Triple
T15482598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Sievert |
E376956
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mike Sievert |
—
|
NE NERFINISHED |
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 Sievert | Statement: [Mike Sievert, name, Mike Sievert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Sievert Context triple: [Mike Sievert, name, Mike Sievert]
-
A.
Mike Sievert
chosen
Mike Sievert is an American business executive best known for leading T-Mobile US through its high-growth, "Un-carrier" strategy and major merger with Sprint.
-
B.
Mike Schuler
Mike Schuler is an American basketball coach best known for his successful tenure as head coach of the Portland Trail Blazers in the late 1980s.
-
C.
Jeff Sauer
Jeff Sauer was a highly respected American ice hockey coach and administrator, best known for his long and successful collegiate coaching career and influential contributions to USA Hockey.
-
D.
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.
-
E.
Keith Suter
Keith Suter is an Australian-based futurist, international affairs analyst, and media commentator known for his work on global politics, conflict resolution, and strategic foresight.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f8cb4388190a3b4c92c3bb4ad4f |
completed | April 16, 2026, 1:46 a.m. |
Created at: April 10, 2026, 3:40 a.m.