Triple
T18204576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ViT |
E435871
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | vision transformer architecture |
C31656
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: vision transformer architecture Context triple: [ViT, instanceOf, vision transformer architecture]
-
A.
visual discovery engine
A visual discovery engine is a system that helps users explore and find relevant content, products, or ideas primarily through images and visual cues rather than text-based search.
-
B.
computer vision algorithm
chosen
A computer vision algorithm is a computational method that processes and interprets visual data from images or videos to automatically extract meaningful information or perform tasks such as detection, recognition, and segmentation.
-
C.
image recognition model
An image recognition model is a computational system that analyzes visual input to automatically identify, classify, and sometimes localize objects, patterns, or features within images.
-
D.
image captioning model
An image captioning model is a system that automatically generates descriptive natural language sentences that explain the content of an input image.
-
E.
imaging architecture
Imaging architecture is the conceptual and technical framework that defines how imaging components, data flows, and processing pipelines are organized and integrated to capture, transform, analyze, and deliver visual information.
- F. None of above.
Provenance (1 batch)
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. |
Created at: April 10, 2026, 10:32 a.m.