Triple
T31486523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NVIDIA L40S |
E803290
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | NVIDIA Ada Lovelace 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 Ada Lovelace GPU Context triple: [NVIDIA L40S, instanceOf, NVIDIA Ada Lovelace 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.
AMD EPYC microprocessor family
The AMD EPYC microprocessor family is a line of high-performance, server-grade x86-64 CPUs designed for data centers and enterprise workloads, offering many cores, large memory capacity, and advanced security and virtualization features.
-
D.
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.
-
E.
Alder Lake processor
An Alder Lake processor is Intel's hybrid CPU architecture that combines high-performance and high-efficiency cores on a single chip to optimize power and speed for modern computing workloads.
- 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_69f348ca04508190ba9379b5329dfd75 |
completed | April 30, 2026, 12:19 p.m. |
Created at: April 30, 2026, 9:35 p.m.