Triple
T6997923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parmigiano Reggiano |
E162263
|
entity |
| Predicate | lactoseContent |
P74137
|
FINISHED |
| Object | naturally very low in lactose |
—
|
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: naturally very low in lactose | Statement: [Parmigiano Reggiano, lactoseContent, naturally very low in lactose]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lactoseContent Context triple: [Parmigiano Reggiano, lactoseContent, naturally very low in lactose]
-
A.
milkComposition
Indicates the specific nutrients and components that make up a given sample of milk.
-
B.
hasSugarContent
Indicates that one entity possesses or contains a specified amount or level of sugar.
-
C.
typicalFatContent
Indicates the usual or characteristic amount of fat contained in something, such as a food or product.
-
D.
proteinContent
Indicates the amount or proportion of protein present in a given entity or substance.
-
E.
hasSugarFreeVariant
Indicates that an item has a corresponding version or option that is formulated without sugar.
- 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_69c68857ffc08190857dc62cd5253777 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbeef57881909245c8a5374a8111 |
completed | March 27, 2026, 7:35 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c67c94819084fdcf0398606027 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8c48ba48190b8d3aa7b8d22816b |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:33 p.m.