Triple
T27386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pacific Ocean |
E547
|
entity |
| Predicate | coversFractionOfWorldOceanArea |
P1885
|
FINISHED |
| Object | about 46 percent |
—
|
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 46 percent | Statement: [Pacific Ocean, coversFractionOfWorldOceanArea, about 46 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversFractionOfWorldOceanArea Context triple: [Pacific Ocean, coversFractionOfWorldOceanArea, about 46 percent]
-
A.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
B.
hasNotableSea
Indicates that an entity is associated with or contains a sea that is considered notable or significant.
-
C.
hasCoastlineOn
Indicates that one entity’s coastline borders or is directly adjacent to a specified body of water.
-
D.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
E.
hasCityOnShore
Indicates that a city is located on or directly adjacent to the shore of a body of water.
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a247798a348190bb943d38300ae3ef |
completed | Feb. 28, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69a24658749881909117b007ec3d8633 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a24778b73c81908e9f2eb8cdbcef73 |
completed | Feb. 28, 2026, 1:40 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.