Triple

T7629557
Position Surface form Disambiguated ID Type / Status
Subject Geumjeongsan E172724 entity
Predicate hasAccessPoint P1985 FINISHED
Object Beomeosa Station E188978 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: Beomeosa Station | Statement: [Geumjeongsan, hasAccessPoint, Beomeosa Station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beomeosa Station
Context triple: [Geumjeongsan, hasAccessPoint, Beomeosa Station]
  • A. Beomeosa Station chosen
    Beomeosa Station is a subway station in Busan, South Korea, serving as a key access point to the nearby Beomeosa Temple and surrounding Geumjeong District area.
  • B. Yangjae Station
    Yangjae Station is a major subway station in southern Seoul, South Korea, serving as an important transit hub on multiple lines within the city’s metro network.
  • C. Yeonsu Station
    Yeonsu Station is a subway station in Incheon, South Korea, serving the Yeonsu District on the Incheon Subway Line 1.
  • D. Myeongnyun Station
    Myeongnyun Station is a metro station in Busan, South Korea, serving the Dongnae District on the Busan Metro network.
  • E. Kwangbok Station
    Kwangbok Station is a stop on the Pyongyang Metro system in North Korea, serving passengers along one of the capital’s main underground transit lines.
  • 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_69c699517e348190bd3348b6889200f2 completed March 27, 2026, 2:50 p.m.
NER Named-entity recognition batch_69c6fa84adb08190885138fc9b908ebf completed March 27, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8a212d0888190a40cdf32ef53d993 completed March 29, 2026, 3:52 a.m.
Created at: March 27, 2026, 3:56 p.m.