Triple
T12284595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Espresso Martini |
E292795
|
entity |
| Predicate | symbolicGarnishMeaning |
P104043
|
FINISHED |
| Object | health, wealth, and happiness |
—
|
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: health, wealth, and happiness | Statement: [Espresso Martini, symbolicGarnishMeaning, health, wealth, and happiness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: symbolicGarnishMeaning Context triple: [Espresso Martini, symbolicGarnishMeaning, health, wealth, and happiness]
-
A.
shapeSymbolism
Indicates how a particular shape is associated with or conveys symbolic meaning within a given context.
-
B.
centralFormulaMeaning
Indicates that one formula or expression represents the primary or core meaning within a larger logical, mathematical, or semantic structure.
-
C.
starMeaning
Indicates that one entity represents or conveys the symbolic or interpretive significance of a star associated with another entity.
-
D.
letterMeaning
Indicates that a particular letter conveys a specific meaning, interpretation, or semantic content.
-
E.
logicalMeaning
Indicates that one entity expresses, encodes, or conveys the logical content, implication, or formal meaning of another.
- 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_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. |
| PDg | Predicate description generation | batch_69d9261b7f088190b69fe6961015fce3 |
completed | April 10, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:52 p.m.