Triple
T196612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korea |
E3830
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Busan |
E4279
|
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 | Statement: [South Korea, hasCity, Busan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Busan Context triple: [South Korea, hasCity, Busan]
-
A.
Busan
chosen
Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
-
B.
Seoul
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
C.
Pyongyang
Pyongyang is the capital and largest city of North Korea, serving as its political, economic, and cultural center.
-
D.
Nara
Nara is an ancient Japanese city renowned for its early role as a national capital, its historic temples, and its culturally significant deer-filled parks.
-
E.
Koreatown
Koreatown is a vibrant Manhattan neighborhood known for its dense concentration of Korean restaurants, shops, and cultural businesses centered around West 32nd Street near the Empire State Building.
- 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a2598594388190a56f36fa036eac84 |
completed | Feb. 28, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a33e3dc3008190a021d1b18438b773 |
completed | Feb. 28, 2026, 7:13 p.m. |
Created at: Feb. 28, 2026, 2:41 a.m.