Triple

T6312471
Position Surface form Disambiguated ID Type / Status
Subject Mary Rodgers E141536 entity
Predicate familyName P18 FINISHED
Object Rodgers E36063 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: Rodgers | Statement: [Mary Rodgers, familyName, Rodgers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rodgers
Context triple: [Mary Rodgers, familyName, Rodgers]
  • A. Rodgers chosen
    Rodgers is a surname of Scottish and Irish origin borne by various notable individuals across sports, entertainment, and other fields.
  • B. Mat Rogers
    Mat Rogers is an Australian former dual-code rugby international who played both rugby league and rugby union at elite levels, including representing Australia and starring in the NRL.
  • C. Ab Rogers
    Ab Rogers is a British designer and architect known for his innovative, playful interior and exhibition designs and for founding Ab Rogers Design.
  • D. Rodger Dodger
    Rodger Dodger is a 2002 dark comedy-drama film about a cynical New York advertising executive who spends a night trying to teach his teenage nephew how to seduce women.
  • E. Rogers Morton
    Rogers Morton was an American Republican politician who served as U.S. Secretary of the Interior and later Secretary of Commerce in the 1970s.
  • 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_69c008d00efc8190a36c05b4b4a3bf4b completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0649ea98c819086509e175812c6c0 completed March 22, 2026, 9:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e469355c81908049e0c68a8c0259 completed March 27, 2026, 1:59 a.m.
Created at: March 22, 2026, 4:28 p.m.