Triple
T17366527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bryonia dioica |
E422200
|
entity |
| Predicate | toxicCompoundClass |
P8036
|
FINISHED |
| Object | cucurbitacins |
—
|
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: cucurbitacins | Statement: [Bryonia dioica, toxicCompoundClass, cucurbitacins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toxicCompoundClass Context triple: [Bryonia dioica, toxicCompoundClass, cucurbitacins]
-
A.
toxicTo
Indicates that one entity causes harm, poisoning, or adverse effects to another when exposed or applied.
-
B.
toxinType
chosen
Indicates the specific kind or category of toxin associated with an entity.
-
C.
hasChemicalClass
Indicates that an entity belongs to, or is categorized under, a particular chemical class based on its structural or compositional characteristics.
-
D.
toxinProduced
Indicates that one entity generates or secretes a substance that is toxic or harmful to another entity or its environment.
-
E.
containsChemical
Indicates that one entity includes or has within it a specified chemical substance.
- 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a50c9ec8190b518fdf80808af53 |
completed | April 19, 2026, 2:13 a.m. |
| PD | Predicate disambiguation | batch_69e3b02662d08190a07d0fb5c04b6f33 |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:44 a.m.