Triple
T26720887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Center for Water Informatics and Technology |
E673696
|
entity |
| Predicate | primaryRegionOfImpact |
P25846
|
FINISHED |
| Object | Pakistan |
—
|
NE NERFINISHED |
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: Pakistan | Statement: [Center for Water Informatics and Technology, primaryRegionOfImpact, Pakistan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryRegionOfImpact Context triple: [Center for Water Informatics and Technology, primaryRegionOfImpact, Pakistan]
-
A.
impactRegion
chosen
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
B.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
C.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
D.
sectorMostAffected
Indicates that a particular sector is the one experiencing the greatest impact or disruption relative to others in a given context.
-
E.
primaryInfluence
Indicates that one entity serves as the main or most significant influencing factor on another entity’s state, behavior, or outcome.
- 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_69eecda481d08190aea69f2f7c745f56 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69fe920a437081908d5174e8cf7a53a6 |
completed | May 9, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69fe919a9a6c8190acb4483f386e6db7 |
completed | May 9, 2026, 1:44 a.m. |
Created at: April 27, 2026, 3:40 a.m.