PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?model ?modelLabel WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R124963> .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark .
  ?contribution orkgp:HAS_MODEL ?model .
  OPTIONAL {
    ?model rdfs:label ?modelLabel .
  }
}

Got 23 rows and 2 columns, showing the first 5 and last 5 rows below:
| model                                                         | modelLabel                       |
| ------------------------------------------------------------- | -------------------------------- |
| CURL (<http://orkg.org/orkg/resource/R123293>)                | CURL (xsd:string)                |
| SAC (<http://orkg.org/orkg/resource/R123322>)                 | SAC (xsd:string)                 |
| C51 noop (<http://orkg.org/orkg/resource/R124890>)            | C51 noop (xsd:string)            |
| Duel noop (<http://orkg.org/orkg/resource/R124891>)           | Duel noop (xsd:string)           |
| Duel hs (<http://orkg.org/orkg/resource/R124892>)             | Duel hs (xsd:string)             |
| ...                                                           | ...                              |
| Prior+Duel hs (<http://orkg.org/orkg/resource/R124919>)       | Prior+Duel hs (xsd:string)       |
| ES FF (1 hour) noop (<http://orkg.org/orkg/resource/R124920>) | ES FF (1 hour) noop (xsd:string) |
| A3C LSTM hs (<http://orkg.org/orkg/resource/R124921>)         | A3C LSTM hs (xsd:string)         |
| Prior+Duel noop (<http://orkg.org/orkg/resource/R124922>)     | Prior+Duel noop (xsd:string)     |
| Reactor 500M (<http://orkg.org/orkg/resource/R124931>)        | Reactor 500M (xsd:string)        |

This query lists the models that have been evaluated on the Atari 2600 Assault dataset, including both their IRIs and (optionally) their human-readable names.

[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 Assault dataset?

[Assistant]
Let's break down the question:

1. The user wants to know which models have been evaluated on the "Atari 2600 Assault" dataset.
2. We need to identify the entity for the "Atari 2600 Assault" dataset in ORKG.
3. Then, we need to find the property that links models to datasets they have been evaluated on (e.g., "evaluated on", "evaluation dataset", etc.).
4. Finally, we need to construct a SPARQL query to list the models evaluated on this dataset.

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

[Tool]
Top 10 entity alternatives:
1. Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>): has type Dataset
2. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132006>): has type Benchmark
3. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R133131>): has type Benchmark
4. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R133033>): has type Benchmark
5. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132931>): has type Benchmark
6. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132830>): has type Benchmark
7. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132731>): has type Benchmark
8. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132630>): has type Benchmark
9. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132525>): has type Benchmark
10. Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132426>): has type Benchmark

[Assistant]
I found several entities related to "Atari 2600 Assault" in ORKG, including one specifically named "Atari 2600 Assault" (Dataset: <http://orkg.org/orkg/resource/R124963>) and several others named "Benchmark Atari 2600 Assault" (all with type Benchmark).

To be comprehensive, I will focus on the main dataset entity (<http://orkg.org/orkg/resource/R124963>) for "Atari 2600 Assault". Next, I need to identify the property that links models to datasets they have been evaluated on. I will search for properties related to "evaluated on" or "evaluation dataset".

[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]
The most relevant property appears to be "Evaluated on" (orkgp:P41838).

Now, I will check how this property is used with the "Atari 2600 Assault" dataset entity (<http://orkg.org/orkg/resource/R124963>) as the object, to find which models have been evaluated on it.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that there are no direct triples using the "Evaluated on" property (orkgp:P41838) with the "Atari 2600 Assault" dataset as the object in the ORKG. However, there may be alternative properties or indirect links (such as through benchmarks or other evaluation-related properties).

To be thorough, I will also check the "evaluate on" property (orkgp:P20042), as it is a close alternative, and see if any models are linked to the dataset using this property.

[Tool] Got no rows and 3 columns

[Assistant]
There are also no direct links using the "evaluate on" property (orkgp:P20042) with the "Atari 2600 Assault" dataset as the object.

Given that there are multiple "Benchmark Atari 2600 Assault" entities, it's possible that models are linked to one of these benchmark entities instead of the dataset entity. I will check if any models are linked to one of the benchmark entities using the "Evaluated on" property. I will start with the first benchmark entity: <http://orkg.org/orkg/resource/R132006>.

[Tool] Got no rows and 3 columns

[Assistant]
There are no direct links using the "Evaluated on" property with the first benchmark entity either. Since there are several benchmark entities, it's possible that a different property is used, or that the evaluation is represented differently in the ORKG.

To cover more ground, I will search for properties related to "evaluation dataset" to see if there is another property commonly used to link models to datasets or benchmarks.

[Tool]
Top 10 property alternatives:
1. On evaluation dataset (orkgp:P18036)
2. Dataset (orkgp:P96045)
3. dataset (orkgp:P2005)
4. Dataset used (orkgp:P25063)
5. Experimental Dataset (orkgp:P15096)
6. dataset description (orkgp:P16037)
7. Datasets used (orkgp:P58072)
8. Datasets (orkgp:P44054)
9. Evaluation results (orkgp:P25064)
10. Uses dataset (orkgp:P18087)

[Assistant]
There are several properties that could potentially link models to datasets, such as "On evaluation dataset" (orkgp:P18036), "Dataset used" (orkgp:P25063), and "Uses dataset" (orkgp:P18087).

I will try the property "On evaluation dataset" (orkgp:P18036) with the "Atari 2600 Assault" dataset entity to see if any models are linked this way.

[Tool] Got no rows and 3 columns

[Assistant]
There are still no direct links using the "On evaluation dataset" property with the "Atari 2600 Assault" dataset.

