Triple
T10851788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Angelico |
E256162
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | PEP 572: Assignment Expressions |
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: Assignment Expressions | Statement: [Chris Angelico, notableWork, PEP 572: Assignment Expressions]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 572: Assignment Expressions Context triple: [Chris Angelico, notableWork, PEP 572: Assignment Expressions]
-
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 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
-
C.
PEP 695
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.
-
D.
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.
-
E.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d75117b76c8190b0fb216b1428c3c7 |
completed | April 9, 2026, 7:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb17d978c8190883b4a56e88859de |
completed | April 14, 2026, 9:28 p.m. |
Created at: April 8, 2026, 9:20 p.m.