Triple
T31137296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NVIDIA A30 |
E793682
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | NVIDIA GPU |
C36935
|
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: NVIDIA GPU Context triple: [NVIDIA A30, instanceOf, NVIDIA GPU]
-
A.
NVIDIA technology
NVIDIA technology encompasses a range of advanced hardware and software solutions—most notably GPUs, AI platforms, and high-performance computing systems—designed to accelerate graphics, data processing, and machine learning workloads across industries.
-
B.
GPU architecture
GPU architecture is the conceptual design and organization of a graphics processing unit’s cores, memory hierarchy, and data paths that enable massively parallel computation for graphics and general-purpose workloads.
-
C.
data center GPU
chosen
A data center GPU is a high-performance graphics processing unit designed for server environments to accelerate large-scale parallel workloads such as AI, HPC, and data analytics with optimized power, cooling, and reliability features.
-
D.
NVIDIA software product
An NVIDIA software product is a program or suite of tools developed by NVIDIA to enable, optimize, or manage graphics, AI, and high-performance computing workloads on NVIDIA hardware platforms.
-
E.
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.
- 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_69f224d2b3a48190aa9dd26fbf6eab1a |
completed | April 29, 2026, 3:33 p.m. |
Created at: April 29, 2026, 9:05 p.m.