Triple
T7387148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XeSS |
E170408
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | AI-driven super sampling technology |
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: AI-driven super sampling technology Context triple: [XeSS, instanceOf, AI-driven super sampling technology]
-
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.
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.
-
C.
real-time rendering technology
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.
-
D.
graphics processing unit
A graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly perform parallel mathematical and geometric calculations to render images, videos, and visual effects for display.
-
E.
GPU-accelerated application
A GPU-accelerated application is software that offloads compute-intensive tasks from the CPU to a graphics processing unit (GPU) to achieve significantly higher performance and parallel processing efficiency.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
Created at: March 27, 2026, 3:08 p.m.