Triple
T10511007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doug Sahm |
E247914
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Sahm |
E825345
|
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: Sahm | Statement: [Doug Sahm, familyName, Sahm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sahm Context triple: [Doug Sahm, familyName, Sahm]
-
A.
Sahm
chosen
Sahm is a German surname most notably borne by Heinrich Sahm, a prominent early 20th-century politician and mayor of Berlin.
-
B.
Arvin
Arvin is a small agricultural city in Southern California’s San Joaquin Valley, known for its farming economy and diverse rural community.
-
C.
Hagey
Hagey is a surname most notably associated with Gerald Hagey, a prominent Canadian academic and founding president of the University of Waterloo.
-
D.
Trulaske
Trulaske is the commonly used name for the Robert J. Trulaske, Sr. College of Business at the University of Missouri, a business school offering undergraduate and graduate programs in fields such as accounting, finance, and management.
-
E.
O'Steen
O'Steen is a surname most notably associated with American film editor Sam O'Steen, known for his work on several acclaimed Hollywood films.
- 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509b542088190868531f84deaf9e4 |
completed | April 7, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dcee1db081908c791867e2438d30 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 6, 2026, 12:27 p.m.