Triple
T18309284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyne Dock Metro station |
E438575
|
entity |
| Predicate | ticketMachineAccepts |
P131280
|
FINISHED |
| Object | cash |
—
|
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: cash | Statement: [Tyne Dock Metro station, ticketMachineAccepts, cash]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketMachineAccepts Context triple: [Tyne Dock Metro station, ticketMachineAccepts, cash]
-
A.
ticketMachines
Indicates that there is a relationship involving ticket machines, typically denoting where they are located, available, or associated with a particular entity or place.
-
B.
ticketTypeAccepted
Indicates that a particular type of ticket is valid for use or accepted in a given context or by a given entity.
-
C.
hasTicketBooths
Indicates that one entity possesses or contains ticket booths used for selling or distributing tickets.
-
D.
ticketAcceptanceArea
Indicates the geographic or operational area within which a given ticket is valid for use or accepted as proof of payment.
-
E.
hasNearbyQuarter
Indicates that one entity is located within a short distance of a specific quarter or district associated with another entity.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021709f88190a8047dd57edc2029 |
completed | April 19, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69e44fdf43d08190bbcfb6b1fe3cc0ee |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e451a0ba208190a5fe92832a8f7a49 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 10:36 a.m.