Triple
T4530209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bee-Line Bus System |
E106276
|
entity |
| Predicate | usesTicketingMethod |
P3383
|
FINISHED |
| Object | on-board fare collection |
—
|
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: on-board fare collection | Statement: [Bee-Line Bus System, usesTicketingMethod, on-board fare collection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesTicketingMethod Context triple: [Bee-Line Bus System, usesTicketingMethod, on-board fare collection]
-
A.
hasTicketing
chosen
Indicates that an entity provides or is associated with a system or mechanism for issuing, managing, or selling tickets.
-
B.
hasTicketIntegration
Indicates that there is an established connection enabling ticket-related data or actions to be shared or synchronized between systems or components.
-
C.
hasTicketRequirement
Indicates that an entity is subject to a specific ticket or admission requirement in order for access, participation, or use to be allowed.
-
D.
usedInE-tickets
Indicates that something (such as a method, technology, or feature) is employed or applied within the context of electronic tickets (e-tickets).
-
E.
hasElectronicTicketing
Indicates that an entity supports or provides electronic ticketing for access, booking, or transactions.
- 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_69bd43f3d6e08190a91824f833d51bbe |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd579ba4188190b4cef6e91772f7e5 |
completed | March 20, 2026, 2:20 p.m. |
| PD | Predicate disambiguation | batch_69bd521cf77c819083852de3094d1377 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:03 p.m.