Triple
T23782374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Basantar |
E587849
|
entity |
| Predicate | casualtiesSideIndia |
P82956
|
FINISHED |
| Object | hundreds 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: hundreds killed and wounded | Statement: [Battle of Basantar, casualtiesSideIndia, hundreds killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesSideIndia Context triple: [Battle of Basantar, casualtiesSideIndia, hundreds killed and wounded]
-
A.
militaryCasualtiesSide
chosen
Indicates the side or party in a conflict to which the recorded military casualties belong.
-
B.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
C.
casualtiesCountry
Indicates that the specified country is the one in which the recorded casualties (deaths or injuries) occurred or to which those casualties belong.
-
D.
casualtiesInflictedOn
Indicates that one party has caused deaths or injuries to another party as a result of a harmful event or action.
-
E.
casualtiesUnion
Indicates a relationship where multiple casualty figures or reports are combined into a single aggregated total.
- 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_69e2490f4ad48190b690878eec3596c6 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1c62d7c608190b5fd0cf35f5faf42 |
completed | April 29, 2026, 8:49 a.m. |
| PD | Predicate disambiguation | batch_69f155f79e34819080f9ddb972b34deb |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:16 p.m.