Failed to generate SPARQL query

[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:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
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 the best performing model benchmarking the Atari 2600 Seaquest dataset in terms of Score metric?

[Assistant]
First, I need to identify the entities and properties involved in the question:

Entities:
- Atari 2600 Seaquest dataset
- Model (benchmarking models)
- Score metric

Properties:
- Benchmarking (model evaluated on dataset)
- Performance/Score metric
- Best performing (highest score)

Step 1: Search for the "Atari 2600 Seaquest" dataset entity in ORKG.
I will search for the entity "Atari 2600 Seaquest" in the ORKG to find its IRI and ensure I am referencing the correct dataset in the query.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX orkgc: <http://orkg.org/orkg/class/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?paper ?title WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R124060> .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark .
  ?paper orkgp:P31 ?contribution ; rdf:type orkgc:Paper .
  OPTIONAL {
    ?paper rdfs:label ?title
  }
}

Got 4 rows and 2 columns:
| paper                                                                                | title                                                                            |
| ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>)        | MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (xsd:string)        |
| A Corpus for Multilingual Docume...lassification in Eight Languages (<http://orkg.org/orkg/resource/R131658>) | A Corpus for Multilingual Docume...lassification in Eight Languages (xsd:string) |
| Massively Multilingual Sentence ...ross-Lingual Transfer and Beyond (<http://orkg.org/orkg/resource/R131694>) | Massively Multilingual Sentence ...ross-Lingual Transfer and Beyond (xsd:string) |
| Bridging the domain gap in cross-lingual document classification (<http://orkg.org/orkg/resource/R131718>)    | Bridging the domain gap in cross-lingual document classification (xsd:string)    |

This query will return a list of research papers (with their IDs and titles) that have performed benchmarks on the MLDoc Zero-Shot English-to-Russian dataset.

[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:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
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] Give me a list of research papers along with their titles and IDs, that have performed benchmarks on the MLDoc Zero-Shot English-to-Russian dataset?

[Assistant]
Let's break down the question:

1. We need to find research papers (entities of type "research paper").
2. These papers must have performed benchmarks (likely a property or relation).
3. The benchmarks must be on the "MLDoc Zero-Shot English-to-Russian dataset".

Step 1: Search for the "MLDoc Zero-Shot English-to-Russian dataset" entity in ORKG to get its IRI.

Let's proceed to search for this dataset entity.

[Tool]
Top 10 entity alternatives:
1. MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R124060>): has type Dataset
2. Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>): has type Benchmark
3. Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131666>): has type Benchmark
4. Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131688>): has type Benchmark
5. Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131702>): has type Benchmark
6. Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131724>): has type Benchmark
7. MLDoc Zero-Shot English-to-Japanese (<http://orkg.org/orkg/resource/R124050>): has type Dataset
8. MLDoc Zero-Shot German-to-French (<http://orkg.org/orkg/resource/R124057>): has type Dataset
9. MLDoc Zero-Shot English-to-Spanish (<http://orkg.org/orkg/resource/R124058>): has type Dataset
10. MLDoc Zero-Shot English-to-Italian (<http://orkg.org/orkg/resource/R124063>): has type Dataset

[Assistant]
The entity "MLDoc Zero-Shot English-to-Russian" with IRI <http://orkg.org/orkg/resource/R124060> is the dataset we are interested in. There are also several benchmark entities related to this dataset.

Next, we need to find research papers that have performed benchmarks on this dataset. Typically, there is a property linking papers to datasets they benchmarked on. Let's search for properties related to "benchmark" or "evaluated on" to identify the correct property to use.

[Tool]
Top 10 property alternatives:
1. Benchmark Performance (orkgp:P59252)
2. Benchmark Time (orkgp:P59253)
3. uses benchmark (orkgp:P38001)
4. Has benchmark (orkgp:P34103)
5. has benchmark (orkgp:hasBenchmark)
6. has benchmark (orkgp:HAS_BENCHMARK)
7. Performance metric (orkgp:P5003)
8. Performance measurment (orkgp:P58068)
9. Performance metrics (orkgp:P49059)
10. performance (orkgp:P5097)

[Tool]
Top 10 property alternatives:
1. Evaluated on (orkgp:P41838)
2. evaluate on (orkgp:P20042)
3. evaluates (orkgp:P37290)
4. evaluate-on (orkgp:P20081)
5. evaluation (orkgp:P5091)
6. evaluation (orkgp:P34)
7. evaluation (orkgp:HAS_EVALUATION)
8. evaluate (orkgp:P20041)
9. method evaluated (orkgp:P82000)
10. Has evaluation (orkgp:HasEvaluation)

[Assistant]
We have several candidate properties that might link research papers to datasets they benchmarked or evaluated on, such as:
- uses benchmark (orkgp:P38001)
- Has benchmark (orkgp:P34103)
- has benchmark (orkgp:hasBenchmark)
- Evaluated on (orkgp:P41838)
- evaluate on (orkgp:P20042)

To be thorough, let's check which of these properties are actually used with the MLDoc Zero-Shot English-to-Russian dataset entity (<http://orkg.org/orkg/resource/R124060>) as the object. This will help us identify the correct property to use in our query.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                                | property                        | object                                                                       |
| -------------------------------------------------------------------------------------- | ------------------------------- | ---------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R124060>) |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131666>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R124060>) |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131688>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R124060>) |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131702>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R124060>) |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131724>) | has dataset (orkgp:HAS_DATASET) | MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R124060>) |

[Assistant]
The property used to link benchmarks to the MLDoc Zero-Shot English-to-Russian dataset is has dataset (orkgp:HAS_DATASET). The subjects of these triples are benchmark entities.

To find the research papers, we need to:
1. Find which papers are associated with these benchmark entities.
2. Identify the property that links research papers to benchmarks.

Let's check what properties are used with one of these benchmark entities (e.g., <http://orkg.org/orkg/resource/R131650>) to see if there is a property linking it to a research paper.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                                | property                          | object                                                                       |
| -------------------------------------------------------------------------------------- | --------------------------------- | ---------------------------------------------------------------------------- |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_67.83 (<http://orkg.org/orkg/resource/R131651>)          |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>) | has dataset (orkgp:HAS_DATASET)   | MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R124060>) |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                  |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>) | rdfs:label                        | Benchmark MLDoc Zero-Shot English-to-Russian (xsd:string)                    |
| Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>) | rdf:type                          | orkgc:Resource                                                               |

[Tool]
Got 1 row and 3 columns:
| subject                                                                  | property                            | object                                                                                |
| ------------------------------------------------------------------------ | ----------------------------------- | -------------------------------------------------------------------------------------- |
| Contribution  MultiFiT, pseudo (<http://orkg.org/orkg/resource/R131643>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark MLDoc Zero-Shot English-to-Russian (<http://orkg.org/orkg/resource/R131650>) |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                 | object                                                                   |
| ------------------------------------------------------------------------------------------------------ | ------------------------ | ------------------------------------------------------------------------ |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | contribution (orkgp:P31) | Contribution  MultiFiT, pseudo (<http://orkg.org/orkg/resource/R131643>) |

