Triple
T27769736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SouthJet Airlines |
E701708
|
entity |
| Predicate | usesFictionalAircraftType |
P163233
|
FINISHED |
| Object | commercial jet airliner |
—
|
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: commercial jet airliner | Statement: [SouthJet Airlines, usesFictionalAircraftType, commercial jet airliner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesFictionalAircraftType Context triple: [SouthJet Airlines, usesFictionalAircraftType, commercial jet airliner]
-
A.
usesAircraftFeature
Indicates that one entity employs or takes advantage of a specific feature or capability of an aircraft.
-
B.
usesCarrierAircraft
Indicates that one entity employs or operates aircraft that are designed to be launched from and recovered by an aircraft carrier.
-
C.
usesAircraftVariant
Indicates that one entity operates or employs a specific variant or version of an aircraft.
-
D.
usedOnAircraftName
Indicates that something is employed or applied on an aircraft identified by a specific name.
-
E.
aircraftTypesUsedOn
Indicates the types or models of aircraft that are used on or assigned to a particular route, service, operation, or context.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f637962be88190b63239f6e4b782f4 |
completed | May 2, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69f63188e7408190af8ce8b93d128c63 |
completed | May 2, 2026, 5:16 p.m. |
| PDg | Predicate description generation | batch_69f6352df6148190bc10772cd40bd7b3 |
completed | May 2, 2026, 5:32 p.m. |
Created at: April 27, 2026, 4:34 p.m.