Triple
T2867089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Sonnenblume |
E63465
|
entity |
| Predicate | belligerentForceType |
P6154
|
FINISHED |
| Object | armoured units |
—
|
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: armoured units | Statement: [Operation Sonnenblume, belligerentForceType, armoured units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belligerentForceType Context triple: [Operation Sonnenblume, belligerentForceType, armoured units]
-
A.
armedForcesInvolved
Indicates that the relationship or event involves the participation or presence of military or armed forces.
-
B.
besiegingForce
Indicates a military group that is surrounding and attacking a target location or force in an attempt to capture or subdue it.
-
C.
typeOfTroops
chosen
Indicates the specific category or kind of military forces involved in or associated with an entity or event.
-
D.
typeOfGovernmentForces
Indicates the specific kind or classification of government-controlled military or security forces involved in a situation or context.
-
E.
warfareType
Indicates the specific kind or category of warfare that characterizes a given conflict or military engagement.
- 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_69ab4c42fb8c8190b36e161d47c03b81 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdfbcebcc81909a78a1787d823e3e |
completed | March 7, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69abdd123ec48190af50a1859aea50b7 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.