Triple
T3923216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maryport and Carlisle Railway |
E93209
|
entity |
| Predicate | notableTraffic |
P621
|
FINISHED |
| Object | coal |
—
|
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: coal | Statement: [Maryport and Carlisle Railway, notableTraffic, coal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableTraffic Context triple: [Maryport and Carlisle Railway, notableTraffic, coal]
-
A.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
-
B.
notableRouteType
Indicates that a route is particularly significant or well-known for a specific type or category (e.g., scenic, historic, commercial).
-
C.
trafficType
chosen
Indicates the category or nature of traffic involved in a given interaction, flow, or connection (e.g., type of network, data, or transport traffic).
-
D.
notableTrain
Indicates that there is a train or rail service associated with the subject that is considered notable or significant in some way.
-
E.
hasTrafficPattern
Indicates that there is a characteristic or recurring flow of traffic associated with an entity, such as its typical volume, direction, or timing of movement.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeed7c2c848190a6d62e2df9b942d4 |
completed | March 9, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69aee7609c4081908000ce12ae827c3f |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:23 p.m.