Triple
T18178440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BrainScript |
E435221
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | CNTK BrainScript |
—
|
NE NERFINISHED |
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: CNTK BrainScript | Statement: [BrainScript, shortName, CNTK BrainScript]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CNTK BrainScript Context triple: [BrainScript, shortName, CNTK BrainScript]
-
A.
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.
-
B.
BrainScript modeling language
chosen
BrainScript modeling language is a domain-specific scripting language used to define and train neural network models within the Microsoft Cognitive Toolkit (CNTK).
-
C.
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.
-
D.
ONNX
ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models that enables interoperability between different deep learning frameworks and tools.
-
E.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df5b68f081908aac8210270f1499 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:31 a.m.