Triple

T29088059
Position Surface form Disambiguated ID Type / Status
Subject Makeda E734170 entity
Predicate notableIn P22 FINISHED
Object Afropean music scene
The Afropean music scene is a cultural and musical movement that blends African and European influences, reflecting the hybrid identities and experiences of the African diaspora in Europe.
 E1848898 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: Afropean music scene | Statement: [Makeda, notableIn, Afropean music scene]
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: Afropean music scene
Triple: [Makeda, notableIn, Afropean music scene]
Generated description
The Afropean music scene is a cultural and musical movement that blends African and European influences, reflecting the hybrid identities and experiences of the African diaspora in Europe.

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_69f05b0c0f28819086eae6e84f2ae472 completed April 28, 2026, 7 a.m.
NER Named-entity recognition batch_69f6614906e08190a6af61758dd099d6 completed May 2, 2026, 8:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a251f91a6948190b8b0c3c9708628d2 completed June 7, 2026, 7:36 a.m.
NEDg Description generation batch_6a252ab734088190af8464174d1727ed completed June 7, 2026, 8:24 a.m.
NED2 Entity disambiguation (via description) batch_6a252e61d81c81908bac119566d9df17 completed June 7, 2026, 8:40 a.m.
Created at: April 28, 2026, 11:02 a.m.