Triple

T20107486
Position Surface form Disambiguated ID Type / Status
Subject Mitchell Froom E490220 entity
Predicate collaboratedWith P435 FINISHED
Object Suzanne Vega 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: Suzanne Vega | Statement: [Mitchell Froom, collaboratedWith, Suzanne Vega]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suzanne Vega
Context triple: [Mitchell Froom, collaboratedWith, Suzanne Vega]
  • A. Suzanne Vega chosen
    Suzanne Vega is an American singer-songwriter known for her literate, folk-inspired songs such as "Luka" and "Tom's Diner," which gained prominence in the 1980s and 1990s.
  • B. Jane Siberry
    Jane Siberry is a Canadian singer-songwriter known for her eclectic, art-pop compositions and distinctive, ethereal vocal style.
  • C. Susan Crow
    Susan Crow is an American former teacher and philanthropist best known as the third wife and longtime partner of legendary singer Tony Bennett.
  • D. Julee Cruise
    Julee Cruise was an American singer and actress best known for her ethereal vocal work on the "Twin Peaks" soundtrack and other David Lynch projects.
  • E. Laura Timmins
    Laura Timmins is the young, observant heroine of the British period drama "Lark Rise to Candleford," whose move from her rural village to a nearby market town frames the series’ coming-of-age narrative.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69da62636cc08190982cc71733a17b8d completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e666ddb09881909ad2aedd1e8a78da completed April 20, 2026, 5:48 p.m.
Created at: April 11, 2026, 11:28 p.m.