PREFIX fb: <http://rdf.freebase.com/ns/>
SELECT ?placeOfBirth WHERE {
  fb:m.0261x8t fb:people.person.place_of_birth ?placeOfBirth
}

Got 1 row and 1 column:
| placeOfBirth               |
| -------------------------- |
| Los Angeles (fb:m.030qb3t) |

Kim Kardashian came from Los Angeles.

[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:
freebase at https://qlever.cs.uni-freiburg.de/api/freebase

You can use the following SPARQL prefixes implicitly in all functions:
fb: <http://rdf.freebase.com/ns/
fbkey: <http://rdf.freebase.com/key/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
owl: <http://www.w3.org/2002/07/owl#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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 kim kardashian come from?

[Assistant]
To answer the question "Where did Kim Kardashian come from?", we need to identify the entity representing Kim Kardashian and then find the place associated with her origin or birthplace.

First, let's search for Kim Kardashian in the Freebase knowledge graph to get her entity identifier.

[Tool]
Top 10 entity alternatives:
1. Kim Kardashian, also known as Kimmie, Kim, Kimberly Noel Kardashian, Kimberly Noel "Kim" Kardashian, Kim West (fb:m.0261x8t): Kimberly "Kim" Kardashian West is an American television and soc...y television personality, with estimated earnings of $6 million., Celebrity
2. Kim Kardashian, also known as Petite Ninja Warriors (fb:m.0745l_5): The models navigate their way through Los Angeles as they attend...e ladies to hang on wires. Model Jessica White is a guest judge., TV Episode
3. Kim Kardashian (fb:m.06xy8z0): John Caparulo, Jen Kirkman, Ross Mathews, TV Episode
4. Kim Kardashian (fb:m.0gg_zfd): TV Episode
5. Kim Kardashian (fb:m.0xtwmq5): Musical Recording
6. Kim Kardashian (fb:m.0kc3ypk): TV Episode
7. Kim Kardashian (fb:m.0g05d33): TV Episode
8. Kim Kardashian (fb:m.06y25xd): TV Episode
9. Kim Kardashian (fb:m.0tm0357): Musical Recording
10. Kim Kardashian (fb:m.0_hgg8w): TV Episode

[Assistant]
From the search results, it's clear that the first entry corresponds to Kim Kardashian, the American reality television star and businesswoman. We'll use the entity identifier `fb:m.0261x8t` for further queries.

Next, we need to find the property that represents her place of birth or origin. Let's search for properties related to "birth" for this entity.

[Tool]
Top 10 property alternatives:
1. Place of birth (fb:people.person.place_of_birth): Location, Person
2. Date of birth (fb:people.person.date_of_birth): Date/Time, Person
3. Gender (fb:people.person.gender): Person, Gender
4. Country of nationality (fb:people.person.nationality): Country, Person
5. Religion (fb:people.person.religion): Religion, Person
6. Religion (fb:base.popstra.celebrity.religion): Religion Choice, Celebrity
7. Name (fb:type.object.name): Object, Text
8. Parents (fb:people.person.parents): Person
9. Family (fb:people.family_member.family): Family member, Family
10. Profession (fb:people.person.profession): Profession, Person

No other items found

[Assistant]
The property `fb:people.person.place_of_birth` seems relevant for finding the place where Kim Kardashian was born. Now, let's construct a SPARQL query to retrieve the place of birth for Kim Kardashian using the entity identifier `fb:m.0261x8t`.

Let's formulate and execute the SPARQL query.

[Tool]
Got 1 row and 1 column:
| placeOfBirth               |
| -------------------------- |
| Los Angeles (fb:m.030qb3t) |

[Assistant]
The SPARQL query successfully returned that Kim Kardashian's place of birth is Los Angeles. Therefore, we can conclude that Kim Kardashian came from Los Angeles.

Now, let's provide the final answer:

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX fb: <http://rdf.freebase.com/ns/>
SELECT ?case ?caseLabel WHERE {
  ?case fb:law.legal_case.court fb:m.07sz1 .
  OPTIONAL {
    ?case rdfs:label ?caseLabel
  }
}
LIMIT 10

Got 10 rows and 2 columns:
| case                                                                                | caseLabel                                                                |
| ----------------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Burwell v. Hobby Lobby (fb:m.0102pqly)                                              | Burwell v. Hobby Lobby (en)                                              |
| Motor Vehicles Manufacturers Ass... Mutual Automobile Insurance Co. (fb:m.010f95gt) | Motor Vehicles Manufacturers Ass... Mutual Automobile Insurance Co. (en) |
| Schuette v. Coalition to Defend Affirmative Action (fb:m.010f9y51)                  | Schuette v. Coalition to Defend Affirmative Action (en)                  |
| Myers v. United States (fb:m.01_0hd)                                                | Myers v. United States (en)                                              |
| Riley v. California (fb:m.010hmy_z)                                                 | Riley v. California (en)                                                 |
| Octane Fitness, LLC v. ICON Health & Fitness, Inc. (fb:m.010hqgjj)                  | Octane Fitness, LLC v. ICON Health & Fitness, Inc. (en)                  |
| McCullen v. Coakley (fb:m.01111jln)                                                 | McCullen v. Coakley (en)                                                 |
| American Broadcasting Cos. v. Aereo, Inc. (fb:m.01115s_f)                           | American Broadcasting Cos. v. Aereo, Inc. (en)                           |
| Harris v. Quinn (fb:m.0113hp9g)                                                     | Harris v. Quinn (en)                                                     |
| Plessy v. Ferguson (fb:m.011x02)                                                    | Caso Plessy contra Ferguson (es)                                         |

Here are some of the notable supreme court cases associated with the Supreme Court of the United States: Burwell v. Hobby Lobby, Motor Vehicles Manufacturers Association of America v. State Farm Mutual Automobile Insurance Co., Schuette v. Coalition to Defend Affirmative Action, Myers v. United States, Riley v. California, Octane Fitness, LLC v. ICON Health & Fitness, Inc., McCullen v. Coakley, American Broadcasting Cos. v. Aereo, Inc., Harris v. Quinn, Plessy v. Ferguson.

