Triple
T11194232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Euston – Crewe |
E264878
|
entity |
| Predicate | railCorridorDirection |
P24810
|
FINISHED |
| Object | north–south |
—
|
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: north–south | Statement: [London Euston – Crewe, railCorridorDirection, north–south]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railCorridorDirection Context triple: [London Euston – Crewe, railCorridorDirection, north–south]
-
A.
railwayTrafficDirection
Indicates the customary side of the track on which trains are operated or expected to run within a given railway system or segment.
-
B.
railCorridorType
Indicates the specific classification or type of a rail corridor associated with the subject (e.g., mainline, branch line, high-speed, freight, etc.).
-
C.
terminusDirection
Indicates the directional orientation or endpoint direction associated with a route, path, or line.
-
D.
transportDirection
chosen
Indicates the directional flow or route along which something is transported from an origin toward a destination.
-
E.
trainNumberDirection
Indicates the specific direction in which a train, identified by its train number, is traveling or scheduled to travel.
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8bf14e481908563b15790af4d20 |
completed | April 9, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69d75cf4461c8190af84060f7db83211 |
completed | April 9, 2026, 8:01 a.m. |
Created at: April 8, 2026, 9:29 p.m.