Triple
T8115042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jinhae Bay |
E189450
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Masan |
E581969
|
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: Masan | Statement: [Jinhae Bay, hasNearbySettlement, Masan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masan Context triple: [Jinhae Bay, hasNearbySettlement, Masan]
-
A.
Masan
chosen
Masan is a city in South Korea that serves as a regional administrative and judicial center, hosting a seat of the country's district courts.
-
B.
Nago
Nago is a coastal city in northern Okinawa, Japan, known for its beaches, subtropical climate, and role as a regional commercial and cultural center.
-
C.
Daigo
Daigo was the era name (nengō) in Japanese history corresponding to the reign of Emperor Daigo in the early 10th century.
-
D.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
E.
Namba City
Namba City is a large shopping and entertainment complex in Osaka’s Namba district, featuring retail stores, restaurants, offices, and a rooftop garden integrated with the surrounding urban landscape.
- 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_69ca82baad008190ab2859712b9b1607 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb43308e0081909ea0463dabd74e4d |
completed | March 31, 2026, 3:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc943bdaa48190971bf57ae4fb5c21 |
completed | April 1, 2026, 3:42 a.m. |
Created at: March 30, 2026, 5:33 p.m.