Triple
T17520216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chainer |
E426662
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | ChainerMN |
—
|
NE NERFINISHED |
How this triple was built (3 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: ChainerMN | Statement: [Chainer, hasComponent, ChainerMN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ChainerMN Context triple: [Chainer, hasComponent, ChainerMN]
-
A.
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.
-
B.
DMLC (Distributed Machine Learning Community)
DMLC (Distributed Machine Learning Community) is an open-source collaborative group that develops scalable machine learning and deep learning systems and tools, including major projects like Apache MXNet and XGBoost.
-
C.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
D.
Horovod
Horovod is an open-source distributed deep learning framework designed to make training models across multiple GPUs and machines fast and easy.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ChainerMN Target entity description: ChainerMN is a distributed deep learning extension for the Chainer framework that enables scalable multi-node, multi-GPU training.
-
A.
Chainer
chosen
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
B.
DMLC (Distributed Machine Learning Community)
DMLC (Distributed Machine Learning Community) is an open-source collaborative group that develops scalable machine learning and deep learning systems and tools, including major projects like Apache MXNet and XGBoost.
-
C.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
D.
Horovod
Horovod is an open-source distributed deep learning framework designed to make training models across multiple GPUs and machines fast and easy.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- F. None of above.
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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.