Triple
T19791562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siege of Cartagena de Indias (1741) |
E475422
|
entity |
| Predicate | hasCasualtiesAttacker |
P110385
|
FINISHED |
| Object | heavy casualties from combat and disease |
—
|
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: heavy casualties from combat and disease | Statement: [Siege of Cartagena de Indias (1741), hasCasualtiesAttacker, heavy casualties from combat and disease]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCasualtiesAttacker Context triple: [Siege of Cartagena de Indias (1741), hasCasualtiesAttacker, heavy casualties from combat and disease]
-
A.
casualtiesAttackersKilled
Indicates the number of attacking forces who were killed as a result of the attack.
-
B.
hasChildCasualties
Indicates that an event, incident, or situation resulted in casualties specifically involving children.
-
C.
casualtiesInflictedOn
Indicates that one party has caused deaths or injuries to another party as a result of a harmful event or action.
-
D.
hasCasualtiesLevel
Indicates the severity or extent of casualties associated with an event, incident, or situation.
-
E.
sustainedHeavyCasualtiesAt
chosen
Indicates that an entity experienced a large number of serious losses (e.g., deaths or injuries) at a specific location or during a specific 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653c37a3c819080f195d58adaaa7b |
completed | April 20, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69e53053ed2881908400becdfada7fd3 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:49 p.m.