Triple
T11016821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pixar Image Computer |
E260385
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image processing computer |
C29018
|
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 processing computer Context triple: [Pixar Image Computer, instanceOf, image processing computer]
-
A.
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.
-
B.
imager
An imager is a component or system that captures, generates, or processes visual representations of data, scenes, or objects into image form.
-
C.
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.
-
D.
image upscaling technology
Image upscaling technology is a set of algorithms and tools that increase the resolution and apparent quality of digital images by intelligently adding or refining pixel data, often using advanced methods like machine learning or deep learning.
-
E.
image service
An image service is a system that stores, processes, and delivers images—often including transformations like resizing, cropping, and format conversion—via programmatic interfaces or web endpoints.
- F. None of above. chosen
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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:25 p.m.