PREFIX orkgp: <http://orkg.org/orkg/predicate/>
PREFIX orkgc: <http://orkg.org/orkg/class/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT DISTINCT ?paper ?title WHERE {
  ?cub_dataset rdfs:label ?dataset_label .
  FILTER ( LCASE( ?dataset_label ) = "cub-200-2011" ) ?benchmark orkgp:HAS_DATASET ?cub_dataset .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark .
  ?paper orkgp:P31 ?contribution .
  ?paper rdf:type orkgc:Paper .
  OPTIONAL {
    ?paper rdfs:label ?title
  }
}

This query will robustly return the titles and IDs of research papers that include a benchmark for the CUB-200-2011 dataset, using the correct property for linking papers to contributions in ORKG.

[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 titles and IDs of research papers that include a benchmark for the CUB-200-2011 dataset?

[Assistant]
First, I need to identify the entity representing the "CUB-200-2011" dataset in the ORKG knowledge graph. Then, I need to find research papers that include a benchmark for this dataset. To do this, I will:

1. Search for the "CUB-200-2011" dataset entity.
2. Search for properties related to "benchmark" to see how benchmarks are represented.
3. Find research papers that have a benchmark for the "CUB-200-2011" dataset and retrieve their titles and IDs.

Let's start by searching for the "CUB-200-2011" entity.

[Tool]
Top 10 entity alternatives:
1. CUB-200-2011 (<http://orkg.org/orkg/resource/R121969>): has type Dataset
2. CUB-200-2011 (<http://orkg.org/orkg/resource/R119383>): has type Dataset
3. Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135099>): has type Benchmark
4. Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135184>): has type Benchmark
5. Imbalanced CUB-200-2011 (<http://orkg.org/orkg/resource/R126025>): has type Dataset
6. CUB-200-2011 5-way (5-shot) (<http://orkg.org/orkg/resource/R119306>): has type Dataset
7. CUB-200-2011 - 0-Shot (<http://orkg.org/orkg/resource/R119169>): has type Dataset
8. CUB-200-2011 5-way (1-shot) (<http://orkg.org/orkg/resource/R119193>): has type Dataset
9. CUB-200-2011, 30 samples per class (<http://orkg.org/orkg/resource/R126964>): has type Dataset
10. CUB-200-2011, 5 samples per class (<http://orkg.org/orkg/resource/R126966>): has type Dataset

[Assistant]
There are several entities related to "CUB-200-2011" in the ORKG knowledge graph, including datasets and benchmarks. The most relevant for our purpose are:

- CUB-200-2011 (Dataset): <http://orkg.org/orkg/resource/R121969>
- CUB-200-2011 (Dataset): <http://orkg.org/orkg/resource/R119383>
- Benchmark CUB-200-2011: <http://orkg.org/orkg/resource/R135099>
- Benchmark CUB-200-2011: <http://orkg.org/orkg/resource/R135184>

Next, I need to determine how research papers are linked to benchmarks or datasets, specifically for the CUB-200-2011 dataset. I will search for properties related to "benchmark" to see how this relationship is represented.

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

[Assistant]
There are several properties related to "benchmark," including:

- uses benchmark (orkgp:P38001)
- Has benchmark (orkgp:P34103)
- has benchmark (orkgp:hasBenchmark)
- has benchmark (orkgp:HAS_BENCHMARK)

To ensure completeness, I will check if any of these properties are used to link research papers to the CUB-200-2011 dataset or its benchmark entities. I will start by listing triples where the object is one of the CUB-200-2011 entities and the property is one of the "benchmark" properties. Let's start with "uses benchmark" (orkgp:P38001) and the dataset entity <http://orkg.org/orkg/resource/R121969>.

[Tool] Got no rows and 3 columns

[Assistant]
It appears that there are no triples using the "uses benchmark" property (orkgp:P38001) with the CUB-200-2011 dataset entity (<http://orkg.org/orkg/resource/R121969>) as the object.

Next, I will try the "Has benchmark" property (orkgp:P34103) with the same dataset entity as the object.

[Tool] Got no rows and 3 columns

[Assistant]
There are no triples using the "Has benchmark" property (orkgp:P34103) with the CUB-200-2011 dataset entity as the object either.

Next, I will try the "has benchmark" property (orkgp:hasBenchmark) with the same dataset entity as the object.

[Tool] Got no rows and 3 columns

[Assistant]
There are still no results using the "has benchmark" property (orkgp:hasBenchmark) with the CUB-200-2011 dataset entity as the object.

Since there are also benchmark entities for CUB-200-2011, I will try listing triples where the object is the benchmark entity <http://orkg.org/orkg/resource/R135099> and the property is "has benchmark" (orkgp:hasBenchmark).

[Tool] Got no rows and 3 columns

[Assistant]
There are no triples using the "has benchmark" property (orkgp:hasBenchmark) with the CUB-200-2011 benchmark entity (<http://orkg.org/orkg/resource/R135099>) as the object.

Given that none of the "benchmark" properties are directly linking papers to the CUB-200-2011 dataset or its benchmark entities, I will try a different approach. I will search for properties related to "dataset" to see if research papers are linked to datasets using a different property.

[Tool]
Top 10 property alternatives:
1. Dataset (orkgp:P96045)
2. dataset (orkgp:P2005)
3. Dataset used (orkgp:P25063)
4. Datasets (orkgp:P44054)
5. Uses dataset (orkgp:P18087)
6. dataset description (orkgp:P16037)
7. Datasets used (orkgp:P58072)
8. dataset format (orkgp:P62072)
9. Dataset name (orkgp:P34063)
10. Experimental Dataset (orkgp:P15096)

[Assistant]
There are several properties related to "dataset," such as:

- Dataset (orkgp:P96045)
- dataset (orkgp:P2005)
- Dataset used (orkgp:P25063)
- Uses dataset (orkgp:P18087)
- Datasets used (orkgp:P58072)

I will check if any research papers are linked to the CUB-200-2011 dataset entity (<http://orkg.org/orkg/resource/R121969>) using the "Uses dataset" property (orkgp:P18087).

[Tool] Got no rows and 3 columns

[Assistant]
There are no triples using the "Uses dataset" property (orkgp:P18087) with the CUB-200-2011 dataset entity as the object.

Given that none of the direct dataset or benchmark properties are yielding results, I will try a broader approach. I will check if any research papers mention the CUB-200-2011 dataset in any property, by listing all triples where the object is <http://orkg.org/orkg/resource/R121969>.

[Tool] Got no rows and 3 columns

[Assistant]
No triples were found where the CUB-200-2011 dataset entity (<http://orkg.org/orkg/resource/R121969>) is the object.

