Triple
T808119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Marengo |
E17481
|
entity |
| Predicate | casualtiesAustrian |
P10775
|
FINISHED |
| Object | several thousand killed and wounded |
—
|
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: several thousand killed and wounded | Statement: [Battle of Marengo, casualtiesAustrian, several thousand killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesAustrian Context triple: [Battle of Marengo, casualtiesAustrian, several thousand killed and wounded]
-
A.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
B.
casualtiesDescription
chosen
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
C.
compensatedAustriaForLossOf
Indicates that an entity provided compensation to Austria for a specific loss or damage.
-
D.
strengthRussoAustrian
Indicates the relative military or political strength between Russian and Austrian forces or interests in a given context.
-
E.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
- 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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ac07fedc8190ab05595f25c1792f |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7221c081908068e66fe720f26d |
completed | March 1, 2026, 9:06 p.m. |
Created at: March 1, 2026, 7:38 p.m.