Triple
T8823430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | cuBLAS |
E209957
|
entity |
| Predicate | relatedLibrary |
P55137
|
FINISHED |
| Object | cuSPARSE |
E213160
|
NE FINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: cuSPARSE | Statement: [cuBLAS, relatedLibrary, cuSPARSE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: cuSPARSE Context triple: [cuBLAS, relatedLibrary, cuSPARSE]
-
A.
cuSPARSE
chosen
cuSPARSE is NVIDIA’s GPU-accelerated library providing high-performance sparse linear algebra routines for CUDA applications.
-
B.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
C.
CUDA libraries
CUDA libraries are a collection of NVIDIA-provided GPU-accelerated software libraries that offer optimized routines for tasks such as linear algebra, deep learning, signal processing, and parallel algorithms on CUDA-enabled hardware.
-
D.
cuDNN
cuDNN is NVIDIA’s GPU-accelerated library of optimized primitives for deep neural networks, widely used to speed up training and inference in frameworks like TensorFlow and PyTorch.
-
E.
BLAS
BLAS (Basic Linear Algebra Subprograms) is a standardized collection of low-level routines for performing common linear algebra operations such as vector and matrix multiplication, widely used as a performance-optimized foundation in scientific computing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
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. |
| NER | Named-entity recognition | batch_69cc6030b25081909d67488b35a72e05 |
completed | April 1, 2026, midnight |
| NED1 | Entity disambiguation (via context triple) | batch_69cf893e08b0819083c2d152d0f9c263 |
completed | April 3, 2026, 9:32 a.m. |
Created at: March 30, 2026, 6:46 p.m.