Triple
T37685322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ord River irrigation scheme |
E938350
|
entity |
| Predicate | stage1AreaIrrigated |
P6177
|
FINISHED |
| Object | approximately 14,000 hectares |
—
|
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: approximately 14,000 hectares | Statement: [Ord River irrigation scheme, stage1AreaIrrigated, approximately 14,000 hectares]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stage1AreaIrrigated Context triple: [Ord River irrigation scheme, stage1AreaIrrigated, approximately 14,000 hectares]
-
A.
hasIrrigationArea
chosen
Indicates that an entity possesses or is associated with a specific area of land equipped or designated for irrigation.
-
B.
hasIrrigatedAgriculture
Indicates that an entity engages in or possesses agricultural activities that are supported or sustained by artificial irrigation systems.
-
C.
hasIrrigation
Indicates that an entity is equipped with or benefits from an irrigation system supplying water.
-
D.
typeOfIrrigation
Indicates the specific irrigation method or system used to supply water to a given area or crop.
-
E.
usedIrrigation
Indicates that an entity applied or employed an irrigation system or method to supply water to land or crops.
- 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_69f76ed881408190bc62a969530a4a53 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb9e8108c8190ae1c7940b1677e95 |
completed | May 6, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fbb141605c8190b9c27d70352522db |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:18 p.m.