Triple

T4887226
Position Surface form Disambiguated ID Type / Status
Subject Deuterocanonical books E109467 entity
Predicate contains P35 FINISHED
Object Susanna E43277 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: Susanna | Statement: [Deuterocanonical books, contains, Susanna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Susanna
Context triple: [Deuterocanonical books, contains, Susanna]
  • A. Susanna chosen
    Susanna is a deuterocanonical addition to the Book of Daniel, telling the story of a virtuous woman falsely accused of adultery and vindicated by the prophet Daniel.
  • B. Susanna
    Susanna is a feminine given name of Hebrew origin, commonly used in various European languages and cultures.
  • C. Susannah
    Susannah is one of the central, romantically entangled characters in Alan Ayckbourn’s comedic stage play "Bedroom Farce."
  • D. Suzanne
    "Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
  • E. Suzanne
    Suzanne is a central character in Steve Martin’s play "Picasso at the Lapin Agile," representing a young woman entangled romantically with both Picasso and other men in the bohemian Parisian setting.
  • 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_69bd440f71348190b99938e59fb7f9a1 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e03a7fc8190bcac63f4b19e586e completed March 20, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69be77975ad08190a427bcf1c01e364f completed March 21, 2026, 10:48 a.m.
Created at: March 20, 2026, 1:28 p.m.