Triple
T849762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keras |
E18356
|
entity |
| Predicate | maintainer |
P2962
|
FINISHED |
| Object | Keras team |
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 team | Statement: [Keras, maintainer, Keras team]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keras team Context triple: [Keras, maintainer, Keras team]
-
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.
ChatGPT Team
ChatGPT Team is a subscription plan designed for collaborative use, allowing multiple users within an organization or group to share access to advanced ChatGPT features and management tools.
-
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.
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.
-
E.
Christian Szegedy
Christian Szegedy is a computer scientist and AI researcher known for his influential work on deep learning and convolutional neural networks, including contributions to the Inception architecture.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac1fac3481909cba7070ce31a9b3 |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3bd8b588190b7a9eb72dce93d07 |
completed | March 4, 2026, 3:15 a.m. |
Created at: March 1, 2026, 7:38 p.m.