Triple
T17795542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of New Market |
E444280
|
entity |
| Predicate | numberOfCadetsEngaged |
P6153
|
FINISHED |
| Object | approximately 257 VMI cadets |
—
|
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 257 VMI cadets | Statement: [Battle of New Market, numberOfCadetsEngaged, approximately 257 VMI cadets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCadetsEngaged Context triple: [Battle of New Market, numberOfCadetsEngaged, approximately 257 VMI cadets]
-
A.
numberOfRegimentsInvolved
Indicates the total count of regiments that participated in or were involved in a specified event or action.
-
B.
numberOfTroopsInvolved
chosen
Indicates the quantity of military personnel participating in or assigned to a specific operation, event, or engagement.
-
C.
numberOfBattalions
Indicates the quantitative relationship specifying how many battalions are associated with a given entity or context.
-
D.
engagedForces
Indicates that one force has actively committed or deployed its military units against another force in combat or operational interaction.
-
E.
hasNumberOfAssailants
Indicates the relationship specifying how many assailants are involved in a given event or situation.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487fafc2c8190b28e791267c47e3c |
completed | April 19, 2026, 7:44 a.m. |
| PD | Predicate disambiguation | batch_69e3d8de28688190844b65acf6af54e6 |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:13 a.m.