Triple
T17797496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rob Oakeshott |
E444330
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Rob Oakeshott |
—
|
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: Rob Oakeshott | Statement: [Rob Oakeshott, name, Rob Oakeshott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rob Oakeshott Context triple: [Rob Oakeshott, name, Rob Oakeshott]
-
A.
Rob Oakeshott
chosen
Rob Oakeshott is an Australian politician best known as a former independent federal MP whose support was pivotal in forming the minority Gillard Labor government in 2010.
-
B.
James Brokenshire
James Brokenshire was a British Conservative politician who served as a Member of Parliament and held several ministerial roles, including Secretary of State for Northern Ireland.
-
C.
Paul Broughton
Paul Broughton is an actor best known for his role in the British television drama series "The Lakes."
-
D.
Ian Rumfitt
Ian Rumfitt is a British philosopher best known for his work in the philosophy of language and logic, particularly on meaning, truth, and inferentialism.
-
E.
John Leeson
John Leeson is a British actor best known for voicing the robotic dog K-9 in the Doctor Who television franchise.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487fbc83481909a30fc7203b64099 |
completed | April 19, 2026, 7:44 a.m. |
Created at: April 10, 2026, 10:13 a.m.