PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?item ?itemLabel WHERE {
  VALUES ?doi {
    "10.1126/SCIENCE.156.3775.636" "10.1145/358027.358042"
  }
  ?item wdt:P356 ?doi .
  OPTIONAL {
    ?item rdfs:label ?itemLabel FILTER ( LANG( ?itemLabel ) = "en" )
  }
}

The item associated with the DOI '10.1126/SCIENCE.156.3775.636' is "How Long Is the Coast of Britain...ilarity and Fractional Dimension" (wd:Q3554851). The item associated with the DOI '10.1145/358027.358042' is "The Complexity of Songs" (wd:Q2410432). Both items have their titles available in English.

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

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

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

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

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

[User] What are the items associated with the DOIs '10.1126/SCIENCE.156.3775.636' and '10.1145/358027.358042', along with their titles if available?

[Assistant]
To answer this question, I need to find the items in Wikidata that are associated with the given DOIs '10.1126/SCIENCE.156.3775.636' and '10.1145/358027.358042'. Then, I will retrieve their titles if available.

Step 1: Identify the property for DOI in Wikidata. The property for DOI is usually "P356".
Step 2: Query Wikidata for items with these DOI values.
Step 3: Retrieve the titles (rdfs:label) of these items if available.

I will start by querying for items with the first DOI '10.1126/SCIENCE.156.3775.636' and get their labels.

[Tool]
Got 1 row and 2 columns:
| item                                                                              | itemLabel                                                                |
| --------------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| How Long Is the Coast of Britain...ilarity and Fractional Dimension (wd:Q3554851) | How Long Is the Coast of Britain...ilarity and Fractional Dimension (en) |

[Assistant]
I found one item associated with the DOI '10.1126/SCIENCE.156.3775.636'. The item is "How Long Is the Coast of Britain...ilarity and Fractional Dimension" (wd:Q3554851).

Next, I will query for the item associated with the DOI '10.1145/358027.358042' and get its label.

[Tool]
Got 1 row and 2 columns:
| item                                  | itemLabel                    |
| ------------------------------------- | ---------------------------- |
| The Complexity of Songs (wd:Q2410432) | The Complexity of Songs (en) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the items associated with the given DOIs and their English titles. The query uses the DOI property (wdt:P356) properly and includes an optional clause to get the English labels. The results match the expected items and titles. The output is clear, complete, and follows the rules for SPARQL queries. No further refinement is needed.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?player ?dob ?gender ( YEAR( NOW () ) - YEAR( ?dob ) AS ?age ) WHERE {
  ?player wdt:P106 wd:Q10833314 ; wdt:P569 ?dob .
  FILTER NOT EXISTS {
    ?player wdt:P570 ?dod
  }
  OPTIONAL {
    ?player wdt:P21 ?gender .
  }
  FILTER ( ( YEAR( NOW () ) - YEAR( ?dob ) ) < 110 )
}
ORDER BY DESC ( ?age )
LIMIT 100

Here are the 100 oldest living tennis players with their dates of birth, genders, and approximate ages (age calculated as current year minus birth year, excluding those 110 years or older), ordered by age in descending order. The query returns the actual IRIs of the players as requested.

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

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

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

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

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

[User] Who are the 100 oldest living tennis players? Include their dates of birth, genders, and ages, and order by age in descending order. Exclude those who are 110 years old or older.

[Assistant]
To answer this question, I need to identify entities that are tennis players, are living, and have a date of birth. Then I need to calculate their ages, filter out those who are 110 years old or older, and order the results by age in descending order, limiting to 100 results. Additionally, I need to include their genders.

Step 1: Identify the entity for "tennis player."
Step 2: Find properties for date of birth, gender, and living status.
Step 3: Construct the SPARQL query to get the oldest living tennis players with the required details.

I will start by searching for the entity "tennis player" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. tennis player (wd:Q10833314): sportsperson who plays tennis
2. Chee-Meng Jimmy Liew, also known as Tennis player (wd:Q27579542): chess player
3. Tennis player (wd:Q50648121): painting by Roy De maistre
4. Tennis Player (wd:Q106776651): painting by James Edward Davis (Princeton y1968-145)
5. Tennis Player (wd:Q106776580): painting by James Edward Davis (Princeton y1968-146)
6. Tennis Player (wd:Q106774483): painting by James Edward Davis (Princeton y1968-147)
7. Tennis Player (wd:Q106767186): painting by James Edward Davis
8. table tennis player, also known as ping-pong player, table tennis athlete (wd:Q13382519): sportsperson taking part in table tennis competitions
9. amateur tennis player, also known as amateur tennis players (wd:Q48995125)
10. beach tennis player (wd:Q30525637)

[Assistant]
The entity for "tennis player" is wd:Q10833314.

Next, I will identify the properties for date of birth, gender, and living status.
- Date of birth is usually wdt:P569.
- Gender is usually wdt:P21.
- Living status can be inferred by the absence of a date of death (wdt:P570).

I will now construct a SPARQL query to find the 100 oldest living tennis players with their date of birth, gender, and age, excluding those who are 110 years old or older, and order by age in descending order.

[Tool]
Failed to call function execute:
failed to parse input: no matching token found from position 80: 'FTIME('%m-%d', NOW()) < STRFTIME('%m-%d', ?dob), 1, 0) AS ?age) WHERE {
  ?player wdt:P106 wd:Q10833314;  # occupation tennis player
          wdt:P569 ?dob.           # date of birth
  FILTER NOT EXISTS { ?player wdt:P570 ?dod }  # no date of death
  OPTIONAL { ?player wdt:P21 ?gender. }         # gender
  FILTER((YEAR(NOW()) - YEAR(?dob) - IF(STRFTIME('%m-%d', NOW()) < STRFTIME('%m-%d', ?dob), 1, 0)) < 110)  # age less than 110
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
} ORDER BY DESC(?age) LIMIT 100'

[Assistant]
The SPARQL endpoint does not support the STRFTIME and IF functions as used. I need to calculate age differently.

I will calculate age approximately by subtracting the year of birth from the current year and then filter out those with age 110 or more. This will be an approximate age calculation without considering the exact month and day.

I will rewrite the query accordingly.

[Tool]
SPARQL execution failed:
Tried to allocate 409.6 MB, but only 290 MB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
The query failed due to memory limitations. I will try to optimize the query by removing optional label retrieval and only retrieving the essential data: player, date of birth, gender, and age. I will retrieve labels in a separate step if needed.

I will also try to limit the number of results earlier by ordering by date of birth ascending (oldest first) and limiting to 100 before calculating age. This might reduce memory usage.

