Triple
T38541695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topo Chico Hard Seltzer |
E924848
|
entity |
| Predicate | hasCalorieContentPerServing |
P51659
|
FINISHED |
| Object | 100 calories |
—
|
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: 100 calories | Statement: [Topo Chico Hard Seltzer, hasCalorieContentPerServing, 100 calories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCalorieContentPerServing Context triple: [Topo Chico Hard Seltzer, hasCalorieContentPerServing, 100 calories]
-
A.
hasCalories
chosen
Indicates that an entity contains a specified amount of caloric energy.
-
B.
commonServingSize
Indicates that two or more food items share the same standard or typical serving size used for comparison or labeling.
-
C.
marketedAsServing
Indicates that something is promoted or advertised as providing service to a particular audience, purpose, or function.
-
D.
hasNumberOfServingsPerRecipe
Indicates the quantity of servings that a single recipe yields.
-
E.
hasServingSizeVariant
Indicates that one item is a version of another that differs specifically in serving size.
- 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_69f76eadeac081909cdfdd0474cb6765 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a006fe981488190b4287289a3327664 |
completed | May 10, 2026, 11:45 a.m. |
| PD | Predicate disambiguation | batch_6a006f6976ec8190ba2c04fbaa946345 |
completed | May 10, 2026, 11:43 a.m. |
Created at: May 3, 2026, 4:32 p.m.