Triple
T768566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virgin Valley black fire opal |
E16228
|
entity |
| Predicate | hasOpticalEffect |
P16366
|
FINISHED |
| Object | play of color |
—
|
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: play of color | Statement: [Virgin Valley black fire opal, hasOpticalEffect, play of color]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpticalEffect Context triple: [Virgin Valley black fire opal, hasOpticalEffect, play of color]
-
A.
usesOpticsType
Indicates that one entity employs or is characterized by a specific type of optical system or technology.
-
B.
visualEffect
chosen
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
C.
hasOpticalImageStabilization
Indicates that a device or component includes a feature that reduces image blur caused by camera movement during capture.
-
D.
hasFilmColorType
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
-
E.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
- 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a765ba688190ab328bb159583077 |
completed | March 1, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69a4a5074c788190a74fc20ad24e2d26 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.