Triple
T17657350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fimbul Ice Shelf |
E440158
|
entity |
| Predicate | hasSnowAccumulation |
P117020
|
FINISHED |
| Object | high annual snowfall typical of coastal Antarctica |
—
|
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: high annual snowfall typical of coastal Antarctica | Statement: [Fimbul Ice Shelf, hasSnowAccumulation, high annual snowfall typical of coastal Antarctica]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSnowAccumulation Context triple: [Fimbul Ice Shelf, hasSnowAccumulation, high annual snowfall typical of coastal Antarctica]
-
A.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
B.
snowAccumulation
chosen
Indicates that snow has collected or built up on a surface or in a location over time.
-
C.
hasSnowIn
Indicates that snow is present or occurs within a specified location or region.
-
D.
hasSnowAndIce
Indicates that the subject is covered with or contains both snow and ice.
-
E.
hasSnowfield
Indicates that an entity possesses, contains, or is characterized by a snow-covered field or area.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46ea2b3308190b9ad752728d98856 |
completed | April 19, 2026, 5:56 a.m. |
| PD | Predicate disambiguation | batch_69e3cddc87188190ac2f049b86038676 |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 9:27 a.m.