Triple
T584783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Leuthen |
E15134
|
entity |
| Predicate | casualtiesAustria |
P10775
|
FINISHED |
| Object | heavy casualties including many prisoners |
—
|
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 casualties including many prisoners | Statement: [Battle of Leuthen, casualtiesAustria, heavy casualties including many prisoners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesAustria Context triple: [Battle of Leuthen, casualtiesAustria, heavy casualties including many prisoners]
-
A.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
B.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
C.
numberOfGermanVictims
Indicates the quantity of victims who are identified as German in the context of the described event or situation.
-
D.
casualtiesDescription
chosen
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
E.
NATOcasualtiesMilitaryKilled
Indicates that members of NATO military forces were killed as casualties.
- 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_69a4935783b8819082b77726ec10cc42 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49b9874c88190bd1e08d4689ea124 |
completed | March 1, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69a494c9315c8190a773e8e00737d8a0 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.