Triple
T23332990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 40 O.B. |
E591495
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Nigel de Grey |
—
|
NE NERFINISHED |
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: Nigel de Grey | Statement: [40 O.B., employer, Nigel de Grey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nigel de Grey Context triple: [40 O.B., employer, Nigel de Grey]
-
A.
Nigel de Grey
chosen
Nigel de Grey was a British cryptanalyst and intelligence officer renowned for his work in codebreaking at Room 40 during World War I.
-
B.
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.
-
C.
David Sinclair
David Sinclair was the son of American novelist and social reformer Upton Sinclair.
-
D.
Geoffrey Hall
Geoffrey Hall is a cinematographer known for his work on films such as "Chopper," contributing distinctive visual style to Australian cinema.
-
E.
Peter Walter
Peter Walter is a prominent biochemist and molecular biologist best known for his work on protein targeting and the unfolded protein response in cells.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197eecc5c81908089eb43bc701196 |
completed | April 29, 2026, 5:32 a.m. |
Created at: April 17, 2026, 5:16 p.m.