Triple
T18204603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ViT |
E435871
|
entity |
| Predicate | treatsImageAs |
P105444
|
FINISHED |
| Object | sequence of patches |
—
|
LITERAL FINISHED |
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: sequence of patches | Statement: [ViT, treatsImageAs, sequence of patches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsImageAs Context triple: [ViT, treatsImageAs, sequence of patches]
-
A.
treatsRightAs
Indicates that one entity provides medical or therapeutic treatment to another entity who is identified as the right-hand participant in the relationship.
-
B.
usesImageModel
chosen
Indicates that one entity employs or relies on an image-based model (such as a computer vision or image generation model) in relation to another entity or task.
-
C.
principalImageType
Indicates the primary or most representative type of image associated with an entity.
-
D.
treatsLightAs
Indicates that an entity regards or handles light in a particular way or manner.
-
E.
containsImage
Indicates that one entity includes or embeds an image as part of its content or structure.
- 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.