Triple
T17039472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caffeine-Free Coca-Cola |
E413406
|
entity |
| Predicate | sugarContentType |
P65313
|
FINISHED |
| Object | sugar-sweetened |
—
|
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: sugar-sweetened | Statement: [Caffeine-Free Coca-Cola, sugarContentType, sugar-sweetened]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sugarContentType Context triple: [Caffeine-Free Coca-Cola, sugarContentType, sugar-sweetened]
-
A.
sugarContentCategory
Indicates the classification of something based on how much sugar it contains (e.g., low, medium, or high sugar content).
-
B.
hasSugarContent
Indicates that one entity possesses or contains a specified amount or level of sugar.
-
C.
sweetenerType
chosen
Indicates the specific kind or category of sweetener associated with or used in relation to an entity.
-
D.
hasSugarFreeVariant
Indicates that an item has a corresponding version or option that is formulated without sugar.
-
E.
isSugarFree
Indicates that something does not contain sugar or has been formulated without added sugar.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d8f5844c819097eade4a2b42ab91 |
completed | April 18, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69e35d60a588819084f53ef9f8b2e7c0 |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.