Triple
T28050893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 江崎玲於奈 |
E708815
|
entity |
| Predicate | hasJapaneseNameReading |
P143799
|
FINISHED |
| Object | えさき れおな |
—
|
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: えさき れおな | Statement: [江崎玲於奈, hasJapaneseNameReading, えさき れおな]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasJapaneseNameReading Context triple: [江崎玲於奈, hasJapaneseNameReading, えさき れおな]
-
A.
JapaneseNameReading
chosen
Indicates that one entity is the reading or pronunciation (e.g., in kana or romaji) of a Japanese name represented by the other entity.
-
B.
hasKanjiReading
Indicates that a written kanji character is associated with a specific reading or pronunciation.
-
C.
hasNameInJapanese
Indicates that an entity is associated with a specific name expressed in the Japanese language.
-
D.
hasNameInKanji
Indicates that an entity is associated with a specific written form of its name in Kanji characters.
-
E.
hasJapaneseSurname
Indicates that the person or entity possesses a surname that is of Japanese origin or is commonly used in Japanese naming conventions.
- 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_69ef9b6df9f48190bbb971d02cbe1b65 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f63fd945f08190b2526b01686a1562 |
completed | May 2, 2026, 6:18 p.m. |
| PD | Predicate disambiguation | batch_69f63710d17c819084cfe96e6df334fd |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 8:33 p.m.