Triple
T281130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autoportrait au chapeau de paille |
E5356
|
entity |
| Predicate | depictsMedium |
P9561
|
FINISHED |
| Object | oil paint |
—
|
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: oil paint | Statement: [Autoportrait au chapeau de paille, depictsMedium, oil paint]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsMedium Context triple: [Autoportrait au chapeau de paille, depictsMedium, oil paint]
-
A.
depicts
Indicates that one entity visually represents, portrays, or shows another entity.
-
B.
depictsPerson
Indicates that one entity visually represents or portrays a specific person.
-
C.
numberOfFiguresDepicted
Indicates the total count of distinct figures shown within a given depiction or representation.
-
D.
usedMedium
Indicates that an action or communication was carried out through a particular medium or channel.
-
E.
depictsInstitution
Indicates that one entity visually represents or portrays an institution as its subject.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69a25e06dd7c8190a8cbb76cee3c6e4b |
completed | Feb. 28, 2026, 3:16 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.