Triple
T6187352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshimura Kaneda |
E138092
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Yoshimura |
E138092
|
NE 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: Yoshimura | Statement: [Yoshimura Kaneda, familyName, Yoshimura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yoshimura Context triple: [Yoshimura Kaneda, familyName, Yoshimura]
-
A.
Yoshimura
chosen
Yoshimura is a Japanese surname borne by various notable individuals across fields such as politics, sports, and the arts.
-
B.
Yuasa
Yuasa is a historic coastal town in Japan renowned as the birthplace of traditional soy sauce production.
-
C.
Murayama
Murayama is a Japanese surname borne by various notable individuals across fields such as politics, science, and the arts.
-
D.
Mibuchi
Mibuchi is a Japanese surname borne by individuals such as Tadahiko Mibuchi.
-
E.
Yamada
Yamada is a common Japanese surname borne by many notable figures across fields such as politics, arts, sports, and academia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c008a8fd408190b7ec6e42934974a6 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06217be288190997032c8c4839fa5 |
completed | March 22, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640a2faf4819089bfbce9710ed22d |
completed | March 27, 2026, 8:32 a.m. |
Created at: March 22, 2026, 4:19 p.m.