Triple
T816373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NumPy |
E17659
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | scikit-learn |
E17661
|
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: scikit-learn | Statement: [NumPy, influenced, scikit-learn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: scikit-learn Context triple: [NumPy, influenced, scikit-learn]
-
A.
scikit-learn
chosen
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
B.
SciPy
SciPy is an open-source Python library that provides advanced scientific and technical computing tools, including modules for optimization, integration, statistics, signal processing, and linear algebra.
-
C.
Keras
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.
-
D.
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.
-
E.
Seaborn
Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab621d2c819083f10bff4f66c482 |
completed | March 1, 2026, 9:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d8d1a448190be8494fa2776615a |
completed | March 3, 2026, 11:23 p.m. |
Created at: March 1, 2026, 7:38 p.m.