Triple
T11456931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gary metropolitan area |
E271551
|
entity |
| Predicate | lakeEffect |
P99666
|
FINISHED |
| Object | subject to lake-effect snow from Lake Michigan |
—
|
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: subject to lake-effect snow from Lake Michigan | Statement: [Gary metropolitan area, lakeEffect, subject to lake-effect snow from Lake Michigan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lakeEffect Context triple: [Gary metropolitan area, lakeEffect, subject to lake-effect snow from Lake Michigan]
-
A.
mouthLake
Indicates the location where a river or stream flows into and forms part of a lake.
-
B.
lakeShape
Indicates the geometric or physical outline/form that a lake possesses.
-
C.
hasGlacialLake
Indicates that one entity possesses, contains, or is associated with a lake formed by glacial activity.
-
D.
hasNearbyLake
Indicates that one entity is located close to or in the vicinity of a lake.
-
E.
riverPhenomenon
Indicates a natural event, condition, or process that occurs in or directly affects a river.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f2138081909408c7916cef99c9 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:35 p.m.