[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:
freebase at https://qlever.cs.uni-freiburg.de/api/freebase

You can use the following SPARQL prefixes implicitly in all functions:
fb: <http://rdf.freebase.com/ns/
fbkey: <http://rdf.freebase.com/key/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
owl: <http://www.w3.org/2002/07/owl#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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 supreme court cases?

[Assistant]
To answer the question "What are the supreme court cases?", we need to identify the relevant entities and properties in the Freebase knowledge graph. We'll start by searching for entities related to "supreme court case".

Let's begin by searching for entities related to "supreme court case".

[Tool]
Top 10 entity alternatives:
1. Contemporary Supreme Court Cases (fb:m.06ty839): Book
2. Famous Supreme Court cases (fb:m.068_pbs): Book
3. Historic Supreme Court Cases (fb:m.06rs5_3): Book
4. Supreme Court Case Selections Act (fb:m.0cn_fjt): The Supreme Court Case Selections Act of 1988 is an act of Congr...from federal district courts and in limited categories of cases.
5. Supreme Court Cases Through Primary Sources (fb:m.068xzgs): Book
6. Supreme Court Cases on Sex Discrimination (fb:m.06g_l7m): Book
7. Supreme Court of Canada - Case Information (fb:m.0bm58y0)
8. Landmark Supreme Court cases: a reference guide (fb:m.090n2c7): Book Edition
9. Case selection in the United States Supreme Court (fb:m.095rz7m): Book Edition
10. Case selection in the United States Supreme Court (fb:m.06nt7lp): Book

[Assistant]
From the search results, it seems that there are several books and acts related to supreme court cases, but no direct entity representing a list of supreme court cases. However, we can infer that the entities like "Contemporary Supreme Court Cases," "Famous Supreme Court cases," and "Historic Supreme Court Cases" might contain information about specific supreme court cases.

Since these are books, they likely contain many cases, but we don't have a direct way to list all supreme court cases from a single entity. Instead, we can try to find properties that relate to supreme court cases and then list some examples.

Let's search for properties related to "supreme court case" to see if we can find any useful ones.

[Tool]
Top 10 property alternatives:
1. Type (fb:type.object.type): Object, Type
2. Name (fb:type.object.name): Object, Text
3. Key (fb:type.object.key): Object, Key
4. Author (fb:book.written_work.author): Written Work, Author
5. Editions (fb:book.book.editions): Book Edition, Book
6. Notable types (fb:common.topic.notable_types): Topic, Type
7. Notable for (fb:common.topic.notable_for): notable_for, Topic

No other items found

[Assistant]
It appears that the properties related to the book "Contemporary Supreme Court Cases" do not directly provide information about individual supreme court cases. Let's try another approach by searching for properties related to "supreme court" in general, which might give us more relevant properties.

I will now search for properties related to "supreme court" to see if we can find any properties that link to supreme court cases.

[Tool]
Top 10 property alternatives:
1. Court (fb:base.morelaw.legal_system.court): Legal system, Court
2. Court (fb:base.morelaw.canadian_judge.court): Canadian Judge, Court
3. Court (fb:base.lawbase.court_decision.court): Court decision, Court
4. Court (fb:base.crime.appeal.court): Appellate court, Appeal
5. Court (fb:law.court_courthouse_relationship.court): Court-courthouse relationship, Court
6. Court (fb:user.rca.default_domain.us_court_decision.court): US Court Decision, Court
7. Court (fb:base.morelaw.legal_case.court): Trial, Court
8. Court (fb:law.judicial_tenure.court): Judicial tenure, Court
9. Court (fb:law.legal_case.court): Legal case, Court
10. Court (fb:base.canadianlaw.case.court): Legal Case, Court

[Assistant]
From the search results, we can see several properties related to "court" that might be useful. Specifically, the property `fb:law.legal_case.court` seems promising as it links legal cases to their respective courts. We can use this property to find supreme court cases by filtering for cases where the court is a supreme court.

Next, let's list some triples involving this property to understand how it is used and to identify the entity representing the U.S. Supreme Court.

I will now list triples involving the property `fb:law.legal_case.court`.

[Tool]
Got 2,322 rows and 3 columns, showing the first 10 rows below:
| subject                                                                           | property                        | object                                                                 |
| --------------------------------------------------------------------------------- | ------------------------------- | ---------------------------------------------------------------------- |
| Massachusetts (fb:m.05k7sb)                                                       | Court (fb:law.legal_case.court) | Massachusetts Supreme Judicial Court (fb:m.02ltkn)                     |
| Burwell v. Hobby Lobby (fb:m.0102pqly)                                            | Court (fb:law.legal_case.court) | Supreme Court of the United States (fb:m.07sz1)                        |
| Brogden v Metropolitan Rly Co (fb:m.012dshl5)                                     | Court (fb:law.legal_case.court) | House of Lords (fb:m.03k6x)                                            |
| Case Concerning Barcelona Traction, Light, and Power Company, Ltd (fb:m.0114ql02) | Court (fb:law.legal_case.court) | International Court of Justice (fb:m.03vls)                            |
| Trial of Gotovina et al (fb:m.0pmh8k5)                                            | Court (fb:law.legal_case.court) | International Criminal Tribunal for the former Yugoslavia (fb:m.03yb8) |
| fb:g.120vzwr7                                                                     | Court (fb:law.legal_case.court) | European Court of Human Rights (fb:m.0bq2z)                            |
| fb:g.122m41g6                                                                     | Court (fb:law.legal_case.court) | Court of Justice of the European Union (fb:m.09gbtd0)                  |
| Peracomo Inc v TELUS Communications Co (fb:m.010gqkk6)                            | Court (fb:law.legal_case.court) | Supreme Court of Canada (fb:m.0x2jd)                                   |
| German Constitutional Court abortion decision, 1975 (fb:m.06b8dl)                 | Court (fb:law.legal_case.court) | Federal Constitutional Court of Germany (fb:m.02xpt)                   |
| Barbados v. Trinidad and Tobago (fb:m.09v3t0z)                                    | Court (fb:law.legal_case.court) | Permanent Court of Arbitration (fb:m.09h8x)                            |
| ...                                                                               | ...                             | ...                                                                    |

