Triple
T10946386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Standards Track PEPs |
E258606
|
entity |
| Predicate | example |
P1259
|
FINISHED |
| Object | PEP 572 |
E51179
|
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: PEP 572 | Statement: [Standards Track PEPs, example, PEP 572]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 572 Context triple: [Standards Track PEPs, example, PEP 572]
-
A.
PEP 572
chosen
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
B.
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.
-
C.
PEP 590
PEP 590 is the Python Enhancement Proposal that introduced the "vectorcall" protocol to speed up and standardize function calls in CPython.
-
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 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770eaaea08190b06e508600d8a305 |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3446b6f1481908365bb9235768859 |
completed | April 18, 2026, 8:44 a.m. |
Created at: April 8, 2026, 9:23 p.m.