Triple
T10061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chile |
E203
|
entity |
| Predicate | drinkingWaterStandard |
P649
|
FINISHED |
| Object | potable in most urban areas |
—
|
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: potable in most urban areas | Statement: [Chile, drinkingWaterStandard, potable in most urban areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drinkingWaterStandard Context triple: [Chile, drinkingWaterStandard, potable in most urban areas]
-
A.
notableStandard
Indicates that one entity is a widely recognized or influential standard that the other entity is associated with or exemplifies.
-
B.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
C.
hasRiver
Indicates that a location or area contains, is traversed by, or is directly associated with a river.
-
D.
basinCountry
Indicates the country or countries within whose territory a river basin or drainage area is primarily located or through which it significantly extends.
-
E.
tributary
Indicates that one watercourse flows into and feeds another, contributing its water to a larger stream, river, or lake.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a240b249788190af8dbf7e80e9c91b |
completed | Feb. 28, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69a23fe52ec48190a4d24101c91434ed |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a240b1551c81908abcae128ea45d00 |
completed | Feb. 28, 2026, 1:11 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.