Triple
T21509936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Pyongan Province |
E530687
|
entity |
| Predicate | hasPortCity |
P2745
|
FINISHED |
| Object | Nampo |
—
|
NE NERFINISHED |
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: [South Pyongan Province, hasPortCity, Nampo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nampo Context triple: [South Pyongan Province, hasPortCity, 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.
Pak Hyŏkkŏse
Pak Hyŏkkŏse is the legendary founding monarch of the ancient Korean kingdom of Silla, traditionally said to have established the state in 57 BCE.
-
D.
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.
-
E.
Yŏngnŭng
Yŏngnŭng is the McCune–Reischauer romanization of Yeongneung, a royal tomb site in Paju, South Korea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c45c81f08190a6b8bbb70a45aae7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea84dfbc8190a23d9a7d6eb2c2b5 |
completed | April 23, 2026, 9:46 a.m. |
Created at: April 16, 2026, 6:25 p.m.