Triple

T8509547
Position Surface form Disambiguated ID Type / Status
Subject Suzanne Duchamp E201416 entity
Predicate givenName P17 FINISHED
Object Suzanne E655678 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 Duchamp, givenName, Suzanne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suzanne
Context triple: [Suzanne Duchamp, givenName, Suzanne]
  • A. Suzanne
    "Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
  • B. 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.
  • C. Suzanne chosen
    Suzanne is a feminine given name of French origin, derived from the Hebrew name Shoshannah meaning “lily.”
  • D. Suzie
    Suzie is a brilliant, tech-savvy girl from Stranger Things who helps Dustin Henderson and his friends by providing crucial scientific and hacking assistance.
  • E. 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.
  • 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_69ca8320e5748190ac2c585a0bba8193 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe5e0cb1881909d1ff6ee9b3a65cc completed March 31, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce88ec84208190ab72c8411e6cc4c2 completed April 2, 2026, 3:19 p.m.
Created at: March 30, 2026, 6:15 p.m.