Triple

T20317241

Position	Surface form	Disambiguated ID	Type / Status
Subject	Statutory Maternity Pay	`E510409`	entity
Predicate	startTimeCondition	`P55446`	FINISHED
Object	starts automatically if employee is off work for a pregnancy-related illness in the 4 weeks before the week the baby is due	`—`	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: starts automatically if employee is off work for a pregnancy-related illness in the 4 weeks before the week the baby is due | Statement: [Statutory Maternity Pay, startTimeCondition, starts automatically if employee is off work for a pregnancy-related illness in the 4 weeks before the week the baby is due]

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_69e0b4c7491c8190961113c4283b10b0`	completed	April 16, 2026, 10:07 a.m.
NER	Named-entity recognition	`batch_69e67788ca3c8190a3496fd54a5870d6`	completed	April 20, 2026, 6:59 p.m.

Created at: April 16, 2026, 11:19 a.m.