Triple
T24144445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belle Tout Lighthouse |
E598336
|
entity |
| Predicate | originalLightCharacteristic |
P30545
|
FINISHED |
| Object | fixed white 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: fixed white light | Statement: [Belle Tout Lighthouse, originalLightCharacteristic, fixed white light]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalLightCharacteristic Context triple: [Belle Tout Lighthouse, originalLightCharacteristic, fixed white light]
-
A.
lightingCharacteristic
chosen
Indicates the specific qualities or properties of how something is lit, such as brightness, color, direction, or style of illumination.
-
B.
originalIllumination
Indicates that an entity provides the initial or primary source of light or illumination for another entity or context.
-
C.
preferredLight
Indicates that one entity favors or is best suited to a particular lighting condition or level of illumination.
-
D.
lightType
Indicates the specific category or kind of light associated with an entity or lighting setup.
-
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_69e288c9e488819093dd1acd91b08b8a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e008efbc8190ac6c12d3ba5dd5d8 |
completed | April 29, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69f1765650fc8190a6bc1eb512b240bf |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 11:29 p.m.