Triple
T31025369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NVIDIA RT Cores |
E790553
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | ray tracing accelerator |
C8436
|
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: ray tracing accelerator Context triple: [NVIDIA RT Cores, instanceOf, ray tracing accelerator]
-
A.
graphics acceleration technology
Graphics acceleration technology is specialized hardware and software that offloads and speeds up the processing of visual and graphical computations, enabling smoother rendering and higher performance for images, videos, and 3D applications.
-
B.
hardware accelerator
chosen
A hardware accelerator is a specialized computing device or component designed to perform specific tasks or algorithms more efficiently and faster than a general-purpose processor.
-
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.
accelerator infrastructure
Accelerator infrastructure encompasses the physical facilities, technical systems, and support services required to design, build, operate, and maintain particle accelerators and their associated experimental environments.
-
E.
3D rendering engine
A 3D rendering engine is a software component that transforms 3D scene data—geometry, materials, lighting, and camera parameters—into 2D images or frames through processes like rasterization or ray tracing.
- 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_69f224c811508190a7de096a5b1f5798 |
completed | April 29, 2026, 3:33 p.m. |
Created at: April 29, 2026, 8:58 p.m.