Triple
T18050998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | typing (Python module) |
E431925
|
entity |
| Predicate | relatedPEP |
P37
|
FINISHED |
| Object | PEP 604 |
—
|
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 604 | Statement: [typing (Python module), relatedPEP, PEP 604]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 604 Context triple: [typing (Python module), relatedPEP, PEP 604]
-
A.
PEP 604
chosen
PEP 604 is a Python Enhancement Proposal that introduced the modern, concise syntax for expressing type unions (using the `|` operator) in Python’s type hints.
-
B.
PEP 624
PEP 624 is a Python Enhancement Proposal that specifies the removal of the Py_UNICODE encoder APIs from the CPython C API to streamline and modernize Unicode handling in Python.
-
C.
PEP 660
PEP 660 is a Python packaging standard that defines how editable installs should work for PEP 517 build backends, enabling consistent development workflows across tools.
-
D.
PEP 614
PEP 614 is a Python Enhancement Proposal that relaxes the grammar restrictions on decorator syntax, allowing more flexible and expressive decorator expressions in Python.
-
E.
PEP 643
PEP 643 is a Python Enhancement Proposal that defines a standardized way to specify and handle metadata for Python packages.
- 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.