Triple
T3439049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yersinia pestis |
E72523
|
entity |
| Predicate | biosafetyConcern |
P30182
|
FINISHED |
| Object | potential bioterrorism agent |
—
|
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: potential bioterrorism agent | Statement: [Yersinia pestis, biosafetyConcern, potential bioterrorism agent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: biosafetyConcern Context triple: [Yersinia pestis, biosafetyConcern, potential bioterrorism agent]
-
A.
zoonoticPotential
Indicates the potential for a disease or pathogen to be transmitted from animals to humans.
-
B.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
-
C.
observationSafety
Indicates that an observation or monitoring activity is conducted in a manner that ensures the safety of the subjects, observers, and environment involved.
-
D.
safetyRelevant
chosen
Indicates that the associated entity, condition, or information has a direct impact on safety or is critical for preventing harm or accidents.
-
E.
notableSafety
Indicates that an entity is recognized for having significant safety characteristics, performance, or impact relative to others.
- 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_69ad85af50288190a854b76653deee6f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb9f6de1481909b789f4b0e1113d3 |
completed | March 8, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69adae00ad588190bef24373b58a2e1a |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:16 p.m.