Triple
T31869317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Conneaut, Ohio |
E813551
|
entity |
| Predicate | hasLakeEffect |
P99666
|
FINISHED |
| Object | snow from Lake Erie |
—
|
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: snow from Lake Erie | Statement: [Conneaut, Ohio, hasLakeEffect, snow from Lake Erie]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLakeEffect Context triple: [Conneaut, Ohio, hasLakeEffect, snow from Lake Erie]
-
A.
lakeEffect
chosen
Indicates a weather phenomenon where a large lake modifies passing air masses, typically enhancing precipitation or altering local atmospheric conditions downwind of the lake.
-
B.
hasLakeLandscape
Indicates that an entity features or is characterized by a landscape that includes a lake.
-
C.
hasNearbyLake
Indicates that one entity is located close to or in the vicinity of a lake.
-
D.
hasLakeRegion
Indicates that a place or geographic area includes or is associated with a specific lake region.
-
E.
hasLakeInFront
Indicates that one entity is situated such that there is a lake directly in front of it.
- 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_69f348ecb07481909c8f72619131b115 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe72dca2f08190beff17de3d2aada6 |
completed | May 8, 2026, 11:33 p.m. |
| PD | Predicate disambiguation | batch_69fe70bca8d08190b810e1e616ceac44 |
completed | May 8, 2026, 11:24 p.m. |
Created at: April 30, 2026, 11:54 p.m.