Triple

T3994685
Position Surface form Disambiguated ID Type / Status
Subject Blansky's Beauties E87071 entity
Predicate starred P5563 FINISHED
Object Caren Kaye
Caren Kaye is an American actress best known for her television work in the 1970s and 1980s, including prominent roles in sitcoms and TV movies.
 E407118 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: Caren Kaye | Statement: [Blansky's Beauties, starred, Caren Kaye]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Caren Kaye
Context triple: [Blansky's Beauties, starred, Caren Kaye]
  • A. Nora Kaye
    Nora Kaye was an acclaimed American ballerina and actress, renowned as one of the leading dramatic dancers of the mid-20th century.
  • B. Charise Castro Smith
    Charise Castro Smith is an American playwright, screenwriter, actress, and filmmaker best known for co-directing and co-writing Disney’s animated film "Encanto."
  • C. Maxene Reynolds
    Maxene Reynolds is the daughter of legendary American actress and singer Debbie Reynolds.
  • D. Gwen Bagni
    Gwen Bagni was an American screenwriter known for her work in mid-20th-century film and television, including adaptations of historical and biographical stories.
  • E. Ina Caro
    Ina Caro is an American historian and travel writer known for her books that explore French history through journeys to its historic sites.
  • 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: Caren Kaye
Triple: [Blansky's Beauties, starred, Caren Kaye]
Generated description
Caren Kaye is an American actress best known for her television work in the 1970s and 1980s, including prominent roles in sitcoms and TV movies.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Caren Kaye
Target entity description: Caren Kaye is an American actress best known for her television work in the 1970s and 1980s, including prominent roles in sitcoms and TV movies.
  • A. Nora Kaye
    Nora Kaye was an acclaimed American ballerina and actress, renowned as one of the leading dramatic dancers of the mid-20th century.
  • B. Charise Castro Smith
    Charise Castro Smith is an American playwright, screenwriter, actress, and filmmaker best known for co-directing and co-writing Disney’s animated film "Encanto."
  • C. Maxene Reynolds
    Maxene Reynolds is the daughter of legendary American actress and singer Debbie Reynolds.
  • D. Gwen Bagni
    Gwen Bagni was an American screenwriter known for her work in mid-20th-century film and television, including adaptations of historical and biographical stories.
  • E. Ina Caro
    Ina Caro is an American historian and travel writer known for her books that explore French history through journeys to its historic sites.
  • 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_69aed94118148190975e6aa4e554cde9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa1f0fb88190aafbfdc98bc8652d completed March 9, 2026, 4:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b54c50f348819090ebfd8b5192c819 completed March 14, 2026, 11:53 a.m.
NEDg Description generation batch_69b550142cb88190b797ea327cff136e completed March 14, 2026, 12:09 p.m.
NED2 Entity disambiguation (via description) batch_69b5507641408190ab3407faf0aee807 completed March 14, 2026, 12:11 p.m.
Created at: March 9, 2026, 3:34 p.m.