Triple
T9651502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Maiwand |
E233344
|
entity |
| Predicate | casualtiesAndLossesBritishIndian |
P17245
|
FINISHED |
| Object | over 900 killed |
—
|
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: over 900 killed | Statement: [Battle of Maiwand, casualtiesAndLossesBritishIndian, over 900 killed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesAndLossesBritishIndian Context triple: [Battle of Maiwand, casualtiesAndLossesBritishIndian, over 900 killed]
-
A.
casualties_British_side
Indicates the number or extent of casualties suffered by the British side in a conflict or incident.
-
B.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
C.
casualtiesUnion
Indicates a relationship where multiple casualty figures or reports are combined into a single aggregated total.
-
D.
casualtiesBritishWounded
Indicates the number of British individuals who were wounded as a result of a specific event or action.
-
E.
casualtiesUKKilled
chosen
Indicates that the relationship specifies the number of people from the UK who were killed in the referenced event or incident.
- 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_69ca848b31648190b57aa55da20285be |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9bb0cce88190b7eacc8b43450d7f |
completed | April 1, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b0263081908cf6df3eb07d71b0 |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:13 p.m.