PREFIX pq: <http://www.wikidata.org/prop/qualifier/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
SELECT ?senator ?senatorLabel WHERE {
  ?senator wdt:P39 wd:Q486282 .
  ?senator p:P39 ?statement .
  ?statement pq:P580 ?start .
  ?statement pq:P582 ?end .
  FILTER ( ?start <= '2013-01-01T00:00:00Z' ^^ xsd:dateTime && ?end >= '2013-12-31T23:59:59Z' ^^ xsd:dateTime ) .
  ?senator wdt:P1376 wd:Q1370 .
  OPTIONAL {
    ?senator rdfs:label ?senatorLabel .
    FILTER ( LANG( ?senatorLabel ) = 'en' )
  }
}

Despite multiple attempts, the SPARQL queries did not yield any results for the Virginia senators in 2013. It is possible that the data is either incomplete or not structured in a way that can be queried effectively with the current approach.

[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 virginia senators 2013?

[Assistant]
To find out who the Virginia senators were in 2013, we need to identify the following:
1. The entity for Virginia.
2. The property for senators or representatives.
3. The time frame for 2013.

Let's start by finding the entity for Virginia.

[Tool]
Got 83 rows and 1 column, showing the first 5 and last 5 rows below:
| ?virginia                |
| ------------------------ |
| Virginia (wd:Q1027286)   |
| Virginia (wd:Q103908392) |
| Virginia (wd:Q104864702) |
| Virginia (wd:Q105731200) |
| Virginia (wd:Q105731207) |
| ...                      |
| Virginia (wd:Q97482399)  |
| Virginia (wd:Q98149339)  |
| Virginia (wd:Q98208996)  |
| Virginia (wd:Q986681)    |
| Virginia (wd:Q99673513)  |

[Assistant]
There are multiple entities with the label "Virginia". We need to filter these to find the correct one, which is likely the U.S. state of Virginia. Let's check the type of each entity to identify the correct one.

We will use the `wdt:P31` property, which typically denotes the instance of something, to filter for the U.S. state.

[Tool]
Got 1 row and 1 column:
| ?virginia           |
| ------------------- |
| Virginia (wd:Q1370) |

[Assistant]
We have identified the correct entity for Virginia, which is `wd:Q1370`. Next, we need to find the property that links Virginia to its senators. The property for senators is likely `wdt:P691` (U.S. Senate seat) or `wdt:P39` (position held).

Let's check if `wdt:P39` can help us find the senators of Virginia.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that `wdt:P39` did not yield any results for Virginia's senators. Let's try another property, such as `wdt:P691`, which specifically refers to U.S. Senate seats.

We will also need to filter the results to ensure they are relevant to the year 2013. We can use the `pq:P580` (start time) and `pq:P582` (end time) qualifiers to filter the time frame.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that `wdt:P691` also did not yield any results. Let's try another approach by looking for the property that directly links a person to their position as a senator. The property `wdt:P39` (position held) might be more appropriate if we specify the position as a U.S. Senator.

We will also need to filter the results to ensure they are relevant to the year 2013. We can use the `pq:P580` (start time) and `pq:P582` (end time) qualifiers to filter the time frame.

Let's try this approach.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that even this approach did not yield any results. Let's try a different strategy by directly searching for the senators of Virginia and then filtering by the year 2013.

We will use the property `wdt:P39` (position held) and the specific position `wd:Q486282` (United States Senator). We will also use the `wdt:P1376` property to link the senator to Virginia.

Let's try this approach again, but this time we will also include the `rdfs:label` to get the names of the senators for better verification.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that our current approach is still not yielding any results. Let's take a step back and verify the properties and entities we are using.

We will try a more direct approach by searching for the senators of Virginia and then filtering by the year 2013. We will use the `wdt:P39` property to find the position held and the `wdt:P1376` property to link the senator to Virginia.

Let's try a simpler query to see if we can find any senators of Virginia without the time filter first.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that even the simpler query without the time filter is not yielding any results. This suggests that the data might not be structured in the way we are querying it.

Let's try a different approach by searching for the senators of Virginia using the `wdt:P39` property and the specific position `wd:Q486282` (United States Senator), but this time we will not use the `wdt:P1376` property to link the senator to Virginia. Instead, we will look for the senators and then manually verify if they are from Virginia.

Let's try this approach.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that even this approach is not yielding any results. Given the complexity and the lack of results, it's possible that the data for Virginia senators in 2013 is not structured in a straightforward way in Wikidata.

Let's try a different approach by searching for the U.S. Senate seats for Virginia and then finding the people who held those positions in 2013.

We will use the property `wdt:P361` (part of) to find the U.S. Senate seats for Virginia and then use `wdt:P39` to find the people who held those positions.

Let's try this approach.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that even this approach is not yielding any results. Given the complexity and the lack of results, it's possible that the data for Virginia senators in 2013 is not structured in a straightforward way in Wikidata.

Let's try a different approach by searching for the U.S. Senate seats for Virginia and then finding the people who held those positions in 2013 using the `wdt:P39` property and the specific position `wd:Q486282` (United States Senator). We will also use the `pq:P580` and `pq:P582` qualifiers to filter the time frame.

We will use the `wdt:P361` property to find the U.S. Senate seats for Virginia and then use `wdt:P39` to find the people who held those positions.

Let's try this approach again, but this time we will explicitly filter for the time period 2013.

[Tool] Got no rows and 2 columns

[Assistant]
Given that multiple approaches have not yielded any results, it's possible that the data for Virginia senators in 2013 is either not present or not structured in a way that can be easily queried using the properties and entities we have tried.

Let's try one more approach by searching for the U.S. Senate seats for Virginia and then finding the people who held those positions in 2013 using a different set of properties and qualifiers. We will use the `wdt:P39` property and the specific position `wd:Q486282` (United States Senator), and we will also use the `pq:P580` and `pq:P582` qualifiers to filter the time frame.

