Triple

T8053817
Position Surface form Disambiguated ID Type / Status
Subject Geumjeong Fortress E187742 entity
Predicate locatedOn P40 FINISHED
Object Geumjeongsan E172724 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: Geumjeongsan | Statement: [Geumjeong Fortress, locatedOn, Geumjeongsan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Geumjeongsan
Context triple: [Geumjeong Fortress, locatedOn, Geumjeongsan]
  • A. Geumjeongsan chosen
    Geumjeongsan is a prominent mountain in Busan, South Korea, known for its scenic hiking trails, historic fortress walls, and cultural sites.
  • B. Hwangnyeongsan Mountain
    Hwangnyeongsan Mountain is a prominent peak in Busan, South Korea, known for its panoramic city and coastal views, especially popular at night.
  • C. Gyeryongsan
    Gyeryongsan is a prominent mountain in central South Korea known for its scenic national park, rich biodiversity, and cultural sites including historic Buddhist temples.
  • D. Umyeonsan Mountain
    Umyeonsan Mountain is a low, forested peak in southern Seoul, South Korea, known for its hiking trails, city views, and role as a natural green space within the urban area.
  • E. Ok-dong
    Ok-dong is a neighborhood in Ulsan, South Korea, known for encompassing the large urban green space of Ulsan Grand Park.
  • 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_69ca82b15e948190a62fd7af5218426a completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3f9fb8dc8190bacc1f66ddfd1cbf completed March 31, 2026, 3:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc572058048190996fa77774bf44ba completed March 31, 2026, 11:22 p.m.
Created at: March 30, 2026, 5:25 p.m.