Given that none of the direct dataset or benchmark entities are linked to models via the most likely properties, it's possible that the evaluation is represented in a more complex structure, such as through an intermediate "evaluation" or "result" entity.

To investigate further, I will search for entities of type "Model" and see how their evaluation datasets are represented. This may help identify the correct property or pattern.

[Tool]
Top 10 entity alternatives:
1. Model (<http://orkg.org/orkg/resource/R150425>): has type Contribution Template
2. Model (<http://orkg.org/orkg/resource/R44212>)
3. Model (<http://orkg.org/orkg/resource/R150408>): has type Contribution Template
4. Model (<http://orkg.org/orkg/resource/R50465>): has type Sentence, has type Model
5. Model (<http://orkg.org/orkg/resource/R50479>): has type Sentence, has type Model
6. Model (<http://orkg.org/orkg/resource/R69780>)
7. Model (<http://orkg.org/orkg/resource/R50384>): has type Sentence, has type Model
8. Model (<http://orkg.org/orkg/resource/R44256>)
9. Model (<http://orkg.org/orkg/resource/R50367>): has type Sentence, has type Model
10. Model (<http://orkg.org/orkg/resource/R50366>): has type Sentence, has type Model

[Tool]
Got 5 rows and 3 columns:
| subject                                        | property                       | object                                                                           |
| ---------------------------------------------- | ------------------------------ | -------------------------------------------------------------------------------- |
| Model (<http://orkg.org/orkg/resource/R50465>) | has content (orkgp:hasContent) | We pre-trained BioBERT using Nav... v1.1 (þPubMed) in this setting. (xsd:string) |
| Model (<http://orkg.org/orkg/resource/R50465>) | rdf:type                       | Model (orkgc:Model)                                                              |
| Model (<http://orkg.org/orkg/resource/R50465>) | rdfs:label                     | Model (xsd:string)                                                               |
| Model (<http://orkg.org/orkg/resource/R50465>) | rdf:type                       | Sentence (orkgc:Sentence)                                                        |
| Model (<http://orkg.org/orkg/resource/R50465>) | rdf:type                       | orkgc:Resource                                                                   |

[Assistant]
Looking at the properties of a "Model" entity (<http://orkg.org/orkg/resource/R50465>), I see that it uses general properties like "has content", "rdf:type", and "rdfs:label", but nothing directly indicating evaluation or dataset links.

Since the direct approach is not yielding results, I will try a broader SPARQL query: I will look for any triples where the object is the "Atari 2600 Assault" dataset (<http://orkg.org/orkg/resource/R124963>), regardless of property, to see if there are any indirect or unexpected links.

[Tool]
Got 24 rows and 3 columns, showing the first 10 rows below:
| subject                                                                | property                        | object                                                       |
| ---------------------------------------------------------------------- | ------------------------------- | ------------------------------------------------------------ |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R131208>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R131903>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132006>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132107>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132208>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132307>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132387>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132426>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132525>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R132630>) | has dataset (orkgp:HAS_DATASET) | Atari 2600 Assault (<http://orkg.org/orkg/resource/R124963>) |
| ...                                                                    | ...                             | ...                                                          |

[Assistant]
From this broader search, I see that several "Benchmark Atari 2600 Assault" entities are linked to the "Atari 2600 Assault" dataset via the property "has dataset" (orkgp:HAS_DATASET). This suggests that models might be evaluated on these benchmark entities rather than directly on the dataset.

Next, I will check if any models are linked to one of these benchmark entities (e.g., <http://orkg.org/orkg/resource/R131208>) using properties like "Evaluated on" (orkgp:P41838) or similar.

[Tool] Got no rows and 3 columns

[Assistant]
There are still no direct links using "Evaluated on" with the benchmark entity. Since the benchmarks themselves might be part of a more complex evaluation structure, I will try a different approach:

I will look for any triples where the object is one of these benchmark entities, regardless of property. This may reveal how models are connected to these benchmarks.

[Tool]
Got 1 row and 3 columns:
| subject                                                      | property                            | object                                                                 |
| ------------------------------------------------------------ | ----------------------------------- | ---------------------------------------------------------------------- |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Atari 2600 Assault (<http://orkg.org/orkg/resource/R131208>) |

[Tool]
Got 45 rows and 3 columns, showing the first 10 rows below:
| subject                                                      | property                                | object                                                                                |
| ------------------------------------------------------------ | --------------------------------------- | -------------------------------------------------------------------------------------- |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | research problem (orkgp:P32)            | Continuous Control (<http://orkg.org/orkg/resource/R123291>)                           |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/aravindsrinivas/curl_rainbow (xsd:string)                           |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Finger, spin (DMControl100k) (<http://orkg.org/orkg/resource/R131172>)       |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has model (orkgp:HAS_MODEL)             | CURL (<http://orkg.org/orkg/resource/R123293>)                                         |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | rdf:type                                | Contribution (orkgc:Contribution)                                                      |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | rdfs:label                              | Contribution 	CURL (xsd:string)                                                        |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/MishaLaskin/curl (xsd:string)                                       |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Ball in cup, catch (DMControl500k) (<http://orkg.org/orkg/resource/R131174>) |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Walker, walk (DMControl100k) (<http://orkg.org/orkg/resource/R131176>)       |
| Contribution  CURL (<http://orkg.org/orkg/resource/R131171>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Reacher, easy (DMControl100k) (<http://orkg.org/orkg/resource/R131178>)      |
| ...                                                          | ...                                     | ...                                                                                |

[Tool]
Got 3 rows and 3 columns:
| subject                                        | property   | object              |
| ---------------------------------------------- | ---------- | ------------------- |
| CURL (<http://orkg.org/orkg/resource/R123293>) | rdf:type   | Model (orkgc:Model) |
| CURL (<http://orkg.org/orkg/resource/R123293>) | rdfs:label | CURL (xsd:string)   |
| CURL (<http://orkg.org/orkg/resource/R123293>) | rdf:type   | orkgc:Resource      |

[Assistant]
From this exploration, I found that the model "CURL" (<http://orkg.org/orkg/resource/R123293>) is linked via a "Contribution" entity (<http://orkg.org/orkg/resource/R131171>) to the "Benchmark Atari 2600 Assault" (<http://orkg.org/orkg/resource/R131208>), which in turn is linked to the "Atari 2600 Assault" dataset.

