Triple
T8823625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thrust |
E209961
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | parallel algorithms library |
C7925
|
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: parallel algorithms library Context triple: [Thrust, instanceOf, parallel algorithms library]
-
A.
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.
-
B.
SIMD instruction set extension
A SIMD instruction set extension is a set of processor instructions that enable performing the same operation simultaneously on multiple data elements to accelerate parallelizable computations.
-
C.
GPU computing framework
chosen
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.
-
D.
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.
-
E.
vector processing extension
A vector processing extension is a hardware or software enhancement to a processor’s instruction set that enables efficient parallel operations on multiple data elements within single instructions, improving performance for data-intensive 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_69ca8364e13081909c85fe80f44fe86f |
completed | March 30, 2026, 2:06 p.m. |
Created at: March 30, 2026, 6:46 p.m.