[Tool]
Got 14 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                      | object                                                                    |
| ------------------------------------------------------------------------------------------------------ | ----------------------------- | ------------------------------------------------------------------------- |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | author (orkgp:P27)            | Jeremy Howard (xsd:string)                                                |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | contribution (orkgp:P31)      | Contribution  MultiFiT, pseudo (<http://orkg.org/orkg/resource/R131643>)  |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | research field (orkgp:P30)    | Computer Sciences (<http://orkg.org/orkg/resource/R132>)                  |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | publication year (orkgp:P29)  | 2019 (xsd:string)                                                         |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | publication month (orkgp:P28) | 9 (xsd:string)                                                            |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | url (orkgp:url)               | https://arxiv.org/abs/1909.04761v2.pdf (xsd:string)                       |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | rdf:type                      | Paper (orkgc:Paper)                                                       |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | rdfs:label                    | MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (xsd:string) |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | author (orkgp:P27)            | Julian Martin Eisenschlos (xsd:string)                                    |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (<http://orkg.org/orkg/resource/R131642>) | author (orkgp:P27)            | Marcin Kardas (xsd:string)                                                |
| ...                                                                                | ...                           | ...                                                                       |

[Assistant]
We have established the following chain:
- The dataset MLDoc Zero-Shot English-to-Russian is linked to several benchmark entities via has dataset (orkgp:HAS_DATASET).
- Each benchmark entity is linked to a "contribution" entity via has benchmark (orkgp:HAS_BENCHMARK).
- Each "contribution" entity is linked to a research paper via contribution (orkgp:P31).
- The research paper entity has type orkgc:Paper and a label (title).

Now, let's write a SPARQL query that:
1. Finds all benchmark entities with has dataset MLDoc Zero-Shot English-to-Russian.
2. Follows has benchmark to the contribution.
3. Follows contribution to the paper.
4. Returns the paper's IRI and title.

Here is the query.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the IRIs (IDs) and titles of research papers that have performed benchmarks on the specified dataset. The query uses appropriate properties to link papers, contributions, benchmarks, and datasets, and includes the title as an optional field. The result is consistent with the user's request. No further refinement is needed.


PREFIX orkgc: <http://orkg.org/orkg/class/>
PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?paper ?title WHERE {
  ?paper rdf:type orkgc:Paper .
  ?paper rdfs:label ?title .
  ?paper orkgp:P31 ?contribution .
  ?contribution orkgp:HAS_BENCHMARK <http://orkg.org/orkg/resource/R131252> .
}

The research paper that has benchmarked models on the Cart Pole (OpenAI Gym) dataset is:
- Title: Mean Actor Critic
- ID: http://orkg.org/orkg/resource/R131248

[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:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
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] Provide a list of research paper titles and IDs that have benchmarked models on the Cart Pole (OpenAI Gym) dataset?

[Assistant]
First, I need to identify the entity representing the "Cart Pole (OpenAI Gym)" dataset in the ORKG knowledge graph. I will search for this entity using relevant keywords.

Step 1: Search for the entity "Cart Pole (OpenAI Gym)" in ORKG.

[Tool]
Top 10 entity alternatives:
1. Cart Pole (OpenAI Gym) (<http://orkg.org/orkg/resource/R123346>): has type Dataset
2. Benchmark Cart Pole (OpenAI Gym) (<http://orkg.org/orkg/resource/R131252>): has type Benchmark
3. OpenAI Gym (<http://orkg.org/orkg/resource/R129281>): has type Problem
4. Lunar Lander (OpenAI Gym) (<http://orkg.org/orkg/resource/R123333>): has type Dataset
5. Benchmark Lunar Lander (OpenAI Gym) (<http://orkg.org/orkg/resource/R131250>): has type Benchmark
6. Gymnetron (<http://orkg.org/orkg/resource/R68503>): has type Contribution
7. cart-double-pendulum-system (<http://orkg.org/orkg/resource/R164069>): Dynamical system consisting of a horizontally movable (actuated)...o pendulum bodies with passive (non-actuated) rotational joints., has type dynamical system
8. cart-pole-system (<http://orkg.org/orkg/resource/R162667>): has type dynamical system
9. cart-triple-pendulum-system (<http://orkg.org/orkg/resource/R163746>): Dynamical system consisting of a horizontally movable (actuated)...e pendulum bodies with passive (non-actuated) rotational joints., has type dynamical system
10. cartpole.balance_sparse (<http://orkg.org/orkg/resource/R123365>): has type Dataset

[Assistant]
The entity "Cart Pole (OpenAI Gym)" with IRI <http://orkg.org/orkg/resource/R123346> is identified as a dataset in ORKG. Next, I need to find research papers that have benchmarked models on this dataset.

To do this, I should look for research papers (likely of type orkgc:Paper) that are linked to this dataset via a property indicating benchmarking or evaluation on a dataset. I will search for properties related to "benchmark" or "evaluation" to identify the correct property to use in the query.

Step 2: Search for properties related to "benchmark" in ORKG.

[Tool]
Top 10 property alternatives:
1. Benchmark Performance (orkgp:P59252)
2. Benchmark Time (orkgp:P59253)
3. uses benchmark (orkgp:P38001)
4. Has benchmark (orkgp:P34103)
5. has benchmark (orkgp:hasBenchmark)
6. has benchmark (orkgp:HAS_BENCHMARK)
7. Performance metric (orkgp:P5003)
8. Performance measurment (orkgp:P58068)
9. Performance metrics (orkgp:P49059)
10. performance (orkgp:P5097)

[Assistant]
There are several properties related to "benchmark," but the most relevant for linking papers to datasets are likely "uses benchmark" (orkgp:P38001), "Has benchmark" (orkgp:P34103), and "has benchmark" (orkgp:hasBenchmark or orkgp:HAS_BENCHMARK).

To ensure completeness, I will check which of these properties are used to link papers to the "Cart Pole (OpenAI Gym)" dataset by listing triples where the object is <http://orkg.org/orkg/resource/R123346>.

Step 3: List triples with the object as the Cart Pole (OpenAI Gym) dataset to see which properties are used.

[Tool]
Got 1 row and 3 columns:
| subject                                                                    | property                        | object                                                           |
| -------------------------------------------------------------------------- | ------------------------------- | ---------------------------------------------------------------- |
| Benchmark Cart Pole (OpenAI Gym) (<http://orkg.org/orkg/resource/R131252>) | has dataset (orkgp:HAS_DATASET) | Cart Pole (OpenAI Gym) (<http://orkg.org/orkg/resource/R123346>) |

[Assistant]
The property "has dataset" (orkgp:HAS_DATASET) is used to link the "Benchmark Cart Pole (OpenAI Gym)" entity to the "Cart Pole (OpenAI Gym)" dataset. This suggests that papers may be linked to the benchmark entity, which in turn is linked to the dataset.

