Triple
T12959359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Jordan |
E310097
|
entity |
| Predicate | nationalityInFilmContext |
P22097
|
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: [Mr. Jordan, nationalityInFilmContext, American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalityInFilmContext Context triple: [Mr. Jordan, nationalityInFilmContext, American]
-
A.
nationalityOfActor
Indicates that a specified nationality is associated with, or belongs to, a particular actor.
-
B.
nationalityOfPersonReferredTo
Indicates that one entity is the country or nationality associated with the person referenced by the other entity.
-
C.
hasDirectorNationality
chosen
Indicates that the nationality of a director is associated with a given entity (such as a film, organization, or work).
-
D.
nationalityInText
Indicates that a person's nationality is mentioned or specified within a given text.
-
E.
nationalityInStory
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:44 p.m.