[Assistant]
From the listed triples, we can see that the property `fb:law.legal_case.court` is used to link legal cases to their respective courts. One of the entries shows a case linked to the "Supreme Court of the United States" (`fb:m.07sz1`).

We can use this property to find all supreme court cases by querying for cases where the court is the "Supreme Court of the United States".

Now, let's construct a SPARQL query to find all supreme court cases associated with the "Supreme Court of the United States".

Here is the SPARQL query:

```sparql
SELECT ?case ?caseLabel
WHERE {
  ?case fb:law.legal_case.court fb:m.07sz1 .
  OPTIONAL { ?case rdfs:label ?caseLabel }
}
LIMIT 10
```

This query selects the cases (?case) and their labels (?caseLabel) where the court is the "Supreme Court of the United States" (`fb:m.07sz1`). The `OPTIONAL` clause is used to include the label of the case if available.

Let's execute this query to get the results.

[Tool]
Got 10 rows and 2 columns:
| case                                                                                | caseLabel                                                                |
| ----------------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Burwell v. Hobby Lobby (fb:m.0102pqly)                                              | Burwell v. Hobby Lobby (en)                                              |
| Motor Vehicles Manufacturers Ass... Mutual Automobile Insurance Co. (fb:m.010f95gt) | Motor Vehicles Manufacturers Ass... Mutual Automobile Insurance Co. (en) |
| Schuette v. Coalition to Defend Affirmative Action (fb:m.010f9y51)                  | Schuette v. Coalition to Defend Affirmative Action (en)                  |
| Myers v. United States (fb:m.01_0hd)                                                | Myers v. United States (en)                                              |
| Riley v. California (fb:m.010hmy_z)                                                 | Riley v. California (en)                                                 |
| Octane Fitness, LLC v. ICON Health & Fitness, Inc. (fb:m.010hqgjj)                  | Octane Fitness, LLC v. ICON Health & Fitness, Inc. (en)                  |
| McCullen v. Coakley (fb:m.01111jln)                                                 | McCullen v. Coakley (en)                                                 |
| American Broadcasting Cos. v. Aereo, Inc. (fb:m.01115s_f)                           | American Broadcasting Cos. v. Aereo, Inc. (en)                           |
| Harris v. Quinn (fb:m.0113hp9g)                                                     | Harris v. Quinn (en)                                                     |
| Plessy v. Ferguson (fb:m.011x02)                                                    | Caso Plessy contra Ferguson (es)                                         |

[Assistant]
The SPARQL query successfully returned a list of supreme court cases associated with the "Supreme Court of the United States". Here are the top 10 cases:

1. Burwell v. Hobby Lobby
2. Motor Vehicles Manufacturers Association of America v. State Farm Mutual Automobile Insurance Co.
3. Schuette v. Coalition to Defend Affirmative Action
4. Myers v. United States
5. Riley v. California
6. Octane Fitness, LLC v. ICON Health & Fitness, Inc.
7. McCullen v. Coakley
8. American Broadcasting Cos. v. Aereo, Inc.
9. Harris v. Quinn
10. Plessy v. Ferguson

These are some of the notable supreme court cases. If you need more cases or further details, please let me know!

I will now finalize the answer with the SPARQL query and the results.

[Tool] Stopped generation process


PREFIX fb: <http://rdf.freebase.com/ns/>
SELECT ?name WHERE {
  fb:m.05znhhq fb:type.object.name ?name .
}

Got 5 rows and 1 column:
| name                   |
| ---------------------- |
| Callista Gingrich (en) |
| Callista Gingrich (fr) |
| Callista Gingrich (it) |
| Callista Gingrich (pl) |
| Callista Gingrich (pt) |

Callista Gingrich

[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:
freebase at https://qlever.cs.uni-freiburg.de/api/freebase

You can use the following SPARQL prefixes implicitly in all functions:
fb: <http://rdf.freebase.com/ns/
fbkey: <http://rdf.freebase.com/key/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
owl: <http://www.w3.org/2002/07/owl#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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 is newt gingrich's wife's name?

[Assistant]
To answer the question "What is Newt Gingrich's wife's name?", we need to identify the entity for Newt Gingrich and then find his spouse. Let's start by searching for Newt Gingrich in the Freebase knowledge graph.

I'll use the `search_entity` function to find Newt Gingrich's entity identifier.

