Triple
T34960833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pancarlık Valley |
E1008248
|
entity |
| Predicate | paintingType |
P3050
|
FINISHED |
| Object | Christian frescoes |
—
|
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: Christian frescoes | Statement: [Pancarlık Valley, paintingType, Christian frescoes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: paintingType Context triple: [Pancarlık Valley, paintingType, Christian frescoes]
-
A.
paintingMethod
Indicates the technique or process used to create a painting or apply paint.
-
B.
artworkType
chosen
Indicates the specific category or kind of artwork that characterizes the relationship between the subject and the artwork.
-
C.
paints
Indicates that one entity applies paint to create, cover, or decorate another entity.
-
D.
paintingSettingOf
Indicates that a painting depicts or provides the visual setting or background context for another entity or scene.
-
E.
paintingProject
Indicates a relationship where an entity undertakes or is associated with the activity of creating, executing, or managing a painting-related task or assignment.
- 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_69f76dc69564819099e9e78aed6ff0a6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4 p.m.