Triple
T17987790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cicuta virosa |
E430279
|
entity |
| Predicate | effectOfPoisoning |
P54346
|
FINISHED |
| Object | convulsions |
—
|
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: convulsions | Statement: [Cicuta virosa, effectOfPoisoning, convulsions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOfPoisoning Context triple: [Cicuta virosa, effectOfPoisoning, convulsions]
-
A.
toxinEffect
chosen
Indicates the harmful impact or physiological response caused by a toxin on a target entity.
-
B.
poisonUsed
Indicates that one entity employed poison as a means to harm, kill, or incapacitate another entity.
-
C.
effectOfDeath
Indicates the causal impact or consequences that a death has on another entity, state, or process.
-
D.
toxicTo
Indicates that one entity causes harm, poisoning, or adverse effects to another when exposed or applied.
-
E.
notableToxicity
Indicates that an entity is recognized for having a significant level or history of toxicity, harm, or detrimental effects in its context or interactions.
- 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_69d8b90364248190a37381adea932f42 |
completed | April 10, 2026, 8:46 a.m. |
| NER | Named-entity recognition | batch_69e4b29d3ad4819096c2600aa2a99f21 |
completed | April 19, 2026, 10:46 a.m. |
| PD | Predicate disambiguation | batch_69e3f90039e4819080527f860dca042e |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:23 a.m.