Triple

T16467123
Position Surface form Disambiguated ID Type / Status
Subject Geoje Island E399961 entity
Predicate near P350 FINISHED
Object Tongyeong E781477 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: Tongyeong | Statement: [Geoje Island, near, Tongyeong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tongyeong
Context triple: [Geoje Island, near, Tongyeong]
  • A. Tongyeong chosen
    Tongyeong is a coastal city in South Gyeongsang Province, South Korea, known for its scenic archipelago, seafood, and maritime history.
  • B. Suncheon
    Suncheon is a city in South Jeolla Province, South Korea, known for its ecological attractions such as the Suncheon Bay Wetland Reserve and its role as a regional administrative and cultural center.
  • C. Gunsan
    Gunsan is a coastal city in North Jeolla Province, South Korea, known for its port, industrial facilities, and longstanding association with nearby military air operations.
  • D. Icheon
    Icheon is a South Korean city renowned for its traditional ceramics and hot spring resorts.
  • E. Gijeon
    Gijeon is an alternative name for the Seoul Capital Area, the densely populated metropolitan region surrounding South Korea’s capital city.
  • 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_69d87f2dac988190b74d6e185fa88ba4 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32dcd707081908fb7ca91a8c09e0a completed April 18, 2026, 7:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006ed25bcc819090ccca4705e4e24f completed May 10, 2026, 11:41 a.m.
Created at: April 10, 2026, 5:11 a.m.