Triple
T33965055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montes Caucasus |
E870826
|
entity |
| Predicate | hasAlbedoContrastWith |
P76599
|
FINISHED |
| Object | Mare Imbrium basaltic plains |
—
|
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: Mare Imbrium basaltic plains | Statement: [Montes Caucasus, hasAlbedoContrastWith, Mare Imbrium basaltic plains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlbedoContrastWith Context triple: [Montes Caucasus, hasAlbedoContrastWith, Mare Imbrium basaltic plains]
-
A.
albedoContrast
chosen
Indicates the degree to which two surfaces or regions differ in their reflectivity (albedo), typically highlighting contrast in brightness.
-
B.
hasAlbedo
Indicates that an entity possesses a specific reflectivity or albedo value, describing how much incoming light it reflects.
-
C.
providesContrastWith
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
D.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
E.
hasHighAlbedo
Indicates that the subject reflects a large proportion of incoming light or radiation from its surface.
- 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_69f3499ce8e88190b66e1d49ad8c7037 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd68abf52881909c5a390c362b7c59 |
completed | May 8, 2026, 4:38 a.m. |
| PD | Predicate disambiguation | batch_69fd6812d0c88190930d8fa2d4b92490 |
completed | May 8, 2026, 4:35 a.m. |
Created at: May 1, 2026, 1:50 a.m.