We will also try to find the U.S. Senate seats for Virginia using the `wdt:P361` property and then use `wdt:P39` to find the people who held those positions.

Let's try this final approach.

[Tool] Got no rows and 2 columns

[Assistant]
Despite multiple attempts, we have not been able to find the Virginia senators for 2013 using the available properties and entities in Wikidata. It's possible that the data is either incomplete or not structured in a way that can be queried effectively with the current approach.

Given the lack of results, I will provide my best attempt at a SPARQL query and explain why it did not yield the expected results.

### Best Attempt at SPARQL Query
```sparql
SELECT ?senator ?senatorLabel WHERE {
  ?senator wdt:P39 wd:Q486282 .
  ?senator p:P39 ?statement .
  ?statement pq:P580 ?start .
  ?statement pq:P582 ?end .
  FILTER(?start <= '2013-01-01T00:00:00Z'^^xsd:dateTime && ?end >= '2013-12-31T23:59:59Z'^^xsd:dateTime) .
  ?senator wdt:P1376 wd:Q1370 .
  OPTIONAL { ?senator rdfs:label ?senatorLabel . FILTER(LANG(?senatorLabel) = 'en') }
}
```

### Explanation
1. **Entities and Properties**:
   - `wd:Q1370` is the entity for Virginia.
   - `wd:Q486282` is the entity for the position of United States Senator.
   - `wdt:P39` is the property for the position held.
   - `wdt:P1376` is the property for the country or state represented.
   - `pq:P580` and `pq:P582` are qualifiers for the start and end times of the position held.

2. **Query Structure**:
   - We searched for entities that hold the position of United States Senator (`wd:Q486282`).
   - We filtered the results to include only those who represented Virginia (`wd:Q1370`).
   - We used the `pq:P580` and `pq:P582` qualifiers to filter the time frame to 2013.

3. **Results**:
   - Despite the logical structure of the query, it did not return any results, suggesting that the data for Virginia senators in 2013 is either not present or not structured in a way that can be queried using the properties and entities we have tried.

### Conclusion
Given the lack of results, it is likely that the data for Virginia senators in 2013 is not available or not structured in a way that can be queried effectively with the current approach. Therefore, I am unable to provide a satisfactory SPARQL query to answer the question.

I will now call the `cancel` function to indicate that I could not find a satisfactory SPARQL query.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?governor WHERE {
  wd:Q812 wdt:P6 ?governor .
}

Ron DeSantis

[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 is the state governor of florida?

[Assistant] To answer the question "Who is the state governor of Florida?", we need to identify the entity for Florida and the property for the governor. Let's start by searching for the entity representing Florida.

[Tool]
Got 1 row and 1 column:
| ?florida          |
| ----------------- |
| Florida (wd:Q812) |

[Assistant]
We have identified the entity for Florida as `wd:Q812`. Next, we need to find the property that represents the governor of a state. We can use the property `wdt:P35` which typically represents the head of government or governor.

Let's construct a query to find the current governor of Florida.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the property `wdt:P35` did not yield any results for the governor of Florida. Let's try another property that might represent the governor, such as `wdt:P6` which is often used for the leader or head of government.

Let's update our query to use `wdt:P6` instead.

[Tool]
Got 1 row and 1 column:
| ?governor                  |
| -------------------------- |
| Ron DeSantis (wd:Q3105215) |

[Assistant]
We have found that the current governor of Florida is Ron DeSantis, represented by the entity `wd:Q3105215`.

Now, let's construct the final SPARQL query to answer the user's question.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?school WHERE {
  wd:Q127417 wdt:P69 ?school
}

Got 3 rows and 1 column:
| ?school                                                            |
| ------------------------------------------------------------------ |
| United States Army Command and General Staff College (wd:Q1115050) |
| Milwaukee High School of the Arts (wd:Q6861744)                    |
| United States Military Academy (wd:Q9219)                          |

The school Douglas MacArthur attended is the United States Military Academy (West Point).

[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 school did douglas macarthur go to?

[Assistant] To answer the question about the school Douglas MacArthur attended, I need to identify the entity for Douglas MacArthur and then find the property that indicates his education or alma mater. Let's start by searching for the entity representing Douglas MacArthur.

[Tool]
Got 3 rows and 1 column:
| ?entity                          |
| -------------------------------- |
| Douglas MacArthur (wd:Q127417)   |
| Douglas MacArthur (wd:Q47509083) |
| Douglas MacArthur (wd:Q47512806) |

[Assistant]
I found three entities with the label "Douglas MacArthur." To ensure we select the correct one, I will check the descriptions or other details of these entities. Specifically, I will look for the most prominent and well-known Douglas MacArthur, who was a famous American general.

Let's check the descriptions of these entities to confirm which one is the correct one.

[Tool]
SPARQL execution failed:
Tried to allocate 25.9 GB, but only 20 GB were available. Clear the cache or allow more memory for QLever during startup

[Assistant]
It seems the query to fetch the descriptions caused a memory issue. Instead, I will try to filter the entities based on their types or other properties to identify the correct Douglas MacArthur. Specifically, I will look for the entity that has a notable position or role, such as being a military officer.

Let's try to filter the entities by checking if they have a property indicating a military career.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that none of the entities with the label "Douglas MacArthur" have the property `P106` (occupation) set to `Q8203` (military personnel). This might be due to the specific property or the way the data is structured. Let's try another approach by checking if any of these entities have a notable position or role that indicates they are the famous American general.

I will check if any of these entities have a notable position or role, such as being a general or a commander.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that none of the entities with the label "Douglas MacArthur" have the property `P39` (position held) set to `Q11694` (Supreme Commander for the Allied Powers). This suggests that the data might be structured differently or the specific properties I'm checking might not be the right ones.

Let's try a different approach by looking at the properties and values of the entities to see if we can identify the correct one. I will fetch the properties and values of the entities to get more context.

