Triple
T16405548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AirAsia |
E398414
|
entity |
| Predicate | notableRouteFocus |
P19187
|
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: [AirAsia, notableRouteFocus, point-to-point short-haul routes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableRouteFocus Context triple: [AirAsia, notableRouteFocus, point-to-point short-haul routes]
-
A.
notableRouteType
chosen
Indicates that a route is particularly significant or well-known for a specific type or category (e.g., scenic, historic, commercial).
-
B.
notableRouteFeature
Indicates that a route is associated with a distinctive or significant feature, such as a landmark, characteristic terrain, or other notable aspect along its path.
-
C.
famousRoute
Indicates that a route is widely known or celebrated, typically due to its historical, cultural, or touristic significance.
-
D.
popularRouteOn
Indicates that a particular route is frequently used or favored on a given transportation line, service, or network.
-
E.
notableSpot
Indicates that a location is recognized as a significant or noteworthy place in some context.
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327d2b4e48190b7153f198639e9cd |
completed | April 18, 2026, 6:42 a.m. |
| PD | Predicate disambiguation | batch_69e226fe1dd08190865c181721f8c348 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:09 a.m.