Triple
T29938818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FSR |
E760439
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | image upscaling algorithm |
C9940
|
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 upscaling algorithm Context triple: [FSR, instanceOf, image upscaling algorithm]
-
A.
image upscaling technology
chosen
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.
-
B.
computer vision algorithm
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.
image generation model
An image generation model is an AI system that creates new images from input data such as text prompts, reference images, or learned patterns, using techniques like deep neural networks and generative modeling.
-
D.
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.
-
E.
AI-powered media enhancement software
AI-powered media enhancement software is a digital tool that uses artificial intelligence to automatically analyze, improve, and optimize audio, video, and images for higher quality and better user experience.
- 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_69f22463f3648190a603c3ff305c660b |
completed | April 29, 2026, 3:31 p.m. |
Created at: April 29, 2026, 6:21 p.m.