Triple
T27439142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elkhorn River |
E690874
|
entity |
| Predicate | drainageAreaLandUse |
P107197
|
FINISHED |
| Object | row-crop agriculture |
—
|
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: row-crop agriculture | Statement: [Elkhorn River, drainageAreaLandUse, row-crop agriculture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drainageAreaLandUse Context triple: [Elkhorn River, drainageAreaLandUse, row-crop agriculture]
-
A.
drainageBasinLandUse
chosen
Indicates how the land within a drainage basin is utilized or managed (e.g., for agriculture, urban development, or natural vegetation).
-
B.
drainageBasinLandCover
Indicates the type or characteristics of land cover present within a drainage basin area.
-
C.
majorLandUse
Indicates the primary way a given area of land is utilized or designated (e.g., residential, commercial, agricultural).
-
D.
otherLandUse
Indicates that the land is used for purposes that do not fall into any of the primary or predefined land-use categories.
-
E.
drainageAreaFeature
Indicates the geographic feature or area from which water drains into a particular water body or drainage system.
- 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_69ef5200fa0481908e28508d6e2c149e |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c663be481908f233d25d28713a4 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 12:44 p.m.