Triple
T32356632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Drewry’s Bluff |
E826748
|
entity |
| Predicate | casualtiesUnionKilledAndWounded |
P121650
|
FINISHED |
| Object | approximately 27 |
—
|
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 27 | Statement: [Battle of Drewry’s Bluff, casualtiesUnionKilledAndWounded, approximately 27]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesUnionKilledAndWounded Context triple: [Battle of Drewry’s Bluff, casualtiesUnionKilledAndWounded, approximately 27]
-
A.
casualtiesUnion
Indicates a relationship where multiple casualty figures or reports are combined into a single aggregated total.
-
B.
casualtiesUnionKilled
Indicates that the number of casualties consists of individuals who were killed and were members of a union.
-
C.
casualtiesUnionWounded
Indicates that the number of casualties specifically refers to Union forces who were wounded.
-
D.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
E.
UnionCasualtiesKilledAndWounded
chosen
Indicates the number of Union forces who were either killed or wounded as a result of a specific military engagement or event.
- 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_69f34915a2588190bb3178f5ec2f48f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6d16f5cb881908eed141afaaa0b51 |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 12:49 a.m.