Triple
T29938960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FSR 2.0 |
E760442
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | temporal upscaling technology |
C9941
|
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: temporal upscaling technology Context triple: [FSR 2.0, instanceOf, temporal upscaling technology]
-
A.
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.
-
B.
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.
-
C.
motion-processing technology
Motion-processing technology refers to systems and algorithms that detect, analyze, and interpret movement from sensors or visual input to enable responsive digital or mechanical actions.
-
D.
high-dynamic-range imaging technology
High-dynamic-range imaging technology is a method of capturing, processing, and displaying images with a wider range of luminance and color than standard imaging, preserving detail in both very bright and very dark areas.
-
E.
real-time rendering technology
chosen
Real-time rendering technology is a class of systems and algorithms that generate and display interactive, visually coherent images or scenes at high frame rates, typically for applications like games, simulations, and virtual reality.
- 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.