Triple

T5278516
Position Surface form Disambiguated ID Type / Status
Subject West Yorkshire Urban Area E119432 entity
Predicate includesTown P847 FINISHED
Object Pudsey E372053 NE FINISHED

How this triple was built (2 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: Pudsey | Statement: [West Yorkshire Urban Area, includesTown, Pudsey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pudsey
Context triple: [West Yorkshire Urban Area, includesTown, Pudsey]
  • A. Pudsey chosen
    Pudsey is a market town in West Yorkshire, England, situated between Leeds and Bradford.
  • B. Paddington Bear
    Paddington Bear is a beloved fictional bear from Peru who wears a duffle coat and hat, loves marmalade sandwiches, and stars in a long-running series of children's books and film adaptations set in London.
  • C. Teddy
    Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
  • D. Teddy
    Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
  • E. Kipper the Dog
    Kipper the Dog is a popular British children's book and animated television character, a friendly, easygoing dog whose gentle adventures are aimed at preschool audiences.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd446d05a8819092ad333a3f9c8d5c completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd84c2eab881908698a14b116a3bfa completed March 20, 2026, 5:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf06d9aab08190ad9905925a849922 completed March 21, 2026, 9 p.m.
Created at: March 20, 2026, 1:51 p.m.