Triple
T628869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Minden |
E15880
|
entity |
| Predicate | strengthFrench |
P6664
|
FINISHED |
| Object | approximately 50,000–55,000 troops |
—
|
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 50,000–55,000 troops | Statement: [Battle of Minden, strengthFrench, approximately 50,000–55,000 troops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: strengthFrench Context triple: [Battle of Minden, strengthFrench, approximately 50,000–55,000 troops]
-
A.
strength
chosen
Indicates the degree of power, intensity, or effectiveness with which an entity can act on, influence, or withstand another entity or force.
-
B.
strengthDescription
Indicates a description or characterization of the degree of strength associated with an entity or relationship.
-
C.
strengthens
Indicates that one entity increases the power, effectiveness, or resilience of another.
-
D.
strengthensFrom
Indicates that one entity becomes stronger, more effective, or more intense as a result of influence, support, or input from another entity.
-
E.
FrenchObjective
Indicates that an entity serves as the goal, target, or object of an action or relation specifically within a French linguistic or contextual framework.
- 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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e5b5a308190a62165f9275e2f5f |
completed | March 1, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69a49d01b29081908be87e4cd7726ff1 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.