Triple
T8911000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Terminals |
E212179
|
entity |
| Predicate | fareZoningRelation |
P24620
|
FINISHED |
| Object | distinct from London 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: distinct from London fare zones | Statement: [London Terminals, fareZoningRelation, distinct from London fare zones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fareZoningRelation Context triple: [London Terminals, fareZoningRelation, distinct from London fare zones]
-
A.
fareZoneHierarchyPosition
Indicates the relative level or rank of a fare zone within a hierarchical fare zone structure.
-
B.
fareZoneIncludes
Indicates that a specified fare zone geographically or logically contains a given location, stop, or segment for fare calculation purposes.
-
C.
fareBoundaryBetween
Indicates that there is a dividing line or zone where one fare region, zone, or pricing scheme ends and another begins.
-
D.
spatialRelation
Indicates a spatial relationship between entities, specifying how one is positioned or located relative to another in space.
-
E.
arealRelation
chosen
Indicates a spatial relationship between areas, such as overlap, containment, adjacency, or relative positioning between two regions.
- 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6523b9348190a7cefac9e73e2004 |
completed | April 1, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69cc5ecf55248190a29f00fbf99f13c4 |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:55 p.m.