Triple
T2863483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiffany glass dome |
E63380
|
entity |
| Predicate | lightingEffect |
P30545
|
FINISHED |
| Object | diffused natural light |
—
|
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: diffused natural light | Statement: [Tiffany glass dome, lightingEffect, diffused natural light]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lightingEffect Context triple: [Tiffany glass dome, lightingEffect, diffused natural light]
-
A.
lightingColor
Indicates the color or hue of the lighting applied to or associated with an entity.
-
B.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
C.
lightingCharacteristic
chosen
Indicates the specific qualities or properties of how something is lit, such as brightness, color, direction, or style of illumination.
-
D.
lightingDesigner
Indicates that an entity is responsible for planning, creating, or supervising the lighting design for a production, event, or environment.
-
E.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
- 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_69ab4c41e8c08190a9e8f5249cc12610 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdfb853908190aa2fd492e9fa5e87 |
completed | March 7, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69abdd123ec48190af50a1859aea50b7 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.