reStructuredText
E255494
reStructuredText is a lightweight, plaintext markup language commonly used in the Python ecosystem for documentation, including PEPs and Sphinx-based docs.
All labels observed (1)
| Label | Occurrences |
|---|---|
| reStructuredText canonical | 4 |
How this entity was disambiguated
This entity first appeared as the object of triple T2313154 — 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: reStructuredText Context triple: [PEP, format, reStructuredText]
-
A.
Markdown
Markdown is a lightweight markup language that uses plain-text formatting syntax to create structured documents, most commonly used for README files, documentation, and web content.
-
B.
Rich Text Format
Rich Text Format (RTF) is a cross-platform document file format developed by Microsoft that preserves basic text formatting and structure while remaining readable by many word processors.
-
C.
LaTeX
LaTeX is a widely used, high-quality typesetting system particularly popular in academia for producing technical and scientific documents with precise control over layout and mathematical notation.
-
D.
DocBook
DocBook is a semantic markup language, originally based on SGML and now commonly used in XML form, designed for authoring and publishing technical documentation and books in a platform-independent way.
-
E.
SimpleText
SimpleText was a basic text-editing and word-processing application bundled with classic Mac OS, providing users with simple tools for creating and editing plain and styled text documents.
- 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: reStructuredText Target entity description: reStructuredText is a lightweight, plaintext markup language commonly used in the Python ecosystem for documentation, including PEPs and Sphinx-based docs.
-
A.
Markdown
Markdown is a lightweight markup language that uses plain-text formatting syntax to create structured documents, most commonly used for README files, documentation, and web content.
-
B.
Rich Text Format
Rich Text Format (RTF) is a cross-platform document file format developed by Microsoft that preserves basic text formatting and structure while remaining readable by many word processors.
-
C.
LaTeX
LaTeX is a widely used, high-quality typesetting system particularly popular in academia for producing technical and scientific documents with precise control over layout and mathematical notation.
-
D.
DocBook
DocBook is a semantic markup language, originally based on SGML and now commonly used in XML form, designed for authoring and publishing technical documentation and books in a platform-independent way.
-
E.
SimpleText
SimpleText was a basic text-editing and word-processing application bundled with classic Mac OS, providing users with simple tools for creating and editing plain and styled text documents.
- F. None of above. chosen
Statements (73)
| Predicate | Object |
|---|---|
| instanceOf |
documentation format
ⓘ
lightweight markup language ⓘ plaintext markup language ⓘ |
| abbreviation | reST ⓘ |
| category |
documentation systems
ⓘ
lightweight markup languages ⓘ |
| commonlyUsedWith |
Docutils
ⓘ
Python Package Index ⓘ
surface form:
PyPI
Python Enhancement Proposals ⓘ Read the Docs ⓘ Sphinx ⓘ |
| designedBy | David Goodger ⓘ |
| designedFor |
easy readability in plain text
ⓘ
unambiguous structure in plain text ⓘ |
| ecosystem | Python ecosystem ⓘ |
| fileExtension |
.rest
ⓘ
.rst ⓘ |
| fullName | reStructuredText ⓘ |
| hasImplementation | Docutils ⓘ |
| influenced |
Python documentation practices
ⓘ
Sphinx markup conventions ⓘ |
| license | BSD-like license (via Docutils) ⓘ |
| originProject | Docutils project ⓘ |
| outputFormats |
EPUB
ⓘ
surface form:
EPUB via Sphinx
HTML ⓘ LaTeX ⓘ ODF via converters ⓘ PDF via LaTeX or other toolchains ⓘ XML ⓘ man pages ⓘ |
| parsedBy |
Docutils
ⓘ
Pandoc ⓘ Sphinx ⓘ |
| primaryUse |
Python documentation
ⓘ
software documentation ⓘ technical documentation ⓘ |
| standardizedIn | Docutils documentation ⓘ |
| supportsExtensionMechanism |
directives
ⓘ
roles ⓘ |
| supportsFeature |
admonitions
ⓘ
bullet lists ⓘ citations ⓘ code blocks ⓘ definition lists ⓘ directives ⓘ embedded images ⓘ enumerated lists ⓘ field lists ⓘ footnotes ⓘ hyperlinks ⓘ inline markup ⓘ inline roles ⓘ lists ⓘ literal blocks ⓘ math markup via extensions ⓘ paragraphs ⓘ section headings ⓘ substitutions ⓘ tables ⓘ |
| supportsInlineMarkup |
emphasis
ⓘ
hyperlink references ⓘ inline literals ⓘ interpreted text ⓘ strong emphasis ⓘ substitution references ⓘ |
| syntaxStyle |
ASCII-based
ⓘ
plain text ⓘ |
| typicalSectionUnderlineChars | = - ` : ' " ~ ^ _ * + # < > ⓘ |
| usedFor |
API reference documentation
ⓘ
Python Enhancement Proposals ⓘ README files ⓘ Sphinx documentation ⓘ project documentation on PyPI ⓘ |
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: reStructuredText Description of subject: reStructuredText is a lightweight, plaintext markup language commonly used in the Python ecosystem for documentation, including PEPs and Sphinx-based docs.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.