Triple
T17818642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiyoshi Ozaki |
E444911
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kiyoshi |
—
|
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: Kiyoshi | Statement: [Kiyoshi Ozaki, givenName, Kiyoshi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiyoshi Context triple: [Kiyoshi Ozaki, givenName, Kiyoshi]
-
A.
Kiyoshi
chosen
Kiyoshi is a Japanese given name commonly used for males and borne by various notable figures in fields such as science, arts, and sports.
-
B.
Shinya
Shinya is a Japanese given name commonly used for males.
-
C.
Yojiro
Yojiro is a Japanese given name commonly used for males.
-
D.
Koichi
Koichi is a Japanese given name commonly used for males and borne by various notable figures in fields such as science, politics, and entertainment.
-
E.
Kinnosuke
Kinnosuke is the given name of the renowned Japanese novelist Natsume Sōseki, a central figure in modern Japanese literature.
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e488812df081909771d51c54c405fe |
completed | April 19, 2026, 7:47 a.m. |
Created at: April 10, 2026, 10:14 a.m.