Triple
T4325872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SciPy Developers |
E96632
|
entity |
| Predicate | supportsProject |
P12986
|
FINISHED |
| Object | NumPy |
E17659
|
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: NumPy | Statement: [SciPy Developers, supportsProject, NumPy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NumPy Context triple: [SciPy Developers, supportsProject, NumPy]
-
A.
NumPy
chosen
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
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.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
D.
Matplotlib
Matplotlib is a widely used Python plotting library for creating static, animated, and interactive visualizations.
-
E.
Dask
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
- 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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3513020f481909ff2fec3934f3002 |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d09861a4819086a88bb42a8ea2e4 |
completed | March 14, 2026, 9:18 p.m. |
Created at: March 12, 2026, 11:13 p.m.