[Tool]
Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| player                                | dob                                 | gender               |
| ------------------------------------- | ----------------------------------- | -------------------- |
| wd:Q27989956                          | 1916-01-01T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   |
| Vera Mattar (wd:Q29473999)            | 1916-01-01T00:00:00Z (xsd:dateTime) | female (wd:Q6581072) |
| Rolf Göpfert (wd:Q116256918)          | 1916-02-16T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   |
| wd:Q12875993                          | 1917-01-01T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   |
| Patricia Henry Yeomans (wd:Q63762498) | 1917-01-01T00:00:00Z (xsd:dateTime) | female (wd:Q6581072) |
| ...                                   | ...                                 | ...                  |
| Tony Pickard (wd:Q3531860)            | 1934-09-13T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   |
| Chuck Swindoll (wd:Q5115734)          | 1934-10-18T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   |
| John W. Frost (wd:Q16095604)          | 1934-10-23T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   |
| Ken Rosewall (wd:Q312728)             | 1934-11-02T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   |
| Pat Hird (wd:Q115515681)              | 1934-11-11T00:00:00Z (xsd:dateTime) | female (wd:Q6581072) |

[Assistant]
I have retrieved 100 oldest living tennis players with their dates of birth and genders. Now I will calculate their ages approximately by subtracting the birth year from the current year and include their labels for better readability. I will also order by age in descending order.

I will now construct the final query with these details.

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The SPARQL query is logically correct in terms of filtering for living tennis players, excluding those 110 years or older, and ordering by age. However, the query failed to execute due to memory limitations, likely because the calculation of age using YEAR(NOW()) - YEAR(?dob) and the large dataset caused high memory usage. To improve, consider optimizing the query by reducing optional clauses or simplifying the age calculation, or by limiting the dataset further before ordering and limiting results. Also, ensure that the query returns the actual IRIs of the players as required. The system should retry with an optimized query to successfully retrieve the answer.

[Assistant]
To optimize the query and reduce memory usage, I will:
- Remove optional label retrieval for gender and player to reduce complexity.
- Calculate age approximately as before.
- Limit the dataset by ordering by date of birth ascending (oldest first) and limiting to 100 before calculating age and filtering.
- Return the actual IRIs of the players as required along with their date of birth, gender, and approximate age.

I will retry with this optimized query.

[Tool]
Got 100 rows and 4 columns, showing the first 5 and last 5 rows below:
| player                                | dob                                 | gender               | age           |
| ------------------------------------- | ----------------------------------- | -------------------- | ------------- |
| wd:Q27989956                          | 1916-01-01T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   | 109 (xsd:int) |
| Vera Mattar (wd:Q29473999)            | 1916-01-01T00:00:00Z (xsd:dateTime) | female (wd:Q6581072) | 109 (xsd:int) |
| Rolf Göpfert (wd:Q116256918)          | 1916-02-16T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   | 109 (xsd:int) |
| wd:Q12875993                          | 1917-01-01T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   | 108 (xsd:int) |
| Patricia Henry Yeomans (wd:Q63762498) | 1917-01-01T00:00:00Z (xsd:dateTime) | female (wd:Q6581072) | 108 (xsd:int) |
| ...                                   | ...                                 | ...                  | ...           |
| Tony Pickard (wd:Q3531860)            | 1934-09-13T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   | 91 (xsd:int)  |
| Chuck Swindoll (wd:Q5115734)          | 1934-10-18T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   | 91 (xsd:int)  |
| John W. Frost (wd:Q16095604)          | 1934-10-23T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   | 91 (xsd:int)  |
| Ken Rosewall (wd:Q312728)             | 1934-11-02T00:00:00Z (xsd:dateTime) | male (wd:Q6581097)   | 91 (xsd:int)  |
| Pat Hird (wd:Q115515681)              | 1934-11-11T00:00:00Z (xsd:dateTime) | female (wd:Q6581072) | 91 (xsd:int)  |

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves tennis players who are alive, their dates of birth, genders, and calculates their ages, excluding those 110 years or older. However, the ordering is by date of birth ascending (ORDER BY ?dob), which means the youngest players appear first, not the oldest as requested. To order by age in descending order, the query should use ORDER BY DESC(?dob) or ORDER BY DESC(?age). Also, the age calculation is approximate since it only subtracts birth year from current year, which may overestimate age if the birthday hasn't occurred yet this year. While this is acceptable for an approximate age, it should be noted. Finally, the query returns 100 results but does not guarantee that these are the oldest 100 players because of the ordering issue. Fixing the ordering will ensure the correct top 100 oldest living tennis players are returned.

[Assistant]
Thank you for the feedback. To address the issues:
- I will order the results by age in descending order to get the oldest players first.
- I will keep the approximate age calculation as the difference between the current year and birth year, noting that it is an approximation.
- This ordering will ensure the top 100 oldest living tennis players are returned.

I will now refine the query accordingly.

[Tool] Stopped generation process


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
SELECT ?person ?personLabel ?position ?positionLabel WHERE {
  ?person p:P39 ?statement .
  ?statement ps:P39 ?position .
  ?statement pq:P748 wd:Q22686 .
  OPTIONAL {
    ?person rdfs:label ?personLabel FILTER ( LANG( ?personLabel ) = "en" )
  }
  OPTIONAL {
    ?position rdfs:label ?positionLabel FILTER ( LANG( ?positionLabel ) = "en" )
  }
}
LIMIT 100

Got 91 rows and 4 columns, showing the first 5 and last 5 rows below:
| person                                | personLabel                | position                                                                          | positionLabel                                                          |
| ------------------------------------- | -------------------------- | --------------------------------------------------------------------------------- | ---------------------------------------------------------------------- |
| Emily Webster Murphy (wd:Q42282721)   | Emily Webster Murphy (en)  | Administrator of General Services (wd:Q102259808)                                 | Administrator of General Services (en)                                 |
| Christopher C. Miller (wd:Q101423348) | Christopher C. Miller (en) | Director of the National Counterterrorism Center (wd:Q104720749)                  | Director of the National Counterterrorism Center (en)                  |
| Amul Roger Thapar (wd:Q4748925)       | Amul Roger Thapar (en)     | Judge of the United States Court of Appeals for the Sixth Circuit (wd:Q107282720) | Judge of the United States Court of Appeals for the Sixth Circuit (en) |
| Brian Jack (wd:Q86493775)             | Brian Jack (en)            | White House Political Director (wd:Q108485661)                                    | White House Political Director (en)                                    |
| Brett Kavanaugh (wd:Q4962244)         | Brett Kavanaugh (en)       | Associate Justice of the Supreme Court of the United States (wd:Q11144)           | Associate Justice of the Supreme Court of the United States (en)       |
| ...                                   | ...                        | ...                                                                               | ...                                                                    |
| Robert C. O'Brien (wd:Q7342642)       | Robert C. O'Brien (en)     | Special Envoy for Hostage Affairs (wd:Q85802513)                                  | Special Envoy for Hostage Affairs (en)                                 |
| Joseph Otting (wd:Q17306111)          | Joseph Otting (en)         | Comptroller of the Currency (wd:Q89682521)                                        | Comptroller of the Currency (en)                                       |
| Elaine Chao (wd:Q263322)              | Elaine Chao (en)           | United States Secretary of Transportation (wd:Q967737)                            | United States Secretary of Transportation (en)                         |
| Eugene Scalia (wd:Q16875330)          | Eugene Scalia (en)         | United States Secretary of Labor (wd:Q967750)                                     | United States Secretary of Labor (en)                                  |
| Betsy DeVos (wd:Q4898286)             | Betsy DeVos (en)           | United States Secretary of Education (wd:Q967762)                                 | United States Secretary of Education (en)                              |

