Triple

T20340633
Position Surface form Disambiguated ID Type / Status
Subject Bernardine E495729 entity
Predicate title P38 FINISHED
Object Bernardine 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: Bernardine | Statement: [Bernardine, title, Bernardine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bernardine
Context triple: [Bernardine, title, Bernardine]
  • A. Bernardine chosen
    Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
  • B. Ernestine
    Ernestine is the given first name of the American actress and sex symbol Jane Russell, known for her roles in classic Hollywood films of the 1940s and 1950s.
  • C. Ernestine
    Ernestine is a character from the television series "Underground," which dramatizes the lives and struggles of enslaved people seeking freedom via the Underground Railroad.
  • D. Christa
    Christa was the first name of Christa McAuliffe, the American teacher and astronaut selected as the first private citizen to fly in space.
  • E. Benedetta
    Benedetta is an Italian feminine given name, equivalent to "Benedicta" and commonly used in Italy and other Italian-speaking communities.
  • 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_69e0b4a3320881909495ae8bc30bc2dc completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6783533a881909a12311bb9c66542 completed April 20, 2026, 7:02 p.m.
Created at: April 16, 2026, 11:23 a.m.