[Tool]
Top 10 entity alternatives:
1. Newt Gingrich, also known as Rep. Newt Gingrich, Rep. Gingrich, Newton Leroy McPherson, Newton Leroy "Newt" Gingrich, Newton Leroy Gingrich (fb:m.018fzs): Newton Leroy "Newt" Gingrich is an American politician, historia...n 1997, and in 1998 passed the first balanced budget since 1969., U.S. Congressperson
2. Newt Gingrich (fb:m.05nfprq): TONIGHT: Author of "Never Call Retreat," Newt Gingrich! Plus, Stephen Colbert with "This Week in God"., TV Episode
3. Newt Gingrich (fb:m.05nfcb3): TV Episode
4. Newt Gingrich (fb:m.05nfcqx): Colin Quinn, TV Episode
5. Newt Gingrich (fb:m.05nfprm): TV Episode
6. Newt Gingrich (fb:m.06y3kvd): TV Episode
7. Newt Gingrich (fb:m.0dl0lms): Richard Shelby releases nominees, and Newt Gingrich discusses the radical Obama administration., TV Episode
8. Newt Gingrich, also known as Newt Gingrich, Jessalyn Gilsig (fb:m.0bmn7ll): TV Episode
9. Newt Gingrich (fb:m.0w81t1v): The media weighs presidential nominees for 2016, Sonia Sotomayor...s, and Newt Gingrich admits he guessed wrong about the election., TV Episode
10. Newt Gingrich (fb:m.0z158s): Musical Recording

[Assistant]
From the search results, it appears that the correct entity for Newt Gingrich is `fb:m.018fzs`. Now, let's proceed to find his spouse. We will use the `list` function to look for triples where `fb:m.018fzs` is the subject and the property is related to a spouse or partner.

Let's execute the `list` function with the subject set to `fb:m.018fzs`.

[Tool]
Got 739 rows and 3 columns, showing the first 10 rows below:
| subject                     | property                                                            | object                                                                   |
| --------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Newt Gingrich (fb:m.018fzs) | Type (fb:type.object.type)                                          | Topic (fb:common.topic)                                                  |
| Newt Gingrich (fb:m.018fzs) | Key (fb:type.object.key)                                            | /authority/daylife/topic/01oscHi9wA2Am                                   |
| Newt Gingrich (fb:m.018fzs) | Name (fb:type.object.name)                                          | Newt Gingrich (ca)                                                       |
| Newt Gingrich (fb:m.018fzs) | Topic equivalent webpage (fb:common.topic.topic_equivalent_webpage) | <http://ar.wikipedia.org/wiki/%46%4A%48%2A_%2C%4A%46%2C%31%4A%2A%34>     |
| Newt Gingrich (fb:m.018fzs) | Notable for (fb:common.topic.notable_for)                           | fb:g.125b19mcc                                                           |
| Newt Gingrich (fb:m.018fzs) | Notable types (fb:common.topic.notable_types)                       | U.S. Congressperson (fb:m.05kpwk1)                                       |
| Newt Gingrich (fb:m.018fzs) | Description (fb:common.topic.description)                           | Newt Gingrich je americký politi...istorik, spisovateľ a futurológ. (sk) |
| Newt Gingrich (fb:m.018fzs) | Article (fb:common.topic.article)                                   | fb:m.018fz_                                                              |
| Newt Gingrich (fb:m.018fzs) | Also known as (fb:common.topic.alias)                               | Newt (en)                                                                |
| Newt Gingrich (fb:m.018fzs) | Gender (fb:people.person.gender)                                    | Male (fb:m.05zppz)                                                       |
| ...                         | ...                                                                 | ...                                                                      |

[Assistant]
The list function did not directly provide information about Newt Gingrich's spouse. However, we can infer that the property related to spouses might not be listed among these initial results. To find the specific property related to spouses, we can use the `search_property_of_entity` function with the entity `fb:m.018fzs` and the query "spouse".

Let's use the `search_property_of_entity` function to find the relevant property.

[Tool]
Top 10 property alternatives:
1. Spouse (or domestic partner) (fb:people.person.spouse_s): Marriage, Person
2. Profession (fb:people.person.profession): Profession, Person
3. Country of nationality (fb:people.person.nationality): Country, Person
4. Gender (fb:people.person.gender): Person, Gender
5. Name (fb:type.object.name): Object, Text
6. Date of birth (fb:people.person.date_of_birth): Date/Time, Person
7. Key (fb:type.object.key): Object, Key
8. Supported by (fb:base.propositions.proposition_issue.supported_by): Proposition Issue, Proposition
9. Place of birth (fb:people.person.place_of_birth): Location, Person
10. Parents (fb:people.person.parents): Person

No other items found

[Assistant]
From the search results, the property `fb:people.person.spouse_s` seems to be the appropriate one to find Newt Gingrich's spouse. Now, I will construct a SPARQL query to retrieve the spouse of Newt Gingrich using this property.

Let's formulate and execute the SPARQL query to find Newt Gingrich's spouse.

```sparql
SELECT ?spouse
WHERE {
  fb:m.018fzs fb:people.person.spouse_s ?spouse .
}
```

I will now execute this query.

[Tool]
Got 3 rows and 1 column:
| spouse       |
| ------------ |
| fb:m.0hybx80 |
| fb:m.0j1782r |
| fb:m.0j17836 |

[Assistant]
The SPARQL query returned three potential spouses for Newt Gingrich. To get their names, we need to further query these entities. Let's retrieve the names of these entities.

Here is the next SPARQL query to get the names of the spouses:

```sparql
SELECT ?spouse ?spouseName
WHERE {
  fb:m.018fzs fb:people.person.spouse_s ?spouse .
  ?spouse fb:type.object.name ?spouseName .
}
```

I will now execute this query to get the names of Newt Gingrich's spouses.

[Tool] Got no rows and 2 columns

[Assistant]
It seems that the direct retrieval of names using `fb:type.object.name` did not yield any results. Let's try another approach by listing the triples for each spouse entity to see if we can find their names.

We will use the `list` function for each spouse entity to inspect their details.

First, let's list the triples for `fb:m.0hybx80`.

