Triple
T17313485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ono |
E420360
|
entity |
| Predicate | hasAlternativeRomanization |
P5923
|
FINISHED |
| Object | Ohno |
E343054
|
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: Ohno | Statement: [Ono, hasAlternativeRomanization, Ohno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ohno Context triple: [Ono, hasAlternativeRomanization, 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.
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.
- 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4399a4194819091d34cd3fffc8072 |
completed | April 19, 2026, 2:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c4603f88190a713bf8260329ac3 |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:43 a.m.