Triple
T21722714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satoshi Ohno |
E536199
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ohno |
—
|
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: Ohno | Statement: [Satoshi Ohno, familyName, Ohno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ohno Context triple: [Satoshi Ohno, familyName, Ohno]
-
A.
Ohno
chosen
Ohno is a Japanese surname borne by various notable individuals across fields such as sports, science, and entertainment.
-
B.
Ono
Ono is a Japanese surname borne by various notable individuals across fields such as academia, politics, and the arts.
-
C.
Ono
Ono is a keen-eyed egret from Disney Junior’s animated series “The Lion Guard,” serving as the team’s observant and intelligent lookout.
-
D.
Ken Ohno
Ken Ohno is an American mathematician known for his work in number theory, particularly in the areas of modular forms and special values of L-functions.
-
E.
Haruka Ono
Haruka Ono is a Japanese individual notable enough to be recognized as a bearer of the surname Ono, though specific widely known public details about her are limited.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd97032c08190820b87a288e77293 |
completed | April 27, 2026, 9:47 p.m. |
Created at: April 16, 2026, 6:47 p.m.