Triple
T20513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Michigan |
E406
|
entity |
| Predicate | rankBySurfaceAreaInGreatLakes |
P1170
|
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 Michigan, rankBySurfaceAreaInGreatLakes, second largest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankBySurfaceAreaInGreatLakes Context triple: [Lake Michigan, rankBySurfaceAreaInGreatLakes, second largest]
-
A.
hasMajorLake
Indicates that a geographic region or area contains at least one significant lake within its boundaries.
-
B.
rankByPopulationInUS
Indicates the relative ordering of entities based on the size of their populations within the United States.
-
C.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
D.
rankByPopulationInUnitedStates
Indicates the relative ordering of entities based on their population size within the United States.
-
E.
areaRank
chosen
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
- 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_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a246f7bd30819085f751c41f6f029e |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a246526f5881909bc2a46e978bd082 |
completed | Feb. 28, 2026, 1:35 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.