Triple
T33743183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnny Depp as Lieutenant Victor |
E864626
|
entity |
| Predicate | basedOnRealEventsIn |
P53505
|
FINISHED |
| Object | Cuba |
—
|
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: Cuba | Statement: [Johnny Depp as Lieutenant Victor, basedOnRealEventsIn, Cuba]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnRealEventsIn Context triple: [Johnny Depp as Lieutenant Victor, basedOnRealEventsIn, Cuba]
-
A.
basedOnRealLife
Indicates that something is derived from, inspired by, or directly adapted from actual real-world events, people, or situations.
-
B.
usesRealHistoricalEvents
chosen
Indicates that the subject incorporates or is based on actual events that occurred in real history.
-
C.
basedOnEventsDescribedIn
Indicates that something is derived from, inspired by, or constructed using the events described in another source.
-
D.
inspiredByRealEventType
Indicates that an event, action, or situation is based on, derived from, or influenced by an actual real-world occurrence.
-
E.
associatedWithFictionalEvent
Indicates that an entity has a connection or involvement with a fictional event, such as being based on, inspired by, or participating in that imagined occurrence.
- F. None of above.
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_69f3498b24b8819096a65009e521d0e1 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fed09a12648190affcd9bacf7ca275 |
completed | May 9, 2026, 6:13 a.m. |
| PD | Predicate disambiguation | batch_69fecf91d6f481908deb60c965c433ed |
completed | May 9, 2026, 6:09 a.m. |
Created at: May 1, 2026, 1:44 a.m.