Triple
T5491079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Misti |
E123700
|
entity |
| Predicate | photoSubjectFor |
P9792
|
FINISHED |
| Object | tourists in Arequipa |
—
|
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: tourists in Arequipa | Statement: [Misti, photoSubjectFor, tourists in Arequipa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: photoSubjectFor Context triple: [Misti, photoSubjectFor, tourists in Arequipa]
-
A.
isPhotographicSubject
chosen
Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
-
B.
depictedSubject
Indicates that one entity visually represents or portrays another entity as its subject in an image or depiction.
-
C.
iconographicSubject
Indicates that one entity serves as the depicted subject or theme represented in the iconography of another entity.
-
D.
portraitSpecialization
Indicates that one entity specializes in creating or working with portraits, distinguishing a focused area of expertise within a broader artistic or professional domain.
-
E.
photographer
Indicates that one entity takes photographs of another entity, typically in a professional or intentional capacity.
- 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_69bd464a2d908190869324ce176779c8 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927f198c8190889b555be9bf9765 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a8df6481908d1643f7342fe6f0 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:10 p.m.