Triple
T20793334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Poet Laureate |
E511833
|
entity |
| Predicate | hasTitleHolder |
P1911
|
FINISHED |
| Object | Ted Kooser |
—
|
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: Ted Kooser | Statement: [United States Poet Laureate, hasTitleHolder, Ted Kooser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ted Kooser Context triple: [United States Poet Laureate, hasTitleHolder, Ted Kooser]
-
A.
Ted Kooser
chosen
Ted Kooser is an American poet, essayist, and former U.S. Poet Laureate known for his accessible, plainspoken verse about Midwestern life.
-
B.
John Kooser
John Kooser was an individual significant enough in local or regional history that a Pennsylvania state park was named in his honor.
-
C.
Doug Mahon
Doug Mahon is a technology entrepreneur best known as a founder of the data storage company Seagate Technology.
-
D.
Donald Hall
Donald Hall was a prominent American poet, essayist, and former U.S. Poet Laureate known for his reflective, rural-themed verse and influential contributions to contemporary poetry.
-
E.
Kay Ryan
Kay Ryan is an American poet known for her concise, witty, and philosophically rich verse, who has received major literary honors for her work.
- 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2aadd7081908c6343821e8c655c |
completed | April 21, 2026, 12:19 a.m. |
Created at: April 16, 2026, 12:38 p.m.