Triple
T6701366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Big Black River Bridge |
E152885
|
entity |
| Predicate | UnionKilledAndWounded |
P72518
|
FINISHED |
| Object | under 300 |
—
|
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: under 300 | Statement: [Battle of Big Black River Bridge, UnionKilledAndWounded, under 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: UnionKilledAndWounded Context triple: [Battle of Big Black River Bridge, UnionKilledAndWounded, under 300]
-
A.
casualtiesUnion
Indicates a relationship where multiple casualty figures or reports are combined into a single aggregated total.
-
B.
wasWoundedIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
-
C.
coalitionCasualties
Indicates that members of a coalition have suffered deaths or injuries as a result of a particular conflict, event, or action.
-
D.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
E.
casualtiesKilledAndMortallyWounded
Indicates that the relationship records the number of individuals who were killed outright or died later from mortal wounds.
- F. None of above. chosen
Provenance (4 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_69c68807adbc8190b8632df42b39eda0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16897e48190b43eda2206b14d6a |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d089c7488190a00853fb12f53b2a |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d1668a7c8190ae93951f9ba2df10 |
completed | March 27, 2026, 6:50 p.m. |
Created at: March 27, 2026, 2:05 p.m.