Triple
T6706342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tennō |
E153012
|
entity |
| Predicate | writtenWithKanji |
P17917
|
FINISHED |
| Object | 天皇 |
—
|
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: 天皇 | Statement: [Tennō, writtenWithKanji, 天皇]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: writtenWithKanji Context triple: [Tennō, writtenWithKanji, 天皇]
-
A.
usesKanjiFrom
Indicates that one writing system, word, or text incorporates or is composed of kanji characters originating from another specified source.
-
B.
kanji
chosen
Indicates that an entity is written in, represented by, or associated with a specific kanji character or set of kanji characters.
-
C.
usesKatakanaFor
Indicates that one entity is written or represented using katakana script in relation to another entity.
-
D.
canBeWrittenWithMultipleKanji
Indicates that the same word or expression can be represented using more than one distinct kanji spelling.
-
E.
writtenWithCharacter
Indicates that something is written using a particular character or set of characters as its writing system or notation.
- 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_69c68808d8d8819087369015270788fe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16897e48190b43eda2206b14d6a |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d089c7488190a00853fb12f53b2a |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:06 p.m.