Triple
T12018243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyoto Tower |
E286080
|
entity |
| Predicate | lightingColorChangesForEvents |
P102812
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Kyoto Tower, lightingColorChangesForEvents, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lightingColorChangesForEvents Context triple: [Kyoto Tower, lightingColorChangesForEvents, true]
-
A.
lightingColor
Indicates the color or hue of the lighting applied to or associated with an entity.
-
B.
lightingDependsOn
Indicates that the state or behavior of lighting is determined or influenced by another specified factor or condition.
-
C.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
D.
usesLightingFor
Indicates that one entity employs or relies on a particular lighting setup, technology, or condition to achieve a purpose or perform an action.
-
E.
lightingPractice
Indicates a relationship where an entity engages in or performs activities to rehearse, refine, or experiment with lighting setups or techniques.
- F. None of above. chosen
Provenance (4 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902b6ebbc8190b13c44a61c6f81b9 |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d91006e14081909838412df082f794 |
completed | April 10, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:47 p.m.