Triple
T12313950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ViaMobilidade lines |
E293550
|
entity |
| Predicate | operationModel |
P9632
|
FINISHED |
| Object | public–private partnership |
—
|
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: public–private partnership | Statement: [ViaMobilidade lines, operationModel, public–private partnership]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operationModel Context triple: [ViaMobilidade lines, operationModel, public–private partnership]
-
A.
operatingModel
chosen
Indicates how an organization structures and manages its processes, resources, and governance to deliver its products or services.
-
B.
executionModel
Indicates how a process, task, or operation is carried out or implemented, specifying the underlying method, strategy, or mechanism of its execution.
-
C.
operationName
Indicates the specific action or procedure being performed or invoked within a process or system.
-
D.
userInteractionModel
Indicates how a user is expected to interact with a system, defining the style, rules, or pattern of those interactions.
-
E.
operationOf
Indicates that one entity is the function, activity, or process carried out by another entity (such as a system, device, or organization).
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec02c008190a56aae60a3d9eff6 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.