Triple
T43370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madame X |
E852
|
entity |
| Predicate | lighting |
P1280
|
FINISHED |
| Object | dramatic contrast emphasizing the sitter’s pale skin |
—
|
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: dramatic contrast emphasizing the sitter’s pale skin | Statement: [Madame X, lighting, dramatic contrast emphasizing the sitter’s pale skin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lighting Context triple: [Madame X, lighting, dramatic contrast emphasizing the sitter’s pale skin]
-
A.
hasLighting
chosen
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
B.
material
Indicates that one entity is physically composed of, made from, or constructed using the substance or material represented by the other entity.
-
C.
colors
Indicates that one entity assigns, describes, or provides the color or colors of another entity.
-
D.
hasAlbedo
Indicates that an entity possesses a specific reflectivity or albedo value, describing how much incoming light it reflects.
-
E.
cinematographyBy
Indicates that the cinematographic work (such as the camera work or visual style of a film or video) is created or supervised by a specified person or entity.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24c083ad081909c1122c8fb29efdc |
completed | Feb. 28, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69a24aba9a2c81909f769a8f22e30c92 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.