Triple
T18050997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | typing (Python module) |
E431925
|
entity |
| Predicate | relatedPEP |
P37
|
FINISHED |
| Object | PEP 585 |
—
|
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 585 | Statement: [typing (Python module), relatedPEP, PEP 585]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 585 Context triple: [typing (Python module), relatedPEP, PEP 585]
-
A.
PEP 585
chosen
PEP 585 is a Python Enhancement Proposal that introduced built-in generic types (like list[int] and dict[str, int]) as a modern replacement for many typing module aliases.
-
B.
PEP 582
PEP 582 is a Python enhancement proposal that introduces a local `__pypackages__` directory for managing project-specific dependencies without using virtual environments.
-
C.
PEP 552
PEP 552 is a Python Enhancement Proposal that introduced deterministic, hash-based .pyc files to improve reproducibility and caching behavior in Python.
-
D.
PEP 578
PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
-
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.