Triple

T10889612
Position Surface form Disambiguated ID Type / Status
Subject Julio Iglesias E257139 entity
Predicate spouse P13 FINISHED
Object Isabel Preysler E346224 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: Isabel Preysler | Statement: [Julio Iglesias, spouse, Isabel Preysler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabel Preysler
Context triple: [Julio Iglesias, spouse, Isabel Preysler]
  • A. Isabel Preysler chosen
    Isabel Preysler is a Spanish-Filipina socialite and television host known for her high-profile relationships and status as a prominent figure in Spanish high society.
  • B. Isabel García
    Isabel García is a Spanish politician known for her work in the European Parliament and her advocacy on social and labor issues.
  • C. Lucía Hiriart
    Lucía Hiriart was a Chilean public figure best known as the influential and controversial wife of military dictator Augusto Pinochet, playing a prominent role during his regime.
  • D. Cristina Banegas
    Cristina Banegas is an acclaimed Argentine actress and director recognized internationally for her powerful performances in film, television, and theater.
  • E. Esther Fernández
    Esther Fernández was a prominent Mexican film actress known for her work during the Golden Age of Mexican cinema.
  • 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_69d6aa848804819081b2713ca0bedf06 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d752041e2c8190b513dc9dc5857fcc completed April 9, 2026, 7:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69e42d4ab9bc81908a2522d1334390fc completed April 19, 2026, 1:18 a.m.
Created at: April 8, 2026, 9:21 p.m.