PEP 622
E51738
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
All labels observed (1)
| Label | Occurrences |
|---|---|
| PEP 622 canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T400418 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 622 Context triple: [Python Enhancement Proposals, hasPart, PEP 622]
-
A.
PEP 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
B.
PEP 0
PEP 0 is the index document that lists and tracks the status of all Python Enhancement Proposals (PEPs) in the Python community.
-
C.
Python Enhancement Proposals
Python Enhancement Proposals (PEPs) are the formal design documents that propose, specify, and document new features, processes, and standards for the Python programming language.
-
D.
PEP 1
PEP 1 is the foundational Python Enhancement Proposal that defines the purpose, structure, and workflow for all other PEPs in the Python community process.
-
E.
Python Steering Council
The Python Steering Council is the core governance body responsible for guiding the development and direction of the Python programming language.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PEP 622 Target entity description: PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
-
A.
PEP 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
B.
PEP 0
PEP 0 is the index document that lists and tracks the status of all Python Enhancement Proposals (PEPs) in the Python community.
-
C.
Python Enhancement Proposals
Python Enhancement Proposals (PEPs) are the formal design documents that propose, specify, and document new features, processes, and standards for the Python programming language.
-
D.
PEP 1
PEP 1 is the foundational Python Enhancement Proposal that defines the purpose, structure, and workflow for all other PEPs in the Python community process.
-
E.
Python Steering Council
The Python Steering Council is the core governance body responsible for guiding the development and direction of the Python programming language.
- F. None of above. chosen
Statements (39)
| Predicate | Object |
|---|---|
| instanceOf | Python Enhancement Proposal ⓘ |
| author |
Brandt Bucher
ⓘ
Guido van Rossum ⓘ Ivan Levkivskyi ⓘ Jelle Zijlstra ⓘ Tobias Kohn ⓘ |
| category | Standards Track ⓘ |
| createdForVersion | Python 3.10 ⓘ |
| definesConcept |
AS patterns
ⓘ
OR patterns ⓘ case block ⓘ class patterns ⓘ guard in pattern matching ⓘ mapping patterns ⓘ match statement ⓘ sequence patterns ⓘ |
| discusses |
implementation considerations
ⓘ
semantics of pattern matching ⓘ syntax for structural pattern matching ⓘ |
| hasNumber | 622 ⓘ |
| hasTitle | Structural Pattern Matching ⓘ |
| influenced |
PEP 634
ⓘ
PEP 635 ⓘ PEP 636 ⓘ |
| language | Python ⓘ |
| motivation | provide a concise way to destructure and match data ⓘ |
| proposesFeature | structural pattern matching ⓘ |
| relatedTo |
PEP 572
ⓘ
PEP 622 reference implementation ⓘ |
| repository | https://peps.python.org/pep-0622/ ⓘ |
| resultedIn | adoption of structural pattern matching in Python 3.10 ⓘ |
| status | Withdrawn ⓘ |
| supersededBy |
PEP 634
ⓘ
PEP 635 ⓘ PEP 636 ⓘ |
| topic |
case patterns
ⓘ
match statement ⓘ pattern matching syntax ⓘ |
| withdrawnReason | split into more focused PEPs 634, 635, and 636 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: PEP 622 Description of subject: PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.