Triple
T13712581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makoni District |
E328809
|
entity |
| Predicate | hasFoodCrop |
P54821
|
FINISHED |
| Object | maize |
—
|
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: maize | Statement: [Makoni District, hasFoodCrop, maize]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFoodCrop Context triple: [Makoni District, hasFoodCrop, maize]
-
A.
containsEdibleCrop
Indicates that one entity includes within it or has present an edible crop as part of its contents or composition.
-
B.
hasCropSpecies
chosen
Indicates that a particular location, field, or agricultural system is associated with or used to cultivate a specified crop species.
-
C.
isCultivatedAs
Indicates that one entity is intentionally grown, tended, or produced by another as a crop, resource, or cultivated organism.
-
D.
hasAgriculturalProduction
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
E.
numberOfCrops
Indicates the quantity or count of crops associated with a given entity or context.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:54 p.m.