Triple
T35711729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Conde, South Dakota |
E1031877
|
entity |
| Predicate | surroundedByLandUse |
P183848
|
FINISHED |
| Object | farmland |
—
|
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: farmland | Statement: [Conde, South Dakota, surroundedByLandUse, farmland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: surroundedByLandUse Context triple: [Conde, South Dakota, surroundedByLandUse, farmland]
-
A.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
B.
landUseIncludes
Indicates that a specified land area contains or permits the specified type(s) of land use within its boundaries.
-
C.
surrounds
Indicates that one entity is located all around another entity, enclosing or encircling it on multiple sides or completely.
-
D.
landUseContext
Indicates the contextual setting or circumstances under which a particular piece of land is used or designated for a specific purpose.
-
E.
hasLandUseCharacter
Indicates that one entity possesses or is associated with a particular type or pattern of land use.
- F. None of above. chosen
Provenance (4 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_69f76e0df1d08190965b1c6dff94c391 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
| PDg | Predicate description generation | batch_69f7a34e80dc8190980d5b7b0b91341d |
completed | May 3, 2026, 7:34 p.m. |
Created at: May 3, 2026, 4:05 p.m.