Given that, I will try the other CUB-200-2011 dataset entity (<http://orkg.org/orkg/resource/R119383>) to see if it is used in any triples.

[Tool]
Got 2 rows and 3 columns:
| subject                                                          | property                        | object                                                 |
| ---------------------------------------------------------------- | ------------------------------- | ------------------------------------------------------ |
| Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135099>) | has dataset (orkgp:HAS_DATASET) | CUB-200-2011 (<http://orkg.org/orkg/resource/R119383>) |
| Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135184>) | has dataset (orkgp:HAS_DATASET) | CUB-200-2011 (<http://orkg.org/orkg/resource/R119383>) |

[Assistant]
I found that both "Benchmark CUB-200-2011" entities (<http://orkg.org/orkg/resource/R135099> and <http://orkg.org/orkg/resource/R135184>) are linked to the CUB-200-2011 dataset entity (<http://orkg.org/orkg/resource/R119383>) via the property "has dataset" (orkgp:HAS_DATASET).

Now, I need to find research papers that include a benchmark for these benchmark entities. I will check which properties are used with these benchmark entities as objects, to see if any research papers are linked to them. Let's start with <http://orkg.org/orkg/resource/R135099>.

[Tool]
Got 1 row and 3 columns:
| subject                                                                        | property                            | object                                                           |
| ------------------------------------------------------------------------------ | ----------------------------------- | ---------------------------------------------------------------- |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135099>) |

[Assistant]
The benchmark entity "Benchmark CUB-200-2011" (<http://orkg.org/orkg/resource/R135099>) is linked to a subject "Contribution  BiLSTM-TDN(ResNet-101)" (<http://orkg.org/orkg/resource/R135098>) via the property "has benchmark" (orkgp:HAS_BENCHMARK).

Now, I need to check if this "Contribution" entity is linked to a research paper, and if so, retrieve the paper's title and ID. I will list all triples where the subject is <http://orkg.org/orkg/resource/R135098>.

[Tool]
Got 8 rows and 3 columns:
| subject                                                                        | property                            | object                                                               |
| ------------------------------------------------------------------------------ | ----------------------------------- | -------------------------------------------------------------------- |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | research problem (orkgp:P32)        | Image Classification (<http://orkg.org/orkg/resource/R38570>)        |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135099>)     |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | has model (orkgp:HAS_MODEL)         | BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R126364>)     |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | rdf:type                            | Contribution (orkgc:Contribution)                                    |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | rdfs:label                          | Contribution 	BiLSTM-TDN(ResNet-101) (xsd:string)                    |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Stanford Cars (<http://orkg.org/orkg/resource/R135101>)    |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Oxford-IIIT Pets (<http://orkg.org/orkg/resource/R135103>) |
| Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) | rdf:type                            | orkgc:Resource                                                       |

[Assistant] The "Contribution  BiLSTM-TDN(ResNet-101)" entity is a contribution, not a paper itself. In ORKG, contributions are typically linked to papers via a property such as "has contribution." I will now search for triples where the object is <http://orkg.org/orkg/resource/R135098> to find the associated research paper.

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                 | object                                                                         |
| --------------------------------------------------------------------------------------------------------- | ------------------------ | ------------------------------------------------------------------------------ |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | contribution (orkgp:P31) | Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) |

[Tool]
Got 15 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                      | object                                                                         |
| --------------------------------------------------------------------------------------------------------- | ----------------------------- | ------------------------------------------------------------------------------ |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | author (orkgp:P27)            | Anping Zeng (xsd:string)                                                       |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | contribution (orkgp:P31)      | Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | research field (orkgp:P30)    | Computer Sciences (<http://orkg.org/orkg/resource/R132>)                       |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | publication year (orkgp:P29)  | 2021 (xsd:string)                                                              |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | publication month (orkgp:P28) | 3 (xsd:string)                                                                 |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | url (orkgp:url)               | https://arxiv.org/abs/2103.07230v1.pdf (xsd:string)                            |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | rdf:type                      | Paper (orkgc:Paper)                                                            |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | rdfs:label                    | Sequential Random Network for Fine-grained Image Classification (xsd:string)   |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | author (orkgp:P27)            | Chaorong Li (xsd:string)                                                       |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | author (orkgp:P27)            | Fengqing Qin (xsd:string)                                                      |
| ...                                                                                | ...                           | ...                                                                            |

[Tool]
Got 1 row and 3 columns:
| subject                                                      | property                            | object                                                           |
| ------------------------------------------------------------ | ----------------------------------- | ---------------------------------------------------------------- |
| Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135184>) |

[Tool]
Got 6 rows and 3 columns:
| subject                                                      | property                            | object                                                                      |
| ------------------------------------------------------------ | ----------------------------------- | --------------------------------------------------------------------------- |
| Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) | research problem (orkgp:P32)        | Fine-Grained Image Classification (<http://orkg.org/orkg/resource/R126762>) |
| Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark CUB-200-2011 (<http://orkg.org/orkg/resource/R135184>)            |
| Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) | has model (orkgp:HAS_MODEL)         | DATL (<http://orkg.org/orkg/resource/R126768>)                              |
| Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) | rdf:type                            | Contribution (orkgc:Contribution)                                           |
| Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) | rdfs:label                          | Contribution 	DATL (xsd:string)                                             |
| Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) | rdf:type                            | orkgc:Resource                                                              |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                 | object                                                       |
| ------------------------------------------------------------------------------------------------------------- | ------------------------ | ------------------------------------------------------------ |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | contribution (orkgp:P31) | Contribution  DATL (<http://orkg.org/orkg/resource/R135183>) |

[Tool]
Got 12 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                      | object                                                                            |
| ------------------------------------------------------------------------------------------------------------- | ----------------------------- | --------------------------------------------------------------------------------- |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | author (orkgp:P27)            | Ashiq Imran (xsd:string)                                                          |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | contribution (orkgp:P31)      | Contribution  ImageNet + iNat on WS-DAN (<http://orkg.org/orkg/resource/R135171>) |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | research field (orkgp:P30)    | Computer Sciences (<http://orkg.org/orkg/resource/R132>)                          |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | publication year (orkgp:P29)  | 2020 (xsd:string)                                                                 |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | publication month (orkgp:P28) | 10 (xsd:string)                                                                   |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | url (orkgp:url)               | https://arxiv.org/abs/2010.03071v1.pdf (xsd:string)                               |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | rdf:type                      | Paper (orkgc:Paper)                                                               |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | rdfs:label                    | Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (xsd:string)  |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | author (orkgp:P27)            | Vassilis Athitsos (xsd:string)                                                    |
| Domain Adaptive Transfer Learnin...ne-grained Visual Categorization (<http://orkg.org/orkg/resource/R135170>) | contribution (orkgp:P31)      | Contribution  DAT (<http://orkg.org/orkg/resource/R135180>)                       |
| ...                                                                                | ...                           | ...                                                                               |

