Triple

T3621306
Position Surface form Disambiguated ID Type / Status
Subject Old School E76732 entity
Predicate mainCharacter P1183 FINISHED
Object Frank Ricard
Frank Ricard is a hard-partying, middle-aged man whose attempts to relive his college days become a central source of comedy and chaos in the film "Old School."
 E527574 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: Frank Ricard | Statement: [Old School, mainCharacter, Frank Ricard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Frank Ricard
Context triple: [Old School, mainCharacter, Frank Ricard]
  • A. Michel Ecochard
    Michel Ecochard was a prominent French architect and urban planner known for his influential modernist designs and large-scale planning projects in the Middle East and North Africa.
  • B. Alain Chevalier
    Alain Chevalier was a French businessman best known as a co-founder and early architect of the luxury goods conglomerate LVMH.
  • C. René Guiette
    René Guiette was a Belgian painter and art critic associated with modernist movements in the early 20th century.
  • D. André Roussin
    André Roussin was a prominent 20th-century French playwright known for his sophisticated comedies of manners and successful Parisian stage works.
  • E. Roland Gallois
    Roland Gallois is a film editor known for his work on the feature film "Slow West."
  • 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: Frank Ricard
Triple: [Old School, mainCharacter, Frank Ricard]
Generated description
Frank Ricard is a hard-partying, middle-aged man whose attempts to relive his college days become a central source of comedy and chaos in the film "Old School."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Frank Ricard
Target entity description: Frank Ricard is a hard-partying, middle-aged man whose attempts to relive his college days become a central source of comedy and chaos in the film "Old School."
  • A. Michel Ecochard
    Michel Ecochard was a prominent French architect and urban planner known for his influential modernist designs and large-scale planning projects in the Middle East and North Africa.
  • B. Alain Chevalier
    Alain Chevalier was a French businessman best known as a co-founder and early architect of the luxury goods conglomerate LVMH.
  • C. René Guiette
    René Guiette was a Belgian painter and art critic associated with modernist movements in the early 20th century.
  • D. André Roussin
    André Roussin was a prominent 20th-century French playwright known for his sophisticated comedies of manners and successful Parisian stage works.
  • E. Roland Gallois
    Roland Gallois is a film editor known for his work on the feature film "Slow West."
  • 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_69ad85dae2fc81908d1ceadbc6af0089 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc2b9aa608190a680b250ecf63156 completed March 8, 2026, 6:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69bff2a7e5b081909d3d64fc62c48eb3 completed March 22, 2026, 1:46 p.m.
NEDg Description generation batch_69bff357b39c8190824415483c4ec843 completed March 22, 2026, 1:49 p.m.
NED2 Entity disambiguation (via description) batch_69bff3ad10b88190b9ef4fff5642ecc4 completed March 22, 2026, 1:50 p.m.
Created at: March 8, 2026, 3:23 p.m.