Triple
T7803250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwangandaegyo |
E180482
|
entity |
| Predicate | lightingControl |
P40123
|
FINISHED |
| Object | computer-controlled LED system |
—
|
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: computer-controlled LED system | Statement: [Gwangandaegyo, lightingControl, computer-controlled LED system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lightingControl Context triple: [Gwangandaegyo, lightingControl, computer-controlled LED system]
-
A.
lightingControlledBy
chosen
Indicates that the lighting system or device is operated, regulated, or influenced by a specified controller or control mechanism.
-
B.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
C.
lightType
Indicates the specific category or kind of light associated with an entity or lighting setup.
-
D.
lightingDesigner
Indicates that an entity is responsible for planning, creating, or supervising the lighting design for a production, event, or environment.
-
E.
lightingColor
Indicates the color or hue of the lighting applied to or associated with an 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:34 p.m.