Triple

T10776403

Position	Surface form	Disambiguated ID	Type / Status
Subject	Midnight in Paris	`E254207`	entity
Predicate	posterTagline	`P7688`	FINISHED
Object	Paris in the morning is beautiful. Paris in the afternoon is charming. Paris in the evening is enchanting. But Paris after midnight is magic.	`—`	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: Paris in the morning is beautiful. Paris in the afternoon is charming. Paris in the evening is enchanting. But Paris after midnight is magic. | Statement: [Midnight in Paris, posterTagline, Paris in the morning is beautiful. Paris in the afternoon is charming. Paris in the evening is enchanting. But Paris after midnight is magic.]

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_69d6aa609f008190a294200aefcb7bd5`	completed	April 8, 2026, 7:20 p.m.
NER	Named-entity recognition	`batch_69d7329d8c908190bddad40685133ea1`	completed	April 9, 2026, 5:01 a.m.

Created at: April 8, 2026, 9:16 p.m.