Triple
T30677185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 隨 |
E780944
|
entity |
| Predicate | equivalentInJapaneseKanji |
P171594
|
FINISHED |
| Object | 隨 |
—
|
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: 隨 | Statement: [隨, equivalentInJapaneseKanji, 隨]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentInJapaneseKanji Context triple: [隨, equivalentInJapaneseKanji, 隨]
-
A.
kanji
Indicates that an entity is written in, represented by, or associated with a specific kanji character or set of kanji characters.
-
B.
JapaneseNameReading
Indicates that one entity is the reading or pronunciation (e.g., in kana or romaji) of a Japanese name represented by the other entity.
-
C.
japaneseKunReading
Indicates that a Japanese kanji character has a specific native Japanese (kun) reading associated with it.
-
D.
translationOfJapaneseName
Indicates that one entity is a translation of a Japanese name given by the other entity.
-
E.
hasNameInKanji
Indicates that an entity is associated with a specific written form of its name in Kanji characters.
- F. None of above. chosen
Provenance (4 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_69f224a7fc208190a07d6d3879b31640 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6a1ac56b88190a820434b65c9fa23 |
completed | May 3, 2026, 1:15 a.m. |
| PD | Predicate disambiguation | batch_69f69fe463248190aa78128abeab1183 |
completed | May 3, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69f6a0e920cc8190a943fdd0594906c5 |
completed | May 3, 2026, 1:12 a.m. |
Created at: April 29, 2026, 8:32 p.m.