Triple
T27567570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bartels–Stewart algorithm |
E695944
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | algorithm in numerical linear algebra |
C49595
|
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: algorithm in numerical linear algebra Context triple: [Bartels–Stewart algorithm, instanceOf, algorithm in numerical linear algebra]
-
A.
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.
-
B.
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.
-
C.
linear system solver
chosen
A linear system solver is a computational component that determines the values of unknown variables that satisfy a given set of linear equations.
-
D.
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.
-
E.
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.
- 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_69ef53891af88190a193c5e2a1dac9b1 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 27, 2026, 1:42 p.m.