Triple
T10857815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Overground |
E256314
|
entity |
| Predicate | ticketingZoneCoverage |
P69645
|
FINISHED |
| Object | multiple TfL fare zones |
—
|
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: multiple TfL fare zones | Statement: [London Overground, ticketingZoneCoverage, multiple TfL fare zones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketingZoneCoverage Context triple: [London Overground, ticketingZoneCoverage, multiple TfL fare zones]
-
A.
ticketingZoneType
Indicates the type or category of ticketing zone that applies within a given area or context.
-
B.
ticketingScope
Indicates the range or domain within which ticketing actions (such as creation, assignment, or management of tickets) are valid or applicable.
-
C.
hasTicketing
Indicates that an entity provides or is associated with a system or mechanism for issuing, managing, or selling tickets.
-
D.
ticketValidityCovers
Indicates that the validity period or scope of a ticket extends to and includes a specified time, location, service, or condition.
-
E.
fareZoneIncludes
chosen
Indicates that a specified fare zone geographically or logically contains a given location, stop, or segment for fare calculation purposes.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d751377da88190a7244bb9d6b0c2ec |
completed | April 9, 2026, 7:11 a.m. |
| PD | Predicate disambiguation | batch_69d70d308dfc81908792f98cfb871392 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:20 p.m.