Triple
T4970259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Freeman's Farm |
E111630
|
entity |
| Predicate | AmericanCasualtiesAndLosses |
P28432
|
FINISHED |
| Object | approximately 300 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: approximately 300 killed and wounded | Statement: [Battle of Freeman's Farm, AmericanCasualtiesAndLosses, approximately 300 killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: AmericanCasualtiesAndLosses Context triple: [Battle of Freeman's Farm, AmericanCasualtiesAndLosses, approximately 300 killed and wounded]
-
A.
casualtiesUnitedStates
Indicates that the event or situation resulted in casualties (deaths and/or injuries) among United States personnel or citizens.
-
B.
UScasualties
chosen
Indicates the number or occurrence of casualties suffered by the United States in a given conflict, event, or situation.
-
C.
casualtiesKilledUS
Indicates that the relationship specifies the number of U.S. individuals who were killed as casualties in an event or incident.
-
D.
PatriotCasualties
Indicates that the event involves casualties suffered by patriot forces or supporters.
-
E.
aircraftDestroyedUS
Indicates that a U.S. aircraft has been destroyed.
- 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_69bd441a0eb481908050fa4273b19eae |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd730a7590819088ab8d49c5c88c2f |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd7146e6e881908a55ab2756b631f6 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:33 p.m.