Triple
T12789665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afro-Saint Lucian people |
E305723
|
entity |
| Predicate | historicalLaborContext |
P1409
|
FINISHED |
| Object | plantation slavery |
—
|
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: plantation slavery | Statement: [Afro-Saint Lucian people, historicalLaborContext, plantation slavery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalLaborContext Context triple: [Afro-Saint Lucian people, historicalLaborContext, plantation slavery]
-
A.
hasHistoricalContext
chosen
Indicates that something is related to, influenced by, or best understood in light of specific past events, conditions, or time periods.
-
B.
historicalOccupationPattern
Indicates a recurring or characteristic pattern in the occupations held by an entity or its members over historical periods.
-
C.
historicalBackground
Indicates that one entity provides contextual historical information or circumstances that help explain the origin, development, or significance of another entity.
-
D.
shareHistoricalContextAs
Indicates that two or more entities are associated with or understood within the same historical background, period, or circumstances.
-
E.
historicalField
Indicates that one entity’s field of study, work, or relevance is situated within the historical domain or concerns past events.
- 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e6a61f48190972e241e70bc392c |
completed | April 10, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69d9640ba0688190973e4e7ec8d4a8e0 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:30 p.m.