Triple

T7453986
Position Surface form Disambiguated ID Type / Status
Subject Marie Stopes E172072 entity
Predicate spouse P13 FINISHED
Object Humphrey Verdon Roe E488575 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: Humphrey Verdon Roe | Statement: [Marie Stopes, spouse, Humphrey Verdon Roe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Humphrey Verdon Roe
Context triple: [Marie Stopes, spouse, Humphrey Verdon Roe]
  • A. Humphrey Verdon Roe chosen
    Humphrey Verdon Roe was a British aviation pioneer and businessman best known for co-founding the aircraft manufacturer Avro and supporting early aviation development in the UK.
  • B. Humphrey Searle
    Humphrey Searle was a 20th-century British composer known for his pioneering use of serialism and his influential orchestral and film scores.
  • C. Charles Vereker
    Charles Vereker was an Irish soldier and politician who served as a Member of Parliament in the late 18th and early 19th centuries.
  • D. John Bowes Morrell
    John Bowes Morrell was a prominent English historian, author, and civic leader from York who played a key role in the founding and development of the University of York.
  • E. Sidney Lanfield
    Sidney Lanfield was an American film and television director best known for his work on Hollywood comedies and genre films from the 1930s through the 1950s.
  • 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_69c68a66554c8190add75c65942c0317 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f3ac5c2081908ab03f8bd4586f94 completed March 27, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8be148dc881909f15e6457f11c775 completed March 29, 2026, 5:52 a.m.
Created at: March 27, 2026, 3:14 p.m.