Triple
T18727850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York–London |
E457952
|
entity |
| Predicate | isOneOfBusiestInternationalRoutes |
P133336
|
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: [New York–London, isOneOfBusiestInternationalRoutes, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isOneOfBusiestInternationalRoutes Context triple: [New York–London, isOneOfBusiestInternationalRoutes, true]
-
A.
majorInternationalRoutesTo
Indicates that there are significant, globally important transportation routes (such as air, sea, or land corridors) connecting one location to another.
-
B.
oneOfBusiestAirportsIn
Indicates that an airport is among the busiest airports within a specified location or region.
-
C.
isMostPopularRouteOn
Indicates that a particular route is the most frequently chosen or favored option on a given transportation line, network, or service.
-
D.
handlesMostPassengerTrafficOf
Indicates that one entity is responsible for managing the largest share of passenger traffic associated with another entity, compared to all similar entities.
-
E.
airlineHubRoute
Indicates a route that connects an airline’s designated hub airport with another airport in its network.
- F. None of above. chosen
Provenance (4 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d74bd9881908cd314e68327c402 |
completed | April 20, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69e48d03766c8190a43f7681842f4f8d |
completed | April 19, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e49a9bcc0c81908df3e513fd6762ff |
completed | April 19, 2026, 9:04 a.m. |
Created at: April 10, 2026, 11:50 a.m.