Triple

T13491538
Position Surface form Disambiguated ID Type / Status
Subject Early E318642 entity
Predicate hasNotableBearer P458 FINISHED
Object Margaret Early E197328 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: Margaret Early | Statement: [Early, hasNotableBearer, Margaret Early]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margaret Early
Context triple: [Early, hasNotableBearer, Margaret Early]
  • A. Margaret Early chosen
    Margaret Early was an American film actress active in the late 1930s and 1940s, known for her wholesome screen presence in Hollywood musicals and comedies.
  • B. Margaret Whitton
    Margaret Whitton was an American actress and director best known for her comedic film roles in the 1980s and 1990s, particularly in sports comedies.
  • C. Margaret Booth
    Margaret Booth was a pioneering American film editor and longtime MGM supervising editor whose career spanned the silent era through Hollywood’s Golden Age.
  • D. Margaret Watson
    Margaret Watson is a fictional character portrayed by American actress and consumer advocate Betty Furness.
  • E. Margaret Nichols
    Margaret Nichols was the wife of American film and television producer Hal Roach.
  • 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_69d806b6bfec819089222715b2e86c8e completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf4c66008190b287e0551889d7c8 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f76ba566c48190808857dd0bc3a871 completed May 3, 2026, 3:37 p.m.
Created at: April 9, 2026, 9:43 p.m.