Triple

T13702357

Position	Surface form	Disambiguated ID	Type / Status
Subject	G.I. Jane	`E328550`	entity
Predicate	plotSummary	`P264`	FINISHED
Object	A female naval officer becomes the first woman to undergo U.S. Navy SEALs training and faces extreme physical and psychological challenges.	`—`	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 female naval officer becomes the first woman to undergo U.S. Navy SEALs training and faces extreme physical and psychological challenges. | Statement: [G.I. Jane, plotSummary, A female naval officer becomes the first woman to undergo U.S. Navy SEALs training and faces extreme physical and psychological challenges.]

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_69d8076ff62081908a7bd79889edd7a0`	completed	April 9, 2026, 8:09 p.m.
NER	Named-entity recognition	`batch_69dcad162158819089280ee1e6b5c2cf`	completed	April 13, 2026, 8:45 a.m.

Created at: April 9, 2026, 9:54 p.m.