Triple

T626346
Position Surface form Disambiguated ID Type / Status
Subject Dike Bridge E15827 entity
Predicate hasNotablePersonInvolved P6467 FINISHED
Object Mary Jo Kopechne E25562 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: Mary Jo Kopechne | Statement: [Dike Bridge, hasNotablePersonInvolved, Mary Jo Kopechne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary Jo Kopechne
Context triple: [Dike Bridge, hasNotablePersonInvolved, Mary Jo Kopechne]
  • A. Mary Jo Kopechne chosen
    Mary Jo Kopechne was a young political campaign worker and former aide to Senator Robert F. Kennedy whose death in 1969 at Chappaquiddick Island became the center of a major political scandal involving Senator Ted Kennedy.
  • B. Joanne Schieble
    Joanne Schieble is an American woman best known as the biological mother of Apple co-founder Steve Jobs.
  • C. Margaret Domka
    Margaret Domka is an American soccer referee known for officiating at the highest levels of the women’s game, including major domestic and international competitions.
  • D. Virginia Katz
    Virginia Katz is a film editor known for her work on major Hollywood productions, including entries in the Twilight Saga.
  • E. Jane Belson
    Jane Belson was a British barrister best known as the wife of author Douglas Adams.
  • 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_69a4935c131c8190a5378c6bf101e8cc completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a515ceb081908c064b2082047c0f completed March 1, 2026, 8:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69adeaaaad508190992c7b50e9397450 completed March 8, 2026, 9:31 p.m.
Created at: March 1, 2026, 7:35 p.m.