Triple
T17313528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Ono |
E420361
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ono |
—
|
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: Ono | Statement: [Ken Ono, familyName, Ono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ono Context triple: [Ken Ono, familyName, Ono]
-
A.
Ono
chosen
Ono is a Japanese surname borne by various notable individuals across fields such as academia, politics, and the arts.
-
B.
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.
-
C.
Ono Niha
Ono Niha are the indigenous Nias people of Indonesia, known for their distinct Austronesian language, megalithic traditions, and elaborate warrior and stone-jumping rituals.
-
D.
Mika Ono
Mika Ono is a Japanese individual known for bearing the surname Ono, though specific widely recognized public achievements or roles are not well documented.
-
E.
Ohno
Ohno is a Japanese surname borne by various notable individuals across fields such as sports, science, and entertainment.
- 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4399a4194819091d34cd3fffc8072 |
completed | April 19, 2026, 2:10 a.m. |
Created at: April 10, 2026, 5:43 a.m.