[Tool]
Got 100 rows and 3 columns, showing the first 5 and last 5 rows below:
| ?entity                        | ?property          | ?value                                          |
| ------------------------------ | ------------------ | ----------------------------------------------- |
| Douglas MacArthur (wd:Q127417) | schema:description | "American Field Marshal/General of the Army"@ms |
| Douglas MacArthur (wd:Q127417) | schema:description | "americký generál a filipínský polní maršál"@cs |
| Douglas MacArthur (wd:Q127417) | schema:description | "Amerikaans officier (1880-1964)"@nl            |
| Douglas MacArthur (wd:Q127417) | schema:description | "Amerikalı general (1880 – 1964)"@tr            |
| Douglas MacArthur (wd:Q127417) | schema:description | "amerikansk general og krigshelt"@nb            |
| ...                            | ...                | ...                                             |
| Douglas MacArthur (wd:Q127417) | schema:name        | "Douglas MacArthur"@szy                         |
| Douglas MacArthur (wd:Q127417) | schema:name        | "Douglas MacArthur"@tl                          |
| Douglas MacArthur (wd:Q127417) | schema:name        | "Douglas MacArthur"@tr                          |
| Douglas MacArthur (wd:Q127417) | schema:name        | "Douglas MacArthur"@vi                          |
| Douglas MacArthur (wd:Q127417) | schema:name        | "Douglas MacArthur"@war                         |

[Assistant]
From the results, it is clear that the entity `wd:Q127417` has multiple descriptions and names that indicate it is the famous American general Douglas MacArthur. Therefore, I will use this entity to find out which school he attended.

Next, I will look for the property that indicates his education or alma mater. The property `P69` (educated at) is commonly used for this purpose.

Let's execute a query to find the school Douglas MacArthur attended.

[Tool]
Got 3 rows and 1 column:
| ?school                                                            |
| ------------------------------------------------------------------ |
| United States Army Command and General Staff College (wd:Q1115050) |
| Milwaukee High School of the Arts (wd:Q6861744)                    |
| United States Military Academy (wd:Q9219)                          |

[Assistant]
The query returned three schools where Douglas MacArthur was educated:

1. United States Army Command and General Staff College (wd:Q1115050)
2. Milwaukee High School of the Arts (wd:Q6861744)
3. United States Military Academy (wd:Q9219)

Among these, the United States Military Academy, also known as West Point, is the most well-known institution where Douglas MacArthur received his primary military education.

Therefore, the school Douglas MacArthur attended is the United States Military Academy.

Here is the final SPARQL query and answer:

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?actor ?actorLabel ?role ?roleLabel WHERE {
  wd:Q192316 wdt:P161 ?actor .
  OPTIONAL {
    ?actor wdt:P181 ?role .
  }
  OPTIONAL {
    ?role rdfs:label ?roleLabel .
    FILTER ( LANG( ?roleLabel ) = 'en' )
  }
  OPTIONAL {
    ?actor rdfs:label ?actorLabel .
    FILTER ( LANG( ?actorLabel ) = 'en' )
  }
}
LIMIT 10

Despite multiple attempts, I was unable to find a satisfactory SPARQL query that returns the actor who played Donna in The West Wing. The data for this specific character and show might not be structured in a way that is easily queryable with the current properties and relationships in the Wikidata knowledge graph.

[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 played donna on west wing?

[Assistant]
To answer the question "Who played Donna on West Wing?", I need to identify the character "Donna" from the TV show "The West Wing" and then find out which actor played this role. Let's start by searching for the TV show "The West Wing" and the character "Donna" in the knowledge graph.

I will first look for the entity representing "The West Wing" and then find the character "Donna" associated with it.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that my initial query did not return any results. I might need to refine the search for "The West Wing" to ensure I am finding the correct entity. Let's try a more direct approach by searching for the exact title "The West Wing" and then look for the character "Donna" associated with it.

I will also check for alternative titles or common names that might be used for the show.

[Tool]
SPARQL execution failed:
HTTPSConnectionPool(host='qlever.cs.uni-freiburg.de', port=443): Read timed out. (read timeout=30)

[Assistant]
It looks like the query timed out, which might be due to the server being busy or the query being too broad. Let's try a more specific query to find the entity for "The West Wing" by using the known identifier for the show, which is `wd:Q192316`.

Once we have the show, we can look for the character "Donna" and the actor who played her.

Let's proceed with this more specific query.

[Tool] Got no rows and 3 columns

[Assistant]
It seems that my query did not return any results, which suggests that the character "Donna" might not be directly linked to the show "The West Wing" in the way I expected. Let's try a different approach by looking for the cast of "The West Wing" and then filtering for the character "Donona".

I will first retrieve the cast of "The West Wing" and then filter for the character "Donna".

[Tool] Got no rows and 4 columns

[Assistant]
It seems that even this approach did not yield any results. Let's take a step back and verify the properties and relationships used in the query.

I will first confirm that "The West Wing" has a cast and then look for the character "Donna" in a more straightforward manner. I will also check if there are any alternative properties or relationships that might be used to link actors to their characters.

Let's start by confirming the cast of "The West Wing" and then filter for the character "Donna".

[Tool] Got no rows and 4 columns

[Assistant]
It appears that the property `wdt:P181` (role in film or TV show) is not yielding any results for "The West Wing". This suggests that the relationship between actors and their characters might be represented differently in the knowledge graph.

Let's try a different approach by looking for the cast of "The West Wing" using the property `wdt:P161` (cast member) and then manually inspect the results to see if we can find the character "Donna" and the corresponding actor.

I will retrieve the cast members of "The West Wing" and their roles, and then filter for the character "Donna" in the results.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that even this approach is not yielding any results. Let's take a different strategy by directly searching for the character "Donna" in the context of "The West Wing" and then finding the actor associated with that character.

I will search for the character "Donna" and then look for the actor who played her.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that the character "Donna" is not directly linked to "The West Wing" through the properties and relationships I've been using. Let's try a more direct approach by searching for the character "Donna Moss" (the full name of the character) and then finding the actor associated with her.

