Triple

T3751353
Position Surface form Disambiguated ID Type / Status
Subject West London E81336 entity
Predicate containsDistrict P22582 FINISHED
Object Paddington E78024 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: Paddington | Statement: [West London, containsDistrict, Paddington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paddington
Context triple: [West London, containsDistrict, Paddington]
  • A. Paddington chosen
    Paddington is a central London district best known for its major railway station, historic canal basin, and association with the fictional Paddington Bear.
  • 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. Paddington (2014 film)
    Paddington (2014 film) is a British family comedy based on Michael Bond’s beloved bear character, following a young Peruvian bear’s misadventures in London after being adopted by the Brown family.
  • D. Paddington 2
    Paddington 2 is a critically acclaimed 2017 family comedy film about the beloved bear Paddington, celebrated for its warmth, humor, and inventive storytelling.
  • E. Mr. Plod
    Mr. Plod is the bumbling village policeman character from Enid Blyton’s Noddy stories, known for trying to keep order in Toyland.
  • 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_69ad8b19b7b08190a6188804e99c53e9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb909bb4819088559f90d718f72f completed March 8, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db34aa5c8190ba3f22ee0f1f4208 completed March 14, 2026, 3:51 a.m.
Created at: March 8, 2026, 3:35 p.m.