Triple
T18799300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | statsmodels |
E459721
|
entity |
| Predicate | uses |
P98
|
FINISHED |
| Object | SciPy |
—
|
NE NERFINISHED |
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 | Statement: [statsmodels, uses, SciPy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SciPy Context triple: [statsmodels, uses, SciPy]
-
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.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
C.
SciPy Developers
SciPy Developers are the community of programmers and contributors responsible for maintaining and advancing the SciPy scientific computing library for Python.
-
D.
Python scientific stack
The Python scientific stack is a collection of interoperable libraries and tools (such as NumPy, SciPy, pandas, and Matplotlib) used for scientific computing, data analysis, and visualization in Python.
-
E.
SciPy Steering Council
The SciPy Steering Council is the leadership group responsible for guiding the development, governance, and strategic direction of the SciPy scientific computing ecosystem in Python.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.