Triple
T15759608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lymington Town railway station |
E382059
|
entity |
| Predicate | hasTicketPurchaseOption |
P3383
|
FINISHED |
| Object | ticket machine |
—
|
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: ticket machine | Statement: [Lymington Town railway station, hasTicketPurchaseOption, ticket machine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTicketPurchaseOption Context triple: [Lymington Town railway station, hasTicketPurchaseOption, ticket machine]
-
A.
hasTicketing
chosen
Indicates that an entity provides or is associated with a system or mechanism for issuing, managing, or selling tickets.
-
B.
hasTicketRequirement
Indicates that an entity is subject to a specific ticket or admission requirement in order for access, participation, or use to be allowed.
-
C.
ticketIncludes
Indicates that a particular ticket grants access to, contains, or covers the specified item, service, or component.
-
D.
hasTicketBooths
Indicates that one entity possesses or contains ticket booths used for selling or distributing tickets.
-
E.
ticketTypeAccepted
Indicates that a particular type of ticket is valid for use or accepted in a given context or by a given entity.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b35ea48190a758ee76a57b5451 |
completed | April 16, 2026, 3 a.m. |
| PD | Predicate disambiguation | batch_69e00531e7ac8190a4190cce4f7fab4c |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:47 a.m.