Triple
T10851733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assignment Expressions |
E256161
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | walrus operator (:=) |
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: walrus operator (:=) | Statement: [Assignment Expressions, alsoKnownAs, walrus operator (:=)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: walrus operator (:=) Context triple: [Assignment Expressions, alsoKnownAs, walrus operator (:=)]
-
A.
PEP 572
chosen
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
B.
Lambda
Lambda is a General Motors mid-size crossover SUV platform used for vehicles such as the Saturn Outlook.
-
C.
PEP 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
-
D.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
E.
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.
- 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_69dff7cc0d648190afb0ce80bac7f3dc |
completed | April 15, 2026, 8:40 p.m. |
Created at: April 8, 2026, 9:20 p.m.