Triple
T8710777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enkutatash |
E206768
|
entity |
| Predicate | beverageTraditions |
P4038
|
FINISHED |
| Object | traditional Ethiopian coffee ceremony |
—
|
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: traditional Ethiopian coffee ceremony | Statement: [Enkutatash, beverageTraditions, traditional Ethiopian coffee ceremony]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: beverageTraditions Context triple: [Enkutatash, beverageTraditions, traditional Ethiopian coffee ceremony]
-
A.
traditionalDrink
chosen
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
B.
beverageSubcategory
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
C.
nationalWineTradition
Indicates that a country or region has an established cultural and historical practice of producing, consuming, and valuing wine.
-
D.
featuresBeverage
Indicates that one entity includes, offers, or presents a particular beverage as part of its contents, services, or characteristics.
-
E.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
- 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_69ca835645e881908f00e3c8b51da81d |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5c3189f88190bb9bb77ba9d28d60 |
completed | March 31, 2026, 11:43 p.m. |
| PD | Predicate disambiguation | batch_69cc456bda508190a9aa0fb92760739e |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:35 p.m.