Triple
T24582749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amery Ice Shelf |
E608295
|
entity |
| Predicate | hasThicknessRange |
P156439
|
FINISHED |
| Object | 200–1400 meters |
—
|
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: 200–1400 meters | Statement: [Amery Ice Shelf, hasThicknessRange, 200–1400 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasThicknessRange Context triple: [Amery Ice Shelf, hasThicknessRange, 200–1400 meters]
-
A.
hasMaximumThickness
Indicates that an entity possesses a specified upper limit on its thickness.
-
B.
thickness
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
C.
hasStratigraphicThickness
Indicates the measured vertical thickness or depth extent of a stratigraphic unit or layer.
-
D.
typicalThicknessFormula
Indicates the standard or commonly used formula for calculating the thickness of something under typical conditions.
-
E.
wallThicknessComparedTo
Indicates how the thickness of one wall relates to the thickness of another wall, typically in terms of being greater, equal, or less.
- F. None of above. chosen
Provenance (4 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_69e2c4ce89248190ad99e18f0638dfbb |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a983a4408190acdf29ccd52be9d4 |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
| PDg | Predicate description generation | batch_69f2a846c5bc81909ba50cee483bea91 |
completed | April 30, 2026, 12:54 a.m. |
Created at: April 18, 2026, 2:29 a.m.