Triple
T8577154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PixelRNN |
E203075
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image generation model |
C24666
|
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 generation model Context triple: [PixelRNN, instanceOf, image generation model]
-
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.
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.
-
C.
generative AI service suite
A generative AI service suite is an integrated collection of tools and APIs that create, transform, and analyze content (such as text, images, code, or audio) using advanced machine learning models to support diverse applications and workflows.
-
D.
art model
An art model is a person who poses for artists, photographers, or art classes to provide a live reference for studying and depicting the human form, expression, or composition.
-
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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:22 p.m.