This suggests that models evaluated on the Atari 2600 Assault dataset are linked through a chain: Model ← Contribution ← has benchmark ← Benchmark Atari 2600 Assault ← has dataset ← Atari 2600 Assault.

I will now write a SPARQL query to find all models that are linked, through a contribution and benchmark, to the "Atari 2600 Assault" dataset.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query correctly retrieves the models evaluated on the Atari 2600 Assault dataset by following the dataset-to-benchmark-to-contribution-to-model path. However, the query only returns the model IRIs and not their human-readable names. For better usability, especially since the answer lists model names, it would be helpful to include the rdfs:label of each model in the SELECT clause using an OPTIONAL clause. This would make the results more informative and user-friendly. The current query is otherwise correct and complete in terms of logic.

[Assistant]
Thank you for your feedback. To improve usability, I will update the SPARQL query to include the rdfs:label of each model using an OPTIONAL clause. This way, the results will show both the model IRIs and their human-readable names.

Here is the refined query:

```sparql
SELECT DISTINCT ?model ?modelLabel WHERE {
  ?benchmark orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R124963> .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark .
  ?contribution orkgp:HAS_MODEL ?model .
  OPTIONAL { ?model rdfs:label ?modelLabel. }
}
```

I will now submit this as the final answer.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the models evaluated on the Atari 2600 Assault dataset by traversing from the dataset to benchmarks, then to contributions, and finally to models. It returns both the model IRIs and their optional labels, as required. The result is relevant and complete for the user's question. No further refinement is needed.


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] Can you provide links to code used in papers that benchmark the SEE model?

[Assistant]
Let's break down the question:

1. We are looking for papers that benchmark the "SEE model".
2. For those papers, we want to find links to code used in them.

Step 1: Identify the "SEE model" entity in the ORKG.
Step 2: Find papers that benchmark the SEE model.
Step 3: For those papers, find code links (usually via properties like "code", "implementation", "github", etc.).

Let's start by searching for the "SEE model" entity in the ORKG.
I will search for the entity "SEE model" in the ORKG to identify its IRI and ensure we are referencing the correct model in subsequent queries.


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 ?contribution ?model ?modelLabel ?f1 WHERE {
  ?contribution orkgp:HAS_BENCHMARK <http://orkg.org/orkg/resource/R129339> .
  OPTIONAL {
    ?contribution orkgp:HAS_MODEL ?model .
    OPTIONAL {
      ?model rdfs:label ?modelLabel .
    }
  }
  OPTIONAL {
    ?contribution orkgp:P18037 ?f1 .
  }
}
ORDER BY DESC ( xsd:float ( ?f1 ) )
LIMIT 1

There is currently no model with an F1 metric recorded for the CoNLL 2012 benchmark dataset in the ORKG. While the 'SpanRel' model is linked to this benchmark, no F1 value is available for it or any other model.

[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] Indicate the model that performed best in terms of F1 metric on the CoNLL 2012 benchmark dataset?

[Assistant]
Let's break down the question:

1. We are looking for a model (likely a research contribution or system) that achieved the best performance in terms of the F1 metric.
2. The performance is specifically on the "CoNLL 2012 benchmark dataset".
3. We need to identify the relevant entities: models, F1 metric, and CoNLL 2012 benchmark dataset.
4. We need to find the property that links models to their F1 metric performance on a dataset.

First, I will search for the "CoNLL 2012" dataset entity in the ORKG.

[Tool]
Top 10 entity alternatives:
1. CoNLL 2012 (<http://orkg.org/orkg/resource/R122123>): has type Dataset
2. Benchmark CoNLL 2012 (<http://orkg.org/orkg/resource/R129339>): has type Benchmark
3. CoNLL (<http://orkg.org/orkg/resource/R46350>)
4. 2012 (<http://orkg.org/orkg/resource/wikidata:Q1990>): has type External
5. CoNLL++ (<http://orkg.org/orkg/resource/R122914>): has type Dataset
6. CoNLL (<http://orkg.org/orkg/resource/R44354>)
7. 2012 (<http://orkg.org/orkg/resource/R203543>)
8. 2012 (<http://orkg.org/orkg/resource/R3052>)
9. 2012 (<http://orkg.org/orkg/resource/R4437>)
10. 2012 (<http://orkg.org/orkg/resource/R4488>)

[Assistant]
I found two highly relevant entities:
- CoNLL 2012 (<http://orkg.org/orkg/resource/R122123>) of type Dataset
- Benchmark CoNLL 2012 (<http://orkg.org/orkg/resource/R129339>) of type Benchmark

Since the question refers to the "CoNLL 2012 benchmark dataset", both may be relevant, but the "Benchmark CoNLL 2012" seems most precise.

Next, I will search for the F1 metric property in the ORKG, as we need to find which model achieved the best F1 score on this benchmark.

