Published

hiragana

vr.tr.hiragana · thing-q48332

Enable an AI agent to recognise hiragana, assess the identity and usability of its written forms, and choose appropriate reading, annotation, conversion or preservation actions.

Thing Registry Information and virtual systems INF.MED

Bundle → Layer → Finding → Questions Classifiers What it is Why it exists Distinguishing features What robots and AI may and may not do Moral aspects Owners Links to other meta-models Identity and identifiers Direct properties Recognition Capabilities and actions Hazards and failure modes Standards and interfaces Context of use Sources

Bundle → Layer → Finding → Questions Filled

5 bundles · 10 layers · 10 findings · 20 questions

Hiragana character identity Establishes which hiragana characters are present and which visible marks participate in their identity.

Script membership

Identifies hiragana within mixed Japanese text and ambiguous visual material.

Hiragana identification basis

Record the evidence for identifying a character or span as hiragana, preserving alternatives when the source is unclear.

Which characters are identifiable as hiragana, and which marks are shared with other Japanese scripts? definition
Does identification come from encoded text, a visible glyph or an interpretation of handwriting? provenance

Size and voicing distinctions

Tracks small kana and voicing distinctions that affect interpretation.

Contrastive kana features

Record whether size and dakuten or handakuten belong to the intended character rather than resulting from rendering or recognition noise.

Is the observed kana small or full-sized, and is that distinction supported by the source? definition
Is a voicing mark present, absent or ambiguous, and would changing it alter the intended reading? boundary

Hiragana reading units Relates hiragana sequences to mora structure and context-sensitive readings.

Mora segmentation

Distinguishes character boundaries from the units relevant to reading and mora counting.

Sequence-to-mora analysis

Record how combinations such as きゃ, small っ and ん contribute to the sequence's interpreted mora structure.

Which characters form a combined reading unit, and which contribute a separate mora? definition
How many morae does the sequence have under the selected reading, and where is the analysis uncertain? measurement

Contextual reading

Captures readings that depend on grammatical or lexical context.

Spelling-reading correspondence

Record the contextual interpretation of particle spellings and vowel sequences rather than treating spelling as a complete phonetic transcription.

Are は, へ or を functioning as particles here, and what reading does that support? definition
What lexical or grammatical evidence supports interpreting a vowel sequence as a long vowel or as separate vowels? provenance

Hiragana orthographic role Explains what hiragana is doing within the surrounding Japanese writing.

Word and grammar functions

Identifies whole-word spelling, particles and kana attached to kanji.

Hiragana functional span

Record the role and extent of each relevant hiragana span, including its attachment to neighbouring kanji when used as okurigana.

Does this span spell an entire word, function as a particle or supply okurigana? definition
Which surrounding word or kanji does the span belong to, and where does that relationship end? boundary

Reading annotation and script choice

Distinguishes reading annotations from body text and records evidence for deliberate hiragana spelling.

Annotation target and writing choice

Record whether hiragana annotates another expression or is the expression's chosen spelling, without inferring authorial intent from script alone.

Is this hiragana a reading annotation, and which exact base-text span does it annotate? boundary
What evidence, if any, establishes that hiragana was deliberately selected in place of a kanji or katakana spelling? provenance

Hiragana conventions and history Assesses spelling and character forms against the conventions relevant to their context.

Applicable spelling convention

Names the orthographic reference used to evaluate the text.

Convention-relative assessment

Record the applicable spelling convention and classify departures only when there is enough contextual evidence.

Which documented convention governs this text, including relevant treatment of じ versus ぢ and ず versus づ? provenance
Does a departure warrant correction, preservation as intentional usage or an unresolved annotation? action

Historical and variant forms

Handles older spellings and forms without assuming contemporary equivalence.

Historical form interpretation

Record source-supported identification of forms such as ゐ and ゑ, and assess how hentaigana or other historical variants relate to this model's scope.

Which dated source or specialist reference supports the identification and interpretation of this historical form? provenance
Would a contemporary transcription discard a spelling or form distinction that the task requires preserving? boundary

Hiragana representation and transformation Assesses digital and visual fidelity and constrains transformations of hiragana text.

Encoding and source fidelity

Checks correspondence among source glyphs, recognised characters and encoded sequences.