Next, I need to find papers that have benchmarked models on this dataset. I will check if there are papers linked to the "Benchmark Cart Pole (OpenAI Gym)" entity, possibly via properties like "has benchmark," "uses benchmark," or similar.

Step 4: List triples where the object is "Benchmark Cart Pole (OpenAI Gym)" to see if papers are linked to it.

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                            | object                                                                     |
| ----------------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------------------- |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Cart Pole (OpenAI Gym) (<http://orkg.org/orkg/resource/R131252>) |

[Assistant]
The entity "Contribution MAC" (<http://orkg.org/orkg/resource/R131249>) is linked to "Benchmark Cart Pole (OpenAI Gym)" via the property "has benchmark" (orkgp:HAS_BENCHMARK). Typically, contributions are associated with research papers in ORKG.

To find the corresponding research paper, I will look for triples where the subject is "Contribution MAC" and see if it is linked to a paper entity.

Step 5: List triples where the subject is "Contribution MAC" to find its associated paper.

[Tool]
Got 15 rows and 3 columns, showing the first 10 rows below:
| subject                                                     | property                                | object                                                                        |
| ----------------------------------------------------------- | --------------------------------------- | ----------------------------------------------------------------------------- |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | research problem (orkgp:P32)            | Continuous Control (<http://orkg.org/orkg/resource/R123291>)                  |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/camall3n/atari-MAC (xsd:string)                            |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Lunar Lander (OpenAI Gym) (<http://orkg.org/orkg/resource/R131250>) |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has model (orkgp:HAS_MODEL)             | MAC (<http://orkg.org/orkg/resource/R119888>)                                 |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | rdf:type                                | Contribution (orkgc:Contribution)                                             |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | rdfs:label                              | Contribution 	MAC (xsd:string)                                                |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/kavosh8/MAC (xsd:string)                                   |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Cart Pole (OpenAI Gym) (<http://orkg.org/orkg/resource/R131252>)    |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R131254>)       |
| Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Atari 2600 Pong (<http://orkg.org/orkg/resource/R131256>)           |
| ...                                                         | ...                                     | ...                                                                           |

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                 | object                                                      |
| ----------------------------------------------------------- | ------------------------ | ----------------------------------------------------------- |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | contribution (orkgp:P31) | Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) |

[Tool] Got no rows and 3 columns

[Tool]
Got 14 rows and 3 columns, showing the first 10 rows below:
| subject                                                     | property                      | object                                                      |
| ----------------------------------------------------------- | ----------------------------- | ----------------------------------------------------------- |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | author (orkgp:P27)            | Abdel-rahman Mohamed (xsd:string)                           |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | contribution (orkgp:P31)      | Contribution  MAC (<http://orkg.org/orkg/resource/R131249>) |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | research field (orkgp:P30)    | Computer Sciences (<http://orkg.org/orkg/resource/R132>)    |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | publication year (orkgp:P29)  | 2017 (xsd:string)                                           |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | publication month (orkgp:P28) | 9 (xsd:string)                                              |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | url (orkgp:url)               | http://arxiv.org/abs/1709.00503v2.pdf (xsd:string)          |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | rdf:type                      | Paper (orkgc:Paper)                                         |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | rdfs:label                    | Mean Actor Critic (xsd:string)                              |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | author (orkgp:P27)            | Cameron Allen (xsd:string)                                  |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | author (orkgp:P27)            | George Konidaris (xsd:string)                               |
| ...                                                         | ...                           | ...                                                         |

[Tool]
Got 1 row and 2 columns:
| paper                                                       | title                          |
| ----------------------------------------------------------- | ------------------------------ |
| Mean Actor Critic (<http://orkg.org/orkg/resource/R131248>) | Mean Actor Critic (xsd:string) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the IRIs and titles of research papers that have benchmarked models on the Cart Pole (OpenAI Gym) dataset. The query returns the paper's identifier and its title, as requested, and does not include any unnecessary computation or filtering outside of SPARQL. The answer is complete and accurate based on the execution result. No further refinement is needed.


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?paper ?paperLabel ?codeLink ?contribCodeLink WHERE {
  ?contribution ?modelProp <http://orkg.org/orkg/resource/R216337> .
  VALUES ?modelProp {
    orkgp:HAS_MODEL orkgp:P1004 orkgp:P20072 orkgp:P31
  }
  ?contribution ?benchProp ?benchmark .
  VALUES ?benchProp {
    orkgp:P38001 orkgp:P34103 orkgp:hasBenchmark orkgp:HAS_BENCHMARK
  }
  ?paper ?contribProp ?contribution .
  OPTIONAL {
    ?paper rdfs:label ?paperLabel
  }
  OPTIONAL {
    ?paper ?codeProp ?codeLink .
    VALUES ?codeProp {
      orkgp:P16033 orkgp:P4077 orkgp:HAS_SOURCE_CODE
    }
  }
  OPTIONAL {
    ?contribution ?contribCodeProp ?contribCodeLink .
    VALUES ?contribCodeProp {
      orkgp:P16033 orkgp:P4077 orkgp:HAS_SOURCE_CODE
    }
  }
}

According to the current ORKG data, there are no papers whose contributions both use the Long Short Transformer model and are explicitly linked to a benchmark. Therefore, no code links are available for such papers at this time.

[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:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
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] List the code links in papers that use the Long Short Transformer model in any benchmark?

[Assistant]
Let's break down the question:

1. We need to find papers that use the "Long Short Transformer" model in any benchmark.
2. For those papers, we need to list their code links.

Step 1: Search for the "Long Short Transformer" model entity in ORKG to get its IRI.

Step 2: Identify how "use in benchmark" is represented in ORKG (likely as a property linking a paper to a benchmark via a model).

Step 3: Find the property for "code link" in ORKG.

Let's start with Step 1: Search for the "Long Short Transformer" model entity.

[Tool]
Top 10 entity alternatives:
1. Long Short Transformer (<http://orkg.org/orkg/resource/R216337>)
2. Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>): has type Contribution
3. Longformer: The Long-Document Transformer (<http://orkg.org/orkg/resource/R130652>): has type Paper
4. long short-term memory (<http://orkg.org/orkg/resource/wikidata:Q6673524>): has type External
5. Long Short -Term Memory (<http://orkg.org/orkg/resource/R186119>): has type Method
6. Long Short-Term Memory (<http://orkg.org/orkg/resource/R55177>)
7. Bi-directional Long short-term Memory (<http://orkg.org/orkg/resource/R206274>): has type Result
8. Long short-term Memory (LSTM) (<http://orkg.org/orkg/resource/R206275>): has type Result
9. Long Short Term Memory (LSTM) (<http://orkg.org/orkg/resource/R230231>): has type Result
10. Long Short Term Memory (LSTM) (<http://orkg.org/orkg/resource/R209134>): has type Result

[Assistant]
The entity "Long Short Transformer" is found with IRI: http://orkg.org/orkg/resource/R216337.

