Triple
T20964173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Country Doctor |
E516322
|
entity |
| Predicate | photographerNationality |
P126233
|
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: [Country Doctor, photographerNationality, American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: photographerNationality Context triple: [Country Doctor, photographerNationality, American]
-
A.
hasPhotographerNationality
chosen
Indicates that a photographer is associated with a specific nationality.
-
B.
photographer
Indicates that one entity takes photographs of another entity, typically in a professional or intentional capacity.
-
C.
notablePhotographer
Indicates that the subject is a photographer who is recognized as notable or significant in some context.
-
D.
designerNationality
Indicates that a designer has a specific national or country affiliation.
-
E.
photographerOfEvent
Indicates that one entity serves as the photographer responsible for capturing images at a particular event.
- 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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb71d644819087e00933fcc26217 |
completed | April 21, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_69e5dbe6976081908abd4e9c8734bae9 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 1:32 p.m.