Triple
T30045160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Middle River (Iowa) |
E763429
|
entity |
| Predicate | hasTypicalLandUseNearby |
P19783
|
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: [Middle River (Iowa), hasTypicalLandUseNearby, farmland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalLandUseNearby Context triple: [Middle River (Iowa), hasTypicalLandUseNearby, farmland]
-
A.
hasNearbyLandUse
chosen
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
B.
hasRecreationalUseNearby
Indicates that there is at least one location or facility for recreational activities situated close to the referenced entity.
-
C.
hasLikelyLandUse
Indicates that an area or parcel is associated with a predicted or most probable type of land use (e.g., residential, commercial, agricultural).
-
D.
adjacentToIndustrialArea
Indicates that one entity is located directly next to or bordering an industrial area.
-
E.
hasNearbyPublicLand
Indicates that one entity is located close to an area of public land, such as parks, reserves, or other publicly accessible open spaces.
- 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_69f22470a89c8190be7273297c0e0d19 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fdd5fba5048190b7d430ae2054a1fd |
completed | May 8, 2026, 12:24 p.m. |
| PD | Predicate disambiguation | batch_69fdd35f76f88190a1854ea27132f9c7 |
completed | May 8, 2026, 12:13 p.m. |
Created at: April 29, 2026, 6:54 p.m.