[Tool]
Top 10 property alternatives:
1. F1 (orkgp:P18037)
2. Best F1 (orkgp:P36078)
3. F1-Score (orkgp:P71208)
4. F1-score (orkgp:P23063)
5. F1-score (True News) (orkgp:P68127)
6. approximate f1 score (orkgp:P71212)
7. F1-Score (Discuss) (orkgp:P67035)
8. F1-Score (Agree) (orkgp:P67033)
9. F1-Score (Real News) (orkgp:P66044)
10. Original Features (F1-score) (orkgp:P68074)

[Assistant]
There are several properties related to F1, but the most general and likely relevant ones are:
- F1 (orkgp:P18037)
- Best F1 (orkgp:P36078)
- F1-Score (orkgp:P71208)
- F1-score (orkgp:P23063)

Since the question asks for the "best" model in terms of F1, "Best F1" (orkgp:P36078) seems most appropriate, but to be thorough, I will consider both "Best F1" and the general "F1" properties.

Next, I will look for models or systems that have an F1 score reported on the "Benchmark CoNLL 2012" dataset. To do this, I will search for triples where the dataset is "Benchmark CoNLL 2012" and see how models and F1 scores are linked.

[Tool]
Got 1 row and 3 columns:
| subject                                                         | property                            | object                                                         |
| --------------------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------- |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark CoNLL 2012 (<http://orkg.org/orkg/resource/R129339>) |

[Assistant]
I found that the entity "Contribution SpanRel" (<http://orkg.org/orkg/resource/R129332>) is linked to "Benchmark CoNLL 2012" via the property "has benchmark" (orkgp:HAS_BENCHMARK).

Now, I need to check if this contribution (likely a model or system) has an F1 score reported, and if there are other contributions linked to this benchmark. I will list all triples for "Contribution SpanRel" to see if it has an F1 score and to understand how the F1 metric is represented.

[Tool]
Got 15 rows and 3 columns, showing the first 10 rows below:
| subject                                                         | property                                | object                                                                  |
| --------------------------------------------------------------- | --------------------------------------- | ----------------------------------------------------------------------- |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | research problem (orkgp:P32)            | Part-Of-Speech Tagging (<http://orkg.org/orkg/resource/R116310>)        |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/jzbjyb/SpanRel (xsd:string)                          |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>)       |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has model (orkgp:HAS_MODEL)             | SpanRel (<http://orkg.org/orkg/resource/R116338>)                       |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | rdf:type                                | Contribution (orkgc:Contribution)                                       |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | rdfs:label                              | Contribution 	SpanRel (xsd:string)                                      |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/neulab/cmu-multinlp (xsd:string)                     |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark SemEval-2010 Task 8 (<http://orkg.org/orkg/resource/R129335>) |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark WLPC (<http://orkg.org/orkg/resource/R129337>)                |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark CoNLL 2012 (<http://orkg.org/orkg/resource/R129339>)          |
| ...                                                             | ...                                     | ...                                                                     |

[Tool]
Got 242 rows and 3 columns, showing the first 10 rows below:
| subject                                                                      | property          | object                               |
| ---------------------------------------------------------------------------- | ----------------- | ------------------------------------ |
| JNLPBA 2004 corpus (<http://orkg.org/orkg/resource/R164373>)                 | F1 (orkgp:P18037) | 72.6 (xsd:string)                    |
| Event Extraction (<http://orkg.org/orkg/resource/R165834>)                   | F1 (orkgp:P18037) | 52.84 (xsd:string)                   |
| AGAC NER Track (<http://orkg.org/orkg/resource/R165696>)                     | F1 (orkgp:P18037) | 60
 (xsd:string)                     |
| Relation Extraction (<http://orkg.org/orkg/resource/R165896>)                | F1 (orkgp:P18037) | 100 Promoter dependence (xsd:string) |
| BioNLP 2009 Event Extraction Focus (<http://orkg.org/orkg/resource/R165729>) | F1 (orkgp:P18037) | 43.12 (xsd:string)                   |
| Task 1. Core event extraction (<http://orkg.org/orkg/resource/R165741>)      | F1 (orkgp:P18037) | 56.04 (xsd:string)                   |
| Event Extraction Focus (<http://orkg.org/orkg/resource/R165801>)             | F1 (orkgp:P18037) | 56.55 (xsd:string)                   |
| Task 2. Event enrichment (<http://orkg.org/orkg/resource/R165742>)           | F1 (orkgp:P18037) | 45.86 (xsd:string)                   |
| Event Extraction Focus (<http://orkg.org/orkg/resource/R165806>)             | F1 (orkgp:P18037) | 55.59 (xsd:string)                   |
| Core Event Extraction (<http://orkg.org/orkg/resource/R165814>)              | F1 (orkgp:P18037) | 50.97 (xsd:string)                   |
| ...                                                                          | ...               | ...                                  |

[Tool]
Got 86 rows and 3 columns, showing the first 10 rows below:
| subject                                                              | property               | object             |
| -------------------------------------------------------------------- | ---------------------- | ------------------ |
| Subtask A - Questions (<http://orkg.org/orkg/resource/R111702>)      | Best F1 (orkgp:P36078) | 0.725 (xsd:string) |
| Subtask B - Answers (<http://orkg.org/orkg/resource/R111703>)        | Best F1 (orkgp:P36078) | 0.524 (xsd:string) |
| Any track (<http://orkg.org/orkg/resource/R76890>)                   | Best F1 (orkgp:P36078) | 81.4 (xsd:string)  |
| Any track (<http://orkg.org/orkg/resource/R76861>)                   | Best F1 (orkgp:P36078) | 80.6 (xsd:string)  |
| Any track (<http://orkg.org/orkg/resource/R76880>)                   | Best F1 (orkgp:P36078) | 64.0 (xsd:string)  |
| Any track (<http://orkg.org/orkg/resource/R76882>)                   | Best F1 (orkgp:P36078) | 63.8 (xsd:string)  |
| Any track (<http://orkg.org/orkg/resource/R76886>)                   | Best F1 (orkgp:P36078) | 63.2 (xsd:string)  |
| Classification experiments (<http://orkg.org/orkg/resource/R187614>) | Best F1 (orkgp:P36078) | 0.74 (xsd:string)  |
| Any track (<http://orkg.org/orkg/resource/R76695>)                   | Best F1 (orkgp:P36078) | 91.77 (xsd:string) |
| Any track (<http://orkg.org/orkg/resource/R76822>)                   | Best F1 (orkgp:P36078) | 71.43 (xsd:string) |
| ...                                                                  | ...                    | ...                |

