Triple

T28562448
Position Surface form Disambiguated ID Type / Status
Subject Randi Reisfeld E722581 entity
Predicate notableWork P4 FINISHED
Object Clueless: Cher’s Frisky Business
Clueless: Cher’s Frisky Business is a young adult novel set in the world of the Clueless franchise, following Cher Horowitz’s fashionable high school adventures in Beverly Hills.
E1827372 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: Clueless: Cher’s Frisky Business | Statement: [Randi Reisfeld, notableWork, Clueless: Cher’s Frisky Business]
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: Clueless: Cher’s Frisky Business
Triple: [Randi Reisfeld, notableWork, Clueless: Cher’s Frisky Business]
Generated description
Clueless: Cher’s Frisky Business is a young adult novel set in the world of the Clueless franchise, following Cher Horowitz’s fashionable high school adventures in Beverly Hills.

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_69f01a5f69d08190ad5c0d2167078dec completed April 28, 2026, 2:24 a.m.
NER Named-entity recognition batch_69f650542a1c8190b6f0e66be3bba62c completed May 2, 2026, 7:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cc36bc5648190b78dcef57759e5c3 completed May 31, 2026, 11:25 p.m.
NEDg Description generation batch_6a1cc49311c08190a86bc140a5ead00b completed May 31, 2026, 11:30 p.m.
NED2 Entity disambiguation (via description) batch_6a1cc514936c8190bdfd952c4c00f5d3 completed May 31, 2026, 11:32 p.m.
Created at: April 28, 2026, 4:05 a.m.