Triple
T16638236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Pyongan Province |
E404263
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Sinuiju |
E287083
|
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: Sinuiju | Statement: [North Pyongan Province, hasMajorCity, Sinuiju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sinuiju Context triple: [North Pyongan Province, hasMajorCity, Sinuiju]
-
A.
Sinuiju, Korea
chosen
Sinuiju, Korea is a North Korean city on the Yalu River bordering China, known as an important industrial and transportation hub.
-
B.
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.
-
C.
Kim Chaek City
Kim Chaek City is an industrial port city in North Hamgyong Province, North Korea, named in honor of the Korean War general and politician Kim Chaek.
-
D.
Nampo
Nampo is a major port city in southwestern North Korea, known for its industrial facilities and strategic location on the Yellow Sea.
-
E.
Nampo-dong
Nampo-dong is a bustling commercial and shopping district in central Busan, South Korea, known for its markets, street food, and proximity to the city’s harbor.
- 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e378eb07388190a10e896bae7f098e |
completed | April 18, 2026, 12:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084b984d081909f76ef874431ff40 |
completed | May 10, 2026, 1:14 p.m. |
Created at: April 10, 2026, 5:18 a.m.