[Tool]
Got 83 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                | object             |
| -------------------------------------------------------------------------------------------------------- | ----------------------- | ------------------ |
| f1, precision&recall score for AIM concept (<http://orkg.org/orkg/resource/R282508>)                     | F1-Score (orkgp:P71208) | 38.38 (xsd:string) |
| Bidirectional Encoder Representations from Transformers (BERT) (<http://orkg.org/orkg/resource/R236264>) | F1-Score (orkgp:P71208) | 86.16 (xsd:string) |
| Generalized Autoregressive Language (XLNet) model (<http://orkg.org/orkg/resource/R236265>)              | F1-Score (orkgp:P71208) | 87.90 (xsd:string) |
| Robustly Optimized BERT Pretraining Approach (RoBERTa) (<http://orkg.org/orkg/resource/R236266>)         | F1-Score (orkgp:P71208) | 89.17 (xsd:string) |
| method1 (<http://orkg.org/orkg/resource/R255294>)                                                        | F1-Score (orkgp:P71208) | 96.60 (xsd:string) |
| method2 (<http://orkg.org/orkg/resource/R255295>)                                                        | F1-Score (orkgp:P71208) | 96.39 (xsd:string) |
| method3 (<http://orkg.org/orkg/resource/R255296>)                                                        | F1-Score (orkgp:P71208) | 82.40 (xsd:string) |
| method4 (<http://orkg.org/orkg/resource/R255297>)                                                        | F1-Score (orkgp:P71208) | 83.37 (xsd:string) |
| Multi-Layer Perceptron (MLP) (<http://orkg.org/orkg/resource/R236212>)                                   | F1-Score (orkgp:P71208) | 83.08 (xsd:string) |
| Multi-Layer Perceptron (MLP) (<http://orkg.org/orkg/resource/R236258>)                                   | F1-Score (orkgp:P71208) | 81.72 (xsd:string) |
| ...                                                                                | ...                     | ...                |

[Tool]
Got 141 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                | object                |
| --------------------------------------------------------------------------------------- | ----------------------- | --------------------- |
| Contribution 1 (<http://orkg.org/orkg/resource/R196706>)                                | F1-score (orkgp:P23063) | 1. 33.8
 (xsd:string) |
| Contribution 1 (<http://orkg.org/orkg/resource/R135550>)                                | F1-score (orkgp:P23063) | 88.6 (xsd:string)     |
| Five fold cross validation results (<http://orkg.org/orkg/resource/R180014>)            | F1-score (orkgp:P23063) | 0.962 (xsd:string)    |
| Performance of ontology classes at each level (<http://orkg.org/orkg/resource/R180016>) | F1-score (orkgp:P23063) | 0.952 (xsd:string)    |
| Random Forest (<http://orkg.org/orkg/resource/R192329>)                                 | F1-score (orkgp:P23063) | 99.2 (xsd:string)     |
| JRip (<http://orkg.org/orkg/resource/R192330>)                                          | F1-score (orkgp:P23063) | 98.9 (xsd:string)     |
| Bagging (<http://orkg.org/orkg/resource/R192331>)                                       | F1-score (orkgp:P23063) | 99.1 (xsd:string)     |
| J48 (<http://orkg.org/orkg/resource/R192332>)                                           | F1-score (orkgp:P23063) | 99.0 (xsd:string)     |
| PART (<http://orkg.org/orkg/resource/R192333>)                                          | F1-score (orkgp:P23063) | 98.8 (xsd:string)     |
| BERT (<http://orkg.org/orkg/resource/R212676>)                                          | F1-score (orkgp:P23063) | 86.0 (xsd:string)     |
| ...                                                                                | ...                     | ...                   |

[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 1 row and 3 columns:
| subject                                                         | property                            | object                                                         |
| --------------------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------- |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark CoNLL 2012 (<http://orkg.org/orkg/resource/R129339>) |

[Tool]
Got 15 rows and 3 columns, showing the first 10 rows below:
| subject                                                         | property                                | object                                                                  |
| --------------------------------------------------------------- | --------------------------------------- | ----------------------------------------------------------------------- |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | research problem (orkgp:P32)            | Part-Of-Speech Tagging (<http://orkg.org/orkg/resource/R116310>)        |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/jzbjyb/SpanRel (xsd:string)                          |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Penn Treebank (<http://orkg.org/orkg/resource/R129333>)       |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has model (orkgp:HAS_MODEL)             | SpanRel (<http://orkg.org/orkg/resource/R116338>)                       |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | rdf:type                                | Contribution (orkgc:Contribution)                                       |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | rdfs:label                              | Contribution 	SpanRel (xsd:string)                                      |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/neulab/cmu-multinlp (xsd:string)                     |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark SemEval-2010 Task 8 (<http://orkg.org/orkg/resource/R129335>) |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark WLPC (<http://orkg.org/orkg/resource/R129337>)                |
| Contribution  SpanRel (<http://orkg.org/orkg/resource/R129332>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark CoNLL 2012 (<http://orkg.org/orkg/resource/R129339>)          |
| ...                                                             | ...                                     | ...                                                                     |

[Tool]
Top 10 property alternatives:
1. has benchmark (orkgp:HAS_BENCHMARK)
2. has model (orkgp:HAS_MODEL)
3. has source code (orkgp:HAS_SOURCE_CODE)

No other items found

[Assistant]
It appears that for the only contribution linked to "Benchmark CoNLL 2012" in the ORKG ("Contribution SpanRel"), there is no explicit F1 metric property attached. There are also no other contributions linked to this benchmark in the current data.