[Tool]
Got 8 rows and 3 columns:
| subject      | property                                                       | object                           |
| ------------ | -------------------------------------------------------------- | -------------------------------- |
| fb:m.0hybx80 | Type (fb:type.object.type)                                     | Marriage (fb:people.marriage)    |
| fb:m.0hybx80 | Has no value (fb:freebase.valuenotation.has_no_value)          | To (fb:m.01z0kt_)                |
| fb:m.0hybx80 | Spouse (fb:people.marriage.spouse)                             | Newt Gingrich (fb:m.018fzs)      |
| fb:m.0hybx80 | Type of union (fb:people.marriage.type_of_union)               | Marriage (fb:m.04ztj)            |
| fb:m.0hybx80 | From (fb:people.marriage.from)                                 | 2000-08-18 (xsd:date)            |
| fb:m.0hybx80 | Location of ceremony (fb:people.marriage.location_of_ceremony) | Alexandria (fb:m.013h9)          |
| fb:m.0hybx80 | rdf:type                                                       | Marriage (fb:people.marriage)    |
| fb:m.0hybx80 | Spouse (fb:people.marriage.spouse)                             | Callista Gingrich (fb:m.05znhhq) |

[Assistant]
From the list of triples for `fb:m.0hybx80`, we can see that `Callista Gingrich (fb:m.05znhhq)` is one of the spouses associated with Newt Gingrich. Let's now check the details of `fb:m.05znhhq` to confirm her name.

Next, let's list the triples for `fb:m.05znhhq`.

```sparql
SELECT ?name
WHERE {
  fb:m.05znhhq fb:type.object.name ?name .
}
```

I will now execute this query to get the name of Callista Gingrich.

[Tool]
Got 5 rows and 1 column:
| name                   |
| ---------------------- |
| Callista Gingrich (en) |
| Callista Gingrich (fr) |
| Callista Gingrich (it) |
| Callista Gingrich (pl) |
| Callista Gingrich (pt) |

[Assistant]
The query has successfully retrieved the names of Callista Gingrich in different languages. Based on the results, we can conclude that Newt Gingrich's wife's name is Callista Gingrich.

Now, I will provide the final answer.

[Tool] Stopped generation process


PREFIX fb: <http://rdf.freebase.com/ns/>
SELECT ?actor WHERE {
  fb:m.02s9tsc fb:film.performance.actor ?actor .
}

Alexa Vega played the role of Carmen Cortez in the film Spy Kids.

[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:
freebase at https://qlever.cs.uni-freiburg.de/api/freebase

You can use the following SPARQL prefixes implicitly in all functions:
fb: <http://rdf.freebase.com/ns/
fbkey: <http://rdf.freebase.com/key/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
owl: <http://www.w3.org/2002/07/owl#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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 carmen cortez in spy kids?

[Assistant]
To answer the question "Who played Carmen Cortez in Spy Kids?", we need to identify the entity corresponding to the character Carmen Cortez from the movie "Spy Kids" and then find out who the actor is that played this role.

Let's start by searching for the entity corresponding to Carmen Cortez in Freebase.

[Tool]
Top 10 entity alternatives:
1. Baby Carmen, also known as Carmen Cortez (fb:m.02ql50): Carmen Elizabeth Juanita Costa Brava Cortez is a fictional chara...had kept that knowledge to himself on her mother's instructions., Film character
2. Carmen (fb:m.09g6g): Carmen is an opera in four acts by the French composer Georges B... were critical, and the French public was generally indifferent., Opera
3. Cortez, also known as Cortez, Colorado, Montezuma County / Cortez city (fb:m.0rc25): The City of Cortez is a Home Rule Municipality that is the count... The city population was 8,482 at the 2010 United States Census., City/Town/Village
4. Carmen Ejogo, also known as Carmen Elizabeth Ejogo, Carmen (fb:m.07fnrq): Carmen Elizabeth Ejogo is a British actress and singer. She bega...e met with King and was given King's blessing for her portrayal., Film actor
5. Carmen, also known as Bizet's Carmen (fb:m.08tbx6): Carmen is a film directed by Francesco Rosi. It is a film versio...ngs, and shot scenes in some of the exact places that Doré drew., Award-Winning Work
6. Carmen, also known as Carlos Saura Dance Trilogy, Part 1: Carmen (fb:m.02pwmj7): Carmen is a 1983 Spanish film adaptation of the novel Carmen by ...sonal lives Bizet's tragic love affair, up to its lethal climax., Award-Winning Work
7. Carmen, also known as Alfalfa County / Carmen town, Carmen, Oklahoma (fb:m.0z3_c): Carmen is a town in Alfalfa County, Oklahoma, United States. The population was 355 at the 2010 census., City/Town/Village
8. Risë Stevens, also known as Carmen, Risë Gus Steenberg, Risë Steenberg, Rise Stevens (fb:m.0953c4): Risë Stevens was an American operatic mezzo-soprano., Musical Artist
9. Carmen, also known as Carmen (London Symphony Orchestra feat. conductor: Claudio Abbado) (fb:m.03_dwxc): Consumer product
10. Carmen, also known as Carmen - Cyc - (fb:m.067_f8b): Carmen is a novella by Prosper Mérimée, written and first publis... of dramatic works, including the famous opera by Georges Bizet., Book

[Assistant]
From the search results, it seems that the most relevant entity for Carmen Cortez from the movie "Spy Kids" is `Baby Carmen, also known as Carmen Cortez (fb:m.02ql50)`.

Next, let's find out who played this character. We'll search for properties related to actors or performers associated with this character.