[Assistant]
I have identified two research papers that include a benchmark for the CUB-200-2011 dataset:

1. Sequential Random Network for Fine-grained Image Classification
   - ID: <http://orkg.org/orkg/resource/R135097>
   - Title: Sequential Random Network for Fine-grained Image Classification

2. Domain Adaptive Transfer Learning for Fine-grained Visual Categorization
   - ID: <http://orkg.org/orkg/resource/R135170>
   - Title: Domain Adaptive Transfer Learning for Fine-grained Visual Categorization

I will now construct a SPARQL query that retrieves the titles and IDs of research papers that include a benchmark for the CUB-200-2011 dataset, using the patterns I have discovered.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The query is mostly correct and returns the IRIs (IDs) and titles of papers that include a benchmark for the CUB-200-2011 dataset. However, the use of VALUES with two hardcoded benchmark IRIs (<http://orkg.org/orkg/resource/R135099> and <http://orkg.org/orkg/resource/R135184>) is problematic. The question asks for papers that include a benchmark for the CUB-200-2011 dataset, but the query assumes that these two IRIs are the only possible benchmarks for CUB-200-2011. This may miss papers that use other IRIs or representations for the CUB-200-2011 dataset.

To improve, the query should first identify the IRI(s) for the CUB-200-2011 dataset (e.g., via rdfs:label or a dataset property), and then find all benchmarks related to it, rather than hardcoding the benchmark IRIs. This will make the query more robust and complete.

[Assistant]
Thank you for your feedback. You are correct: hardcoding the benchmark IRIs is not robust. To improve, I will:

1. Dynamically identify all IRIs for the CUB-200-2011 dataset (using rdfs:label).
2. Find all benchmarks that are linked to any of these dataset IRIs (using properties like orkgp:HAS_DATASET).
3. Retrieve all papers that include a benchmark for any of these benchmarks, and return their IDs and titles.

Let me explain the improved approach step by step:

Step 1: Find all dataset IRIs with the label "CUB-200-2011".
Step 2: Find all benchmarks that have these datasets via orkgp:HAS_DATASET.
Step 3: Find all contributions that have these benchmarks via orkgp:HAS_BENCHMARK.
Step 4: Find all papers that have these contributions via orkgp:P31.
Step 5: Return the paper IRIs and their titles.

Now, I will write the improved SPARQL query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is well-structured and follows the logical path from dataset to benchmark to contribution to paper. However, the property orkgp:P31 is used for linking papers to contributions, which may not be correct. In ORKG, the property linking papers to contributions is typically orkg:hasContribution or similar, not P31 (which is often used for 'instance of' in Wikidata). Please verify the correct property for linking papers to contributions in the ORKG schema and update the query accordingly. This may resolve the issue of getting no results.

[Assistant]
Thank you for the feedback. You are correct: the property orkgp:P31 is likely not the correct one for linking papers to contributions in ORKG. Instead, it is typically orkgp:hasContribution or a similar property.

To proceed, I will:
1. Search for the correct property that links papers to contributions in ORKG.
2. Update the query to use this property.

First, I will search for properties related to "contribution" in ORKG.

[Tool]
Top 10 property alternatives:
1. contribution (orkgp:P31)
2. Contribution description (orkgp:P41880)
3. contribution facet (orkgp:P97005)
4. contributor (orkgp:P49016)
5. has contribution type (orkgp:P40065)
6. compare contribution (orkgp:compareContribution)
7. Major Contributions (orkgp:P15063)
8. Donor (orkgp:P41897)
9. include (orkgp:P20088)
10. contribution:research_aplication (orkgp:P37219)

[Tool]
Top 10 property alternatives:
1. has contribution type (orkgp:P40065)
2. contribution (orkgp:P31)
3. Contribution description (orkgp:P41880)
4. has (orkgp:P18055)
5. has (orkgp:P18022)
6. Has (orkgp:P18071)
7. has name (orkgp:P7111)
8. has stakeholder (orkgp:P41259)
9. Has participant (orkgp:P18058)
10. has reference (orkgp:P7102)

[Tool]
Got 15 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                      | object                                                                         |
| --------------------------------------------------------------------------------------------------------- | ----------------------------- | ------------------------------------------------------------------------------ |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | author (orkgp:P27)            | Anping Zeng (xsd:string)                                                       |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | contribution (orkgp:P31)      | Contribution  BiLSTM-TDN(ResNet-101) (<http://orkg.org/orkg/resource/R135098>) |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | research field (orkgp:P30)    | Computer Sciences (<http://orkg.org/orkg/resource/R132>)                       |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | publication year (orkgp:P29)  | 2021 (xsd:string)                                                              |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | publication month (orkgp:P28) | 3 (xsd:string)                                                                 |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | url (orkgp:url)               | https://arxiv.org/abs/2103.07230v1.pdf (xsd:string)                            |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | rdf:type                      | Paper (orkgc:Paper)                                                            |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | rdfs:label                    | Sequential Random Network for Fine-grained Image Classification (xsd:string)   |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | author (orkgp:P27)            | Chaorong Li (xsd:string)                                                       |
| Sequential Random Network for Fine-grained Image Classification (<http://orkg.org/orkg/resource/R135097>) | author (orkgp:P27)            | Fengqing Qin (xsd:string)                                                      |
| ...                                                                                | ...                           | ...                                                                            |

[Assistant]
From the triple listing, it appears that the property orkgp:P31 (with label "contribution") is indeed used to link a paper to its contribution in ORKG, at least for the example paper "Sequential Random Network for Fine-grained Image Classification".

Given this, the previous query structure is correct for this dataset. However, to make the query even more robust, I will ensure that the property orkgp:P31 is used, and I will also add a check that the paper is of type orkgc:Paper.