Given this, the best possible SPARQL query is to return the model(s) associated with the only contribution linked to the "Benchmark CoNLL 2012" benchmark, even though no F1 score is explicitly recorded. I will write a query that returns the model(s) for contributions evaluated on this benchmark, along with any F1 score if available (using OPTIONAL).

Here is the SPARQL query:

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is structurally correct and follows the rules, returning the IRIs for the contribution and model. However, the answer is incomplete because it does not actually provide an F1 metric value for the model, which is the core of the user's question. The query correctly attempts to retrieve the F1 metric, but since the value is missing in the data, the answer should explicitly state that no F1 metric is recorded for any model on the CoNLL 2012 benchmark in the ORKG, rather than implying that 'SpanRel' is the best (since it is the only one present, but with no metric). The answer should clarify that no model with an F1 metric is available for this benchmark in the current data.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The answer and the SPARQL query are correct given the current state of the knowledge graph. The query correctly attempts to retrieve the model with the highest F1 metric for the CoNLL 2012 benchmark, but no F1 value is available for any model. The answer accurately reflects this situation. 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 ?dataset .
  FILTER ( ?dataset = <http://orkg.org/orkg/resource/R117710> || ?dataset = <http://orkg.org/orkg/resource/R119108> ) ?benchmark orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

Got 2 rows and 2 columns:
| metric                                                     | metricLabel                   |
| ---------------------------------------------------------- | ----------------------------- |
| Accuracy (<http://orkg.org/orkg/resource/R111697>)         | Accuracy (xsd:string)         |
| Percentage error (<http://orkg.org/orkg/resource/R116138>) | Percentage error (xsd:string) |

The metrics of evaluation over the Fashion-MNIST dataset in ORKG are Accuracy and Percentage error.

[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 are the metrics of evaluation over the Fashion-MNIST dataset?

[Assistant]
Let's break down the question:

1. The user wants to know about "metrics of evaluation" used "over the Fashion-MNIST dataset".
2. So, we need to:
   - Identify the entity for "Fashion-MNIST dataset".
   - Identify properties related to "metrics of evaluation" (likely evaluation metrics, performance metrics, etc.).
   - Find the metrics that are associated with the Fashion-MNIST dataset.

Step 1: Search for the entity "Fashion-MNIST dataset" in ORKG.

[Tool]
Top 10 entity alternatives:
1. Fashion-MNIST (<http://orkg.org/orkg/resource/R119108>): has type Dataset
2. Fashion-MNIST (<http://orkg.org/orkg/resource/R117710>): has type Dataset
3. Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134874>): has type Benchmark
4. Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134960>): has type Benchmark
5. Rotated Fashion-MNIST (<http://orkg.org/orkg/resource/R116086>): has type Dataset

[Assistant]
I found several entities related to "Fashion-MNIST" in ORKG, with the most relevant being:

- Fashion-MNIST (<http://orkg.org/orkg/resource/R119108>) - Dataset
- Fashion-MNIST (<http://orkg.org/orkg/resource/R117710>) - Dataset

I'll proceed with the first one: <http://orkg.org/orkg/resource/R119108>.

Next, I need to identify properties related to "metrics of evaluation" for this dataset. I'll search for properties with keywords like "metric", "evaluation", or "performance".

[Tool]
No property items found

No other items found

[Assistant] It appears there are no properties directly labeled as "metric" for the Fashion-MNIST dataset entity. Sometimes, evaluation metrics may be associated with related terms like "evaluation", "performance", or "measure". I'll try searching with the keyword "evaluation" next.

[Tool]
No property items found

No other items found

[Assistant] No properties labeled "evaluation" are directly associated with the Fashion-MNIST dataset entity either. Sometimes, metrics are linked through other relationships, such as "used in", "has metric", or "performance". I'll try searching with the keyword "performance" next.

[Tool]
No property items found

No other items found

[Assistant]
There are still no direct property matches for "performance" on the Fashion-MNIST dataset entity.

Another approach is to look for how the Fashion-MNIST dataset is used in the context of experiments or papers, and then see if metrics are associated with those. Often, datasets are linked to tasks or results, which in turn have associated metrics.

I'll list all triples where the Fashion-MNIST dataset entity is the object, to see if it is referenced in any experiments, tasks, or results.

[Tool] Got no rows and 3 columns

[Assistant]
There are no triples where the Fashion-MNIST dataset entity (<http://orkg.org/orkg/resource/R119108>) is used as the object.

Given that, I'll try the second Fashion-MNIST entity (<http://orkg.org/orkg/resource/R117710>) to see if it has more connections or relevant properties. I'll search for properties related to "metric" for this entity.

[Tool]
No property items found

No other items found

[Assistant]
There are no properties labeled "metric" for the second Fashion-MNIST entity either.

Since direct property searches are not yielding results, I'll try a broader approach: searching for all properties of the first Fashion-MNIST entity to see if any of them are related to evaluation metrics, even if not labeled as such.

[Tool]
Got 3 rows and 3 columns:
| subject                                                 | property   | object                       |
| ------------------------------------------------------- | ---------- | ---------------------------- |
| Fashion-MNIST (<http://orkg.org/orkg/resource/R119108>) | rdf:type   | Dataset (orkgc:Dataset)      |
| Fashion-MNIST (<http://orkg.org/orkg/resource/R119108>) | rdfs:label | 	 Fashion-MNIST (xsd:string) |
| Fashion-MNIST (<http://orkg.org/orkg/resource/R119108>) | rdf:type   | orkgc:Resource               |

[Assistant]
The only properties directly associated with the Fashion-MNIST entity are its type and label. There are no direct links to evaluation metrics.

