Triple
T13112499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hwaseong |
E311006
|
entity |
| Predicate | neighboringCity |
P988
|
FINISHED |
| Object | Ansan |
E415511
|
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: Ansan | Statement: [Hwaseong, neighboringCity, Ansan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ansan Context triple: [Hwaseong, neighboringCity, Ansan]
-
A.
Ansan
chosen
Ansan is a coastal industrial city in South Korea known for its manufacturing base, multicultural population, and proximity to Seoul.
-
B.
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.
-
C.
Anseong
Anseong is a city in Gyeonggi Province, South Korea, known for its traditional culture, agricultural heritage, and annual Baudeogi Festival.
-
D.
Dongducheon
Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
-
E.
Gwacheon
Gwacheon is a small city in South Korea known for hosting major government offices, cultural institutions, and the Seoul Grand Park complex.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9817f8ee8819084078b4bec5e4f18 |
completed | April 10, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe249a972081909b30302865396eb1 |
completed | May 8, 2026, 5:59 p.m. |
Created at: April 9, 2026, 9:05 p.m.