Triple
T10812864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O'Hara |
E255145
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Ohara |
E346815
|
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: Ohara | Statement: [O'Hara, hasVariant, Ohara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ohara Context triple: [O'Hara, hasVariant, Ohara]
-
A.
Ōhira
chosen
Ōhira is a Japanese surname most notably associated with Masayoshi Ōhira, a former Prime Minister of Japan.
-
B.
Hinohara
Hinohara is a rural village in western Tokyo, Japan, known for its mountainous terrain, forests, and outdoor recreation areas.
-
C.
Ichihara
Ichihara is a coastal industrial city in Chiba Prefecture, Japan, known for its large petrochemical complexes and proximity to Tokyo Bay.
-
D.
Wakamatsu
Wakamatsu is a ward in the city of Kitakyushu, Japan, known historically as a port and industrial area on the northern coast of Kyushu.
-
E.
Oimachi
Oimachi is a commercial and residential district in Tokyo known for its busy train hub, shopping streets, and convenient access to central Shinagawa and other parts of the city.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733eadda48190b2b1183ee60102cb |
completed | April 9, 2026, 5:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de853692f08190914cbeaf1a558730 |
completed | April 14, 2026, 6:19 p.m. |
Created at: April 8, 2026, 9:18 p.m.