Triple

T3739025
Position Surface form Disambiguated ID Type / Status
Subject Ursula Burns E79654 entity
Predicate name P16 FINISHED
Object Ursula Burns E79654 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: Ursula Burns | Statement: [Ursula Burns, name, Ursula Burns]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ursula Burns
Context triple: [Ursula Burns, name, Ursula Burns]
  • A. Ursula Burns chosen
    Ursula Burns is an American business executive best known for serving as CEO of Xerox, becoming the first Black woman to lead a Fortune 500 company.
  • B. Linda Gelsinger
    Linda Gelsinger is the wife of Intel CEO Pat Gelsinger and is known for her involvement in Christian ministry and family-focused activities alongside her husband.
  • C. Peggy Knudsen
    Peggy Knudsen was an American film and television actress active in the 1940s and 1950s, often appearing in dramas and crime films.
  • D. Jo Ann Kelleher
    Jo Ann Kelleher is best known as the wife of Herb Kelleher, the co-founder and longtime CEO of Southwest Airlines.
  • E. Anne Mulcahy
    Anne Mulcahy is an American business executive best known for leading Xerox Corporation’s turnaround as its CEO and chairwoman in the early 2000s.
  • 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_69ad8b115610819095b02007da5ca3cb completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb404b908190b6b4ee583dee3cc9 completed March 8, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db23ff3c81908d19295a7ce4a39c completed March 14, 2026, 3:51 a.m.
Created at: March 8, 2026, 3:34 p.m.