Given this, the next best approach is to look for papers, experiments, or resources that reference Fashion-MNIST and see if they mention evaluation metrics. I'll search for entities related to "Fashion-MNIST" that might represent experiments, results, or papers, and then look for metrics associated with those.

[Tool]
Top 10 entity alternatives:
1. Fashion-MNIST (<http://orkg.org/orkg/resource/R119108>): has type Dataset
2. Fashion-MNIST (<http://orkg.org/orkg/resource/R117710>): has type Dataset
3. Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134874>): has type Benchmark
4. Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134960>): has type Benchmark
5. Rotated Fashion-MNIST (<http://orkg.org/orkg/resource/R116086>): has type Dataset

[Assistant] Among the search results, there are entities labeled as "Benchmark Fashion-MNIST" which might be more likely to have evaluation metrics associated with them. I'll check the properties of the first benchmark entity: <http://orkg.org/orkg/resource/R134874>.

[Tool]
Got 5 rows and 3 columns:
| subject                                                           | property                          | object                                                                     |
| ----------------------------------------------------------------- | --------------------------------- | -------------------------------------------------------------------------- |
| Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134874>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Percentage error_4.14 (<http://orkg.org/orkg/resource/R134875>) |
| Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134874>) | has dataset (orkgp:HAS_DATASET)   | Fashion-MNIST (<http://orkg.org/orkg/resource/R117710>)                    |
| Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134874>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                                |
| Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134874>) | rdfs:label                        | Benchmark Fashion-MNIST (xsd:string)                                       |
| Benchmark Fashion-MNIST (<http://orkg.org/orkg/resource/R134874>) | rdf:type                          | orkgc:Resource                                                             |

[Assistant]
The "Benchmark Fashion-MNIST" entity (<http://orkg.org/orkg/resource/R134874>) has a property "evaluation" (orkgp:HAS_EVALUATION) pointing to "Evaluation Percentage error_4.14" (<http://orkg.org/orkg/resource/R134875>). This suggests that "Percentage error" is used as an evaluation metric.

To confirm and possibly find more metrics, I'll check the properties of the evaluation entity <http://orkg.org/orkg/resource/R134875>.

[Tool]
Got 5 rows and 3 columns:
| subject                                                                    | property                  | object                                                     |
| -------------------------------------------------------------------------- | ------------------------- | ---------------------------------------------------------- |
| Evaluation Percentage error_4.14 (<http://orkg.org/orkg/resource/R134875>) | value (orkgp:HAS_VALUE)   | 4.14 (xsd:string)                                          |
| Evaluation Percentage error_4.14 (<http://orkg.org/orkg/resource/R134875>) | metric (orkgp:HAS_METRIC) | Percentage error (<http://orkg.org/orkg/resource/R116138>) |
| Evaluation Percentage error_4.14 (<http://orkg.org/orkg/resource/R134875>) | rdf:type                  | Evaluation (orkgc:Evaluation)                              |
| Evaluation Percentage error_4.14 (<http://orkg.org/orkg/resource/R134875>) | rdfs:label                | Evaluation Percentage error_4.14 (xsd:string)              |
| Evaluation Percentage error_4.14 (<http://orkg.org/orkg/resource/R134875>) | rdf:type                  | orkgc:Resource                                             |

[Assistant]
The evaluation entity confirms that "Percentage error" (<http://orkg.org/orkg/resource/R116138>) is the metric used in this benchmark for Fashion-MNIST.

To generalize, I should look for all metrics associated with any benchmark or evaluation over the Fashion-MNIST dataset. I'll write a SPARQL query that finds all metrics (and their IRIs) used in evaluations of benchmarks that use the Fashion-MNIST dataset.

Here's the plan:
- Find all benchmarks that have Fashion-MNIST as a dataset.
- For each benchmark, find evaluations.
- For each evaluation, find the metric used.

I'll now construct and execute this SPARQL query.

[Tool]
Got 2 rows and 2 columns:
| metric                                                     | metricLabel                   |
| ---------------------------------------------------------- | ----------------------------- |
| Accuracy (<http://orkg.org/orkg/resource/R111697>)         | Accuracy (xsd:string)         |
| Percentage error (<http://orkg.org/orkg/resource/R116138>) | Percentage error (xsd:string) |

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the identifiers of the evaluation metrics used over the Fashion-MNIST dataset, along with their optional labels. The answer is accurate and complete according to the query result and the rules provided. No further refinement is necessary.


PREFIX orkgc: <http://orkg.org/orkg/class/>
PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?metric ?metricLabel WHERE {
  ?benchmark a orkgc:Benchmark ; orkgp:HAS_DATASET <http://orkg.org/orkg/resource/R122670> ; orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL {
    ?metric rdfs:label ?metricLabel
  }
}

The only evaluation metric currently recorded in ORKG for benchmarking models on the Yelp Fine-grained classification dataset is 'Error'. No other metrics are listed.

[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 Yelp Fine-grained classification dataset?

[Assistant]
Let's break down the question:

1. The user wants to know about evaluation metrics.
2. The context is benchmarking models.
3. The dataset in question is the "Yelp Fine-grained classification dataset".

Step 1: Identify the entity for "Yelp Fine-grained classification dataset".
I'll search for this dataset in ORKG.

Step 2: Once identified, I will look for properties or relations connecting this dataset to evaluation metrics, possibly via benchmarking or evaluation studies.

Step 3: I will search for properties related to "evaluation metric" and see how they are used in the context of this dataset.

