Triple

T13709309
Position Surface form Disambiguated ID Type / Status
Subject Can't Buy Me Love E328727 entity
Predicate starring P1507 FINISHED
Object Tina Caspary
Tina Caspary is an American actress and dancer best known for her roles in 1980s teen films and for her work as a choreographer in film and television.
 E1113098 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: Tina Caspary | Statement: [Can't Buy Me Love, starring, Tina Caspary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tina Caspary
Context triple: [Can't Buy Me Love, starring, Tina Caspary]
  • A. Tina Hirsch
    Tina Hirsch is an American film editor known for her work on numerous feature films and television projects.
  • B. Cynthia Scheider
    Cynthia Scheider is an American film editor known for her work on movies such as "The Taking of Pelham One Two Three" and "Kramer vs. Kramer."
  • C. Erika Peters
    Erika Peters is a German-born actress known for her film and television work in the 1950s and 1960s.
  • D. Janine Melnitz
    Janine Melnitz is the Ghostbusters’ sharp-tongued, no-nonsense receptionist who provides comic relief and grounded support to the team.
  • E. Lisa Eilbacher
    Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
  • 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: Tina Caspary
Triple: [Can't Buy Me Love, starring, Tina Caspary]
Generated description
Tina Caspary is an American actress and dancer best known for her roles in 1980s teen films and for her work as a choreographer in film and television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tina Caspary
Target entity description: Tina Caspary is an American actress and dancer best known for her roles in 1980s teen films and for her work as a choreographer in film and television.
  • A. Tina Hirsch
    Tina Hirsch is an American film editor known for her work on numerous feature films and television projects.
  • B. Cynthia Scheider
    Cynthia Scheider is an American film editor known for her work on movies such as "The Taking of Pelham One Two Three" and "Kramer vs. Kramer."
  • C. Erika Peters
    Erika Peters is a German-born actress known for her film and television work in the 1950s and 1960s.
  • D. Janine Melnitz
    Janine Melnitz is the Ghostbusters’ sharp-tongued, no-nonsense receptionist who provides comic relief and grounded support to the team.
  • E. Lisa Eilbacher
    Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
  • 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_69d80770b9bc81909f70c8c317d53cff completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dd43949e6c8190ae5e4fa119cde33a completed April 13, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69fde15abe6c8190a6212861bbce790e completed May 8, 2026, 1:12 p.m.
NEDg Description generation batch_69fde41944f4819099f41860272bca49 completed May 8, 2026, 1:24 p.m.
NED2 Entity disambiguation (via description) batch_69fde4981c98819092e30a61892a6e78 completed May 8, 2026, 1:26 p.m.
Created at: April 9, 2026, 9:54 p.m.