Triple
T32158951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Varner–Hogg Plantation |
E821373
|
entity |
| Predicate | usedForcedLaborOf |
P27275
|
FINISHED |
| Object | enslaved African Americans |
—
|
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: enslaved African Americans | Statement: [Varner–Hogg Plantation, usedForcedLaborOf, enslaved African Americans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedForcedLaborOf Context triple: [Varner–Hogg Plantation, usedForcedLaborOf, enslaved African Americans]
-
A.
usedForcedLabor
chosen
Indicates that an entity compelled people to work against their will, typically under coercion, threat, or without fair compensation.
-
B.
wasEnslavedIn
Indicates that an entity was held in a state of slavery within a specified place or context.
-
C.
useOfTorture
Indicates the intentional infliction or use of torture by one entity upon another, typically to punish, coerce, intimidate, or extract information.
-
D.
exploitedFor
Indicates that one entity is unfairly or abusively used by another entity as a resource, means, or advantage for the latter’s benefit.
-
E.
hasInmateLabor
Indicates that an entity utilizes or is associated with labor performed by incarcerated individuals.
- 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_69f34905e098819082191a6922a6d607 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 1, 2026, 12:32 a.m.