Triple

T9759417
Position Surface form Disambiguated ID Type / Status
Subject Lili E236631 entity
Predicate editedBy P1954 FINISHED
Object Harold F. Kress E155211 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: Harold F. Kress | Statement: [Lili, editedBy, Harold F. Kress]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harold F. Kress
Context triple: [Lili, editedBy, Harold F. Kress]
  • A. Harold F. Kress chosen
    Harold F. Kress was an American film editor renowned for his work on numerous classic Hollywood films and for winning multiple Academy Awards for Best Film Editing.
  • B. Arthur M. Bueche
    Arthur M. Bueche was an influential engineer and research executive whose leadership in advancing technology and public policy in industry led to a prestigious engineering award being named in his honor.
  • C. Harold M. Shaw
    Harold M. Shaw was an early American film director and actor known for his pioneering work in silent cinema during the 1910s.
  • D. Harold C. Mayer
    Harold C. Mayer was an American financier best known as one of the co-founders of the investment bank Bear Stearns.
  • E. Robert L. Lippert
    Robert L. Lippert was an American film producer and distributor known for his prolific output of low-budget genre movies from the 1940s through the 1960s.
  • 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_69ca84d64f6c8190a4ed4e9f5936eda5 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda049995c81908569ec61805642b2 completed April 1, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c41022908190a5f55291a2323691 completed April 5, 2026, 2:08 a.m.
Created at: March 30, 2026, 8:24 p.m.