Triple
T4526625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Santiago de Cuba |
E106193
|
entity |
| Predicate | combatantLosses |
P28346
|
FINISHED |
| Object | Spanish fleet largely sunk or beached |
—
|
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: Spanish fleet largely sunk or beached | Statement: [Battle of Santiago de Cuba, combatantLosses, Spanish fleet largely sunk or beached]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: combatantLosses Context triple: [Battle of Santiago de Cuba, combatantLosses, Spanish fleet largely sunk or beached]
-
A.
coalitionCasualties
Indicates that members of a coalition have suffered deaths or injuries as a result of a particular conflict, event, or action.
-
B.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
C.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
D.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
E.
aircraftLosses
chosen
Indicates the number or occurrence of aircraft that have been destroyed, damaged beyond repair, or otherwise lost.
- 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_69bd43f3d6e08190a91824f833d51bbe |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57760f4481908f69ce82be63d7f8 |
completed | March 20, 2026, 2:19 p.m. |
| PD | Predicate disambiguation | batch_69bd521cf77c819083852de3094d1377 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:03 p.m.