Triple
T20503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Michigan |
E406
|
entity |
| Predicate | onlyGreatLakeWhollyInUSA |
P1570
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Lake Michigan, onlyGreatLakeWhollyInUSA, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: onlyGreatLakeWhollyInUSA Context triple: [Lake Michigan, onlyGreatLakeWhollyInUSA, true]
-
A.
hasMajorLake
Indicates that a geographic region or area contains at least one significant lake within its boundaries.
-
B.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
C.
basinCountry
Indicates the country or countries within whose territory a river basin or drainage area is primarily located or through which it significantly extends.
-
D.
hasCoastlineOn
Indicates that one entity’s coastline borders or is directly adjacent to a specified body of water.
-
E.
usedByUSStatePortion
Indicates that something (such as a resource, facility, or infrastructure) is utilized or operated by a specific portion or subdivision of a U.S. state.
- 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_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. |
| PDg | Predicate description generation | batch_69a246f4d7908190a947f6da251c6f3b |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.