Triple

T5989187
Position Surface form Disambiguated ID Type / Status
Subject Maurice Jarre E133300 entity
Predicate spouse P13 FINISHED
Object Laura Devon
Laura Devon was an American actress and singer active in film and television during the 1960s.
E561019 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: Laura Devon | Statement: [Maurice Jarre, spouse, Laura Devon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Devon
Context triple: [Maurice Jarre, spouse, Laura Devon]
  • A. Florence Dempsey
    Florence Dempsey is a spirited young reporter character in the 1933 horror film "Mystery of the Wax Museum," known for her sharp wit and investigative tenacity.
  • B. Ellen French
    Ellen French was an American socialite from a prominent family, best known as the wife of wealthy businessman and sportsman Alfred Gwynne Vanderbilt.
  • C. Bess Laurence
    Bess Laurence is the daughter of Amy March and Laurie in Louisa May Alcott’s "Little Women" series, often portrayed as a sweet and musically gifted child.
  • D. Hattie Shaw
    Hattie Shaw is a skilled MI6 field agent and the sister of Deckard Shaw in the Fast & Furious franchise, prominently featured in "Fast & Furious Presents: Hobbs & Shaw."
  • E. Kathryn Erbe
    Kathryn Erbe is an American actress best known for her role as Detective Alexandra Eames on the television series "Law & Order: Criminal Intent."
  • 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: Laura Devon
Triple: [Maurice Jarre, spouse, Laura Devon]
Generated description
Laura Devon was an American actress and singer active in film and television during the 1960s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Laura Devon
Target entity description: Laura Devon was an American actress and singer active in film and television during the 1960s.
  • A. Florence Dempsey
    Florence Dempsey is a spirited young reporter character in the 1933 horror film "Mystery of the Wax Museum," known for her sharp wit and investigative tenacity.
  • B. Ellen French
    Ellen French was an American socialite from a prominent family, best known as the wife of wealthy businessman and sportsman Alfred Gwynne Vanderbilt.
  • C. Bess Laurence
    Bess Laurence is the daughter of Amy March and Laurie in Louisa May Alcott’s "Little Women" series, often portrayed as a sweet and musically gifted child.
  • D. Hattie Shaw
    Hattie Shaw is a skilled MI6 field agent and the sister of Deckard Shaw in the Fast & Furious franchise, prominently featured in "Fast & Furious Presents: Hobbs & Shaw."
  • E. Kathryn Erbe
    Kathryn Erbe is an American actress best known for her role as Detective Alexandra Eames on the television series "Law & Order: Criminal Intent."
  • 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_69c0087010d081908bb8142342d63330 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04dc76fd481908cc3f327e532a1a6 completed March 22, 2026, 8:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c10854969c8190b9be249f26ad2f47 completed March 23, 2026, 9:31 a.m.
NEDg Description generation batch_69c109bf2fb4819091915b2e10b629b8 completed March 23, 2026, 9:37 a.m.
NED2 Entity disambiguation (via description) batch_69c10a5e061c81909e8085f3210452dc completed March 23, 2026, 9:39 a.m.
Created at: March 22, 2026, 4:04 p.m.