Triple
T17818588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yukio Ozaki |
E444911
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Yukio |
—
|
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: Yukio | Statement: [Yukio Ozaki, givenName, Yukio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yukio Context triple: [Yukio Ozaki, givenName, Yukio]
-
A.
Yukio
chosen
Yukio is a Japanese given name commonly used for males and borne by several notable figures in politics, arts, and entertainment.
-
B.
Yasu
Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
-
C.
Yoshio
Yoshio is a Japanese masculine given name that can be written with various kanji characters and is borne by numerous real and fictional individuals.
-
D.
Yūsaku
Yūsaku is a Japanese masculine given name used by various notable figures in fields such as art, entertainment, and sports.
-
E.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
- 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.