Triple
T8965382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bloomington |
E214116
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object | Tim Busse |
E484182
|
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: Tim Busse | Statement: [Bloomington, hasMayor, Tim Busse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tim Busse Context triple: [Bloomington, hasMayor, Tim Busse]
-
A.
Tim Busse
chosen
Tim Busse is an American local politician who serves as the mayor of Bloomington, Minnesota.
-
B.
Kevin Biegel
Kevin Biegel is an American television writer and producer best known for co-creating the sitcom Cougar Town and working on shows like Scrubs and Enlisted.
-
C.
Chris Bauermeister
Chris Bauermeister is an American bassist best known as a founding member of the influential punk rock band Jawbreaker.
-
D.
Chris Weinke
Chris Weinke is a former American football quarterback best known for leading Florida State University to a national championship and winning the Heisman Trophy before playing in the NFL.
-
E.
Eric Wetzels
Eric Wetzels is a Dutch politician who serves as the chairperson of the People's Party for Freedom and Democracy (VVD).
- 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_69ca839cd6008190a1546a701a56710c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc674c4be8819090d46aba8ab40af3 |
completed | April 1, 2026, 12:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc95514408190ad442069daec0459 |
completed | April 3, 2026, 2:06 p.m. |
Created at: March 30, 2026, 7:01 p.m.