Triple

T19844910
Position Surface form Disambiguated ID Type / Status
Subject Yeongneung and Nyeongneung E476834 entity
Predicate locatedIn P40 FINISHED
Object Yeoju NE NERFINISHED

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: Yeoju | Statement: [Yeongneung and Nyeongneung, locatedIn, Yeoju]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yeoju
Context triple: [Yeongneung and Nyeongneung, locatedIn, Yeoju]
  • A. Yeoju chosen
    Yeoju is a city in South Korea known for its rich historical heritage, including royal tombs and ceramics, and its scenic riverside landscapes.
  • B. Jeonju
    Jeonju is a historic city in southwestern South Korea known for its well-preserved Hanok Village, rich culinary traditions, and cultural heritage.
  • C. Sacheon
    Sacheon is a coastal city in South Gyeongsang Province, South Korea, known for its fishing industry, maritime transport, and aerospace manufacturing.
  • D. 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.
  • E. Jeongeup
    Jeongeup is a city in South Korea known for its location in North Jeolla Province and its cultural and historical heritage.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e51d39d081909bcfafeaaf3d2fcc completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658085a148190a305bde0897dfe84 completed April 20, 2026, 4:44 p.m.
Created at: April 10, 2026, 1:51 p.m.