Triple

T31728491
Position Surface form Disambiguated ID Type / Status
Subject Goyang Aram Nuri Arts Center E809792 entity
Predicate hasPart P35 FINISHED
Object Aram Opera House
Aram Opera House is a major performance venue within the Goyang Aram Nuri Arts Center in Goyang, South Korea, known for hosting opera, classical music, and large-scale cultural events.
E1976494 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: Aram Opera House | Statement: [Goyang Aram Nuri Arts Center, hasPart, Aram Opera House]
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: Aram Opera House
Triple: [Goyang Aram Nuri Arts Center, hasPart, Aram Opera House]
Generated description
Aram Opera House is a major performance venue within the Goyang Aram Nuri Arts Center in Goyang, South Korea, known for hosting opera, classical music, and large-scale cultural events.

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_69f348e009c8819095d77df52c645b9c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6aafe1f708190b25ad76aea93f75d completed May 3, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b9475bcd08190a8b53c1233234532 completed June 12, 2026, 5:09 a.m.
NEDg Description generation batch_6a2b96da70e88190bcd2c6e6db9b933f completed June 12, 2026, 5:19 a.m.
NED2 Entity disambiguation (via description) batch_6a2b9a88b51081909d5eb9b74cabfb1f completed June 12, 2026, 5:35 a.m.
Created at: April 30, 2026, 11:20 p.m.