Triple
T10922737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aapravasi Ghat |
E257986
|
entity |
| Predicate | numberOfIndenturedLaborersProcessed |
P11975
|
FINISHED |
| Object | over 450000 |
—
|
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 450000 | Statement: [Aapravasi Ghat, numberOfIndenturedLaborersProcessed, over 450000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfIndenturedLaborersProcessed Context triple: [Aapravasi Ghat, numberOfIndenturedLaborersProcessed, over 450000]
-
A.
numberOfImmigrantsProcessed
chosen
Indicates the total count of immigrants that have been processed in a given context or system.
-
B.
numberOfEnslaved
Indicates the quantity of individuals who were held in a state of enslavement in relation to a given entity or context.
-
C.
estimatedNumberOfPeopleDeported
Indicates the approximate count of individuals who were forcibly removed or expelled from a place or country.
-
D.
laborNumber
Indicates a relationship where a specific labor or work assignment is identified or referenced by a unique number.
-
E.
labourNumber
Indicates the identifier or count assigned to a specific unit of labor or work activity associated with an entity.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708d1fb88190bb33b72d4330ce11 |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:22 p.m.