Triple
T38188782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London–Manchester |
E1005393
|
entity |
| Predicate | isMajorBusinessTravelCorridor |
P101129
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [London–Manchester, isMajorBusinessTravelCorridor, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMajorBusinessTravelCorridor Context triple: [London–Manchester, isMajorBusinessTravelCorridor, true]
-
A.
isMajorBusinessRoute
chosen
Indicates that a route serves as a primary corridor for significant commercial or business-related traffic and activities.
-
B.
hasMajorCityOnRoute
Indicates that a major city lies along, or is directly served by, a specified route or path between locations.
-
C.
isMajorTransportAxisIn
Indicates that a route or corridor functions as a primary or heavily used transportation pathway within a specified area or region.
-
D.
isMajorCargoAirport
Indicates that an airport primarily handles large volumes of cargo traffic and serves as a significant freight hub.
-
E.
isMajorPassengerRoute
Indicates that a transportation route serves as a primary corridor carrying a high volume of passenger traffic between locations.
- 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_69f76dbc22c481908139b694ffde7a0c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fea2d0a4d08190aa06aeb902a02d5a |
completed | May 9, 2026, 2:58 a.m. |
| PD | Predicate disambiguation | batch_69fea24698348190b9b992a8e7cdbcd0 |
completed | May 9, 2026, 2:56 a.m. |
Created at: May 3, 2026, 4:29 p.m.