Let's start by searching for the "Yelp Fine-grained classification dataset" entity.

[Tool]
Top 10 entity alternatives:
1. Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R122670>): has type Dataset
2. Benchmark Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R130137>): has type Benchmark
3. Fine-grained Event Classification (<http://orkg.org/orkg/resource/R140999>): has type Problem
4. Fine-Grained Image Classification (<http://orkg.org/orkg/resource/R126762>): has type Problem
5. Fine-grained Sentiment Classification (<http://orkg.org/orkg/resource/R150023>)
6. Yelp Binary classification (<http://orkg.org/orkg/resource/R122706>): has type Dataset
7. SST-5 Fine-grained classification (<http://orkg.org/orkg/resource/R122621>): has type Dataset
8. Benchmark Yelp Binary classification (<http://orkg.org/orkg/resource/R130141>): has type Benchmark
9. Benchmark SST-5 Fine-grained classification (<http://orkg.org/orkg/resource/R130404>): has type Benchmark
10. Benchmark SST-5 Fine-grained classification (<http://orkg.org/orkg/resource/R131155>): has type Benchmark

[Assistant]
I found the following relevant entities in ORKG:
- Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R122670>): type Dataset
- Benchmark Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R130137>): type Benchmark

The most relevant for evaluation metrics used in benchmarking is likely the "Benchmark Yelp Fine-grained classification" entity. Next, I will look for properties or relations from this entity that connect to evaluation metrics.

I'll search for properties related to "evaluation metric" for this benchmark entity.

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

No other items found

[Assistant]
The most relevant property found is "evaluation" (orkgp:HAS_EVALUATION). This likely links the benchmark to evaluations, which may contain information about evaluation metrics.

Next, I will list triples where the subject is "Benchmark Yelp Fine-grained classification" and the property is "evaluation" to see what entities are linked as evaluations. Then, I will explore those evaluation entities for their associated metrics.

[Tool]
Got 1 row and 3 columns:
| subject                                                                              | property                          | object                                                           |
| ------------------------------------------------------------------------------------ | --------------------------------- | ---------------------------------------------------------------- |
| Benchmark Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R130137>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation Error_27.80 (<http://orkg.org/orkg/resource/R130138>) |

[Assistant]
The benchmark "Benchmark Yelp Fine-grained classification" has an evaluation entity: "Evaluation Error_27.80" (<http://orkg.org/orkg/resource/R130138>).

Now, I will explore the properties of this evaluation entity to see if it specifies which evaluation metric(s) were used.

[Tool]
Got 5 rows and 3 columns:
| subject                                                          | property                  | object                                          |
| ---------------------------------------------------------------- | ------------------------- | ----------------------------------------------- |
| Evaluation Error_27.80 (<http://orkg.org/orkg/resource/R130138>) | value (orkgp:HAS_VALUE)   | 27.80 (xsd:string)                              |
| Evaluation Error_27.80 (<http://orkg.org/orkg/resource/R130138>) | metric (orkgp:HAS_METRIC) | Error (<http://orkg.org/orkg/resource/R119433>) |
| Evaluation Error_27.80 (<http://orkg.org/orkg/resource/R130138>) | rdf:type                  | Evaluation (orkgc:Evaluation)                   |
| Evaluation Error_27.80 (<http://orkg.org/orkg/resource/R130138>) | rdfs:label                | Evaluation Error_27.80 (xsd:string)             |
| Evaluation Error_27.80 (<http://orkg.org/orkg/resource/R130138>) | rdf:type                  | orkgc:Resource                                  |

[Tool]
Got 3 rows and 3 columns:
| subject                                         | property   | object                |
| ----------------------------------------------- | ---------- | --------------------- |
| Error (<http://orkg.org/orkg/resource/R119433>) | rdf:type   | Metric (orkgc:Metric) |
| Error (<http://orkg.org/orkg/resource/R119433>) | rdfs:label | Error (xsd:string)    |
| Error (<http://orkg.org/orkg/resource/R119433>) | rdf:type   | orkgc:Resource        |

[Assistant]
The only evaluation metric explicitly linked to the "Benchmark Yelp Fine-grained classification" in ORKG is "Error".

To answer the user's question about commonly used evaluation metrics, I should check if there are other evaluations or metrics associated with this dataset or benchmark. I'll search for other evaluations or metrics linked to the "Yelp Fine-grained classification" dataset entity as well.

I'll list all triples where the subject is "Yelp Fine-grained classification" (<http://orkg.org/orkg/resource/R122670>) to see if there are other evaluations or metrics.

[Tool]
Got 3 rows and 3 columns:
| subject                                                                    | property   | object                                        |
| -------------------------------------------------------------------------- | ---------- | --------------------------------------------- |
| Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R122670>) | rdf:type   | Dataset (orkgc:Dataset)                       |
| Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R122670>) | rdfs:label | Yelp Fine-grained classification (xsd:string) |
| Yelp Fine-grained classification (<http://orkg.org/orkg/resource/R122670>) | rdf:type   | orkgc:Resource                                |

[Assistant]
There are no additional evaluations or metrics directly linked to the "Yelp Fine-grained classification" dataset entity in ORKG. The only evaluation metric found in the context of benchmarking on this dataset is "Error".

To provide a comprehensive answer, I will write a SPARQL query that retrieves all evaluation metrics used in benchmarking models on the "Yelp Fine-grained classification" dataset, including their IRIs and labels if available.

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves all evaluation metrics used for benchmarking models on the Yelp Fine-grained classification dataset by querying for benchmarks associated with the dataset and extracting their metrics. The query returns the metric IRIs and optionally their labels, adhering to the guidelines. The answer accurately reflects the query result. No further refinement is needed.