The people appointed by Donald Trump and the positions for which they were appointed include:
- Emily Webster Murphy: Administrator of General Services
- Christopher C. Miller: Director of the National Counterterrorism Center
- Amul Roger Thapar: Judge of the United States Court of Appeals for the Sixth Circuit
- Brian Jack: White House Political Director
- Brett Kavanaugh: Associate Justice of the Supreme Court of the United States
- Robert C. O'Brien: Special Envoy for Hostage Affairs
- Joseph Otting: Comptroller of the Currency
- Elaine Chao: United States Secretary of Transportation
- Eugene Scalia: United States Secretary of Labor
- Betsy DeVos: United States Secretary of Education

This list is not exhaustive but provides examples of people appointed by Donald Trump and their respective positions.

[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 were the people appointed by Donald Trump, and which position was this for each person?

[Assistant]
To answer this question, I need to identify the entity for "Donald Trump" and then find the people he appointed along with the positions for which they were appointed.

Step 1: Search for the entity "Donald Trump" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Donald Trump, also known as President Donald Trump, President Donald J. Trump, President Trump, President Donald J Trump, Trump (wd:Q22686): president of the United States (2017–2021, 2025–present), real estate entrepreneur, chief executive officer, television producer, business magnate, businessperson, game show host, film producer, entrepreneur, politician, merchant, investor, writer, actor
2. Donald Trump, also known as Donald L. Trump, the other Donald Trump, Skip Trump, Dr. Donald L. Trump, Donald Lynn "Skip" Trump (wd:Q27947481): American physician, oncologist, physician, executive
3. Donald Trump (wd:Q5295230): 2011 song by Mac Miller
4. Donald Trump (wd:Q23001025): segment of an episode of Last Week Tonight
5. Donald Trump III, also known as Donald J. Trump III, Donald Trump, Donald J. Trump, Donald John Trump, Donald John Trump III (wd:Q23000825): son of Donald Trump Jr. (born 2009)
6. Donald Trump (wd:Q104421475): character Donald Trump in Anthony Davis's opera The Central Park Five
7. Donald Trump Jr., also known as Donald Trump, Jr., Donald Trump Junior, Don Jr, Don Jr., Don Trump Jr (wd:Q3713655): American businessman and reality television personality (born 1977), television presenter, businessperson, entrepreneur
8. second impeachment of Donald Trump, also known as 2021 efforts to remove Donald Trump from office, 2021 impeachment of Donald Trump, 2nd impeachment of Donald Trump, Donald J. Trump Impeachment, Donald Trump Impeachment (wd:Q104713215): 2021 United States presidential impeachment
9. second inauguration of Donald Trump, also known as Trump inauguration 2025, the 60th Presidential Inauguration, second Trump inauguration, second inauguration of Trump, inauguration of Donald Trump (wd:Q126616223): 2025 United States presidential inauguration
10. first presidency of Donald Trump, also known as Donald Trump I, First Donald Trump Administration, First Donald Trump presidency, First presidency of Donald Trump, First Trump presidency (wd:Q27809653): U.S. presidential administration from 2017 to 2021

[Assistant]
The entity for Donald Trump is wd:Q22686. Now I need to find the people appointed by Donald Trump and the positions for which they were appointed.

Step 2: I will search for properties related to "appointed" for the entity Donald Trump (wd:Q22686) to identify the relevant property that links Donald Trump to the people he appointed.

[Tool]
Top 10 property alternatives:
1. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as p/wdt): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
2. position held, also known as function, held position, holds position, job, office held (wd:P39 as p/wdt): subject currently or formerly holds the object position or public office
3. employer, also known as working at, working for, working place, works at, works for (wd:P108 as p/wdt): person or organization for which the subject works or worked, affiliation
4. occupation, also known as job, work, vocation, profession, employment (wd:P106 as p/wdt): occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39)
5. subject has role, also known as has role, role, subject had role, subject of qualified statement has role, subject of statement has role (wd:P2868 as p/wdt): role/generic identity of the item ("subject") in the context of ...acting roles, use P453 ("character role"). For persons, use P39.
6. significant person, also known as significant people, sigper, sign. person, relevant person, relevant people (wd:P3342 as p/wdt): person linked to the item in any possible way
7. 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
8. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as p/wdt): award or recognition received by a person, organization or creative work
9. 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
10. 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

No other items found

[Assistant]
The property "position held" (wdt:P39) is relevant for finding positions held by people. However, I need to find the people appointed by Donald Trump specifically. The direct property for "appointed by" or "appointed" does not appear in the top results for Donald Trump.

Step 3: I will try to find people who have a property indicating they were appointed by Donald Trump. This might be represented as a qualifier or a statement on the person's position held with an "appointed by" qualifier.

I will search for people who have a position held (wdt:P39) with a qualifier indicating "appointed by" Donald Trump (wd:Q22686). To do this, I will first search for the property "appointed by" to confirm its identifier.

[Tool]
Top 10 property alternatives:
1. appointed by, also known as named by (wd:P748 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): who appointed the person to the office, can be used as a qualifier
2. consecrator, also known as has consecrator, ordainer, ordained by, has ordainer, consecrated by (wd:P1598 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): bishop who presided as consecrator or co-consecrator of this bishop, significant person
3. nominated for, also known as nominee for, shortlisted for, selected for award, selected for, nominee for award (wd:P1411 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): award nomination received by a person, organisation or creative work (inspired from "award received" (Property:P166))
4. issued by, also known as issuing body, issuing agent, issuer, identifier issuing body, identifier issued by (wd:P2378 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): organisation or other agent that issues or allocates an identifier, code, classification number, etc.
5. founded by, also known as starter, co-founded by, started by, institutor, instituted by (wd:P112 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): founder or co-founder of this organization, religion, place or entity, creator
6. maintained by, also known as custodian, maintenance, maintainer, held by, administrator (wd:P126 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): person or organization in charge of keeping the subject (for instance an infrastructure) in functioning order
7. academic appointment (wd:P8413 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): this person has been appointed to a role within the given higher...stitution or department; distinct from employment or affiliation
8. authority, also known as executive authority, governed by, governing authority, governing body, has authority (wd:P797 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): entity having executive power on given entity
9. position held, also known as function, held position, holds position, job, office held (wd:P39 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): subject currently or formerly holds the object position or public office
10. winner, also known as nominee, prizewinner, titleholder, top dog, victor (wd:P1346 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): 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

[Assistant]
The property "appointed by" is wd:P748. This property can be used as a qualifier to indicate who appointed a person to an office.

Step 4: I will now construct a query to find people who have a position held (wdt:P39) with a qualifier "appointed by" (wdt:P748) Donald Trump (wd:Q22686). I will retrieve the people and the positions they held.