I will search for the character "Donna Moss" and then look for the actor who played her.

[Tool] Got no rows and 4 columns

[Assistant]
It seems that even searching for the full name "Donna Moss" did not yield any results. This suggests that the character "Donna" might not be directly linked to "The West Wing" in the way I expected, or the data might be structured differently.

Let's try a different approach by searching for the actor who played "Donna" directly. I will search for the actor and then verify if they played the character "Donna" in "The West Wing".

I will start by searching for the actor who is known for playing "Donna" in "The West Wing" and then verify the character and the show.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that even this approach did not yield any results. Given the complexity and the lack of success in finding the character "Donna" and the actor who played her, it's possible that the data for this specific character and show is not structured in a way that is easily queryable with the current properties and relationships.

Let's try one more approach by searching for the actor who is known for playing a significant role in "The West Wing" and then manually inspecting the results to see if we can find the character "Donna".

I will retrieve the cast members of "The West Wing" and their roles, and then manually inspect the results to see if we can find the character "Donna" and the corresponding actor.

[Tool] Got no rows and 4 columns

[Assistant]
Given that multiple approaches have not yielded any results, it seems that the data for the character "Donna" and the actor who played her in "The West Wing" might not be structured in a way that is easily queryable with the current properties and relationships in the Wikidata knowledge graph.

Since I have tried various methods and still have not found the answer, I will provide my best attempt at a SPARQL query and explain why I could not find a satisfactory answer.

Here is my best attempt:

```sparql
SELECT ?actor ?actorLabel ?role ?roleLabel WHERE {
  wd:Q192316 wdt:P161 ?actor .
  OPTIONAL { ?actor wdt:P181 ?role . }
  OPTIONAL { ?role rdfs:label ?roleLabel . FILTER(LANG(?roleLabel) = 'en') }
  OPTIONAL { ?actor rdfs:label ?actorLabel . FILTER(LANG(?actorLabel) = 'en') }
} LIMIT 10
```

This query attempts to retrieve the cast members of "The West Wing" and their roles, but it did not return any results.

I will now provide an explanation and my best attempt at a SPARQL query.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?country ?countryLabel WHERE {
  ?country wdt:P37 wd:Q188 .
  ?country wdt:P31 wd:Q6256 .
  OPTIONAL {
    ?country rdfs:label ?countryLabel .
    FILTER ( LANG( ?countryLabel ) = 'en' )
  }
}

Got 5 rows and 2 columns:
| ?country                | ?countryLabel |
| ----------------------- | ------------- |
| Germany (wd:Q183)       | Germany       |
| Belgium (wd:Q31)        | Belgium       |
| Luxembourg (wd:Q32)     | Luxembourg    |
| Liechtenstein (wd:Q347) | Liechtenstein |
| Switzerland (wd:Q39)    | Switzerland   |

The countries where German is an official language are Germany, Belgium, Luxembourg, Liechtenstein, and Switzerland.

[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 countries speak german officially?

[Assistant]
To answer the question "Which countries speak German officially?", we need to identify the countries where German is an official language. Here's the step-by-step approach:

1. **Identify the entity for the German language**: We need to find the Wikidata identifier for the German language.
2. **Identify the property for official languages**: We need to find the property that indicates a language is an official language of a country.
3. **Construct the SPARQL query**: We will query for countries where German is an official language.

Let's start by finding the Wikidata identifier for the German language.

### Step 1: Find the Wikidata identifier for the German language

I will search for the German language in Wikidata.

[Tool]
Got 29 rows and 1 column, showing the first 5 and last 5 rows below:
| ?germanLanguage        |
| ---------------------- |
| German (wd:Q104086862) |
| German (wd:Q115261558) |
| German (wd:Q12750187)  |
| German (wd:Q12905935)  |
| German (wd:Q13039329)  |
| ...                    |
| German (wd:Q57477652)  |
| German (wd:Q5807368)   |
| German (wd:Q83603297)  |
| German (wd:Q91079802)  |
| German (wd:Q97353908)  |

[Assistant]
It seems there are multiple entities with the label "German". However, the most common and widely recognized entity for the German language is likely to be the one with the identifier `wd:Q188`.

Let's confirm this by checking the properties and labels of `wd:Q188` to ensure it is indeed the German language.

### Step 2: Confirm the German language entity

I will check the properties and labels of `wd:Q188` to ensure it is the correct entity for the German language.

[Tool]
Got 1 row and 1 column:
| ?label |
| ------ |
| German |

[Assistant]
The entity `wd:Q188` is indeed the German language. Now, let's find the property that indicates a language is an official language of a country.

### Step 3: Identify the property for official languages

The property for official languages in Wikidata is `wdt:P37` (official language).

### Step 4: Construct the SPARQL query

We will now construct a SPARQL query to find the countries where German (`wd:Q188`) is an official language.

```sparql
SELECT ?country ?countryLabel
WHERE {
  ?country wdt:P37 wd:Q188 .
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = 'en') }
}
```

This query will return the countries where German is an official language along with their English labels.

Let's execute this query.

[Tool]
Got 1,703 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?country                            | ?countryLabel       |
| ----------------------------------- | ------------------- |
| Electorate of Hesse (wd:Q100594799) | Electorate of Hesse |
| Bözberg (wd:Q1020189)               | Bözberg             |
| Fraubrunnen (wd:Q10317)             | Fraubrunnen         |
| Biel/Bienne (wd:Q1034)              | Biel/Bienne         |
| Jegenstorf (wd:Q10341)              | Jegenstorf          |
| ...                                 | ...                 |
| Free City of Danzig (wd:Q901974)    | Free City of Danzig |
| Bülach (wd:Q9093)                   | Bülach              |
| Danube Commission (wd:Q911204)      | Danube Commission   |
| Drusselstein (wd:Q91901863)         | Drusselstein        |
| Bad Ragaz (wd:Q95759)               | Bad Ragaz           |

[Assistant]
The query returned a large number of results, including many subnational entities and historical states. To focus on current sovereign countries, we need to filter the results to include only those that are classified as countries.

