Triple

T31216312
Position Surface form Disambiguated ID Type / Status
Subject Empirical analysis of the Mariel boatlift and its impact on Miami labor markets E795879 entity
Predicate conclusion P374 FINISHED
Object labor markets can absorb large immigration inflows with limited adverse effects on natives in the short run 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: labor markets can absorb large immigration inflows with limited adverse effects on natives in the short run | Statement: [Empirical analysis of the Mariel boatlift and its impact on Miami labor markets, conclusion, labor markets can absorb large immigration inflows with limited adverse effects on natives in the short run]

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_69f224d9d52c8190a61f68ded37fa755 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69c2b9bd08190ba440c060ebef476 completed May 3, 2026, 12:51 a.m.
Created at: April 29, 2026, 9:10 p.m.