Triple
T27476730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piedmont (Georgia) |
E693482
|
entity |
| Predicate | hasErosionIssues |
P156966
|
FINISHED |
| Object | soil erosion from agriculture and development |
—
|
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: soil erosion from agriculture and development | Statement: [Piedmont (Georgia), hasErosionIssues, soil erosion from agriculture and development]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasErosionIssues Context triple: [Piedmont (Georgia), hasErosionIssues, soil erosion from agriculture and development]
-
A.
hasErosionEffect
Indicates that one entity causes or exhibits an erosive impact or degradation effect on another entity or surface.
-
B.
hasErosionFeature
Indicates that one entity exhibits or contains a specific erosion-related feature or form resulting from erosional processes.
-
C.
hasErosionRole
Indicates that an entity participates in or contributes to an erosion process in a specified role or capacity.
-
D.
erosionConcern
chosen
Indicates concern that a location, structure, or resource is being or may be negatively affected by erosion.
-
E.
hasErosionalState
Indicates the current degree or condition of erosion affecting an entity’s surface or structure.
- 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_69ef5381f2648190a2392d0fab833095 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62e451dd08190b9cbe3a9a2a4ffa6 |
completed | May 2, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69f623aaf40081909f947431424a1d55 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 12:57 p.m.