Triple
T12572974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucas–Kanade optical flow algorithm |
E295649
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | computer vision algorithm |
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: computer vision algorithm Context triple: [Lucas–Kanade optical flow algorithm, instanceOf, computer vision algorithm]
-
A.
computer vision research laboratory
A computer vision research laboratory is a specialized facility where researchers develop, test, and evaluate algorithms and systems that enable machines to interpret and understand visual information from the world.
-
B.
image processing computer
An image processing computer is a specialized computing system designed to efficiently capture, analyze, transform, and interpret digital images using dedicated hardware and software algorithms.
-
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.
camera feature
A camera feature is a distinct capability or setting of a camera system that enhances how images or videos are captured, processed, or presented.
-
E.
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.
- F. None of above. chosen
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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
Created at: April 8, 2026, 11:50 p.m.