Triple
T15833580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokuji Hayakawa |
E383929
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hayakawa |
—
|
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: Hayakawa | Statement: [Tokuji Hayakawa, familyName, Hayakawa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hayakawa Context triple: [Tokuji Hayakawa, familyName, Hayakawa]
-
A.
Hayakawa
chosen
Hayakawa is a Japanese surname borne by numerous notable individuals in fields such as film, politics, and academia.
-
B.
Kiyokawa
Kiyokawa is a small rural village in Kanagawa Prefecture, Japan, known for its mountainous scenery and outdoor recreation.
-
C.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
D.
Tanaka
Tanaka is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and other fields.
-
E.
Hiranaka
Hiranaka is a Japanese surname borne by individuals such as former professional boxer Akinobu Hiranaka.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e11e6670d48190a456581dd951f168 |
completed | April 16, 2026, 5:37 p.m. |
Created at: April 10, 2026, 4:49 a.m.