Triple
T21752456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nescafé |
E536947
|
entity |
| Predicate | hasProductVariant |
P455
|
FINISHED |
| Object | Nescafé Decaf |
—
|
NE NERFINISHED |
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: Nescafé Decaf | Statement: [Nescafé, hasProductVariant, Nescafé Decaf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nescafé Decaf Context triple: [Nescafé, hasProductVariant, Nescafé Decaf]
-
A.
Nescafé
chosen
Nescafé is a globally popular brand of instant coffee and related coffee products owned by Nestlé.
-
B.
Sanka Coffie
Sanka Coffie is the laid-back, humorous pushcart driver and brakeman who provides comic relief and heart in the Jamaican bobsled team in the film "Cool Runnings."
-
C.
Coffee-Mate
Coffee-Mate is a popular non-dairy coffee creamer brand known for its wide variety of flavored and powdered creamers used to enhance coffee.
-
D.
Café au Lait
Café au Lait is one of the short, conversational vignettes in Jim Jarmusch’s film "Coffee and Cigarettes," featuring characters chatting over coffee in a minimalist, black-and-white setting.
-
E.
Koffee
Koffee is a Jamaican reggae and dancehall singer, songwriter, and rapper known for her Grammy-winning EP "Rapture" and hit single "Toast."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d8b8b9c8190b1f6a8bc25d69dbb |
completed | April 28, 2026, 2:38 a.m. |
Created at: April 16, 2026, 6:50 p.m.