Triple

T2392896
Position Surface form Disambiguated ID Type / Status
Subject Women’s Basketball Hall of Fame E48981 entity
Predicate hasNotableInductee P304 FINISHED
Object Nancy Lieberman E302388 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: Nancy Lieberman | Statement: [Women’s Basketball Hall of Fame, hasNotableInductee, Nancy Lieberman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nancy Lieberman
Context triple: [Women’s Basketball Hall of Fame, hasNotableInductee, Nancy Lieberman]
  • A. Nancy Lieberman chosen
    Nancy Lieberman is a pioneering American basketball player and coach, widely regarded as one of the greatest figures in women's basketball history and a trailblazer for women in the sport.
  • B. Janet Margolin
    Janet Margolin was an American film and television actress best known for her roles in movies such as "David and Lisa" and Woody Allen's "Annie Hall."
  • C. Nancy Goodman
    Nancy Goodman is an American diplomat, businesswoman, and philanthropist best known for founding the Susan G. Komen Breast Cancer Foundation.
  • D. Liz Gorinsky
    Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
  • E. Paula Weinstein
    Paula Weinstein was an American film producer and studio executive known for overseeing acclaimed dramas and major Hollywood productions.
  • 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_69a88aa5f63081908d07fd302029fcbd completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc876d48881909e4d6f5ebe430012 completed March 7, 2026, 6:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69b360793f908190b0341c003207cc6f completed March 13, 2026, 12:55 a.m.
Created at: March 4, 2026, 7:57 p.m.