Here is the refined query:

[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 ?paperURL ?codeReference ?benchmark WHERE {
  VALUES ?model {
    <http://orkg.org/orkg/resource/R121047> <http://orkg.org/orkg/resource/R120886>
  }
  ?contribution orkgp:HAS_MODEL ?model .
  ?contribution orkgp:HAS_BENCHMARK ?benchmark .
  ?contribution orkgp:HAS_SOURCE_CODE ?codeReference .
  ?paper orkgp:P31 ?contribution .
  OPTIONAL {
    ?paper rdfs:label ?paperLabel
  }
  OPTIONAL {
    ?paper orkgp:url ?paperURL
  }
}

Got 10 rows and 5 columns:
| paper                                                                                | paperLabel                                                    | paperURL                                            | codeReference                                                    | benchmark                                                  |
| ------------------------------------------------------------------------------------------ | ------------------------------------------------------------- | --------------------------------------------------- | ---------------------------------------------------------------- | ---------------------------------------------------------- |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/facebookresearch/adaptive-span (xsd:string)   | Benchmark enwik8 (<http://orkg.org/orkg/resource/R130671>) |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/facebookresearch/adaptive-span (xsd:string)   | Benchmark Text8 (<http://orkg.org/orkg/resource/R130686>)  |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/memory-transformer-xl (xsd:string) | Benchmark enwik8 (<http://orkg.org/orkg/resource/R130671>) |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/memory-transformer-xl (xsd:string) | Benchmark Text8 (<http://orkg.org/orkg/resource/R130686>)  |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/reformer-pytorch (xsd:string)      | Benchmark enwik8 (<http://orkg.org/orkg/resource/R130671>) |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/reformer-pytorch (xsd:string)      | Benchmark Text8 (<http://orkg.org/orkg/resource/R130686>)  |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/routing-transformer (xsd:string)   | Benchmark enwik8 (<http://orkg.org/orkg/resource/R130671>) |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/routing-transformer (xsd:string)   | Benchmark Text8 (<http://orkg.org/orkg/resource/R130686>)  |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/x-transformers (xsd:string)        | Benchmark enwik8 (<http://orkg.org/orkg/resource/R130671>) |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | Augmenting Self-attention with Persistent Memory (xsd:string) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) | https://github.com/lucidrains/x-transformers (xsd:string)        | Benchmark Text8 (<http://orkg.org/orkg/resource/R130686>)  |

You can find code references in the paper "Augmenting Self-attention with Persistent Memory" (https://arxiv.org/abs/1907.01470v1.pdf), which used the All-attention network - 18 layers model for benchmarking. The code references include:
- https://github.com/facebookresearch/adaptive-span
- https://github.com/lucidrains/memory-transformer-xl
- https://github.com/lucidrains/reformer-pytorch
- https://github.com/lucidrains/routing-transformer
- https://github.com/lucidrains/x-transformers
The benchmarks used include Text8 and enwik8.

[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] Where can I find code references in papers that have used the All-attention network - 18 layers model for benchmarking purposes?

[Assistant]
Let's break down the question:

1. We are looking for code references (likely code repositories or links) in papers.
2. The papers must have used the "All-attention network - 18 layers" model.
3. The usage context is for benchmarking purposes.

Step 1: Identify the entity for "All-attention network - 18 layers" in ORKG.
I'll search for this model in the ORKG knowledge graph.

[Tool]
Top 10 entity alternatives:
1. All-attention network - 18 layers (<http://orkg.org/orkg/resource/R121047>): has type Model
2. Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>): has type Contribution
3. All-attention network (18 layers) (<http://orkg.org/orkg/resource/R120886>): has type Model
4. Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>): has type Contribution
5. All-attention network - 36 layers (<http://orkg.org/orkg/resource/R121045>): has type Model
6. Contribution  All-attention network - 36 layers (<http://orkg.org/orkg/resource/R130681>): has type Contribution
7. All-attention network (36 layers) (<http://orkg.org/orkg/resource/R120904>): has type Model
8. Contribution  All-attention network (36 layers) (<http://orkg.org/orkg/resource/R130674>): has type Contribution
9. Transformer-XL (18 layers) (<http://orkg.org/orkg/resource/R120889>): has type Model
10. Contribution  Transformer-XL (18 layers) (<http://orkg.org/orkg/resource/R130606>): has type Contribution

[Assistant]
I found two relevant entities for "All-attention network - 18 layers" in the ORKG knowledge graph:
- All-attention network - 18 layers (<http://orkg.org/orkg/resource/R121047>)
- All-attention network (18 layers) (<http://orkg.org/orkg/resource/R120886>)

Both are of type Model and seem to refer to the same concept, possibly with minor naming differences. For completeness, I will consider both in the next steps.

Next, I need to find papers that have used either of these models for benchmarking purposes and have code references.

Step 2: Identify properties related to "benchmarking" and "code reference" in ORKG. I'll start by searching for properties related to "benchmark" and "code".

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

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

[Tool]
Top 10 property alternatives:
1. code (orkgp:P16033)
2. source code (orkgp:P4077)
3. reference (orkgp:P41817)
4. reference (orkgp:reference)
5. reference URL (<http://orkg.org/orkg/predicate/wikidata:P854>)
6. code repository (orkgp:P49000)
7. code List (orkgp:P4005)
8. has source code (orkgp:HAS_SOURCE_CODE)
9. coding (orkgp:P59061)
10. Reference data (orkgp:P41058)

[Tool]
Top 10 property alternatives:
1. code repository (orkgp:P49000)
2. Has repository (orkgp:P35177)
3. Data repositories (orkgp:P15671)
4. Code repositories (orkgp:P36041)
5. Github (orkgp:P35178)
6. Open source (orkgp:P15199)
7. Documentation (orkgp:P41835)
8. Resource (orkgp:Resource)
9. sourceOrganization (<http://orkg.org/orkg/predicate/SCHEMAORG:sourceOrganization>)
10. source code (orkgp:P4077)

[Assistant]
For "benchmarking," the most relevant properties are:
- uses benchmark (orkgp:P38001)
- Has benchmark (orkgp:P34103)
- has benchmark (orkgp:hasBenchmark)
- has benchmark (orkgp:HAS_BENCHMARK)

For "code reference," the most relevant properties are:
- code (orkgp:P16033)
- source code (orkgp:P4077)
- code repository (orkgp:P49000)
- has source code (orkgp:HAS_SOURCE_CODE)

Step 3: Find papers (or contributions) that use either "All-attention network - 18 layers" or "All-attention network (18 layers)" as a benchmark and have code references.

I'll start by checking how the model entity is used in the graph, e.g., as an object of "uses benchmark" or similar properties.

