Triple
T21727686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nancy Reddin Kienholz |
E536315
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nancy Reddin Kienholz |
—
|
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: Nancy Reddin Kienholz | Statement: [Nancy Reddin Kienholz, name, Nancy Reddin Kienholz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy Reddin Kienholz Context triple: [Nancy Reddin Kienholz, name, Nancy Reddin Kienholz]
-
A.
Nancy Reddin Kienholz
chosen
Nancy Reddin Kienholz is an American mixed-media and installation artist known for her collaborative, politically charged assemblage works with her husband, Ed Kienholz.
-
B.
Nancy Hoffman
Nancy Hoffman is a notable individual recognized for achievements significant enough to be distinctly associated with the surname Hoffman.
-
C.
Nancy Kinder
Nancy Kinder is a philanthropist and civic leader known for her significant support of cultural and educational institutions, particularly in Houston, Texas.
-
D.
Joan Snyder
Joan Snyder is best known as the wife of famed American sports commentator and Las Vegas bookmaker Jimmy "The Greek" Snyder.
-
E.
Marilyn Goldin
Marilyn Goldin is a screenwriter best known for her work on the film "The Big Blue."
- 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_69e0c46d3284819099a4f9d5a704eb95 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69effd03c6ec8190a2f0445c1f3a45b4 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 16, 2026, 6:48 p.m.