Triple

T6937212
Position Surface form Disambiguated ID Type / Status
Subject Suzanne Mubarak E160582 entity
Predicate givenName P17 FINISHED
Object Suzanne E302682 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: Suzanne | Statement: [Suzanne Mubarak, givenName, Suzanne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suzanne
Context triple: [Suzanne Mubarak, givenName, Suzanne]
  • A. Suzanne chosen
    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.
  • B. Suzanne
    "Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
  • C. Susanna
    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.
  • D. Susanna
    Susanna is a feminine given name of Hebrew origin, commonly used in various European languages and cultures.
  • E. Susy
    Susy is the nickname of Susy Clemens, the daughter of American author Mark Twain.
  • 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_69c6884f3db4819080ad65da69386206 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6da606328819095eb852f7a0842dc completed March 27, 2026, 7:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69c75861cd548190a215d616b68101d9 completed March 28, 2026, 4:26 a.m.
Created at: March 27, 2026, 2:27 p.m.