Triple
T15361406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ResNeXt |
E367297
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image recognition 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: image recognition architecture Context triple: [ResNeXt, instanceOf, image recognition architecture]
-
A.
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.
-
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.
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.
-
D.
network architecture
A network architecture is the structured design and organization of hardware, software, protocols, and communication paths that define how data flows and services are delivered within a computer network.
-
E.
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.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
Created at: April 10, 2026, 3:18 a.m.