Triple
T1181028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Money Monster |
E25136
|
entity |
| Predicate | storyBy |
P1955
|
FINISHED |
| Object | Alan Di Fiore |
E198413
|
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: Alan Di Fiore | Statement: [Money Monster, storyBy, Alan Di Fiore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alan Di Fiore Context triple: [Money Monster, storyBy, Alan Di Fiore]
-
A.
Alan Di Fiore
chosen
Alan Di Fiore is a Canadian screenwriter and producer known for his work in film and television, including co-writing the thriller "Money Monster."
-
B.
Greg D'Auria
Greg D'Auria is a film editor known for his work on major Hollywood productions, including the science fiction film "Star Trek Beyond."
-
C.
Mike Fiore
Mike Fiore is an American college baseball player best known for being the inaugural recipient of the prestigious Dick Howser Trophy, awarded to the nation's top collegiate player.
-
D.
Michael De Luca
Michael De Luca is an American film producer and studio executive known for overseeing and producing a wide range of major Hollywood films across genres.
-
E.
Robert DiNozzi
Robert DiNozzi is a film producer best known for his work on the thriller movie "Flightplan."
- 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_69a494267b4c819088c97a59182bf56a |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd32c5f48190b4e2d39fa052cbb7 |
completed | March 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adb5ac8a2481908394095e9a6edc6a |
completed | March 8, 2026, 5:45 p.m. |
Created at: March 1, 2026, 7:45 p.m.