Triple
T23484437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lee Dong-hwi |
E570494
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Busan, South Korea |
—
|
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: Busan, South Korea | Statement: [Lee Dong-hwi, placeOfBirth, Busan, South Korea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Busan, South Korea Context triple: [Lee Dong-hwi, placeOfBirth, Busan, South Korea]
-
A.
Busan, South Korea
chosen
Busan, South Korea is the country’s second-largest city and a major coastal hub known for its busy port, beaches, and international film festival.
-
B.
Jinju, South Korea
Jinju, South Korea is a historic city in South Gyeongsang Province known for its riverside fortress, role in the Imjin War, and annual lantern festival.
-
C.
Daegu, South Korea
Daegu, South Korea is a major city in the southeastern part of the country known for its role as an industrial, cultural, and educational center.
-
D.
Gunsan, South Korea
Gunsan, South Korea is a coastal industrial city in North Jeolla Province known for its port, manufacturing facilities, and role as a regional transportation hub.
-
E.
Ulsan, South Korea
Ulsan, South Korea is a major industrial port city known as a global hub for automobile and ship manufacturing.
- 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_69e245b0b01481908f636939bedd804c |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a752c678819087e5c50b8cf87d3d |
completed | April 29, 2026, 6:38 a.m. |
Created at: April 17, 2026, 6:03 p.m.