Triple
T35237115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Normalized Cuts for image segmentation |
E1017405
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image segmentation 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: image segmentation algorithm Context triple: [Normalized Cuts for image segmentation, instanceOf, image segmentation algorithm]
-
A.
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.
-
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.
image
An image is a visual representation of objects, scenes, or concepts captured or generated in a two-dimensional form.
-
E.
semantic segmentation dataset class
A semantic segmentation dataset class manages and provides access to images and their corresponding pixel-wise labeled masks, enabling efficient loading, preprocessing, and batching for training and evaluating segmentation models.
- 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_69f76de235048190b990070c23c51b6b |
completed | May 3, 2026, 3:46 p.m. |
Created at: May 3, 2026, 4:02 p.m.