Triple

T13804704
Position Surface form Disambiguated ID Type / Status
Subject Loni Anderson E331729 entity
Predicate portrayed P1668 FINISHED
Object Jennifer Marlowe E845435 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: Jennifer Marlowe | Statement: [Loni Anderson, portrayed, Jennifer Marlowe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jennifer Marlowe
Context triple: [Loni Anderson, portrayed, Jennifer Marlowe]
  • A. Jennifer Marlowe chosen
    Jennifer Marlowe is a glamorous, intelligent, and unflappable receptionist character from the WKRP in Cincinnati television franchise, known for subverting "dumb blonde" stereotypes.
  • B. Aileen Marlowe
    Aileen Marlowe was the wife of American film and television actor Hugh Marlowe.
  • C. Emily Greenhouse
    Emily Greenhouse is an American journalist and literary editor who serves as editor in chief of the influential magazine The New York Review of Books.
  • D. Beth Seaton
    Beth Seaton is a central character in the 1978 equestrian drama film "International Velvet," serving as one of the key figures in the protagonist's journey through competitive horse riding.
  • E. Lindy Robbins
    Lindy Robbins is an American songwriter known for crafting hit pop songs for major artists across the contemporary music industry.
  • 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_69d81c59f8808190a851bc56afdc55e9 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de026c36108190a7436034a730a261 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fcdeefd61c81908d189237af45467a completed May 7, 2026, 6:50 p.m.
Created at: April 9, 2026, 10:12 p.m.