Triple
T7316596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korea Bay |
E168427
|
entity |
| Predicate | adjacentToCity |
P5707
|
FINISHED |
| Object | Nampo |
E627467
|
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: Nampo | Statement: [Korea Bay, adjacentToCity, Nampo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nampo Context triple: [Korea Bay, adjacentToCity, Nampo]
-
A.
Nampo
chosen
Nampo is a major port city in southwestern North Korea, known for its industrial facilities and strategic location on the Yellow Sea.
-
B.
Sinuiju, Korea
Sinuiju, Korea is a North Korean city on the Yalu River bordering China, known as an important industrial and transportation hub.
-
C.
Hungnam
Hungnam is a port city on North Korea’s east coast that served as a major industrial center and the site of a large-scale UN evacuation during the Korean War.
-
D.
Wonsan
Wonsan is a port city on North Korea’s east coast, known for its strategic military importance and role as a regional transportation and industrial hub.
-
E.
Pyongyang
Pyongyang is the capital and largest city of North Korea, serving as its political, economic, and cultural center.
- 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_69c68a5251508190ad68df4151cfeb04 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ef162d488190bf1c63b71b20a294 |
completed | March 27, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7fa78d8e08190a5658f2df7fbf667 |
completed | March 28, 2026, 3:57 p.m. |
Created at: March 27, 2026, 3:02 p.m.