PREFIX orkgp: <http://orkg.org/orkg/predicate/>
SELECT DISTINCT ?model WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R124988> .
  OPTIONAL {
    ?benchmark orkgp:HAS_MODEL ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:P20072 ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:P1004 ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:P71209 ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:P43024 ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:P59015 ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:hasModel ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:P9018 ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:HAS_MODEL ?model .
  }
  OPTIONAL {
    ?benchmark orkgp:HAS_EVALUATION ?evaluation .
    ?evaluation ?evalModelProp ?model .
    FILTER ( ?evalModelProp IN ( orkgp:HAS_MODEL , orkgp:P20072 , orkgp:P1004 , orkgp:P71209 , orkgp:P43024 , orkgp:P59015 , orkgp:hasModel , orkgp:P9018 , orkgp:HAS_MODEL ) )
  }
  FILTER ( BOUND( ?model ) )
}

There are currently no models in the ORKG that are explicitly linked as being evaluated on the Atari 2600 Skiing dataset, either directly or indirectly via benchmarks or evaluations.

[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] Can you list the models that have been evaluated on the Atari 2600 Skiing dataset?

[Assistant]
Let's break down the question:

1. We need to identify the entity for the "Atari 2600 Skiing dataset".
2. We need to find models that have been evaluated on this dataset.
3. We need to determine the property that links models to datasets they have been evaluated on.

First, I will search for the "Atari 2600 Skiing dataset" entity in the ORKG.

[Tool]
Top 10 entity alternatives:
1. Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>): has type Dataset
2. Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133348>): has type Benchmark
3. Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>): has type Benchmark
4. Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R134193>): has type Benchmark
5. Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R134278>): has type Benchmark
6. Atari 2600 Enduro (<http://orkg.org/orkg/resource/R124982>): has type Dataset
7. Atari 2600 HERO (<http://orkg.org/orkg/resource/R125016>): has type Dataset
8. Atari 2600 Gravitar (<http://orkg.org/orkg/resource/R125024>): has type Dataset
9. Atari 2600 Gopher (<http://orkg.org/orkg/resource/R124929>): has type Dataset
10. Atari 2600 Frostbite (<http://orkg.org/orkg/resource/R125005>): has type Dataset

[Assistant]
The most relevant entity for the "Atari 2600 Skiing dataset" appears to be "Atari 2600 Skiing" with IRI <http://orkg.org/orkg/resource/R124988> and type Dataset.

Next, I need to determine how models are linked to datasets they have been evaluated on. I will search for properties related to "evaluated on" or "evaluation" in the ORKG.

[Tool]
Top 10 property alternatives:
1. Evaluated on (orkgp:P41838)
2. evaluates (orkgp:P37290)
3. evaluation (orkgp:P5091)
4. evaluation (orkgp:P34)
5. evaluation (orkgp:HAS_EVALUATION)
6. evaluate (orkgp:P20041)
7. evaluate on (orkgp:P20042)
8. Has evaluation (orkgp:HasEvaluation)
9. method evaluated (orkgp:P82000)
10. Means of evaluation (orkgp:P36082)

[Tool] Got no rows and 3 columns

[Assistant]
It appears that querying for models evaluated on the "Atari 2600 Skiing" dataset using the property "Evaluated on" (orkgp:P41838) did not return any results. However, there are several other properties related to evaluation, such as "evaluates", "evaluation", "evaluate on", and "method evaluated".

Next, I will try the property "evaluate on" (orkgp:P20042) to see if it links any models to the Atari 2600 Skiing dataset.

[Tool] Got no rows and 3 columns

[Assistant]
The property "evaluate on" (orkgp:P20042) also did not return any results for models evaluated on the Atari 2600 Skiing dataset.

I'll try the property "evaluates" (orkgp:P37290) next, as it might be used in the reverse direction (i.e., from model to dataset).

[Tool] Got no rows and 3 columns

[Assistant]
The property "evaluates" (orkgp:P37290) also did not return any results for models evaluated on the Atari 2600 Skiing dataset.

Since the direct properties did not yield results, I will now check if the dataset entity (<http://orkg.org/orkg/resource/R124988>) is used as an object in any triple, to see if there are other relevant properties or links.

[Tool]
Got 4 rows and 3 columns:
| subject                                                               | property                        | object                                                      |
| --------------------------------------------------------------------- | ------------------------------- | ----------------------------------------------------------- |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133348>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R134193>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R134278>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |

[Assistant]
The dataset "Atari 2600 Skiing" is linked to several "Benchmark Atari 2600 Skiing" entities using the property "has dataset" (orkgp:HAS_DATASET). These benchmarks might contain information about models evaluated on this dataset.

Next, I will check what properties are associated with one of these benchmark entities (e.g., <http://orkg.org/orkg/resource/R133317>) to see if there are links to models.

[Tool]
Got 5 rows and 3 columns:
| subject                                                               | property                          | object                                                            |
| --------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------- |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Score_-23582 (<http://orkg.org/orkg/resource/R133318>) |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | has dataset (orkgp:HAS_DATASET)   | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>)       |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                       |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | rdfs:label                        | Benchmark Atari 2600 Skiing (xsd:string)                          |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | rdf:type                          | orkgc:Resource                                                    |

[Tool]
Got 5 rows and 3 columns:
| subject                                                           | property                  | object                                          |
| ----------------------------------------------------------------- | ------------------------- | ----------------------------------------------- |
| Evaluation Score_-23582 (<http://orkg.org/orkg/resource/R133318>) | value (orkgp:HAS_VALUE)   | -23582 (xsd:string)                             |
| Evaluation Score_-23582 (<http://orkg.org/orkg/resource/R133318>) | metric (orkgp:HAS_METRIC) | Score (<http://orkg.org/orkg/resource/R119875>) |
| Evaluation Score_-23582 (<http://orkg.org/orkg/resource/R133318>) | rdf:type                  | Evaluation (orkgc:Evaluation)                   |
| Evaluation Score_-23582 (<http://orkg.org/orkg/resource/R133318>) | rdfs:label                | Evaluation Score_-23582 (xsd:string)            |
| Evaluation Score_-23582 (<http://orkg.org/orkg/resource/R133318>) | rdf:type                  | orkgc:Resource                                  |

[Tool]
Got 1 row and 3 columns:
| subject                                                               | property                          | object                                                            |
| --------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------- |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Score_-23582 (<http://orkg.org/orkg/resource/R133318>) |

[Assistant]
The evaluation node (<http://orkg.org/orkg/resource/R133318>) is linked to the benchmark, but there is no direct indication of which model was evaluated. It is possible that the model information is stored in another property of the evaluation or benchmark node.

I will now search for properties of the evaluation node (<http://orkg.org/orkg/resource/R133318>) that might indicate the model used.

[Tool]
Top 10 property alternatives:
1. value (orkgp:HAS_VALUE)
2. metric (orkgp:HAS_METRIC)

No other items found

[Assistant]
The evaluation node only has properties for "value" and "metric", and does not directly reference a model. Since the benchmark node also does not seem to have a direct model link, it's possible that the ORKG does not explicitly store which models were evaluated on the Atari 2600 Skiing dataset, at least not in the explored structure.

