Triple
T387694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upper Peninsula of Michigan |
E8813
|
entity |
| Predicate | landCover |
P2022
|
FINISHED |
| Object | heavily forested |
—
|
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: heavily forested | Statement: [Upper Peninsula of Michigan, landCover, heavily forested]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landCover Context triple: [Upper Peninsula of Michigan, landCover, heavily forested]
-
A.
hasLandCoverage
chosen
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
-
B.
landscapeType
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
C.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
D.
ecoregionsInclude
Indicates that one ecoregion spatially contains or encompasses another ecoregion or area within its boundaries.
-
E.
ecoregion
Indicates that one entity is located within, associated with, or belongs to the same ecological region as another entity.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec5828d881909e8810061c02480c |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e967d84c8190a6b647f78d95d4e4 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.