[Tool]
Top 10 property alternatives:
1. Portrayed in films (fb:film.film_character.portrayed_in_films): Film performance, Film character
2. Name (fb:type.object.name): Object, Text
3. Programs in which this was a regular character (fb:tv.tv_character.appeared_in_tv_program): TV Regular Performance, TV Character
4. Key (fb:type.object.key): Object, Key
5. Type (fb:type.object.type): Object, Type
6. Gender (fb:fictional_universe.fictional_character.gender): Fictional Character, Character Gender
7. Notable for (fb:common.topic.notable_for): notable_for, Topic
8. Also known as (fb:common.topic.alias): Topic, Text

No other items found

[Assistant]
It appears that the direct property search did not yield a clear property indicating the actor who played the role. However, the property `Portrayed in films (fb:film.film_character.portrayed_in_films)` might be useful to find the film and then trace back to the actor.

Let's list triples involving the entity `fb:m.02ql50` to see if we can find any relevant information about the actor playing Carmen Cortez.

[Tool]
Got 67 rows and 3 columns, showing the first 10 rows below:
| subject                   | property                                                            | object                                                                   |
| ------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Baby Carmen (fb:m.02ql50) | Type (fb:type.object.type)                                          | Topic (fb:common.topic)                                                  |
| Baby Carmen (fb:m.02ql50) | Key (fb:type.object.key)                                            | /en/carmen_cortez                                                        |
| Baby Carmen (fb:m.02ql50) | Name (fb:type.object.name)                                          | Baby Carmen (en)                                                         |
| Baby Carmen (fb:m.02ql50) | Topic equivalent webpage (fb:common.topic.topic_equivalent_webpage) | <http://en.wikipedia.org/wiki/Carmen_Cortez>                             |
| Baby Carmen (fb:m.02ql50) | Notable for (fb:common.topic.notable_for)                           | fb:g.1257kj8ps                                                           |
| Baby Carmen (fb:m.02ql50) | Notable types (fb:common.topic.notable_types)                       | Film character (fb:m.02nsjl9)                                            |
| Baby Carmen (fb:m.02ql50) | Description (fb:common.topic.description)                           | Carmen Elizabeth Juanita Costa B...lf on her mother's instructions. (en) |
| Baby Carmen (fb:m.02ql50) | Article (fb:common.topic.article)                                   | fb:m.02ql55                                                              |
| Baby Carmen (fb:m.02ql50) | Also known as (fb:common.topic.alias)                               | Carmen Cortez (en)                                                       |
| Baby Carmen (fb:m.02ql50) | Portrayed in films (fb:film.film_character.portrayed_in_films)      | fb:m.02s9tsc                                                             |
| ...                       | ...                                                                 | ...                                                                      |

[Assistant]
From the listed triples, we can see that the property `Portrayed in films (fb:film.film_character.portrayed_in_films)` links the character to a film. Let's follow this link to find the film entity and then look for the actor who played the role.

The film entity is `fb:m.02s9tsc`. Now, let's search for properties related to actors or performers associated with this film entity.

[Tool]
Top 10 property alternatives:
1. Actor (fb:film.performance.actor): Film performance, Film actor
2. Character (fb:film.performance.character): Film performance, Film character
3. Film (fb:film.performance.film): Film performance, Film
4. Type (fb:type.object.type): Object, Type

No other items found

[Assistant]
The property `Actor (fb:film.performance.actor)` seems to be the most relevant for finding the actor who played the role in the film. Now, let's construct a SPARQL query to find the actor who played Carmen Cortez in the film "Spy Kids".

We will first find the film entity and then use the `Actor (fb:film.performance.actor)` property to get the actor's information.

Let's build the SPARQL query step-by-step:

1. Find the film entity where Carmen Cortez is portrayed.
2. Use the `Actor (fb:film.performance.actor)` property to get the actor's information.

Here is the initial SPARQL query:

```sparql
SELECT ?actor
WHERE {
  fb:m.02ql50 fb:film.film_character.portrayed_in_films ?film .
  ?performance fb:film.performance.film ?film .
  ?performance fb:film.performance.actor ?actor .
}
```

Let's execute this query to see if it returns the correct information.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that the initial SPARQL query did not return any results. Let's refine our approach by directly linking the character to the actor through the film entity.

Since we know the film entity is `fb:m.02s9tsc`, we can modify the query to directly connect the character to the actor via the film.

Here is the refined SPARQL query:

```sparql
SELECT ?actor
WHERE {
  fb:m.02ql50 fb:film.film_character.portrayed_in_films fb:m.02s9tsc .
  ?performance fb:film.performance.film fb:m.02s9tsc .
  ?performance fb:film.performance.actor ?actor .
}
```

Let's execute this refined query to see if it returns the correct information.

[Tool] Got no rows and 1 columns

[Assistant]
It seems that even the refined query did not return any results. Let's try another approach by directly searching for the actor who played the role of Carmen Cortez in the film "Spy Kids".

We can search for the object of the property `Actor (fb:film.performance.actor)` where the film is "Spy Kids".

Let's execute this search:

```sparql
SELECT ?actor
WHERE {
  fb:m.02s9tsc fb:film.performance.actor ?actor .
}
```

This query will directly find the actor who played the role in the film "Spy Kids".

Let's execute this query now.

[Tool]
Got 1 row and 1 column:
| actor                    |
| ------------------------ |
| Alexa Vega (fb:m.02z7h0) |

[Assistant]
The SPARQL query has returned the correct information. Alexa Vega played the role of Carmen Cortez in the film "Spy Kids".

Now, let's finalize the answer using the `answer` function.

[Tool] Stopped generation process


PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX fb: <http://rdf.freebase.com/ns/>
SELECT ?formOfGovernment ?formLabel WHERE {
  fb:m.09c7w0 fb:location.country.form_of_government ?formOfGovernment .
  OPTIONAL {
    ?formOfGovernment rdfs:label ?formLabel .
    FILTER ( lang( ?formLabel ) = 'en' )
  }
}

Got 3 rows and 2 columns:
| formOfGovernment                      | formLabel                    |
| ------------------------------------- | ---------------------------- |
| Presidential system (fb:m.01d9r3)     | Presidential system (en)     |
| Constitutional republic (fb:m.0c9rw5) | Constitutional republic (en) |
| Federal republic (fb:m.0j729)         | Federal republic (en)        |

The United States is a presidential constitutional federal republic.

