Triple
T8823472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | cuDNN |
E209958
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | MXNet |
E234123
|
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: MXNet | Statement: [cuDNN, usedBy, MXNet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MXNet Context triple: [cuDNN, usedBy, MXNet]
-
A.
MXNet
chosen
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
B.
Theano
Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
-
C.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
D.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
E.
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
- 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.