Triple

T32188690

Position	Surface form	Disambiguated ID	Type / Status
Subject	Freeway	`E822174`	entity
Predicate	mainCharacter	`P1183`	FINISHED
Object	Vanessa Lutz Vanessa Lutz is the tough, foul-mouthed teenage protagonist of the 1996 dark comedy film "Freeway," portrayed by Reese Witherspoon in a modern, gritty retelling of "Little Red Riding Hood."	`E2001850`	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: Vanessa Lutz | Statement: [Freeway, mainCharacter, Vanessa Lutz]

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: Vanessa Lutz
Triple: [Freeway, mainCharacter, Vanessa Lutz]

Generated description

Vanessa Lutz is the tough, foul-mouthed teenage protagonist of the 1996 dark comedy film "Freeway," portrayed by Reese Witherspoon in a modern, gritty retelling of "Little Red Riding Hood."

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_69f3490819cc81909bae1f8ce99423c5`	completed	April 30, 2026, 12:20 p.m.
NER	Named-entity recognition	`batch_69f6bac372ac81908c1c7ac6eb579d53`	completed	May 3, 2026, 3:02 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3056ef681c8190a4003ff45cdcf91e`	completed	June 15, 2026, 7:47 p.m.
NEDg	Description generation	`batch_6a305b2b439c81909a057a3fc72d4a61`	completed	June 15, 2026, 8:06 p.m.
NED2	Entity disambiguation (via description)	`batch_6a305b977fa48190a19a2569afe41cc9`	completed	June 15, 2026, 8:07 p.m.

Created at: May 1, 2026, 12:35 a.m.