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.