Triple
T17116080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuniaki |
E415342
|
entity |
| Predicate | hasMultipleKanjiForms |
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: [Kuniaki, hasMultipleKanjiForms, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMultipleKanjiForms Context triple: [Kuniaki, hasMultipleKanjiForms, true]
-
A.
canBeWrittenWithMultipleKanji
chosen
Indicates that the same word or expression can be represented using more than one distinct kanji spelling.
-
B.
usesHanjaVariants
Indicates that one entity employs or incorporates alternative Hanja (Chinese character) forms corresponding to another entity.
-
C.
hasNameInKanji
Indicates that an entity is associated with a specific written form of its name in Kanji characters.
-
D.
hasDistinctLetterForms
Indicates that the related writing system or symbol set uses different visual shapes or styles for the same letter in different contexts (such as position, case, or usage).
-
E.
usesKanjiFrom
Indicates that one writing system, word, or text incorporates or is composed of kanji characters originating from another specified source.
- 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3e80528588190a877dcc6d6d3a392 |
completed | April 18, 2026, 8:22 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.