Triple
T7130536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Watlington railway station |
E166173
|
entity |
| Predicate | hasUnstaffedTicketOffice |
P75022
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Watlington railway station, hasUnstaffedTicketOffice, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnstaffedTicketOffice Context triple: [Watlington railway station, hasUnstaffedTicketOffice, yes]
-
A.
hasStaffedTicketOffice
Indicates that a location or facility has a ticket office that is staffed by personnel.
-
B.
hasBookingOffice
Indicates that one entity maintains or is associated with a booking office where reservations or ticketing services are handled for it.
-
C.
hasClerk
Indicates that an entity is served, assisted, or managed by a clerk associated with it.
-
D.
hasStaffedHours
Indicates that specific hours or time periods are assigned during which staff are present and available.
-
E.
hasProperOffice
Indicates that an entity maintains an officially designated, appropriate office or place of business.
- F. None of above. chosen
Provenance (4 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_69c6888350588190870cd552b427a1cd |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e66dc2388190bdec018f1cc6b20a |
completed | March 27, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c7289881909f3b533c384f9ed4 |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a213508190a40aca39f9eee7d5 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:44 p.m.