Triple
T10299977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Carillon |
E241601
|
entity |
| Predicate | forceStrengthFrench |
P24882
|
FINISHED |
| Object | approximately 3,600 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 3,600 soldiers | Statement: [Battle of Carillon, forceStrengthFrench, approximately 3,600 soldiers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: forceStrengthFrench Context triple: [Battle of Carillon, forceStrengthFrench, approximately 3,600 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.
FrenchOutcome
Indicates that an event, action, or process results in an outcome that is specifically French in nature, context, or origin.
-
E.
FrenchCommander
Indicates that an entity serves as a military commander associated with France.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ee10f88190b1615c49b8f24a26 |
completed | April 7, 2026, 9:48 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f35e548190be3b4d92d65d2d20 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:44 a.m.