Triple
T11003202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Long-term Recurrent Convolutional Networks for Visual Recognition and Description |
E260050
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | computer vision paper |
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 paper Context triple: [Long-term Recurrent Convolutional Networks for Visual Recognition and Description, instanceOf, computer vision paper]
-
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.
landmark paper in machine learning
A landmark paper in machine learning is a highly influential publication that introduces foundational theories, algorithms, or empirical results that significantly shape subsequent research and practice in the field.
-
C.
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.
-
D.
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.
-
E.
optics paper
An optics paper is a scholarly article that presents original research, theoretical analysis, or experimental results related to the behavior, properties, and applications of light and optical systems.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:25 p.m.