Triple
T21244084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Taymyr |
E523556
|
entity |
| Predicate | populationDensityAroundLake |
P728
|
FINISHED |
| Object | very low |
—
|
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: very low | Statement: [Lake Taymyr, populationDensityAroundLake, very low]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationDensityAroundLake Context triple: [Lake Taymyr, populationDensityAroundLake, very low]
-
A.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given region.
-
B.
hasPopulationDensity
chosen
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
C.
hasNearbyLakeRegion
Indicates that one region is located close to a lake or lake-dominated area.
-
D.
hasPopulationDensityType
Indicates the classification of an area based on how densely populated it is (e.g., urban, suburban, rural).
-
E.
numberOfLakes
Indicates the quantity of lakes associated with a given entity.
- 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_69e0b513b89c81908b27147e91368db2 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7352621488190bd74c57798c7d658 |
completed | April 21, 2026, 8:28 a.m. |
| PD | Predicate disambiguation | batch_69e5f61239708190ab7b3c83ae848a0d |
completed | April 20, 2026, 9:46 a.m. |
Created at: April 16, 2026, 3:47 p.m.