Triple

T2720902
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth line E60076 entity
Predicate passesThrough P225 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: [Elizabeth line, passesThrough, Paddington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paddington
Context triple: [Elizabeth line, passesThrough, 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 (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.
  • C. 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.
  • D. Mr. Plod
    Mr. Plod is the bumbling village policeman character from Enid Blyton’s Noddy stories, known for trying to keep order in Toyland.
  • E. Mr. Brown – Paddington
    Mr. Brown in *Paddington* is the cautious yet kind-hearted London father who gradually embraces and protects the lovable bear Paddington as part of his family.
  • 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_69ab4b746d248190958e052045c09255 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdab1cb808190b0789c76bc9cb090 completed March 7, 2026, 7:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc033d94481908e4709529ab93442 completed March 10, 2026, 6:54 a.m.
Created at: March 6, 2026, 9:55 p.m.