Triple

T18205142
Position Surface form Disambiguated ID Type / Status
Subject Swin Transformer E435882 entity
Predicate inspiredBy P9 FINISHED
Object Vision Transformer (ViT) — NE NERFINISHED

How this triple was built (2 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: Vision Transformer (ViT) | Statement: [Swin Transformer, inspiredBy, Vision Transformer (ViT)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vision Transformer (ViT)
Context triple: [Swin Transformer, inspiredBy, Vision Transformer (ViT)]
  • 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. 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.
  • C. DeiT
    DeiT is a family of data-efficient vision transformer models designed for image classification with reduced training data requirements and strong performance.
  • D. 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.
  • E. Transformer-XL
    Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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.
Created at: April 10, 2026, 10:32 a.m.