Triple
T1029401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yellow Sea |
E22214
|
entity |
| Predicate | hasSeaSurfaceFreezing |
P19069
|
FINISHED |
| Object | in northern parts during winter |
—
|
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: in northern parts during winter | Statement: [Yellow Sea, hasSeaSurfaceFreezing, in northern parts during winter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeaSurfaceFreezing Context triple: [Yellow Sea, hasSeaSurfaceFreezing, in northern parts during winter]
-
A.
hasSeaIce
Indicates that one entity possesses, contains, or is covered by sea ice in relation to another context or location.
-
B.
hasSeaIceExtent
Indicates that a specified area or region possesses a measurable amount or coverage of sea ice over a given space or time.
-
C.
requiresFrostFreeSeason
Indicates that the subject depends on a period without frost to grow, develop, or function properly.
-
D.
hasIcebergs
Indicates that one entity (typically a body of water or region) contains or is characterized by the presence of icebergs.
-
E.
freezesOver
chosen
Indicates that a liquid surface becomes solid due to low temperatures, typically forming a layer of ice over it.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b95d35888190a20593a278175df7 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b7276180819085c6b23501a6a6e0 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.