Triple
T18050994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | typing (Python module) |
E431925
|
entity |
| Predicate | relatedPEP |
P37
|
FINISHED |
| Object | PEP 526 |
—
|
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: PEP 526 | Statement: [typing (Python module), relatedPEP, PEP 526]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 526 Context triple: [typing (Python module), relatedPEP, PEP 526]
-
A.
PEP 526
chosen
PEP 526 is a Python Enhancement Proposal that introduced a standard syntax for variable and attribute type annotations in Python.
-
B.
PEP 566
PEP 566 is a Python Enhancement Proposal that defines a standardized, extensible metadata format for Python packages to improve distribution and tooling interoperability.
-
C.
PEP 570
PEP 570 is the Python Enhancement Proposal that introduced positional-only parameters to Python function definitions, formalizing a syntax for arguments that must be passed by position.
-
D.
PEP 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
E.
PEP 560
PEP 560 is a Python Enhancement Proposal that optimizes and refines the implementation of typing and generic types in Python, improving performance and simplifying the internal mechanics of the typing module.
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4bff57ea08190a30a87993f7d3299 |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 10:25 a.m.