Triple

T9995493
Position Surface form Disambiguated ID Type / Status
Subject Peter Capaldi E197191 entity
Predicate characterPortrayed P1507 FINISHED
Object Mr. Curry
Mr. Curry is a fussy, self-important neighbor character in the Paddington Bear stories, known for his grumpiness and frequent complaints about Paddington.
 E834528 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: Mr. Curry | Statement: [Peter Capaldi, characterPortrayed, Mr. Curry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mr. Curry
Context triple: [Peter Capaldi, characterPortrayed, Mr. Curry]
  • A. Mr. Franks
    Mr. Franks is a music producer best known for his work with the hip-hop collective Legend.
  • B. Mr. Jackson
    Mr. Jackson is a messy, intrusive toad character in Beatrix Potter’s children’s story "The Tale of Mrs. Tittlemouse."
  • C. Mr. Brown
    Mr. Brown is the kind-hearted but often flustered father figure from the "Paddington" film series.
  • D. Mr. Brown
    Mr. Brown is one of the color-coded hijackers in the crime thriller "The Taking of Pelham One Two Three," known for his role in the subway train hostage plot.
  • E. Mr. Brown
    Mr. Brown is a comically eccentric, churchgoing older man known for his loud outfits, over-the-top reactions, and frequent appearances in Tyler Perry’s Madea franchise.
  • 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: Mr. Curry
Triple: [Peter Capaldi, characterPortrayed, Mr. Curry]
Generated description
Mr. Curry is a fussy, self-important neighbor character in the Paddington Bear stories, known for his grumpiness and frequent complaints about Paddington.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mr. Curry
Target entity description: Mr. Curry is a fussy, self-important neighbor character in the Paddington Bear stories, known for his grumpiness and frequent complaints about Paddington.
  • A. Mr. Franks
    Mr. Franks is a music producer best known for his work with the hip-hop collective Legend.
  • B. Mr. Jackson
    Mr. Jackson is a messy, intrusive toad character in Beatrix Potter’s children’s story "The Tale of Mrs. Tittlemouse."
  • C. Mr. Brown
    Mr. Brown is the kind-hearted but often flustered father figure from the "Paddington" film series.
  • D. Mr. Brown
    Mr. Brown is one of the color-coded hijackers in the crime thriller "The Taking of Pelham One Two Three," known for his role in the subway train hostage plot.
  • E. Mr. Brown
    Mr. Brown is a comically eccentric, churchgoing older man known for his loud outfits, over-the-top reactions, and frequent appearances in Tyler Perry’s Madea franchise.
  • 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_69ca82f3b61c81908ecc2c1c96dbc2e4 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdcb99ac74819091f20816478ea375 completed April 2, 2026, 1:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69d258336ab8819098d4878b8c106d86 completed April 5, 2026, 12:40 p.m.
NEDg Description generation batch_69d259cb8c4c8190b9169745751855ca completed April 5, 2026, 12:47 p.m.
NED2 Entity disambiguation (via description) batch_69d25a30ad98819084dcd305e709c34d completed April 5, 2026, 12:48 p.m.
Created at: March 30, 2026, 8:50 p.m.