PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?player ( COUNT( ?award ) AS ?awardCount ) WHERE {
  VALUES ?player {
    wd:Q41421 wd:Q25369
  }
  ?player wdt:P166 ?award .
}
GROUP BY ?player

The query successfully returned the awards received by Michael Jordan and Kobe Bean Bryant. Now, let's count the number of awards for each player.

### Step 2: Count the number of awards received by Michael Jordan and Kobe Bean Bryant

```sparql
SELECT ?player (COUNT(?award) AS ?awardCount)
WHERE {
  VALUES ?player { wd:Q41421 wd:Q25369 }
  ?player wdt:P166 ?award .
}
GROUP BY ?player
```

Let's execute this query to get the counts.

[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 has won more NBA awards, Michael Jordan or Kobe Bean Bryant？

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

[Tool]
Example 1:
Question:
Give me all spacecrafts that flew to Mars.

SPARQL:
SELECT ?s1 where {
  ?s1 wdt:P31 wd:Q389459
}

Using entities:
Mars rover, also known as Mars rovers (wd:Q389459): space exploration vehicle designed to move across the surface of Mars

Using properties:
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:
For which label did Elvis record his first album?

SPARQL:
SELECT DISTINCT ?label WHERE {
  wd:Q59150527 wdt:P527 ?uri .
  ?uri wdt:P577 ?date .
  ?uri wdt:P264 ?label .
}
ORDER BY ?date
LIMIT 1

Using properties:
record label, also known as label, labels (wd:P264 as wdt): brand and trademark associated with the marketing of subject music recordings and music videos
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).
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:
What is the profession of Frank Herbert?

SPARQL:
SELECT ?o1 WHERE {
  wd:Q7934 wdt:P106 ?o1 .
}

Using entities:
Frank Herbert, also known as Jr. Franklin Patrick Herbert, Jr. Franklin Herbert, Frank Patrick Herbert, Jr. Frank Patrick Herbert, Franklin Herbert (wd:Q7934): American writer (1920–1986), science fiction writer, prose writer, photographer, journalist, novelist, writer

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)

[Assistant] Based on the examples, we need to identify the entities for Michael Jordan and Kobe Bean Bryant, and then find the property for NBA awards. Let's start by searching for these entities.

[Tool]
Top 10 entity alternatives:
1. Michael Jordan, also known as His Airness, Air Jordan (wd:Q41421): American basketball player and businessman (born 1963), basketball player, baseball player, autobiographer, spokesperson, entrepreneur
2. Michael I. Jordan, also known as Michael Irwin Jordan, Michael Jordan (wd:Q3308285): American computer scientist, University of California, Berkeley, artificial intelligence researcher, computer scientist, university teacher, statistician
3. Michael Jordan, also known as Michael William Jordan (wd:Q6831716): English footballer (born 1984), association football player
4. Michael Jordan (wd:Q65029442): American football offensive lineman, American football player
5. Mike Jordan, also known as Michael Jordan, Michael H. Jordan, Michael Henry Jordan, M. H. Jordan (wd:Q6847437): American baseball player and politician (1863-1940), baseball player, politician
6. Michael Jordan, also known as Mike Jordan (wd:Q27069141): American football cornerback, American football player
7. Michael Jordan (wd:Q6831715): Irish politician, politician, farmer
8. Michael Jordan (wd:Q6831719): British mycologist, writer, and television presenter, television presenter, mushroom gatherer, mycologist, announcer, writer
9. Michael Jordan (wd:Q1928047): German draughtsperson, artist and comics artist, university teacher, comics artist, draftsperson, artist
10. Michael Jordan (wd:Q100983906): college basketball player (2001–2001) New Mexico, basketball player

[Assistant] The correct entity for Michael Jordan is `wd:Q41421`. Now let's search for Kobe Bean Bryant.

[Tool]
Top 10 entity alternatives:
1. Kobe Bryant, also known as Kobe Bean Bryant, Kobe B. Bryant, Kobe, KB24, Black Mamba (wd:Q25369): American basketball player (1978–2020), professional athlete, basketball player, screenwriter
2. Kobe Bryant Day (wd:Q98969272): proposed Federal Holiday
3. death of Kobe Bryant, also known as Kobe Bryant death, Kobe Bryant's death (wd:Q83737550): 26 January 2020 death of Kobe Bryant in helicopter crash
4. Kobe Bryant Boulevard (wd:Q107299206): proposed street renaming in Los Angeles
5. NBA All-Star Game Kobe Bryant Most Valuable Player Award, also known as Kobe Bryant Most Valuable Player Award, Kobe Bryant MVP Award, NBA All-Star Game Kobe Bryant MVP Award (wd:Q31391): basketball award of the National Basketball Association (NBA)
6. Kobe Bryant China Fund (wd:Q6424297): for education, sports, and culture programs for children from China and the United States
7. Statue of Kobe Bryant (wd:Q125101336): sculpture in Los Angeles, California, U.S
8. Kobe Bryant/Ice Cube (wd:Q125446155): episode of All That (S4 E19)
9. Kobe Bryant sexual assault case (wd:Q6424298): legal case
10. Kobe Bryant in NBA Courtside (wd:Q3198120): 1998 video game

[Assistant] The correct entity for Kobe Bean Bryant is `wd:Q25369`. Next, we need to find the property that represents NBA awards. Let's search for properties related to awards.

[Tool]
Top 10 property alternatives:
1. grants, also known as awards, bestows, confers, gives out (wd:P5460 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): confers degree, honor, award, prize, title, certificate or medal denoting achievement to a person or organization
2. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): award or recognition received by a person, organization or creative work
3. conferred by, also known as presented by, officiated by, issued by, granted by, given by (wd:P1027 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): person or organization who grants an award, certification, grant, or role
4. for work, also known as awarded for work, citation for award, for their work on, nominated for work (wd:P1686 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier of award received (P166) to specify the work that an award was given to the creator for
5. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): winner of a competition or similar event, not to be used for awa...ssibly qualified with "for work" (P1686)) or for wars or battles, participating team, participant
6. commemorates, also known as monument to, memorial to, memorializes, marks, in memory of (wd:P547 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): what the place, monument, memorial, or holiday, commemorates, main subject
7. nominee, also known as award nominee (wd:P2453 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): qualifier used with “nominated for” to specify which person or organization was nominated
8. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
9. nominated by (wd:P4353 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): who nominated a person for an office or award; can be used as a qualifier
10. product or material produced, also known as products, product or service provided, product, material, or service produced or provided, product, producer of (wd:P1056 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): material or product produced or sold by an organization, industry, facility, or process, material or product produced by an organization, industry, facility, or process, immediate cause of

[Assistant]
The property `award received` (wd:P166) seems to be the most relevant for our query. Now, let's construct a SPARQL query to count the number of NBA awards received by Michael Jordan and Kobe Bean Bryant.

We will start with a simple query to list the awards received by both players and then count them.

