Triple

T34366799
Position Surface form Disambiguated ID Type / Status
Subject Julianne Hough E882032 entity
Predicate notableRole P22 FINISHED
Object Ariel Moore in Footloose (2011)
Ariel Moore in Footloose (2011) is the rebellious small-town preacher’s daughter whose love of dancing and defiance of strict local rules drive much of the film’s emotional and romantic storyline.
E2092698 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: Ariel Moore in Footloose (2011) | Statement: [Julianne Hough, notableRole, Ariel Moore in Footloose (2011)]
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: Ariel Moore in Footloose (2011)
Triple: [Julianne Hough, notableRole, Ariel Moore in Footloose (2011)]
Generated description
Ariel Moore in Footloose (2011) is the rebellious small-town preacher’s daughter whose love of dancing and defiance of strict local rules drive much of the film’s emotional and romantic storyline.

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_69f349be5c9c81908dc726ae1f4c68f2 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f7182e6c948190b4dc6763255302f4 completed May 3, 2026, 9:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3704abf27c8190a24571305c358eb5 completed June 20, 2026, 9:22 p.m.
NEDg Description generation batch_6a37058d9864819088afc4a2160ad876 completed June 20, 2026, 9:26 p.m.
NED2 Entity disambiguation (via description) batch_6a37061b69fc81908c02244b45d74771 completed June 20, 2026, 9:28 p.m.
Created at: May 1, 2026, 1:58 a.m.