Triple
T11215922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python generic class definitions |
E265439
|
entity |
| Predicate | formalizedIn |
P6279
|
FINISHED |
| Object | PEP 695 |
E52336
|
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 695 | Statement: [Python generic class definitions, formalizedIn, PEP 695]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 695 Context triple: [Python generic class definitions, formalizedIn, PEP 695]
-
A.
PEP 695
chosen
PEP 695 is a Python Enhancement Proposal that introduces a new, more concise syntax for type parameter declarations to improve the language’s support for generics and static typing.
-
B.
PEP 657
PEP 657 is a Python enhancement proposal that improves error reporting by adding fine-grained location information (such as per-expression line and column data) to tracebacks.
-
C.
PEP 636
PEP 636 is a Python Enhancement Proposal that serves as a tutorial-style guide to the structural pattern matching feature introduced in Python 3.10.
-
D.
PEP 649
PEP 649 is a Python enhancement proposal that introduces a new, lazy evaluation scheme for type annotations to improve performance and forward-reference handling.
-
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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8e8eef48190932a85784ce15c86 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e8a68e4404819096c5023c7eca4b6a |
completed | April 22, 2026, 10:44 a.m. |
Created at: April 8, 2026, 9:30 p.m.