Triple
T27697151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 53rd Electronic Warfare Group |
E698328
|
entity |
| Predicate | typeOfEquipmentTested |
P104198
|
FINISHED |
| Object | airborne electronic warfare systems |
—
|
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: airborne electronic warfare systems | Statement: [53rd Electronic Warfare Group, typeOfEquipmentTested, airborne electronic warfare systems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfEquipmentTested Context triple: [53rd Electronic Warfare Group, typeOfEquipmentTested, airborne electronic warfare systems]
-
A.
typeOfEquipmentUsed
Indicates that a particular piece or category of equipment is utilized in performing a specific action, process, or activity.
-
B.
typeOfEquipmentManaged
Indicates the specific kind or category of equipment that an entity is responsible for managing.
-
C.
intendedEquipmentType
Indicates that a particular piece of equipment is the planned or designated type to be used in a given context or activity.
-
D.
testedDeviceType
chosen
Indicates that one entity is the type or category of device on which another entity has been tested.
-
E.
equipmentTypeTrainedOn
Indicates the type of equipment on which an entity has received training or is qualified to operate.
- 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_69ef590ea74081908f0cd7500d85fa27 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69fdee770af48190aca2670db50f8b49 |
completed | May 8, 2026, 2:08 p.m. |
| PD | Predicate disambiguation | batch_69fdecec98a08190a357d816dc2a6dbe |
completed | May 8, 2026, 2:02 p.m. |
Created at: April 27, 2026, 2:55 p.m.