### Step 5: Refine the query to include only sovereign countries

We can use the property `wdt:P31` (instance of) to filter for entities that are instances of a country (`wd:Q6256`).

Here is the refined query:

```sparql
SELECT ?country ?countryLabel
WHERE {
  ?country wdt:P37 wd:Q188 .
  ?country wdt:P31 wd:Q6256 .
  OPTIONAL { ?country rdfs:label ?countryLabel . FILTER(LANG(?countryLabel) = 'en') }
}
```

This query will return only the sovereign countries where German is an official language.

Let's execute this refined query.

[Tool]
Got 5 rows and 2 columns:
| ?country                | ?countryLabel |
| ----------------------- | ------------- |
| Germany (wd:Q183)       | Germany       |
| Belgium (wd:Q31)        | Belgium       |
| Luxembourg (wd:Q32)     | Luxembourg    |
| Liechtenstein (wd:Q347) | Liechtenstein |
| Switzerland (wd:Q39)    | Switzerland   |

[Assistant]
The refined query has returned the sovereign countries where German is an official language. These countries are:

- Germany
- Belgium
- Luxembourg
- Liechtenstein
- Switzerland

### Final Answer

The countries where German is an official language are Germany, Belgium, Luxembourg, Liechtenstein, and Switzerland.

I will now provide the final SPARQL query and answer.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?yearOfBirth WHERE {
  wd:Q352 wdt:P569 ?birthDate .
  BIND( STR( YEAR( ?birthDate ) ) AS ?yearOfBirth )
}

Got 1 row and 1 column:
| ?yearOfBirth |
| ------------ |
| 1889         |

[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 year was hitler was born?

[Assistant] To answer the question about the year Hitler was born, I need to identify the entity for Adolf Hitler and the property for birth date. Let's start by searching for the entity representing Adolf Hitler.

[Tool]
Got 9 rows and 1 column:
| ?entity                      |
| ---------------------------- |
| Adolf Hitler (wd:Q100345867) |
| Adolf Hitler (wd:Q112235833) |
| Adolf Hitler (wd:Q112243291) |
| Adolf Hitler (wd:Q112253120) |
| Adolf Hitler (wd:Q120332530) |
| Adolf Hitler (wd:Q20463000)  |
| Adolf Hitler (wd:Q352)       |
| Adolf Hitler (wd:Q47510643)  |
| Adolf Hitler (wd:Q78526535)  |

[Assistant] There are multiple entities with the label "Adolf Hitler". The most likely correct entity is `wd:Q352`, which is the main entry for Adolf Hitler in Wikidata. Next, I will check the birth date property for this entity.

[Tool]
Got 1 row and 1 column:
| ?birthDate           |
| -------------------- |
| 1889-04-20T00:00:00Z |

[Assistant]
The birth date of Adolf Hitler is 1889-04-20. To answer the question specifically about the year, I will extract only the year from the birth date.

Here is the final SPARQL query:

```sparql
SELECT YEAR(?birthDate) AS ?yearOfBirth
WHERE {
  wd:Q352 wdt:P569 ?birthDate
}
```

Executing this query will give us the year of birth.

[Tool]
SPARQL execution failed:
failed to parse input: parse error at position 7 for token YEAR with content 'YEAR' (the input most likely does not follow the grammar)

[Assistant]
It seems that the `YEAR` function is not supported in the SPARQL endpoint I am using. Instead, I will use the `STRDT` function to convert the birth date to a string and then extract the first four characters to get the year.

Here is the revised SPARQL query:

```sparql
SELECT STRDT(SUBSTR(STR(?birthDate), 1, 4), xsd:gYear) AS ?yearOfBirth
WHERE {
  wd:Q352 wdt:P569 ?birthDate
}
```

Let's execute this query.

[Tool]
SPARQL execution failed:
failed to parse input: parse error at position 7 for token STRDT with content 'STRDT' (the input most likely does not follow the grammar)

[Assistant]
It appears that the `STRDT` function is also not supported in this SPARQL endpoint. Instead, I will use the `SUBSTR` function directly to extract the year from the birth date string.

Here is the revised SPARQL query:

```sparql
SELECT SUBSTR(STR(?birthDate), 1, 4) AS ?yearOfBirth
WHERE {
  wd:Q352 wdt:P569 ?birthDate
}
```

Let's execute this query.

[Tool]
SPARQL execution failed:
failed to parse input: parse error at position 7 for token SUBSTR with content 'SUBSTR' (the input most likely does not follow the grammar)

[Assistant]
It seems that the `SUBSTR` function is also not supported in this SPARQL endpoint. Given these limitations, I will use a simpler approach to extract the year from the birth date.

I will use the `BIND` function to create a new variable that extracts the year from the birth date string.

Here is the revised SPARQL query:

```sparql
SELECT ?yearOfBirth
WHERE {
  wd:Q352 wdt:P569 ?birthDate .
  BIND (STR(YEAR(?birthDate)) AS ?yearOfBirth)
}
```

Let's execute this query.

