Triple

T19754642
Position Surface form Disambiguated ID Type / Status
Subject Un Lun Dun E474471 entity
Predicate setting P1957 FINISHED
Object UnLondon
UnLondon is a fantastical, alternate version of London in China Miéville’s novel "Un Lun Dun," populated by discarded objects, strange creatures, and magical urban landscapes.
 E1393739 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: UnLondon | Statement: [Un Lun Dun, setting, UnLondon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UnLondon
Context triple: [Un Lun Dun, setting, UnLondon]
  • A. More London
    More London is a modern riverside business and leisure development on the south bank of the River Thames in central London, known for its offices, public spaces, and views of Tower Bridge.
  • 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. Londiani
    Londiani is a town in Kenya’s Rift Valley region, known as a local commercial and transport hub within Kericho County.
  • D. "London"
    London is the capital and largest city of the United Kingdom, renowned as a global center for finance, culture, and history.
  • E. London
    London is a major Ethereum network upgrade that introduced significant changes to the protocol’s fee market and transaction pricing mechanisms.
  • 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: UnLondon
Triple: [Un Lun Dun, setting, UnLondon]
Generated description
UnLondon is a fantastical, alternate version of London in China Miéville’s novel "Un Lun Dun," populated by discarded objects, strange creatures, and magical urban landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UnLondon
Target entity description: UnLondon is a fantastical, alternate version of London in China Miéville’s novel "Un Lun Dun," populated by discarded objects, strange creatures, and magical urban landscapes.
  • A. More London
    More London is a modern riverside business and leisure development on the south bank of the River Thames in central London, known for its offices, public spaces, and views of Tower Bridge.
  • 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. Londiani
    Londiani is a town in Kenya’s Rift Valley region, known as a local commercial and transport hub within Kericho County.
  • D. "London"
    London is the capital and largest city of the United Kingdom, renowned as a global center for finance, culture, and history.
  • E. London
    London is a major Ethereum network upgrade that introduced significant changes to the protocol’s fee market and transaction pricing mechanisms.
  • 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_69d8e51940a0819087bd2996f98da668 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6529dada081909c5b4d65247c6032 completed April 20, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07bd67064081908839a408b0e746e1 completed May 16, 2026, 12:42 a.m.
NEDg Description generation batch_6a07bde28e848190aaa9c4a06b31bdb6 completed May 16, 2026, 12:44 a.m.
NED2 Entity disambiguation (via description) batch_6a07bed9c62c819088d8e32f92b3b147 completed May 16, 2026, 12:48 a.m.
Created at: April 10, 2026, 1:48 p.m.