Triple
T5034806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Umpqua River Lighthouse |
E113396
|
entity |
| Predicate | hasLensType |
P55795
|
FINISHED |
| Object | first-order Fresnel lens |
—
|
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: first-order Fresnel lens | Statement: [Umpqua River Lighthouse, hasLensType, first-order Fresnel lens]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLensType Context triple: [Umpqua River Lighthouse, hasLensType, first-order Fresnel lens]
-
A.
lensType
chosen
Indicates the specific kind or category of lens associated with or used by an entity.
-
B.
cameraLensType
Indicates the specific type or category of lens used or associated with a camera.
-
C.
usesLensMount
Indicates that one device or component is designed to accept, attach to, or operate with a specific type of lens mount.
-
D.
usesLensBrand
Indicates that one entity employs or operates using a lens produced by a specific brand.
-
E.
laterLensType
Indicates that one lens type occurs or is used at a later time than another lens type in a temporal sequence.
- 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_69bd44384298819089c49e7c330ec7b8 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73b8646c8190b3cc20193e4639ee |
completed | March 20, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69bd71509e9c8190a60c1d8d04936a12 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:36 p.m.