Triple

T13351752
Position Surface form Disambiguated ID Type / Status
Subject FBI ABSCAM operation E318085 entity
Predicate notableParticipant P6467 FINISHED
Object John Good
John Good is a former FBI agent best known for his key role in the ABSCAM undercover corruption investigation of the late 1970s and early 1980s.
 E1034756 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: John Good | Statement: [FBI ABSCAM operation, notableParticipant, John Good]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Good
Context triple: [FBI ABSCAM operation, notableParticipant, John Good]
  • A. Jonathan Good
    Jonathan Good is an American professional wrestler best known for his time in WWE as Dean Ambrose and in AEW as Jon Moxley.
  • B. Joseph Good
    Joseph Good was an early settler and prominent local figure after whom the town of Goodsprings, Nevada, was named.
  • C. John Goodland
    John Goodland is a notable individual associated with Grand Chute, Wisconsin, recognized for his significance to the local community.
  • D. John Bunn
    John Bunn was an American basketball coach and administrator known for his influential roles in college basketball and contributions to the sport’s development.
  • E. John Hough
    John Hough is a British film and television director best known for his work in horror and genre cinema during the 1970s and 1980s.
  • 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: John Good
Triple: [FBI ABSCAM operation, notableParticipant, John Good]
Generated description
John Good is a former FBI agent best known for his key role in the ABSCAM undercover corruption investigation of the late 1970s and early 1980s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Good
Target entity description: John Good is a former FBI agent best known for his key role in the ABSCAM undercover corruption investigation of the late 1970s and early 1980s.
  • A. Jonathan Good
    Jonathan Good is an American professional wrestler best known for his time in WWE as Dean Ambrose and in AEW as Jon Moxley.
  • B. Joseph Good
    Joseph Good was an early settler and prominent local figure after whom the town of Goodsprings, Nevada, was named.
  • C. John Goodland
    John Goodland is a notable individual associated with Grand Chute, Wisconsin, recognized for his significance to the local community.
  • D. John Bunn
    John Bunn was an American basketball coach and administrator known for his influential roles in college basketball and contributions to the sport’s development.
  • E. John Hough
    John Hough is a British film and television director best known for his work in horror and genre cinema during the 1970s and 1980s.
  • 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_69d806b5a3c08190b42c267fb092f98a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99e8c2f1c819094f0970f35f18afa completed April 11, 2026, 1:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f71f47fd7c8190b8d98a181acd7710 completed May 3, 2026, 10:11 a.m.
NEDg Description generation batch_69f7204b6f108190bca6a0140620e03e completed May 3, 2026, 10:15 a.m.
NED2 Entity disambiguation (via description) batch_69f720fbf0bc81908c68cf2844938e45 completed May 3, 2026, 10:18 a.m.
Created at: April 9, 2026, 9:32 p.m.