Triple
T10382039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koudougou |
E244663
|
entity |
| Predicate | hasNearbyAgriculturalActivity |
P93890
|
FINISHED |
| Object | cotton cultivation |
—
|
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: cotton cultivation | Statement: [Koudougou, hasNearbyAgriculturalActivity, cotton cultivation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyAgriculturalActivity Context triple: [Koudougou, hasNearbyAgriculturalActivity, cotton cultivation]
-
A.
hasAgriculturalCharacter
Indicates that something possesses qualities, features, or uses typical of agriculture or farming activities.
-
B.
hasNearbyHorseFarm
Indicates that one entity is located close to or in the vicinity of a horse farm.
-
C.
hasAgriculturalProduction
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
D.
locatedInAgriculturalRegion
Indicates that an entity is situated within a region primarily characterized by agricultural activities or land use.
-
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. 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e992d8e08190aaa9a04925f52ccc |
completed | April 7, 2026, 11:25 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb0e7a88190bec0b7a52c70dfe2 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e91ce2008190af252c140370b7f2 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 12:04 p.m.