Triple

T19128009
Position Surface form Disambiguated ID Type / Status
Subject Reality Bites E468236 entity
Predicate character P662 FINISHED
Object Troy Dyer
Troy Dyer is the brooding, sardonic slacker and aspiring musician who serves as the romantic antihero in the 1994 film "Reality Bites."
 E1377537 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: Troy Dyer | Statement: [Reality Bites, character, Troy Dyer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Troy Dyer
Context triple: [Reality Bites, character, Troy Dyer]
  • A. Troy Berry
    Troy Berry is an American politician who has served as a Democratic member of the Arkansas House of Representatives.
  • B. Troy Dannen
    Troy Dannen is a collegiate sports executive who serves as the athletic director for the University of Washington Huskies, overseeing the school's athletic programs and operations.
  • C. Troy Barlow
    Troy Barlow is a U.S. Army reservist and one of the central soldiers in the 1999 war film "Three Kings," portrayed by Mark Wahlberg.
  • D. Troy Verges
    Troy Verges is an American songwriter and record producer known for his work in contemporary country and pop music.
  • E. Sean Duryee
    Sean Duryee is a law enforcement official who serves as the head of the California Highway Patrol, overseeing highway safety and state-level traffic enforcement operations.
  • 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: Troy Dyer
Triple: [Reality Bites, character, Troy Dyer]
Generated description
Troy Dyer is the brooding, sardonic slacker and aspiring musician who serves as the romantic antihero in the 1994 film "Reality Bites."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Troy Dyer
Target entity description: Troy Dyer is the brooding, sardonic slacker and aspiring musician who serves as the romantic antihero in the 1994 film "Reality Bites."
  • A. Troy Berry
    Troy Berry is an American politician who has served as a Democratic member of the Arkansas House of Representatives.
  • B. Troy Dannen
    Troy Dannen is a collegiate sports executive who serves as the athletic director for the University of Washington Huskies, overseeing the school's athletic programs and operations.
  • C. Troy Barlow
    Troy Barlow is a U.S. Army reservist and one of the central soldiers in the 1999 war film "Three Kings," portrayed by Mark Wahlberg.
  • D. Troy Verges
    Troy Verges is an American songwriter and record producer known for his work in contemporary country and pop music.
  • E. Sean Duryee
    Sean Duryee is a law enforcement official who serves as the head of the California Highway Patrol, overseeing highway safety and state-level traffic enforcement operations.
  • 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_69d8dd0796a48190b34ce4cd9d3f3be5 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e3ceb5808190b3b53d9e8df3605a completed April 20, 2026, 8:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a073b161f748190ba56a95ba8ce7286 completed May 15, 2026, 3:26 p.m.
NEDg Description generation batch_6a073ba7f03481908ed5c06a46c0a029 completed May 15, 2026, 3:28 p.m.
NED2 Entity disambiguation (via description) batch_6a073cae2df481908ed9a0ee0bc14fd5 completed May 15, 2026, 3:33 p.m.
Created at: April 10, 2026, 12:05 p.m.