Triple

T26617058
Position Surface form Disambiguated ID Type / Status
Subject Kevin Chamberlin E668089 entity
Predicate televisionRole P1668 FINISHED
Object Bertram Winkle in Jessie
Bertram Winkle in Jessie is the grumpy yet lovable butler of the Ross family on the Disney Channel sitcom "Jessie," known for his sarcastic humor and reluctant affection for the kids.
E1735605 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: Bertram Winkle in Jessie | Statement: [Kevin Chamberlin, televisionRole, Bertram Winkle in Jessie]
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: Bertram Winkle in Jessie
Triple: [Kevin Chamberlin, televisionRole, Bertram Winkle in Jessie]
Generated description
Bertram Winkle in Jessie is the grumpy yet lovable butler of the Ross family on the Disney Channel sitcom "Jessie," known for his sarcastic humor and reluctant affection for the kids.

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_69ee9cfe16088190a3dddd68e3c7b1ea completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f615adea108190900f6809d6cdeb81 completed May 2, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11ec27bac08190b0da2d3d83dc5cd2 completed May 23, 2026, 6:04 p.m.
NEDg Description generation batch_6a11edc0dcb881909cf23e6303439681 completed May 23, 2026, 6:11 p.m.
NED2 Entity disambiguation (via description) batch_6a11eee584a48190aa152f30f2c59f69 completed May 23, 2026, 6:16 p.m.
Created at: April 27, 2026, 2:19 a.m.