Triple

T14890925
Position Surface form Disambiguated ID Type / Status
Subject Suwon E359751 entity
Predicate hasLandmark P105 FINISHED
Object Suwon City Hall
Suwon City Hall is the main municipal government building and administrative center serving the city of Suwon in South Korea.
 E1127823 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: Suwon City Hall | Statement: [Suwon, hasLandmark, Suwon City Hall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Suwon City Hall
Context triple: [Suwon, hasLandmark, Suwon City Hall]
  • A. Ulsan City Hall
    Ulsan City Hall is the main municipal government building and administrative center serving the city of Ulsan, South Korea.
  • B. Incheon City Hall
    Incheon City Hall is the main administrative and governmental headquarters of the metropolitan city of Incheon, South Korea.
  • C. Busan Metropolitan City Hall
    Busan Metropolitan City Hall is the main administrative headquarters of the Busan metropolitan government in South Korea.
  • D. 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.
  • E. Seoul Metropolitan Council building
    The Seoul Metropolitan Council building is the official headquarters of the city’s legislative body, where ordinances and policies for Seoul are debated and enacted.
  • 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: Suwon City Hall
Triple: [Suwon, hasLandmark, Suwon City Hall]
Generated description
Suwon City Hall is the main municipal government building and administrative center serving the city of Suwon in South Korea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Suwon City Hall
Target entity description: Suwon City Hall is the main municipal government building and administrative center serving the city of Suwon in South Korea.
  • A. Ulsan City Hall
    Ulsan City Hall is the main municipal government building and administrative center serving the city of Ulsan, South Korea.
  • B. Incheon City Hall
    Incheon City Hall is the main administrative and governmental headquarters of the metropolitan city of Incheon, South Korea.
  • C. Busan Metropolitan City Hall
    Busan Metropolitan City Hall is the main administrative headquarters of the Busan metropolitan government in South Korea.
  • D. 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.
  • E. Seoul Metropolitan Council building
    The Seoul Metropolitan Council building is the official headquarters of the city’s legislative body, where ordinances and policies for Seoul are debated and enacted.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded5f883288190af602633fa7d6860 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe7e83418081908280a9ed8ddb9fd7 completed May 9, 2026, 12:23 a.m.
NEDg Description generation batch_69fe7f9ea5848190af5edd6d2d22c117 completed May 9, 2026, 12:28 a.m.
NED2 Entity disambiguation (via description) batch_69fe7ff91dec8190aa9f0d42a8cd00e0 completed May 9, 2026, 12:29 a.m.
Created at: April 10, 2026, 2:10 a.m.