Triple
T34674380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | cuDF |
E890457
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | GPU-accelerated DataFrame library |
C15487
|
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 DataFrame library Context triple: [cuDF, instanceOf, GPU-accelerated DataFrame library]
-
A.
GPU-accelerated array library
chosen
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.
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.
-
C.
data-parallel execution engine
A data-parallel execution engine is a system that coordinates the simultaneous processing of independent data partitions across multiple compute resources to accelerate large-scale 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_69f349d9c59481908b36baa0be093aea |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 2:05 a.m.