Next, we need to find papers that use this model in any benchmark. This likely involves properties connecting papers to models and benchmarks. Let's search for properties related to "benchmark" and "model" to see how these relationships are represented in ORKG.

I'll start by searching for properties related to "benchmark".

[Tool]
Top 10 property alternatives:
1. Benchmark Performance (orkgp:P59252)
2. Benchmark Time (orkgp:P59253)
3. uses benchmark (orkgp:P38001)
4. Has benchmark (orkgp:P34103)
5. has benchmark (orkgp:hasBenchmark)
6. has benchmark (orkgp:HAS_BENCHMARK)
7. Performance metric (orkgp:P5003)
8. Performance measurment (orkgp:P58068)
9. Performance metrics (orkgp:P49059)
10. performance (orkgp:P5097)

[Assistant]
There are several properties related to benchmarks, such as:
- uses benchmark (orkgp:P38001)
- Has benchmark (orkgp:P34103)
- has benchmark (orkgp:hasBenchmark)
- has benchmark (orkgp:HAS_BENCHMARK)

Next, let's search for properties related to "model" to see how a paper might be linked to the "Long Short Transformer" model.

[Tool]
Top 10 property alternatives:
1. model (orkgp:P20072)
2. model (orkgp:P1004)
3. models (orkgp:P71209)
4. Models (orkgp:P43024)
5. model type (orkgp:P71176)
6. Proposed model (orkgp:P59015)
7. Mathematical model (orkgp:P41907)
8. has model (orkgp:hasModel)
9. Has model (orkgp:P9018)
10. has model (orkgp:HAS_MODEL)

[Assistant]
There are several properties related to "model", such as:
- model (orkgp:P20072)
- model (orkgp:P1004)
- has model (orkgp:hasModel)
- Has model (orkgp:P9018)
- Proposed model (orkgp:P59015)

Now, let's check how the "Long Short Transformer" model is used in the knowledge graph. I'll list triples where the subject is the Long Short Transformer entity to see how it's connected to papers or benchmarks.

[Tool]
Got 2 rows and 3 columns:
| subject                                                          | property   | object                              |
| ---------------------------------------------------------------- | ---------- | ----------------------------------- |
| Long Short Transformer (<http://orkg.org/orkg/resource/R216337>) | rdf:type   | orkgc:Resource                      |
| Long Short Transformer (<http://orkg.org/orkg/resource/R216337>) | rdfs:label | Long Short Transformer (xsd:string) |

[Assistant]
The "Long Short Transformer" entity currently only has basic type and label information—no direct links to papers or benchmarks.

Let's try the reverse: list triples where "Long Short Transformer" is the object, to see if any papers or contributions reference it via a "model" or similar property.

[Tool]
Got 2 rows and 3 columns:
| subject                                                                  | property                    | object                                                           |
| ------------------------------------------------------------------------ | --------------------------- | ---------------------------------------------------------------- |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>)                 | has model (orkgp:HAS_MODEL) | Long Short Transformer (<http://orkg.org/orkg/resource/R216337>) |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | model (orkgp:P1004)         | Long Short Transformer (<http://orkg.org/orkg/resource/R216337>) |

[Assistant]
We found two subjects referencing the Long Short Transformer model:
1. Contribution 1 (<http://orkg.org/orkg/resource/R216346>) via has model (orkgp:HAS_MODEL)
2. Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) via model (orkgp:P1004)

The first is a "Contribution", which is likely part of a paper. Let's check what paper or resource this contribution belongs to, and whether it is linked to any benchmarks and code links.

I'll list triples where the subject is Contribution 1 to see its properties.

[Tool]
Got 4 rows and 3 columns:
| subject                                                  | property                    | object                                                           |
| -------------------------------------------------------- | --------------------------- | ---------------------------------------------------------------- |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | has model (orkgp:HAS_MODEL) | Long Short Transformer (<http://orkg.org/orkg/resource/R216337>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | rdf:type                    | Contribution (orkgc:Contribution)                                |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | rdfs:label                  | Contribution 1 (xsd:string)                                      |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | rdf:type                    | orkgc:Resource                                                   |

[Assistant]
Contribution 1 is linked to the Long Short Transformer model, but there is no direct information about benchmarks or code links in these triples.

Let's check if this contribution is part of a paper. Usually, contributions are linked to papers via a property like "has contribution" or similar. I'll search for triples where Contribution 1 is the object.

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                 | object                                                   |
| ------------------------------------------------------------------------------------------------------------- | ------------------------ | -------------------------------------------------------- |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | contribution (orkgp:P31) | Contribution 1 (<http://orkg.org/orkg/resource/R216346>) |

[Assistant]
Contribution 1 is part of the paper "Long-Short Transformer: Efficient Transformers for Language and Vision" (<http://orkg.org/orkg/resource/R216344>).

Now, let's check if this paper is linked to any benchmarks and if it has a code link. I'll list all triples for this paper to see its properties.

[Tool]
Got 14 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                     | object                                                                                |
| ------------------------------------------------------------------------------------------------------------- | ---------------------------- | ------------------------------------------------------------------------------------------- |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Anima Anandkumar (xsd:string)                                                               |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | contribution (orkgp:P31)     | Contribution 1 (<http://orkg.org/orkg/resource/R216346>)                                    |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | research field (orkgp:P30)   | Natural Language Processing (<http://orkg.org/orkg/resource/R145261>)                       |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | publication year (orkgp:P29) | 2021 (xsd:string)                                                                           |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | venue (orkgp:HAS_VENUE)      | Advances in Neural Information Processing Systems (<http://orkg.org/orkg/resource/R216345>) |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | rdf:type                     | Paper (orkgc:Paper)                                                                         |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | rdfs:label                   | Long-Short Transformer: Efficien...sformers for Language and Vision (xsd:string)            |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Bryan Catanzaro (xsd:string)                                                                |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Chaowei Xiao (xsd:string)                                                                   |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Chen Zhu (xsd:string)                                                                       |
| ...                                                                                | ...                          | ...                                                                                |

[Tool]
Got 14 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                     | object                                                                                |
| ------------------------------------------------------------------------------------------------------------- | ---------------------------- | ------------------------------------------------------------------------------------------- |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Anima Anandkumar (xsd:string)                                                               |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | contribution (orkgp:P31)     | Contribution 1 (<http://orkg.org/orkg/resource/R216346>)                                    |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | research field (orkgp:P30)   | Natural Language Processing (<http://orkg.org/orkg/resource/R145261>)                       |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | publication year (orkgp:P29) | 2021 (xsd:string)                                                                           |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | venue (orkgp:HAS_VENUE)      | Advances in Neural Information Processing Systems (<http://orkg.org/orkg/resource/R216345>) |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | rdf:type                     | Paper (orkgc:Paper)                                                                         |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | rdfs:label                   | Long-Short Transformer: Efficien...sformers for Language and Vision (xsd:string)            |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Bryan Catanzaro (xsd:string)                                                                |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Chaowei Xiao (xsd:string)                                                                   |
| Long-Short Transformer: Efficien...sformers for Language and Vision (<http://orkg.org/orkg/resource/R216344>) | author (orkgp:P27)           | Chen Zhu (xsd:string)                                                                       |
| ...                                                                                | ...                          | ...                                                                                |

[Assistant] From the triples, there is no explicit benchmark or code link property shown for this paper. Let's search for properties related to "code" to see how code links might be represented in ORKG.

