Triple
T6679727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tübingen |
E151945
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Ann Arbor |
E46153
|
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: Ann Arbor | Statement: [Tübingen, twinTown, Ann Arbor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Arbor Context triple: [Tübingen, twinTown, Ann Arbor]
-
A.
Ann Arbor
chosen
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.
-
B.
East Lansing
East Lansing is a city in central Michigan best known as the home of Michigan State University.
-
C.
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.
-
D.
Ann Arbor metropolitan area
The Ann Arbor metropolitan area is a Michigan urban region centered on the city of Ann Arbor and its surrounding communities, known for its university-driven economy, research institutions, and vibrant cultural life.
-
E.
Portland, Michigan
Portland, Michigan is a small city in Ionia County known for its historic downtown, multiple riverfront parks, and extensive network of pedestrian bridges and trails.
- 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_69c687f830bc81909eb8b04dbb8450b1 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b11df8d88190bf19fcb4e7a0bdb3 |
completed | March 27, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c700790c388190bba9f0440470ebf5 |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:03 p.m.