Triple
T830498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Point Sur Lighthouse |
E17952
|
entity |
| Predicate | originalLens |
P20486
|
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: [Point Sur Lighthouse, originalLens, first-order Fresnel lens]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalLens Context triple: [Point Sur Lighthouse, originalLens, first-order Fresnel lens]
-
A.
originalLensOrder
Indicates that one lens precedes another in the original, intended ordering of lenses.
-
B.
originalLabel
Indicates the primary or initial label or name originally assigned to an entity before any changes or translations.
-
C.
usesOpticsType
Indicates that one entity employs or is characterized by a specific type of optical system or technology.
-
D.
originallyIn
Indicates that something first appeared, was created, or was initially located within a particular context, source, or place.
-
E.
originalType
Indicates that one entity represents the initial or source type from which another entity is derived, transformed, or reclassified.
- 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_69a4937c9c188190aaa216f6b466f452 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abb384988190949d2df65662f76d |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa79a6488190a634388e071ed9b7 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4893e481908632102d240466dc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.