Given the available data, I am unable to construct a SPARQL query that reliably lists the models evaluated on the Atari 2600 Skiing dataset. The relevant links between models and evaluations or benchmarks are missing or not explicitly represented in the ORKG.

If you have more context or a different dataset structure, I can try again. Otherwise, I will stop here.

[Tool] Stopped generation process

[User]
Feedback (status=retry):
The explanation correctly notes that the knowledge graph may lack explicit links between models and their evaluations on the Atari 2600 Skiing dataset. However, the provided SPARQL query (SELECT ?model WHERE { ?model ?p ?o . FILTER(false) }) is a placeholder that will never return any results, regardless of the data. A better approach would be to attempt to find any possible connection, even if indirect, such as searching for evaluations or benchmarks related to the Atari 2600 Skiing dataset and then checking if any models are associated with those evaluations. If no such links exist, the query should at least demonstrate an attempt to traverse plausible relationships (e.g., via properties like 'evaluatedOn', 'hasModel', 'benchmark', etc.). Please retry with a more targeted query, and only conclude no answer is possible if such attempts yield no results.

[Assistant]
Thank you for the feedback. I agree that a better approach is to attempt to traverse plausible relationships, even if indirect, between the Atari 2600 Skiing dataset, benchmarks/evaluations, and possible models. Here’s my revised step-by-step plan:

1. The dataset entity is Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>).
2. This dataset is linked to several benchmark entities via orkgp:HAS_DATASET.
3. Benchmarks are linked to evaluation nodes via orkgp:HAS_EVALUATION.
4. I will now search for properties on benchmark and evaluation nodes that might reference a model (e.g., "has model", "method", "approach", "algorithm", etc.).
5. If such a property exists, I will construct a query that traverses: dataset → benchmark → evaluation → model.

First, I will search for properties on a benchmark node (e.g., <http://orkg.org/orkg/resource/R133317>) that might reference a model.

[Tool]
Top 10 property alternatives:
1. has dataset (orkgp:HAS_DATASET)
2. evaluation (orkgp:HAS_EVALUATION)

No other items found

[Assistant] There are no properties directly referencing a model on the benchmark node. Next, I will search for properties related to "model" on the evaluation node (<http://orkg.org/orkg/resource/R133318>), as this is a common place to store such information.

[Tool]
Top 10 property alternatives:
1. value (orkgp:HAS_VALUE)
2. metric (orkgp:HAS_METRIC)

No other items found

[Assistant]
There are no properties directly referencing a model on the evaluation node either. Since "model" did not yield results, I will now try searching for properties related to "method", "approach", or "algorithm" on the evaluation node, as these are sometimes used to refer to models in research knowledge graphs.

I'll start by searching for "method".

[Tool]
Top 10 property alternatives:
1. metric (orkgp:HAS_METRIC)
2. value (orkgp:HAS_VALUE)

No other items found

[Assistant] The evaluation node does not have any properties related to "method". Next, I will try "approach".

[Tool]
Top 10 property alternatives:
1. value (orkgp:HAS_VALUE)
2. metric (orkgp:HAS_METRIC)

No other items found

[Assistant] No properties related to "approach" are present on the evaluation node. I will try "algorithm" as a last common alternative.

[Tool]
Top 10 property alternatives:
1. metric (orkgp:HAS_METRIC)
2. value (orkgp:HAS_VALUE)

No other items found

[Assistant] None of the typical properties for linking models to evaluations or benchmarks are present on the evaluation node. As a final attempt, I will search for properties related to "model" at the property level in the ORKG, to see if there are any relevant properties that might be used elsewhere (e.g., "has model", "model used", etc.).

[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)

[Tool]
Got 658 rows and 3 columns, showing the first 10 rows below:
| subject                                                                   | property                    | object                                                      |
| ------------------------------------------------------------------------- | --------------------------- | ----------------------------------------------------------- |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>)   | has model (orkgp:HAS_MODEL) | DDQN (tuned) hs (<http://orkg.org/orkg/resource/R124898>)   |
| Contribution  DQN hs (<http://orkg.org/orkg/resource/R132805>)            | has model (orkgp:HAS_MODEL) | DQN hs (<http://orkg.org/orkg/resource/R124911>)            |
| Contribution  Prior+Duel hs (<http://orkg.org/orkg/resource/R132906>)     | has model (orkgp:HAS_MODEL) | Prior+Duel hs (<http://orkg.org/orkg/resource/R124919>)     |
| Contribution  DQN noop (<http://orkg.org/orkg/resource/R132706>)          | has model (orkgp:HAS_MODEL) | DQN noop (<http://orkg.org/orkg/resource/R124908>)          |
| Contribution  Prior hs (<http://orkg.org/orkg/resource/R132500>)          | has model (orkgp:HAS_MODEL) | Prior hs (<http://orkg.org/orkg/resource/R124895>)          |
| Contribution  Prior noop (<http://orkg.org/orkg/resource/R132401>)        | has model (orkgp:HAS_MODEL) | Prior noop (<http://orkg.org/orkg/resource/R124894>)        |
| Contribution  A3C FF hs (<http://orkg.org/orkg/resource/R133504>)         | has model (orkgp:HAS_MODEL) | A3C FF hs (<http://orkg.org/orkg/resource/R124913>)         |
| Contribution  A3C FF (1 day) hs (<http://orkg.org/orkg/resource/R133605>) | has model (orkgp:HAS_MODEL) | A3C FF (1 day) hs (<http://orkg.org/orkg/resource/R124914>) |
| Contribution  A3C LSTM hs (<http://orkg.org/orkg/resource/R133706>)       | has model (orkgp:HAS_MODEL) | A3C LSTM hs (<http://orkg.org/orkg/resource/R124921>)       |
| Contribution  Duel noop (<http://orkg.org/orkg/resource/R131981>)         | has model (orkgp:HAS_MODEL) | Duel noop (<http://orkg.org/orkg/resource/R124891>)         |
| ...                                                                       | ...                         | ...                                                         |

[Tool]
Got 4 rows and 3 columns:
| subject                                                               | property                        | object                                                      |
| --------------------------------------------------------------------- | ------------------------------- | ----------------------------------------------------------- |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133317>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R133348>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R134193>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |
| Benchmark Atari 2600 Skiing (<http://orkg.org/orkg/resource/R134278>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Skiing (<http://orkg.org/orkg/resource/R124988>) |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Assistant]
I have systematically checked all plausible paths:
- The Atari 2600 Skiing dataset is linked to several benchmark entities.
- These benchmarks are not linked to any models via the "has model" property.
- Evaluation nodes associated with these benchmarks also do not reference any models, nor do they use alternative properties such as "method", "approach", or "algorithm".

Given this, I will now construct a SPARQL query that attempts to traverse all plausible relationships: from the dataset to benchmarks, from benchmarks to evaluations, and from evaluations or benchmarks to any model using a variety of possible properties. If no results are returned, this will demonstrate that the knowledge graph does not currently contain such links.

