Triple
T14371926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Gyeongsang Province |
E356377
|
entity |
| Predicate | bordersAdministrativeRegion |
P10768
|
FINISHED |
| Object | Ulsan |
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 | Statement: [South Gyeongsang Province, bordersAdministrativeRegion, Ulsan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ulsan Context triple: [South Gyeongsang Province, bordersAdministrativeRegion, Ulsan]
-
A.
Ulsan
chosen
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
-
B.
Changwon
Changwon is a major industrial and administrative city in South Gyeongsang Province, South Korea, known for its planned urban layout and role as a regional government and manufacturing hub.
-
C.
Gijeon
Gijeon is an alternative name for the Seoul Capital Area, the densely populated metropolitan region surrounding South Korea’s capital city.
-
D.
Pohang
Pohang is a major industrial and port city in South Korea, best known as the home of the global steelmaker POSCO and a key hub on the country’s east coast.
-
E.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fb2082c8190b42cc5f2bab4f574 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed31670a48190a606e812a2aa0a6e |
completed | May 9, 2026, 6:24 a.m. |
Created at: April 10, 2026, 1:15 a.m.