Triple
T825453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SciPy |
E17842
|
entity |
| Predicate | hasModule |
P12988
|
FINISHED |
| Object | scipy.stats |
E17842
|
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: scipy.stats | Statement: [SciPy, hasModule, scipy.stats]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: scipy.stats Context triple: [SciPy, hasModule, scipy.stats]
-
A.
SciPy
chosen
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.
-
B.
Gaussian distribution
The Gaussian distribution, also known as the normal distribution, is a fundamental continuous probability distribution characterized by its symmetric bell-shaped curve and central role in statistics and the natural sciences.
-
C.
Maxwell–Boltzmann statistics
Maxwell–Boltzmann statistics is a classical statistical framework in physics that describes the distribution of speeds or energies among distinguishable, non-quantum particles in thermal equilibrium.
-
D.
scikit-learn
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.
-
E.
Monte Carlo method
The Monte Carlo method is a computational technique that uses random sampling to approximate numerical results, especially for complex integrals, simulations, and probabilistic 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_69a4937c9c188190aaa216f6b466f452 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2b503d48190bd4f33548a22d5fe |
completed | March 1, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d9577f081908aa31b1926e04bb8 |
completed | March 3, 2026, 11:24 p.m. |
Created at: March 1, 2026, 7:38 p.m.