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                    | object                                                                      |
| ----------------------------------------------------------------------------------------- | --------------------------- | --------------------------------------------------------------------------- |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has model (orkgp:HAS_MODEL) | All-attention network - 18 layers (<http://orkg.org/orkg/resource/R121047>) |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                    | object                                                                      |
| ----------------------------------------------------------------------------------------- | --------------------------- | --------------------------------------------------------------------------- |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has model (orkgp:HAS_MODEL) | All-attention network (18 layers) (<http://orkg.org/orkg/resource/R120886>) |

[Tool]
Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                                | object                                                                      |
| ----------------------------------------------------------------------------------------- | --------------------------------------- | --------------------------------------------------------------------------- |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | research problem (orkgp:P32)            | Language Modelling (<http://orkg.org/orkg/resource/R120872>)                |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/facebookresearch/adaptive-span (xsd:string)              |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark Text8 (<http://orkg.org/orkg/resource/R130686>)                   |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has model (orkgp:HAS_MODEL)             | All-attention network - 18 layers (<http://orkg.org/orkg/resource/R121047>) |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | rdf:type                                | Contribution (orkgc:Contribution)                                           |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | rdfs:label                              | Contribution 	All-attention network - 18 layers (xsd:string)                |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/memory-transformer-xl (xsd:string)            |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/reformer-pytorch (xsd:string)                 |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/routing-transformer (xsd:string)              |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/x-transformers (xsd:string)                   |
| ...                                                                                | ...                                     | ...                                                                         |

[Tool]
Got 11 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                                | object                                                                      |
| ----------------------------------------------------------------------------------------- | --------------------------------------- | --------------------------------------------------------------------------- |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | research problem (orkgp:P32)            | Language Modelling (<http://orkg.org/orkg/resource/R120872>)                |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/facebookresearch/adaptive-span (xsd:string)              |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has benchmark (orkgp:HAS_BENCHMARK)     | Benchmark enwik8 (<http://orkg.org/orkg/resource/R130671>)                  |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has model (orkgp:HAS_MODEL)             | All-attention network (18 layers) (<http://orkg.org/orkg/resource/R120886>) |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | rdf:type                                | Contribution (orkgc:Contribution)                                           |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | rdfs:label                              | Contribution 	All-attention network (18 layers) (xsd:string)                |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/memory-transformer-xl (xsd:string)            |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/reformer-pytorch (xsd:string)                 |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/routing-transformer (xsd:string)              |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/x-transformers (xsd:string)                   |
| ...                                                                                | ...                                     | ...                                                                         |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                            | object                                                    |
| ----------------------------------------------------------------------------------------- | ----------------------------------- | --------------------------------------------------------- |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark Text8 (<http://orkg.org/orkg/resource/R130686>) |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                            | object                                                     |
| ----------------------------------------------------------------------------------------- | ----------------------------------- | ---------------------------------------------------------- |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has benchmark (orkgp:HAS_BENCHMARK) | Benchmark enwik8 (<http://orkg.org/orkg/resource/R130671>) |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                                | property                                | object                                                           |
| ----------------------------------------------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/facebookresearch/adaptive-span (xsd:string)   |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/memory-transformer-xl (xsd:string) |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/reformer-pytorch (xsd:string)      |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/routing-transformer (xsd:string)   |
| Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/x-transformers (xsd:string)        |

[Tool]
Got 5 rows and 3 columns:
| subject                                                                                | property                                | object                                                           |
| ----------------------------------------------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/facebookresearch/adaptive-span (xsd:string)   |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/memory-transformer-xl (xsd:string) |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/reformer-pytorch (xsd:string)      |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/routing-transformer (xsd:string)   |
| Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) | has source code (orkgp:HAS_SOURCE_CODE) | https://github.com/lucidrains/x-transformers (xsd:string)        |

[Tool] Got no rows and 3 columns

[Tool] Got no rows and 3 columns

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                 | object                                                                                |
| ------------------------------------------------------------------------------------------ | ------------------------ | ----------------------------------------------------------------------------------------- |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | contribution (orkgp:P31) | Contribution  All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>) |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property                 | object                                                                                |
| ------------------------------------------------------------------------------------------ | ------------------------ | ----------------------------------------------------------------------------------------- |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | contribution (orkgp:P31) | Contribution  All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>) |

[Tool]
Got 16 rows and 3 columns, showing the first 10 rows below:
| subject                                                                                | property                      | object                                                                                |
| ------------------------------------------------------------------------------------------ | ----------------------------- | ----------------------------------------------------------------------------------------- |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | author (orkgp:P27)            | Armand Joulin (xsd:string)                                                                |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | contribution (orkgp:P31)      | Contribution  All-attention network (36 layers) (<http://orkg.org/orkg/resource/R130674>) |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | research field (orkgp:P30)    | Computer Sciences (<http://orkg.org/orkg/resource/R132>)                                  |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | publication year (orkgp:P29)  | 2019 (xsd:string)                                                                         |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | publication month (orkgp:P28) | 7 (xsd:string)                                                                            |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | url (orkgp:url)               | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string)                                       |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | rdf:type                      | Paper (orkgc:Paper)                                                                       |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | rdfs:label                    | Augmenting Self-attention with Persistent Memory (xsd:string)                             |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | author (orkgp:P27)            | Edouard Grave (xsd:string)                                                                |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | author (orkgp:P27)            | Guillaume Lample (xsd:string)                                                             |
| ...                                                                                | ...                           | ...                                                                                |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property        | object                                              |
| ------------------------------------------------------------------------------------------ | --------------- | --------------------------------------------------- |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | url (orkgp:url) | https://arxiv.org/abs/1907.01470v1.pdf (xsd:string) |

[Tool]
Got 1 row and 3 columns:
| subject                                                                                | property   | object                                                        |
| ------------------------------------------------------------------------------------------ | ---------- | ------------------------------------------------------------- |
| Augmenting Self-attention with Persistent Memory (<http://orkg.org/orkg/resource/R130669>) | rdfs:label | Augmenting Self-attention with Persistent Memory (xsd:string) |

[Assistant]
Here's what I have found so far:

- The model "All-attention network - 18 layers" is represented by two entities in ORKG:
  - All-attention network - 18 layers (<http://orkg.org/orkg/resource/R121047>)
  - All-attention network (18 layers) (<http://orkg.org/orkg/resource/R120886>)
- Both are used in contributions:
  - Contribution All-attention network - 18 layers (<http://orkg.org/orkg/resource/R130685>)
  - Contribution All-attention network (18 layers) (<http://orkg.org/orkg/resource/R130670>)
- Both contributions are part of the paper "Augmenting Self-attention with Persistent Memory" (<http://orkg.org/orkg/resource/R130669>), which is available at https://arxiv.org/abs/1907.01470v1.pdf.
- Both contributions have code references via the property "has source code (orkgp:HAS_SOURCE_CODE)" and are associated with benchmarking (property "has benchmark (orkgp:HAS_BENCHMARK)").

Now, I will write a SPARQL query that retrieves:
- The paper IRI
- The paper label
- The paper URL
- The code references
- The benchmark(s) used

for papers that have a contribution using either of the two model IRIs for benchmarking purposes and that provide code references.

I will use OPTIONAL for the label and URL, and ensure the query is general for future extensibility.

Here is the SPARQL query:

[Tool] Stopped generation process

[User]
Feedback (status=done):
The SPARQL query correctly retrieves the IRIs of the papers, code references, and benchmarks associated with the All-attention network - 18 layers model, as requested. The query also includes optional clauses for human-readable labels and URLs, which are not required but helpful for presentation. The answer provided matches the query results and is accurate and complete according to the user's question. No further refinement is necessary.


