Triple
T13257729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobby Whitman |
E315704
|
entity |
| Predicate | threatContext |
P109184
|
FINISHED |
| Object | deadly training exercise |
—
|
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: deadly training exercise | Statement: [Bobby Whitman, threatContext, deadly training exercise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: threatContext Context triple: [Bobby Whitman, threatContext, deadly training exercise]
-
A.
threatType
Indicates the specific category or nature of a threat that one entity poses or represents in relation to another.
-
B.
threatCategory
Indicates the classification of a threat according to its type, severity, or nature within a defined risk or security framework.
-
C.
threat
Indicates a relationship where one entity expresses or poses potential harm, danger, or negative consequences toward another entity.
-
D.
threatStatus
Indicates the level or category of risk or danger posed by one entity to another or to a defined system or environment.
-
E.
threatFactors
Indicates that certain conditions, elements, or circumstances contribute to increasing the risk or likelihood of a harmful or adverse outcome.
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f60911081909fa346a054f76c9f |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99cf7f9c48190a6a4f452b4a2aefa |
completed | April 11, 2026, 12:59 a.m. |
Created at: April 9, 2026, 9:25 p.m.