Triple
T10463211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Italian Job |
E246727
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Michael Deeley |
E226087
|
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: Michael Deeley | Statement: [The Italian Job, producer, Michael Deeley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Deeley Context triple: [The Italian Job, producer, Michael Deeley]
-
A.
Michael Deeley
chosen
Michael Deeley is a British film producer best known for his work on acclaimed films such as "The Deer Hunter" and "Blade Runner."
-
B.
Justin Deeley
Justin Deeley is an American actor and model best known for his television roles, including a prominent part on the series "Drop Dead Diva."
-
C.
Nick Daley
Nick Daley is a fictional character from the "Night at the Museum" film series, known as the son of protagonist Larry Daley.
-
D.
Ian Donnelly
Ian Donnelly is a theoretical physicist and linguist who serves as one of the central human protagonists in the science fiction film "Arrival," working alongside Louise Banks to communicate with extraterrestrial visitors.
-
E.
James Daly
James Daly was an American actor known for his work in television and film during the mid-20th century, including roles on series like "Medical Center."
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50884fac48190af22e181b1492557 |
completed | April 7, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89fd865688190b0b5708481f397f4 |
completed | April 10, 2026, 6:59 a.m. |
Created at: April 6, 2026, 12:19 p.m.