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 ?value ?metric WHERE {
  <http://orkg.org/orkg/resource/R130407> orkgp:HAS_EVALUATION ?evaluation .
  ?evaluation orkgp:HAS_VALUE ?value .
  ?evaluation orkgp:HAS_METRIC ?metric .
  OPTIONAL {
    ?evaluation rdfs:label ?label
  }
}
ORDER BY DESC ( xsd:decimal ( ?value ) )
LIMIT 1

The ORKG knowledge graph does not contain a direct link between the best performing evaluation for the CoNLL++ dataset and a model entity (IRI or identifier). All available properties and triples have been checked, and the only information available is the F1 score for the benchmark. Therefore, it is not possible to return the model's IRI as requested.

[System]
You are a question answering assistant. Your job is to generate a SPARQL query to answer a given user question.

You have access to the following knowledge graphs:
orkg at http://localhost:7054

You can use the following SPARQL prefixes implicitly in all functions:
foaf: <http://xmlns.com/foaf/0.1/
orkgc: <http://orkg.org/orkg/class/
orkgp: <http://orkg.org/orkg/predicate/
orkgsh: <http://orkg.org/orkg/shapes/
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs: <http://www.w3.org/2000/01/rdf-schema#
skos: <http://www.w3.org/2004/02/skos/core#
xsd: <http://www.w3.org/2001/XMLSchema#
owl: <http://www.w3.org/2002/07/owl#
dct: <http://purl.org/dc/terms/
dc: <http://purl.org/dc/elements/1.1/
prov: <http://www.w3.org/ns/prov#
schema: <http://schema.org/
geo: <http://www.opengis.net/ont/geosparql#
geosparql: <http://www.opengis.net/ont/geosparql#
gn: <http://www.geonames.org/ontology#
bd: <http://www.bigdata.com/rdf#
hint: <http://www.bigdata.com/queryHints#
wikibase: <http://wikiba.se/ontology#
qb: <http://purl.org/linked-data/cube#
void: <http://rdfs.org/ns/void#

You should follow a step-by-step approach to generate the SPARQL query:
1. Determine possible entities and properties implied by the user question.
2. Use the functions to search for entities and properties in the knowledge graphs. Where applicable, constrain the searches with already identified entities and properties.
3. Gradually build up the SPARQL query using the identified entities and properties. Start with simple queries and add more complexity as needed. Use the execute function to get feedback on intermediate queries. You can go back and forth between knowledge graph searches and SPARQL query executions to refine the query.
4. Use the answer or cancel function to finalize your answer and stop the generation process.

Additional rules:
1. Explain your thought process before and after each step and function call.
2. Do not assume or make up any entities or properties without verifying them with the knowledge graphs and functions.
3. The SPARQL query should always return the actual identifiers / IRIs of the items in its result. It additionally may return labels or other human-readable information, but they are optional and should be put within optional clauses unless explicitly requested by the user.
4. Do not stop early if there are still obvious improvements to be made to the SPARQL query. For example, keep refining your SPARQL query if its result contains irrelevant items or is missing items you expected.
5. Do not perform additional computation (e.g. filtering, sorting, calculations) on the result of the SPARQL query to determine the answer. All computation should be done solely within SPARQL.
6. For questions with a "True" or "False" answer the SPARQL query should be an ASK query.
7. Do not use 'SERVICE wikibase:label { bd:serviceParam wikibase:language ...' in SPARQL queries. It is not SPARQL standard and unsupported by the used QLever SPARQL endpoints. Use rdfs:label or similar properties to get labels instead.

[User] What is the best performing model benchmarking the CoNLL++ dataset in terms of F1 metric?

[Assistant]
First, I need to identify the following:

1. The entity representing the "CoNLL++ dataset".
2. The property representing "benchmarking" or "evaluated on dataset".
3. The property representing "model".
4. The property representing "F1 metric" or "F1 score".
5. How to retrieve the "best performing" model, i.e., the one with the highest F1 score.

Let's proceed step by step.

Step 1: Search for the "CoNLL++ dataset" entity in ORKG.

[Tool]
Top 10 entity alternatives:
1. CoNLL-2003 dataset (<http://orkg.org/orkg/resource/R172675>): has type Material
2. CoNLL-2003 German Dataset (<http://orkg.org/orkg/resource/R162715>)
3. CoNLL-2003 English Dataset (<http://orkg.org/orkg/resource/R162714>)
4. CoNLL-2002 Spanish Dataset (<http://orkg.org/orkg/resource/R162724>)
5. CoNLL-2002 Dutch Dataset (<http://orkg.org/orkg/resource/R162725>)
6. CoNLL 2003 English NER dataset (<http://orkg.org/orkg/resource/R163796>)
7. Zero-Resource Transfer From CoNLL-2003 English dataset. (<http://orkg.org/orkg/resource/R121398>): has type Model
8. Dataset (<http://orkg.org/orkg/resource/R178304>): has type Contribution Template
9. Dataset (<http://orkg.org/orkg/resource/R216575>): has type Contribution, has type R216526
10. Dataset (<http://orkg.org/orkg/resource/R182198>): has type Contribution

[Assistant]
I did not find an exact match for "CoNLL++ dataset", but the closest matches are "CoNLL-2003 dataset" and its language-specific variants. It is possible that "CoNLL++" refers to an extension or variant of CoNLL-2003, but without more context, I will proceed with "CoNLL-2003 dataset" (http://orkg.org/orkg/resource/R172675) as the most likely intended entity.

Next, I need to identify the property used for benchmarking or evaluation on a dataset. I will search for properties related to "benchmark" or "evaluated on".

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

[Assistant]
There are several properties related to benchmarking, such as "Benchmark Performance" (orkgp:P59252), "uses benchmark" (orkgp:P38001), and "Has benchmark" (orkgp:P34103). To find which one is used for linking models to datasets, I will check for triples where the "CoNLL-2003 dataset" is the object for these properties.

I will start by listing triples where "CoNLL-2003 dataset" is the object.

[Tool]
Got 1 row and 3 columns:
| subject                                                  | property                  | object                                                       |
| -------------------------------------------------------- | ------------------------- | ------------------------------------------------------------ |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | material (orkgp:MATERIAL) | CoNLL-2003 dataset (<http://orkg.org/orkg/resource/R172675>) |

[Assistant]
The only triple found links a "Contribution" to the "CoNLL-2003 dataset" via the property "material" (orkgp:MATERIAL). This suggests that contributions (which may describe model results) are associated with datasets via this property.

Next, I need to find how models and their F1 scores are represented within such contributions. I will look for properties related to "model" and "F1" within the context of the contribution entity (<http://orkg.org/orkg/resource/R172674>).

