Triple
T29821507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Novi (1799) |
E757255
|
entity |
| Predicate | frenchStrengthApprox |
P24882
|
FINISHED |
| Object | about 36,000–40,000 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: about 36,000–40,000 troops | Statement: [Battle of Novi (1799), frenchStrengthApprox, about 36,000–40,000 troops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frenchStrengthApprox Context triple: [Battle of Novi (1799), frenchStrengthApprox, about 36,000–40,000 troops]
-
A.
approximateStrengthFrancoSpanish
Indicates an estimated or inferred level of strength or intensity in the relationship or interaction between Franco and Spanish entities.
-
B.
approximateGallicStrength
Indicates an estimation or rough calculation of the level or magnitude of Gallic strength in a given context.
-
C.
approximateStrengthFrancoBavarian
Indicates an estimated or inferred degree of strength or intensity in the Franco-Bavarian relationship or interaction.
-
D.
strengthFrance
chosen
Indicates a relationship where a level, measure, or attribute of strength is associated specifically with France.
-
E.
lengthInFrance
Indicates that the specified length or duration applies specifically within the context of France (e.g., under French conditions, jurisdiction, or territory).
- 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_69f2245701c88190ad42415a0956c4ed |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f675675250819089db3b30f1f05f87 |
completed | May 2, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69f673c4abec8190bc2379e66f4af0a9 |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 5:29 p.m.