Triple

T16155654
Position Surface form Disambiguated ID Type / Status
Subject Margaret Elinor Belsham E392033 entity
Predicate spouse P13 FINISHED
Object Christopher Cockerell E78210 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: Christopher Cockerell | Statement: [Margaret Elinor Belsham, spouse, Christopher Cockerell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christopher Cockerell
Context triple: [Margaret Elinor Belsham, spouse, Christopher Cockerell]
  • A. Christopher Cockerell chosen
    Christopher Cockerell was a British engineer and inventor best known for creating the hovercraft.
  • B. Charles Robert Cockerell
    Charles Robert Cockerell was a prominent 19th-century British architect known for his influential neoclassical designs and scholarly contributions to architectural history.
  • C. Jonathan Dyson
    Jonathan Dyson is the Chief Fire Officer leading the North Yorkshire Fire and Rescue Service in England.
  • D. John Whittle
    John Whittle was an Australian soldier and recipient of the Victoria Cross, recognized for his bravery during World War I.
  • E. Douglas Cockerell
    Douglas Cockerell was a prominent British bookbinder and teacher, renowned for his influential work in fine binding and book conservation in the early 20th century.
  • 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_69d87f1c65e48190aa2b4c472e9bafc4 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21e5902a08190ad8694955ef6073a completed April 17, 2026, 11:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff7ae46dc81908cba9152a6080c3a completed May 10, 2026, 3:12 a.m.
Created at: April 10, 2026, 5:01 a.m.