Triple

T6167373

Position	Surface form	Disambiguated ID	Type / Status
Subject	Office for Civil Rights	`E137600`	entity
Predicate	missionStatement	`P79`	FINISHED
Object	to ensure equal access to education and to promote educational excellence through vigorous enforcement of civil rights in our nation’s schools	`—`	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: to ensure equal access to education and to promote educational excellence through vigorous enforcement of civil rights in our nation’s schools | Statement: [Office for Civil Rights, missionStatement, to ensure equal access to education and to promote educational excellence through vigorous enforcement of civil rights in our nation’s schools]

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_69c008a68c508190a8d78245c865960e`	completed	March 22, 2026, 3:20 p.m.
NER	Named-entity recognition	`batch_69c05d647ae48190a1db07b4f4a06e67`	completed	March 22, 2026, 9:21 p.m.

Created at: March 22, 2026, 4:17 p.m.