Triple
T29852341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melilotus officinalis |
E758096
|
entity |
| Predicate | toxicityNote |
P167667
|
FINISHED |
| Object | spoiled or moldy plant material can form dicoumarol |
—
|
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: spoiled or moldy plant material can form dicoumarol | Statement: [Melilotus officinalis, toxicityNote, spoiled or moldy plant material can form dicoumarol]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toxicityNote Context triple: [Melilotus officinalis, toxicityNote, spoiled or moldy plant material can form dicoumarol]
-
A.
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.
-
B.
toxicityType
Indicates the specific kind or category of toxic effect associated with a substance, action, or exposure.
-
C.
toxicityRoute
Indicates the route or pathway through which a toxic effect or substance is delivered or exposed to an entity.
-
D.
toxicTo
Indicates that one entity causes harm, poisoning, or adverse effects to another when exposed or applied.
-
E.
toxinType
Indicates the specific kind or category of toxin associated with an entity.
- 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_69f2245a82cc8190a387e7d0118d710b |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67648f35c8190ab466e413b6dbcb5 |
completed | May 2, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69f66ac32b60819092290b2de35988d3 |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66bd123108190b451eb6e23842adb |
completed | May 2, 2026, 9:25 p.m. |
Created at: April 29, 2026, 5:44 p.m.