Triple

T7418727
Position Surface form Disambiguated ID Type / Status
Subject Circus E171190 entity
Predicate hasPart P35 FINISHED
Object Magdalene E439755 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: Magdalene | Statement: [Circus, hasPart, Magdalene]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Magdalene
Context triple: [Circus, hasPart, Magdalene]
  • A. Magdalene chosen
    Magdalene is the birth name of the iconic German-American actress and singer Marlene Dietrich, renowned for her roles in classic Hollywood cinema and her distinctive, androgynous style.
  • B. Magdalene Shaw
    Magdalene Shaw is a sharp-witted, tough matriarch and career criminal in the Fast & Furious franchise, known as the mother of Deckard and Owen Shaw.
  • C. Our Lady
    Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
  • D. Bernardine
    Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
  • E. Saint Martha
    Saint Martha is a New Testament figure, sister of Mary and Lazarus, venerated as a saint for her hospitality and service to Jesus.
  • 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_69c68a625d048190af70eb8b63bec5a0 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f2e93ffc8190beb5a1d3eb6c5d23 completed March 27, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69c81ef30ee88190a484f4b735913676 completed March 28, 2026, 6:33 p.m.
Created at: March 27, 2026, 3:11 p.m.