### Step 1: List the awards received by Michael Jordan and Kobe Bean Bryant

```sparql
SELECT ?player ?award
WHERE {
  VALUES ?player { wd:Q41421 wd:Q25369 }
  ?player wdt:P166 ?award .
}
```

Let's execute this query to see the results.

[Tool]
Failed to call function execute:
failed to parse input: no matching token found from position 21: '\nWHERE {\n  VALUES ?player { wd:Q41421 wd:Q25369 }\n  ?player wdt:P166 ?award .\n}'

[Assistant]
It looks like there was an issue with the SPARQL query. Let's try again with a corrected version of the query.

### Corrected Query: List the awards received by Michael Jordan and Kobe Bean Bryant

```sparql
SELECT ?player ?award
WHERE {
  VALUES ?player { wd:Q41421 wd:Q25369 }
  ?player wdt:P166 ?award .
}
```

Let's execute this query to see the results.

[Tool]
Got 33 rows and 2 columns, showing the first 5 and last 5 rows below:
| player                     | award                                                                |
| -------------------------- | -------------------------------------------------------------------- |
| Kobe Bryant (wd:Q25369)    | NBA All-Defensive Team (wd:Q1465181)                                 |
| Kobe Bryant (wd:Q25369)    | NBA Most Valuable Player Award (wd:Q222047)                          |
| Kobe Bryant (wd:Q25369)    | NBA All-Star Game Kobe Bryant Most Valuable Player Award (wd:Q31391) |
| Kobe Bryant (wd:Q25369)    | BET Award for Athlete of the Century (wd:Q4835465)                   |
| Kobe Bryant (wd:Q25369)    | All-NBA Team (wd:Q674359)                                            |
| ...                        | ...                                                                  |
| Michael Jordan (wd:Q41421) | All-NBA Team (wd:Q674359)                                            |
| Michael Jordan (wd:Q41421) | Bill Russell NBA Finals Most Valuable Player Award (wd:Q739499)      |
| Michael Jordan (wd:Q41421) | Associated Press Athlete of the Year (wd:Q743122)                    |
| Michael Jordan (wd:Q41421) | wd:Q845884                                                           |
| Michael Jordan (wd:Q41421) | Best NBA Player ESPY Award (wd:Q943106)                              |

[Assistant]
The query successfully returned the awards received by Michael Jordan and Kobe Bean Bryant. Now, let's count the number of awards for each player.

### Step 2: Count the number of awards received by Michael Jordan and Kobe Bean Bryant

```sparql
SELECT ?player (COUNT(?award) AS ?awardCount)
WHERE {
  VALUES ?player { wd:Q41421 wd:Q25369 }
  ?player wdt:P166 ?award .
}
GROUP BY ?player
```

Let's execute this query to get the counts.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?work ?genre WHERE {
  ?work wdt:P50 wd:Q550311 ; wdt:P136 ?genre .
  FILTER ( ?genre = wd:Q5366097 )
}

Yu Yu Hakusho (wd:Q18225473)

[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] among the other representative work of the author of hunter x hunter, which one is talking about middle school student?

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

[Tool]
Example 1:
Question:
How high is the Yokohama Marine Tower?

SPARQL:
SELECT DISTINCT ?num WHERE {
  wd:Q1207989 wdt:P2048 ?num .
}

Using entities:
Yokohama Marine Tower (wd:Q1207989): lighthouse in Japan

Using properties:
height, also known as heighth, size, depth over terrain (wd:P2048 as wdt): vertical length of an entity, length

Example 2:
Question:
How many films did Leonardo DiCaprio star in?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) AS ?c ) WHERE {
  ?uri wdt:P31 wd:Q11424 ; wdt:P161 wd:Q38111 .
}

Using entities:
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
Leonardo DiCaprio (wd:Q38111): American actor and film producer (born 1974), television actor, environmentalist, film producer, screenwriter, 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
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 3:
Question:
Whom did Lance Bass marry?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q443120 wdt:P26 ?uri .
}

Using entities:
Lance Bass (wd:Q443120): American singer, dancer, actor, and film and television producer, television producer, television actor, autobiographer, film producer, stage actor, voice actor, film actor, dancer, singer, actor

Using properties:
spouse, also known as married partner, married to, marry, partner, spouses (wd:P26 as wdt): the subject has the object as their spouse (husband, wife, partn...etc.). Use "unmarried partner" (P451) for non-married companions, significant person, relative

[Assistant] Based on the examples, we need to identify the author of Hunter x Hunter and then find out which of their other works talk about middle school students. Let's start by searching for the author of Hunter x Hunter.

[Tool]
Top 10 entity alternatives:
1. Hunter × Hunter, also known as HUNTER × HUNTER, Hunter x Hunter, HXH (wd:Q696071): Japanese manga series
2. Hunter × Hunter, also known as H×H, Hunter x Hunter, HxH (wd:Q70209136): 2011 Japanese anime television series
3. Hunter × Hunter, also known as Hunter x Hunter, HxH (wd:Q21232458): 1999 Japanese anime television series
4. Monster Hunter Generations, also known as Monster Hunter X (wd:Q20856979): 2015 Japanese video game
5. Hunter Xcite (wd:Q60767092): sailboat class
6. Xavier Hunter (wd:Q100806788): basketball player
7. Hunter × Hunter: Phantom Rouge, also known as Hunter X Hunter: Phantom Rouge (wd:Q11398237): 2013 film by Yūzō Satō
8. City Hunter XYZ (wd:Q97170832): fan film by Filip Wong
9. Monster Hunter Generations Ultimate, also known as Monster Hunter XX (wd:Q30302563): 2017 video game
10. XX: Beautiful Hunter (wd:Q8042573): 1994 film by Masaru Konuma

[Assistant] It seems like the first result is the manga series "Hunter × Hunter". Let's check who the author of this manga is.

[Tool]
Got 1 row and 3 columns:
| subject                      | property         | object                         |
| ---------------------------- | ---------------- | ------------------------------ |
| Hunter × Hunter (wd:Q696071) | author (wdt:P50) | Yoshihiro Togashi (wd:Q550311) |

[Assistant]
The author of "Hunter × Hunter" is Yoshihiro Togashi. Now, let's find out which of his other works involve middle school students.

First, we need to identify the works of Yoshihiro Togashi. We can use the `wdt:P50` property to find works authored by him.

[Tool]
Got 4 rows and 1 column:
| work                               |
| ---------------------------------- |
| Yu Yu Hakusho (wd:Q18225473)       |
| Ten de Shōwaru Cupid (wd:Q3518005) |
| Level E (wd:Q483557)               |
| Hunter × Hunter (wd:Q696071)       |

[Assistant]
Yoshihiro Togashi has authored the following works:
- Yu Yu Hakusho (wd:Q18225473)
- Ten de Shōwaru Cupid (wd:Q3518005)
- Level E (wd:Q483557)
- Hunter × Hunter (wd:Q696071)

Next, we need to determine which of these works involve middle school students. We can search for properties related to characters or themes in these works to see if they mention middle school students.

Let's start by checking if any of these works have a property that indicates the presence of middle school students.

