Triple
T29877362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Poison Spring |
E758774
|
entity |
| Predicate | UnionCasualtiesKilled |
P125463
|
FINISHED |
| Object | over 100 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 100 killed | Statement: [Battle of Poison Spring, UnionCasualtiesKilled, over 100 killed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: UnionCasualtiesKilled Context triple: [Battle of Poison Spring, UnionCasualtiesKilled, over 100 killed]
-
A.
UnionCasualtiesKilledAndWounded
Indicates the number of Union forces who were either killed or wounded as a result of a specific military engagement or event.
-
B.
casualtiesUnionKilled
chosen
Indicates that the number of casualties consists of individuals who were killed and were members of a union.
-
C.
casualtiesCiviliansKilled
Indicates that the relationship records the number of civilian deaths resulting from a specific event or action.
-
D.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
-
E.
governmentCasualties
Indicates that members of a government (such as officials, employees, or security forces) were killed, injured, or otherwise became casualties in an event or 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_69f2245d0d7081909e37ee328542bcd7 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f676caee048190952d49763046a768 |
completed | May 2, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 5:56 p.m.