Triple
T4509754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osan Air Base |
E102019
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Osan |
E223589
|
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: Osan | Statement: [Osan Air Base, locatedNear, Osan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osan Context triple: [Osan Air Base, locatedNear, 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.
Ozaki
Ozaki is a Japanese surname borne by various notable figures in politics, literature, and the arts.
-
C.
Dairen
Dairen, now known as Dalian, is a major port city in northeastern China that historically served as an important strategic and commercial hub under various foreign leases and administrations.
-
D.
Ota
Ota is a historically significant Awori town in southwestern Nigeria that has grown into a major industrial and educational hub.
-
E.
Osakasayama
Osakasayama is a suburban city in Osaka Prefecture, Japan, known for its residential character and proximity to the Osaka metropolitan area.
- 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_69bd43d6251c81909deecce3e6e9d69c |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd571138b88190b68bbfc4300aaf9d |
completed | March 20, 2026, 2:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd7f7c1bd08190bc7b7028a512a466 |
completed | March 20, 2026, 5:10 p.m. |
Created at: March 20, 2026, 1:01 p.m.