Triple
T18205263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VisionEncoderDecoderModel |
E435885
|
entity |
| Predicate | supportsEncoderModel |
P57888
|
FINISHED |
| Object | ViTModel |
—
|
NE NERFINISHED |
How this triple was built (3 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: ViTModel | Statement: [VisionEncoderDecoderModel, supportsEncoderModel, ViTModel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ViTModel Context triple: [VisionEncoderDecoderModel, supportsEncoderModel, ViTModel]
-
A.
ViT
chosen
ViT (Vision Transformer) is a deep learning model architecture that applies the transformer framework to image recognition tasks by treating images as sequences of patches.
-
B.
VisionEncoderDecoderModel
VisionEncoderDecoderModel is a Hugging Face Transformers architecture that combines a vision encoder with a text decoder to perform tasks like image captioning and visual question answering.
-
C.
Swin Transformer
Swin Transformer is a hierarchical vision transformer architecture that uses shifted windows for efficient and scalable image recognition and related computer vision tasks.
-
D.
DeiT
DeiT is a family of data-efficient vision transformer models designed for image classification with reduced training data requirements and strong performance.
-
E.
PWSFTviT
PWSFTviT is the renowned Łódź Film School in Poland, one of Europe’s leading film and television academies known for training many acclaimed filmmakers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsEncoderModel Context triple: [VisionEncoderDecoderModel, supportsEncoderModel, ViTModel]
-
A.
supportsModelType
Indicates that an entity is compatible with, or can operate using, a specified model type.
-
B.
supportsModelingOf
Indicates that one entity provides the capability or functionality needed to represent, simulate, or model another entity or process.
-
C.
supportsModelVariant
Indicates that one entity is capable of operating with, being compatible with, or otherwise accommodating a specific variant of a model.
-
D.
supportsContributionModel
Indicates that one entity enables or is compatible with a particular model or framework for making contributions (such as donations, content, or resources).
-
E.
supportsModelFamily
chosen
Indicates that one entity provides compatibility, functionality, or resources necessary for the operation or use of a particular model family.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.