Triple

T17730791
Position Surface form Disambiguated ID Type / Status
Subject Roger Peralta E442579 entity
Predicate spouse P13 FINISHED
Object Karen Peralta NE NERFINISHED

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: Karen Peralta | Statement: [Roger Peralta, spouse, Karen Peralta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karen Peralta
Context triple: [Roger Peralta, spouse, Karen Peralta]
  • A. Karen Peralta chosen
    Karen Peralta is a character in the television series "Brooklyn Nine-Nine," known as the mother of main protagonist Jake Peralta.
  • B. Lorna Cepeda
    Lorna Cepeda is a Colombian actress best known for her comedic role as Patricia Fernández in the popular telenovela "Yo soy Betty, la fea."
  • C. Patricia Alvaran
    Patricia Alvaran is known as the former wife of American actor Tom Berenger.
  • D. Lorraine Vélez
    Lorraine Vélez is an American actress and singer known for her work in musical theatre and television, including roles in productions like "Rent" and various stage and screen performances.
  • E. Nancy Colmenares
    Nancy Colmenares is best known as the first wife of former Venezuelan president Hugo Chávez, with whom she had three children.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d8b9ec79688190b86bdcef85a7b3aa completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e478e69f288190900027641952f198 completed April 19, 2026, 6:40 a.m.
Created at: April 10, 2026, 10:08 a.m.