Triple
T18027140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sean Thornton |
E431282
|
entity |
| Predicate | nationalityDescriptorInFilm |
P32391
|
FINISHED |
| Object | Yank (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: Yank (American) | Statement: [Sean Thornton, nationalityDescriptorInFilm, Yank (American)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalityDescriptorInFilm Context triple: [Sean Thornton, nationalityDescriptorInFilm, Yank (American)]
-
A.
hasDirectorNationality
Indicates that the nationality of a director is associated with a given entity (such as a film, organization, or work).
-
B.
nationalityOfActor
Indicates that a specified nationality is associated with, or belongs to, a particular actor.
-
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.
nationalityInText
Indicates that a person's nationality is mentioned or specified within a given text.
- 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_69d8b9050fb48190890155145deb0a66 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9c7be308190af0d77c0df6d94ce |
completed | April 19, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69e3f904b8048190add43883cd7cb191 |
completed | April 18, 2026, 9:35 p.m. |
Created at: April 10, 2026, 10:24 a.m.