Here is the query:

```sparql
SELECT DISTINCT ?model WHERE {
  # Find benchmarks for the Atari 2600 Skiing dataset
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R124988> .
  # Try to find models linked directly to the benchmark
  OPTIONAL { ?benchmark orkgp:HAS_MODEL ?model . }
  OPTIONAL { ?benchmark orkgp:P20072 ?model . }
  OPTIONAL { ?benchmark orkgp:P1004 ?model . }
  OPTIONAL { ?benchmark orkgp:P71209 ?model . }
  OPTIONAL { ?benchmark orkgp:P43024 ?model . }
  OPTIONAL { ?benchmark orkgp:P59015 ?model . }
  OPTIONAL { ?benchmark orkgp:hasModel ?model . }
  OPTIONAL { ?benchmark orkgp:P9018 ?model . }
  OPTIONAL { ?benchmark orkgp:HAS_MODEL ?model . }
  # Try to find models via evaluations
  OPTIONAL {
    ?benchmark orkgp:HAS_EVALUATION ?evaluation .
    ?evaluation ?evalModelProp ?model .
    FILTER(?evalModelProp IN (
      orkgp:HAS_MODEL, orkgp:P20072, orkgp:P1004, orkgp:P71209, orkgp:P43024,
      orkgp:P59015, orkgp:hasModel, orkgp:P9018, orkgp:HAS_MODEL
    ))
  }
  FILTER(BOUND(?model))
}
```

Now, I will execute this query to see if any models are linked to the Atari 2600 Skiing dataset, directly or indirectly.

[Tool] Got no rows and 1 columns

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query is well-constructed for the question. It attempts to find all models evaluated on the Atari 2600 Skiing dataset by checking for models linked directly to benchmarks or via evaluations, using a comprehensive set of possible model properties. It also ensures that only bound model results are returned. The answer correctly reflects that no such models are currently found in the ORKG. No further refinement is needed.


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/R117257> .
  ?benchmark orkgp:HAS_EVALUATION ?evaluation .
  {
    ?evaluation orkgp:HAS_METRIC ?metric .
  } UNION {
    ?evaluation orkgp:P2006 ?metric .
  } UNION {
    ?evaluation orkgp:P24010 ?metric .
  }
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

The evaluation metrics commonly used when benchmarking models on the WMT2014 English-German dataset, according to the ORKG knowledge graph, are:
- BLEU (http://orkg.org/orkg/resource/R116443)
- BLEU score (http://orkg.org/orkg/resource/R117121)
These are distinct entities in the knowledge graph, and no other metrics were found for this dataset using the available metric properties.

[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 WMT2014 English-German dataset?

[Assistant]
First, I need to identify the entity for the "WMT2014 English-German dataset" in the ORKG knowledge graph. Then, I will look for properties or relations related to evaluation metrics or benchmarking. This will help me find which metrics are commonly used in this context.

Step 1: Search for the entity "WMT2014 English-German" in ORKG.

[Tool]
Top 10 entity alternatives:
1. WMT2014 English-German (<http://orkg.org/orkg/resource/R117257>): has type Dataset
2. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129779>): has type Benchmark
3. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129807>): has type Benchmark
4. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129841>): has type Benchmark
5. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129827>): has type Benchmark
6. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129757>): has type Benchmark
7. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129748>): has type Benchmark
8. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129735>): has type Benchmark
9. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129715>): has type Benchmark
10. Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129704>): has type Benchmark

[Assistant]
I found several entities related to "WMT2014 English-German" in ORKG, including a dataset entity and multiple benchmark entities. The most relevant for evaluation metrics would likely be the dataset entity (http://orkg.org/orkg/resource/R117257) and the associated benchmark entities.

Next, I will investigate what properties are associated with the dataset entity to see if there are direct links to evaluation metrics, or if I need to explore the benchmark entities further.

[Tool]
Got 3 rows and 3 columns:
| subject                                                          | property   | object                              |
| ---------------------------------------------------------------- | ---------- | ----------------------------------- |
| WMT2014 English-German (<http://orkg.org/orkg/resource/R117257>) | rdf:type   | Dataset (orkgc:Dataset)             |
| WMT2014 English-German (<http://orkg.org/orkg/resource/R117257>) | rdfs:label | WMT2014 English-German (xsd:string) |
| WMT2014 English-German (<http://orkg.org/orkg/resource/R117257>) | rdf:type   | orkgc:Resource                      |

[Assistant]
The dataset entity itself does not have direct links to evaluation metrics. Since benchmarking is typically associated with benchmarks rather than the dataset entity itself, I will now explore one of the benchmark entities related to "WMT2014 English-German" to see if they are linked to evaluation metrics.

I'll start by listing the properties of the first benchmark entity: http://orkg.org/orkg/resource/R129779.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                    | property                          | object                                                                |
| -------------------------------------------------------------------------- | --------------------------------- | --------------------------------------------------------------------- |
| Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129779>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation BLEU score_21.54 (<http://orkg.org/orkg/resource/R129780>) |
| Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129779>) | has dataset (orkgp:HAS_DATASET)   | WMT2014 English-German (<http://orkg.org/orkg/resource/R117257>)      |
| Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129779>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                           |
| Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129779>) | rdfs:label                        | Benchmark WMT2014 English-German (xsd:string)                         |
| Benchmark WMT2014 English-German (<http://orkg.org/orkg/resource/R129779>) | rdf:type                          | orkgc:Resource                                                        |

[Tool]
Got 5 rows and 3 columns:
| subject                                                               | property                  | object                                               |
| --------------------------------------------------------------------- | ------------------------- | ---------------------------------------------------- |
| Evaluation BLEU score_21.54 (<http://orkg.org/orkg/resource/R129780>) | value (orkgp:HAS_VALUE)   | 21.54 (xsd:string)                                   |
| Evaluation BLEU score_21.54 (<http://orkg.org/orkg/resource/R129780>) | metric (orkgp:HAS_METRIC) | BLEU score (<http://orkg.org/orkg/resource/R117121>) |
| Evaluation BLEU score_21.54 (<http://orkg.org/orkg/resource/R129780>) | rdf:type                  | Evaluation (orkgc:Evaluation)                        |
| Evaluation BLEU score_21.54 (<http://orkg.org/orkg/resource/R129780>) | rdfs:label                | Evaluation BLEU score_21.54 (xsd:string)             |
| Evaluation BLEU score_21.54 (<http://orkg.org/orkg/resource/R129780>) | rdf:type                  | orkgc:Resource                                       |

[Tool]
Top 10 property alternatives:
1. Evaluation metrics (orkgp:P41532)
2. Has evaluation metrics (orkgp:P35217)
3. Performance metric (orkgp:P5003)
4. Automatic Evaluation Metrics (orkgp:P37517)
5. evaluation (orkgp:P5091)
6. evaluation (orkgp:P34)
7. evaluation (orkgp:HAS_EVALUATION)
8. Evaluation method (orkgp:P59089)
9. Performance metrics (orkgp:P49059)
10. Evaluation results (orkgp:P25064)

[Assistant]
From the benchmark entity, I found that the BLEU score is used as an evaluation metric for the WMT2014 English-German dataset. However, to answer the question about which metrics are "commonly used," I need to generalize across all benchmark entities associated with this dataset and aggregate the metrics used.

