Triple
T25741810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Finnish Armoured Division |
E648237
|
entity |
| Predicate | usedEquipmentModel |
P2728
|
FINISHED |
| Object | T-26 light tank |
E803858
|
NE 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: T-26 light tank | Statement: [Finnish Armoured Division, usedEquipmentModel, T-26 light tank]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedEquipmentModel Context triple: [Finnish Armoured Division, usedEquipmentModel, T-26 light tank]
-
A.
usedEquipmentFrom
Indicates that one entity has utilized or operated equipment that originated from or was provided by another entity.
-
B.
usesEquipment
chosen
Indicates that an entity employs or operates a particular piece of equipment to perform an action or fulfill a function.
-
C.
typeOfEquipmentUsed
Indicates that a particular piece or category of equipment is utilized in performing a specific action, process, or activity.
-
D.
usedInDeviceModel
Indicates that something (such as a component, material, or technology) is utilized within or incorporated into a particular device model.
-
E.
usedHelicopterModel
Indicates that a particular helicopter model was employed or utilized in relation to a given event, operation, or activity.
- F. None of above.
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_69e7ab306eec8190b05c312c6ab186b8 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f5fd1b3010819098da2e7d46dfc326 |
completed | May 2, 2026, 1:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10cc05f5808190a9b05ef5fe13daf4 |
completed | May 22, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69f4938262ac8190b41f922d0407d272 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 22, 2026, 3:45 a.m.