[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:
freebase at https://qlever.cs.uni-freiburg.de/api/freebase

You can use the following SPARQL prefixes implicitly in all functions:
fb: <http://rdf.freebase.com/ns/
fbkey: <http://rdf.freebase.com/key/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
owl: <http://www.w3.org/2002/07/owl#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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 type of democracy is the united states?

[Assistant]
To answer the question "What type of democracy is the United States?", we need to identify the relevant entity for the United States and then find properties or relationships that describe its form of government or type of democracy.

First, let's search for the entity representing the United States in Freebase.

[Tool]
Top 10 entity alternatives:
1. United States of America, also known as USA, US, United States, U.S.A., U.S. of A. (fb:m.09c7w0): The United States of America, commonly referred to as the United...s emerged from 13 British colonies located along the East Coast., Country
2. Federal government of the United States, also known as American Government, US Government, US Federal government, United States: Federal Government, United States government (fb:m.01bqks): The government of the United States of America is the federal go...ften used, and the term "National Government" is sometimes used., Organization founder
3. United States (fb:m.0dvxqk): United States is a half-hour comedy-drama that NBC added to its ... Bob can go out and get yourselves one redhead with two straws.", TV Program
4. United States (fb:m.0dmmqxb): Musical Recording
5. United States (fb:m.0829w2d): Book Edition
6. United States... (fb:m.03987_d): Musical Release
7. United States (fb:m.0qhhg_4): Consumer product
8. United States (fb:m.05q8l00): United States is the first full length hard rock collaborative a...oration has been described as a cross between Queen and Mr. Big., Musical Album
9. United States (fb:m.0qhgg6l): Consumer product
10. United States (fb:m.011kfnhv): Musical Release

[Assistant]
From the search results, the first entry "United States of America" (fb:m.09c7w0) is clearly the correct entity representing the United States as a country. Next, we need to find properties or relationships that describe its form of government or type of democracy. We can start by searching for properties related to "government" or "democracy" for this entity.

Let's search for properties related to "government" for the United States entity.

[Tool]
Top 10 property alternatives:
1. Government (fb:government.governmental_jurisdiction.government): Governmental Jurisdiction, Government
2. Government bodies (fb:government.governmental_jurisdiction.government_bodies): Governmental Jurisdiction, Governmental Body
3. Form of Government (fb:location.country.form_of_government): Form of Government, Country
4. Government positions (fb:government.governmental_jurisdiction.government_positions): Government Office or Title, Governmental Jurisdiction
5. Governing officials (fb:government.governmental_jurisdiction.governing_officials): Governmental Jurisdiction, Government Position Held
6. Agencies (fb:government.governmental_jurisdiction.agencies): Governmental Jurisdiction, Government Agency
7. Administrative Divisions (fb:location.country.administrative_divisions): Administrative Division, Country
8. President (fb:user.exooo.default_domain.head_of_state.president): Employment tenure, Head of state
9. Courts (fb:law.court_jurisdiction_area.courts): Court jurisdiction area, Court
10. Official website (fb:common.topic.official_website): Topic, URI

No other items found

[Assistant]
From the search results, the property "Form of Government" (fb:location.country.form_of_government) seems to be the most relevant for describing the type of democracy in the United States. Now, let's construct a SPARQL query to retrieve the value of this property for the United States entity.

I will now create a SPARQL query to find the form of government for the United States.

[Tool]
Got 3 rows and 1 column:
| formOfGovernment                      |
| ------------------------------------- |
| Presidential system (fb:m.01d9r3)     |
| Constitutional republic (fb:m.0c9rw5) |
| Federal republic (fb:m.0j729)         |

[Assistant]
The SPARQL query returned three forms of government for the United States:

1. Presidential system (fb:m.01d9r3)
2. Constitutional republic (fb:m.0c9rw5)
3. Federal republic (fb:m.0j729)

These indicate that the United States is a presidential constitutional federal republic. To provide a clear answer, we can include the labels for these forms of government in our final response.

Now, I will modify the SPARQL query to include optional clauses to retrieve the labels for these forms of government.

[Tool]
Got 3 rows and 2 columns:
| formOfGovernment                      | formLabel                    |
| ------------------------------------- | ---------------------------- |
| Presidential system (fb:m.01d9r3)     | Presidential system (en)     |
| Constitutional republic (fb:m.0c9rw5) | Constitutional republic (en) |
| Federal republic (fb:m.0j729)         | Federal republic (en)        |

[Assistant]
The SPARQL query returned the labels for the forms of government for the United States:

1. Presidential system
2. Constitutional republic
3. Federal republic

Therefore, the United States is a presidential constitutional federal republic.

I will now finalize the answer using the answer function.

[Tool] Stopped generation process


PREFIX fb: <http://rdf.freebase.com/ns/>
SELECT ?school WHERE {
  fb:m.025s5v9 fb:people.person.education ?school .
}

Michelle Obama graduated from the following schools: fb:m.02wn0g2, fb:m.02wpt7p, fb:m.0n1db15.

[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:
freebase at https://qlever.cs.uni-freiburg.de/api/freebase

You can use the following SPARQL prefixes implicitly in all functions:
fb: <http://rdf.freebase.com/ns/
fbkey: <http://rdf.freebase.com/key/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
owl: <http://www.w3.org/2002/07/owl#
xsd: <http://www.w3.org/2001/XMLSchema#
foaf: <http://xmlns.com/foaf/0.1/
skos: <http://www.w3.org/2004/02/skos/core#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
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 michelle obama graduated from?

