Triple

T16097940
Position Surface form Disambiguated ID Type / Status
Subject Hot Bench E390533 entity
Predicate originalJudge P68922 FINISHED
Object Tanya Acker
Tanya Acker is an American civil litigator and television personality best known as a judge on the courtroom reality show "Hot Bench."
 E1197886 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: Tanya Acker | Statement: [Hot Bench, originalJudge, Tanya Acker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tanya Acker
Context triple: [Hot Bench, originalJudge, Tanya Acker]
  • A. Kate Sacker
    Kate Sacker is a sharp, ambitious Assistant U.S. Attorney in the television drama "Billions," known for her legal acumen and political savvy.
  • B. Karen Akers
    Karen Akers is an American actress and cabaret singer known for her work on Broadway and in film, particularly in sophisticated musical and dramatic roles.
  • C. Tanya Vogel
    Tanya Vogel is a collegiate sports administrator best known for serving as the athletic director at George Washington University.
  • D. Jane Brucker
    Jane Brucker is an American actress best known for playing Lisa Houseman, the protagonist’s older sister, in the classic 1987 film "Dirty Dancing."
  • E. Erika Peters
    Erika Peters is a German-born actress known for her film and television work in the 1950s and 1960s.
  • 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: Tanya Acker
Triple: [Hot Bench, originalJudge, Tanya Acker]
Generated description
Tanya Acker is an American civil litigator and television personality best known as a judge on the courtroom reality show "Hot Bench."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tanya Acker
Target entity description: Tanya Acker is an American civil litigator and television personality best known as a judge on the courtroom reality show "Hot Bench."
  • A. Kate Sacker
    Kate Sacker is a sharp, ambitious Assistant U.S. Attorney in the television drama "Billions," known for her legal acumen and political savvy.
  • B. Karen Akers
    Karen Akers is an American actress and cabaret singer known for her work on Broadway and in film, particularly in sophisticated musical and dramatic roles.
  • C. Tanya Vogel
    Tanya Vogel is a collegiate sports administrator best known for serving as the athletic director at George Washington University.
  • D. Jane Brucker
    Jane Brucker is an American actress best known for playing Lisa Houseman, the protagonist’s older sister, in the classic 1987 film "Dirty Dancing."
  • E. Erika Peters
    Erika Peters is a German-born actress known for her film and television work in the 1950s and 1960s.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21a00f6808190a60939ef7ce727a7 completed April 17, 2026, 11:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff79a96d08190af69cbb18037f66e completed May 10, 2026, 3:12 a.m.
NEDg Description generation batch_69fff86a556c819096bc008e1ca76e8c completed May 10, 2026, 3:15 a.m.
NED2 Entity disambiguation (via description) batch_69fff926120081909f1042bf3a16ea10 completed May 10, 2026, 3:19 a.m.
Created at: April 10, 2026, 4:59 a.m.