Triple
T12284594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Espresso Martini |
E292795
|
entity |
| Predicate | commonGarnishCount |
P56695
|
FINISHED |
| Object | three coffee beans |
—
|
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: three coffee beans | Statement: [Espresso Martini, commonGarnishCount, three coffee beans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonGarnishCount Context triple: [Espresso Martini, commonGarnishCount, three coffee beans]
-
A.
isTypicallyGarnishedWith
chosen
Indicates that one item is commonly used as a garnish or decorative finishing element for another.
-
B.
numberOfEntremeses
Indicates the quantity or count of entremeses (short theatrical pieces) associated with a given entity or event.
-
C.
sharesCuisineWith
Indicates that two entities offer or are associated with the same type or style of cuisine.
-
D.
numberOfWings
Indicates the quantity of wings that an entity possesses.
-
E.
hasApproximateNumberOfVarieties
Indicates that an entity is associated with an estimated or non-exact count of different varieties or types.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9261e1570819084bb4fdb44aa6aea |
completed | April 10, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69d91c4d9a9c8190aeb7beaf9792d8f0 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:52 p.m.