Triple

T13792306
Position Surface form Disambiguated ID Type / Status
Subject Jumping the Broom E331427 entity
Predicate editedBy P1954 FINISHED
Object Terilyn A. Shropshire E169992 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: Terilyn A. Shropshire | Statement: [Jumping the Broom, editedBy, Terilyn A. Shropshire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terilyn A. Shropshire
Context triple: [Jumping the Broom, editedBy, Terilyn A. Shropshire]
  • A. Terilyn A. Shropshire chosen
    Terilyn A. Shropshire is an American film editor known for her work on numerous acclaimed films and television projects.
  • B. Courtney H. Hodges
    Courtney H. Hodges was a senior U.S. Army general in World War II who commanded First Army in the European Theater, playing a key role in the liberation of Western Europe.
  • C. Kimberly J. Brown
    Kimberly J. Brown is an American actress best known for playing Marnie Piper in Disney Channel’s Halloweentown film series.
  • D. Cherelle L. Parker
    Cherelle L. Parker is an American politician who serves as the mayor of Philadelphia and is the first woman elected to the position in the city's history.
  • E. Tracey E. Edmonds
    Tracey E. Edmonds is an American television and film producer and businesswoman known for her work in entertainment and media entrepreneurship.
  • 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0258a1408190a837d17c6d6a2bd4 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c7007d5c8190800456e32101903f completed May 3, 2026, 10:06 p.m.
Created at: April 9, 2026, 10:11 p.m.