Triple
T2463711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michigan State College |
E55193
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | East Lansing, Michigan |
E52734
|
NE 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: East Lansing, Michigan | Statement: [Michigan State College, locatedIn, East Lansing, Michigan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: East Lansing, Michigan Context triple: [Michigan State College, locatedIn, East Lansing, Michigan]
-
A.
East Lansing
chosen
East Lansing is a city in central Michigan best known as the home of Michigan State University.
-
B.
Ann Arbor
Ann Arbor is a vibrant college town in southeastern Michigan best known as the home of the University of Michigan and a center for education, research, and arts.
-
C.
Lansing, Michigan
Lansing, Michigan is the capital city of the U.S. state of Michigan and a historic center of automobile manufacturing and industry.
-
D.
Berkley, Michigan
Berkley, Michigan is a small suburban city in Oakland County known for its tree-lined neighborhoods, family-friendly community, and proximity to Detroit.
-
E.
Cedar Springs, Michigan
Cedar Springs, Michigan is a small city in Kent County known historically for its natural springs and lumber industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd12059788190a6493f64bb725aed |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98a63a44819092c017b8624e3dc8 |
completed | March 10, 2026, 4:05 a.m. |
Created at: March 6, 2026, 9:44 p.m.