Triple

T155706
Position Surface form Disambiguated ID Type / Status
Subject The Bodyguard E3174 entity
Predicate mainCharacter P1183 FINISHED
Object Frank Farmer
Frank Farmer is the stoic former Secret Service agent turned professional bodyguard who is hired to protect a famous singer in the film "The Bodyguard."
 E55812 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: Frank Farmer | Statement: [The Bodyguard, mainCharacter, Frank Farmer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Frank Farmer
Context triple: [The Bodyguard, mainCharacter, Frank Farmer]
  • A. William Nolan
    William Nolan is an editor known for his work on editions of classic adventure literature, including "The Mark of Zorro."
  • B. Philip Sabes
    Philip Sabes is a neuroscientist and entrepreneur known for his work on brain–computer interfaces and for co-founding the neurotechnology company Neuralink.
  • C. Charles Begole
    Charles Begole was an American mountaineer best known as one of the first climbers to reach the summit of Mount Whitney in the 19th century.
  • D. Eric Eldred
    Eric Eldred is an American digital rights activist and publisher known for his work promoting free access to literature and for co-founding the Creative Commons movement.
  • E. Keith Fraase
    Keith Fraase is a film editor best known for his work on the movie "Chappaquiddick."
  • 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: Frank Farmer
Triple: [The Bodyguard, mainCharacter, Frank Farmer]
Generated description
Frank Farmer is the stoic former Secret Service agent turned professional bodyguard who is hired to protect a famous singer in the film "The Bodyguard."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Frank Farmer
Target entity description: Frank Farmer is the stoic former Secret Service agent turned professional bodyguard who is hired to protect a famous singer in the film "The Bodyguard."
  • A. William Nolan
    William Nolan is an editor known for his work on editions of classic adventure literature, including "The Mark of Zorro."
  • B. Philip Sabes
    Philip Sabes is a neuroscientist and entrepreneur known for his work on brain–computer interfaces and for co-founding the neurotechnology company Neuralink.
  • C. Charles Begole
    Charles Begole was an American mountaineer best known as one of the first climbers to reach the summit of Mount Whitney in the 19th century.
  • D. Eric Eldred
    Eric Eldred is an American digital rights activist and publisher known for his work promoting free access to literature and for co-founding the Creative Commons movement.
  • E. Keith Fraase
    Keith Fraase is a film editor best known for his work on the movie "Chappaquiddick."
  • 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_69a2527757ec819090b8becb2cf1a862 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a2582eb6408190beb38213c7d7d968 completed Feb. 28, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69a43e6744888190b4514f057fc98498 completed March 1, 2026, 1:25 p.m.
NEDg Description generation batch_69a43ffa92e88190a40155190dbf8b9c completed March 1, 2026, 1:32 p.m.
NED2 Entity disambiguation (via description) batch_69a44046b4cc819085012cce79176fc6 completed March 1, 2026, 1:33 p.m.
Created at: Feb. 28, 2026, 2:31 a.m.