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

[Assistant]
The query failed because the SERVICE wikibase:label is not supported by the QLever SPARQL endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional clauses to get the labels.

I will now execute the corrected query.

[Tool]
Got 91 rows and 4 columns, showing the first 5 and last 5 rows below:
| person                                | personLabel                | position                                                                          | positionLabel                                                          |
| ------------------------------------- | -------------------------- | --------------------------------------------------------------------------------- | ---------------------------------------------------------------------- |
| Emily Webster Murphy (wd:Q42282721)   | Emily Webster Murphy (en)  | Administrator of General Services (wd:Q102259808)                                 | Administrator of General Services (en)                                 |
| Christopher C. Miller (wd:Q101423348) | Christopher C. Miller (en) | Director of the National Counterterrorism Center (wd:Q104720749)                  | Director of the National Counterterrorism Center (en)                  |
| Amul Roger Thapar (wd:Q4748925)       | Amul Roger Thapar (en)     | Judge of the United States Court of Appeals for the Sixth Circuit (wd:Q107282720) | Judge of the United States Court of Appeals for the Sixth Circuit (en) |
| Brian Jack (wd:Q86493775)             | Brian Jack (en)            | White House Political Director (wd:Q108485661)                                    | White House Political Director (en)                                    |
| Brett Kavanaugh (wd:Q4962244)         | Brett Kavanaugh (en)       | Associate Justice of the Supreme Court of the United States (wd:Q11144)           | Associate Justice of the Supreme Court of the United States (en)       |
| ...                                   | ...                        | ...                                                                               | ...                                                                    |
| Robert C. O'Brien (wd:Q7342642)       | Robert C. O'Brien (en)     | Special Envoy for Hostage Affairs (wd:Q85802513)                                  | Special Envoy for Hostage Affairs (en)                                 |
| Joseph Otting (wd:Q17306111)          | Joseph Otting (en)         | Comptroller of the Currency (wd:Q89682521)                                        | Comptroller of the Currency (en)                                       |
| Elaine Chao (wd:Q263322)              | Elaine Chao (en)           | United States Secretary of Transportation (wd:Q967737)                            | United States Secretary of Transportation (en)                         |
| Eugene Scalia (wd:Q16875330)          | Eugene Scalia (en)         | United States Secretary of Labor (wd:Q967750)                                     | United States Secretary of Labor (en)                                  |
| Betsy DeVos (wd:Q4898286)             | Betsy DeVos (en)           | United States Secretary of Education (wd:Q967762)                                 | United States Secretary of Education (en)                              |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The answer correctly lists multiple people appointed by Donald Trump along with their respective positions, which matches the SPARQL query results. The SPARQL query is well-constructed, using the qualifier 'appointed by' (P748) to filter appointments made by Donald Trump (Q22686) and retrieving both the person and position with their English labels. The answer also clarifies that the list is not exhaustive, which is appropriate given the query's LIMIT 100. Overall, the output is accurate, complete, and follows best practices for SPARQL queries and answer presentation.


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
SELECT ?person ?personLabel ?eloRating WHERE {
  ?person p:P1087 ?statement .
  ?statement ps:P1087 ?eloRating .
  ?statement pq:P585 ?date .
  FILTER ( ?date >= "2023-01-01T00:00:00Z" ^^ xsd:dateTime && ?date < "2023-02-01T00:00:00Z" ^^ xsd:dateTime ) OPTIONAL {
    ?person rdfs:label ?personLabel FILTER ( LANG( ?personLabel ) = "en" )
  }
}
ORDER BY DESC ( ?eloRating )
LIMIT 100

Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                              | personLabel                 | eloRating          |
| ----------------------------------- | --------------------------- | ------------------ |
| Yu Yangyi (wd:Q377866)              | Yu Yangyi (en)              | 2729 (xsd:decimal) |
| Francisco Vallejo Pons (wd:Q543256) | Francisco Vallejo Pons (en) | 2710 (xsd:decimal) |
| Hans Niemann (wd:Q59137809)         | Hans Niemann (en)           | 2706 (xsd:decimal) |
| Kirill Alekseenko (wd:Q18923582)    | Kirill Alekseenko (en)      | 2686 (xsd:decimal) |
| Amin Tabatabaei (wd:Q23710569)      | Amin Tabatabaei (en)        | 2686 (xsd:decimal) |
| ...                                 | ...                         | ...                |
| Nikolas Theodorou (wd:Q27533120)    | Nikolas Theodorou (en)      | 2590 (xsd:decimal) |
| Shamsiddin Vokhidov (wd:Q27533974)  | Shamsiddin Vokhidov (en)    | 2589 (xsd:decimal) |
| Viktor Erdős (wd:Q2524265)          | Viktor Erdős (en)           | 2588 (xsd:decimal) |
| Ivan Sokolov (wd:Q709548)           | Ivan Sokolov (en)           | 2588 (xsd:decimal) |
| Peter Michalík (wd:Q539326)         | Peter Michalík (en)         | 2588 (xsd:decimal) |

The SPARQL query retrieves the top 100 people with the highest Elo ratings in January 2023, including their Elo rating, ordered by Elo rating in descending order. It uses an optional clause to get the English labels of the people without using the unsupported SERVICE wikibase:label clause.

[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 were the top 100 people with the highest Elo ratings in January 2023? Please include their Elo rating, and order by their Elo rating in descending order.

[Assistant]
To answer this question, I need to find the top 100 people with the highest Elo ratings in January 2023. Elo rating is commonly used in chess and some other competitive games. So, I will first check if there is a property for Elo rating and if there is a way to filter by date (January 2023). Then, I will query for the top 100 people ordered by their Elo rating in descending order.

Step 1: Search for the property related to Elo rating in Wikidata.

[Tool]
Top 10 property alternatives:
1. Elo rating (wd:P1087 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): quantitative measure of one's game-playing ability, particularly in classical chess
2. review score, also known as rating (score), review rating (wd:P444 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): review score received by a creative work or other entity
3. rating (wd:P4271 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): qualifier to indicate a score given by the referenced source indicating the quality or completeness of the statement
4. number of reviews/ratings, also known as number of reviews, ratings, reviews, number of ratings (wd:P7887 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): qualifier on review score to state the number of user reviews, user ratings or critic reviews
5. dan/kyu rank, also known as go rank, wushu rank, shogi rank, renju rank, kendo rank (wd:P468 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): rank system used in several board games (e.g. go, shogi, renju), martial arts (e.g. judo, kendo, wushu) and some other games
6. ranking, also known as peak position, place, placing, position, rank (wd:P1352 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): subject's ordinal position as qualitatively evaluated relative to other members of a group
7. score method, also known as scoring system, score type, scoring metric, scoring method, score system (wd:P1443 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): qualifier of "points/goal scored by (P1363)"
8. league level above, also known as level above (league), promotion to (wd:P2499 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): the league above this sports league, next higher rank
9. series ordinal, also known as ordinal, unit number, sort order, sorting order, series number (wd:P1545 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): position of an item in its parent series (most frequently a 1-ba...d from "ranking" defined as a property for evaluating a quality)
10. military or police rank, also known as military rank, naval rank, police rank, rank (wd:P410 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): military or police rank achieved by a person (should usually hav...ualifier), or military or police rank associated with a position

[Assistant] The property for Elo rating is wd:P1087. Now I need to find people with Elo ratings and filter for the date January 2023. I will check how Elo ratings are recorded for people and if there is a way to filter by date. I will look for examples of people with Elo ratings.

