Triple
T16171526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russell, Majors and Waddell |
E392447
|
entity |
| Predicate | businessRisk |
P26874
|
FINISHED |
| Object | high operating costs of the Pony Express |
—
|
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: high operating costs of the Pony Express | Statement: [Russell, Majors and Waddell, businessRisk, high operating costs of the Pony Express]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: businessRisk Context triple: [Russell, Majors and Waddell, businessRisk, high operating costs of the Pony Express]
-
A.
riskBasis
chosen
Indicates the underlying factor, condition, or rationale that forms the basis for assessing or assigning risk in a given context.
-
B.
riskType
Indicates the category or nature of risk associated with an entity, event, or relationship.
-
C.
riskBased
Indicates that something is determined, prioritized, or managed according to the level or assessment of risk involved.
-
D.
riskElement
Indicates that one entity is a risk-related component, factor, or contributor associated with another entity within a risk context.
-
E.
riskFeature
Indicates that one entity possesses or exhibits a characteristic, condition, or attribute that increases the likelihood or severity of a negative outcome for another entity or situation.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb7ab1481908fc35bfc8c56e5f2 |
completed | April 17, 2026, 11:51 a.m. |
| PD | Predicate disambiguation | batch_69e219d642708190ba31a90dce76a210 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.