Triple

T5673567
Position Surface form Disambiguated ID Type / Status
Subject Che! E125031 entity
Predicate castMember P1668 FINISHED
Object Barbara Luna E144107 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: Barbara Luna | Statement: [Che!, castMember, Barbara Luna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barbara Luna
Context triple: [Che!, castMember, Barbara Luna]
  • A. Barbara Luna chosen
    Barbara Luna is an American actress known for her numerous film and television roles from the 1950s onward, including appearances in productions such as the historical drama "Che!" and the original "Star Trek" series.
  • B. Lunita Laredo
    Lunita Laredo is the mother of Irish author and poker entrepreneur Molly Bloom, whose life inspired the film "Molly's Game."
  • C. Barbara De Fina
    Barbara De Fina is an American film producer best known for her frequent collaborations with director Martin Scorsese on acclaimed films throughout the 1980s and 1990s.
  • D. Elizabeth Alda
    Elizabeth Alda is an American actress, best known for her role in the film "The Four Seasons" and as the daughter of actor and director Alan Alda.
  • E. Lina Romay
    Lina Romay was a Mexican-born actress and singer best known as a featured performer in Hollywood films of the 1940s and 1950s.
  • 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_69c008295c808190acfe78915e7d656a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023700ba88190ba7c829785f20c82 completed March 22, 2026, 5:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c097dbdbc081909dd228c61461c5c8 completed March 23, 2026, 1:31 a.m.
Created at: March 22, 2026, 3:43 p.m.