Triple
T19791563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siege of Cartagena de Indias (1741) |
E475422
|
entity |
| Predicate | hasCasualtiesDefender |
P84402
|
FINISHED |
| Object | significant but lower than attacker |
—
|
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: significant but lower than attacker | Statement: [Siege of Cartagena de Indias (1741), hasCasualtiesDefender, significant but lower than attacker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCasualtiesDefender Context triple: [Siege of Cartagena de Indias (1741), hasCasualtiesDefender, significant but lower than attacker]
-
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.
sustainedHeavyCasualtiesAt
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.
-
D.
hasCasualtiesLevel
chosen
Indicates the severity or extent of casualties associated with an event, incident, or situation.
-
E.
casualtiesInflictedOn
Indicates that one party has caused deaths or injuries to another party as a result of a harmful event or action.
- 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.