Triple
T30906442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Artemisia vulgaris |
E787312
|
entity |
| Predicate | allergenicPotential |
P81999
|
FINISHED |
| Object | can cause allergic reactions |
—
|
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: can cause allergic reactions | Statement: [Artemisia vulgaris, allergenicPotential, can cause allergic reactions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: allergenicPotential Context triple: [Artemisia vulgaris, allergenicPotential, can cause allergic reactions]
-
A.
allergenicity
chosen
Indicates that one entity has the capacity to cause an allergic reaction in another entity.
-
B.
isAllergenFor
Indicates that one entity acts as an allergen that can trigger an allergic reaction in another entity.
-
C.
notableAllergenicGenera
Indicates that the subject is associated with genera that are particularly notable for causing allergic reactions.
-
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_69f224be300c8190a6513ce1ee0a7026 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6927febd0819088a01859f80a7e56 |
completed | May 3, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69f68b7ec098819080480998038de940 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:50 p.m.