[Tool]
Got 65,536 rows and 3 columns, showing the first 10 rows below:
| subject                           | property               | object             |
| --------------------------------- | ---------------------- | ------------------ |
| Magnus Carlsen (wd:Q106807)       | Elo rating (wdt:P1087) | 2832 (xsd:decimal) |
| Mikhail Tal (wd:Q102664)          | Elo rating (wdt:P1087) | 2525 (xsd:decimal) |
| Vasily Smyslov (wd:Q104148)       | Elo rating (wdt:P1087) | 2480 (xsd:decimal) |
| Bent Larsen (wd:Q108807)          | Elo rating (wdt:P1087) | 2415 (xsd:decimal) |
| Harika Dronavalli (wd:Q109147059) | Elo rating (wdt:P1087) | 2502 (xsd:decimal) |
| Jan-Krzysztof Duda (wd:Q10860652) | Elo rating (wdt:P1087) | 1834 (xsd:decimal) |
| Andor Lilienthal (wd:Q113167)     | Elo rating (wdt:P1087) | 2385 (xsd:decimal) |
| Anna Cramling (wd:Q104539456)     | Elo rating (wdt:P1087) | 2045 (xsd:decimal) |
| Jon Ludvig Hammer (wd:Q1152824)   | Elo rating (wdt:P1087) | 2596 (xsd:decimal) |
| Carlos Guimard (wd:Q1042965)      | Elo rating (wdt:P1087) | 2325 (xsd:decimal) |
| ...                               | ...                    | ...                |

[Assistant] Elo ratings are recorded as a direct property (wdt:P1087) of people, but these seem to be current or peak ratings without a date qualifier. To get Elo ratings specifically for January 2023, I need to check if there is a qualifier or statement with a date for Elo rating. I will check the statements for a specific person with Elo rating to see if there is a date qualifier. I will check the statements for Magnus Carlsen (wd:Q106807) as an example.

[Tool]
Got 1 row and 3 columns:
| subject                     | property               | object             |
| --------------------------- | ---------------------- | ------------------ |
| Magnus Carlsen (wd:Q106807) | Elo rating (wdt:P1087) | 2832 (xsd:decimal) |

[Assistant] The direct property wdt:P1087 for Elo rating does not have qualifiers or dates. I will check the statement property ps:P1087 for Magnus Carlsen to see if there are qualifiers including dates.

[Tool]
Got 173 rows and 3 columns, showing the first 10 rows below:
| subject                     | property             | object                                         |
| --------------------------- | -------------------- | ---------------------------------------------- |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-03428782-3A91-4A25-8C71-12F8260947A0 |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-049260CC-04C8-40C0-A6E8-7BEA2B1BCE0A |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-07DE7F45-8BAC-436C-850E-B06B0C411C33 |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-0B688EA4-412E-41F6-BFEA-4277E1857970 |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-0B83E356-575F-449B-B7D3-4D94626185C6 |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-0E3583C6-82D1-44F2-92AB-0D569C842F0E |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-0F1CE930-4560-42A6-ABDF-28258AF00BD5 |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-1030EE52-676D-4C10-8B46-2CCEC0652BDE |
| Magnus Carlsen (wd:Q106807) | Elo rating (p:P1087) | s:Q106807-112BE085-C5E6-4690-BE51-700B06F3A516 |
| ...                         | ...                  | ...                                            |

[Assistant] There are multiple statements for Elo rating for Magnus Carlsen with statement nodes. I will check the qualifiers for one of these statements to see if there is a date qualifier. I will check the qualifiers for the first statement node s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B.

[Tool]
Got 7 rows and 3 columns:
| subject                                        | property                 | object                                       |
| ---------------------------------------------- | ------------------------ | -------------------------------------------- |
| s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B | Elo rating (ps:P1087)    | 2882 (xsd:decimal)                           |
| s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B | point in time (pq:P585)  | 2014-05-01T00:00:00Z (xsd:dateTime)          |
| s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B | wikibase:rank            | wikibase:NormalRank                          |
| s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B | rdf:type                 | wikibase:Statement                           |
| s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B | prov:wasDerivedFrom      | ref:a13cb5b476f8dd365e5c22a686a3ad7791ac569f |
| s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B | Elo rating (psv:P1087)   | v:7eb179c14755792dee594f7aebfaf7a7           |
| s:Q106807-011A7F8B-2AAE-4C51-B47F-9ACD6F9C315B | point in time (pqv:P585) | v:e0e14f5714aca22bfe702639db968957           |

[Assistant] The Elo rating statements have a qualifier "point in time" (pq:P585) which indicates the date of the rating. This means I can filter Elo ratings by the point in time qualifier. Now I will write a query to get the top 100 people with the highest Elo ratings in January 2023, including their Elo rating, ordered by Elo rating in descending order.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct in its logic: it retrieves persons with Elo ratings (P1087) at a point in time (P585) in January 2023, orders them by Elo rating descending, and limits to 100 results. However, the query uses the SERVICE wikibase:label clause, which is not supported by the QLever endpoint and causes the query to fail with an HTTP 301 error. To fix this, remove the SERVICE wikibase:label clause and instead optionally retrieve labels using rdfs:label or similar properties. Also, ensure the date filtering is correct and that the query returns the person identifiers and their Elo ratings as required. After these adjustments, the query should run successfully and return the desired results.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the top 100 people with the highest Elo ratings in January 2023, including their Elo ratings, and orders them in descending order as requested. It uses the appropriate properties for Elo rating (P1087) and point in time (P585) with a correct date filter for January 2023. The optional clause for English labels is properly included without using the unsupported SERVICE wikibase:label. The query result matches the question requirements and is well-structured. No further refinement is necessary.


PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?item WHERE {
  ?item wdt:P2093 "Fred Smith" .
}

Got 10 rows and 1 column:
| item                                                                                |
| ----------------------------------------------------------------------------------- |
| Can Unions Be Sued for Following the Law? (wd:Q128767199)                           |
| A Better Hope: Resources for a C...sm, Democracy, and Postmodernity (wd:Q128929105) |
| Computerized Deep Structure Indexing System – A critical appraisal (wd:Q129292383)  |
| Term and cross reference structu...d Deep Structure Indexing System (wd:Q129294127) |
| FORTRAN programming standards (wd:Q130959327)                                       |
| Global effect of PEG-IFN-alpha a...gene expression in PBMC in vitro (wd:Q47190483)  |
| Northampton General Hospital. (wd:Q55115073)                                        |
| FOR THE PERIOD JANUARY-SEPTEMBER, 2003 (wd:Q58549138)                               |
| 24 Hour Service at Georgia Southern University: 1989–2007 (wd:Q58602086)            |
| Cardiac epithelioid hemangioendothelioma with 8-year follow-up (wd:Q80335651)       |

The items that have exactly the string "Fred Smith" listed under their author name string (property P2093) are:
- Can Unions Be Sued for Following the Law? (wd:Q128767199)
- A Better Hope: Resources for a C...sm, Democracy, and Postmodernity (wd:Q128929105)
- Computerized Deep Structure Indexing System – A critical appraisal (wd:Q129292383)
- Term and cross reference structu...d Deep Structure Indexing System (wd:Q129294127)
- FORTRAN programming standards (wd:Q130959327)
- Global effect of PEG-IFN-alpha a...gene expression in PBMC in vitro (wd:Q47190483)
- Northampton General Hospital. (wd:Q55115073)
- FOR THE PERIOD JANUARY-SEPTEMBER, 2003 (wd:Q58549138)
- 24 Hour Service at Georgia Southern University: 1989–2007 (wd:Q58602086)
- Cardiac epithelioid hemangioendothelioma with 8-year follow-up (wd:Q80335651)

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

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

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

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

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

[User] Which items have exactly the string "Fred Smith" listed under its author name string?

[Assistant]
To answer this question, I need to find items that have an author name string exactly matching "Fred Smith."

Step 1: Identify the property used for author name string in Wikidata. Usually, author name strings are represented by the property "author name string" (P2093).

Step 2: Query for items that have the property P2093 with the exact string "Fred Smith."

I will now construct and execute a SPARQL query to find such items.

[Tool]
Got 10 rows and 1 column:
| item                                                                                |
| ----------------------------------------------------------------------------------- |
| Can Unions Be Sued for Following the Law? (wd:Q128767199)                           |
| A Better Hope: Resources for a C...sm, Democracy, and Postmodernity (wd:Q128929105) |
| Computerized Deep Structure Indexing System – A critical appraisal (wd:Q129292383)  |
| Term and cross reference structu...d Deep Structure Indexing System (wd:Q129294127) |
| FORTRAN programming standards (wd:Q130959327)                                       |
| Global effect of PEG-IFN-alpha a...gene expression in PBMC in vitro (wd:Q47190483)  |
| Northampton General Hospital. (wd:Q55115073)                                        |
| FOR THE PERIOD JANUARY-SEPTEMBER, 2003 (wd:Q58549138)                               |
| 24 Hour Service at Georgia Southern University: 1989–2007 (wd:Q58602086)            |
| Cardiac epithelioid hemangioendothelioma with 8-year follow-up (wd:Q80335651)       |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves items that have exactly the string "Fred Smith" as their author name string (property P2093). The query is simple and precise, matching the user's question requirements. The answer lists the items with their labels and QIDs, which is helpful for understanding. No further refinement is necessary.


PREFIX ps: <http://www.wikidata.org/prop/statement/>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
SELECT ?person ?personLabel ?year WHERE {
  ?person wdt:P166 wd:Q55455135 .
  ?person p:P166 ?statement .
  ?statement ps:P166 wd:Q55455135 ; pq:P585 ?year .
  OPTIONAL {
    ?person rdfs:label ?personLabel FILTER ( LANG( ?personLabel ) = "en" )
  }
}
ORDER BY ?year

Got 60 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                                   | personLabel                    | year                                |
| ---------------------------------------- | ------------------------------ | ----------------------------------- |
| Manuel Alvar (wd:Q1891429)               | Manuel Alvar (en)              | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Ludwig Buisson (wd:Q1563209)             | Ludwig Buisson (en)            | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Christian Adolf Isermeyer (wd:Q15226554) | Christian Adolf Isermeyer (en) | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Richard Hoggart (wd:Q1534871)            | Richard Hoggart (en)           | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Nicolás Guillén (wd:Q334118)             | Nicolás Guillén (en)           | 1976-01-01T00:00:00Z (xsd:dateTime) |
| ...                                      | ...                            | ...                                 |
| Kaija Saariaho (wd:Q241432)              | Kaija Saariaho (en)            | 2015-01-01T00:00:00Z (xsd:dateTime) |
| Richard Dyer (wd:Q7325332)               | Richard Dyer (en)              | 2018-12-06T00:00:00Z (xsd:dateTime) |
| Patricio Guzmán (wd:Q2057315)            | Patricio Guzmán (en)           | 2019-03-12T00:00:00Z (xsd:dateTime) |
| Ibrahima Thioub (wd:Q1656025)            | Ibrahima Thioub (en)           | 2020-01-20T00:00:00Z (xsd:dateTime) |
| Trina Robbins (wd:Q458714)               | Trina Robbins (en)             | 2023-11-30T00:00:00Z (xsd:dateTime) |

The people who have received an honorary doctorate from Bordeaux Montaigne University (wd:Q55455135) along with the year they received it are listed below, ordered by year:

- Manuel Alvar (1974)
- Ludwig Buisson (1974)
- Christian Adolf Isermeyer (1974)
- Richard Hoggart (1974)
- Nicolás Guillén (1976)
- ...
- Kaija Saariaho (2015)
- Richard Dyer (2018)
- Patricio Guzmán (2019)
- Ibrahima Thioub (2020)
- Trina Robbins (2023)

[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 received an honorary doctorate from Bordeaux Montaigne University? Please also include the year they received the doctorate and order results by year

[Assistant]
To answer this question, I need to identify the entity for Bordeaux Montaigne University and the property for honorary doctorate recipients. Then I will query for people who have received an honorary doctorate from this university along with the year they received it, and order the results by year.

Step 1: Search for the entity "Bordeaux Montaigne University" in Wikidata.

[Tool]
Top 10 entity alternatives:
1. Bordeaux Montaigne University, also known as University of Bordeaux 3, Michel de Montaigne University (wd:Q13342): French university founded in 1971
2. honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135)
3. University of Bordeaux, also known as UB, u-bordeaux.fr (wd:Q13344): university in France
4. Bordeaux Segalen University, also known as University of Bordeaux 2 (wd:Q13340): former French university abolished and replaced in 2014
5. IUT Bordeaux Montaigne, also known as University Institute of Journalism Technology of Bordeaux, IUT Michel de Montaigne (wd:Q3152663)
6. Bordeaux University Foundation (wd:Q117197454)
7. University of Bordeaux (wd:Q20791505): French university from 1896 to 1971
8. University of Bordeaux 1 (wd:Q13338): former French university founded in 1971, replaced and abolished in 2014
9. prix Montaigne de Bordeaux (wd:Q16671140): French literary prize
10. Bordeaux University Law Library (wd:Q97667454): University law and economics library

[Assistant] The entity for Bordeaux Montaigne University is wd:Q13342. Now I need to find the property related to honorary doctorate recipients. I will search for properties related to "honorary doctorate" or "honorary degree" to find the relevant property.

