Triple

T6774703
Position Surface form Disambiguated ID Type / Status
Subject Onsan National Industrial Complex E155126 entity
Predicate adjacentTo P224 FINISHED
Object Onsan-eup E153850 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: Onsan-eup | Statement: [Onsan National Industrial Complex, adjacentTo, Onsan-eup]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Onsan-eup
Context triple: [Onsan National Industrial Complex, adjacentTo, Onsan-eup]
  • A. Soreang
    Soreang is a suburban district and the administrative center of Bandung Regency in West Java, Indonesia, situated within the greater Bandung metropolitan area.
  • B. Ungjin
    Ungjin was an ancient city in the Korean kingdom of Baekje that served as one of its historical capitals and a key political and cultural center.
  • C. Wiryeseong
    Wiryeseong was the first capital city of the ancient Korean kingdom of Baekje, located in the Han River basin near present-day Seoul.
  • D. Balgüe
    Balgüe is a small rural village on Ometepe Island in Lake Nicaragua, known for its scenic setting near volcanic landscapes and eco-tourism lodges.
  • E. Ulju-gun chosen
    Ulju-gun is a county-level administrative district located within the metropolitan city of Ulsan in South Korea, known for its mix of industrial facilities and natural landscapes.
  • 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_69c68812ef7c819099369f51febb725c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d24ddaf08190baffbff991eeb458 completed March 27, 2026, 6:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712ca48d88190b9f47b23264d4264 completed March 27, 2026, 11:29 p.m.
Created at: March 27, 2026, 2:13 p.m.