Triple
T2236676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Sahagún |
E49296
|
entity |
| Predicate | FrenchKilledAndWounded |
P14905
|
FINISHED |
| Object | over 100 |
—
|
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: over 100 | Statement: [Battle of Sahagún, FrenchKilledAndWounded, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FrenchKilledAndWounded Context triple: [Battle of Sahagún, FrenchKilledAndWounded, over 100]
-
A.
FrenchCasualties
chosen
Indicates that the relationship specifies the number or extent of casualties suffered by French forces in a given event or context.
-
B.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
C.
frenchTroopsEvacuated
Indicates that French military forces withdrew or were removed from a particular location or situation.
-
D.
NATOcasualtiesMilitaryKilled
Indicates that members of NATO military forces were killed as casualties.
-
E.
FrenchRole
Indicates a role or position that an entity holds specifically within a French context (e.g., in France or related to French institutions, culture, or language).
- 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc09573848190bf91eddcc2fa0061 |
completed | March 7, 2026, 6:07 a.m. |
| PD | Predicate disambiguation | batch_69abbdafc07881909101266a33ae7031 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.