Triple
T4297643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlton, New York |
E99753
|
entity |
| Predicate | hasRuralLandUse |
P43475
|
FINISHED |
| Object | agricultural land |
—
|
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: agricultural land | Statement: [Charlton, New York, hasRuralLandUse, agricultural land]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRuralLandUse Context triple: [Charlton, New York, hasRuralLandUse, agricultural land]
-
A.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
B.
hasRuralHinterland
Indicates that a place or urban area is associated with and served by a surrounding rural region that supports it economically, socially, or functionally.
-
C.
hasAgriculturalCharacter
Indicates that something possesses qualities, features, or uses typical of agriculture or farming activities.
-
D.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
E.
hasLandUseCharacter
chosen
Indicates that one entity possesses or is associated with a particular type or pattern of land use.
- 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_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3509bff808190a86fade7ccfc3611 |
completed | March 12, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69b347fe55a88190b77bab0c0f38e1aa |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:08 p.m.