Triple

T2308441
Position Surface form Disambiguated ID Type / Status
Subject Marty E51894 entity
Predicate cinematographyBy P1953 FINISHED
Object Joseph LaShelle E26112 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: Joseph LaShelle | Statement: [Marty, cinematographyBy, Joseph LaShelle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joseph LaShelle
Context triple: [Marty, cinematographyBy, Joseph LaShelle]
  • A. Joseph LaShelle chosen
    Joseph LaShelle was an American cinematographer renowned for his work on numerous classic Hollywood films and for winning an Academy Award for Best Cinematography.
  • B. Taye Diggs
    Taye Diggs is an American actor and singer known for his roles in Broadway musicals like "Rent" and films such as "How Stella Got Her Groove Back" and "The Best Man."
  • C. Orlando Jones
    Orlando Jones was an early 18th-century Virginia planter and colonial official connected to the prominent Jones and Dandridge families.
  • D. Aldis Hodge
    Aldis Hodge is an American actor known for his versatile film and television roles, including prominent performances in projects like "Leverage," "One Night in Miami...," and "Black Adam."
  • E. Terrence Howard
    Terrence Howard is an American actor and singer known for his roles in films like "Hustle & Flow" and the TV series "Empire."
  • 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_69a88b0bb30c81908ded03b006d29387 completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc607cf4881908ba4ea2a7f5dedc9 completed March 7, 2026, 6:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7f3a007c8190b5c8683ced9d77fa completed March 9, 2026, 8:05 a.m.
Created at: March 4, 2026, 7:49 p.m.