Triple
T5150130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 20th Maine Volunteer Infantry Regiment |
E116171
|
entity |
| Predicate | killedAndDiedOfWoundsApproximate |
P661
|
FINISHED |
| Object | about 150 men |
—
|
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: about 150 men | Statement: [20th Maine Volunteer Infantry Regiment, killedAndDiedOfWoundsApproximate, about 150 men]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: killedAndDiedOfWoundsApproximate Context triple: [20th Maine Volunteer Infantry Regiment, killedAndDiedOfWoundsApproximate, about 150 men]
-
A.
wasWoundedIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
-
B.
deathApprox
Indicates that an entity’s death occurred at an approximate, rather than exact, time or date.
-
C.
casualtiesEstimate
chosen
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
-
D.
fatalInjury
Indicates that an entity causes or sustains an injury that directly results in death.
-
E.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
- 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_69bd4446c0e08190a7c29dc74976bf03 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd78d7f4d081908d59adcd86f52f1d |
completed | March 20, 2026, 4:42 p.m. |
| PD | Predicate disambiguation | batch_69bd77ae2f10819098bb8939106e1281 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:43 p.m.