Triple

T5581133
Position Surface form Disambiguated ID Type / Status
Subject Peabo Bryson E146641 entity
Predicate performedWith P9966 FINISHED
Object Melissa Manchester E452196 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: Melissa Manchester | Statement: [Peabo Bryson, performedWith, Melissa Manchester]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Melissa Manchester
Context triple: [Peabo Bryson, performedWith, Melissa Manchester]
  • A. Melissa Manchester chosen
    Melissa Manchester is an American singer-songwriter and actress known for her emotive pop and adult contemporary hits, including classics like "Midnight Blue" and "Don't Cry Out Loud."
  • B. Laura Branigan
    Laura Branigan was an American pop singer and actress best known for her powerful vocals and 1980s hits such as "Gloria" and "Self Control."
  • C. Barbara West
    Barbara West is an actress known for her role in the acclaimed Australian psychological horror film "The Babadook."
  • D. Jennifer Warnes
    Jennifer Warnes is an American singer-songwriter and interpreter of Leonard Cohen’s work, known for her rich vocals and hit duets such as “(I’ve Had) The Time of My Life.”
  • E. Donna Dixon
    Donna Dixon is an American actress and former model known for her roles in 1980s comedies and for her long career in film and television.
  • 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_69c0090287a08190b4098411effe970c completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0208147a48190b2cdb42b9c9814a3 completed March 22, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0285baa648190bf8e94740ea62466 completed March 22, 2026, 5:35 p.m.
Created at: March 22, 2026, 3:37 p.m.