Triple

T26767129
Position Surface form Disambiguated ID Type / Status
Subject Gijang County, Busan E674968 entity
Predicate hasAttraction P105 FINISHED
Object East Busan Tourism Complex
East Busan Tourism Complex is a large coastal leisure and tourism district in Gijang County, Busan, featuring resorts, shopping, entertainment, and scenic ocean views.
E1741315 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: East Busan Tourism Complex | Statement: [Gijang County, Busan, hasAttraction, East Busan Tourism Complex]
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: East Busan Tourism Complex
Triple: [Gijang County, Busan, hasAttraction, East Busan Tourism Complex]
Generated description
East Busan Tourism Complex is a large coastal leisure and tourism district in Gijang County, Busan, featuring resorts, shopping, entertainment, and scenic ocean views.

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_69eecda85298819097ee1c38a3d772e7 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f619289e008190a0f99326509ff9cb completed May 2, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12095051ac8190a80b4ce38366e990 completed May 23, 2026, 8:08 p.m.
NEDg Description generation batch_6a120a6fdd60819090c963c9a5577186 completed May 23, 2026, 8:13 p.m.
NED2 Entity disambiguation (via description) batch_6a120b329b30819089e007135e13dc21 completed May 23, 2026, 8:16 p.m.
Created at: April 27, 2026, 4 a.m.