Triple
T31486433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ada Optical Flow Accelerator |
E803288
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | optical flow engine |
C45085
|
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: optical flow engine Context triple: [Ada Optical Flow Accelerator, instanceOf, optical flow engine]
-
A.
optical flow-based method
An optical flow-based method is a technique that estimates the motion of objects, surfaces, or edges in a visual scene by analyzing the apparent pixel intensity changes between consecutive image frames.
-
B.
motion-processing technology
Motion-processing technology refers to systems and algorithms that detect, analyze, and interpret movement from sensors or visual input to enable responsive digital or mechanical actions.
-
C.
video co-processor
chosen
A video co-processor is a specialized hardware component that offloads and accelerates video-related tasks—such as decoding, encoding, rendering, and image processing—from the main CPU or GPU to improve performance and efficiency.
-
D.
depth sensor
A depth sensor is a device that measures the distance from itself to objects in its field of view, typically using technologies like time-of-flight, structured light, or stereo vision to produce depth information.
-
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.
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_69f348ca04508190ba9379b5329dfd75 |
completed | April 30, 2026, 12:19 p.m. |
Created at: April 30, 2026, 9:35 p.m.