Triple
T3888067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syntagma metro station |
E87991
|
entity |
| Predicate | hasTypeOfTicketing |
P3383
|
FINISHED |
| Object | automatic ticket machines |
—
|
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: automatic ticket machines | Statement: [Syntagma metro station, hasTypeOfTicketing, automatic ticket machines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfTicketing Context triple: [Syntagma metro station, hasTypeOfTicketing, automatic ticket machines]
-
A.
hasTicketing
chosen
Indicates that an entity provides or is associated with a system or mechanism for issuing, managing, or selling tickets.
-
B.
hasElectronicTicketing
Indicates that an entity supports or provides electronic ticketing for access, booking, or transactions.
-
C.
hasTicketFormat
Indicates that an entity’s ticket is expressed or structured in a particular format.
-
D.
hasTicketRequirement
Indicates that an entity is subject to a specific ticket or admission requirement in order for access, participation, or use to be allowed.
-
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_69aed9466d548190939f5217a23ed4ac |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecad4bf081909ae45a69d22468fa |
completed | March 9, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69aee759609c8190985e96ec6d96dedd |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:21 p.m.