Triple

T15629350
Position Surface form Disambiguated ID Type / Status
Subject 純一郎 E375766 entity
Predicate hasKanjiVariantCount P59069 FINISHED
Object multiple possible kanji combinations LITERAL FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: multiple possible kanji combinations | Statement: [純一郎, hasKanjiVariantCount, multiple possible kanji combinations]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasKanjiVariantCount
Context triple: [純一郎, hasKanjiVariantCount, multiple possible kanji combinations]
  • A. usesHanjaVariants
    Indicates that one entity employs or incorporates alternative Hanja (Chinese character) forms corresponding to another entity.
  • B. canBeWrittenWithMultipleKanji chosen
    Indicates that the same word or expression can be represented using more than one distinct kanji spelling.
  • C. hasVariantReadingsWith
    Indicates a relationship where two textual items are linked because they exhibit differing or alternative readings of (typically) the same underlying content.
  • D. hasNumberOfVarnas
    Indicates the relationship that specifies how many varnas (distinct categories or classes) are associated with a given entity.
  • E. hasNameInKanji
    Indicates that an entity is associated with a specific written form of its name in Kanji characters.
  • F. None of above.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d85cd035a48190b73d5579ab73969a completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04eb4301881908c7157227fdf79b6 completed April 16, 2026, 2:51 a.m.
PD Predicate disambiguation batch_69deda868d4481908f4bce1c64d2902a completed April 15, 2026, 12:23 a.m.
Created at: April 10, 2026, 4:14 a.m.