[Tool]
Top 10 property alternatives:
1. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as p/wdt): educational institution attended by subject, affiliation
2. 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)
3. given name, also known as name, proper name, personal name, middle name, forename (wd:P735 as p/wdt): first name or another given name of this person; values used with the property should not link disambiguations nor family names, name
4. sibling, also known as is sister of, is the brother of, is the sibling of, is the sister of, sib (wd:P3373 as p/wdt): the subject and the object have at least one common parent (brot...law, etc.) and step-siblings (step-brothers, step-sisters, etc.), relative
5. date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as p/wdt): date on which the subject was born, inception
6. 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)
7. sex or gender, also known as gender or sex, sex, gender identity, gender expression, gender (wd:P21 as p/wdt): sex or gender identity of human or animal. For human: male, fema...m, female organism. Groups of same gender use subclass of (P279)
8. place of birth, also known as POB, location of birth, location born, born in, birthplace (wd:P19 as p/wdt): most specific known birth location of a person, animal or fictional character, location
9. X (Twitter) username, also known as twitter.com/, X username, X handle, x.com/, X account (wd:P2002 as p/wdt): username on X (formerly Twitter); do not include the '@' symbol, website username or ID
10. country of citizenship, also known as subject of (country), nation of citizenship, national of, nationality, nation (wd:P27 as p/wdt): the object is a country that recognizes the subject as its citizen, country

No other items found

[Assistant]
It seems that there are no direct properties related to "middle school student" for Yoshihiro Togashi's works. However, we can try to find properties related to characters or themes in these works and see if any of them mention middle school students.

Let's check the properties of each work to see if they provide any relevant information.

We'll start with "Yu Yu Hakusho" (wd:Q18225473).

[Tool]
Got 1 row and 3 columns:
| subject                      | property              | object                      |
| ---------------------------- | --------------------- | --------------------------- |
| Yu Yu Hakusho (wd:Q18225473) | instance of (wdt:P31) | manga series (wd:Q21198342) |

[Assistant] "Yu Yu Hakusho" is a manga series. Let's check if it has any properties that might indicate the presence of middle school students, such as characters or themes.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that "Yu Yu Hakusho" does not have a property specifically indicating the presence of middle school students. Let's try another approach by checking the characters of "Yu Yu Hakusho" to see if any of them are middle school students.

We can use the `wdt:P674` property, which indicates the main characters of a work.

[Tool]
Got 8 rows and 1 column:
| character                      |
| ------------------------------ |
| Kurama (wd:Q1122114)           |
| Mukuro (wd:Q11637247)          |
| Shinobu Sensui (wd:Q125862827) |
| Genkai (wd:Q17217577)          |
| Koenma (wd:Q18276574)          |
| Hiei (wd:Q3272545)             |
| Yusuke Urameshi (wd:Q4388204)  |
| Kazuma Kuwabara (wd:Q4388502)  |

[Assistant]
The main characters of "Yu Yu Hakusho" are:
- Kurama (wd:Q1122114)
- Mukuro (wd:Q11637247)
- Shinobu Sensui (wd:Q125862827)
- Genkai (wd:Q17217577)
- Koenma (wd:Q18276574)
- Hiei (wd:Q3272545)
- Yusuke Urameshi (wd:Q4388204)
- Kazuma Kuwabara (wd:Q4388502)

Let's check if any of these characters are middle school students. We can look for properties like "occupation" or "age" to determine if they are middle school students.

[Tool]
Got 4 rows and 2 columns:
| character                     | occupation             |
| ----------------------------- | ---------------------- |
| Kurama (wd:Q1122114)          | scientist (wd:Q901)    |
| Genkai (wd:Q17217577)         | psychic (wd:Q2917466)  |
| Yusuke Urameshi (wd:Q4388204) | detective (wd:Q842782) |
| Kazuma Kuwabara (wd:Q4388502) | psychic (wd:Q2917466)  |

[Assistant]
The occupations of the main characters of "Yu Yu Hakusho" are:
- Kurama (wd:Q1122114): Scientist
- Genkai (wd:Q17217577): Psychic
- Yusuke Urameshi (wd:Q4388204): Detective
- Kazuma Kuwabara (wd:Q4388502): Psychic

None of these occupations directly indicate that the characters are middle school students. Let's check the age of these characters to see if they are around the age of middle school students (typically 11-14 years old).

We can use the `wdt:P569` property to check the date of birth of these characters, if available, and calculate their age.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the dates of birth for the main characters of "Yu Yu Hakusho" are not available in Wikidata. Since we couldn't find any information about their ages, let's move on to the next work by Yoshihiro Togashi.

Next, we'll check "Ten de Shōwaru Cupid" (wd:Q3518005). Let's see if this work involves middle school students by checking its characters or themes.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there is no information about the characters of "Ten de Shōwaru Cupid" in Wikidata. Let's move on to the next work, "Level E" (wd:Q483557).

We'll check the characters of "Level E" to see if any of them are middle school students.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there is no information about the characters of "Level E" in Wikidata either.

Since we have checked the main works of Yoshihiro Togashi and found no direct information about middle school students, let's try a different approach. We can search for the works of Yoshihiro Togashi and filter them based on the theme or genre to see if any of them are set in a middle school setting.

Let's search for works by Yoshihiro Togashi that are related to schools or middle school students.

[Tool]
Got 10 rows and 2 columns:
| work                               | genre                                         |
| ---------------------------------- | --------------------------------------------- |
| Yu Yu Hakusho (wd:Q18225473)       | adventure anime and manga (wd:Q15712918)      |
| Yu Yu Hakusho (wd:Q18225473)       | school anime and manga (wd:Q5366097)          |
| Yu Yu Hakusho (wd:Q18225473)       | supernatural anime and manga (wd:Q61942616)   |
| Ten de Shōwaru Cupid (wd:Q3518005) | ecchi (wd:Q219559)                            |
| Level E (wd:Q483557)               | comedy anime and manga (wd:Q15286013)         |
| Level E (wd:Q483557)               | science fiction anime and manga (wd:Q5366020) |
| Hunter × Hunter (wd:Q696071)       | action anime and manga (wd:Q15637293)         |
| Hunter × Hunter (wd:Q696071)       | fantasy anime and manga (wd:Q15637301)        |
| Hunter × Hunter (wd:Q696071)       | adventure anime and manga (wd:Q15712918)      |
| Hunter × Hunter (wd:Q696071)       | comedy drama anime and manga (wd:Q15712927)   |

[Assistant]
The genres of Yoshihiro Togashi's works are as follows:
- **Yu Yu Hakusho (wd:Q18225473)**:
  - Adventure anime and manga (wd:Q15712918)
  - School anime and manga (wd:Q5366097)
  - Supernatural anime and manga (wd:Q61942616)
- **Ten de Shōwaru Cupid (wd:Q3518005)**:
  - Ecchi (wd:Q219559)
- **Level E (wd:Q483557)**:
  - Comedy anime and manga (wd:Q15286013)
  - Science fiction anime and manga (wd:Q5366020)
- **Hunter × Hunter (wd:Q696071)**:
  - Action anime and manga (wd:Q15637293)
  - Fantasy anime and manga (wd:Q15637301)
  - Adventure anime and manga (wd:Q15712918)
  - Comedy drama anime and manga (wd:Q15712927)

Among these, "Yu Yu Hakusho" is listed as a "school anime and manga" (wd:Q5366097). This suggests that it might involve middle school students.

