Triple

T10882118
Position Surface form Disambiguated ID Type / Status
Subject NVIDIA RAPIDS E256948 entity
Predicate instanceOf P0 FINISHED
Object GPU-accelerated data science framework 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 data science framework
Context triple: [NVIDIA RAPIDS, instanceOf, GPU-accelerated data science framework]
  • A. 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.
  • B. 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.
  • C. 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.
  • 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. machine learning framework
    A machine learning framework is a software library or platform that provides tools, abstractions, and workflows to design, train, evaluate, and deploy machine learning models efficiently.
  • 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_69d6aa848804819081b2713ca0bedf06 completed April 8, 2026, 7:20 p.m.
Created at: April 8, 2026, 9:21 p.m.