Triple

T6387849
Position Surface form Disambiguated ID Type / Status
Subject Room 40 E143744 entity
Predicate employed P7 FINISHED
Object Nigel de Grey
Nigel de Grey was a British cryptanalyst and intelligence officer renowned for his work in codebreaking at Room 40 during World War I.
 E589716 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: Nigel de Grey | Statement: [Room 40, employed, Nigel de Grey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nigel de Grey
Context triple: [Room 40, employed, Nigel de Grey]
  • A. Spencer de Grey
    Spencer de Grey is a prominent British architect and senior partner at Foster + Partners, known for his leadership in major international architectural projects.
  • B. David Sinclair
    David Sinclair was the son of American novelist and social reformer Upton Sinclair.
  • C. David Sinclair (biologist)
    David Sinclair is an Australian biologist and Harvard Medical School professor best known for his pioneering research on aging, sirtuins, and longevity therapeutics.
  • D. Walter Bodmer
    Walter Bodmer is a prominent British human geneticist and immunologist known for his influential work on the genetics of human populations and cancer.
  • E. Paul Torday
    Paul Torday was a British novelist best known for his satirical debut novel "Salmon Fishing in the Yemen," which brought him widespread recognition later in life.
  • 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: Nigel de Grey
Triple: [Room 40, employed, Nigel de Grey]
Generated description
Nigel de Grey was a British cryptanalyst and intelligence officer renowned for his work in codebreaking at Room 40 during World War I.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nigel de Grey
Target entity description: Nigel de Grey was a British cryptanalyst and intelligence officer renowned for his work in codebreaking at Room 40 during World War I.
  • A. Spencer de Grey
    Spencer de Grey is a prominent British architect and senior partner at Foster + Partners, known for his leadership in major international architectural projects.
  • B. David Sinclair
    David Sinclair was the son of American novelist and social reformer Upton Sinclair.
  • C. David Sinclair (biologist)
    David Sinclair is an Australian biologist and Harvard Medical School professor best known for his pioneering research on aging, sirtuins, and longevity therapeutics.
  • D. Walter Bodmer
    Walter Bodmer is a prominent British human geneticist and immunologist known for his influential work on the genetics of human populations and cancer.
  • E. Paul Torday
    Paul Torday was a British novelist best known for his satirical debut novel "Salmon Fishing in the Yemen," which brought him widespread recognition later in life.
  • 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_69c008dac1ec81909cef8157ccd69962 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06869dfb88190aeb84c6c61414888 completed March 22, 2026, 10:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6388224fc8190aabd6e6d75887367 completed March 27, 2026, 7:57 a.m.
NEDg Description generation batch_69c6397756c481909ca13339c2186c0a completed March 27, 2026, 8:01 a.m.
NED2 Entity disambiguation (via description) batch_69c63a0a4a108190b474555d8cb1540c completed March 27, 2026, 8:04 a.m.
Created at: March 22, 2026, 4:34 p.m.