Triple
T4878142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Itaewon |
E109253
|
entity |
| Predicate | partOfUrbanArea |
P294
|
FINISHED |
| Object | Seoul metropolitan area |
E166134
|
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: Seoul metropolitan area | Statement: [Itaewon, partOfUrbanArea, Seoul metropolitan area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seoul metropolitan area Context triple: [Itaewon, partOfUrbanArea, Seoul metropolitan area]
-
A.
Seoul Capital Area
chosen
The Seoul Capital Area is South Korea’s largest metropolitan region, encompassing Seoul, Incheon, and surrounding Gyeonggi Province, and serving as the country’s political, economic, and cultural hub.
-
B.
Seoul
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
C.
Yongin
Yongin is a rapidly growing city in the Seoul Capital Area of South Korea, known for attractions like Everland Resort and the Korean Folk Village.
-
D.
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.
-
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_69bd440e9d64819083e82cf33b4d9570 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6dbd69d48190a8397d67af8f5fc8 |
completed | March 20, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba4d76808190843622211d1eac6f |
completed | March 21, 2026, 3:33 p.m. |
Created at: March 20, 2026, 1:27 p.m.