Triple

T15275479
Position Surface form Disambiguated ID Type / Status
Subject Ryan Hansen E365126 entity
Predicate characterPortrayed P1507 FINISHED
Object Andy
Andy is a fictional character portrayed by actor Ryan Hansen, known for his comedic and often charmingly awkward roles.
 E1146704 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: Andy | Statement: [Ryan Hansen, characterPortrayed, Andy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Andy
Context triple: [Ryan Hansen, characterPortrayed, Andy]
  • A. Andy
    Andy is the central character in the 1991 Australian psychological drama film "Proof," around whom the story’s exploration of trust, perception, and relationships revolves.
  • B. Andy
    Andy is the central protagonist of the British film "Life Is Sweet," around whom the story’s domestic and emotional themes revolve.
  • C. Andy
    Andy is a common English given name, often used as a diminutive of Andrew.
  • D. Andy
    Andy is the immortal warrior leader portrayed by Charlize Theron in the action-fantasy film "The Old Guard."
  • E. Andy
    Andy is one of the central teenage protagonists in the 1985 adventure film "The Goonies," known for her cheerleader background and budding romance with Brand.
  • 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: Andy
Triple: [Ryan Hansen, characterPortrayed, Andy]
Generated description
Andy is a fictional character portrayed by actor Ryan Hansen, known for his comedic and often charmingly awkward roles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Andy
Target entity description: Andy is a fictional character portrayed by actor Ryan Hansen, known for his comedic and often charmingly awkward roles.
  • A. Andy
    Andy is a recurring friend of Ray Barone on the sitcom "Everybody Loves Raymond," known for his awkward, self-deprecating humor.
  • B. Andy
    Andy is a central character in the play "Animals Out of Paper," serving as a gifted but troubled high school student whose passion for origami deeply influences the story’s emotional arc.
  • C. Andy
    Andy is one of Snoopy’s lesser-known beagle siblings from the Peanuts comic strip, recognizable by his shaggy fur and laid-back demeanor.
  • D. Andy
    Andy is the central protagonist of the British film "Life Is Sweet," around whom the story’s domestic and emotional themes revolve.
  • E. Andy
    Andy is one of the central teenage protagonists in the 1985 adventure film "The Goonies," known for her cheerleader background and budding romance with Brand.
  • 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00952731c8190bf6a5e6e10c95b94 completed April 15, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee608217881909c9f7f7c753cf0a8 completed May 9, 2026, 7:45 a.m.
NEDg Description generation batch_69fee7a3e9a081908b6b2addc66c0c75 completed May 9, 2026, 7:52 a.m.
NED2 Entity disambiguation (via description) batch_69fee7fa2fe48190b7ba9b3cda2b8f31 completed May 9, 2026, 7:53 a.m.
Created at: April 10, 2026, 3:14 a.m.