Triple
T1305540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RYR |
E27866
|
entity |
| Predicate | airlineServiceArea |
P26826
|
FINISHED |
| Object | point-to-point short-haul routes |
—
|
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: point-to-point short-haul routes | Statement: [RYR, airlineServiceArea, point-to-point short-haul routes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airlineServiceArea Context triple: [RYR, airlineServiceArea, point-to-point short-haul routes]
-
A.
airportServesAs
Indicates that an airport functions in a particular role or capacity (such as primary, secondary, or hub) for a specified area, organization, or service.
-
B.
airlineHub
Indicates that a particular location (typically an airport or city) serves as a central hub or primary operational base for an airline.
-
C.
airportServed
Indicates that a particular airport provides service to, or is used for air travel to and from, a given location or area.
-
D.
hasRegionalAirport
Indicates that a place or region possesses or is served by a regional airport.
-
E.
hasAirportCodeRegion
Indicates that an airport code is associated with, or belongs to, a specific geographic or administrative region.
- 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c13524d481909e8f5bb2ab91f6e4 |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4bee8544c8190874efd9bae9bccf9 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf60545c8190901ccfb2cb7c4b41 |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:51 p.m.