Triple

T4038134
Position Surface form Disambiguated ID Type / Status
Subject Harry Steenbock E83876 entity
Predicate name P16 FINISHED
Object Harry Steenbock E83876 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: Harry Steenbock | Statement: [Harry Steenbock, name, Harry Steenbock]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harry Steenbock
Context triple: [Harry Steenbock, name, Harry Steenbock]
  • A. Harry Steenbock chosen
    Harry Steenbock was an American biochemist best known for discovering how to use ultraviolet light to fortify foods with vitamin D, a breakthrough that helped eradicate rickets.
  • B. Robert Florey
    Robert Florey was a French-American film director and screenwriter known for his work in early Hollywood horror and experimental cinema.
  • C. Isaac Herbert Kempner
    Isaac Herbert Kempner was an American businessman, civic leader, and philanthropist from Galveston, Texas, known for his role in the sugar industry and local public service.
  • D. Harry Marks
    Harry Marks was a co-founder of the TED conferences, helping to establish the influential global platform for sharing ideas across technology, entertainment, and design.
  • E. Jerome Kellogg
    Jerome Kellogg was a physicist known as a notable student of Nobel laureate Isidor Isaac Rabi.
  • 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_69aed92f7cf0819098e0539bdcc3767f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb3656f08190aa5286d951013646 completed March 9, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b55646e5d881909eadd0640a4f1796 completed March 14, 2026, 12:36 p.m.
Created at: March 9, 2026, 3:37 p.m.