Triple
T19757119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rich Kinder |
E474529
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Nancy Kinder |
—
|
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 Kinder | Statement: [Rich Kinder, spouse, Nancy Kinder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy Kinder Context triple: [Rich Kinder, spouse, Nancy Kinder]
-
A.
Nancy Kinder
chosen
Nancy Kinder is a philanthropist and civic leader known for her significant support of cultural and educational institutions, particularly in Houston, Texas.
-
B.
Joan Snyder
Joan Snyder is best known as the wife of famed American sports commentator and Las Vegas bookmaker Jimmy "The Greek" Snyder.
-
C.
Nancy Schön
Nancy Schön is an American sculptor best known for her beloved public bronze sculptures, including the iconic "Make Way for Ducklings" installation in Boston.
-
D.
Nancy Wilner
Nancy Wilner is best known as the first wife of American actor Robert Culp, with whom she was married in the 1950s.
-
E.
Melissa Winogrand
Melissa Winogrand is known as one of the children of influential American street photographer Garry Winogrand.
- 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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6531c42408190b856341a6c6a4101 |
completed | April 20, 2026, 4:23 p.m. |
Created at: April 10, 2026, 1:48 p.m.