[Tool]
Top 10 property alternatives:
1. conferred by, also known as presented by, officiated by, issued by, granted by, given by (wd:P1027 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): person or organization who grants an award, certification, grant, or role
2. award received, also known as win, winner of, recognition title, received award, prize received (wd:P166 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): award or recognition received by a person, organization or creative work
3. professorship, also known as professorship position, named professorship, named chair, endowed professorship, chair (wd:P803 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): professorship position held by this academic person, position held
4. student of, also known as professor, studied under, pupil of, tutor, mentor (wd:P1066 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): person who has taught this person, significant person
5. category for employees of the organization, also known as Academic staff, category for employees of the organisation, Professor (wd:P4195 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): Wikimedia category for people who work or worked at this organization
6. UGentMemorialis professor ID, also known as UGent Memorialis ID, UGentMemorialis ID (wd:P3159 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): identifier for a professor at the Ghent University
7. doctoral advisor, also known as PhD supervisor, promotor, supervisor, thesis advisor, advisor (wd:P184 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): person who supervised the doctorate or PhD thesis of the subject, supervised by, student of
8. commemorates, also known as monument to, memorial to, memorializes, marks, in memory of (wd:P547 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): what the place, monument, memorial, or holiday, commemorates, main subject
9. doctoral student, also known as Ph.D. student, supervisee, supervised doctoral student, doctoral candidate (wd:P185 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): doctoral student(s) of a professor, student
10. rector, also known as chancellor, university president, Rector (academia), president (wd:P1075 as psv/pqn/prv/wdtn/pr/ps/p/pqv/prn/wdt/psn/pq): senior official in an educational institution., director / manager, chairperson

[Assistant] The property "award received" (wdt:P166) is relevant for honorary doctorate recipients since honorary doctorates are awards. Now I will query for people who have received an honorary doctorate from Bordeaux Montaigne University (wd:Q13342) using the property "award received" (wdt:P166) and filter for honorary doctorates conferred by this university. I will also try to get the year they received the doctorate and order the results by year.

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

[Assistant] The query failed due to the use of SERVICE wikibase:label which is not supported by the endpoint. I will rewrite the query without using SERVICE wikibase:label and instead use optional rdfs:label for the person. Also, I will check if the honorary doctorate is linked directly to the university Bordeaux Montaigne University. I will try to find the property that links the honorary doctorate award to the university.

[Tool]
Got 81 rows and 3 columns, showing the first 10 rows below:
| subject                                                                | property                                          | object                                            |
| ---------------------------------------------------------------------- | ------------------------------------------------- | ------------------------------------------------- |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | instance of (wdt:P31)                             | award (wd:Q618779)                                |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | country (wdt:P17)                                 | France (wd:Q142)                                  |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | subclass of (wdt:P279)                            | honorary doctorate (wd:Q11415564)                 |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | topic's main category (wdt:P910)                  | wd:Q97402146                                      |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | conferred by (wdt:P1027)                          | Bordeaux Montaigne University (wd:Q13342)         |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | category for recipients of this award (wdt:P2517) | wd:Q97402146                                      |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | wdt:P31279                                        | award (wd:Q618779)                                |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | schema:description                                | a francia egyetem kitüntető címe (hu)             |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | schema:name                                       | æresdoktor ved Université Bordeaux Montaigne (nb) |
| honorary doctorate of the Bordeaux Montaigne University (wd:Q55455135) | rdf:type                                          | wikibase:Item                                     |
| ...                                                                    | ...                                               | ...                                               |

[Assistant] The honorary doctorate of Bordeaux Montaigne University (wd:Q55455135) is an instance of award (wd:Q618779) and is conferred by Bordeaux Montaigne University (wd:Q13342). Now I will query for people who have received this honorary doctorate (wd:Q55455135) as an award (wdt:P166) and get the year they received it (qualifier P585). I will also include the person's label and order the results by year.

[Tool]
Got 60 rows and 3 columns, showing the first 5 and last 5 rows below:
| person                                   | personLabel                    | year                                |
| ---------------------------------------- | ------------------------------ | ----------------------------------- |
| Manuel Alvar (wd:Q1891429)               | Manuel Alvar (en)              | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Ludwig Buisson (wd:Q1563209)             | Ludwig Buisson (en)            | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Christian Adolf Isermeyer (wd:Q15226554) | Christian Adolf Isermeyer (en) | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Richard Hoggart (wd:Q1534871)            | Richard Hoggart (en)           | 1974-01-01T00:00:00Z (xsd:dateTime) |
| Nicolás Guillén (wd:Q334118)             | Nicolás Guillén (en)           | 1976-01-01T00:00:00Z (xsd:dateTime) |
| ...                                      | ...                            | ...                                 |
| Kaija Saariaho (wd:Q241432)              | Kaija Saariaho (en)            | 2015-01-01T00:00:00Z (xsd:dateTime) |
| Richard Dyer (wd:Q7325332)               | Richard Dyer (en)              | 2018-12-06T00:00:00Z (xsd:dateTime) |
| Patricio Guzmán (wd:Q2057315)            | Patricio Guzmán (en)           | 2019-03-12T00:00:00Z (xsd:dateTime) |
| Ibrahima Thioub (wd:Q1656025)            | Ibrahima Thioub (en)           | 2020-01-20T00:00:00Z (xsd:dateTime) |
| Trina Robbins (wd:Q458714)               | Trina Robbins (en)             | 2023-11-30T00:00:00Z (xsd:dateTime) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves people who have received an honorary doctorate from Bordeaux Montaigne University, along with the year they received it, and orders the results by year as requested. The query uses the appropriate properties (wdt:P166 for award received and pq:P585 for the point in time) and includes optional English labels for readability. The execution result shows a reasonable number of results with correct ordering by year. The answer is complete and well-formatted. No further refinement is necessary.


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
SELECT ?praenomen ?praenomenLabelEn ?praenomenLabelLa WHERE {
  ?praenomen wdt:P31 wd:Q1240901 .
  ?praenomen rdfs:label ?praenomenLabelEn .
  FILTER ( LANG( ?praenomenLabelEn ) = "en" ) OPTIONAL {
    ?praenomen rdfs:label ?praenomenLabelLa .
    FILTER ( LANG( ?praenomenLabelLa ) = "la" )
  }
}

Got 82 rows and 3 columns, showing the first 5 and last 5 rows below:
| praenomen               | praenomenLabelEn | praenomenLabelLa |
| ----------------------- | ---------------- | ---------------- |
| Fausta (wd:Q100325240)  | Fausta (en)      | Fausta (la)      |
| Potitus (wd:Q100742089) | Potitus (en)     | Potitus (la)     |
| Paculla (wd:Q100785430) | Paculla (en)     | Paculla (la)     |
| Hispala (wd:Q100933398) | Hispala (en)     | Hispala (la)     |
| Novius (wd:Q100934274)  | Novius (en)      | Novius (la)      |
| ...                     | ...              | ...              |
| Herius (wd:Q94301903)   | Herius (en)      | Herius (la)      |
| Pacuvius (wd:Q94554559) | Pacuvius (en)    | Pacuvius (la)    |
| Numa (wd:Q98346041)     | Numa (en)        | Numa (la)        |
| Ancus (wd:Q98660919)    | Ancus (en)       | Ancus (la)       |
| Sthenius (wd:Q99228944) | Sthenius (en)    | Sthenius (la)    |

I refined the query to retrieve praenomina in ancient Rome with their English labels and optional native labels in Latin (language code 'la'). This provides the native Latin labels (e.g., 'Octavus') if available alongside the English labels.

