Triple

T28665422
Position Surface form Disambiguated ID Type / Status
Subject The Hard Times of RJ Berger E725573 entity
Predicate characterPortrayedBy P1507 FINISHED
Object Amber Lancaster
Amber Lancaster is an American model, actress, and television personality best known for her work on MTV’s "The Hard Times of RJ Berger" and as a model on "The Price Is Right."
E1841148 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: Amber Lancaster | Statement: [The Hard Times of RJ Berger, characterPortrayedBy, Amber Lancaster]
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: Amber Lancaster
Triple: [The Hard Times of RJ Berger, characterPortrayedBy, Amber Lancaster]
Generated description
Amber Lancaster is an American model, actress, and television personality best known for her work on MTV’s "The Hard Times of RJ Berger" and as a model on "The Price Is Right."

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_69f01d85be388190b669a0e401e2f2c4 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f655a43fb4819087b2979884dbd497 completed May 2, 2026, 7:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24ec2070388190a64b7c042fff35e5 completed June 7, 2026, 3:57 a.m.
NEDg Description generation batch_6a24f0319b708190b5d3a875fbc6810c completed June 7, 2026, 4:14 a.m.
NED2 Entity disambiguation (via description) batch_6a24f43f7cb881909d591ee6248b6c2b completed June 7, 2026, 4:31 a.m.
Created at: April 28, 2026, 5 a.m.