PEP 636
E257816
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.
All labels observed (3)
| Label | Occurrences |
|---|---|
| PEP 636 canonical | 10 |
| PEP 636 structural pattern matching tutorial | 1 |
| Python 3.10 pattern matching PEP series | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2301345 — 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 636 Context triple: [PEP 622, supersededBy, PEP 636]
-
A.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
B.
PEP 635
PEP 635 is a Python Enhancement Proposal that provides a detailed rationale and motivation for the structural pattern matching feature introduced 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 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 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
- 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 636 Target entity description: 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.
-
A.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
B.
PEP 635
PEP 635 is a Python Enhancement Proposal that provides a detailed rationale and motivation for the structural pattern matching feature introduced 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 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 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf | Python Enhancement Proposal ⓘ |
| author |
Brandt Bucher
ⓘ
Guido van Rossum ⓘ Ivan Levkivskyi ⓘ Jelle Zijlstra ⓘ Sven Marnach ⓘ Tal Ben-Nun ⓘ Tobias Kohn ⓘ |
| belongsToProject |
CPython
ⓘ
linked to:
Python reference implementation (CPython)
|
| category | Tutorial ⓘ |
| created | 2020-10-05 ⓘ |
| describesChange | addition of match and case statements to Python ⓘ |
| describesFeature | structural pattern matching ⓘ |
| discussedOn | python-dev mailing list ⓘ |
| explainsConcept |
AS patterns
ⓘ
OR patterns ⓘ capture patterns ⓘ class patterns ⓘ guard in case clauses ⓘ literal patterns ⓘ mapping patterns ⓘ pattern matching semantics ⓘ sequence patterns ⓘ value patterns ⓘ wildcard pattern ⓘ |
| explainsDifferenceFrom | traditional switch/case statements in other languages ⓘ |
| explainsSyntaxElement |
case pattern
ⓘ
match statement ⓘ |
| explainsUseCase |
handling abstract syntax trees
ⓘ
parsing structured data ⓘ replacing chains of if/elif statements ⓘ |
| governs | tutorial examples for structural pattern matching ⓘ |
| hostedAt | https://peps.python.org/pep-0636/ ⓘ |
| language | English ⓘ |
| motivates | use of structural pattern matching for clearer control flow ⓘ |
| partOf | Python 3.10 pattern matching PEP series ⓘ |
| PEPNumber | 636 ⓘ |
| purpose | Explain structural pattern matching by examples ⓘ |
| relatedTo |
PEP 634
ⓘ
PEP 635 ⓘ |
| standardizedIn |
Python 3.10 language specification
ⓘ
linked to:
Python 3 language specification
|
| status | Accepted ⓘ |
| targetAudience | Python programmers ⓘ |
| title |
Structural Pattern Matching: Tutorial
ⓘ
linked to:
Structural Pattern Matching
|
| type | Informational PEP ⓘ |
| versionIntroduced | Python 3.10 ⓘ |
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 636 Description of subject: 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.
Referenced by (12)
Full triples — surface form annotated when it differs from this entity's canonical label.