[Tool]
Top 10 property alternatives:
1. code (orkgp:P16033)
2. coding (orkgp:P59061)
3. code List (orkgp:P4005)
4. source code (orkgp:P4077)
5. CODEN (orkgp:P74032)
6. has source code (orkgp:HAS_SOURCE_CODE)
7. programming language (orkgp:P21)
8. encodes (orkgp:P20179)
9. Inter-code agreement (orkgp:P18074)
10. encoding (<http://orkg.org/orkg/predicate/SCHEMAORG:encoding>)

[Assistant]
The most relevant properties for code links are:
- code (orkgp:P16033)
- source code (orkgp:P4077)
- has source code (orkgp:HAS_SOURCE_CODE)

Let's check if any of these properties are used for the paper "Long-Short Transformer: Efficient Transformers for Language and Vision". I'll list triples with this paper as subject and these properties.

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant] None of the code-related properties are directly linked to the paper entity. Sometimes, code links may be attached to the contribution entity. Let's check if any of these code properties are used for Contribution 1.

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
There are no code links attached to either the paper or its contribution using the main code-related properties.

To be thorough, let's check if any code links are attached to the "Long Short Transformer Summary" entity, which also references the model.

[Tool]
Got 8 rows and 3 columns:
| subject                                                                  | property                                     | object                                                           |
| ------------------------------------------------------------------------ | -------------------------------------------- | ---------------------------------------------------------------- |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | model (orkgp:P1004)                          | Long Short Transformer (<http://orkg.org/orkg/resource/R216337>) |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | Efficiency algorithm category (orkgp:P68001) | Fixed Patterns (<http://orkg.org/orkg/resource/R211082>)         |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | Decode (orkgp:P68002)                        | true (xsd:string)                                                |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | model has time complexity (orkgp:P68003)     | $$O(kN)$$ (xsd:string)                                           |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | rdf:type                                     | Contribution (orkgc:Contribution)                                |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | rdfs:label                                   | Long Short Transformer Summary (xsd:string)                      |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | Efficiency algorithm category (orkgp:P68001) | Low-rank Methods (<http://orkg.org/orkg/resource/R211086>)       |
| Long Short Transformer Summary (<http://orkg.org/orkg/resource/R216335>) | rdf:type                                     | orkgc:Resource                                                   |

[Assistant]
The "Long Short Transformer Summary" entity does not have any code link properties either.

Since the question asks for code links in papers that use the Long Short Transformer model in any benchmark, and we've checked the only paper and contributions referencing this model, it appears that there are currently no code links available in ORKG for these entities.

I will now construct a SPARQL query that would retrieve code links for papers using the Long Short Transformer model in any benchmark, but based on the current data, the result will be empty.

Here is the query:

```sparql
SELECT DISTINCT ?paper ?codeLink WHERE {
  ?contribution ?modelProp <http://orkg.org/orkg/resource/R216337> .
  ?paper ?contribProp ?contribution .
  OPTIONAL {
    ?paper ?codeProp ?codeLink .
    FILTER(?codeProp IN (orkgp:P16033, orkgp:P4077, orkgp:HAS_SOURCE_CODE))
  }
}
```

This query:
- Finds contributions referencing the Long Short Transformer model.
- Finds papers that have these contributions.
- Optionally retrieves code links from the paper using known code properties.

I will now provide this as the answer, noting that the result is currently empty.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct in its intent: it tries to find papers that use the Long Short Transformer model and retrieve their code links. However, there are some issues to refine:

1. The query currently retrieves papers that "use" the Long Short Transformer model by looking for contributions linked to the model entity. However, it does not check if the paper uses the model in any benchmark, as requested by the user. It should ensure that the paper uses the model in the context of a benchmark (e.g., by checking for benchmark-related properties or entities in the contribution).
2. The OPTIONAL clause for code links is good, but it would be better to always return the paper IRI and only optionally the code link, as per the guidelines.
3. The answer says there are no code links, but the query result shows three papers (with empty codeLink fields). It would be more precise to clarify that these papers were found but none have code links, rather than saying there are no code links available at all.

Suggestion: Refine the query to ensure that the papers actually use the Long Short Transformer model in a benchmark, and clarify the answer to reflect the actual query result.

[Assistant]
Thank you for the feedback. Here’s how I’ll proceed to refine the answer:

1. I need to ensure that the papers not only use the Long Short Transformer model, but do so in the context of a benchmark. This means the contribution should reference both the model and a benchmark.
2. I’ll look for benchmark-related properties (such as orkgp:P38001, orkgp:P34103, orkgp:hasBenchmark, orkgp:HAS_BENCHMARK) in the contribution.
3. I’ll keep the OPTIONAL for code links, but always return the paper IRI.
4. I’ll clarify in the answer that papers were found using the model in a benchmark, but none have code links.

First, I’ll check which benchmark properties are actually used in the contributions referencing the Long Short Transformer model.

[Tool]
Got 4 rows and 3 columns:
| subject                                                  | property                    | object                                                           |
| -------------------------------------------------------- | --------------------------- | ---------------------------------------------------------------- |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | has model (orkgp:HAS_MODEL) | Long Short Transformer (<http://orkg.org/orkg/resource/R216337>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | rdf:type                    | Contribution (orkgc:Contribution)                                |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | rdfs:label                  | Contribution 1 (xsd:string)                                      |
| Contribution 1 (<http://orkg.org/orkg/resource/R216346>) | rdf:type                    | orkgc:Resource                                                   |

[Assistant]
Contribution 1, which references the Long Short Transformer model, does not have any benchmark-related properties in its direct triples.

To be thorough, let's check if any of the other benchmark properties (orkgp:P38001, orkgp:P34103, orkgp:hasBenchmark, orkgp:HAS_BENCHMARK) are used anywhere in the knowledge graph, and if so, whether they are used with Contribution 1.

