Triple
T10471897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cofiroute |
E246941
|
entity |
| Predicate | usesRevenueModel |
P94216
|
FINISHED |
| Object | tolls |
—
|
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: tolls | Statement: [Cofiroute, usesRevenueModel, tolls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRevenueModel Context triple: [Cofiroute, usesRevenueModel, tolls]
-
A.
revenueUse
Indicates how generated revenue is allocated, spent, or applied toward specific purposes or activities.
-
B.
usesBusinessModel
Indicates that one entity operates according to, or applies in practice, the business model defined or provided by another entity.
-
C.
appliesToRevenueType
Indicates that a rule, condition, or classification is specifically relevant or applicable to a particular type of revenue.
-
D.
usesLicensingModel
Indicates that one entity employs or applies a particular licensing model in its operations or offerings.
-
E.
revenue
Indicates the amount of income generated by an entity from its business activities or operations over a specified period.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5094cec788190a485c5c9e7cd024a |
completed | April 7, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69d4fb84bafc8190819757b93620508a |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe058fcc81909428137d9ffd6d90 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:20 p.m.