To confirm, let's check the plot or setting of "Yu Yu Hakusho" to see if it is indeed set in a middle school or involves middle school students.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that there is no direct information about the setting of "Yu Yu Hakusho" in Wikidata. However, since "Yu Yu Hakusho" is listed as a "school anime and manga," it is reasonable to infer that it likely involves middle school students.

Given the information we have, the work of Yoshihiro Togashi that talks about middle school students is "Yu Yu Hakusho."

Let's finalize our answer with a SPARQL query that confirms this inference.

```sparql
SELECT ?work ?genre WHERE {
  ?work wdt:P50 wd:Q550311 ;
        wdt:P136 ?genre .
  FILTER(?genre = wd:Q5366097)
}
```

This query will return the work and genre where the genre is "school anime and manga," which should confirm that "Yu Yu Hakusho" is the work involving middle school students.

[Tool]
Got 1 row and 2 columns:
| work                         | genre                                |
| ---------------------------- | ------------------------------------ |
| Yu Yu Hakusho (wd:Q18225473) | school anime and manga (wd:Q5366097) |

[Assistant]
The SPARQL query confirms that "Yu Yu Hakusho" (wd:Q18225473) is a work by Yoshihiro Togashi that is classified as a "school anime and manga" (wd:Q5366097), which suggests that it involves middle school students.

Therefore, the answer to the question is:

**Among the other representative works of the author of Hunter x Hunter, "Yu Yu Hakusho" is the one that talks about middle school students.**

Here is the final SPARQL query:

```sparql
SELECT ?work ?genre WHERE {
  ?work wdt:P50 wd:Q550311 ;
        wdt:P136 ?genre .
  FILTER(?genre = wd:Q5366097)
}
```

And the answer:

**Yu Yu Hakusho (wd:Q18225473)**

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q1067089 wdt:P31 | wdt:P279 * wd:Q82821 .
}

False

[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 throat singing a tradition?

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

[Tool]
Example 1:
Question:
What is the largest city in Australia?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P31 / wdt:P279 * wd:Q515 ; wdt:P17 wd:Q408 ; wdt:P1082 ?population .
}
ORDER BY DESC ( ?population )
LIMIT 1

Using entities:
Australia, also known as AU, The Commonwealth of Australia, Stria, Straya, Stralia (wd:Q408): country in Oceania
city, also known as cities (wd:Q515): large human settlement

Using properties:
population, also known as inhabitants, human population (wd:P1082 as wdt): number of people inhabiting the place; number of people of subject
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
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:
Which books were written by Danielle Steel?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P50 wd:Q72653 .
  ?uri wdt:P136 ?genre .
  ?genre wdt:P279 * wd:Q571 .
}

Using entities:
book, also known as books (wd:Q571): medium for recording information (words or images) typically on bound pages or more abstractly in electronic or audio form
Danielle Steel, also known as Danielle Fernandes Dominique Schülein-Steel, Danielle Fernandes Dominique Schuelein-Steel (wd:Q72653): children's writer, American writer, novelist, writer

Using properties:
genre, also known as genre of music, type of music, type of film, music genre, literary genre (wd:P136 as wdt): creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
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
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator

Example 3:
Question:
In which city is the headquarter of Air China?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q51747 wdt:P159 ?uri
}

Using entities:
Air China, also known as CCA, Air China Limited, CA (wd:Q51747): flag carrier of the People's Republic of China

Using properties:
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place

[Assistant] Based on the examples, we need to identify the entity for 'throat singing' and then check if it is classified as a tradition. Let's first search for the entity 'throat singing'.

[Tool]
Top 10 entity alternatives:
1. overtone singing, also known as overtone chanting, throat singing, harmonic singing (wd:Q691771): singing technique
2. throat singing, also known as throat song, guttural singing (wd:Q1067089): vocal practices found in different cultures of the world and tha... involve a wide range of techniques for producing voice and song
3. Inuit throat singing, also known as Iirngaaq, Inuit vocal games, Katadjak, katajjaq, Nipaquhiit (wd:Q2956224): form of musical performance uniquely found among the Inuit
4. undertone singing, also known as throat singing (wd:Q109584422): vocal technique
5. Tuvan throat singing, also known as Tuvan overtone singing, xɵɵmej, xöömэi, xöömey, xöömej (wd:Q108757333): music genre
6. Mongolian throat singing, also known as hôômij, khoomei, khöömii, khöömiĭ, Mongolian overtone singing (wd:Q16801007): music genre
7. Throat Singing in Kangirsuk (wd:Q85809428): 2019 film
8. Central Asian throat singing (wd:Q110606359): music genre
9. Overtone focusing in biphonic tuvan throat singing (wd:Q89673823): scientific article published on 17 February 2020
10. 'With singing hearts and throaty roarings...' (wd:Q119399998): painting by Jock McFadyen (b.1950), IWM (Imperial War Museums)

[Assistant]
From the search results, the most relevant entity seems to be 'throat singing' (wd:Q1067089). Now, let's check if this entity is classified as a tradition. We can look for properties such as 'instance of' (wdt:P31) or 'subclass of' (wdt:P279) to see if it is related to traditions.

Let's run a SPARQL query to check this.

[Tool]
Got 103 rows and 1 column, showing the first 5 and last 5 rows below:
| tradition                               |
| --------------------------------------- |
| voice type (wd:Q1063547)                |
| singing style (wd:Q112613966)           |
| music genre (wd:Q188451)                |
| vocal technique (wd:Q3516862)           |
| manifestation (wd:Q286583)              |
| ...                                     |
| physicochemical process (wd:Q111752858) |
| type (wd:Q21146257)                     |
| pattern (wd:Q2083958)                   |
| wd:Q20937557                            |
| wave (wd:Q37172)                        |

[Assistant]
The results show that 'throat singing' (wd:Q1067089) is classified under various categories such as 'voice type', 'singing style', 'music genre', and 'vocal technique'. However, it does not appear to be directly classified as a 'tradition' or a 'cultural practice'.

To further confirm, let's check if 'tradition' or 'cultural practice' is one of the superclasses of these categories. We can do this by checking the hierarchy of the classes listed in the results.

Let's run another SPARQL query to check if any of these classes are subclasses of 'tradition' or 'cultural practice'.

First, let's search for the entity 'tradition' to get its IRI.

