Triple
T32203624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Curupayty |
E822611
|
entity |
| Predicate | casualtiesTripleAlliance |
P30659
|
FINISHED |
| Object | several thousand killed and wounded |
—
|
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: several thousand killed and wounded | Statement: [Battle of Curupayty, casualtiesTripleAlliance, several thousand killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesTripleAlliance Context triple: [Battle of Curupayty, casualtiesTripleAlliance, several thousand killed and wounded]
-
A.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
-
B.
casualtiesUnion
Indicates a relationship where multiple casualty figures or reports are combined into a single aggregated total.
-
C.
coalitionCasualties
chosen
Indicates that members of a coalition have suffered deaths or injuries as a result of a particular conflict, event, or action.
-
D.
casualtiesCountry
Indicates that the specified country is the one in which the recorded casualties (deaths or injuries) occurred or to which those casualties belong.
-
E.
UnionCasualtiesKilledAndWounded
Indicates the number of Union forces who were either killed or wounded as a result of a specific military engagement or 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_69f349093174819086e633c190a51aa8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c8159edc8190b1c87015e0c820e8 |
completed | May 3, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f42fbc8190a06eb1044c9e6094 |
completed | May 3, 2026, 3:41 a.m. |
Created at: May 1, 2026, 12:36 a.m.