Triple
T29787587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cedar Creek watershed |
E756305
|
entity |
| Predicate | includesLandUseType |
P37665
|
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: [Cedar Creek watershed, includesLandUseType, agricultural land]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesLandUseType Context triple: [Cedar Creek watershed, includesLandUseType, agricultural land]
-
A.
landUseIncludes
chosen
Indicates that a specified land area contains or permits the specified type(s) of land use within its boundaries.
-
B.
servesLandUseType
Indicates that one entity functions to support, accommodate, or provide services for a specified land use type.
-
C.
hasLandUseCharacter
Indicates that one entity possesses or is associated with a particular type or pattern of land use.
-
D.
hasLikelyLandUse
Indicates that an area or parcel is associated with a predicted or most probable type of land use (e.g., residential, commercial, agricultural).
-
E.
crossesLandUseType
Indicates that one entity extends across or passes through an area characterized by a particular land use type.
- 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_69f22451fb748190bbdbab401280affb |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_6a00037bf4148190a58593d30efdd3f8 |
completed | May 10, 2026, 4:03 a.m. |
| PD | Predicate disambiguation | batch_6a0000b7af608190b718fc4111bcdad8 |
completed | May 10, 2026, 3:51 a.m. |
Created at: April 29, 2026, 5:10 p.m.