Triple
T8658473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shahid Haghani Metro Station |
E205482
|
entity |
| Predicate | ticketingSystemType |
P61444
|
FINISHED |
| Object | electronic ticketing |
—
|
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: electronic ticketing | Statement: [Shahid Haghani Metro Station, ticketingSystemType, electronic ticketing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketingSystemType Context triple: [Shahid Haghani Metro Station, ticketingSystemType, electronic ticketing]
-
A.
ticketTypeExample
Indicates that an entity serves as an example or illustrative instance of a particular ticket type.
-
B.
primaryTicketingSystem
chosen
Indicates that one ticketing system is designated as the main or default system used for handling tickets in a given context.
-
C.
ticketSystem
Indicates a relationship where an entity is managed, tracked, or processed through a ticket-based system for handling requests, issues, or tasks.
-
D.
ticketClassSystem
Indicates that an entity is classified within a particular ticketing or fare class system that defines categories or levels of tickets.
-
E.
ticketingCode
Indicates the specific fare or booking code associated with a ticket that defines its pricing, rules, and conditions of use.
- 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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc486d576081908ad28749c7971432 |
completed | March 31, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:30 p.m.