Triple
T24991673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | candela |
E625463
|
entity |
| Predicate | dependsOnHumanVisionModel |
P154870
|
FINISHED |
| Object | photopic luminous efficiency function V(λ) |
—
|
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: photopic luminous efficiency function V(λ) | Statement: [candela, dependsOnHumanVisionModel, photopic luminous efficiency function V(λ)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dependsOnHumanVisionModel Context triple: [candela, dependsOnHumanVisionModel, photopic luminous efficiency function V(λ)]
-
A.
requiresModelingOf
chosen
Indicates that one entity depends on another entity being represented or simulated in a model in order for it to be properly defined, analyzed, or executed.
-
B.
containsVisionOf
Indicates that one entity includes, depicts, or embodies a visual representation or image of another entity.
-
C.
requiresFineTuningOf
Indicates that one entity needs the adjustment, calibration, or refinement of another entity in order to function correctly or optimally.
-
D.
visualizedIn
Indicates that something is represented or depicted within a particular visual medium, view, or visualization.
-
E.
inspectionRequired
Indicates that an entity must undergo an inspection before it can proceed, be used, or be approved.
- 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_69e2ff2611c081908710457fbe6d376b |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f44a4639e8819090ce27c835eec2f0 |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442c0c2e88190acd7f170f10ccef6 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 6:03 a.m.