Triple
T22100176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbet Schroeder |
E546147
|
entity |
| Predicate | workedWith |
P398
|
FINISHED |
| Object | Jeremy Irons |
—
|
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: Jeremy Irons | Statement: [Barbet Schroeder, workedWith, Jeremy Irons]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeremy Irons Context triple: [Barbet Schroeder, workedWith, 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.
Brian Michael Cox
Brian Michael Cox is a Grammy-winning American songwriter and record producer known for his work on numerous R&B and pop hits.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1291440048190992c48893ced0b34 |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.