Triple
T22971227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beagle |
E571190
|
entity |
| Predicate | subjectAircraftServiceStatus |
P66892
|
FINISHED |
| Object | retired from front-line service |
—
|
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: retired from front-line service | Statement: [Beagle, subjectAircraftServiceStatus, retired from front-line service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectAircraftServiceStatus Context triple: [Beagle, subjectAircraftServiceStatus, retired from front-line service]
-
A.
aircraftStatus
chosen
Indicates the current operational condition or state of an aircraft (e.g., active, grounded, in maintenance, or decommissioned).
-
B.
ICAOStatus
Indicates the current operational or certification status of an entity as defined by ICAO (e.g., active, suspended, revoked, or pending).
-
C.
militaryWingStatus
Indicates the status or condition of an entity’s military wing, such as whether it is active, inactive, designated, or otherwise classified.
-
D.
aircraftInFleet
Indicates that a particular aircraft is included as a member of a specified fleet.
-
E.
aircraftTypeManaged
Indicates that one entity is responsible for managing or overseeing a particular type or category of aircraft.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1823370fc819084a13d6e4eb6e44e |
completed | April 29, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:48 p.m.