Triple

T3342402
Position Surface form Disambiguated ID Type / Status
Subject The District E70288 entity
Predicate starring P1507 FINISHED
Object Roger Aaron Brown
Roger Aaron Brown is an American character actor known for his extensive work in film and television, including prominent roles in crime and drama series.
 E353962 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: Roger Aaron Brown | Statement: [The District, starring, Roger Aaron Brown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Roger Aaron Brown
Context triple: [The District, starring, Roger Aaron Brown]
  • A. Ryan Brown
    Ryan Brown is a film editor known for his work on the movie "Horse Girl."
  • B. Mark Brown
    Mark Brown is an American filmmaker and screenwriter best known for writing and directing the romantic comedy film "Two Can Play That Game."
  • C. Warrick Brown
    Warrick Brown is a fictional crime scene investigator and forensic analyst on the television series "CSI: Crime Scene Investigation."
  • D. Malcolm Brown
    Malcolm Brown is an American football running back known for his career in the NFL, including playing for the St. Louis/Los Angeles Rams and Miami Dolphins.
  • E. Malcolm Brown
    Malcolm Brown was an American film art director known for his work on classic Hollywood productions, including the World War II drama "Thirty Seconds Over Tokyo."
  • 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: Roger Aaron Brown
Triple: [The District, starring, Roger Aaron Brown]
Generated description
Roger Aaron Brown is an American character actor known for his extensive work in film and television, including prominent roles in crime and drama series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Roger Aaron Brown
Target entity description: Roger Aaron Brown is an American character actor known for his extensive work in film and television, including prominent roles in crime and drama series.
  • A. Ryan Brown
    Ryan Brown is a film editor known for his work on the movie "Horse Girl."
  • B. Mark Brown
    Mark Brown is an American filmmaker and screenwriter best known for writing and directing the romantic comedy film "Two Can Play That Game."
  • C. Warrick Brown
    Warrick Brown is a fictional crime scene investigator and forensic analyst on the television series "CSI: Crime Scene Investigation."
  • D. Malcolm Brown
    Malcolm Brown is an American football running back known for his career in the NFL, including playing for the St. Louis/Los Angeles Rams and Miami Dolphins.
  • E. Malcolm Brown
    Malcolm Brown was an American film art director known for his work on classic Hollywood productions, including the World War II drama "Thirty Seconds Over Tokyo."
  • 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_69ad85a405e48190b6e68de7cf9f319e completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1ee711481909c0d921f1b5b8562 completed March 8, 2026, 5:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69b33426f73881908eb0759c47eb08d7 completed March 12, 2026, 9:46 p.m.
NEDg Description generation batch_69b334e5171c8190a01bb6fef5644825 completed March 12, 2026, 9:49 p.m.
NED2 Entity disambiguation (via description) batch_69b3390c50b08190b6239b5f0d1eb4ba completed March 12, 2026, 10:07 p.m.
Created at: March 8, 2026, 3:12 p.m.