Unicode normalization
E564765
Unicode normalization is a set of standardized processes that convert equivalent Unicode text sequences into a consistent canonical form to ensure reliable comparison, searching, and processing of text across systems.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Unicode Normalization Forms | 3 |
| Unicode normalization canonical | 3 |
| NFKD | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T6025017 — 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: Unicode normalization Context triple: [Mark Davis, contributedTo, Unicode normalization]
-
A.
Unicode Technical Standard #35
Unicode Technical Standard #35 is a Unicode Consortium specification that defines the Locale Data Markup Language (LDML) and related mechanisms for internationalization, including formatting of dates, times, numbers, and other locale-sensitive data.
-
B.
Unicode Technical Standard #10
Unicode Technical Standard #10 is the specification that defines the Unicode Collation Algorithm, providing a standardized method for comparing and sorting Unicode text across languages and platforms.
-
C.
Unicode
Unicode is a universal character encoding standard that assigns unique code points to virtually all written scripts, symbols, and emojis used in modern computing.
-
D.
Unicode ICU
Unicode ICU (International Components for Unicode) is a widely used open-source library that provides robust, cross-platform support for Unicode text handling, internationalization, and localization in software applications.
-
E.
Unicode Core Specification
The Unicode Core Specification is the primary technical standard that defines the Unicode character set, its encoding model, and the fundamental rules for representing and processing text in modern computing systems.
- 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: Unicode normalization Target entity description: Unicode normalization is a set of standardized processes that convert equivalent Unicode text sequences into a consistent canonical form to ensure reliable comparison, searching, and processing of text across systems.
-
A.
Unicode Technical Standard #35
Unicode Technical Standard #35 is a Unicode Consortium specification that defines the Locale Data Markup Language (LDML) and related mechanisms for internationalization, including formatting of dates, times, numbers, and other locale-sensitive data.
-
B.
Unicode Technical Standard #10
Unicode Technical Standard #10 is the specification that defines the Unicode Collation Algorithm, providing a standardized method for comparing and sorting Unicode text across languages and platforms.
-
C.
Unicode
Unicode is a universal character encoding standard that assigns unique code points to virtually all written scripts, symbols, and emojis used in modern computing.
-
D.
Unicode ICU
Unicode ICU (International Components for Unicode) is a widely used open-source library that provides robust, cross-platform support for Unicode text handling, internationalization, and localization in software applications.
-
E.
Unicode Core Specification
The Unicode Core Specification is the primary technical standard that defines the Unicode character set, its encoding model, and the fundamental rules for representing and processing text in modern computing systems.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
Unicode standard feature
ⓘ
text processing standard ⓘ |
| addresses |
multiple representations of the same abstract text
ⓘ
precomposed and decomposed character sequences ⓘ |
| alsoKnownAs | UAX #15 ⓘ |
| appliesTo | Unicode text ⓘ |
| definedBy | Unicode Consortium NERFINISHED ⓘ |
| definesForm |
NFC
ⓘ
NFD ⓘ NFKC ⓘ NFKD ⓘ |
| ensures | canonically equivalent strings have identical binary representation ⓘ |
| example | é can be U+00E9 or 'e' + U+0301 ⓘ |
| hasFullName |
Normalization Form C
ⓘ
Normalization Form D NERFINISHED ⓘ Normalization Form KC NERFINISHED ⓘ Normalization Form KD NERFINISHED ⓘ |
| hasProperty |
closed under normalization (idempotent)
ⓘ
stable across Unicode versions for assigned characters ⓘ |
| hasPurpose |
to convert equivalent Unicode text sequences into a consistent form
ⓘ
to enable consistent text processing across systems ⓘ to enable reliable text comparison ⓘ to enable reliable text searching ⓘ |
| hasSpecification | Unicode Standard Annex #15 NERFINISHED ⓘ |
| idempotent | applying the same normalization form twice yields the same result ⓘ |
| isImportantFor |
collation
ⓘ
search indexing ⓘ security-sensitive string comparison ⓘ string equality ⓘ text rendering consistency ⓘ |
| NFC | canonical composition form ⓘ |
| NFCPreferredFor | data interchange ⓘ |
| NFD | canonical decomposition form ⓘ |
| NFDPreferredFor | internal text processing in some systems ⓘ |
| NFKC | compatibility composition form ⓘ |
| NFKCPreferredFor | identifier comparison in some contexts ⓘ |
| NFKD | compatibility decomposition form ⓘ |
| NFKDPreferredFor | text analysis and searching in some contexts ⓘ |
| partOf | Unicode Standard NERFINISHED ⓘ |
| reliesOn |
Unicode character decomposition mappings
ⓘ
canonical combining class ⓘ composition exclusions ⓘ |
| usedBy |
databases
ⓘ
programming languages ⓘ search engines ⓘ text editors ⓘ |
| usesConcept |
canonical equivalence
ⓘ
compatibility equivalence ⓘ |
| usesOperation |
canonical composition
ⓘ
canonical decomposition ⓘ compatibility decomposition ⓘ |
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: Unicode normalization Description of subject: Unicode normalization is a set of standardized processes that convert equivalent Unicode text sequences into a consistent canonical form to ensure reliable comparison, searching, and processing of text across systems.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Unicode Normalization Forms
this entity surface form:
Unicode Normalization Forms
this entity surface form:
Unicode Normalization Forms
this entity surface form:
NFKD