Triple
T14423138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naver Corporation |
E357631
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Seongnam |
E408723
|
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: Seongnam | Statement: [Naver Corporation, headquartersLocation, Seongnam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seongnam Context triple: [Naver Corporation, headquartersLocation, Seongnam]
-
A.
Seongnam
chosen
Seongnam is a major satellite city of Seoul in South Korea, known for its planned urban development and role as a residential and commercial hub.
-
B.
Suwon
Suwon is a major South Korean city best known for its UNESCO-listed Hwaseong Fortress and as a key cultural and economic center just south of Seoul.
-
C.
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.
-
D.
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.
-
E.
Ulsan
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91102c3c81908f571a1fff3bdd47 |
completed | April 14, 2026, 7:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01d196d88190a8fa54468b2de1bb |
completed | May 9, 2026, 9:43 a.m. |
Created at: April 10, 2026, 1:18 a.m.