Triple

T16125258
Position Surface form Disambiguated ID Type / Status
Subject Susanna Nelson E391251 entity
Predicate givenName P17 FINISHED
Object Susanna E364408 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: [Susanna Nelson, givenName, Susanna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Susanna
Context triple: [Susanna Nelson, givenName, Susanna]
  • A. 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.
  • B. Susanna
    Susanna is a thoughtful, observant teenage girl who befriends the shy protagonist during his summer vacation in the coming-of-age film "The Way Way Back."
  • C. Susanna chosen
    Susanna is a feminine given name of Hebrew origin, commonly used in various European languages and cultures.
  • D. Susannah
    Susannah is one of the central, romantically entangled characters in Alan Ayckbourn’s comedic stage play "Bedroom Farce."
  • E. Suzanne
    Suzanne is a feminine given name of French origin, derived from the Hebrew name Shoshannah meaning “lily.”
  • 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_69d87f1bb0988190b490d273dbf3fd03 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e2020408a88190bf3dfc893d577c55 completed April 17, 2026, 9:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff2abf9b08190a375abc842a0e7d0 completed May 10, 2026, 2:51 a.m.
Created at: April 10, 2026, 5 a.m.