Triple
T1349078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Huron |
E28839
|
entity |
| Predicate | rankInSizeAmongGreatLakes |
P26867
|
FINISHED |
| Object | second largest |
—
|
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: second largest | Statement: [Lake Huron, rankInSizeAmongGreatLakes, second largest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInSizeAmongGreatLakes Context triple: [Lake Huron, rankInSizeAmongGreatLakes, second largest]
-
A.
rankByVolumeInGreatLakes
Indicates the relative ordering of entities based on their water volume within the Great Lakes.
-
B.
positionInGreatLakesChain
Indicates the relative location or ordering of an entity within the sequence of the Great Lakes.
-
C.
isLargestFreshwaterLakeIn
Indicates that a lake is the largest freshwater lake within the specified geographic region or area.
-
D.
numberOfLakes
Indicates the quantity of lakes associated with a given entity.
-
E.
onlyGreatLakeWhollyInUSA
Indicates that the subject is the only Great Lake whose entire area lies within the territorial boundaries of the United States.
- 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c268745481908be649526c18e558 |
completed | March 1, 2026, 10:49 p.m. |
| PD | Predicate disambiguation | batch_69a4bef5857c81909ae984feb85a26ca |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf60545c8190901ccfb2cb7c4b41 |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:56 p.m.