Triple
T32963077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Stormberg |
E843291
|
entity |
| Predicate | BoerCasualtiesKilledAndWounded |
P37678
|
FINISHED |
| Object | approximately 30–40 |
—
|
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: approximately 30–40 | Statement: [Battle of Stormberg, BoerCasualtiesKilledAndWounded, approximately 30–40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: BoerCasualtiesKilledAndWounded Context triple: [Battle of Stormberg, BoerCasualtiesKilledAndWounded, approximately 30–40]
-
A.
BoerCasualties
chosen
Indicates the number or occurrence of casualties suffered by Boer forces in a conflict or engagement.
-
B.
ZuluCasualties
Indicates the number or extent of casualties suffered by Zulu forces in a particular conflict or event.
-
C.
dutchCasualties
Indicates that the relationship specifies the number or occurrence of casualties suffered by Dutch entities in a given event or context.
-
D.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
E.
battleCasualty
Indicates that an entity was killed, wounded, or otherwise harmed as a direct result of a specific battle or armed conflict.
- 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_69f3494af2808190ad98cec2f1bc0fe6 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d26f27dc8190ae426a3e1573933e |
completed | May 3, 2026, 4:43 a.m. |
Created at: May 1, 2026, 1:21 a.m.