Triple

T15308713
Position Surface form Disambiguated ID Type / Status
Subject Kill the Messenger E365971 entity
Predicate starring P1507 FINISHED
Object Paz Vega E237879 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: Paz Vega | Statement: [Kill the Messenger, starring, Paz Vega]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paz Vega
Context triple: [Kill the Messenger, starring, Paz Vega]
  • A. Paz Vega chosen
    Paz Vega is a Spanish actress known for her roles in films such as "Sex and Lucía," "Spanglish," and various international productions.
  • B. Ana Torrent
    Ana Torrent is a Spanish actress best known for her acclaimed childhood performances in films like "The Spirit of the Beehive" and "Cría cuervos."
  • C. Ana Ayora
    Ana Ayora is an American actress best known for her roles in films like "The Big Wedding" and appearances in television series such as "Bosch" and "In the Dark."
  • D. Carmen Maura
    Carmen Maura is a renowned Spanish actress, closely associated with director Pedro Almodóvar, celebrated for her versatile performances in both comedic and dramatic roles.
  • E. Sara García
    Sara García was a legendary Mexican film actress, famously known as the “Grandmother of Mexican Cinema” for her iconic maternal roles in classic Golden Age movies.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03cd176708190b0f6ba17aed92f8e completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff364bafd0819086b8aab1c216c6fa completed May 9, 2026, 1:27 p.m.
Created at: April 10, 2026, 3:16 a.m.