Triple

T18829951
Position Surface form Disambiguated ID Type / Status
Subject Jo Bonner E460497 entity
Predicate name P16 FINISHED
Object Jo Bonner NE NERFINISHED

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 Bonner | Statement: [Jo Bonner, name, Jo Bonner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jo Bonner
Context triple: [Jo Bonner, name, Jo Bonner]
  • A. Jo Bonner chosen
    Jo Bonner is an American politician and former U.S. Representative from Alabama who later transitioned into academic leadership and became president of the University of South Alabama.
  • B. Bill Crawford
    Bill Crawford is a former United States Air Force enlisted airman and janitor who was belatedly recognized with the Medal of Honor for his heroic actions during World War II.
  • C. Thad Cochran
    Thad Cochran was a long-serving Republican U.S. Senator from Mississippi known for his influence on federal appropriations and agricultural policy.
  • D. Sam Nunn
    Sam Nunn is a former United States Senator from Georgia known for his influential work on national security, defense policy, and nuclear nonproliferation.
  • E. Allen J. Ellender
    Allen J. Ellender was a long-serving Democratic U.S. Senator from Louisiana known for his influential roles in congressional leadership and appropriations during the mid-20th century.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dcf94c288190a06dea029ae4b223 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5a9981be88190b709c0e72ad3f7e6 completed April 20, 2026, 4:20 a.m.
Created at: April 10, 2026, 11:56 a.m.