Triple

T32451133
Position Surface form Disambiguated ID Type / Status
Subject XXL Freshman 2016 freestyle E829285 entity
Predicate distributionChannel P1486 FINISHED
Object XXL YouTube channel
The XXL YouTube channel is the official video platform of XXL magazine, featuring hip-hop content such as freestyles, interviews, cyphers, and coverage of its annual Freshman class.
E2007203 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: XXL YouTube channel | Statement: [XXL Freshman 2016 freestyle, distributionChannel, XXL YouTube channel]
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: XXL YouTube channel
Triple: [XXL Freshman 2016 freestyle, distributionChannel, XXL YouTube channel]
Generated description
The XXL YouTube channel is the official video platform of XXL magazine, featuring hip-hop content such as freestyles, interviews, cyphers, and coverage of its annual Freshman class.

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_69f3491d2e5c819092b1c9535beff8ec completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c310edd08190a9f8e14056c45bd1 completed May 3, 2026, 3:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34667a829481909a0dad637badbba0 completed June 18, 2026, 9:43 p.m.
NEDg Description generation batch_6a34674302f081908ce094e58ee8360c completed June 18, 2026, 9:46 p.m.
NED2 Entity disambiguation (via description) batch_6a34682ebe448190b88c760af0af9dfa completed June 18, 2026, 9:50 p.m.
Created at: May 1, 2026, 12:56 a.m.