Triple

T9010528
Position Surface form Disambiguated ID Type / Status
Subject Madeleine E215457 entity
Predicate hasVariant P455 FINISHED
Object Magdalene
Magdalene is a feminine given name, traditionally associated with Mary Magdalene from the New Testament and often used in various European languages.
 E439755 NE FINISHED

How this triple was built (4 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: [Madeleine, hasVariant, Magdalene]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Magdalene
Context triple: [Madeleine, hasVariant, Magdalene]
  • A. Magdalene
    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. St Mary Magdalene
    St Mary Magdalene is a Christian church dedicated to Mary Magdalene, serving as the parish church for the village of Mulbarton in Norfolk, England.
  • C. 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.
  • D. Our Lady
    Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
  • E. Bernardine
    Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Magdalene
Triple: [Madeleine, hasVariant, Magdalene]
Generated description
Magdalene is a feminine given name, traditionally associated with Mary Magdalene from the New Testament and often used in various European languages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Magdalene
Target entity description: Magdalene is a feminine given name, traditionally associated with Mary Magdalene from the New Testament and often used in various European languages.
  • 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. St Mary Magdalene
    St Mary Magdalene is a Christian church dedicated to Mary Magdalene, serving as the parish church for the village of Mulbarton in Norfolk, England.
  • C. 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.
  • D. Our Lady
    Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
  • E. Bernardine
    Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
  • F. None of above.

Provenance (5 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_69ca83a2bf088190986ee7a8eb90407d completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc69c1571881908d0b144786b5ee1f completed April 1, 2026, 12:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfeb671e488190920fb1780e4ad48d completed April 3, 2026, 4:31 p.m.
NEDg Description generation batch_69cfedb37584819083038f1498b00886 completed April 3, 2026, 4:41 p.m.
NED2 Entity disambiguation (via description) batch_69cfee063adc8190a1f2bd447f137e56 completed April 3, 2026, 4:42 p.m.
Created at: March 30, 2026, 7:06 p.m.