Triple
T12474524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Australia and United Kingdom |
E298141
|
entity |
| Predicate | hasAirTravelLink |
P105188
|
FINISHED |
| Object | long-haul flights |
—
|
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: long-haul flights | Statement: [Australia and United Kingdom, hasAirTravelLink, long-haul flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAirTravelLink Context triple: [Australia and United Kingdom, hasAirTravelLink, long-haul flights]
-
A.
hasAirlines
Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
-
B.
hasCityPair
Indicates a relationship that links two cities considered as a connected or associated pair, often for purposes such as travel, trade, or comparison.
-
C.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
-
D.
hasTravelNetwork
Indicates that one entity possesses, manages, or is connected through a system or infrastructure enabling travel or transportation between locations.
-
E.
connectsWithAirport
Indicates that there is a direct transportation or operational link established between an entity and an airport.
- 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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e626dbc8190ac7dcdb542ba9b0c |
completed | April 10, 2026, 7:24 p.m. |
| PD | Predicate disambiguation | batch_69d94d3f701c81909dd0e00251ac8553 |
completed | April 10, 2026, 7:19 p.m. |
| PDg | Predicate description generation | batch_69d94e5f8d04819086d1ad4d62364005 |
completed | April 10, 2026, 7:24 p.m. |
Created at: April 8, 2026, 9:56 p.m.