Triple

T23007243
Position Surface form Disambiguated ID Type / Status
Subject Roger Greenberg E572809 entity
Predicate hasSibling P363 FINISHED
Object Phillip Greenberg 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: Phillip Greenberg | Statement: [Roger Greenberg, hasSibling, Phillip Greenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Phillip Greenberg
Context triple: [Roger Greenberg, hasSibling, Phillip Greenberg]
  • A. Michael Greenberg
    Michael Greenberg is an American businessman best known as the co-founder and longtime executive leader of the global footwear company Skechers.
  • B. Michael Greenberg
    Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
  • C. Mike Greenberg
    Mike Greenberg is an American television and radio sportscaster best known as a longtime ESPN personality and co-host of popular sports talk shows.
  • D. Jeffrey Greenstein
    Jeffrey Greenstein is a film producer known for his work on action and genre movies, including the war drama "The Outpost."
  • E. Phil Greenberg chosen
    Phil Greenberg is an immunologist and biotech entrepreneur known for pioneering work in cancer immunotherapy and co-founding Juno Therapeutics.
  • 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_69e245b6a3ac81908087599eefe3e365 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1835802a881908fe817c3fa728a82 completed April 29, 2026, 4:04 a.m.
Created at: April 17, 2026, 3:51 p.m.