Triple
T27720621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Rib River |
E698942
|
entity |
| Predicate | landCoverInDrainageBasin |
P116511
|
FINISHED |
| Object | forested landscape |
—
|
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: forested landscape | Statement: [Big Rib River, landCoverInDrainageBasin, forested landscape]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landCoverInDrainageBasin Context triple: [Big Rib River, landCoverInDrainageBasin, forested landscape]
-
A.
drainageBasinLandCover
chosen
Indicates the type or characteristics of land cover present within a drainage basin area.
-
B.
drainageBasinLandUse
Indicates how the land within a drainage basin is utilized or managed (e.g., for agriculture, urban development, or natural vegetation).
-
C.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
-
D.
vegetationCoverage
Indicates the extent or proportion of an area that is covered by vegetation.
-
E.
naturalRegionType
Indicates the type or category of natural region to which a given area or place belongs.
- 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_69ef591012dc8190a6f1ec994f9f7ff7 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f6363b50f08190a0d8e16d748388f5 |
completed | May 2, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69f62c1a92648190835a2c5250d8c758 |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 3:06 p.m.