Triple
T16000727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pop card (Tyne and Wear Metro) |
E388082
|
entity |
| Predicate | ticketingScheme |
P56945
|
FINISHED |
| Object | Pop brand |
—
|
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: Pop brand | Statement: [Pop card (Tyne and Wear Metro), ticketingScheme, Pop brand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketingScheme Context triple: [Pop card (Tyne and Wear Metro), ticketingScheme, Pop brand]
-
A.
ticketingScope
Indicates the range or domain within which ticketing actions (such as creation, assignment, or management of tickets) are valid or applicable.
-
B.
ticketingCode
Indicates the specific fare or booking code associated with a ticket that defines its pricing, rules, and conditions of use.
-
C.
ticketClassSystem
chosen
Indicates that an entity is classified within a particular ticketing or fare class system that defines categories or levels of tickets.
-
D.
ticketFormat
Indicates the specific structure, layout, or template in which a ticket is represented or issued.
-
E.
ticketingCompatibleWith
Indicates that two systems, services, or components can interoperate or be used together within the same ticketing or reservation workflow without conflict.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:55 a.m.