollywood Walk of Fame opened on January 1, 1960.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ( COUNT( ?language ) AS ?count ) WHERE {
  wd:Q843 wdt:P37 ?language .
}

Got 1 row and 1 column:
| ?count |
| ------ |
| 2      |

Pakistan has 2 official languages.

[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] How many official languages has Pakistan ?

[Assistant] To answer the question about the number of official languages in Pakistan, I need to identify the relevant entities and properties in the knowledge graph. Let's start by searching for the entity representing Pakistan and then look for properties related to official languages.

[Tool]
Top 10 entity alternatives:
1. Pakistan, also known as Islamic Republic of Pakistan, PAK, pk, Republic of Pakistan (wd:Q843): sovereign state in South Asia
2. Dominion of Pakistan, also known as Pakistan (wd:Q2006542): period of Pakistani history between 1947 to 1956
3. West Pakistan, also known as Pakistan (wd:Q2342822): Western Wing of Pakistan between 1947–1970
4. Pakistan (wd:Q4121082): village in Purnia district, Bihar, India
5. Urdu 1, also known as Pakistan (wd:Q2786023): Pakistani Urdu-language television network
6. Pakistan (wd:Q57206806): village in Cameroon
7. Pakistan (wd:Q122963846): 2023 single by D-Block Europe and Clavish
8. Khan Bela, also known as Liaqatpur, Pakistan, Rahim Yar Khan (wd:Q31308479): Khan Bela is a city in Rahim Yar Khan District, Punjab, Pakistan...is {{cvt|90|km|mile}} from Khan Bela and Bahawalpur is {{cvt|105
9. Pakistan (wd:Q87704056): scientific article published on 01 November 1987
10. Pakistan (wd:Q99573810): scientific article published on 01 October 2020

No other items found

[Assistant] The top result for "Pakistan" is the sovereign state in South Asia, which is the entity we are interested in. Now, let's search for properties related to official languages to see if there is a direct property that links Pakistan to its official languages.

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as p/wdt): language designated as official by this item, language used
2. native label, also known as official title, original label, original language label, original language name, original language title (wd:P1705 as p/wdt): label for the items in their official language  or their original language, name
3. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as p/wdt): language widely used (spoken or written) in this place or at this event or organisation
4. official name, also known as long title, name, official title, registered name, full title (wd:P1448 as p/wdt): official name of the subject in its official language(s), name
5. official religion, also known as established religion, state religion (wd:P3075 as p/wdt): official religion in this administrative entity
6. basic form of government, also known as form of government, government, type of government (wd:P122 as p/wdt): subject's government
7. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as p/wdt): seat of government of a country, province, state or other type of administrative territorial entity
8. official website, also known as site (official), home page, site official, URL, URL (official) (wd:P856 as p/wdt): 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
9. office held by head of government, also known as title of head of government, position of head of government, office of head of government, head of government's office, position held by head of government (wd:P1313 as p/wdt): political office that is fulfilled by the head of the government of this item
10. head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as p/wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson

No other items found

[Assistant]
The property `P37` (official language) seems to be the most relevant for our query. Let's use this property to find the official languages of Pakistan.

I will now construct a SPARQL query to retrieve the official languages of Pakistan using the identified property.

```sparql
SELECT ?language
WHERE {
  wd:Q843 wdt:P37 ?language .
}
```

Let's execute this query to get the results.

[Tool]
Got 2 rows and 1 column:
| ?language          |
| ------------------ |
| Urdu (wd:Q1617)    |
| English (wd:Q1860) |

[Assistant]
The query returned two official languages for Pakistan: Urdu and English. Therefore, Pakistan has 2 official languages.

Now, I will provide the final SPARQL query and the answer to the user's question.

```sparql
SELECT (COUNT(?language) AS ?count)
WHERE {
  wd:Q843 wdt:P37 ?language .
}
```

This query will count the number of official languages for Pakistan.

Let's execute this final query to confirm the count.

[Tool]
Got 1 row and 1 column:
| ?count |
| ------ |
| 2      |

[Assistant]
The final query confirms that Pakistan has 2 official languages.

Here is the final SPARQL query and the answer to the user's question:

```sparql
SELECT (COUNT(?language) AS ?count)
WHERE {
  wd:Q843 wdt:P37 ?language .
}
```

Answer: Pakistan has 2 official languages.

[Tool] Stopped generation process