Triple
T21039794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Sinatra as Major Bennett Marco |
E518289
|
entity |
| Predicate | filmNationalityOfActor |
P32391
|
FINISHED |
| Object | American |
—
|
LITERAL 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: American | Statement: [Frank Sinatra as Major Bennett Marco, filmNationalityOfActor, American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmNationalityOfActor Context triple: [Frank Sinatra as Major Bennett Marco, filmNationalityOfActor, American]
-
A.
nationalityOfActor
Indicates that a specified nationality is associated with, or belongs to, a particular actor.
-
B.
hasDirectorNationality
Indicates that the nationality of a director is associated with a given entity (such as a film, organization, or work).
-
C.
hasCinematographerNationality
Indicates that a cinematographer is associated with a specific nationality.
-
D.
portrayalNationalityOfActor
chosen
Indicates that an actor portrays a character of a specified nationality in a performance or work.
-
E.
filmCountryOfOrigin
Indicates the country where a film was originally produced or created.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fceed9148190903adb3b55f65242 |
completed | April 21, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf6728881908a2a43a5c8804a2a |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:14 p.m.