Triple
T10002311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeongdo Island |
E197355
|
entity |
| Predicate | hasViewOf |
P854
|
FINISHED |
| Object | Busan skyline |
E154500
|
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: Busan skyline | Statement: [Yeongdo Island, hasViewOf, Busan skyline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Busan skyline Context triple: [Yeongdo Island, hasViewOf, Busan skyline]
-
A.
Busan city center
Busan city center is the bustling downtown core of Busan, South Korea, known for its dense shopping streets, markets, and entertainment districts.
-
B.
Busan waterfront
chosen
The Busan waterfront is a bustling coastal area in South Korea’s second-largest city, known for its busy port, seafood markets, and scenic seaside promenades.
-
C.
Ulsan city center
Ulsan city center is the main commercial and administrative hub of Ulsan, South Korea, characterized by dense urban development, shopping districts, and business facilities.
-
D.
Busan Tower
Busan Tower is a prominent observation tower in Busan, South Korea, offering panoramic views of the city and its harbor.
-
E.
Busan, South Korea
Busan, South Korea is the country’s second-largest city and a major coastal hub known for its busy port, beaches, and international film festival.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcc9078788190a4e75dd7ff830c63 |
completed | April 2, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d25857f0988190aebe8951f7c0ef86 |
completed | April 5, 2026, 12:40 p.m. |
Created at: March 30, 2026, 8:51 p.m.