Triple
T748220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greenland |
E15389
|
entity |
| Predicate | iceSheetCoverage |
P18733
|
FINISHED |
| Object | about 80 percent of land area |
—
|
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: about 80 percent of land area | Statement: [Greenland, iceSheetCoverage, about 80 percent of land area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: iceSheetCoverage Context triple: [Greenland, iceSheetCoverage, about 80 percent of land area]
-
A.
numberOfPlayersOnIcePerTeam
Indicates the count of players from a single team who are on the ice at the same time during play.
-
B.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
C.
playsOnRinkType
Indicates that an entity participates in a game or activity on a specific type of rink surface or rink configuration.
-
D.
homeIceAdvantageBasedOn
Indicates that the degree of home-ice advantage is determined or influenced by the specified factor or condition.
-
E.
hasIcebergs
Indicates that one entity (typically a body of water or region) contains or is characterized by the presence of icebergs.
- 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_69a49358aa308190adbc9b5a0a2adcf9 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a62f31888190b80cb0a7220f8d80 |
completed | March 1, 2026, 8:48 p.m. |
| PD | Predicate disambiguation | batch_69a4a5004f708190a984ee221716e19c |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a58c0a84819094f07658dc651b36 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.