Triple
T16076511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LINPACK |
E389991
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | numerical linear algebra library |
C36936
|
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: numerical linear algebra library Context triple: [LINPACK, instanceOf, numerical linear algebra library]
-
A.
GPU-accelerated BLAS library
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.
result in linear algebra
In linear algebra, a result is a proven statement or conclusion—such as a theorem, lemma, or corollary—that follows logically from definitions and previously established facts about vectors, matrices, and linear transformations.
-
D.
Julia library
A Julia library is a reusable collection of Julia modules, functions, and types that extends the language’s capabilities for specific tasks or domains.
-
E.
parallel programming library
A parallel programming library is a collection of tools, abstractions, and APIs that enable developers to write programs that execute multiple computations concurrently across multiple cores, processors, or machines to improve performance and scalability.
- F. None of above. chosen
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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
Created at: April 10, 2026, 4:57 a.m.