Triple
T226641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Huron |
E4327
|
entity |
| Predicate | shoreLength |
P1568
|
FINISHED |
| Object | approximately 6,157 kilometers including islands |
—
|
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: approximately 6,157 kilometers including islands | Statement: [Lake Huron, shoreLength, approximately 6,157 kilometers including islands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shoreLength Context triple: [Lake Huron, shoreLength, approximately 6,157 kilometers including islands]
-
A.
shorelineLength
chosen
Indicates the total measured extent of a land area’s boundary where it meets a body of water.
-
B.
coastType
Indicates the specific kind or classification of a coastline associated with a geographic area.
-
C.
hasCoastline
Indicates that a geographic entity is bordered by and directly touches a sea or ocean along part of its boundary.
-
D.
hasCityOnShore
Indicates that a city is located on or directly adjacent to the shore of a body of water.
-
E.
coastlineFeature
Indicates that a geographic entity is a specific type of feature located along or forming part of a coastline.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d10ac248190a98dedabf5358668 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b5877588190af694d060377f027 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.