Triple
T926946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laptev Sea |
E20003
|
entity |
| Predicate | hasTypicalIceRegime |
P22448
|
FINISHED |
| Object | frozen most of the year |
—
|
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: frozen most of the year | Statement: [Laptev Sea, hasTypicalIceRegime, frozen most of the year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalIceRegime Context triple: [Laptev Sea, hasTypicalIceRegime, frozen most of the year]
-
A.
hasSeaIce
Indicates that one entity possesses, contains, or is covered by sea ice in relation to another context or location.
-
B.
hasIcebergs
Indicates that one entity (typically a body of water or region) contains or is characterized by the presence of icebergs.
-
C.
hasSeaIceExtent
Indicates that a specified area or region possesses a measurable amount or coverage of sea ice over a given space or time.
-
D.
hasWaterIce
Indicates that one entity contains, possesses, or is characterized by the presence of water in solid (ice) form.
-
E.
hasGlacier
Indicates that one entity possesses, contains, or is characterized by the presence of a glacier.
- 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_69a493af3dc48190adb7263e6e445ea1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b388f0bc8190a087222636135ba5 |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b2970a4c8190b22cb2fd4706f62b |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b385176081909e3e8c3f647c1fd4 |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:40 p.m.