Triple
T1368702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 163rd Rifle Division |
E30060
|
entity |
| Predicate | equipmentLosses |
P26999
|
FINISHED |
| Object | heavy losses in heavy weapons and vehicles |
—
|
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: heavy losses in heavy weapons and vehicles | Statement: [163rd Rifle Division, equipmentLosses, heavy losses in heavy weapons and vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equipmentLosses Context triple: [163rd Rifle Division, equipmentLosses, heavy losses in heavy weapons and vehicles]
-
A.
spares
Indicates that one entity chooses not to harm, punish, or destroy another entity when it has the power or opportunity to do so.
-
B.
damageYear
Indicates the year in which the damage to an entity occurred or was recorded.
-
C.
economicDamage
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
-
D.
economicDamageApprox
Indicates that one entity has caused or is associated with an estimated or approximate amount of economic damage to another entity or system.
-
E.
usedEquipmentFrom
Indicates that one entity has utilized or operated equipment that originated from or was provided by another entity.
- F. None of above. chosen
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_69a498f912008190a376a98b207b2071 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c2d497f88190993d16a208ced43d |
completed | March 1, 2026, 10:51 p.m. |
| PD | Predicate disambiguation | batch_69a4befb08b88190be966fa1aadd4bcd |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bfc2134c81909cbaaa151d96e9a8 |
completed | March 1, 2026, 10:37 p.m. |
Created at: March 1, 2026, 7:57 p.m.