Standards Track PEPs
E258606
Standards Track PEPs are Python Enhancement Proposals that introduce or change core Python features, syntax, or standard library behavior and, once accepted, are intended to be implemented in the language.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Standards Track PEP | 5 |
| Standards Track PEPs canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2313332 — 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: Standards Track PEPs Context triple: [PEP 0, hasSection, Standards Track PEPs]
-
A.
PEP 1 – PEP Purpose and Guidelines
PEP 1 – PEP Purpose and Guidelines is the foundational Python Enhancement Proposal that defines the goals, structure, and workflow for all other PEPs in the Python development process.
-
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 13: Python Language Governance
PEP 13: Python Language Governance is the Python Enhancement Proposal that defines the structure, responsibilities, and election process of the Python Steering Council, establishing the project's formal governance model.
-
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. 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: Standards Track PEPs Target entity description: Standards Track PEPs are Python Enhancement Proposals that introduce or change core Python features, syntax, or standard library behavior and, once accepted, are intended to be implemented in the language.
-
A.
PEP 1 – PEP Purpose and Guidelines
PEP 1 – PEP Purpose and Guidelines is the foundational Python Enhancement Proposal that defines the goals, structure, and workflow for all other PEPs in the Python development process.
-
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 13: Python Language Governance
PEP 13: Python Language Governance is the Python Enhancement Proposal that defines the structure, responsibilities, and election process of the Python Steering Council, establishing the project's formal governance model.
-
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. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
Python Enhancement Proposal category
ⓘ
software development process artifact ⓘ |
| appliesTo |
Python programming language
ⓘ
linked to:
Python
|
| approvedBy | Python Steering Council ⓘ |
| changeRequires | new PEP or PEP amendment ⓘ |
| definedIn | PEP 1 ⓘ |
| distinguishedFrom |
Informational PEPs
ⓘ
linked to:
PEPs
Process PEPs ⓘ |
| documentFormat | reStructuredText ⓘ |
| example |
PEP 484
ⓘ
PEP 572 ⓘ PEP 585 ⓘ PEP 8 ⓘ |
| governedBy | PEP 1 ⓘ |
| hasIdentifierFormat | PEP NNN ⓘ |
| hasSubcategory |
C API PEPs
ⓘ
linked to:
C API
Core Python PEPs ⓘ
linked to:
Python Enhancement Proposals
Language Syntax PEPs ⓘ Standard Library PEPs ⓘ |
| hostedOn |
python.org
ⓘ
linked to:
Python
|
| intendedOutcome | be implemented in the Python language ⓘ |
| language | English ⓘ |
| lifecycleStage |
accepted
ⓘ
draft ⓘ final ⓘ rejected ⓘ superseded ⓘ withdrawn ⓘ |
| mustInclude |
motivation section
ⓘ
rationale section ⓘ reference implementation or examples ⓘ specification section ⓘ |
| partOf | Python Enhancement Proposal process ⓘ |
| previouslyApprovedBy |
Benevolent Dictator For Life
ⓘ
linked to:
Benevolent Dictator For Life (historical)
|
| purpose |
change existing core Python features
ⓘ
define implementation details for Python features ⓘ introduce new core Python features ⓘ modify Python standard library behavior ⓘ modify Python syntax ⓘ |
| repository | https://github.com/python/peps ⓘ |
| requires |
backwards compatibility analysis
ⓘ
detailed technical specification ⓘ discussion on python-dev or related forums ⓘ reference implementation or implementation plan ⓘ |
| reviewedBy | Python core developers ⓘ |
| scope |
CPython reference implementation
ⓘ
Python language specification ⓘ
linked to:
Python 3 language specification
Python standard library ⓘ |
| statusAfterAcceptance |
accepted
ⓘ
final ⓘ |
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: Standards Track PEPs Description of subject: Standards Track PEPs are Python Enhancement Proposals that introduce or change core Python features, syntax, or standard library behavior and, once accepted, are intended to be implemented in the language.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Standards Track PEPs