Triple
T4984345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bloomington, Minnesota |
E111963
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Tim Busse
Tim Busse is an American local politician who serves as the mayor of Bloomington, Minnesota.
|
E484182
|
NE FINISHED |
How this triple was built (4 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, Minnesota, hasMayor, Tim Busse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tim Busse Context triple: [Bloomington, Minnesota, hasMayor, Tim Busse]
-
A.
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.
-
B.
Eric Wetzels
Eric Wetzels is a Dutch politician who serves as the chairperson of the People's Party for Freedom and Democracy (VVD).
-
C.
Ken Hutchison
Ken Hutchison was a Scottish actor known for his intense character roles in film and television during the 1970s and 1980s.
-
D.
Brian Schmetzer
Brian Schmetzer is an American soccer coach best known for leading Seattle Sounders FC to multiple MLS Cup titles and establishing the club as a perennial league contender.
-
E.
Ken Schretzmann
Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tim Busse Triple: [Bloomington, Minnesota, hasMayor, Tim Busse]
Generated description
Tim Busse is an American local politician who serves as the mayor of Bloomington, Minnesota.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tim Busse Target entity description: Tim Busse is an American local politician who serves as the mayor of Bloomington, Minnesota.
-
A.
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.
-
B.
Eric Wetzels
Eric Wetzels is a Dutch politician who serves as the chairperson of the People's Party for Freedom and Democracy (VVD).
-
C.
Ken Hutchison
Ken Hutchison was a Scottish actor known for his intense character roles in film and television during the 1970s and 1980s.
-
D.
Brian Schmetzer
Brian Schmetzer is an American soccer coach best known for leading Seattle Sounders FC to multiple MLS Cup titles and establishing the club as a perennial league contender.
-
E.
Ken Schretzmann
Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
- F. None of above. chosen
Provenance (5 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_69bd441adc208190b70a033a0741d01e |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7255d7b4819098b537df5b1a4c3c |
completed | March 20, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be8a1891c48190b85bec5e97f75e44 |
completed | March 21, 2026, 12:07 p.m. |
| NEDg | Description generation | batch_69be8afcf5f0819094fd6351a8f377cc |
completed | March 21, 2026, 12:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be8b80af18819091efdfe242b7b477 |
completed | March 21, 2026, 12:13 p.m. |
Created at: March 20, 2026, 1:33 p.m.