Triple
T11593051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastern Basin of the Aral Sea |
E274931
|
entity |
| Predicate | mainCauseOfDesiccation |
P100489
|
FINISHED |
| Object | large-scale irrigation projects |
—
|
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: large-scale irrigation projects | Statement: [Eastern Basin of the Aral Sea, mainCauseOfDesiccation, large-scale irrigation projects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCauseOfDesiccation Context triple: [Eastern Basin of the Aral Sea, mainCauseOfDesiccation, large-scale irrigation projects]
-
A.
causeOfTreeDieOff
Indicates a factor or event that leads to or is responsible for the widespread death or decline of trees.
-
B.
droughtType
Indicates the specific category or classification of a drought affecting an area or system.
-
C.
hasDrySeasonCause
Indicates that one factor or condition is the underlying cause of a location or region experiencing a dry season.
-
D.
droughtTolerance
Indicates the degree to which an entity can maintain normal function and survival under conditions of limited water availability.
-
E.
canDryOut
Indicates that one entity has the ability or tendency to cause another entity to lose moisture and become dry.
- F. None of above. chosen
Provenance (4 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8946594348190935106132fd18028 |
completed | April 10, 2026, 6:10 a.m. |
| PD | Predicate disambiguation | batch_69d85dd20d188190863d1190d4c16048 |
completed | April 10, 2026, 2:17 a.m. |
| PDg | Predicate description generation | batch_69d87f2e67108190ac36bf47aac12fa8 |
completed | April 10, 2026, 4:40 a.m. |
Created at: April 8, 2026, 9:38 p.m.