Triple
T9190847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masurian Lake District |
E220584
|
entity |
| Predicate | hasApproximateNumberOfLakes |
P3323
|
FINISHED |
| Object | over 2000 |
—
|
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: over 2000 | Statement: [Masurian Lake District, hasApproximateNumberOfLakes, over 2000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfLakes Context triple: [Masurian Lake District, hasApproximateNumberOfLakes, over 2000]
-
A.
numberOfLakes
chosen
Indicates the quantity of lakes associated with a given entity.
-
B.
hasManyLakes
Indicates that an entity possesses or is associated with a large number of lakes.
-
C.
hasMajorLake
Indicates that a geographic region or area contains at least one significant lake within its boundaries.
-
D.
hasFreshwaterLake
Indicates that one entity possesses, contains, or is associated with a freshwater lake.
-
E.
hasLakeThatRepresents
Indicates a relationship where a lake serves as a symbolic or representative feature for something, such as a place, concept, or 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_69ca83e7ba70819088b74866d9da2c30 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd5bf25c081909e651b67ef8ecc33 |
completed | April 1, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69cc66090e5881908889dc1213815626 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:24 p.m.