Triple
T18016443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faster R-CNN |
E431008
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | computer vision model |
C4178
|
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 model Context triple: [Faster R-CNN, instanceOf, computer vision model]
-
A.
image recognition model
chosen
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.
-
B.
computer vision algorithm
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.
deep learning model
A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
-
D.
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.
-
E.
computer vision research work
A computer vision research work is a scholarly study that develops, analyzes, or evaluates algorithms and systems enabling machines to interpret and understand visual information from images or videos.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
Created at: April 10, 2026, 10:24 a.m.