My plan:
1. Find all benchmark entities linked to the WMT2014 English-German dataset.
2. For each benchmark, find the evaluation metrics used.
3. Aggregate and list the unique metrics.

I will now write a SPARQL query to find all evaluation metrics used in benchmarks for the WMT2014 English-German dataset.

[Tool]
Got 2 rows and 2 columns:
| metric                                               | metricLabel             |
| ---------------------------------------------------- | ----------------------- |
| BLEU (<http://orkg.org/orkg/resource/R116443>)       | BLEU (xsd:string)       |
| BLEU score (<http://orkg.org/orkg/resource/R117121>) | BLEU score (xsd:string) |

[Tool]
Failed to call function search_object_of_property:
synonyms not found in row 0:  Cosine similarity

[Assistant]
The SPARQL query shows that the BLEU metric (with two slightly different labels: "BLEU" and "BLEU score") is the only evaluation metric currently associated with benchmarks for the WMT2014 English-German dataset in the ORKG knowledge graph.

To summarize: BLEU is the commonly used evaluation metric for benchmarking models on the WMT2014 English-German dataset, according to the available data in ORKG.

Here is the SPARQL query that retrieves this information:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct and retrieves the metrics used in benchmarks involving the WMT2014 English-German dataset. However, it only returns the IRIs of the metrics, not their human-readable labels. While the answer text provides the label 'BLEU', this is not guaranteed by the query result. To improve the query, it should include OPTIONAL clauses to retrieve the rdfs:label (or similar) for each metric, so that the answer can reliably include human-readable metric names. Additionally, the answer should mention all metrics found, not just BLEU, if more are present in the results.

[Assistant]
Thank you for the feedback. You are correct: to ensure the answer includes human-readable names for all metrics, the SPARQL query should use an OPTIONAL clause to retrieve rdfs:label for each metric. This way, the answer can reliably mention all metrics found, not just BLEU.

I will update the query accordingly and provide an improved answer.

Here is the refined SPARQL query and answer:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct and follows the rules, as it returns the actual IRIs of the metrics and includes optional labels. However, there is a minor redundancy: 'BLEU' and 'BLEU score' may refer to the same metric, and it would be helpful to check if they are actually distinct entities in the knowledge graph or just duplicates with different labels. Additionally, the query could be further refined to ensure that all relevant metrics are included, especially if there are other metrics used for this dataset in the knowledge graph. Consider checking for alternative property paths or synonyms for metrics, and possibly filtering out duplicates based on their IRIs or labels if appropriate. Otherwise, the query structure is sound.

[Assistant]
Thank you for the detailed feedback. To address your points:

1. BLEU and BLEU score may be duplicate entities with different labels. I should check if their IRIs are distinct or if they refer to the same concept.
2. To ensure completeness, I should consider alternative property paths or synonyms for metrics, as there might be other ways metrics are linked in the knowledge graph.
3. If duplicates are found (same metric with different labels), I should filter or group them accordingly.

Step 1: Check if BLEU and BLEU score are distinct entities by examining their IRIs and any linking properties (e.g., owl:sameAs, skos:exactMatch, or similar).

Step 2: Search for alternative properties that might be used to link evaluations to metrics.

Step 3: Refine the query to include all relevant metrics and filter/group duplicates if necessary.

Let's start by listing all properties and relations for both BLEU (<http://orkg.org/orkg/resource/R116443>) and BLEU score (<http://orkg.org/orkg/resource/R117121>).

[Tool]
Got 3 rows and 3 columns:
| subject                                        | property   | object                |
| ---------------------------------------------- | ---------- | --------------------- |
| BLEU (<http://orkg.org/orkg/resource/R116443>) | rdf:type   | Metric (orkgc:Metric) |
| BLEU (<http://orkg.org/orkg/resource/R116443>) | rdfs:label | BLEU (xsd:string)     |
| BLEU (<http://orkg.org/orkg/resource/R116443>) | rdf:type   | orkgc:Resource        |

[Tool]
Got 3 rows and 3 columns:
| subject                                              | property   | object                  |
| ---------------------------------------------------- | ---------- | ----------------------- |
| BLEU score (<http://orkg.org/orkg/resource/R117121>) | rdf:type   | Metric (orkgc:Metric)   |
| BLEU score (<http://orkg.org/orkg/resource/R117121>) | rdfs:label | BLEU score (xsd:string) |
| BLEU score (<http://orkg.org/orkg/resource/R117121>) | rdf:type   | orkgc:Resource          |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Top 10 property alternatives:
1. metric (orkgp:P2006)
2. metric (orkgp:HAS_METRIC)
3. Uses metric (orkgp:P24010)
4. is measured in (orkgp:P44022)
5. is measured in (orkgp:P57078)
6. measured by (orkgp:P7069)
7. Measured in (orkgp:P43238)
8. measured (orkgp:P36033)
9. Measurement type (orkgp:P15511)
10. measure (orkgp:P4006)

[Tool]
Top 10 property alternatives:
1. evaluation (orkgp:P5091)
2. evaluation (orkgp:P34)
3. evaluation (orkgp:HAS_EVALUATION)
4. Evaluated on (orkgp:P41838)
5. Means of evaluation (orkgp:P36082)
6. Evaluation method (orkgp:P59089)
7. evaluates (orkgp:P37290)
8. type of evaluation (orkgp:P82001)
9. Evaluation results (orkgp:P25064)
10. evaluate (orkgp:P20041)

[Tool]
Top 10 property alternatives:
1. score (orkgp:P82004)
2. scores (orkgp:P41859)
3. Best score (orkgp:P44146)
4. Average Score (orkgp:P36042)
5. Baseline score (orkgp:P44182)
6. # points (orkgp:P15319)
7. F1-Score (orkgp:P71208)
8. F1-score (orkgp:P23063)
9. F-score (orkgp:P59137)
10. F_β -score (orkgp:P59108)

[Tool]
Got 2 rows and 2 columns:
| metric                                               | metricLabel             |
| ---------------------------------------------------- | ----------------------- |
| BLEU (<http://orkg.org/orkg/resource/R116443>)       | BLEU (xsd:string)       |
| BLEU score (<http://orkg.org/orkg/resource/R117121>) | BLEU score (xsd:string) |

[Assistant]
After checking the knowledge graph, BLEU (<http://orkg.org/orkg/resource/R116443>) and BLEU score (<http://orkg.org/orkg/resource/R117121>) are distinct entities, each with their own IRI and label, and there are no explicit equivalence links (such as owl:sameAs or skos:exactMatch) between them. Both are of type Metric. No other evaluation metrics were found for the WMT2014 English-German dataset using alternative metric properties.

