Triple
T5259465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Dark Spot |
E118786
|
entity |
| Predicate | atmosphericLayer |
P36580
|
FINISHED |
| Object | upper atmosphere |
—
|
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: upper atmosphere | Statement: [Great Dark Spot, atmosphericLayer, upper atmosphere]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: atmosphericLayer Context triple: [Great Dark Spot, atmosphericLayer, upper atmosphere]
-
A.
hasAtmosphericFeature
Indicates that one entity possesses or exhibits a particular feature or characteristic of its atmosphere.
-
B.
hasAtmosphere
Indicates that an astronomical body possesses a surrounding layer of gases held by its gravity.
-
C.
hasGeneralAtmosphere
Indicates that one entity is characterized by or associated with a particular overall mood, tone, or ambient quality provided by another entity.
-
D.
layerOf
chosen
Indicates that one entity forms a distinct layer or stratum of another entity within a structured or composite whole.
-
E.
atmosphericStudyType
Indicates the specific kind or category of study being conducted on the atmosphere or atmospheric phenomena.
- 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_69bd446a42c88190b7ecbef006561d55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7bcced6881909bdb7ac5471a37fe |
completed | March 20, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69bd77c55224819096c0bcfcfae79bd3 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:50 p.m.