Triple

T10089072
Position Surface form Disambiguated ID Type / Status
Subject Edna Best E215294 entity
Predicate spouse P13 FINISHED
Object Seymour Beard
Seymour Beard was the husband of British actress Edna Best, known primarily in relation to her life and career.
E848546 NE FINISHED

How this triple was built (4 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: Seymour Beard | Statement: [Edna Best, spouse, Seymour Beard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seymour Beard
Context triple: [Edna Best, spouse, Seymour Beard]
  • A. Seymour Platt
    Seymour Platt is the son of Christine Keeler, the British model and showgirl central to the 1960s Profumo affair political scandal.
  • B. Seymour Nebenzal
    Seymour Nebenzal was a prominent German-American film producer known for his work in Weimar cinema and later in Hollywood, including influential films such as Fritz Lang’s "M."
  • C. Elwood Bredell
    Elwood Bredell was an American cinematographer best known for his work on classic Hollywood films and film noir in the 1930s and 1940s.
  • D. Edwin Blashfield
    Edwin Blashfield was an American muralist and painter best known for his large-scale allegorical works in prominent public buildings across the United States.
  • E. Sidney Badgley
    Sidney Badgley was a Canadian-born architect known for designing prominent churches and public buildings across North America in the late 19th and early 20th centuries.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Seymour Beard
Triple: [Edna Best, spouse, Seymour Beard]
Generated description
Seymour Beard was the husband of British actress Edna Best, known primarily in relation to her life and career.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Seymour Beard
Target entity description: Seymour Beard was the husband of British actress Edna Best, known primarily in relation to her life and career.
  • A. Seymour Platt
    Seymour Platt is the son of Christine Keeler, the British model and showgirl central to the 1960s Profumo affair political scandal.
  • B. Seymour Nebenzal
    Seymour Nebenzal was a prominent German-American film producer known for his work in Weimar cinema and later in Hollywood, including influential films such as Fritz Lang’s "M."
  • C. Elwood Bredell
    Elwood Bredell was an American cinematographer best known for his work on classic Hollywood films and film noir in the 1930s and 1940s.
  • D. Edwin Blashfield
    Edwin Blashfield was an American muralist and painter best known for his large-scale allegorical works in prominent public buildings across the United States.
  • E. Sidney Badgley
    Sidney Badgley was a Canadian-born architect known for designing prominent churches and public buildings across North America in the late 19th and early 20th centuries.
  • F. None of above. chosen

Provenance (5 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_69ca83a1eed081908b2e9580f2ebeea7 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd057e32881908bf630559af94906 completed April 2, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d3697d5b008190b274a086172c7a7d completed April 6, 2026, 8:06 a.m.
NEDg Description generation batch_69d37540b60c8190a52b03c57b4e3708 completed April 6, 2026, 8:56 a.m.
NED2 Entity disambiguation (via description) batch_69d3763948188190a47f48076fc767cb completed April 6, 2026, 9 a.m.
Created at: March 30, 2026, 9:01 p.m.