Triple
T25749545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Zimbabweans |
E648436
|
entity |
| Predicate | consequenceOfLandReform |
P146599
|
FINISHED |
| Object | loss of many large-scale commercial farms |
—
|
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: loss of many large-scale commercial farms | Statement: [White Zimbabweans, consequenceOfLandReform, loss of many large-scale commercial farms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: consequenceOfLandReform Context triple: [White Zimbabweans, consequenceOfLandReform, loss of many large-scale commercial farms]
-
A.
consequenceOfReforms
chosen
Indicates that something occurs as a result of, or is caused by, a set of reforms.
-
B.
agriculturalImpact
Indicates the effect that an action, condition, or entity has on agricultural systems, productivity, or practices.
-
C.
agriculturalImplication
Indicates a relationship where one factor, event, or condition has consequences, effects, or relevance specifically within an agricultural context.
-
D.
hasLandownershipInfluenceOn
Indicates that one entity’s ownership or control of land affects, shapes, or exerts power over another entity or situation.
-
E.
livelihoodChange
Indicates a change in a person’s or group’s means of making a living, such as improvements, declines, or shifts in income-generating activities.
- 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_69e7ab314d788190b3abe19e114080e1 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f5fd7d0d988190bf868b15d37daa28 |
completed | May 2, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 22, 2026, 4:33 a.m.