[Tool]
Got 165 rows and 3 columns, showing the first 10 rows below:
| subject                                                                  | property                      | object                                                                       |
| ------------------------------------------------------------------------ | ----------------------------- | ---------------------------------------------------------------------------- |
| Performance Study (<http://orkg.org/orkg/resource/R111492>)              | uses benchmark (orkgp:P38001) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R111634>) |
| Contribution1-All (<http://orkg.org/orkg/resource/R191263>)              | uses benchmark (orkgp:P38001) | Drug-Drug Interactions (<http://orkg.org/orkg/resource/R114522>)             |
| Contribution (<http://orkg.org/orkg/resource/R111215>)                   | uses benchmark (orkgp:P38001) | Custom Benchmark (<http://orkg.org/orkg/resource/R135737>)                   |
| LUBM-Q1 2-hop (<http://orkg.org/orkg/resource/R188221>)                  | uses benchmark (orkgp:P38001) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R135532>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R209029>)                 | uses benchmark (orkgp:P38001) | true (xsd:string)                                                            |
| LUBM R3F Q1 Setting 3 (<http://orkg.org/orkg/resource/R114112>)          | uses benchmark (orkgp:P38001) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R77032>)  |
| YAGO-QSC1 (<http://orkg.org/orkg/resource/R135212>)                      | uses benchmark (orkgp:P38001) | YAGO (<http://orkg.org/orkg/resource/R78321>)                                |
| DBLP-QSC1 (<http://orkg.org/orkg/resource/R135213>)                      | uses benchmark (orkgp:P38001) | DBLP (<http://orkg.org/orkg/resource/R114515>)                               |
| LUBM(1.0)-Q09 Cluster Sampling (<http://orkg.org/orkg/resource/R135327>) | uses benchmark (orkgp:P38001) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)    |
| SP2Bench SP6 HSP (<http://orkg.org/orkg/resource/R135463>)               | uses benchmark (orkgp:P38001) | SP2Bench (<http://orkg.org/orkg/resource/R135465>)                           |
| ...                                                                      | ...                           | ...                                                                          |

[Tool]
Got 3 rows and 3 columns:
| subject                                                       | property                     | object                                                                         |
| ------------------------------------------------------------- | ---------------------------- | ------------------------------------------------------------------------------ |
| (Main) Contribution (<http://orkg.org/orkg/resource/R109826>) | Has benchmark (orkgp:P34103) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)      |
| (Main) Contribution (<http://orkg.org/orkg/resource/R109826>) | Has benchmark (orkgp:P34103) | Lehigh University Benchmark 2.0 (<http://orkg.org/orkg/resource/R135306>)      |
| (Main) Contribution (<http://orkg.org/orkg/resource/R109826>) | Has benchmark (orkgp:P34103) | University Ontology Benchmark (UOBM) (<http://orkg.org/orkg/resource/R135307>) |

[Tool]
Got 5 rows and 3 columns:
| subject                                                       | property                           | object                                                                       |
| ------------------------------------------------------------- | ---------------------------------- | ---------------------------------------------------------------------------- |
| (Main) Contribution (<http://orkg.org/orkg/resource/R186169>) | has benchmark (orkgp:hasBenchmark) | LUBM (Lehigh University Benchmark) (<http://orkg.org/orkg/resource/R135532>) |
| Static ordering (<http://orkg.org/orkg/resource/R109830>)     | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)    |
| Static ordering (<http://orkg.org/orkg/resource/R109830>)     | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 2.0 (<http://orkg.org/orkg/resource/R135306>)    |
| Dynamic ordering (<http://orkg.org/orkg/resource/R135319>)    | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 1.0 (<http://orkg.org/orkg/resource/R135305>)    |
| Dynamic ordering (<http://orkg.org/orkg/resource/R135319>)    | has benchmark (orkgp:hasBenchmark) | Lehigh University Benchmark 2.0 (<http://orkg.org/orkg/resource/R135306>)    |

[Tool]
Got 2,628 rows and 3 columns, showing the first 10 rows below:
| subject                                                                   | property                            | object                                                                  |
| ------------------------------------------------------------------------- | ----------------------------------- | ----------------------------------------------------------------------- |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>)   | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R132606>) |
| Contribution  DQN hs (<http://orkg.org/orkg/resource/R132805>)            | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R132806>) |
| Contribution  Prior+Duel hs (<http://orkg.org/orkg/resource/R132906>)     | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R132907>) |
| Contribution  DQN noop (<http://orkg.org/orkg/resource/R132706>)          | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R132707>) |
| Contribution  Prior hs (<http://orkg.org/orkg/resource/R132500>)          | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R132501>) |
| Contribution  Prior noop (<http://orkg.org/orkg/resource/R132401>)        | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R132402>) |
| Contribution  A3C FF hs (<http://orkg.org/orkg/resource/R133504>)         | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R133505>) |
| Contribution  A3C FF (1 day) hs (<http://orkg.org/orkg/resource/R133605>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R133606>) |
| Contribution  A3C LSTM hs (<http://orkg.org/orkg/resource/R133706>)       | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R133707>) |
| Contribution  Duel noop (<http://orkg.org/orkg/resource/R131981>)         | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Seaquest (<http://orkg.org/orkg/resource/R131982>) |
| ...                                                                       | ...                                 | ...                                                                     |

[Tool] Got no rows and 3 columns

[Assistant]
Contribution 1 (the only contribution referencing the Long Short Transformer model) does not have any of the benchmark properties attached. This means, according to the current ORKG data, there are no contributions that both reference the Long Short Transformer model and are explicitly linked to a benchmark.

However, to be thorough, I will write a SPARQL query that would return papers whose contributions reference both the Long Short Transformer model and any benchmark (using any of the benchmark properties found), and also optionally return code links. This will make the query robust for future data as well.

Here is the refined SPARQL query:

```sparql
SELECT DISTINCT ?paper ?codeLink WHERE {
  ?contribution ?modelProp <http://orkg.org/orkg/resource/R216337> .
  ?contribution ?benchProp ?benchmark .
  FILTER(?benchProp IN (orkgp:P38001, orkgp:P34103, orkgp:hasBenchmark, orkgp:HAS_BENCHMARK))
  ?paper ?contribProp ?contribution .
  OPTIONAL {
    ?paper ?codeProp ?codeLink .
    FILTER(?codeProp IN (orkgp:P16033, orkgp:P4077, orkgp:HAS_SOURCE_CODE))
  }
}
```

This query ensures:
- The contribution references the Long Short Transformer model.
- The contribution references a benchmark via any known benchmark property.
- The paper is linked to the contribution.
- Code links are returned if available.

Since the result will be empty with current data, I will clarify that no such papers exist in the current ORKG data.

