Triple
T14066863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hiroshi Hara |
E338497
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hara |
—
|
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: Hara | Statement: [Hiroshi Hara, familyName, Hara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hara Context triple: [Hiroshi Hara, familyName, Hara]
-
A.
Hara
chosen
Hara is a Japanese surname borne by various notable figures in politics, arts, and sports.
-
B.
Harada
Harada is a fictional character from the X-Men film universe, depicted as a skilled Japanese warrior and bodyguard in "The Wolverine."
-
C.
Haebaru
Haebaru is a town in Okinawa Prefecture, Japan, forming part of the greater Naha metropolitan area.
-
D.
Niihama
Niihama is an industrial city in western Japan known for its copper mining history and location along the Seto Inland Sea in Ehime Prefecture.
-
E.
Haruna
Haruna was a Japanese Kongō-class fast battleship that served in the Imperial Japanese Navy during both World Wars and saw extensive action in the Pacific Theater.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de568b81f08190a571004261c0e8e4 |
completed | April 14, 2026, 3 p.m. |
Created at: April 9, 2026, 10:21 p.m.