Triple

T24061150

Position	Surface form	Disambiguated ID	Type / Status
Subject	My Name Is Julia Ross	`E595955`	entity
Predicate	plotSummary	`P264`	FINISHED
Object	A young woman is hired as a secretary and awakens in a seaside mansion where she is told she is someone else and part of a sinister scheme.	`—`	LITERAL FINISHED

How this triple was built (1 step)

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: A young woman is hired as a secretary and awakens in a seaside mansion where she is told she is someone else and part of a sinister scheme. | Statement: [My Name Is Julia Ross, plotSummary, A young woman is hired as a secretary and awakens in a seaside mansion where she is told she is someone else and part of a sinister scheme.]

Provenance (2 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_69e288c25c008190850cf447940ab181`	completed	April 17, 2026, 7:23 p.m.
NER	Named-entity recognition	`batch_69f1da55903c8190ad5d578e33a9dae9`	completed	April 29, 2026, 10:15 a.m.

Created at: April 17, 2026, 10:38 p.m.