Triple
T11782360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Lakes wine region |
E280180
|
entity |
| Predicate | hasAgriculturalBenefitFrom |
P69869
|
FINISHED |
| Object | lake-effect snow |
—
|
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: lake-effect snow | Statement: [Great Lakes wine region, hasAgriculturalBenefitFrom, lake-effect snow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgriculturalBenefitFrom Context triple: [Great Lakes wine region, hasAgriculturalBenefitFrom, lake-effect snow]
-
A.
agricultureUse
Indicates that something is used for, involved in, or designated for agricultural activities or purposes.
-
B.
usedAgriculture
Indicates that an entity employed agricultural methods, practices, or resources for cultivation, production, or related purposes.
-
C.
hasAgriculturalProduction
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
D.
agriculturalFocus
Indicates that an entity is primarily concerned with, oriented toward, or specializing in agriculture or farming-related activities.
-
E.
agriculturalImpact
chosen
Indicates the effect that an action, condition, or entity has on agricultural systems, productivity, or practices.
- 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a8c2e8b08190a31b1e284fca2aee |
completed | April 10, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69d8a242cd8c819086ed6c5f292dc8cb |
completed | April 10, 2026, 7:09 a.m. |
Created at: April 8, 2026, 9:42 p.m.