Triple
T12293390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington State Route 26 |
E293021
|
entity |
| Predicate | primaryLandUseAlongRoute |
P14072
|
FINISHED |
| Object | dryland wheat farming |
—
|
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: dryland wheat farming | Statement: [Washington State Route 26, primaryLandUseAlongRoute, dryland wheat farming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryLandUseAlongRoute Context triple: [Washington State Route 26, primaryLandUseAlongRoute, dryland wheat farming]
-
A.
roadUse
Indicates that an entity utilizes or travels on a particular road or roadway for movement or transport.
-
B.
primaryLandUse
chosen
Indicates the main or dominant way in which a given piece of land is utilized or designated (e.g., residential, agricultural, commercial).
-
C.
majorLandUse
Indicates the primary way a given area of land is utilized or designated (e.g., residential, commercial, agricultural).
-
D.
primaryRailUse
Indicates the main or predominant purpose for which a rail line, segment, or facility is used (e.g., passenger, freight, or mixed traffic).
-
E.
roadType
Indicates the classification or category of a road based on its functional or physical characteristics.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91d23def88190adbaa282dd03d6c6 |
completed | April 10, 2026, 3:54 p.m. |
| PD | Predicate disambiguation | batch_69d91c4ec6c4819085880bdefdd0f354 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:52 p.m.