Triple
T23639087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Elizabeth Land |
E583834
|
entity |
| Predicate | iceThicknessCharacteristic |
P29933
|
FINISHED |
| Object | very thick ice cover |
—
|
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: very thick ice cover | Statement: [Princess Elizabeth Land, iceThicknessCharacteristic, very thick ice cover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: iceThicknessCharacteristic Context triple: [Princess Elizabeth Land, iceThicknessCharacteristic, very thick ice cover]
-
A.
typicalIceThickness
chosen
Indicates the usual or characteristic thickness of ice under normal or representative conditions.
-
B.
iceFeature
Indicates a relationship where a geographic or environmental feature is composed of, covered by, or characterized by ice.
-
C.
hasIceSurface
Indicates that an entity possesses or is characterized by a surface composed primarily of ice.
-
D.
iceClass
Indicates a classification relationship specifying the level or category of ice-strengthening or ice-navigation capability assigned to a vessel or structure.
-
E.
hasTypicalIceRegime
Indicates that there is a characteristic or commonly occurring pattern of ice conditions associated with the referenced entity.
- 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_69e248fe1c2c8190ac914d2442ff3d26 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b27fc22c8190abda7398b9fb928c |
completed | April 29, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f118d7903c8190bb590a71771e93af |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:48 p.m.