Triple
T16155685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jun’ya |
E392034
|
entity |
| Predicate | canBeWrittenWithMultipleKanjiCombinations |
P59069
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Jun’ya, canBeWrittenWithMultipleKanjiCombinations, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeWrittenWithMultipleKanjiCombinations Context triple: [Jun’ya, canBeWrittenWithMultipleKanjiCombinations, true]
-
A.
canBeWrittenWithMultipleKanji
chosen
Indicates that the same word or expression can be represented using more than one distinct kanji spelling.
-
B.
canBeWrittenAsKana
Indicates that something (typically text or a term) is able to be represented using Japanese kana characters.
-
C.
usesHanjaVariants
Indicates that one entity employs or incorporates alternative Hanja (Chinese character) forms corresponding to another entity.
-
D.
canRepresentMultipleChineseCharacters
Indicates that a given form (such as a sound, syllable, or written unit) is capable of corresponding to more than one distinct Chinese character.
-
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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21e5902a08190ad8694955ef6073a |
completed | April 17, 2026, 11:49 a.m. |
| PD | Predicate disambiguation | batch_69e1828abb608190a99d86bce1d77de2 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:01 a.m.