Triple

T12529695
Position Surface form Disambiguated ID Type / Status
Subject Teresa Wright E299527 entity
Predicate workedWith P398 FINISHED
Object Sam Wood E48311 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: Sam Wood | Statement: [Teresa Wright, workedWith, Sam Wood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sam Wood
Context triple: [Teresa Wright, workedWith, Sam Wood]
  • A. Sam Wood chosen
    Sam Wood was an American film director best known for his work during Hollywood’s Golden Age, including classics such as "A Night at the Opera," "Goodbye, Mr. Chips," and "The Pride of the Yankees."
  • B. Sam Wood
    Sam Wood is a small-town police officer who becomes entangled in a racially charged murder investigation in John Ball’s novel "In the Heat of the Night."
  • C. Roger Spottiswoode
    Roger Spottiswoode is a British-Canadian film director and editor known for directing a range of Hollywood features, including the James Bond film "Tomorrow Never Dies."
  • D. Lee M. Russell
    Lee M. Russell was an American Democratic politician who served as governor of Mississippi in the early 20th century.
  • E. Christopher Landon
    Christopher Landon is an American film director, screenwriter, and producer best known for his work on the "Paranormal Activity" series and the "Happy Death Day" films.
  • 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_69d6ada5cdd48190860d9ce30aff69be completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d95469d100819087c83bc55e3ec9ce completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6718cd6288190ad080f469f334caf completed May 2, 2026, 9:50 p.m.
Created at: April 8, 2026, 9:57 p.m.