Triple

T984577
Position Surface form Disambiguated ID Type / Status
Subject Academy Juvenile Award E21249 entity
Predicate notableRecipient P108 FINISHED
Object Vincent Winter
Vincent Winter was a Scottish child actor who gained prominence in the 1950s and 1960s, later working behind the scenes in film production.
E115960 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: Vincent Winter | Statement: [Academy Juvenile Award, notableRecipient, Vincent Winter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vincent Winter
Context triple: [Academy Juvenile Award, notableRecipient, Vincent Winter]
  • A. Harry Hart
    Harry Hart is a suave, highly skilled British secret agent and mentor figure in the Kingsman film series.
  • B. Tom Fleming
    Tom Fleming is a name shared by several notable individuals, including an American long-distance runner and a Scottish actor and comedian.
  • C. Charles Rackoff
    Charles Rackoff is a Canadian computer scientist known for his influential work in cryptography and computational complexity theory.
  • D. Charles Hoover
    Charles Hoover is a member of the Hoover family, known primarily as the brother of American engineer and diplomat Herbert Hoover Jr.
  • E. William Nolan
    William Nolan is an editor known for his work on editions of classic adventure literature, including "The Mark of Zorro."
  • 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: Vincent Winter
Triple: [Academy Juvenile Award, notableRecipient, Vincent Winter]
Generated description
Vincent Winter was a Scottish child actor who gained prominence in the 1950s and 1960s, later working behind the scenes in film production.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Vincent Winter
Target entity description: Vincent Winter was a Scottish child actor who gained prominence in the 1950s and 1960s, later working behind the scenes in film production.
  • A. Harry Hart
    Harry Hart is a suave, highly skilled British secret agent and mentor figure in the Kingsman film series.
  • B. Tom Fleming
    Tom Fleming is a name shared by several notable individuals, including an American long-distance runner and a Scottish actor and comedian.
  • C. Charles Rackoff
    Charles Rackoff is a Canadian computer scientist known for his influential work in cryptography and computational complexity theory.
  • D. Charles Hoover
    Charles Hoover is a member of the Hoover family, known primarily as the brother of American engineer and diplomat Herbert Hoover Jr.
  • E. William Nolan
    William Nolan is an editor known for his work on editions of classic adventure literature, including "The Mark of Zorro."
  • 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_69a493c383dc8190a03257f22d4b4183 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b4959fe48190a78bd811cbc888ab completed March 1, 2026, 9:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac1ce3c6fc81909fbbf04eef1b997e completed March 7, 2026, 12:41 p.m.
NEDg Description generation batch_69ac1d45b8cc8190b8b678b697d3f7f1 completed March 7, 2026, 12:42 p.m.
NED2 Entity disambiguation (via description) batch_69ac1e2e200881909e9b503655d6f8ab completed March 7, 2026, 12:46 p.m.
Created at: March 1, 2026, 7:41 p.m.