[Tool]
Got 1 row and 1 column:
| ?yearOfBirth |
| ------------ |
| 1889         |

[Assistant]
The query has successfully returned the year of birth for Adolf Hitler, which is 1889.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?yearOfBirth
WHERE {
  wd:Q352 wdt:P569 ?birthDate .
  BIND (STR(YEAR(?birthDate)) AS ?yearOfBirth)
}
```

Answer: 1889

Now, I will provide the final answer using the `answer` function.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?song ?songLabel WHERE {
  ?song wdt:P674 wd:Q34086 .
  OPTIONAL {
    ?song rdfs:label ?songLabel .
    FILTER ( LANG( ?songLabel ) = 'en' )
  }
}

Despite trying multiple properties and approaches, the Wikidata knowledge graph does not seem to have direct or indirect links to the songs Justin Bieber wrote. The properties P170 (creator), P800 (notable work), P802 (lyrics by), and P50 (author) did not yield any relevant results. The only results found were related to documentaries or albums, not individual songs. Therefore, it is not possible to provide a definitive list of songs Justin Bieber wrote using the current state of the Wikidata knowledge graph.

[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 all the songs justin bieber wrote?

[Assistant] To answer the question about all the songs Justin Bieber wrote, we need to identify the entity for Justin Bieber and the property that links him to the songs he wrote. Let's start by finding the entity for Justin Bieber.

[Tool]
Got 3 rows and 1 column:
| ?justinBieber                 |
| ----------------------------- |
| Justin Bieber (wd:Q110037270) |
| Justin Bieber (wd:Q127000216) |
| Justin Bieber (wd:Q34086)     |

[Assistant]
There are multiple entities with the label "Justin Bieber". The most likely correct entity is `wd:Q34086`, which is the main entity for Justin Bieber. Now, let's find the property that links Justin Bieber to the songs he wrote. This property is likely to be something like "lyrics by" or "composer".

Let's check the properties related to music composition and songwriting.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the property "lyrics by" does not exist in the exact form. Let's try another approach by looking for properties related to songwriting or composition. We can also look at the properties directly linked to Justin Bieber to see if there is a relevant property.

Let's check the properties directly linked to Justin Bieber.

[Tool]
Got 100 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?property          | ?propertyLabel |
| ------------------ | -------------- |
| schema:description |                |
| schema:description |                |
| schema:description |                |
| schema:description |                |
| schema:description |                |
| ...                | ...            |
| schema:name        |                |
| schema:name        |                |
| schema:name        |                |
| schema:name        |                |
| schema:name        |                |

[Assistant]
The query returned many properties, but none of them seem to be directly related to songwriting or composition. Let's try a more specific approach by looking for properties that link Justin Bieber to musical works. We can use the property `P170` (creator) or `P800` (notable work) to find the songs he wrote.

Let's try using `P170` (creator) to find the songs Justin Bieber wrote.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that using the `P170` (creator) property did not yield any results. Let's try another property, such as `P800` (notable work), which might link Justin Bieber to the songs he wrote.

Let's use `P800` (notable work) to find the songs Justin Bieber wrote.

[Tool] Got no rows and 2 columns

[Assistant]
Using the `P800` (notable work) property also did not yield any results. Let's try another approach by looking for properties related to songwriting or composition more broadly. One common property for songwriting is `P802` (lyrics by).

Let's use `P802` (lyrics by) to find the songs Justin Bieber wrote.

[Tool] Got no rows and 2 columns

[Assistant]
Using the `P802` (lyrics by) property also did not yield any results. It seems that Justin Bieber's contributions to songwriting might not be directly linked through these properties. Let's try another property, such as `P170` (creator) but in a different context, where Justin Bieber is the creator of the song.

We can also try using `P50` (author) to see if it yields any results.

Let's use `P50` (author) to find the songs Justin Bieber wrote.

[Tool]
Got 3 rows and 2 columns:
| ?song                                              | ?songLabel                          |
| -------------------------------------------------- | ----------------------------------- |
| First Step 2 Forever: My Story (wd:Q10282627)      | First Step 2 Forever: My Story      |
| Justin Bieber: Just Getting Started (wd:Q19879423) | Justin Bieber: Just Getting Started |
| Justin Bieber: First Step 2 Forever (wd:Q48833526) | Justin Bieber: First Step 2 Forever |

[Assistant]
The query using `P50` (author) returned some results, but these appear to be books or documentaries rather than songs. It seems that Justin Bieber's songwriting contributions might not be directly linked through these properties.

Given the lack of direct links, let's try a more comprehensive approach by looking for songs where Justin Bieber is listed as a songwriter in any capacity. We can use a combination of properties and qualifiers to find this information.

Let's try using `P170` (creator) with qualifiers to find the songs Justin Bieber wrote.

[Tool] Got no rows and 2 columns

[Assistant]
Using `P170` (creator) with qualifiers also did not yield any results. It seems that Justin Bieber's songwriting contributions might not be directly linked through these properties in the Wikidata knowledge graph.

Given the lack of direct links, it's possible that the information about Justin Bieber's songwriting is not well-represented in Wikidata. However, we can still try a more general approach by looking for songs where Justin Bieber is involved in any way, and then filter those results to find the ones where he is a songwriter.

Let's try a more general query to find songs associated with Justin Bieber and then filter those results to find the ones where he is a songwriter.

[Tool]
Got 1 row and 2 columns:
| ?song                                              | ?songLabel                          |
| -------------------------------------------------- | ----------------------------------- |
| Justin Bieber: First Step 2 Forever (wd:Q48833526) | Justin Bieber: First Step 2 Forever |

[Assistant]
The query using `P674` (cast member) returned a single result, which appears to be a documentary or album rather than a song. It seems that the information about Justin Bieber's songwriting contributions is not well-represented in the Wikidata knowledge graph.

Given the lack of direct and indirect links, it's possible that the information about Justin Bieber's songwriting is not available in the current state of the Wikidata knowledge graph. Therefore, I cannot provide a definitive list of songs he wrote using the available data.

I will now provide a detailed explanation and my best attempt at a SPARQL query.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?palace WHERE {
  ?palace rdfs:label 'Imperial Palace, Tokyo' @en .
  wd:Q34479 wdt:P551 | wdt:P1308 | wdt:P276 ?palace .
}

Despite multiple attempts to find the place of residence for Emperor Hirohito in the Wikidata knowledge graph, no direct or indirect links were found. The commonly known information is that Emperor Hirohito resided in the Imperial Palace in Tokyo, but this information is not explicitly recorded in the knowledge graph.

[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] where did emperor hirohito live?

[Assistant]
To answer the question about where Emperor Hirohito lived, we need to identify the entity for Emperor Hirohito and then find the property that indicates his place of residence. Let's start by identifying the entity for Emperor Hirohito.

### Step 1: Identify the Entity for Emperor Hirohito
I will search for the entity corresponding to Emperor Hirohito in the Wikidata knowledge graph.

### Step 2: Find the Property for Place of Residence
Once we have the entity for Emperor Hirohito, we will look for the property that indicates his place of residence.

Let's start with the first step.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the label 'Emperor Hirohito' did not return any results. This might be because the label is not exactly 'Emperor Hirohito'. Let's try another common name for him, such as 'Hirohito' or 'Emperor Shōwa', which is another name he is known by.

### Step 1: Identify the Entity for Emperor Hirohito (Alternative Names)
I will search for the entity corresponding to Emperor Hirohito using alternative names.

### Step 2: Find the Property for Place of Residence
Once we have the entity for Emperor Hirohito, we will look for the property that indicates his place of residence.

Let's try the alternative names.

[Tool]
Got 4 rows and 1 column:
| ?hirohito               |
| ----------------------- |
| Hirohito (wd:Q11709840) |
| wd:Q1620231             |
| Hirohito (wd:Q34479)    |
| Hirohito (wd:Q47510159) |

[Assistant]
We have multiple results for the label 'Hirohito'. Among these, `wd:Q34479` is the most likely candidate for Emperor Hirohito, as it is the most commonly referenced entity for him. Let's confirm this by checking the labels and descriptions of these entities.

### Step 1: Confirm the Correct Entity for Emperor Hirohito
I will check the labels and descriptions of the entities to confirm which one is Emperor Hirohito.

### Step 2: Find the Property for Place of Residence
Once we have confirmed the correct entity, we will look for the property that indicates his place of residence.

Let's confirm the correct entity.

[Tool]
Got 4 rows and 3 columns:
| ?hirohito               | ?label   | ?description                       |
| ----------------------- | -------- | ---------------------------------- |
| Hirohito (wd:Q11709840) | Hirohito | Japanese male given name (ひろひと)    |
| wd:Q1620231             | Hirohito | Wikimedia disambiguation page      |
| Hirohito (wd:Q34479)    | Hirohito | Emperor of Japan from 1926 to 1989 |
| Hirohito (wd:Q47510159) | Hirohito | painting by Mike Hinge             |

[Assistant]
From the results, `wd:Q34479` is the correct entity for Emperor Hirohito, as it is described as "Emperor of Japan from 1926 to 1989".

### Step 2: Find the Property for Place of Residence
Now that we have confirmed the correct entity, we need to find the property that indicates his place of residence. The property `P551` (place of residence) is commonly used for this purpose.

Let's construct a SPARQL query to find the place of residence for Emperor Hirohito.

### Step 3: Construct the SPARQL Query
I will write a SPARQL query to retrieve the place of residence for Emperor Hirohito.

Let's execute the query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the property `P551` (place of residence) does not return any results for Emperor Hirohito. This might be because the information is stored differently or under a different property. Let's try another property that might indicate his place of residence, such as `P1308` (official residence).

### Step 3: Try Another Property for Place of Residence
I will write a SPARQL query to retrieve the official residence for Emperor Hirohito.

Let's execute the query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the property `P1308` (official residence) also does not return any results for Emperor Hirohito. This suggests that the information might be stored under a different property or might not be directly linked in the way we are querying.

### Step 4: Explore Related Properties
Let's explore related properties that might provide indirect information about his place of residence, such as `P39` (position held) and `P276` (place of activity).

### Step 5: Construct the SPARQL Query
I will write a SPARQL query to retrieve the place of activity for positions held by Emperor Hirohito.

Let's execute the query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the properties `P39` (position held) and `P276` (place of activity) also do not return any results for Emperor Hirohito. This suggests that the information about his place of residence might not be directly available or might be stored in a more complex way.

### Step 6: Check for Historical Residences
Since Emperor Hirohito was the Emperor of Japan, it is likely that he resided in the Imperial Palace in Tokyo. Let's check if there is a direct link to the Imperial Palace or if it is mentioned in any related properties.

### Step 7: Construct the SPARQL Query
I will write a SPARQL query to check if there is a direct link to the Imperial Palace or any related properties.

Let's execute the query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that none of the properties `P551` (place of residence), `P1308` (official residence), or `P276` (place of activity) return any results for Emperor Hirohito. This suggests that the information about his place of residence might not be directly linked in the way we are querying.

### Step 8: Check for Indirect Information
Let's try to find indirect information about his place of residence by looking at related entities, such as the Imperial Palace in Tokyo, which is a well-known residence of the Japanese emperors.

### Step 9: Construct the SPARQL Query
I will write a SPARQL query to check if there is any indirect link to the Imperial Palace in Tokyo.

Let's execute the query.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that even the indirect approach did not yield any results. Given that Emperor Hirohito was the Emperor of Japan, it is widely known that he resided in the Imperial Palace in Tokyo. However, this information might not be explicitly linked in the Wikidata knowledge graph.

### Step 10: Final Attempt
Let's try one more time by directly searching for the Imperial Palace in Tokyo and see if there is any connection to Emperor Hirohito.

### Step 11: Construct the SPARQL Query
I will write a SPARQL query to check if the Imperial Palace in Tokyo is associated with Emperor Hirohito.

Let's execute the query.

[Tool] Got no rows and 1 columns

[Assistant]
It appears that even the direct search for the Imperial Palace in Tokyo and its association with Emperor Hirohito did not yield any results. This suggests that the specific information about Emperor Hirohito's place of residence might not be explicitly recorded in the Wikidata knowledge graph.

Given the historical context, it is widely known that Emperor Hirohito resided in the Imperial Palace in Tokyo. However, since we cannot find a direct link in the knowledge graph, we will have to conclude that the information is not available in the current form.

### Final Answer
Since we could not find a direct or indirect link to Emperor Hirohito's place of residence in the Wikidata knowledge graph, we will provide the commonly known information that he resided in the Imperial Palace in Tokyo.

Here is the final SPARQL query and answer:

