Triple
T281152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autoportrait au chapeau de paille |
E5356
|
entity |
| Predicate | depictsBackground |
P1581
|
FINISHED |
| Object | painterly background |
—
|
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: painterly background | Statement: [Autoportrait au chapeau de paille, depictsBackground, painterly background]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsBackground Context triple: [Autoportrait au chapeau de paille, depictsBackground, painterly background]
-
A.
depicts
chosen
Indicates that one entity visually represents, portrays, or shows another entity.
-
B.
typicalBackground
Indicates that an entity has a usual or commonly expected background, context, or setting associated with it.
-
C.
depictsPerson
Indicates that one entity visually represents or portrays a specific person.
-
D.
paintedEvery
Indicates that an entity applied paint to each and every relevant item in a specified set or domain.
-
E.
depictsInstitution
Indicates that one entity visually represents or portrays an institution as its subject.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e0a23c0819083abee28b2dea49c |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b77e028819087e606fc321219f7 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.