Triple
T13089987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Second Manassas |
E310435
|
entity |
| Predicate | casualtiesUnionApproximate |
P43331
|
FINISHED |
| Object | over 10,000 |
—
|
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: over 10,000 | Statement: [Second Manassas, casualtiesUnionApproximate, over 10,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesUnionApproximate Context triple: [Second Manassas, casualtiesUnionApproximate, over 10,000]
-
A.
casualtiesUnion
chosen
Indicates a relationship where multiple casualty figures or reports are combined into a single aggregated total.
-
B.
englishCasualtiesKilledAndWounded
Indicates the number of English individuals who were either killed or wounded as a result of a particular event or conflict.
-
C.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
D.
casualtiesEstimate
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
-
E.
casualtiesUKKilled
Indicates that the relationship specifies the number of people from the UK who were killed in the referenced event or incident.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d98138a1d481908a139f2f67eb3472 |
completed | April 10, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69d9803f6c508190bfadfbc2d00c2c64 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:03 p.m.