Triple
T17504242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naoyoshi |
E426269
|
entity |
| Predicate | canBeWrittenWithKanjiMeaning |
P76133
|
FINISHED |
| Object | honest and good |
—
|
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: honest and good | Statement: [Naoyoshi, canBeWrittenWithKanjiMeaning, honest and good]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeWrittenWithKanjiMeaning Context triple: [Naoyoshi, canBeWrittenWithKanjiMeaning, honest and good]
-
A.
canBeWrittenWithMultipleKanji
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.
canBeWrittenIn
Indicates that something is capable of being expressed, encoded, or represented using a particular language, notation, or medium.
-
D.
meaningDependsOnKanji
chosen
Indicates that the meaning of something (e.g., a word or expression) is determined by, or varies according to, the specific kanji characters used.
-
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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45214d44c8190b1bf04bf24ab8e81 |
completed | April 19, 2026, 3:55 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f5fbcc8190a6ea9639bf5650da |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:48 a.m.