Triple
T10585025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reagan |
E249832
|
entity |
| Predicate | hasGivenNameBearer |
P458
|
FINISHED |
| Object |
Reagan Dunn
Reagan Dunn is an American attorney and politician who serves on the King County Council in Washington State and is known for his work on public safety and criminal justice issues.
|
E891151
|
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: Reagan Dunn | Statement: [Reagan, hasGivenNameBearer, Reagan Dunn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reagan Dunn Context triple: [Reagan, hasGivenNameBearer, Reagan Dunn]
-
A.
Reagan Gomez-Preston
Reagan Gomez-Preston is an American actress and voice actress best known for her roles in television sitcoms and animated series.
-
B.
Shauneen Bruder
Shauneen Bruder is a Canadian business leader and executive who has served as chancellor of the University of Guelph.
-
C.
Crystal Dunn
Crystal Dunn is an American professional soccer player and World Cup champion known for her versatility and impact for club and country.
-
D.
Kaitlyn Dunn
Kaitlyn Dunn is a person notable enough to be specifically referenced as a bearer of the surname Dunn.
-
E.
McKaley Miller
McKaley Miller is an American actress best known for her role as Rose Hattenbarger on the television series "Hart of Dixie."
- 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: Reagan Dunn Triple: [Reagan, hasGivenNameBearer, Reagan Dunn]
Generated description
Reagan Dunn is an American attorney and politician who serves on the King County Council in Washington State and is known for his work on public safety and criminal justice issues.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Reagan Dunn Target entity description: Reagan Dunn is an American attorney and politician who serves on the King County Council in Washington State and is known for his work on public safety and criminal justice issues.
-
A.
Reagan Gomez-Preston
Reagan Gomez-Preston is an American actress and voice actress best known for her roles in television sitcoms and animated series.
-
B.
Shauneen Bruder
Shauneen Bruder is a Canadian business leader and executive who has served as chancellor of the University of Guelph.
-
C.
Crystal Dunn
Crystal Dunn is an American professional soccer player and World Cup champion known for her versatility and impact for club and country.
-
D.
Kaitlyn Dunn
Kaitlyn Dunn is a person notable enough to be specifically referenced as a bearer of the surname Dunn.
-
E.
McKaley Miller
McKaley Miller is an American actress best known for her role as Rose Hattenbarger on the television series "Hart of Dixie."
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d52768da9c8190add1db88bf2e16ea |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dff76eef4c8190b4fe681a9431207d |
completed | April 15, 2026, 8:39 p.m. |
| NEDg | Description generation | batch_69e0b498df2481908c964d53b1782774 |
completed | April 16, 2026, 10:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e11e21fc2c8190878a877ecd3b465e |
completed | April 16, 2026, 5:36 p.m. |
Created at: April 6, 2026, 12:39 p.m.