[Tool]
Got 14 rows and 3 columns, showing the first 10 rows below:
| subject                                                  | property                      | object                                                                                |
| -------------------------------------------------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------------- |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | research problem (orkgp:P32)  | Named Entity Recognition (<http://orkg.org/orkg/resource/R69806>)                                             |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | method (orkgp:METHOD)         | Sequence-labelling with BLSTM (<http://orkg.org/orkg/resource/R172681>)                                       |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | description (orkgp:P14)       | In this paper, we present a nove...mpare it to existing approaches. (xsd:string)                              |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | result (orkgp:RESULT)         | F1 score (<http://orkg.org/orkg/resource/R172677>)                                                            |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | material (orkgp:MATERIAL)     | 50-dimensional word embeddings r...dia and the Reuters RCV-1 corpus (<http://orkg.org/orkg/resource/R172678>) |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | Tagging scheme (orkgp:P43239) | BIOES (<http://orkg.org/orkg/resource/R172683>)                                                               |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | rdf:type                      | Contribution (orkgc:Contribution)                                                                             |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | rdfs:label                    | Contribution 1 (xsd:string)                                                                                |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | method (orkgp:METHOD)         | Extracting Character Features Using a Convolutional Neural Network (<http://orkg.org/orkg/resource/R172682>)  |
| Contribution 1 (<http://orkg.org/orkg/resource/R172674>) | material (orkgp:MATERIAL)     | Stanford’s GloVe embeddings trai...ords from Wikipedia and Web text (<http://orkg.org/orkg/resource/R172679>) |
| ...                                                      | ...                           | ...                                                                                |

[Assistant]
From the triples, I see that:

- The "method" property (orkgp:METHOD) is used to indicate the model or approach.
- The "result" property (orkgp:RESULT) points to "F1 score" (http://orkg.org/orkg/resource/R172677).
- There may be multiple "method" and "material" entries per contribution.

To find the F1 value, I need to look at the "F1 score" entity and see how its value is represented.

[Tool]
Got 5 rows and 3 columns:
| subject                                            | property                 | object                                                        |
| -------------------------------------------------- | ------------------------ | ------------------------------------------------------------- |
| F1 score (<http://orkg.org/orkg/resource/R172677>) | has value (orkgp:P41999) | 86.28 on OntoNotes using BiLSTM-CNN + emb + lex (xsd:string)  |
| F1 score (<http://orkg.org/orkg/resource/R172677>) | rdf:type                 | Result (orkgc:Result)                                         |
| F1 score (<http://orkg.org/orkg/resource/R172677>) | rdfs:label               | F1 score (xsd:string)                                         |
| F1 score (<http://orkg.org/orkg/resource/R172677>) | has value (orkgp:P41999) | 91.62 on CoNLL-2003 using BiLSTM-CNN + emb + lex (xsd:string) |
| F1 score (<http://orkg.org/orkg/resource/R172677>) | rdf:type                 | orkgc:Resource                                                |

[Assistant]
The F1 score values are stored as strings in the "has value" (orkgp:P41999) property, and the string includes the score, dataset, and model information (e.g., "91.62 on CoNLL-2003 using BiLSTM-CNN + emb + lex").

To answer the question, I need to:

1. Find all F1 score values mentioning "CoNLL-2003".
2. Extract the numeric F1 value and the model name from the string.
3. Select the model with the highest F1 value.

Since the values are stored as strings, I will write a SPARQL query that:

- Finds all F1 score values containing "CoNLL-2003".
- Extracts the numeric value and model name using string functions.
- Orders by the numeric value descending and returns the top result.

Let's construct the query.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query is close to correct, but there are some issues that need refinement:

1. The query is extracting the model name and F1 score by parsing a string (?value) using SUBSTR, STRBEFORE, and STRAFTER, which is fragile and not robust. Ideally, the F1 score and model should be stored as separate properties in the knowledge graph, not embedded in a string. If this is not possible, the string parsing should be more robust.
2. The query returns the model name as a string, not as an IRI/identifier, which goes against the guideline that results should include the actual identifiers/IRIs of the items.
3. The FILTER(CONTAINS(?value, "CoNLL-2003")) is hardcoded for "CoNLL-2003" instead of "CoNLL++" as in the user question. If "CoNLL++" is a different dataset, this query may not be answering the user's question correctly.
4. The answer refers to "CoNLL-2003" instead of "CoNLL++". Please verify if the dataset is correct.

Suggestions:
- Try to extract the model as an IRI if possible.
- Make sure the dataset being filtered matches the user's question ("CoNLL++").
- If string parsing is unavoidable, ensure it is robust and well-documented.
- If the knowledge graph does not contain the required structure, mention this limitation in the answer.

[Assistant]
Thank you for the detailed feedback. I will address each point step by step:

1. The F1 score and model should ideally be stored as separate properties, but in the current ORKG data, they are embedded in a string. I will check if there are any triples that directly link a model (as an IRI) to an F1 score for the relevant dataset.
2. I will attempt to extract the model as an IRI, not just as a string.
3. I will verify if "CoNLL++" exists as a dataset in ORKG, or if only "CoNLL-2003" is available.
4. I will clarify in the answer if "CoNLL++" is not present and that the answer is based on "CoNLL-2003".

Step 1: Search for "CoNLL++" in ORKG entities to ensure the correct dataset is used.

[Tool]
Top 10 entity alternatives:
1. CoNLL (<http://orkg.org/orkg/resource/R46350>)
2. CoNLL++ (<http://orkg.org/orkg/resource/R122914>): has type Dataset
3. CoNLL (<http://orkg.org/orkg/resource/R44354>)
4. Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>): has type Benchmark
5. CoNLL-2003 (<http://orkg.org/orkg/resource/R156982>)
6. CoNLL-2002 (<http://orkg.org/orkg/resource/R162723>)
7. CoNLL 2000 (<http://orkg.org/orkg/resource/R122957>): has type Dataset
8. CoNLL 2019 (<http://orkg.org/orkg/resource/R122231>): has type Dataset
9. CoNLL Spanish (<http://orkg.org/orkg/resource/R124033>): has type Dataset
10. CoNLL German (<http://orkg.org/orkg/resource/R124041>): has type Dataset

[Assistant]
There is an entity for "CoNLL++" (<http://orkg.org/orkg/resource/R122914>) and "Benchmark CoNLL++" (<http://orkg.org/orkg/resource/R130407>), both related to datasets/benchmarks. I will use <http://orkg.org/orkg/resource/R122914> as the dataset for this query.

Step 2: Find contributions or results that benchmark models on "CoNLL++". I will list triples where "CoNLL++" is the object to see how it is used.

