Triple
T33743178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnny Depp as Lieutenant Victor |
E864626
|
entity |
| Predicate | countryOfStorySetting |
P10686
|
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, countryOfStorySetting, Cuba]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfStorySetting Context triple: [Johnny Depp as Lieutenant Victor, countryOfStorySetting, Cuba]
-
A.
countryOfSetting
chosen
Indicates the country in which the setting or context of something (such as a story, event, or work) takes place.
-
B.
nationalityOfFictionalSetting
Indicates that a fictional setting is associated with, or belongs to, a particular nationality or country.
-
C.
countryOfFictionalContext
Indicates that a work of fiction is primarily set in, or contextually associated with, a particular country.
-
D.
nationalityInStory
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
-
E.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
- 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_6a00ccadb6908190ab810a7c05315fa8 |
completed | May 10, 2026, 6:21 p.m. |
| PD | Predicate disambiguation | batch_6a00cc0f86b88190a0d2c43618558f86 |
completed | May 10, 2026, 6:18 p.m. |
Created at: May 1, 2026, 1:44 a.m.