Triple
T7505129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Raab |
E177367
|
entity |
| Predicate | combatantStrengthFrenchSide |
P24882
|
FINISHED |
| Object | about 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 40,000 troops | Statement: [Battle of Raab, combatantStrengthFrenchSide, about 40,000 troops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: combatantStrengthFrenchSide Context triple: [Battle of Raab, combatantStrengthFrenchSide, about 40,000 troops]
-
A.
FrenchUnit
Indicates that a unit or entity is associated with France, typically by origin, affiliation, or national identity.
-
B.
FrenchCasualties
Indicates that the relationship specifies the number or extent of casualties suffered by French forces in a given event or context.
-
C.
approximateStrengthFrancoSpanish
Indicates an estimated or inferred level of strength or intensity in the relationship or interaction between Franco and Spanish entities.
-
D.
strengthFrance
chosen
Indicates a relationship where a level, measure, or attribute of strength is associated specifically with France.
-
E.
FrancoSpanishCasualtiesKilledAndWounded
Indicates the number of people from Franco-Spanish forces who were killed or wounded as casualties in a conflict or event.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f81b431481908214b69c6c8d83bc |
completed | March 27, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d266d88190982cf5d2ee2e9564 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:44 p.m.