Triple

T36936512
Position Surface form Disambiguated ID Type / Status
Subject Gwangju, South Korea E913634 entity
Predicate hasMuseum P105 FINISHED
Object Asia Culture Center
The Asia Culture Center is a major cultural complex in Gwangju that hosts exhibitions, performances, and research focused on contemporary Asian arts and culture.
 E2203970 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: Asia Culture Center | Statement: [Gwangju, South Korea, hasMuseum, Asia Culture Center]
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: Asia Culture Center
Triple: [Gwangju, South Korea, hasMuseum, Asia Culture Center]
Generated description
The Asia Culture Center is a major cultural complex in Gwangju that hosts exhibitions, performances, and research focused on contemporary Asian arts and culture.

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_69f76e8a6a5c81909c1febf32bf3fe23 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9fdfc6a1c8190ba9fd414a889fc72 completed May 5, 2026, 2:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e163f375c8190849c11ae18ff5e95 completed June 26, 2026, 6:03 a.m.
NEDg Description generation batch_6a3e16dc0e0c8190b479a685e0e7dbbc completed June 26, 2026, 6:06 a.m.
NED2 Entity disambiguation (via description) batch_6a3e17f993208190aeae199e9d7839bd completed June 26, 2026, 6:11 a.m.
Created at: May 3, 2026, 4:13 p.m.