Triple
T552831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qatar Airways |
E11877
|
entity |
| Predicate | servesNumberOfDestinationsApproximate |
P13065
|
FINISHED |
| Object | 170+ destinations |
—
|
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: 170+ destinations | Statement: [Qatar Airways, servesNumberOfDestinationsApproximate, 170+ destinations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesNumberOfDestinationsApproximate Context triple: [Qatar Airways, servesNumberOfDestinationsApproximate, 170+ destinations]
-
A.
servesDestinationCount
chosen
Indicates the number of distinct destinations that an entity (such as a service, route, or provider) serves.
-
B.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
C.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
D.
numberOfStations
Indicates the total count of stations associated with or contained by a given entity.
-
E.
servesAsGatewayTo
Indicates that one entity functions as an entry point, access route, or intermediary channel that enables reaching or connecting to another entity.
- 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_69a4932941d08190815efd422f0b4ca7 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4991b296481908cf27e1d1ec67052 |
completed | March 1, 2026, 7:52 p.m. |
| PD | Predicate disambiguation | batch_69a494bc1f8c8190904356f3a8e801de |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:32 p.m.