Triple

T20268537
Position Surface form Disambiguated ID Type / Status
Subject Marry Me E499030 entity
Predicate producer P490 FINISHED
Object Daniel Farris
Daniel Farris is a music producer known for his work on the song "Marry Me."
E1438062 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: Daniel Farris | Statement: [Marry Me, producer, Daniel Farris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Farris
Context triple: [Marry Me, producer, Daniel Farris]
  • A. Michael George Farr
    Michael George Farr, better known as Mike Leander, was a British arranger, songwriter, and record producer noted for his work with artists such as Gary Glitter and the Beatles.
  • B. John Farris
    John Farris is an American novelist and screenwriter best known for his horror and suspense fiction, including the novel that inspired Brian De Palma’s film "The Fury."
  • C. David Fursdon
    David Fursdon is a British public servant and landowner who serves as the ceremonial representative of the Crown in the county of Devon.
  • D. David Fanning
    David Fanning is a television producer and journalist best known for founding and long serving as the executive producer of the PBS investigative documentary series Frontline.
  • E. Michael Fessier
    Michael Fessier was an American screenwriter and author known for his work on Hollywood films in the 1930s and 1940s, often contributing to romantic comedies and musicals.
  • 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: Daniel Farris
Triple: [Marry Me, producer, Daniel Farris]
Generated description
Daniel Farris is a music producer known for his work on the song "Marry Me."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Daniel Farris
Target entity description: Daniel Farris is a music producer known for his work on the song "Marry Me."
  • A. Michael George Farr
    Michael George Farr, better known as Mike Leander, was a British arranger, songwriter, and record producer noted for his work with artists such as Gary Glitter and the Beatles.
  • B. John Farris
    John Farris is an American novelist and screenwriter best known for his horror and suspense fiction, including the novel that inspired Brian De Palma’s film "The Fury."
  • C. David Fursdon
    David Fursdon is a British public servant and landowner who serves as the ceremonial representative of the Crown in the county of Devon.
  • D. David Fanning
    David Fanning is a television producer and journalist best known for founding and long serving as the executive producer of the PBS investigative documentary series Frontline.
  • E. Michael Fessier
    Michael Fessier was an American screenwriter and author known for his work on Hollywood films in the 1930s and 1940s, often contributing to romantic comedies and musicals.
  • 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_69da6275fa6c8190952924930adee150 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e675dbb7ac8190a40c527f2c02ca50 completed April 20, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a08acc90ddc8190a78c414350feeae6 completed May 16, 2026, 5:43 p.m.
NEDg Description generation batch_6a08add512548190a900997f6b4126e7 completed May 16, 2026, 5:48 p.m.
NED2 Entity disambiguation (via description) batch_6a08ae3414388190bd5cf3cfce098d0c completed May 16, 2026, 5:49 p.m.
Created at: April 11, 2026, 11:42 p.m.