Triple
T19295970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snyder’s of Hanover Pretzel Pieces |
E482562
|
entity |
| Predicate | mayContainAllergens |
P23191
|
FINISHED |
| Object | soy |
—
|
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: soy | Statement: [Snyder’s of Hanover Pretzel Pieces, mayContainAllergens, soy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayContainAllergens Context triple: [Snyder’s of Hanover Pretzel Pieces, mayContainAllergens, soy]
-
A.
isAllergenFor
Indicates that one entity acts as an allergen that can trigger an allergic reaction in another entity.
-
B.
containsAllergenicCompound
chosen
Indicates that the subject entity includes one or more compounds known to cause allergic reactions.
-
C.
mayBeGlutenFreeIf
Indicates that something has the potential to be gluten-free under certain conditions or assumptions.
-
D.
isNonAllergenic
Indicates that something does not cause allergic reactions or is free from common allergens.
-
E.
actualAllergenComparison
Indicates a comparison between the actual allergen associated with one entity and the allergen associated with another entity, typically to determine if they match, differ, or how they relate.
- 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_69d8e8cf61b0819096fe3e4107827c4e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fc8533c08190822a917ffa32812d |
completed | April 20, 2026, 10:14 a.m. |
| PD | Predicate disambiguation | batch_69e4dd0bc7508190a6f9d56bd4c3404f |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:31 p.m.