Triple
T10917268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Pine |
E257857
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Gwynne Gilford |
E513547
|
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: Gwynne Gilford | Statement: [Robert Pine, spouse, Gwynne Gilford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gwynne Gilford Context triple: [Robert Pine, spouse, Gwynne Gilford]
-
A.
Gwynne Gilford
chosen
Gwynne Gilford is an American former actress who appeared in film and television in the 1970s and 1980s.
-
B.
Alicia Gwynn
Alicia Gwynn is the widow of Hall of Fame baseball player Tony Gwynn and a longtime community advocate and philanthropist associated with San Diego and baseball-related charitable work.
-
C.
Eileen Morrow
Eileen Morrow is a person notable enough to be recognized as a significant bearer of the surname Morrow.
-
D.
Rose Nylund
Rose Nylund is a sweet, naive, and hilariously literal-minded Midwestern woman portrayed by Betty White on the classic sitcom "The Golden Girls."
-
E.
Ann Wedgeworth
Ann Wedgeworth was an American character actress best known for her Tony Award-winning stage work and memorable roles in films and television series such as "Three's Company."
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7707ebdcc8190b42cafe21c667c82 |
completed | April 9, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e46283f41c8190ac3e1f196c5e4ca0 |
completed | April 19, 2026, 5:05 a.m. |
Created at: April 8, 2026, 9:22 p.m.