Triple
T1728758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Ligny |
E37559
|
entity |
| Predicate | PrussianCasualtiesApprox |
P6773
|
FINISHED |
| Object | 16000–20000 |
—
|
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: 16000–20000 | Statement: [Battle of Ligny, PrussianCasualtiesApprox, 16000–20000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: PrussianCasualtiesApprox Context triple: [Battle of Ligny, PrussianCasualtiesApprox, 16000–20000]
-
A.
PrussianStrength
Indicates a relationship where an entity possesses or exhibits the military, political, or institutional power characteristic of Prussia.
-
B.
casualtiesGermanWounded
Indicates that the relationship specifies the number of German individuals who were wounded (but not killed) as casualties in a particular event or context.
-
C.
numberOfGermanVictims
Indicates the quantity of victims who are identified as German in the context of the described event or situation.
-
D.
PolishCasualties
Indicates the number or extent of casualties suffered by Polish forces or population in a given conflict or event.
-
E.
militaryCasualtiesEstimate
chosen
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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aadb7bda1081908f2c41c520c9c55c |
completed | March 6, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69aa61c0a0288190bce9d60062a84b69 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.