Triple
T10851764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assignment Expressions |
E256161
|
entity |
| Predicate | documentationSection |
P55181
|
FINISHED |
| Object | Python Language Reference – Expressions – Assignment expressions |
E256161
|
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: Python Language Reference – Expressions – Assignment expressions | Statement: [Assignment Expressions, documentationSection, Python Language Reference – Expressions – Assignment expressions]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Python Language Reference – Expressions – Assignment expressions Context triple: [Assignment Expressions, documentationSection, Python Language Reference – Expressions – Assignment expressions]
-
A.
Assignment Expressions
chosen
Assignment Expressions are a Python language feature introduced by PEP 572 that allow assigning values to variables as part of larger expressions using the “walrus” operator (:=).
-
B.
PEP 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
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 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted 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.