Triple
T29911272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SCNLight |
E759679
|
entity |
| Predicate | hasLightType |
P73043
|
FINISHED |
| Object | ambient |
—
|
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: ambient | Statement: [SCNLight, hasLightType, ambient]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLightType Context triple: [SCNLight, hasLightType, ambient]
-
A.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
B.
lightType
chosen
Indicates the specific category or kind of light associated with an entity or lighting setup.
-
C.
hasLightVariationCause
Indicates that one thing is the reason or source for a change or variation in light associated with another thing.
-
D.
hasLightingEffect
Indicates that one entity applies, produces, or is associated with a particular lighting effect on another entity or environment.
-
E.
hasLightness
Indicates that one entity possesses or exhibits a certain degree or quality of lightness (such as brightness or value) in relation to another.
- 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_69f224600590819085e148a01c056ef6 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fd09840ea88190a2e6d7e577ade717 |
completed | May 7, 2026, 9:52 p.m. |
| PD | Predicate disambiguation | batch_69fd064c49988190afadddbd04d7cb94 |
completed | May 7, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:10 p.m.