Triple
T30609011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Kenneth Ronald Murray |
E779125
|
entity |
| Predicate | spouseWorkplace |
P73253
|
FINISHED |
| Object | Bletchley Park |
—
|
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: Bletchley Park | Statement: [John Kenneth Ronald Murray, spouseWorkplace, Bletchley Park]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseWorkplace Context triple: [John Kenneth Ronald Murray, spouseWorkplace, Bletchley Park]
-
A.
spousePlaceOfWork
chosen
Indicates that the place of work specified belongs to the spouse of the referenced person.
-
B.
spouseInWork
Indicates that two entities are spouses within the context of a particular work (such as a book, film, or series), rather than in real life.
-
C.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
D.
spouseWorkWith
Indicates that a person’s spouse works together with a specified person, typically as colleagues in the same workplace or professional context.
-
E.
spouseIndustry
Indicates the industry or sector in which a person's spouse is employed or primarily involved.
- F. None of above.
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_69f224a21fc08190abd9d8dd9eb6bb4c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: April 29, 2026, 8:26 p.m.