Triple

T24286181
Position Surface form Disambiguated ID Type / Status
Subject KNN Audience Award E605674 entity
Predicate namedAfter P63 FINISHED
Object KNN (Korea New Network)
KNN (Korea New Network) is a South Korean regional broadcasting company based in Busan, known for its television and radio services and involvement in local cultural events and awards.
 E1629460 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: KNN (Korea New Network) | Statement: [KNN Audience Award, namedAfter, KNN (Korea New Network)]
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: KNN (Korea New Network)
Triple: [KNN Audience Award, namedAfter, KNN (Korea New Network)]
Generated description
KNN (Korea New Network) is a South Korean regional broadcasting company based in Busan, known for its television and radio services and involvement in local cultural events and awards.

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_69e295480d0c8190846fc3c2e2da1d4c completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f28f56cdc08190a1e06f67dffd4769 completed April 29, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9ca8ca88190a04321f5b0a1ab38 completed May 22, 2026, 3:13 a.m.
NEDg Description generation batch_6a0fcde5d2688190a3d356d26a249ed5 completed May 22, 2026, 3:30 a.m.
NED2 Entity disambiguation (via description) batch_6a0fce64c9248190b8b0f4adac5a2f6a completed May 22, 2026, 3:32 a.m.
Created at: April 18, 2026, 12:08 a.m.