Triple
T7376841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Ulsan |
E170145
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Ulsan Metropolitan City |
E28895
|
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: Ulsan Metropolitan City | Statement: [University of Ulsan, region, Ulsan Metropolitan City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ulsan Metropolitan City Context triple: [University of Ulsan, region, Ulsan Metropolitan City]
-
A.
Ulsan
chosen
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
-
B.
Busan metropolitan area
The Busan metropolitan area is a major South Korean urban and economic hub centered on the port city of Busan, known for its extensive transportation links, coastal location, and role as a key gateway for international trade.
-
C.
Incheon
Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
-
D.
Gijeon
Gijeon is an alternative name for the Seoul Capital Area, the densely populated metropolitan region surrounding South Korea’s capital city.
-
E.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
- 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_69c68a5bfaac81909ce7f001dfb70c76 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1a8b18c8190ad1a19521eda2319 |
completed | March 27, 2026, 9:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e712b2ff1081908ccf311e1133ab72 |
completed | April 21, 2026, 6:01 a.m. |
Created at: March 27, 2026, 3:07 p.m.