Triple

T20557029
Position Surface form Disambiguated ID Type / Status
Subject Jinju Station E504744 entity
Predicate hasNearbyLandmark P2064 FINISHED
Object Jinju City Hall
Jinju City Hall is the main municipal government building and administrative center serving the city of Jinju in South Korea.
 E1437551 NE FINISHED

How this triple was built (4 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: Jinju City Hall | Statement: [Jinju Station, hasNearbyLandmark, Jinju City Hall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jinju City Hall
Context triple: [Jinju Station, hasNearbyLandmark, Jinju City Hall]
  • A. Busan Metropolitan City Hall
    Busan Metropolitan City Hall is the main administrative headquarters of the Busan metropolitan government in South Korea.
  • B. Ulsan City Hall
    Ulsan City Hall is the main municipal government building and administrative center serving the city of Ulsan, South Korea.
  • C. Gwangju City Hall
    Gwangju City Hall is the main municipal government building and administrative center of Gwangju, a major city in South Korea.
  • D. Incheon City Hall
    Incheon City Hall is the main administrative and governmental headquarters of the metropolitan city of Incheon, South Korea.
  • E. Daejeon City Hall
    Daejeon City Hall is the main municipal government complex of Daejeon, South Korea, housing the city’s administrative offices and executive leadership.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jinju City Hall
Triple: [Jinju Station, hasNearbyLandmark, Jinju City Hall]
Generated description
Jinju City Hall is the main municipal government building and administrative center serving the city of Jinju in South Korea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jinju City Hall
Target entity description: Jinju City Hall is the main municipal government building and administrative center serving the city of Jinju in South Korea.
  • A. Busan Metropolitan City Hall
    Busan Metropolitan City Hall is the main administrative headquarters of the Busan metropolitan government in South Korea.
  • B. Ulsan City Hall
    Ulsan City Hall is the main municipal government building and administrative center serving the city of Ulsan, South Korea.
  • C. Gwangju City Hall
    Gwangju City Hall is the main municipal government building and administrative center of Gwangju, a major city in South Korea.
  • D. Incheon City Hall
    Incheon City Hall is the main administrative and governmental headquarters of the metropolitan city of Incheon, South Korea.
  • E. Daejeon City Hall
    Daejeon City Hall is the main municipal government complex of Daejeon, South Korea, housing the city’s administrative offices and executive leadership.
  • F. None of above. chosen

Provenance (5 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_69e0b4b6587c8190aee63dc7cff244ea completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a5de9c008190b8620628fb285e90 completed April 20, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a08a576d990819099ac2858ea7df968 completed May 16, 2026, 5:12 p.m.
NEDg Description generation batch_6a08a68c08dc8190a09854800df82686 completed May 16, 2026, 5:17 p.m.
NED2 Entity disambiguation (via description) batch_6a08a7331cc88190a9ed6056e5743e69 completed May 16, 2026, 5:19 p.m.
Created at: April 16, 2026, 11:38 a.m.