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

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

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

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

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

[User] What are the praenomen in anicent Rome, and what are their native labels (e.g. 'Octavus'), if available?

[Assistant] To answer the question about praenomen in ancient Rome and their native labels, I will first search for the entity "praenomen" in Wikidata to identify the relevant item. Then, I will look for properties or related entities that describe the praenomen and their native labels.

[Tool]
Top 10 entity alternatives:
1. praenomen, also known as first names of Romans, Roman first names, Roman given names, Roman praenomen (wd:Q1240901): first aspect of a personal name ancient Rome
2. praenomen, also known as Roman praenomen (wd:P2358): standard part of the name of a Roman (and related cultures), link to items for specific Roman praenomen only
3. Marcus, also known as M., Marcus (praenomen), Marcvs (wd:Q6757977): ancient Roman praenomen
4. Tiberius, also known as Ti., Tib., Tiberius (praenomen) (wd:Q21485162): ancient Roman praenomen
5. A Note on the use of the Praenomen (wd:Q56209493)

[Assistant]
The entity "praenomen" in ancient Rome is represented by wd:Q1240901, which refers to the first aspect of a personal name in ancient Rome. There is also a property wd:P2358 related to the standard part of the name of a Roman (praenomen).

Next, I will query for the specific praenomen names (Roman first names) and their native labels if available. I will look for items that are instances of or subclasses of "praenomen" (wd:Q1240901) and retrieve their labels and native labels.

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

[Assistant] The query failed due to the use of SERVICE wikibase:label, which is not supported by the endpoint. I will rewrite the query to get the English labels without using SERVICE wikibase:label and instead use rdfs:label with a filter for English. I will also check if the property P2358 (native label) is correct and available for these items.

[Tool]
Got 82 rows and 3 columns, showing the first 5 and last 5 rows below:
| praenomen               | praenomenLabel | nativeLabel |
| ----------------------- | -------------- | ----------- |
| Fausta (wd:Q100325240)  | Fausta (en)    |             |
| Potitus (wd:Q100742089) | Potitus (en)   |             |
| Paculla (wd:Q100785430) | Paculla (en)   |             |
| Hispala (wd:Q100933398) | Hispala (en)   |             |
| Novius (wd:Q100934274)  | Novius (en)    |             |
| ...                     | ...            | ...         |
| Herius (wd:Q94301903)   | Herius (en)    |             |
| Pacuvius (wd:Q94554559) | Pacuvius (en)  |             |
| Numa (wd:Q98346041)     | Numa (en)      |             |
| Ancus (wd:Q98660919)    | Ancus (en)     |             |
| Sthenius (wd:Q99228944) | Sthenius (en)  |             |

[Assistant] I found 82 items that are instances of praenomen (Roman first names) with their English labels. However, the nativeLabel property (wdt:P2358) is mostly empty for these items. I will now check if there is another property that might represent the native label or the original Latin form of these praenomina. I will search for properties related to "native label" or "Latin" for one of these praenomina, for example, "Octavus" if available, or a generic praenomen.

[Tool]
Top 10 property alternatives:
1. instance of, also known as unitary element of class, has type, unique individual of, type, item of type (wd:P31 as p/wdt): that class of which this subject is a particular example and mem...is a subclass of mountain (and an instance of volcanic landform)
2. subclass of, also known as rdfs:subClassOf, sort of, subcategory of, subgenre of, subtype of (wd:P279 as p/wdt): this item is a subclass (subset) of that item; ALL instances of ...stance of volcanic landform); Everest is an instance of mountain
3. topic's main category, also known as article's main category, category, category for this topic, catmain, has category (wd:P910 as p/wdt): main Wikimedia category
4. Wikidata property, also known as main property, property for this item, property, has property (wd:P1687 as p/wdt): main Wikidata property for this item
5. part of, also known as of, within, system of, subsystem of, subgroup of (wd:P361 as p/wdt): object of which the subject is a part (if this subject is alread... of "has part" (P527, see also "has parts of the class" (P2670))
6. Encyclopædia Britannica Online ID, also known as Encyclopaedia Britannica Online ID, Encyclopedia Britannica Online ID, EBID, EB ID, Britannica online ID (wd:P1417 as p/wdt): identifier for an article in the online version of Encyclopædia Britannica
7. TDKIV term ID (wd:P5398 as p/wdt): identifier for a term used by the Czech Terminology Database of ...e (TDKIV) produced by the National Library of the Czech Republic
8. facet of, also known as subitem of, topic of, subtopic of, subject in, section in (wd:P1269 as p/wdt): topic of which this item is an aspect; item that offers a broader perspective on the same topic
9. followed by, also known as continued as, then, successor, succeeded by, sequel is (wd:P156 as p/wdt): immediately following item in a series of which the subject is a...een replaced, e.g. political offices, use "replaced by" (P1366)]
10. has list, also known as list, topic's list, topic list, related list, list for this topic (wd:P2354 as p/wdt): Wikimedia list related to this subject

No other items found

[Assistant]
There is no specific property directly related to "native label" or "Latin label" for the praenomen entity (wd:Q1240901). The property P2358, which I initially considered, does not seem to be widely used for native labels of praenomina.

Given this, I will provide the list of praenomina (Roman first names) with their English labels as the best available information. The native labels (e.g., 'Octavus') are not consistently available in the data.

I will now prepare the final SPARQL query to return the praenomina and their English labels.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query correctly retrieves praenomen instances and their English labels, which answers part of the question. However, the user also asked for native labels (e.g., 'Octavus'), which are not consistently available in the current results. To improve, the query should attempt to retrieve labels in the original Latin language or other native languages as optional values, rather than filtering only for English labels. This can be done by including optional clauses for labels in other languages (e.g., Latin) and returning them alongside the English labels. This refinement would better address the user's request for native labels.

[Assistant] Thank you for the feedback. To better address the user's request for native labels, I will refine the query to retrieve the English labels as well as optional labels in Latin (language code "la") for the praenomina. This way, we can provide the native Latin labels if available alongside the English labels. I will update the query accordingly.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the praenomina in ancient Rome (instances of wd:Q1240901) along with their English labels and optional native Latin labels. The use of FILTER to get English and Latin labels is appropriate, and the OPTIONAL clause ensures Latin labels are included if available. The query returns a comprehensive list of 82 items, which is reasonable for this category. The answer clearly explains the approach and provides example results, fulfilling the user's request accurately and completely. No further refinement is necessary.