Therefore, the refined SPARQL query below retrieves all unique metric IRIs and their optional labels used in benchmarks for the WMT2014 English-German dataset, considering multiple possible property paths for metrics. The answer lists all metrics found, noting that BLEU and BLEU score are present as separate entities in the knowledge graph.

Here is the improved query and answer:

[Tool] Stopped generation process


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?paper ?paperLabel ?code_link ?benchmark ?benchmarkLabel WHERE {
  ?paper orkgp:P31 ?contribution .
  ?contribution orkgp:P37365 ?result .
  ?result orkgp:HAS_MODEL <http://orkg.org/orkg/resource/R116669> .
  ?result orkgp:HAS_BENCHMARK ?benchmark .
  ?result orkgp:HAS_SOURCE_CODE ?code_link .
  OPTIONAL {
    ?paper rdfs:label ?paperLabel .
  }
  OPTIONAL {
    ?benchmark rdfs:label ?benchmarkLabel .
  }
}

Got 2 rows and 5 columns:
| paper                                                                                | paperLabel                                                                    | code_link                                     | benchmark                                           | benchmarkLabel         |
| ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | --------------------------------------------- | --------------------------------------------------- | ---------------------- |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | DocRED: A large-scale document-level relation extraction dataset (xsd:string) | https://github.com/thunlp/DocRED (xsd:string) | Benchmark (<http://orkg.org/orkg/resource/R226586>) | Benchmark (xsd:string) |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | DocRED: A large-scale document-level relation extraction dataset (xsd:string) | https://github.com/thunlp/DocRED (xsd:string) | Benchmark (<http://orkg.org/orkg/resource/R226617>) | Benchmark (xsd:string) |

This query lists code links in papers that use the DocRED-BiLSTM model in the context of any benchmark, and also optionally retrieves human-readable labels for the papers and benchmarks if available.

[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 DocRED-BiLSTM model in any benchmark?

[Assistant]
Let's break down the question:

1. We are looking for papers (entities of type "paper").
2. These papers must use the "DocRED-BiLSTM" model in any benchmark.
3. For these papers, we want to list their code links.

Step 1: Search for the "DocRED-BiLSTM" model entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>): has type Model

[Assistant]
I found the "DocRED-BiLSTM" model entity: http://orkg.org/orkg/resource/R116669.

Step 2: Next, I need to find papers that use this model in any benchmark. To do this, I should look for properties relating papers to models used (such as "uses model", "model used", etc.).

I'll search for properties related to "model" to identify the correct property.

[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)

[Tool] Got no rows and 3 columns

[Assistant]
It appears that the property orkgp:P20072 ("model") did not return any results for papers using the "DocRED-BiLSTM" model. There are several other similar properties related to "model".

Step 3: I will try the next most likely property, orkgp:P1004 ("model"), to see if it links papers to the "DocRED-BiLSTM" model.

[Tool]
Got 2 rows and 3 columns:
| subject                                                                 | property            | object                                                  |
| ----------------------------------------------------------------------- | ------------------- | ------------------------------------------------------- |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>)      | model (orkgp:P1004) | DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>) |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | model (orkgp:P1004) | DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>) |

[Assistant]
The property orkgp:P1004 ("model") links the "DocRED-BiLSTM" model to two resources:
- DocRED (Human-annotated)
- DocRED (Distantly Supervised)

These appear to be benchmarks or datasets, not papers. The question asks for papers that use this model in any benchmark.

Step 4: I need to find papers that use the "DocRED-BiLSTM" model in any benchmark. This likely involves a chain: Paper → uses Model in Benchmark. I will look for properties linking papers to benchmarks, and/or benchmarks to models.

First, let's check what properties are associated with the "DocRED (Human-annotated)" resource to see if there is a link to papers.

[Tool]
Got 25 rows and 3 columns, showing the first 10 rows below:
| subject                                                            | property                                                             | object                                                                            |
| ------------------------------------------------------------------ | -------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | research problem (orkgp:P32)                                         | relation extraction (<http://orkg.org/orkg/resource/R172695>)                     |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | data source (orkgp:P17001)                                           | Wikidata (<http://orkg.org/orkg/resource/wikidata:Q2013>)                         |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Results (orkgp:P37365)                                               | result1 (<http://orkg.org/orkg/resource/R226583>)                                 |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Data domains (orkgp:P41878)                                          | Cross-domain (<http://orkg.org/orkg/resource/R206323>)                            |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Baseline (orkgp:P36029)                                              | convolutional neural network (<http://orkg.org/orkg/resource/wikidata:Q17084460>) |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | description (<http://orkg.org/orkg/predicate/SCHEMAORG:description>) | DocRED contains 132,375 entities...nts that are manually annotated. (xsd:string)  |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Data coverage (orkgp:P41872)                                         | Scholarly article full-text (<http://orkg.org/orkg/resource/R145812>)             |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | name (<http://orkg.org/orkg/predicate/SCHEMAORG:name>)               | DocRED (xsd:string)                                                               |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | inLanguage (<http://orkg.org/orkg/predicate/SCHEMAORG:inLanguage>)   | English (<http://orkg.org/orkg/resource/wikidata:Q1860>)                          |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | assesses (<http://orkg.org/orkg/predicate/SCHEMAORG:assesses>)       | document-level RE (xsd:string)                                                    |
| ...                                                                | ...                                                                  | ...                                                                               |

[Assistant]
From the properties of "DocRED (Human-annotated)", I see a property "Results (orkgp:P37365)" linking to "result1". This might represent a result entry, possibly connecting a paper, model, and benchmark.

Step 5: Let's check what properties "result1" (http://orkg.org/orkg/resource/R226583) has, to see if it links to a paper and a code link.

[Tool]
Got 7 rows and 3 columns:
| subject                                           | property                                | object                                                        |
| ------------------------------------------------- | --------------------------------------- | ------------------------------------------------------------- |
| result1 (<http://orkg.org/orkg/resource/R226583>) | research problem (orkgp:P32)            | relation extraction (<http://orkg.org/orkg/resource/R172695>) |
| result1 (<http://orkg.org/orkg/resource/R226583>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/thunlp/DocRED (xsd:string)                 |
| result1 (<http://orkg.org/orkg/resource/R226583>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark (<http://orkg.org/orkg/resource/R226586>)           |
| result1 (<http://orkg.org/orkg/resource/R226583>) | has model (orkgp:HAS_MODEL)             | DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>)       |
| result1 (<http://orkg.org/orkg/resource/R226583>) | rdf:type                                | R107801 (orkgc:C17002)                                        |
| result1 (<http://orkg.org/orkg/resource/R226583>) | rdfs:label                              | result1 (xsd:string)                                          |
| result1 (<http://orkg.org/orkg/resource/R226583>) | rdf:type                                | orkgc:Resource                                                |

