Triple

T1925504
Position Surface form Disambiguated ID Type / Status
Subject Queen Mary University of London E40819 entity
Predicate city P40 FINISHED
Object London E1817 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: London | Statement: [Queen Mary University of London, city, London]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: London
Context triple: [Queen Mary University of London, city, London]
  • A. London, England chosen
    London, England is the capital and largest city of the United Kingdom, renowned as a global center for finance, culture, and politics.
  • B. Allondon
    Allondon is a small river in western Switzerland and neighboring France, known for flowing through the Geneva region and its natural, relatively unspoiled surroundings.
  • C. York, England
    York, England is a historic walled city in northern England renowned for its medieval architecture, including York Minster, and its rich Roman and Viking heritage.
  • D. London Victoria
    London Victoria is a major central London railway terminus and Underground station, serving as a key hub for commuter, regional, and Gatwick Airport services.
  • E. City of London
    The City of London is the historic and financial core of Greater London, renowned as one of the world’s leading global finance and business centers.
  • 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_69a8864711648190b07bed24ed76258e completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb260da088190ac53bfc9437e112b completed March 7, 2026, 5:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1fcb534881908415237d24b4d8b9 completed March 9, 2026, 1:18 a.m.
Created at: March 4, 2026, 7:35 p.m.