Triple
T1886591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airbus A380 |
E39977
|
entity |
| Predicate | largestOperatorFleetSize |
P5710
|
FINISHED |
| Object | over 100 aircraft |
—
|
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: over 100 aircraft | Statement: [Airbus A380, largestOperatorFleetSize, over 100 aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: largestOperatorFleetSize Context triple: [Airbus A380, largestOperatorFleetSize, over 100 aircraft]
-
A.
isOneOfLargestAirlinesIn
Indicates that an airline ranks among the largest airlines operating within a specified geographic region or country.
-
B.
aircraftOperator
Indicates that one entity operates, manages, or is responsible for the use of a particular aircraft.
-
C.
airlineFleetCategory
Indicates the classification category of an airline based on the characteristics or size of its aircraft fleet.
-
D.
numberOfPlanes
chosen
Indicates the quantity of planes associated with or involved in a given entity or situation.
-
E.
airTrainOperator
Indicates that one entity operates or manages the air train service associated with 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_69a88633e4fc8190b7eb40463e048ec5 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb12032c881909cd93e3601906f48 |
completed | March 7, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69abafe61bc48190ac9ead027df930e1 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.