Triple
T15945396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sidama |
E386669
|
entity |
| Predicate | coffeeProductionRole |
P121076
|
FINISHED |
| Object | major coffee-producing community in Ethiopia |
—
|
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: major coffee-producing community in Ethiopia | Statement: [Sidama, coffeeProductionRole, major coffee-producing community in Ethiopia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coffeeProductionRole Context triple: [Sidama, coffeeProductionRole, major coffee-producing community in Ethiopia]
-
A.
teaProductionRegion
Indicates the region or area where tea is produced or cultivated.
-
B.
coffeeOrganization
Indicates a relationship where an organization is involved with coffee, such as producing, distributing, selling, or promoting it.
-
C.
coffeeIntroducedBy
Indicates that a particular coffee was brought, presented, or made known to someone by a specific person or source.
-
D.
viticulturalRole
Indicates the specific function, responsibility, or involvement an entity has within viticulture or grape-growing activities.
-
E.
coffeeProcessingMethods
Indicates the methods or techniques used to transform raw coffee cherries or beans into a consumable coffee product.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e17d48cc9c8190b03fd07ae2e9dfd8 |
completed | April 17, 2026, 12:22 a.m. |
Created at: April 10, 2026, 4:53 a.m.