Triple
T29938243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | cuSOLVER |
E760426
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | GPU-accelerated linear algebra library |
C25137
|
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: GPU-accelerated linear algebra library Context triple: [cuSOLVER, instanceOf, GPU-accelerated linear algebra library]
-
A.
GPU-accelerated BLAS library
chosen
A GPU-accelerated BLAS library is a collection of highly optimized linear algebra routines that offload matrix and vector computations to graphics processing units to achieve significantly higher performance than CPU-only implementations.
-
B.
GPU-accelerated array library
A GPU-accelerated array library is a software toolkit that provides high-level, NumPy-like array operations executed on graphics processing units to enable massively parallel, high-performance numerical computing.
-
C.
numerical linear algebra library
A numerical linear algebra library is a collection of optimized routines and data structures for performing matrix and vector operations, decompositions, and related numerical computations.
-
D.
GPU computing framework
A GPU computing framework is a software platform that enables developers to write, manage, and optimize parallel programs that execute on graphics processing units for high-performance computation.
-
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_69f22463f3648190a603c3ff305c660b |
completed | April 29, 2026, 3:31 p.m. |
Created at: April 29, 2026, 6:21 p.m.