Triple
T18799248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Noisseville |
E459720
|
entity |
| Predicate | GermanCasualtiesAndLosses |
P14574
|
FINISHED |
| Object | approximately 3500 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: approximately 3500 killed and wounded | Statement: [Battle of Noisseville, GermanCasualtiesAndLosses, approximately 3500 killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: GermanCasualtiesAndLosses Context triple: [Battle of Noisseville, GermanCasualtiesAndLosses, approximately 3500 killed and wounded]
-
A.
GermanLoss
Indicates that Germany experiences a loss, defeat, or negative outcome in the specified context or event.
-
B.
numberOfGermanVictims
chosen
Indicates the quantity of victims who are identified as German in the context of the described event or situation.
-
C.
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.
-
D.
germanUnit
Indicates that an entity is a military or organizational unit that belongs to, originates from, or is associated with Germany.
-
E.
objectiveGermany
Indicates that an entity has Germany as its objective, target, or goal in a given context.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
| PD | Predicate disambiguation | batch_69e48d16dd34819096e096d0c0e4c15c |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:53 a.m.