Triple
T30181794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Rail sectorisation era |
E767220
|
entity |
| Predicate | mainPassengerSector |
P80196
|
FINISHED |
| Object | InterCity |
—
|
NE NERFINISHED |
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: InterCity | Statement: [British Rail sectorisation era, mainPassengerSector, InterCity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainPassengerSector Context triple: [British Rail sectorisation era, mainPassengerSector, InterCity]
-
A.
primaryPassengerGroup
Indicates the main group of passengers that is most directly associated with or served by a given entity or context.
-
B.
majorPassengerService
chosen
Indicates that a transportation facility or route provides primary or significant passenger service as one of its main functions.
-
C.
publicPassengerService
Indicates that an entity provides transportation services that are available to the general public for carrying passengers.
-
D.
hasPassengerTerminalSector
Indicates that a passenger terminal is divided into or associated with a specific sector or subsection within it.
-
E.
passengerSystem
Indicates a relationship where an entity functions as or belongs to a passenger-related system (such as a transport or service system designed for passengers).
- 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_69f2247cc3d88190811dec3face94bf5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6e6029a10819098ff21f58079e70e |
completed | May 3, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d5e8188190b1e1c2e5d1b77031 |
completed | May 3, 2026, 5:57 a.m. |
Created at: April 29, 2026, 7:26 p.m.