Here is the final answer:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct in its structure and intent, but there are a few areas that could be improved for completeness and robustness:

1. The query assumes that the model property linking the contribution to the Long Short Transformer is known and fixed as '?modelProp'. However, it does not specify which property or set of properties are considered as 'uses model' or similar. It would be better to explicitly list the relevant properties (e.g., orkgp:P31, orkgp:usesModel, etc.) in a FILTER or VALUES clause, unless <http://orkg.org/orkg/resource/R216337> is always used with a single property.

2. The query does not retrieve labels for the papers or code links, which could be useful for human readability, though not strictly necessary unless requested.

3. The OPTIONAL clause for code links is good, but it might be more robust to also check for code links at the contribution level, as sometimes code links are attached to contributions rather than papers.

4. The query could be made more readable by using VALUES for the properties instead of multiple FILTER IN clauses.

Overall, the query is close, but a bit of refinement as described above would make it more robust and complete.

[Assistant]
Thank you for the detailed feedback. Here’s how I will refine the query:

1. I will explicitly use VALUES to list all relevant model properties (e.g., orkgp:HAS_MODEL, orkgp:P1004, orkgp:P20072, orkgp:P31, etc.) and all relevant benchmark properties (e.g., orkgp:P38001, orkgp:P34103, orkgp:hasBenchmark, orkgp:HAS_BENCHMARK).
2. I will also use VALUES for code link properties (e.g., orkgp:P16033, orkgp:P4077, orkgp:HAS_SOURCE_CODE).
3. I will retrieve code links both from the paper and the contribution, using OPTIONAL for both.
4. I will add OPTIONAL retrieval of paper labels for readability, but keep them optional as per the guidelines.

Here is the refined SPARQL query:

```sparql
SELECT DISTINCT ?paper ?paperLabel ?codeLink ?contribCodeLink WHERE {
  # Find contributions that use the Long Short Transformer model
  ?contribution ?modelProp <http://orkg.org/orkg/resource/R216337> .
  VALUES ?modelProp { orkgp:HAS_MODEL orkgp:P1004 orkgp:P20072 orkgp:P31 }
  # Contribution must also reference a benchmark
  ?contribution ?benchProp ?benchmark .
  VALUES ?benchProp { orkgp:P38001 orkgp:P34103 orkgp:hasBenchmark orkgp:HAS_BENCHMARK }
  # Find the paper that has this contribution
  ?paper ?contribProp ?contribution .
  # Optionally get paper label
  OPTIONAL { ?paper rdfs:label ?paperLabel }
  # Optionally get code links from the paper
  OPTIONAL {
    ?paper ?codeProp ?codeLink .
    VALUES ?codeProp { orkgp:P16033 orkgp:P4077 orkgp:HAS_SOURCE_CODE }
  }
  # Optionally get code links from the contribution
  OPTIONAL {
    ?contribution ?contribCodeProp ?contribCodeLink .
    VALUES ?contribCodeProp { orkgp:P16033 orkgp:P4077 orkgp:HAS_SOURCE_CODE }
  }
}
```

This query is robust, readable, and covers all the requested aspects.

I will clarify in the answer:
According to the current ORKG data, there are no papers whose contributions both use the Long Short Transformer model and are explicitly linked to a benchmark. Therefore, no code links are available for such papers at this time.

Here is the final answer:

[Tool] Stopped generation process


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?metric ?metricLabel WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R118775> ; orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

The commonly used evaluation metric when benchmarking models on the UCF101 (finetuned) dataset is 3-fold Accuracy.

[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:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
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 evaluation metrics are commonly used when benchmarking models on the UCF101 (finetuned) dataset?

[Assistant]
Let's break down the question:

1. The user is asking about "evaluation metrics" (entities: metrics) that are "commonly used" (so, likely, properties or relations indicating usage frequency or standard practice) when "benchmarking models" (entities: models, benchmarking) on the "UCF101 (finetuned) dataset" (entity: UCF101, possibly with a 'finetuned' variant).

2. First, I need to identify the entity for "UCF101 (finetuned) dataset" in ORKG.

3. Then, I need to find evaluation metrics associated with benchmarking models on this dataset.

Step 1: Search for the UCF101 (finetuned) dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. UCF101 (<http://orkg.org/orkg/resource/R118612>): has type Dataset
2. Benchmark UCF101 (<http://orkg.org/orkg/resource/R130115>): has type Benchmark
3. Benchmark UCF101 (<http://orkg.org/orkg/resource/R130108>): has type Benchmark
4. Benchmark UCF101 (<http://orkg.org/orkg/resource/R130101>): has type Benchmark
5. Benchmark UCF101 (<http://orkg.org/orkg/resource/R130094>): has type Benchmark
6. Benchmark UCF101 (<http://orkg.org/orkg/resource/R129992>): has type Benchmark
7. Benchmark UCF101 (<http://orkg.org/orkg/resource/R129974>): has type Benchmark
8. Baseline UCF101 (<http://orkg.org/orkg/resource/R118672>): has type Model
9. UCF101-24 (<http://orkg.org/orkg/resource/R119004>): has type Dataset
10. UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>): has type Dataset

[Assistant]
I found an entity named "UCF101 (finetuned)" (<http://orkg.org/orkg/resource/R118775>) in ORKG, which matches the dataset mentioned in your question.

Next, I need to find which evaluation metrics are commonly used when benchmarking models on this dataset. To do this, I will search for properties related to "evaluation metric" or "metric" for this dataset entity.

[Tool]
No property items found

No other items found

[Assistant]
It appears there are no direct properties labeled "metric" associated with the "UCF101 (finetuned)" dataset entity in ORKG. However, evaluation metrics might be linked indirectly, for example, through benchmarking results, model evaluations, or related papers.

Next, I will search for properties related to "evaluation" for this dataset entity to see if metrics are referenced in that context.

[Tool]
No property items found

No other items found

[Assistant]
There are no direct properties labeled "evaluation" for the "UCF101 (finetuned)" dataset entity either. Since metrics are often reported in the context of benchmarking or model evaluation, it's possible that the dataset is referenced in benchmarking resources or papers, which in turn mention evaluation metrics.

My next step is to look for benchmark or evaluation results that reference the "UCF101 (finetuned)" dataset and then see which metrics are used in those contexts. I will search for triples where "UCF101 (finetuned)" is the object, to find related benchmarking or evaluation activities.

[Tool]
Got 4 rows and 3 columns:
| subject                                                                | property                        | object                                                       |
| ---------------------------------------------------------------------- | ------------------------------- | ------------------------------------------------------------ |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129964>) | has dataset (orkgp:HAS_DATASET) | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>) |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129981>) | has dataset (orkgp:HAS_DATASET) | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>) |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130086>) | has dataset (orkgp:HAS_DATASET) | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>) |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130122>) | has dataset (orkgp:HAS_DATASET) | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>) |

[Assistant]
I found several "Benchmark UCF101 (finetuned)" resources that use the "UCF101 (finetuned)" dataset. These are likely to contain information about evaluation metrics.

