Triple
T8616577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Ulm |
E204053
|
entity |
| Predicate | hasFrenchStrength |
P24882
|
FINISHED |
| Object | approximately 200,000 soldiers |
—
|
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 200,000 soldiers | Statement: [Battle of Ulm, hasFrenchStrength, approximately 200,000 soldiers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrenchStrength Context triple: [Battle of Ulm, hasFrenchStrength, approximately 200,000 soldiers]
-
A.
approximateStrengthFrancoSpanish
Indicates an estimated or inferred level of strength or intensity in the relationship or interaction between Franco and Spanish entities.
-
B.
strengthFrance
chosen
Indicates a relationship where a level, measure, or attribute of strength is associated specifically with France.
-
C.
approximateStrengthFrancoBavarian
Indicates an estimated or inferred degree of strength or intensity in the Franco-Bavarian relationship or interaction.
-
D.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
E.
approximateGallicStrength
Indicates an estimation or rough calculation of the level or magnitude of Gallic strength 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_69ca832ceab8819096e4a9f546695079 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4703b57c81909511de72fa5c38d7 |
completed | March 31, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69cc455437488190b7506f820daf6e32 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:25 p.m.