[Tool]
Top 10 entity alternatives:
1. tradition, also known as custom, folk custom (wd:Q82821): custom passed down through generations
2. common law, also known as tradition, customary law system, custom, common law system, case law system (wd:Q30216): law created by judicial precedent
3. heritage, also known as tradition (wd:Q2434238): property, custom, or other material, immaterial or natural objec...rations, and conserved for its importance for future generations
4. Tradition (wd:Q9019596): sculpture by Agapit Vallmitjana i Barbany
5. Rosa 'Tradition', also known as Tradition (wd:Q83670721): rose cultivar
6. Tradition (wd:Q7832266): journal
7. Tradition (wd:Q7832265): reggae band
8. Tradition (wd:Q7832263): album by Michael Angelo Batio
9. Tradition (wd:Q7832267): song
10. Tradition, also known as Tradition, thou art for suckling children (wd:Q19107178): 1905 Stephen Crane poem

[Assistant]
The most relevant entity for 'tradition' is 'tradition' (wd:Q82821), which is defined as a custom passed down through generations. Now, let's check if any of the classes associated with 'throat singing' (wd:Q1067089) are subclasses of 'tradition' (wd:Q82821).

We will run a SPARQL query to check this.

[Tool] Got no rows and 1 columns

[Assistant]
The SPARQL query did not return any results, indicating that none of the classes associated with 'throat singing' (wd:Q1067089) are subclasses of 'tradition' (wd:Q82821).

Therefore, based on the information available in Wikidata, throat singing is not classified as a tradition.

Let's finalize this with an ASK query to confirm our conclusion.

[Tool] False

[Assistant]
The ASK query confirms that throat singing (wd:Q1067089) is not classified as a tradition (wd:Q82821) in Wikidata.

Therefore, the answer to the question "Is throat singing a tradition?" is False.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?causeOfDeath ?causeOfDeathLabel WHERE {
  wd:Q1048 wdt:P509 ?causeOfDeath .
  OPTIONAL {
    ?causeOfDeath rdfs:label ?causeOfDeathLabel .
    FILTER ( LANG( ?causeOfDeathLabel ) = "en" )
  }
}

Got 1 row and 2 columns:
| causeOfDeath          | causeOfDeathLabel |
| --------------------- | ----------------- |
| bleeding (wd:Q166019) | bleeding (en)     |

Julius Caesar, the author of the phrase 'Alea iacta est,' died of bleeding, which is consistent with being assassinated.

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

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

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

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

[User] what did the author of the phrase: "Alea iacta est" die of?

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

[Tool]
Example 1:
Question:
In which year was Rachel Stevens born?

SPARQL:
SELECT DISTINCT ?year WHERE {
  wd:Q241665 wdt:P569 ?date .
  BIND( YEAR( ?date ) as ?year )
}

Using entities:
Rachel Stevens, also known as Rachel Lauren Stevens (wd:Q241665): British singer, actress and model, singer-songwriter, recording artist, film actor, singer, actor, model

Using properties:
date of birth, also known as year of birth, birthdate, DOB, born on, born (wd:P569 as wdt): date on which the subject was born, inception

Example 2:
Question:
Who developed the video game World of Warcraft?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q131007 wdt:P178 ?uri .
}

Using entities:
World of Warcraft, also known as Vanilla WoW, World of Warcraft Vanilla, WoW, WoW Vanilla (wd:Q131007): 2004 video game by Blizzard Entertainment

Using properties:
developer, also known as written by, video game developer, video game dev, game developer, game dev (wd:P178 as wdt): organization or person that developed the item, creator

Example 3:
Question:
Give me all Methodist politicians.

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P106 wd:Q82955 ; wdt:P140 wd:Q33203 .
}

Using entities:
Methodism, also known as Methodist church, Methodist movement, Methodists (wd:Q33203): group of historically related denominations of Protestant Christianity
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)
religion or worldview, also known as believes, worldview, religious affiliation, religion or world view, religion (wd:P140 as wdt): religion of a person, organization or religious building, or associated with this subject

[Assistant] Based on the examples, we need to first identify the author of the phrase "Alea iacta est". Let's search for this phrase and see if we can find the author.

[Tool]
Top 10 entity alternatives:
1. Alea iacta est, also known as Alea iacta sunt (wd:Q271723): phrase
2. President's message. Alea iacta est (wd:Q84096493): scientific article published on 01 January 2012
3. The ‘Anthropocene’: alea iacta est (wd:Q129631880): scholarly article published on 22 January 2024
4. Strategic initiatives: Alea iacta est! (wd:Q85732933): scientific article published on 01 September 2010
5. Clinical-grade Computational Pathology: Alea Iacta Est (wd:Q92540890): scientific article published on 11 December 2019
6. Particle Therapy in Australia: iacta alea est! (wd:Q99715552): scientific article published on 25 September 2020
7. Alea Jacta Est (wd:Q4714058): album by WarCry
8. Alea Jacta Est (wd:Q25409653): 2012 video game
9. Alea iacta est: a new approach to cardiac output monitoring? (wd:Q45391246): scientific article published in August 2013
10. Alea Jacta Est Tour (wd:Q4714056)

[Assistant] The top result is the phrase itself, "Alea iacta est" (wd:Q271723). Now, let's find out who said this phrase. We'll look for the author of this phrase.

[Tool]
Got 238 rows and 3 columns, showing the first 10 rows below:
| subject                     | property                                                  | object                                                   |
| --------------------------- | --------------------------------------------------------- | -------------------------------------------------------- |
| Alea iacta est (wd:Q271723) | instance of (wdt:P31)                                     | winged words (wd:Q1935136)                               |
| Alea iacta est (wd:Q271723) | published in (wdt:P1433)                                  | The Twelve Caesars (wd:Q1229963)                         |
| Alea iacta est (wd:Q271723) | author (wdt:P50)                                          | Julius Caesar (wd:Q1048)                                 |
| Alea iacta est (wd:Q271723) | language of work or name (wdt:P407)                       | Latin (wd:Q397)                                          |
| Alea iacta est (wd:Q271723) | inception (wdt:P571)                                      | -048-01-11T00:00:00Z (xsd:dateTime)                      |
| Alea iacta est (wd:Q271723) | described by source (wdt:P1343)                           | Brockhaus and Efron Encyclopedic Dictionary (wd:Q602358) |
| Alea iacta est (wd:Q271723) | Freebase ID (wdt:P646)                                    | /m/09w4dk                                                |
| Alea iacta est (wd:Q271723) | Quora topic ID (wdt:P3417)                                | Alea-Iacta-Est                                           |
| Alea iacta est (wd:Q271723) | Gran Enciclopèdia Catalana ID (former scheme) (wdt:P1296) | 21024157                                                 |
| Alea iacta est (wd:Q271723) | Gran Enciclopèdia Catalana ID (wdt:P12385)                | alea-iacta-est                                           |
| ...                         | ...                                                       | ...                                                      |

[Assistant]
From the list, we can see that the author of the phrase "Alea iacta est" is Julius Caesar (wd:Q1048). Now, let's find out what Julius Caesar died of.

We'll search for the cause of death of Julius Caesar.

[Tool]
Got 1 row and 3 columns:
| subject                  | property                  | object                |
| ------------------------ | ------------------------- | --------------------- |
| Julius Caesar (wd:Q1048) | cause of death (wdt:P509) | bleeding (wd:Q166019) |

[Assistant]
Julius Caesar died of bleeding, which suggests he was assassinated. To confirm this, let's run a SPARQL query to get the cause of death and any additional relevant information.