Next, I will examine one of these benchmark resources (for example, <http://orkg.org/orkg/resource/R129964>) to see which properties (possibly metrics) are associated with it.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                | property                          | object                                                                    |
| ---------------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------- |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129964>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R129965>) |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129964>) | has dataset (orkgp:HAS_DATASET)   | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>)              |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129964>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                               |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129964>) | rdfs:label                        | Benchmark UCF101 (finetuned) (xsd:string)                                 |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129964>) | rdf:type                          | orkgc:Resource                                                            |

[Assistant]
The benchmark resource <http://orkg.org/orkg/resource/R129964> has an "evaluation" property pointing to "Evaluation 3-fold Accuracy_91.5" (<http://orkg.org/orkg/resource/R129965>). This suggests that "Accuracy" is one of the evaluation metrics used.

To confirm and expand on this, I will check the properties of the evaluation resource <http://orkg.org/orkg/resource/R129965> to see if it specifies the metric more explicitly and to look for other possible metrics.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                   | property                  | object                                                    |
| ------------------------------------------------------------------------- | ------------------------- | --------------------------------------------------------- |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R129965>) | value (orkgp:HAS_VALUE)   | 91.5 (xsd:string)                                         |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R129965>) | metric (orkgp:HAS_METRIC) | 3-fold Accuracy (<http://orkg.org/orkg/resource/R118614>) |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R129965>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R129965>) | rdfs:label                | Evaluation 3-fold Accuracy_91.5 (xsd:string)              |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R129965>) | rdf:type                  | orkgc:Resource                                            |

[Assistant]
The evaluation resource specifies the metric as "3-fold Accuracy" (<http://orkg.org/orkg/resource/R118614>). This confirms that accuracy (specifically, 3-fold accuracy) is used as an evaluation metric.

To get a more comprehensive answer, I will check the other benchmark resources that use the "UCF101 (finetuned)" dataset to see if they use additional metrics.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                | property                          | object                                                                    |
| ---------------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------- |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129981>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation 3-fold Accuracy_95.5 (<http://orkg.org/orkg/resource/R129982>) |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129981>) | has dataset (orkgp:HAS_DATASET)   | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>)              |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129981>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                               |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129981>) | rdfs:label                        | Benchmark UCF101 (finetuned) (xsd:string)                                 |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R129981>) | rdf:type                          | orkgc:Resource                                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                   | property                  | object                                                    |
| ------------------------------------------------------------------------- | ------------------------- | --------------------------------------------------------- |
| Evaluation 3-fold Accuracy_95.5 (<http://orkg.org/orkg/resource/R129982>) | value (orkgp:HAS_VALUE)   | 95.5 (xsd:string)                                         |
| Evaluation 3-fold Accuracy_95.5 (<http://orkg.org/orkg/resource/R129982>) | metric (orkgp:HAS_METRIC) | 3-fold Accuracy (<http://orkg.org/orkg/resource/R118614>) |
| Evaluation 3-fold Accuracy_95.5 (<http://orkg.org/orkg/resource/R129982>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation 3-fold Accuracy_95.5 (<http://orkg.org/orkg/resource/R129982>) | rdfs:label                | Evaluation 3-fold Accuracy_95.5 (xsd:string)              |
| Evaluation 3-fold Accuracy_95.5 (<http://orkg.org/orkg/resource/R129982>) | rdf:type                  | orkgc:Resource                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                | property                          | object                                                                    |
| ---------------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------- |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130086>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R130087>) |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130086>) | has dataset (orkgp:HAS_DATASET)   | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>)              |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130086>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                               |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130086>) | rdfs:label                        | Benchmark UCF101 (finetuned) (xsd:string)                                 |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130086>) | rdf:type                          | orkgc:Resource                                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                   | property                  | object                                                    |
| ------------------------------------------------------------------------- | ------------------------- | --------------------------------------------------------- |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R130087>) | value (orkgp:HAS_VALUE)   | 91.5 (xsd:string)                                         |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R130087>) | metric (orkgp:HAS_METRIC) | 3-fold Accuracy (<http://orkg.org/orkg/resource/R118614>) |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R130087>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R130087>) | rdfs:label                | Evaluation 3-fold Accuracy_91.5 (xsd:string)              |
| Evaluation 3-fold Accuracy_91.5 (<http://orkg.org/orkg/resource/R130087>) | rdf:type                  | orkgc:Resource                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                | property                          | object                                                                    |
| ---------------------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------------------- |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130122>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation 3-fold Accuracy_89.0 (<http://orkg.org/orkg/resource/R130123>) |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130122>) | has dataset (orkgp:HAS_DATASET)   | UCF101 (finetuned) (<http://orkg.org/orkg/resource/R118775>)              |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130122>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                               |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130122>) | rdfs:label                        | Benchmark UCF101 (finetuned) (xsd:string)                                 |
| Benchmark UCF101 (finetuned) (<http://orkg.org/orkg/resource/R130122>) | rdf:type                          | orkgc:Resource                                                            |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                   | property                  | object                                                    |
| ------------------------------------------------------------------------- | ------------------------- | --------------------------------------------------------- |
| Evaluation 3-fold Accuracy_89.0 (<http://orkg.org/orkg/resource/R130123>) | value (orkgp:HAS_VALUE)   | 89.0 (xsd:string)                                         |
| Evaluation 3-fold Accuracy_89.0 (<http://orkg.org/orkg/resource/R130123>) | metric (orkgp:HAS_METRIC) | 3-fold Accuracy (<http://orkg.org/orkg/resource/R118614>) |
| Evaluation 3-fold Accuracy_89.0 (<http://orkg.org/orkg/resource/R130123>) | rdf:type                  | Evaluation (orkgc:Evaluation)                             |
| Evaluation 3-fold Accuracy_89.0 (<http://orkg.org/orkg/resource/R130123>) | rdfs:label                | Evaluation 3-fold Accuracy_89.0 (xsd:string)              |
| Evaluation 3-fold Accuracy_89.0 (<http://orkg.org/orkg/resource/R130123>) | rdf:type                  | orkgc:Resource                                            |

[Assistant]
After checking all the benchmark resources using the "UCF101 (finetuned)" dataset, I found that the only evaluation metric explicitly mentioned is "3-fold Accuracy".

To summarize, the commonly used evaluation metric when benchmarking models on the UCF101 (finetuned) dataset in ORKG is "3-fold Accuracy".

Now, I will write a SPARQL query that retrieves all distinct evaluation metrics used in benchmarks for the "UCF101 (finetuned)" dataset, along with their IRIs and optional labels.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the identifiers of the evaluation metrics used for benchmarking models on the UCF101 (finetuned) dataset, along with their optional labels. The answer accurately reflects the result of the query, and the query adheres to the specified rules. No further refinement is necessary.