```sparql
SELECT ?palace WHERE {
  ?palace rdfs:label 'Imperial Palace, Tokyo'@en .
}
```

Answer: Emperor Hirohito is known to have resided in the Imperial Palace in Tokyo.

However, since the query did not return any results, we will use the `cancel` function to explain the situation.

[Tool] Stopped generation process


PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
SELECT ?tunis ?coordinates WHERE {
  wd:Q3572 wdt:P625 ?coordinates .
}

The coordinates of Tunis, the capital city of Tunisia, are POINT(10.180000 36.800833).

[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] where is tunis on a map?

[Assistant]
To answer the question "Where is Tunis on a map?", we need to find the geographic coordinates of Tunis. We can use the `geo:hasGeometry` property to get the geographic information of Tunis.

Let's start by identifying the entity for Tunis and then retrieve its coordinates.

[Tool]
Got 15 rows and 2 columns, showing the first 5 and last 5 rows below:
| ?tunis                                  | ?coordinates                 |
| --------------------------------------- | ---------------------------- |
| Saigon (wd:Q105076255)                  | POINT(106.650000 10.800000)  |
| Reykjavík (wd:Q1764)                    | POINT(-21.935000 64.147500)  |
| Luxembourg (wd:Q1842)                   | POINT(6.132778 49.610555)    |
| Cherrapunji (wd:Q211030)                | POINT(91.724167 25.279167)   |
| Shravasti (wd:Q2594448)                 | POINT(82.050619 27.517073)   |
| ...                                     | ...                          |
| La Plata (wd:Q44059)                    | POINT(-57.950000 -34.933333) |
| Marinha Grande (wd:Q49286541)           | POINT(-8.932222 39.747778)   |
| Garowe (wd:Q822679)                     | POINT(48.483333 8.400000)    |
| capital of the Netherlands (wd:Q834162) | POINT(4.892222 52.373056)    |
| La Guaira (wd:Q873405)                  | POINT(-66.933056 10.600000)  |

[Assistant]
It seems that the query returned multiple results, including some that are not Tunis. Let's refine the query to specifically look for Tunis, the capital city of Tunisia.

We will use the label "Tunis" to filter the results and ensure we get the correct entity.

Here is the refined query:

```sparql
SELECT ?tunis ?coordinates WHERE {
  ?tunis wdt:P31 wd:Q5119; # Instance of city
         rdfs:label "Tunis"@en; # Label is "Tunis"
         wdt:P625 ?coordinates. # Coordinates
}
```

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
It appears that the query did not return any results, which suggests that the label "Tunis" might not be the exact match or there might be another way to identify Tunis.

Let's try a different approach by searching for Tunis directly and then retrieving its coordinates.

Here is the updated query:

```sparql
SELECT ?tunis ?coordinates WHERE {
  ?tunis rdfs:label "Tunis"@en;
         wdt:P625 ?coordinates.
}
```

This query will search for any entity labeled "Tunis" and retrieve its coordinates.

Let's execute this query.

[Tool]
Got 10 rows and 2 columns:
| ?tunis                | ?coordinates                 |
| --------------------- | ---------------------------- |
| Tunis (wd:Q12203526)  | POINT(31.682800 26.616700)   |
| Tunis (wd:Q127431474) | POINT(10.165790 36.818970)   |
| Tunis (wd:Q14222206)  | POINT(30.492119 29.397361)   |
| Tunis (wd:Q16902089)  | POINT(-80.842800 48.838000)  |
| Tunis (wd:Q24626846)  | POINT(124.612556 -9.765206)  |
| Tunis (wd:Q26610612)  | POINT(-3.595974 50.382556)   |
| Tunis (wd:Q3572)      | POINT(10.180000 36.800833)   |
| Tunis (wd:Q63530619)  | POINT(149.167778 -30.365000) |
| Tunis (wd:Q65110542)  | POINT(22.262656 63.129123)   |
| Tunis (wd:Q96411338)  | POINT(-96.527222 30.544722)  |

[Assistant]
The query returned multiple results, but we need to identify the correct Tunis, which is the capital city of Tunisia. The correct entity for Tunis, the capital of Tunisia, is likely to be the one with the most relevant coordinates and context.

From the results, the entity `wd:Q3572` has coordinates that are consistent with the location of Tunis, the capital of Tunisia. These coordinates are `POINT(10.180000 36.800833)`.

Let's confirm this by executing a more specific query to ensure we are getting the correct entity.

Here is the final query:

```sparql
SELECT ?tunis ?coordinates WHERE {
  ?tunis rdfs:label "Tunis"@en;
         wdt:P625 ?coordinates;
         wdt:P31 wd:Q5119. # Instance of city
}
```

This query will filter the results to only include entities that are instances of cities.

Let's execute this query.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the query did not return any results when filtering for instances of cities. This suggests that the entity `wd:Q3572` might not be explicitly classified as a city, or there might be another way to identify it.

Let's try a different approach by directly querying for the entity `wd:Q3572` and retrieving its coordinates.

Here is the query:

```sparql
SELECT ?tunis ?coordinates WHERE {
  wd:Q3572 wdt:P625 ?coordinates.
}
```

This query will directly retrieve the coordinates for the entity `wd:Q3572`.

Let's execute this query.

[Tool]
Got 1 row and 2 columns:
| ?tunis | ?coordinates               |
| ------ | -------------------------- |
|        | POINT(10.180000 36.800833) |

[Assistant]
The query successfully returned the coordinates for Tunis, the capital city of Tunisia. The coordinates are `POINT(10.180000 36.800833)`.

Now, let's provide the final answer to the user's question.

Here is the final SPARQL query and the answer:

```sparql
SELECT ?tunis ?coordinates WHERE {
  wd:Q3572 wdt:P625 ?coordinates.
}
```

Answer: The coordinates of Tunis, the capital city of Tunisia, are `POINT(10.180000 36.800833)`.

[Tool] Stopped generation process