Here is the SPARQL query:

```sparql
SELECT ?causeOfDeath ?causeOfDeathLabel
WHERE {
  wd:Q1048 wdt:P509 ?causeOfDeath .
  OPTIONAL { ?causeOfDeath rdfs:label ?causeOfDeathLabel . FILTER(LANG(?causeOfDeathLabel) = "en") }
}
```

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| causeOfDeath          | causeOfDeathLabel |
| --------------------- | ----------------- |
| bleeding (wd:Q166019) | bleeding (en)     |

[Assistant]
The SPARQL query confirms that Julius Caesar died of bleeding. Given the historical context, this indicates that he was assassinated.

Therefore, the author of the phrase "Alea iacta est" died of bleeding, which is consistent with being assassinated.

Here is the final SPARQL query and answer:

```sparql
SELECT ?causeOfDeath ?causeOfDeathLabel
WHERE {
  wd:Q1048 wdt:P509 ?causeOfDeath .
  OPTIONAL { ?causeOfDeath rdfs:label ?causeOfDeathLabel . FILTER(LANG(?causeOfDeathLabel) = "en") }
}
```

Answer: Julius Caesar, the author of the phrase "Alea iacta est," died of bleeding, which is consistent with being assassinated.

[Tool] Stopped generation process


PREFIX p: <http://www.wikidata.org/prop/>
PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?event ?eventLabel WHERE {
  wd:Q5577 p:P793 ?statement .
  ?statement ps:P793 ?event .
  ?event rdfs:label ?eventLabel .
  FILTER ( LANG( ?eventLabel ) = 'en' )
}

The key event related to Salvador Dalí is his exhumation.

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

You have access to the following knowledge graphs:
wikidata at https://qlever.cs.uni-freiburg.de/api/wikidata-sebastian

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

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

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

[User] What was the key event related to Salvador Dalí?

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

[Tool]
Example 1:
Question:
List all boardgames by GMT.

SPARQL:
SELECT ?uri WHERE {
  ?uri wdt:P31 wd:Q131436 .
}

Using entities:
board game, also known as boardgame (wd:Q131436): game that involves counters or pieces moved or placed on a pre-marked surface or "board", according to a set of rules

Using properties:
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:
Who created Batman?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q2695156 wdt:P170 ?uri .
}

Using entities:
Batman, also known as the Bat-Man, the Batman, the Caped Crusader, the Bat, Sir Hemingford Grey (wd:Q2695156): fictional character in DC Comics, chief executive officer, businessperson, martial artist, philanthropist, vigilante, detective, superhero

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)

Example 3:
Question:
How many theories did Albert Einstein come up with?

SPARQL:
SELECT ( COUNT( DISTINCT ?uri ) as ?cnt ) WHERE {
  wd:Q937 wdt:P800 ?uri .
}

Using entities:
Albert Einstein (wd:Q937): German-born theoretical physicist (1879–1955), physicist

Using properties:
notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as wdt): notable scientific, artistic or literary work, or other work of significance among subject's works

[Assistant] Based on the examples, we need to identify the key event related to Salvador Dalí. Let's first search for Salvador Dalí in the knowledge graph.

[Tool]
Top 10 entity alternatives:
1. Salvador Dalí (wd:Q5577): Spanish artist (1904–1989), jewelry designer, graphic artist, film director, screenwriter, scenographer, photographer, illustrator, holographer, film actor, sculptor, painter, writer, actor
2. Salvador Dalí, also known as Salvador Dali (wd:Q3945890): 1966 short film by Andy Warhol
3. Salvador Dalí, also known as Salvador Dali, Salvador Dalì (wd:Q28669534): 2016 song by Marracash and Guè Pequeno
4. Salvador Dalí (wd:Q3945892): 1970 film
5. Salvador Dalí (wd:Q76627906): photograph by Man Ray
6. Salvador Dalí (wd:Q110890669): 1941 exhibition at Museum of Modern Art
7. Salvador Dalí (wd:Q104633152): exhibition in 1970
8. Salvador Dali (wd:Q76456803): print in the National Gallery of Art (NGA 147625)
9. Salvador Dali (wd:Q103927804): painting by Otto Duecker
10. Salvador Dalí Desert, also known as Dali Desert, Dalí Desert, Desierto Dali, Salvador Dali Desert (wd:Q1382079): desert in Bolivia

[Assistant] The top result is the Spanish artist Salvador Dalí (wd:Q5577). Now let's look at the properties related to key events or notable works for Salvador Dalí.

[Tool]
Top 10 property alternatives:
1. significant event, also known as significant occurrence, significant incident, significant achievment, sign. event, outcome (wd:P793 as p/wdt): significant or notable events associated with the subject
2. participant in, also known as participated in, present at, takes part, took part, took part in (wd:P1344 as p/wdt): event in which a person or organization was/is a participant; inverse of P710 or P1923, significant event, part of
3. educated at, also known as graduated from, graduate of, place of education, school attended, schooled at (wd:P69 as p/wdt): educational institution attended by subject, affiliation
4. field of work, also known as scientific discipline, scientific area, responsible for, research on, research fields (wd:P101 as p/wdt): specialization of a person or organization; see P106 for the occupation, interested in
5. notable work, also known as artwork, wrote, writer of, works, work (wd:P800 as p/wdt): notable scientific, artistic or literary work, or other work of significance among subject's works
6. work location, also known as work place, working from, working at, worked from, worked at (wd:P937 as p/wdt): location where persons or organisations were actively participating in employment, business or other work, significant place, location
7. described by source, also known as mentioned in, source of item, reviewed in, mentioned in news article, talked about in (wd:P1343 as p/wdt): work where this item is described, attested in
8. date of death, also known as DOD, deathdate, died, died on, year of death (wd:P570 as p/wdt): dissolved, abolished or demolished date, date on which the subject died, end time
9. movement, also known as subculture, trend, scientific movement, school, philosophical movement (wd:P135 as p/wdt): literary, artistic, scientific or philosophical movement or scen...ed with this person or work. For political ideologies use P1142.
10. 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)

No other items found

[Assistant] The property "significant event" (P793) seems to be the most relevant for finding key events related to Salvador Dalí. Let's use this property to find the key events associated with him.

[Tool]
Got 1 row and 1 column:
| event       |
| ----------- |
| wd:Q1898510 |

[Assistant] The query returned one event associated with Salvador Dalí, which is represented by the identifier `wd:Q1898510`. To understand what this event is, let's fetch its label.

[Tool]
Got 1 row and 1 column:
| eventLabel      |
| --------------- |
| exhumation (en) |

[Assistant]
The key event related to Salvador Dalí is his exhumation.

Here is the final SPARQL query and answer:

```sparql
SELECT ?event ?eventLabel WHERE {
  wd:Q5577 p:P793 ?statement .
  ?statement ps:P793 ?event .
  ?event rdfs:label ?eventLabel .
  FILTER(LANG(?eventLabel) = 'en')
}
```

Answer: The key event related to Salvador Dalí is his exhumation.

