Triple
T268667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Como |
E5575
|
entity |
| Predicate | rankByDepthInEurope |
P1536
|
FINISHED |
| Object | one of the deepest lakes in Europe |
—
|
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: one of the deepest lakes in Europe | Statement: [Lake Como, rankByDepthInEurope, one of the deepest lakes in Europe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByDepthInEurope Context triple: [Lake Como, rankByDepthInEurope, one of the deepest lakes in Europe]
-
A.
depthRank
chosen
Indicates the relative ordering of entities based on how deep or distant they are along a specified depth dimension or hierarchy.
-
B.
areaRank
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
-
C.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
D.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
E.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
- 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25dae4a0c8190a66cf6ed3889851c |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b70d99c819085d8381a313a2a34 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.