Triple
T825469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SciPy |
E17842
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | CPython |
E9268
|
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: CPython | Statement: [SciPy, compatibleWith, CPython]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CPython Context triple: [SciPy, compatibleWith, CPython]
-
A.
Python reference implementation (CPython)
chosen
Python reference implementation (CPython) is the original and most widely used implementation of the Python programming language, written in C and serving as the de facto standard for Python behavior and compatibility.
-
B.
Cython
Cython is a programming language and compiler that extends Python with static typing and direct C/C++ integration to generate fast, optimized extension modules.
-
C.
Python
Python is a monstrous serpent or dragon from Greek mythology, best known for being slain by the god Apollo at Delphi.
-
D.
Python
Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
-
E.
PyPy
PyPy is a high-performance alternative Python interpreter featuring a Just-In-Time (JIT) compiler designed to significantly speed up the execution of Python programs.
- 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_69a4ab7eb0a08190889463edb0e7bd59 |
completed | March 1, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7b84547508190b82c2012f4342529 |
completed | March 4, 2026, 4:42 a.m. |
Created at: March 1, 2026, 7:38 p.m.