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                        | object                                            |
| ----------------------------------------------------------- | ------------------------------- | ------------------------------------------------- |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | has dataset (orkgp:HAS_DATASET) | CoNLL++ (<http://orkg.org/orkg/resource/R122914>) |

[Assistant]
The only triple found links "Benchmark CoNLL++" to "CoNLL++" via the property "has dataset" (orkgp:HAS_DATASET). This suggests that benchmarking results may be associated with the "Benchmark CoNLL++" entity.

Step 3: List triples where "Benchmark CoNLL++" is the subject to see if there are models, results, or F1 scores directly linked.

[Tool]
Got 5 rows and 3 columns:
| subject                                                     | property                          | object                                                        |
| ----------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------- |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | has dataset (orkgp:HAS_DATASET)   | CoNLL++ (<http://orkg.org/orkg/resource/R122914>)             |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                   |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | rdfs:label                        | Benchmark CoNLL++ (xsd:string)                                |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | rdf:type                          | orkgc:Resource                                                |

[Assistant]
There is an evaluation entity "Evaluation F1_93.42" (<http://orkg.org/orkg/resource/R130408>) linked via the property "evaluation" (orkgp:HAS_EVALUATION). This may contain the F1 score and possibly the model.

Step 4: List triples where "Evaluation F1_93.42" is the subject to see if the model and F1 score are stored as separate properties.

[Tool]
Got 5 rows and 3 columns:
| subject                                                       | property                  | object                                       |
| ------------------------------------------------------------- | ------------------------- | -------------------------------------------- |
| Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) | value (orkgp:HAS_VALUE)   | 93.42 (xsd:string)                           |
| Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) | metric (orkgp:HAS_METRIC) | F1 (<http://orkg.org/orkg/resource/R114483>) |
| Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) | rdf:type                  | Evaluation (orkgc:Evaluation)                |
| Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) | rdfs:label                | Evaluation F1_93.42 (xsd:string)             |
| Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) | rdf:type                  | orkgc:Resource                               |

[Assistant]
The evaluation entity contains the F1 value (as a string) and the metric (F1), but there is no direct link to a model. To find the model, I need to see if there are other entities linking to this evaluation, possibly via a "has evaluation" or similar property.

Step 5: List triples where "Evaluation F1_93.42" is the object to see if a model is linked as the subject.

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                          | object                                                        |
| ----------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------- |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) |

[Assistant]
The only entity linking to this evaluation is "Benchmark CoNLL++", not a model. This suggests that the model information may not be directly linked in the knowledge graph for this evaluation.

Step 6: Search for other evaluations or models associated with "Benchmark CoNLL++" to see if any are linked as IRIs. I will search for properties of "Benchmark CoNLL++" related to "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 linking a model entity to "Benchmark CoNLL++". The only properties are "has dataset" and "evaluation".

Step 7: Search for models associated with the "CoNLL++" dataset in general, in case they are linked elsewhere.

[Tool]
No property items found

No other items found

[Assistant]
There are no properties directly linking a model entity to the "CoNLL++" dataset either.

Summary:
- The knowledge graph contains an F1 evaluation (93.42) for "Benchmark CoNLL++" on the "CoNLL++" dataset.
- However, there is no direct link to a model entity (as an IRI) for this evaluation.
- The only available information is the F1 score and the dataset, but not the model identifier.

Given these limitations, I will write a SPARQL query that retrieves all F1 evaluations for "Benchmark CoNLL++" on "CoNLL++", returning the F1 value and metric, but I will note in the answer that the model IRI is not available in the current knowledge graph structure.

Here is the refined query and answer.

[Tool] Stopped generation process

[User]
Feedback (status=refine):
The SPARQL query retrieves the evaluation with the highest F1 value for the CoNLL++ dataset, but it does not return the model that achieved this result. The user's question specifically asks for the best performing model, so the query should be refined to include the model's IRI or identifier associated with the evaluation. Please update the query to also return the model (e.g., ?model) that was benchmarked in this evaluation, ensuring that the answer directly addresses the user's request.

[Assistant]
Thank you for the feedback. Since the previous exploration showed that the direct link between the evaluation and a model entity is not obvious, I need to investigate further if there is any way to retrieve the model IRI associated with the evaluation for the CoNLL++ dataset.

Step 1: List all triples where the evaluation entity (<http://orkg.org/orkg/resource/R130408>) is the object, to see if any model is linked as the subject via any property.

[Tool]
Got 1 row and 3 columns:
| subject                                                     | property                          | object                                                        |
| ----------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------- |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) |

[Assistant]
The only subject linking to the evaluation is "Benchmark CoNLL++", not a model.

Step 2: List all triples where "Benchmark CoNLL++" is the subject, to see if a model is linked via any property.

[Tool]
Got 5 rows and 3 columns:
| subject                                                     | property                          | object                                                        |
| ----------------------------------------------------------- | --------------------------------- | ------------------------------------------------------------- |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | evaluation (orkgp:HAS_EVALUATION) | Evaluation F1_93.42 (<http://orkg.org/orkg/resource/R130408>) |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | has dataset (orkgp:HAS_DATASET)   | CoNLL++ (<http://orkg.org/orkg/resource/R122914>)             |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | rdf:type                          | Benchmark (orkgc:Benchmark)                                   |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | rdfs:label                        | Benchmark CoNLL++ (xsd:string)                                |
| Benchmark CoNLL++ (<http://orkg.org/orkg/resource/R130407>) | rdf:type                          | orkgc:Resource                                                |

[Assistant]
There is no direct link from "Benchmark CoNLL++" to a model entity.

Step 3: Search for all properties related to "model" in the ORKG, to see if there are alternative properties used to link models to benchmarks or evaluations.

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

[Assistant]
There are several properties for "model" or "has model" (e.g., orkgp:hasModel, orkgp:HAS_MODEL, orkgp:P20072, etc.). I will check if any of these properties are used with "Benchmark CoNLL++" or the evaluation entity.

Step 4: List triples where "Benchmark CoNLL++" is the subject and the property is any of the "model" properties.

[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] Got no rows and 3 columns

[Assistant]
None of the "model" properties are used with "Benchmark CoNLL++" as the subject.

Step 5: List triples where the evaluation entity (<http://orkg.org/orkg/resource/R130408>) is the subject and the property is any of the "model" properties.

[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] Got no rows and 3 columns

[Assistant]
None of the "model" properties are used with the evaluation entity either.

Conclusion:
- The ORKG knowledge graph does not currently contain a direct link between the best performing evaluation for CoNLL++ and a model entity (IRI or identifier).
- All available evidence points to the F1 score being present, but not the model.

I will provide a SPARQL query that retrieves the best F1 score for CoNLL++ and explain in the answer that the model IRI is not available in the current knowledge graph structure.

[Tool] Stopped generation process