Triple

T17876586
Position Surface form Disambiguated ID Type / Status
Subject Kate Read E446968 entity
Predicate relative P37 FINISHED
Object Cousin Monique NE NERFINISHED

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: Cousin Monique | Statement: [Kate Read, relative, Cousin Monique]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cousin Monique
Context triple: [Kate Read, relative, Cousin Monique]
  • A. Monique
    Monique is the given name of the American comedian and Academy Award–winning actress Mo'Nique.
  • B. Monika
    Monika is a feminine given name commonly used in various European countries and beyond.
  • C. Miss Quentin
    Miss Quentin is a rebellious and troubled young woman in William Faulkner’s novel "The Sound and the Fury," whose defiance highlights the decay and dysfunction of the Compson family.
  • D. Mama Nadine
    Mama Nadine is a central character in the film "I Love You to Death," known for her strong-willed, protective, and often darkly comedic presence within the story.
  • E. Marnie
    Marnie is a 1964 psychological thriller film directed by Alfred Hitchcock, starring Tippi Hedren and Sean Connery, about a troubled woman with a mysterious past and compulsive thieving.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

Provenance (2 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_69d8b9f4c22c819093c2680434472894 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e49aa614b48190bdc9e905e9e6d5e0 completed April 19, 2026, 9:04 a.m.
Created at: April 10, 2026, 10:18 a.m.