[Tool]
Got 2 rows and 3 columns:
| subject                                           | property                    | object                                                  |
| ------------------------------------------------- | --------------------------- | ------------------------------------------------------- |
| result1 (<http://orkg.org/orkg/resource/R226583>) | has model (orkgp:HAS_MODEL) | DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>) |
| result1 (<http://orkg.org/orkg/resource/R226615>) | has model (orkgp:HAS_MODEL) | DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>) |

[Tool]
Got 7 rows and 3 columns:
| subject                                           | property                                | object                                                        |
| ------------------------------------------------- | --------------------------------------- | ------------------------------------------------------------- |
| result1 (<http://orkg.org/orkg/resource/R226615>) | research problem (orkgp:P32)            | relation extraction (<http://orkg.org/orkg/resource/R172695>) |
| result1 (<http://orkg.org/orkg/resource/R226615>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/thunlp/DocRED (xsd:string)                 |
| result1 (<http://orkg.org/orkg/resource/R226615>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark (<http://orkg.org/orkg/resource/R226617>)           |
| result1 (<http://orkg.org/orkg/resource/R226615>) | has model (orkgp:HAS_MODEL)             | DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>)       |
| result1 (<http://orkg.org/orkg/resource/R226615>) | rdf:type                                | R107801 (orkgc:C17002)                                        |
| result1 (<http://orkg.org/orkg/resource/R226615>) | rdfs:label                              | result1 (xsd:string)                                          |
| result1 (<http://orkg.org/orkg/resource/R226615>) | rdf:type                                | orkgc:Resource                                                |

[Tool]
Got 3,814 rows and 3 columns, showing the first 10 rows below:
| subject                                                                     | property                                | object                                                                |
| --------------------------------------------------------------------------- | --------------------------------------- | --------------------------------------------------------------------- |
| Contribution  DDQN (tuned) hs (<http://orkg.org/orkg/resource/R132605>)     | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/1jsingh/rl_navigation (xsd:string)                 |
| Contribution  A3C FF hs (<http://orkg.org/orkg/resource/R133504>)           | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/4rChon/NL-FuN (xsd:string)                         |
| Contribution  Duel noop (<http://orkg.org/orkg/resource/R131981>)           | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/170928/-Review-Dueling-Deep-Q-Network (xsd:string) |
| Contribution  XDC (<http://orkg.org/orkg/resource/R129978>)                 | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/HumamAlwassel/XDC (xsd:string)                     |
| Contribution  DQN Best (<http://orkg.org/orkg/resource/R133922>)            | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/alfredvc/paac (xsd:string)                         |
| Contribution  ES FF (1 hour) noop (<http://orkg.org/orkg/resource/R133938>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/alisidd/Evolution-Strategies (xsd:string)          |
| Contribution  Bootstrapped DQN (<http://orkg.org/orkg/resource/R133208>)    | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/MayarLotfy/MCDO (xsd:string)                       |
| Contribution  Gorila (<http://orkg.org/orkg/resource/R133108>)              | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/londoed/Gorila (xsd:string)                        |
| Contribution  A2C + SIL (<http://orkg.org/orkg/resource/R133404>)           | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/alhabk/SGEE--pytorch (xsd:string)                  |
| Contribution  POP3D (<http://orkg.org/orkg/resource/R133822>)               | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/cxxgtxy/POP3D (xsd:string)                         |
| ...                                                                         | ...                                     | ...                                                                   |

[Tool] Got no rows and 3 columns

[Tool]
Got 21,990 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                 | object                                                                   |
| ------------------------------------------------------------------------------------------------------------- | ------------------------ | ------------------------------------------------------------------------ |
| Tadalafil Treatment Delays the O...y in Dystrophin‐Deficient Hearts (<http://orkg.org/orkg/resource/R249264>) | contribution (orkgp:P31) | Contribution (<http://orkg.org/orkg/resource/R249265>)                   |
| The quiescent intracluster mediu... the core of the Perseus cluster (<http://orkg.org/orkg/resource/R245906>) | contribution (orkgp:P31) | HCI AstroMissions  Hitomi 2016 (<http://orkg.org/orkg/resource/R245926>) |
| The quiescent intracluster mediu... the core of the Perseus cluster (<http://orkg.org/orkg/resource/R245924>) | contribution (orkgp:P31) | HCI AstroMissions  Hitomi 2016 (<http://orkg.org/orkg/resource/R245943>) |
| The quiescent intracluster mediu... the core of the Perseus cluster (<http://orkg.org/orkg/resource/R245942>) | contribution (orkgp:P31) | HCI AstroMissions  Hitomi 2016 (<http://orkg.org/orkg/resource/R245944>) |
| Measurements of resonant scatter...us Cluster core with Hitomi SXS* (<http://orkg.org/orkg/resource/R281617>) | contribution (orkgp:P31) | ASTRO X-ray Stars HITOMI 2018 (<http://orkg.org/orkg/resource/R281619>)  |
| Towards a city digital twin (<http://orkg.org/orkg/resource/R160598>)                                         | contribution (orkgp:P31) | Contribution (<http://orkg.org/orkg/resource/R160599>)                   |
| A SARS-CoV-2 protein interaction...als targets for drug repurposing (<http://orkg.org/orkg/resource/R51267>)  | contribution (orkgp:P31) | Contribution 1 (<http://orkg.org/orkg/resource/R51346>)                  |
| NNE: A Dataset for Nested Named ... Recognition in English Newswire (<http://orkg.org/orkg/resource/R211524>) | contribution (orkgp:P31) | NNE Corpus (<http://orkg.org/orkg/resource/R211526>)                     |
| Dose-dependent effects of inspir...ining in neuromuscular disorders (<http://orkg.org/orkg/resource/R225565>) | contribution (orkgp:P31) | Contribution (<http://orkg.org/orkg/resource/R225567>)                   |
| The update of the Italian Food Composition Database (<http://orkg.org/orkg/resource/R203907>)                 | contribution (orkgp:P31) | UIDCD 2003 (<http://orkg.org/orkg/resource/R203934>)                     |
| ...                                                                                | ...                      | ...                                                                      |

[Assistant]
Here's what I've found so far:
- The "DocRED-BiLSTM" model is used in two result resources (result1: R226583 and another result1: R226615).
- Both results have a source code link: https://github.com/thunlp/DocRED.
- These results are not directly linked to a paper entity, but are likely contributions or results within a paper.

To answer the question, I need to find the paper(s) that these results belong to. In ORKG, results are often linked to papers via a "has contribution" or "contribution" property.

Step 6: Let's search for papers that have a contribution pointing to either R226583 or R226615.

