Triple
T9750395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Kooser |
E236425
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ted Kooser |
E236425
|
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: Ted Kooser | Statement: [Ted Kooser, name, Ted Kooser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ted Kooser Context triple: [Ted Kooser, name, 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.
Doug Mahon
Doug Mahon is a technology entrepreneur best known as a founder of the data storage company Seagate Technology.
-
C.
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.
-
D.
Stephen Dunn
Stephen Dunn was an American poet and Pulitzer Prize winner known for his accessible, reflective verse exploring everyday life and human relationships.
-
E.
Mark Strand
Mark Strand was a Pulitzer Prize–winning Canadian-born American poet, essayist, and translator known for his spare, meditative verse and for serving as U.S. Poet Laureate.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f6bb4088190ab8d52ef61d24bee |
completed | April 1, 2026, 10:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1b020829481908456e7977c5f9adb |
completed | April 5, 2026, 12:43 a.m. |
Created at: March 30, 2026, 8:24 p.m.