Triple
T11719693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuser family |
E278593
|
entity |
| Predicate | hasNotableMember |
P304
|
FINISHED |
| Object | Fred E. Kuser |
—
|
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: Fred E. Kuser | Statement: [Kuser family, hasNotableMember, Fred E. Kuser]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fred E. Kuser Context triple: [Kuser family, hasNotableMember, Fred E. Kuser]
-
A.
Fred E. Kuser
chosen
Fred E. Kuser is a notable individual associated with the Kuser family, recognized for his prominence within that lineage.
-
B.
Charles O. Baumann
Charles O. Baumann was an early American film producer and studio executive who played a significant role in the development of the motion picture industry in the early 20th century.
-
C.
Frank E. Bunts
Frank E. Bunts was an American physician and surgeon best known as one of the founding doctors of the Cleveland Clinic, a major academic medical center.
-
D.
Ralph Meeker
Ralph Meeker was an American actor best known for his tough-guy roles in film noir and drama during the 1950s and 1960s.
-
E.
Fred Luddy
Fred Luddy is an American entrepreneur and software executive best known as the founder of the cloud-based enterprise software company ServiceNow.
- 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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4c26e4c8190ae30d906b4fd4221 |
completed | April 10, 2026, 7:20 a.m. |
Created at: April 8, 2026, 9:40 p.m.