Triple
T17934086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OSN |
E448406
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Osan |
—
|
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: Osan | Statement: [OSN, serves, Osan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osan Context triple: [OSN, serves, Osan]
-
A.
Osan
chosen
Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a regional transportation and commercial hub.
-
B.
Osuga
Osuga is a river, known as the Osuga River, whose name is used in at least one language to refer to this waterway.
-
C.
Osaka Hai
Osaka Hai is a prestigious Grade 1 flat horse race in Japan for older thoroughbreds, run over 2000 meters at Hanshin Racecourse each spring.
-
D.
Oza
"Oza" is a prominent poetic work by Russian poet Andrei Voznesensky, reflecting his innovative style and experimental approach to verse.
-
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 (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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a5547b7881909dc41bb7dd34194f |
completed | April 19, 2026, 9:50 a.m. |
Created at: April 10, 2026, 10:21 a.m.