Triple
T1614049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Provo, Utah |
E34672
|
entity |
| Predicate | mayor |
P185
|
FINISHED |
| Object |
Brett M. Taylor
Brett M. Taylor is an American politician serving as the mayor of Provo, Utah.
|
E194153
|
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: Brett M. Taylor | Statement: [Provo, Utah, mayor, Brett M. Taylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brett M. Taylor Context triple: [Provo, Utah, mayor, Brett M. Taylor]
-
A.
Eric M. Taylor
Eric M. Taylor was a notable figure significant enough in his community or field to have the Eric M. Taylor Center named in his honor.
-
B.
Jonathan M. Daniels
Jonathan M. Daniels was a civil rights activist and Episcopal seminarian who was killed in 1965 while protecting a young Black girl during the struggle for racial equality in Alabama.
-
C.
Michael V. Drake
Michael V. Drake is an American academic leader and physician who has served as president of both The Ohio State University and the University of California system.
-
D.
Jonathan Lisco
Jonathan Lisco is an American television writer, producer, and showrunner known for his work on series such as Animal Kingdom, Halt and Catch Fire, and Jack & Bobby.
-
E.
Mark Rosenberg
Mark Rosenberg was an American film producer known for his work on notable movies of the 1980s and early 1990s.
- 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: Brett M. Taylor Triple: [Provo, Utah, mayor, Brett M. Taylor]
Generated description
Brett M. Taylor is an American politician serving as the mayor of Provo, Utah.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brett M. Taylor Target entity description: Brett M. Taylor is an American politician serving as the mayor of Provo, Utah.
-
A.
Eric M. Taylor
Eric M. Taylor was a notable figure significant enough in his community or field to have the Eric M. Taylor Center named in his honor.
-
B.
Jonathan M. Daniels
Jonathan M. Daniels was a civil rights activist and Episcopal seminarian who was killed in 1965 while protecting a young Black girl during the struggle for racial equality in Alabama.
-
C.
Michael V. Drake
Michael V. Drake is an American academic leader and physician who has served as president of both The Ohio State University and the University of California system.
-
D.
Jonathan Lisco
Jonathan Lisco is an American television writer, producer, and showrunner known for his work on series such as Animal Kingdom, Halt and Catch Fire, and Jack & Bobby.
-
E.
Mark Rosenberg
Mark Rosenberg was an American film producer known for his work on notable movies of the 1980s and early 1990s.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9098f384c81909ef836ee779466e2 |
completed | March 5, 2026, 4:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8ab5e8b88190b95af87391e5bb16 |
completed | March 8, 2026, 2:41 p.m. |
| NEDg | Description generation | batch_69ad95738df081909e56178cc1e0ab74 |
completed | March 8, 2026, 3:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad97a1a44c8190a9d8fe05837ec9ff |
completed | March 8, 2026, 3:37 p.m. |
Created at: March 4, 2026, 7:28 p.m.