[Assistant]
To answer the question "Where did Michelle Obama graduate from?", we need to identify the entity corresponding to Michelle Obama and then find the property that indicates her educational background, specifically where she graduated from.

First, let's search for the entity corresponding to Michelle Obama.

[Tool]
Top 10 entity alternatives:
1. Michelle Obama, also known as The Closer, Renaissance, FLOTUS, Mooch, Michelle Robinson Obama (fb:m.025s5v9): Michelle LaVaughn Robinson Obama is an American lawyer and write...y awareness, nutrition, physical activity, and healthful eating., Celebrity
2. Michelle Obama (fb:m.05ng7x9): TV Episode
3. Michelle Obama (fb:m.0jtjc1l): Troops and their families face unique economic challenges, Steph...pundit, and Michelle Obama voices support for military families., TV Episode
4. Michelle Obama (fb:m.0kb0lr_): Jon recalls a conversation with Roger Ailes, examines the good a..., and talks to Michelle Obama about the importance of gardening., TV Episode
5. Michelle Obama, also known as First Lady Michelle Obama (fb:m.0lcl317): Oprah and the staff welcome first lady Michelle Obama. Then, Harpo takes on a challenge to go vegan for one week., TV Episode
6. Michelle Obama (fb:m.0j9tzr1): TV Episode
7. Michelle Obama (fb:m.0gyxqp0): Michelle Obama is a fictional character in the computer animated comedy TV series The Simpsons., Film character
8. Michelle Obama (fb:m.05ld044)
9. Michelle Obama (fb:m.03s87_g)
10. Michelle Obama, Brandy (fb:m.0w8brbs): TV Episode

[Assistant]
From the search results, it appears that the correct entity for Michelle Obama is `fb:m.025s5v9`. Now, I will proceed to find the property that indicates where she graduated from.

Next, I'll list some triples involving Michelle Obama to see what properties are associated with her.

[Tool]
Got 782 rows and 3 columns, showing the first 10 rows below:
| subject                       | property                                                            | object                                                                   |
| ----------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| Michelle Obama (fb:m.025s5v9) | Type (fb:type.object.type)                                          | Topic (fb:common.topic)                                                  |
| Michelle Obama (fb:m.025s5v9) | Key (fb:type.object.key)                                            | /authority/daylife/topic/0dwKd1i6ledJN                                   |
| Michelle Obama (fb:m.025s5v9) | Name (fb:type.object.name)                                          | Michelle Obama (ca)                                                      |
| Michelle Obama (fb:m.025s5v9) | Topic equivalent webpage (fb:common.topic.topic_equivalent_webpage) | <http://ar.wikipedia.org/wiki/%45%4A%34%4A%44_%23%48%28%27%45%27>        |
| Michelle Obama (fb:m.025s5v9) | Notable for (fb:common.topic.notable_for)                           | fb:g.12582g7kh                                                           |
| Michelle Obama (fb:m.025s5v9) | Notable types (fb:common.topic.notable_types)                       | Celebrity (fb:m.02_7frv)                                                 |
| Michelle Obama (fb:m.025s5v9) | Description (fb:common.topic.description)                           | Michelle LaVaughn Obama er en am...ste afro-amerikanske førstedame. (da) |
| Michelle Obama (fb:m.025s5v9) | Article (fb:common.topic.article)                                   | fb:m.025s5vd                                                             |
| Michelle Obama (fb:m.025s5v9) | Also known as (fb:common.topic.alias)                               | First Lady Michelle Obama (en)                                           |
| Michelle Obama (fb:m.025s5v9) | Gender (fb:people.person.gender)                                    | Female (fb:m.02zsn)                                                      |
| ...                           | ...                                                                 | ...                                                                      |

[Assistant]
From the listed triples, there isn't a direct indication of where Michelle Obama graduated from. However, we can search for properties related to education for Michelle Obama to find the relevant information.

Let's search for properties related to education for Michelle Obama.

[Tool]
Top 10 property alternatives:
1. Education (fb:people.person.education): Education, Person
2. Article (fb:common.topic.article): Document, Topic
3. Official website (fb:common.topic.official_website): Topic, URI
4. Religion (fb:people.person.religion): Religion, Person
5. Profession (fb:people.person.profession): Profession, Person
6. Parents (fb:people.person.parents): Person
7. Description (fb:common.topic.description): Topic, Text
8. Member of (fb:organization.organization_member.member_of): Organization membership, Organization member
9. Web Link(s) (fb:common.topic.webpage): Webpage, Topic
10. Children (fb:people.person.children): Person

No other items found

[Assistant]
The property `Education (fb:people.person.education)` seems to be the most relevant for finding out where Michelle Obama graduated from. Let's construct a SPARQL query to retrieve this information.

I will now build and execute a SPARQL query to find where Michelle Obama graduated from.

[Tool]
Got 3 rows and 1 column:
| school       |
| ------------ |
| fb:m.02wn0g2 |
| fb:m.02wpt7p |
| fb:m.0n1db15 |

[Assistant]
The SPARQL query returned three schools where Michelle Obama received her education. To provide more context, let's fetch the names of these schools.

I will now extend the SPARQL query to include the labels of these schools.

[Tool]
Got 3 rows and 2 columns:
| school       | schoolLabel |
| ------------ | ----------- |
| fb:m.02wn0g2 |             |
| fb:m.02wpt7p |             |
| fb:m.0n1db15 |             |

[Assistant]
It appears that the labels for the schools are not directly available in the current dataset. We can still provide the identifiers of the schools where Michelle Obama graduated from.

Here are the identifiers of the schools:

- fb:m.02wn0g2
- fb:m.02wpt7p
- fb:m.0n1db15

We can now finalize our SPARQL query and provide the answer.

[Tool] Stopped generation process