Triple
T4199270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lighthouse at Honfleur |
E86026
|
entity |
| Predicate | colorTechnique |
P51617
|
FINISHED |
| Object | division of color into small dots |
—
|
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: division of color into small dots | Statement: [The Lighthouse at Honfleur, colorTechnique, division of color into small dots]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorTechnique Context triple: [The Lighthouse at Honfleur, colorTechnique, division of color into small dots]
-
A.
colorTheory
Indicates a relationship where principles or concepts about how colors interact, combine, or affect perception are applied or referenced between entities.
-
B.
texture
Indicates the surface quality or feel of an entity as perceived by touch or appearance, such as being smooth, rough, soft, or coarse.
-
C.
artisticTechnique
Indicates the method, style, or process used to create or execute an artistic work.
-
D.
featuresTechnique
chosen
Indicates that something incorporates or makes use of a particular technique as part of its content or execution.
-
E.
supportsColorSampling
Indicates that one entity can perform or accommodate color sampling operations on another entity or its data.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af036243b4819097efe6b796823cd9 |
completed | March 9, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69af01959c4881909eb1adcb3bdadbe6 |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:48 p.m.