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Got 1 row and 3 columns:
| subject                                                            | property               | object                                            |
| ------------------------------------------------------------------ | ---------------------- | ------------------------------------------------- |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Results (orkgp:P37365) | result1 (<http://orkg.org/orkg/resource/R226583>) |

[Tool]
Got 1 row and 3 columns:
| subject                                                                 | property               | object                                            |
| ----------------------------------------------------------------------- | ---------------------- | ------------------------------------------------- |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | Results (orkgp:P37365) | result1 (<http://orkg.org/orkg/resource/R226615>) |

[Tool]
Got 25 rows and 3 columns, showing the first 10 rows below:
| subject                                                            | property                                                             | object                                                                            |
| ------------------------------------------------------------------ | -------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | research problem (orkgp:P32)                                         | relation extraction (<http://orkg.org/orkg/resource/R172695>)                     |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | data source (orkgp:P17001)                                           | Wikidata (<http://orkg.org/orkg/resource/wikidata:Q2013>)                         |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Results (orkgp:P37365)                                               | result1 (<http://orkg.org/orkg/resource/R226583>)                                 |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Data domains (orkgp:P41878)                                          | Cross-domain (<http://orkg.org/orkg/resource/R206323>)                            |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Baseline (orkgp:P36029)                                              | convolutional neural network (<http://orkg.org/orkg/resource/wikidata:Q17084460>) |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | description (<http://orkg.org/orkg/predicate/SCHEMAORG:description>) | DocRED contains 132,375 entities...nts that are manually annotated. (xsd:string)  |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | Data coverage (orkgp:P41872)                                         | Scholarly article full-text (<http://orkg.org/orkg/resource/R145812>)             |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | name (<http://orkg.org/orkg/predicate/SCHEMAORG:name>)               | DocRED (xsd:string)                                                               |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | inLanguage (<http://orkg.org/orkg/predicate/SCHEMAORG:inLanguage>)   | English (<http://orkg.org/orkg/resource/wikidata:Q1860>)                          |
| DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>) | assesses (<http://orkg.org/orkg/predicate/SCHEMAORG:assesses>)       | document-level RE (xsd:string)                                                    |
| ...                                                                | ...                                                                  | ...                                                                               |

[Tool]
Got 24 rows and 3 columns, showing the first 10 rows below:
| subject                                                                 | property                                                             | object                                                                           |
| ----------------------------------------------------------------------- | -------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | research problem (orkgp:P32)                                         | relation extraction (<http://orkg.org/orkg/resource/R172695>)                    |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | data source (orkgp:P17001)                                           | Wikidata (<http://orkg.org/orkg/resource/wikidata:Q2013>)                        |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | Results (orkgp:P37365)                                               | result1 (<http://orkg.org/orkg/resource/R226615>)                                |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | Data domains (orkgp:P41878)                                          | Cross-domain (<http://orkg.org/orkg/resource/R206323>)                           |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | description (<http://orkg.org/orkg/predicate/SCHEMAORG:description>) | A collection of large-scale dist... weakly supervised RE scenarios. (xsd:string) |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | Data coverage (orkgp:P41872)                                         | Scholarly article full-text (<http://orkg.org/orkg/resource/R145812>)            |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | name (<http://orkg.org/orkg/predicate/SCHEMAORG:name>)               | DocRED (xsd:string)                                                              |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | inLanguage (<http://orkg.org/orkg/predicate/SCHEMAORG:inLanguage>)   | English (<http://orkg.org/orkg/resource/wikidata:Q1860>)                         |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | assesses (<http://orkg.org/orkg/predicate/SCHEMAORG:assesses>)       | document-level RE (xsd:string)                                                   |
| DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>) | url (<http://orkg.org/orkg/predicate/SCHEMAORG:url>)                 | https://github.com/thunlp/DocRED (xsd:string)                                    |
| ...                                                                     | ...                                                                  | ...                                                                              |

[Tool]
Top 10 property alternatives:
1. Paper (orkgp:P15330)
2. has paper (orkgp:HasPaper)
3. Paper type (orkgp:P41847)
4. type of the paper (orkgp:P91000)
5. Has ORKG Paper (orkgp:P35034)
6. paper:type (orkgp:P41745)
7. Papers (orkgp:P15336)
8. paper: Theory / Concept / Model (orkgp:P43091)
9. paper: Theory / Construct / Model (orkgp:P43142)
10. paper:journal (orkgp:P37235)

[Tool] Got no rows and 3 columns

[Tool]
Top 10 entity alternatives:
1. DocRED (<http://orkg.org/orkg/resource/R116647>): has type Dataset
2. DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>): has type Benchmark Dataset, has type Contribution, has type Dataset
3. DocRED-LSTM (<http://orkg.org/orkg/resource/R116671>): has type Model
4. DocRED-Context-Aware (<http://orkg.org/orkg/resource/R116670>): has type Model
5. DocRED-BiLSTM (<http://orkg.org/orkg/resource/R116669>): has type Model
6. DocRED-CNN (<http://orkg.org/orkg/resource/R116672>): has type Model
7. DocRED (Distantly Supervised) (<http://orkg.org/orkg/resource/R220302>): has type Benchmark Dataset, has type Contribution, has type Dataset
8. DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>): has type Paper

[Tool]
Got 19 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                     | object                                                                                |
| ---------------------------------------------------------------------------------------------------------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------- |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | author (orkgp:P27)           |  Lixin Huang (xsd:string)                                                                                |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | contribution (orkgp:P31)     | DocRED (Human-annotated) (<http://orkg.org/orkg/resource/R172698>)                                            |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | research field (orkgp:P30)   | Natural Language Processing (<http://orkg.org/orkg/resource/R145261>)                                         |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | publication year (orkgp:P29) | 2019 (xsd:string)                                                                                |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | venue (orkgp:HAS_VENUE)      | 57th Annual Meeting of the Assoc...putational Linguistics (ACL2019) (<http://orkg.org/orkg/resource/R172697>) |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | url (orkgp:url)              | https://aclanthology.org/P19-1074.pdf (xsd:string)                                                            |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | rdf:type                     | Paper (orkgc:Paper)                                                                                |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | rdfs:label                   | DocRED: A large-scale document-level relation extraction dataset (xsd:string)                                 |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | author (orkgp:P27)           |  Zhiyuan Liu (xsd:string)                                                                                |
| DocRED: A large-scale document-level relation extraction dataset (<http://orkg.org/orkg/resource/R172696>) | author (orkgp:P27)           | Deming Ye (xsd:string)                                                                                |
| ...                                                                                | ...                          | ...                                                                                |

[Assistant]
I have found that the paper "DocRED: A large-scale document-level relation extraction dataset" (http://orkg.org/orkg/resource/R172696) has a contribution "DocRED (Human-annotated)" (http://orkg.org/orkg/resource/R172698), which in turn is linked to the result using the "DocRED-BiLSTM" model and provides the code link.

Therefore, the code link for the DocRED-BiLSTM model in the context of a benchmark (DocRED) is associated with this paper.

Now, I will construct a SPARQL query that:
- Finds papers with a contribution that uses the "DocRED-BiLSTM" model in any benchmark (via result nodes).
- Returns the code links (if available).

Let's write the query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct, but it does not strictly ensure that the paper is using the DocRED-BiLSTM model in the context of a benchmark. The query currently finds papers with a contribution that has a result using the DocRED-BiLSTM model and a code link, but it does not check for any benchmark association. To fully answer the question, the query should also ensure that the result is associated with a benchmark (e.g., via a property like orkgp:HAS_BENCHMARK or similar). Please refine the query to include this condition if such a property exists in the knowledge graph.

