Triple
T2543086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sherborne School |
E57829
|
entity |
| Predicate | hasNotableAlumni |
P51
|
FINISHED |
| Object | Jeremy Irons |
E181880
|
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: Jeremy Irons | Statement: [Sherborne School, hasNotableAlumni, Jeremy Irons]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeremy Irons Context triple: [Sherborne School, hasNotableAlumni, Jeremy Irons]
-
A.
Jeremy Irons
chosen
Jeremy Irons is an acclaimed English actor known for his distinctive voice and versatile performances in film, television, and theatre.
-
B.
John Hurt
John Hurt was an acclaimed English actor known for his distinctive voice and powerful performances in films such as "The Elephant Man," "Alien," and "Midnight Express."
-
C.
Michael York
Michael York is an English actor known for his roles in films such as "Cabaret," "Logan's Run," and the "Austin Powers" series.
-
D.
Joseph Fiennes
Joseph Fiennes is an English actor known for his roles in films such as "Shakespeare in Love" and various historical and dramatic productions in both cinema and television.
-
E.
Bill Nighy
Bill Nighy is an English actor known for his distinctive voice and acclaimed performances in films such as "Love Actually," "Pirates of the Caribbean," and "About Time."
- 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_69ab4a5212d88190b989ce129f2ad87f |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd2bf6cac819083a9ab9d041d8641 |
completed | March 7, 2026, 7:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98ab023481908ab51febe79b963c |
completed | March 10, 2026, 4:06 a.m. |
Created at: March 6, 2026, 9:47 p.m.