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
ASK WHERE {
  wd:Q3616 wdt:P1082 ?iranCapitalPopulation .
  wd:Q64 wdt:P1082 ?germanyCapitalPopulation .
  FILTER ( ?iranCapitalPopulation > ?germanyCapitalPopulation )
}

True

[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 the capital of Iran bigger than that of Germany?

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

[Tool]
Example 1:
Question:
What is the last work of Dan Brown?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  ?uri wdt:P50 wd:Q7345 ; wdt:P577 ?date .
}
ORDER BY DESC ( ?date )
LIMIT 1

Using entities:
Dan Brown, also known as Daniel Gerhard Brown, Dan Gerhard Brown (wd:Q7345): American novelist, prose writer, researcher, journalist, musician, novelist, teacher, writer

Using properties:
author, also known as writer, written by, poet, playwright, maker (wd:P50 as wdt): main creator(s) of a written work (use on works, not humans); us...hor name string) when Wikidata item is unknown or does not exist, creator
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 2:
Question:
Who is the governor of Wyoming?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q1214 wdt:P6 ?uri .
}

Using entities:
Wyoming, also known as The Equality State, The Cowboy State, Wyoming, United States, Wyo., Big Wyoming (wd:Q1214): state of the United States of America

Using properties:
head of government, also known as head of national government, mayor, premier, president, prime minister (wd:P6 as wdt): head of the executive power of this town, city, municipality, state, country, or other governmental body, director / manager, chairperson

Example 3:
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

[Assistant]
Based on the examples, we need to find the capitals of Iran and Germany, and then compare their sizes. We'll start by identifying the entities for Iran and Germany, and the properties for capital and population size.

First, let's search for the entities representing Iran and Germany.

[Tool]
Top 10 entity alternatives:
1. Iran, also known as ir, IRI, IRN, Islamic Rep. Iran, Islamic Republic of Iran (wd:Q794): country in Western Asia
2. Qajar Iran, also known as Guarded Domains of Persia, Sublime State of Persia, Qajar Persia, Persia, Iran (wd:Q63158027): country in Western Asia (1789–1925)
3. Iran (wd:Q2043606): village in North Ossetia, Russia
4. Iran, also known as Īrān (wd:Q10859757): Iranian newspaper
5. IRAN, also known as Iran: Journal of the British Institute of Persian Studies (wd:Q5973372): academic journal
6. Irane Man, also known as Iran (wd:Q100939484): 2018 studio by Homayoun Shajarian & Sohrab Pournazeri
7. IRAN (wd:Q108434787): herbarium
8. Iran, also known as Iran (first name), Iran (given name) (wd:Q21286079): male given name
9. Iran, also known as IMO 5327817 (wd:Q101778560): ship built in 1941
10. Iran (wd:Q96382820): About Iran

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

[Assistant]
From the search results, we can see that the correct entities for Iran and Germany are:

- Iran: `wd:Q794`
- Germany: `wd:Q183`

Next, we need to find the properties for capital and population size. Let's search for these properties.

### Step 1: Find the property for "capital"
We will search for the property related to the capital of a country.

[Tool]
Top 10 property alternatives:
1. capital of, also known as national capital of, parish seat of, provincial capital of, seat of, state capital (wd:P1376 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): country, state, department, canton or other administrative division of which the municipality is the governmental seat, located in the administrative territorial entity
2. capital, also known as administrative seat, seat of government, seat, principal place, location of capital (wd:P36 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): seat of government of a country, province, state or other type of administrative territorial entity
3. capital cost, also known as budget, cost, unit cost (wd:P2130 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): amount of money spent on building or producing an object
4. authorised capital, also known as authorized capital (wd:P12651 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): maximum amount of share capital that the company is authorised to issue to shareholders
5. number of matches played/races/starts, also known as gp, starts, races, number of games played, mp (wd:P1350 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): matches or games a player or a team played during an event. Also...matches a player officially appeared in during the whole career.
6. uses capitalization for, also known as capitalises, capitalizes, uses capitalisation for (wd:P6106 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): lexical classes and subclasses of words as well as other uses in the language where the first letter is written as a capital letter
7. market capitalization, also known as market cap, market capitalisation (wd:P2226 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): total value of issued shares by a publicly traded company
8. location, also known as neighbourhood, whereabouts, vilage, venue, town (wd:P276 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): location of the object, structure or event. In the case of an ad...ity use P706. Use P7153 for locations associated with the object
9. 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the item is located on the territory of the following administra...if the item falls only partially into the administrative entity., location, part of
10. nominal share capital (wd:P8247 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): total of the nominal values of the issued ordinary and preference shares of the entity

[Assistant]
From the search results, the correct property for the capital of a country is:

- Capital: `wdt:P36`

### Step 2: Find the property for "population"
Now, let's search for the property related to the population of a city or country.

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

[Assistant]
From the search results, the correct property for the population of a city or country is:

- Population: `wdt:P1082`

### Step 3: Build the SPARQL Query
Now that we have the entities and properties, we can build the SPARQL query to find the capitals of Iran and Germany and their populations. We will then compare the populations to determine which capital is larger.

Let's start by finding the capitals of Iran and Germany.

```sparql
SELECT ?iranCapital ?germanyCapital WHERE {
  wd:Q794 wdt:P36 ?iranCapital .
  wd:Q183 wdt:P36 ?germanyCapital .
}
```

Let's execute this query to get the capitals.

[Tool]
Got 1 row and 2 columns:
| iranCapital       | germanyCapital  |
| ----------------- | --------------- |
| Tehran (wd:Q3616) | Berlin (wd:Q64) |

[Assistant]
We have found the capitals of Iran and Germany:

- Capital of Iran: `Tehran` (wd:Q3616)
- Capital of Germany: `Berlin` (wd:Q64)

Next, we need to find the populations of these cities. We will use the `wdt:P1082` property to get the population.

Let's build the query to get the populations of Tehran and Berlin.

```sparql
SELECT ?iranCapitalPopulation ?germanyCapitalPopulation WHERE {
  wd:Q3616 wdt:P1082 ?iranCapitalPopulation .
  wd:Q64 wdt:P1082 ?germanyCapitalPopulation .
}
```

Let's execute this query to get the populations.

[Tool]
Got 1 row and 2 columns:
| iranCapitalPopulation | germanyCapitalPopulation |
| --------------------- | ------------------------ |
| 8693706 (xsd:decimal) | 3782202 (xsd:decimal)    |

[Assistant]
We have found the populations of the capitals:

- Population of Tehran: 8,693,706
- Population of Berlin: 3,782,202

Since the population of Tehran is greater than the population of Berlin, the capital of Iran is indeed bigger than the capital of Germany.

To provide a final answer, we will use an ASK query to confirm this.

```sparql
ASK WHERE {
  wd:Q3616 wdt:P1082 ?iranCapitalPopulation .
  wd:Q64 wdt:P1082 ?germanyCapitalPopulation .
  FILTER(?iranCapitalPopulation > ?germanyCapitalPopulation)
}
```

Let's execute this ASK query to get the final answer.

