Triple

T15367892
Position Surface form Disambiguated ID Type / Status
Subject Employee of the Month (2006 film) E367463 entity
Predicate editedBy P1954 FINISHED
Object Tom Lewis E1043563 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: Tom Lewis | Statement: [Employee of the Month (2006 film), editedBy, Tom Lewis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tom Lewis
Context triple: [Employee of the Month (2006 film), editedBy, Tom Lewis]
  • A. Tom Lewis chosen
    Tom Lewis is a film editor known for his work on the crime-comedy movie "The Whole Nine Yards."
  • B. Tom Lewis
    Tom Lewis was the husband of American actress Loretta Young, known primarily for his marriage to the Hollywood star.
  • C. Andrew Lewis
    Andrew Lewis was an 18th-century American pioneer, surveyor, and military leader in the French and Indian War and the American Revolutionary War, noted for his role in the Battle of Point Pleasant.
  • D. Peter Mullen
    Peter Mullen is a British Anglican priest and writer known for his traditionalist views and commentary on religious and social issues.
  • E. Stephen Rennicks
    Stephen Rennicks is an Irish composer best known for his film scores, particularly his long-standing collaboration with director Lenny Abrahamson.
  • 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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4a7cdc8190b7b48c97e774c306 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff0b4e968c8190a16824ee3ede13b2 completed May 9, 2026, 10:24 a.m.
Created at: April 10, 2026, 3:18 a.m.