Triple
T20037418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inoue |
E497312
|
entity |
| Predicate | hasAlternativeSpelling |
P457
|
FINISHED |
| Object | Inouye |
—
|
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: Inouye | Statement: [Inoue, hasAlternativeSpelling, Inouye]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inouye Context triple: [Inoue, hasAlternativeSpelling, Inouye]
-
A.
Mark Inouye
Mark Inouye is an American classical trumpeter best known as the principal trumpet of the San Francisco Symphony.
-
B.
Daniel Akaka
Daniel Akaka was a long-serving U.S. Senator from Hawaii known for his advocacy on Native Hawaiian rights and veterans’ issues.
-
C.
Daniel Inouye
chosen
Daniel Inouye was a highly decorated World War II veteran and long-serving U.S. senator from Hawaii who became nationally prominent for his role on the Senate Watergate Committee.
-
D.
Ken Inouye
Ken Inouye is the son of the late U.S. Senator Daniel Inouye and has worked as a music industry executive and political consultant.
-
E.
Richard Hashimoto
Richard Hashimoto is a film producer best known for his work on the 1988 dark comedy-fantasy movie "Beetlejuice."
- 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e935ac8190900cdb4f0cfde505 |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:36 p.m.