Triple
T11678328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike McGinn |
E277549
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mike McGinn |
E277549
|
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: Mike McGinn | Statement: [Mike McGinn, name, Mike McGinn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike McGinn Context triple: [Mike McGinn, name, Mike McGinn]
-
A.
Mike McGinn
chosen
Mike McGinn is an American politician and former mayor of Seattle, Washington, known for his advocacy on environmental and transportation issues.
-
B.
James Donlan
James Donlan was an American character actor of the early 20th century, known for his prolific work in Hollywood films during the 1920s and 1930s.
-
C.
Ed Murray
Ed Murray is an American politician who served as the 53rd mayor of Seattle and previously spent many years in the Washington State Legislature.
-
D.
Howard Cunningham
Howard Cunningham is the affable, old-fashioned Milwaukee hardware store owner and father figure on the classic American sitcom "Happy Days."
-
E.
Michael Nutter
Michael Nutter is an American politician who served as the 98th mayor of Philadelphia from 2008 to 2016.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a46000a48190888ad1a6ade052e3 |
completed | April 10, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f08fbb28548190af494ddf1a1ff986 |
completed | April 28, 2026, 10:45 a.m. |
Created at: April 8, 2026, 9:40 p.m.