Triple

T14026996
Position Surface form Disambiguated ID Type / Status
Subject Lorena Bernal E337485 entity
Predicate name P16 FINISHED
Object Lorena Bernal E337485 NE FINISHED

How this triple was built (2 steps)

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: Lorena Bernal | Statement: [Lorena Bernal, name, Lorena Bernal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lorena Bernal
Context triple: [Lorena Bernal, name, Lorena Bernal]
  • A. Lorena Bernal chosen
    Lorena Bernal is an Argentine-born Spanish actress, model, and former Miss Spain who has also worked as a television presenter.
  • B. Sofía García
    Sofía García is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the García surname.
  • C. Sofía García
    Sofía García is one of the four Dominican-American sisters at the center of Julia Alvarez’s novel, whose rebellious and free-spirited nature often puts her at odds with her traditional family and their immigrant expectations.
  • D. Claudia Ramírez
    Claudia Ramírez is a Mexican actress known for her work in film and television, particularly in acclaimed Mexican cinema of the late 20th century.
  • E. Yalitza Aparicio
    Yalitza Aparicio is a Mexican actress and former preschool teacher who gained international acclaim and an Academy Award nomination for her debut performance in Alfonso Cuarón’s film "Roma."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2fa830ac81908cb7df7c9e81e42a completed April 14, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe967cea3c81909d9b600501897a41 completed May 9, 2026, 2:05 a.m.
Created at: April 9, 2026, 10:20 p.m.