Triple
T6042519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RMSProp |
E134579
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object | Keras |
E18356
|
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: Keras | Statement: [RMSProp, implementedIn, Keras]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keras Context triple: [RMSProp, implementedIn, Keras]
-
A.
Keras
chosen
Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
-
B.
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.
-
C.
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.
-
D.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
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 (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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056e108fc81908775d176ff960fad |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1139793708190b14c83d4197a33a0 |
completed | March 23, 2026, 10:19 a.m. |
Created at: March 22, 2026, 4:08 p.m.