Triple

T13672924
Position Surface form Disambiguated ID Type / Status
Subject Café Society E327796 entity
Predicate character P662 FINISHED
Object Vonnie
Vonnie is a central character in Woody Allen's 2016 romantic comedy-drama film "Café Society," portrayed as a charming young woman caught in a bittersweet love triangle in 1930s Hollywood and New York.
 E1052066 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: Vonnie | Statement: [Café Society, character, Vonnie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vonnie
Context triple: [Café Society, character, Vonnie]
  • A. Vonnie
    Vonnie is the given first name of Von Miller, the prominent American football linebacker and Super Bowl MVP.
  • B. Vickie
    Vickie is a band member and love interest of Robin Buckley in the television series "Stranger Things."
  • C. Vickie
    Vickie is a feminine given name, often used as a diminutive of Victoria.
  • D. Vickie
    Vickie is the birth name of American model, actress, and television personality Anna Nicole Smith, who became famous as a Playboy Playmate and pop culture figure.
  • E. Vicki
    Vicki is a young companion of the First Doctor in the classic British science fiction television series Doctor Who.
  • 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: Vonnie
Triple: [Café Society, character, Vonnie]
Generated description
Vonnie is a central character in Woody Allen's 2016 romantic comedy-drama film "Café Society," portrayed as a charming young woman caught in a bittersweet love triangle in 1930s Hollywood and New York.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vonnie
Target entity description: Vonnie is a central character in Woody Allen's 2016 romantic comedy-drama film "Café Society," portrayed as a charming young woman caught in a bittersweet love triangle in 1930s Hollywood and New York.
  • A. Vonnie
    Vonnie is the given first name of Von Miller, the prominent American football linebacker and Super Bowl MVP.
  • B. Vickie
    Vickie is a band member and love interest of Robin Buckley in the television series "Stranger Things."
  • C. Vickie
    Vickie is a feminine given name, often used as a diminutive of Victoria.
  • D. Vickie
    Vickie is the birth name of American model, actress, and television personality Anna Nicole Smith, who became famous as a Playboy Playmate and pop culture figure.
  • E. Vicki
    Vicki is a young companion of the First Doctor in the classic British science fiction television series Doctor Who.
  • 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_69d8076f1fa8819094664a59b55010df completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc65aab348190a6611f5765f8392d completed April 12, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78b1222648190a70f50e6e5c34593 completed May 3, 2026, 5:51 p.m.
NEDg Description generation batch_69f78c00d0dc81909ac411e048719d20 completed May 3, 2026, 5:55 p.m.
NED2 Entity disambiguation (via description) batch_69f78c5edd4c8190bf23a470f595a636 completed May 3, 2026, 5:56 p.m.
Created at: April 9, 2026, 9:53 p.m.