[Assistant]
Thank you for the feedback. You are correct: to strictly answer the question, the query should ensure that the result node is associated with a benchmark, not just a model and code link.

Step 1: I have already seen that the result nodes (e.g., http://orkg.org/orkg/resource/R226583) have a property orkgp:HAS_BENCHMARK linking to a benchmark entity.

Step 2: I will refine the query to require that the result node has both:
- orkgp:HAS_MODEL DocRED-BiLSTM
- orkgp:HAS_BENCHMARK ?benchmark
- orkgp:HAS_SOURCE_CODE ?code_link

This will ensure that only results using the model in the context of a benchmark are considered.

Here is the refined query:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is mostly correct and returns the relevant code links, papers, and benchmarks. However, it currently returns the IRIs for papers and benchmarks, but the code_link is returned as a literal string. This is acceptable, but for completeness, it would be beneficial to also optionally retrieve human-readable labels for the papers and benchmarks using OPTIONAL clauses with rdfs:label. This would make the results more user-friendly if labels are available, while still adhering to the requirement that IRIs are always included. The current query is otherwise correct and does not need major changes.

[Assistant]
Thank you for the feedback. To make the results more user-friendly, I will add OPTIONAL clauses to retrieve rdfs:label for both the paper and the benchmark, while still always returning their IRIs and the code link.

Here is the refined query:

[Tool] Stopped generation process


PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?evaluation ?metric ?metricLabel ?value WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R125977> ; orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric ; orkgp:HAS_VALUE ?value .
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}
ORDER BY DESC ( xsd:float ( ?value ) )
LIMIT 1

The highest benchmark result achieved on the WOS-5736 dataset is an Accuracy of 90.93.

[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 highest benchmark result achieved on the WOS-5736 dataset, including the metric and its value?

[Assistant]
Let's break down the question:

1. We need to identify the WOS-5736 dataset in the ORKG knowledge graph.
2. We need to find benchmark results associated with this dataset.
3. For each result, we need to find the metric and its value.
4. We need to find the highest value among these results, and report the corresponding metric and value.

First, I'll search for the "WOS-5736" dataset entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. WOS-5736 (<http://orkg.org/orkg/resource/R125977>): has type Dataset
2. Benchmark WOS-5736 (<http://orkg.org/orkg/resource/R134498>): has type Benchmark

[Assistant]
The entity "WOS-5736" with IRI <http://orkg.org/orkg/resource/R125977> is identified as a Dataset. Now, I need to find benchmark results associated with this dataset.

I'll search for properties related to "benchmark result" or similar for this dataset.

[Tool]
No property items found

No other items found

[Assistant]
It appears there is no direct property labeled "benchmark" for the WOS-5736 dataset entity. However, there is another entity called "Benchmark WOS-5736" (<http://orkg.org/orkg/resource/R134498>), which might represent benchmark results for this dataset.

I will now check what properties are associated with the "Benchmark WOS-5736" entity to see if it contains benchmark results, metrics, and values.

[Tool]
Got 5 rows and 3 columns:
| subject                                                      | property                          | object                                                              |
| ------------------------------------------------------------ | --------------------------------- | ------------------------------------------------------------------- |
| Benchmark WOS-5736 (<http://orkg.org/orkg/resource/R134498>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_90.93 (<http://orkg.org/orkg/resource/R134499>) |
| Benchmark WOS-5736 (<http://orkg.org/orkg/resource/R134498>) | has dataset (orkgp:HAS_DATASET)   | WOS-5736 (<http://orkg.org/orkg/resource/R125977>)                  |
| Benchmark WOS-5736 (<http://orkg.org/orkg/resource/R134498>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                         |
| Benchmark WOS-5736 (<http://orkg.org/orkg/resource/R134498>) | rdfs:label                        | Benchmark WOS-5736 (xsd:string)                                     |
| Benchmark WOS-5736 (<http://orkg.org/orkg/resource/R134498>) | rdf:type                          | orkgc:Resource                                                      |

[Assistant]
The "Benchmark WOS-5736" entity has an evaluation property pointing to "Evaluation Accuracy_90.93" (<http://orkg.org/orkg/resource/R134499>). This likely represents a benchmark result with a metric (Accuracy) and a value (90.93).

Next, I will inspect the "Evaluation Accuracy_90.93" entity to extract the metric and value explicitly, and check if there are other evaluations (in case there are multiple results).

[Tool]
Got 5 rows and 3 columns:
| subject                                                             | property                  | object                                             |
| ------------------------------------------------------------------- | ------------------------- | -------------------------------------------------- |
| Evaluation Accuracy_90.93 (<http://orkg.org/orkg/resource/R134499>) | value (orkgp:HAS_VALUE)   | 90.93 (xsd:string)                                 |
| Evaluation Accuracy_90.93 (<http://orkg.org/orkg/resource/R134499>) | metric (orkgp:HAS_METRIC) | Accuracy (<http://orkg.org/orkg/resource/R111697>) |
| Evaluation Accuracy_90.93 (<http://orkg.org/orkg/resource/R134499>) | rdf:type                  | Evaluation (orkgc:Evaluation)                      |
| Evaluation Accuracy_90.93 (<http://orkg.org/orkg/resource/R134499>) | rdfs:label                | Evaluation Accuracy_90.93 (xsd:string)             |
| Evaluation Accuracy_90.93 (<http://orkg.org/orkg/resource/R134499>) | rdf:type                  | orkgc:Resource                                     |

[Assistant]
The evaluation result for "Benchmark WOS-5736" is:
- Metric: Accuracy (<http://orkg.org/orkg/resource/R111697>)
- Value: 90.93

To ensure completeness, I should check if there are any other evaluations linked to "Benchmark WOS-5736" that might have a higher value for any metric.

I'll check if there are multiple evaluations linked to "Benchmark WOS-5736".

[Tool]
Got 1 row and 3 columns:
| subject                                                      | property                          | object                                                              |
| ------------------------------------------------------------ | --------------------------------- | ------------------------------------------------------------------- |
| Benchmark WOS-5736 (<http://orkg.org/orkg/resource/R134498>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Accuracy_90.93 (<http://orkg.org/orkg/resource/R134499>) |

[Assistant]
There is only one evaluation result linked to "Benchmark WOS-5736", which is "Evaluation Accuracy_90.93". Therefore, the highest benchmark result achieved on the WOS-5736 dataset is:

- Metric: Accuracy
- Value: 90.93

I will now construct a SPARQL query that retrieves the metric and value for the highest benchmark result on the WOS-5736 dataset, ensuring the query is general enough in case more results are added in the future.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the highest benchmark result for the WOS-5736 dataset, including the metric and its value, by ordering the results in descending order by value and limiting the output to the top result. The query also returns the IRIs for the evaluation and metric, and includes the metric label as an optional field, which is appropriate. The answer is accurate and complete according to the requirements.