Kana representation equivalence

Record whether differences reflect equivalent encodings, distinct characters or errors, especially around combining voicing marks and small kana.

Do differing encodings represent the same intended kana, or do they preserve a meaningful character distinction? boundary
Which source comparison verifies that OCR or transcription retained small kana and voicing marks correctly? provenance

Controlled kana conversion

Defines task-appropriate normalisation, katakana conversion and romanisation.

Transformation contract

Record the conversion's purpose, convention, contextual dependencies and recoverability before changing hiragana.

Should the transformation preserve written spelling or represent contextual reading, and which conversion convention applies? action
Which distinctions or ambiguities would be lost, and what original text or annotation must accompany the result? boundary

Classifiers Filled

Family: Thing Registry
Category: Information and virtual systems
Entry kind: thing
Plane: INF
Domain: INF.MED
Other names and narrower kinds: okurigana

What it is Filled

This model owns hiragana as a Japanese writing subsystem, including character identity, written sequences, contextual reading, orthographic roles and representation integrity; individual texts supply the contexts in which these are assessed.

In scope

Identification of hiragana characters and sequences, including small kana and voiced or semi-voiced forms
Relationships between written hiragana, mora structure and context-dependent readings
Hiragana spelling conventions, grammatical uses and relationships to neighbouring scripts
Distinctions among contemporary, historical and nonstandard forms or usages
Integrity of hiragana across handwritten, printed and digitally encoded representations

Out of scope

Japanese vocabulary, syntax and discourse beyond what is needed to interpret hiragana usage
Kanji identity, meanings and internal character structure
Katakana inventory and usage except where comparison or conversion involves hiragana
Japanese phonetics and speech performance beyond the reading distinctions represented by hiragana
Document authorship, copyright and access permissions
General font engineering, keyboard design and text storage infrastructure

Why it exists Filled

Enable an AI agent to recognise hiragana, assess the identity and usability of its written forms, and choose appropriate reading, annotation, conversion or preservation actions.

Distinguishing features Filled

Determine script identity from the character or interpreted glyph rather than roundness alone: hiragana あ and katakana ア can represent corresponding readings while remaining different script characters.
Distinguish hiragana from kanji by its kana identity and contextual phonographic use; a cursive appearance alone does not establish that a handwritten form is hiragana.
Test small versus full-sized character identity: ゃ and や, or っ and つ, cannot be treated as interchangeable visual variants.
Separate script identity from encoded block membership: a combining voicing mark or shared punctuation may participate in hiragana text without independently identifying that text as hiragana.
Distinguish hiragana spelling from a phonetic transcription: particle uses of は, へ and を require contextual reading rather than a single invariant sound assignment.

What robots and AI may and may not do Filled

Must not

Convert hiragana to kanji or katakana where the author chose hiragana on purpose.
Present a machine reading of a name in hiragana as its certain pronunciation.
Replace historical kana spellings in old texts without saying so.

Only with a human decision

Publishing a transliteration of a person's name for official documents.

May

Read, transcribe and convert hiragana text, stating the romanization system used.
Add readings to kanji in hiragana to help learners when asked.

Moral aspects Filled

Script choices carry meaning, tone and identity in Japanese writing.

Who is affected

Readers and writers of Japanese
Learners
People named in texts

Owners Filled

Steward

Nobody owns it; it belongs to the Japanese language and its users.

Links to other meta-models Filled

parent

Q187659 - registry parent class

What else AI and robots need to interact with it Filled

Sources Filled

Wikidata item Q48332: hiragana - identity and sense of the item
Wikipedia: Hiragana - general description of the item

Open questions

Does the registry intend hiragana to include hentaigana and other historical forms directly, or should this model link to separately owned historical-script models?
Which authoritative orthographic references and editions should govern contemporary spelling, historical spelling and okurigana assessment?
Which specialist small-kana usages, dialect spellings and expressive spellings require explicit coverage rather than an unresolved-use category?
Which shared marks, including iteration and prolonged-sound marks where encountered, should this model interpret directly and which should belong to a neighbouring Japanese writing model?
Which romanisation and modernisation conventions should be supported, and what evidence is required before resolving an ambiguous reading during conversion?

Machine files

Card as JSON Specification Card backlog vercy.card/1

Provenance

thing registry research (pass 2) · unreviewed

Built from: models/things/publications/thing-q48332/spec.json