Triple
T1893384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NVIDIA CUDA |
E41922
|
entity |
| Predicate | includes |
P1393
|
FINISHED |
| Object |
CUDA Math Library
The CUDA Math Library is a collection of highly optimized mathematical functions provided by NVIDIA for accelerating numerical computations on CUDA-enabled GPUs.
|
E41922
|
NE FINISHED |
How this triple was built (4 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: CUDA Math Library | Statement: [NVIDIA CUDA, includes, CUDA Math Library]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CUDA Math Library Context triple: [NVIDIA CUDA, includes, CUDA Math Library]
-
A.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
B.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
C.
OpenCL
OpenCL is an open, cross-platform framework for writing programs that execute across heterogeneous systems including CPUs, GPUs, and other processors.
-
D.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
-
E.
OpenACC
OpenACC is a directive-based parallel programming standard designed to simplify the development of portable, high-performance code on heterogeneous systems such as GPUs and multicore CPUs.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: CUDA Math Library Triple: [NVIDIA CUDA, includes, CUDA Math Library]
Generated description
The CUDA Math Library is a collection of highly optimized mathematical functions provided by NVIDIA for accelerating numerical computations on CUDA-enabled GPUs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CUDA Math Library Target entity description: The CUDA Math Library is a collection of highly optimized mathematical functions provided by NVIDIA for accelerating numerical computations on CUDA-enabled GPUs.
-
A.
NVIDIA CUDA
chosen
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
B.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
C.
OpenCL
OpenCL is an open, cross-platform framework for writing programs that execute across heterogeneous systems including CPUs, GPUs, and other processors.
-
D.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
-
E.
OpenACC
OpenACC is a directive-based parallel programming standard designed to simplify the development of portable, high-performance code on heterogeneous systems such as GPUs and multicore CPUs.
- F. None of above.
Provenance (5 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1480a6c81909fcf5cce4c42fed4 |
completed | March 7, 2026, 5:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69addf6aba788190bb30420375b5db7f |
completed | March 8, 2026, 8:43 p.m. |
| NEDg | Description generation | batch_69addfe604708190b85d6f7261197537 |
completed | March 8, 2026, 8:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ade0919d748190a4326d4abca2c9eb |
completed | March 8, 2026, 8:48 p.m. |
Created at: March 4, 2026, 7:34 p.m.