Triple
T18415461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zenko Suzuki |
E441874
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Zenko |
—
|
NE NERFINISHED |
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: Zenko | Statement: [Zenko Suzuki, givenName, Zenko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zenko Context triple: [Zenko Suzuki, givenName, Zenko]
-
A.
Zenko
chosen
Zenko is a Japanese given name most notably borne by Zenko Suzuki, who served as Prime Minister of Japan in the early 1980s.
-
B.
Zennichimaro
Zennichimaro is the childhood name of Nichiren, the influential 13th-century Japanese Buddhist monk who founded the Nichiren school of Buddhism.
-
C.
Kinnosuke
Kinnosuke is the given name of the renowned Japanese novelist Natsume Sōseki, a central figure in modern Japanese literature.
-
D.
Motonari
Motonari is a Japanese given name most famously associated with the Sengoku-period warlord Mōri Motonari, known for his strategic prowess and unification of much of western Honshu.
-
E.
Gyōda
Gyōda is a city in Saitama Prefecture, Japan, known for its historic Oshi Castle and well-preserved traditional townscape.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51a278fdc8190aa81a2ade682d942 |
completed | April 19, 2026, 6:08 p.m. |
Created at: April 10, 2026, 10:47 a.m.