Triple
T14025576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MiG-29K |
E337447
|
entity |
| Predicate | MiG-29KUB_role |
P53999
|
FINISHED |
| Object | two-seat trainer and combat variant |
—
|
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: two-seat trainer and combat variant | Statement: [MiG-29K, MiG-29KUB_role, two-seat trainer and combat variant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MiG-29KUB_role Context triple: [MiG-29K, MiG-29KUB_role, two-seat trainer and combat variant]
-
A.
Tu-95MSRole
Indicates the specific operational role or mission type assigned to the Tu-95MS aircraft within its usage or deployment context.
-
B.
mainFighterAircraft
Indicates that an aircraft serves as the primary fighter aircraft for a given country, organization, or military force.
-
C.
notableAircraftRole
chosen
Indicates that an aircraft is notably associated with performing a particular role or function.
-
D.
basedAircraftRole
Indicates that an aircraft is regularly stationed at a particular location in a specified operational role or function.
-
E.
isMilitaryAssetOf
Indicates that something functions as a military resource, equipment, or facility that belongs to, is controlled by, or is used by a particular 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2fa6ca7481908976ce748a1957b1 |
completed | April 14, 2026, 12:14 p.m. |
| PD | Predicate disambiguation | batch_69de05a802ac819090604025aae6a4d5 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:20 p.m.