Triple
T2016269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Chancellorsville |
E44000
|
entity |
| Predicate | unionCasualtiesAndLosses |
P28432
|
FINISHED |
| Object | approximately 17,000 |
—
|
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 17,000 | Statement: [Battle of Chancellorsville, unionCasualtiesAndLosses, approximately 17,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: unionCasualtiesAndLosses Context triple: [Battle of Chancellorsville, unionCasualtiesAndLosses, approximately 17,000]
-
A.
coalitionCasualties
Indicates that members of a coalition have suffered deaths or injuries as a result of a particular conflict, event, or action.
-
B.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
C.
UScasualties
chosen
Indicates the number or occurrence of casualties suffered by the United States in a given conflict, event, or situation.
-
D.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
E.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
- 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8cb16048190bc626685fbb5f707 |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb7a03a1c81909ad50d56667db2d5 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:38 p.m.