Triple
T12284713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lemon Drop Martini |
E292799
|
entity |
| Predicate | hasTypicalGarnish |
P56695
|
FINISHED |
| Object | lemon twist |
—
|
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: lemon twist | Statement: [Lemon Drop Martini, hasTypicalGarnish, lemon twist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalGarnish Context triple: [Lemon Drop Martini, hasTypicalGarnish, lemon twist]
-
A.
isTypicallyGarnishedWith
chosen
Indicates that one item is commonly used as a garnish or decorative finishing element for another.
-
B.
hasGravy
Indicates that one entity is accompanied by, covered with, or served with gravy in relation to another entity or context.
-
C.
hasStapleFood
Indicates that an entity’s primary or regularly consumed basic food item is another specified entity.
-
D.
hasMainIngredient
Indicates that one entity is the primary or most significant ingredient used to make another entity.
-
E.
hasFoodOption
Indicates that an entity offers, provides, or includes a particular type of food or dining option.
- 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.