Triple

T10449786
Position Surface form Disambiguated ID Type / Status
Subject Herb Kelleher E246388 entity
Predicate spouse P13 FINISHED
Object Jo Ann Kelleher E246388 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: Jo Ann Kelleher | Statement: [Herb Kelleher, spouse, Jo Ann Kelleher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jo Ann Kelleher
Context triple: [Herb Kelleher, spouse, Jo Ann Kelleher]
  • A. Jo Ann Kelleher chosen
    Jo Ann Kelleher is best known as the wife of Herb Kelleher, the co-founder and longtime CEO of Southwest Airlines.
  • B. Pamela Eells O'Connell
    Pamela Eells O'Connell is an American television producer and writer best known for her work on family-oriented sitcoms such as "The Suite Life of Zack & Cody" and its spin-offs.
  • C. Janet Healy
    Janet Healy is a film producer known for her work on animated features, including the 2012 adaptation of Dr. Seuss's "The Lorax."
  • D. Betsy McCaughey
    Betsy McCaughey is an American politician, writer, and former Lieutenant Governor of New York known for her conservative commentary and opposition to certain health care reforms.
  • E. Patricia Breslin
    Patricia Breslin was an American actress known for her roles in 1950s–60s film and television, including appearances on shows like "The Twilight Zone" and "Peyton Place."
  • 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_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fe09af04819083db42f4de4cb0a9 completed April 7, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69d9881d84588190a9117064a0950ac1 completed April 10, 2026, 11:30 p.m.
Created at: April 6, 2026, 12:17 p.m.