[Tool] True

[Assistant]
The ASK query confirms that the population of Tehran (the capital of Iran) is indeed greater than the population of Berlin (the capital of Germany).

Therefore, the answer to the question "Is the capital of Iran bigger than that of Germany?" is **True**.

Here is the final SPARQL query and answer:

```sparql
ASK WHERE {
  wd:Q3616 wdt:P1082 ?iranCapitalPopulation .
  wd:Q64 wdt:P1082 ?germanyCapitalPopulation .
  FILTER(?iranCapitalPopulation > ?germanyCapitalPopulation)
}
```

Answer: True

[Tool] Stopped generation process


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ( COUNT( ?language ) AS ?count ) WHERE {
  wd:Q30 wdt:P37 ?language .
}

Got 1 row and 1 column:
| count       |
| ----------- |
| 1 (xsd:int) |

The United States of America has 1 official language.

[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 does the united states of america have?

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

[Tool]
Example 1:
Question:
When was the De Beers company founded?

SPARQL:
SELECT ?date WHERE {
  wd:Q471891 wdt:P571 ?date .
}

Using entities:
De Beers, also known as De Beers Group, The De Beers Group of Companies (wd:Q471891): International corporation that specialises in diamond exploratio...retail, rough diamond sales and industrial diamond manufacturing

Using properties:
inception, also known as initial release date, founded in, founded on, founded on date, founding date (wd:P571 as wdt): time when an entity begins to exist; for date of official opening use P1619, start time

Example 2:
Question:
Who discovered Ceres?

SPARQL:
SELECT DISTINCT ?uri WHERE {
  wd:Q596 wdt:P61 ?uri .
}

Using entities:
Ceres, also known as (1) Ceres, 1 Ceres, Ceres (dwarf planet) (wd:Q596): dwarf planet in the Solar System and largest asteroid of the main asteroid belt

Using properties:
discoverer or inventor, also known as inventor or discoverer, inventor, invented by, invented, introduced by (wd:P61 as wdt): subject who discovered, first described, invented, or developed this discovery or invention or scientific hypothesis or theory

Example 3:
Question:
Which museum in New York has the most visitors?

SPARQL:
SELECT ?s1 WHERE {
  ?s1 wdt:P159 wd:Q60 .
  ?s1 wdt:P31 wd:Q33506 .
  ?s1 wdt:P1174 ?o1
}
order by ?o1
limit 1

Using entities:
museum, also known as museal institution, museum-style institution (wd:Q33506): institution that holds artifacts and other objects of scientific, artistic, cultural, historical, or other importance
New York City, also known as Capital of the World, Big Apple, the five boroughs, The City So Nice They Named It Twice, NY, NY (wd:Q60): most populous city in the United States

Using properties:
visitors per year, also known as annual visitors, yearly visitors (wd:P1174 as wdt): number of people visiting a location or an event each year
headquarters location, also known as headquarters locality, seat, headquarters town, HQ, HQ location (wd:P159 as wdt): city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building, significant place
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] Based on the examples, we need to identify the relevant entity and property for our question. We are looking for the number of official languages in the United States of America. Let's start by searching for the entity representing the United States of America.

[Tool]
Top 10 entity alternatives:
1. United States, also known as the U.S. of A, United States of America, the US of America, United States of North America, U. S. (wd:Q30): country primarily located in North America
2. Istadus Unidus the America, also known as United States of America (wd:Q29045433): Wikimedia permanent duplicate item
3. United States of America (wd:Q100270731): scholarly article
4. United States of America (wd:Q103971355): encyclopedic article; Grove Art Online
5. United States of America (wd:Q105870683): vocal track by Kikki Danielsson; 1980 studio recording
6. United States of America (wd:Q105870692): song written and composed by Donna Fargo, originally recorded by Donna Fargo and released in 1974
7. United States of America (wd:Q106315054): vocal track by Donna Fargo; 1974 studio recording
8. United States of America, also known as Star Trek USA, USA (wd:Q99281400): country of the United States of America as depicted in Star Trek
9. United States of America (wd:L350484-S1)
10. United States (of America) (wd:L399000-S1)

[Assistant] The correct entity for the United States of America is `wd:Q30`. Next, we need to find the property that represents the official language(s) of a country. Let's search for a property related to "official language".

[Tool]
Top 10 property alternatives:
1. official language, also known as language spoken, spoken in, speaking, language official (wd:P37 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): 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 prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): label for the items in their official language  or their original language, name
3. Ethnologue language status, also known as EGIDS, language status (wd:P3823 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): language status identifier by Ethnologue.com using EGIDS scale
4. language regulatory body, also known as language authority, regulatory body (wd:P1018 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): regulatory body of a language
5. languages spoken, written or signed, also known as language of expression, language, wrote language, writes language, uses language (wd:P1412 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): language(s) that a person or a people speaks, writes or signs, including the native language(s)
6. native language, also known as first language, L1 speaker of, language native, mother tongue (wd:P103 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): language or languages a person has learned from early childhood, languages spoken, written or signed
7. language used, also known as languages used, medium of instruction, used language, uses language, working language (wd:P2936 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): language widely used (spoken or written) in this place or at this event or organisation
8. language of work or name, also known as language of the reference, language of URL, language of website, language of work, named in language (wd:P407 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): language associated with this creative work (such as books, show...guage" (P103) and "languages spoken, written or signed" (P1412))
9. programmed in, also known as language, programming language, written in (wd:P277 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): the programming language(s) in which the software is developed
10. writing language, also known as languages of writings, languages written, language written, writing languages, language of works (wd:P6886 as prv/wdtn/ps/pq/prn/pr/p/wdt/psn/pqv/pqn/psv): language in which the writer has written their work, languages spoken, written or signed

[Assistant]
The property `wd:P37` represents the official language(s) of a country. Now, let's construct a SPARQL query to find the official languages of the United States of America (`wd:Q30`).

We will start with a simple query to list the official languages:

```sparql
SELECT ?language WHERE {
  wd:Q30 wdt:P37 ?language .
}
```

Let's execute this query to see the results.

[Tool]
Got 1 row and 1 column:
| language           |
| ------------------ |
| English (wd:Q1860) |

[Assistant]
The query returned one official language for the United States of America, which is English (`wd:Q1860`).

To provide the final answer, we can count the number of official languages. Since the query only returned one language, we can conclude that the United States of America has one official language.

Here is the final SPARQL query and answer:

```sparql
SELECT (COUNT(?language) AS ?count) WHERE {
  wd:Q30 wdt:P37 ?language .
}
```

Let's execute this query to confirm the count.

[Tool]
Got 1 row and 1 column:
| count       |
| ----------- |
| 1 (xsd:int) |

[Assistant]
The query confirms that the United States of America has one official language.

Here is the final SPARQL query and answer:

```sparql
SELECT (COUNT(?language) AS ?count) WHERE {
  wd:Q30 wdt:P37 ?language .
}
```

The United States of America has 1 official language.

[Tool] Stopped generation process