Triple
T15982626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Molly Evangeline Goodman |
E387611
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Anna Beth Goodman |
E1188697
|
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: Anna Beth Goodman | Statement: [Molly Evangeline Goodman, hasRelative, Anna Beth Goodman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Beth Goodman Context triple: [Molly Evangeline Goodman, hasRelative, Anna Beth Goodman]
-
A.
Anna Beth Goodman
chosen
Anna Beth Goodman is an American businesswoman and the wife of actor John Goodman, known for running a children’s clothing line in New Orleans.
-
B.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
C.
Beverly Goldberg
Beverly Goldberg is the overbearing yet loving 1980s matriarch from the TV sitcom "The Goldbergs," known for her smothering parenting style and bold personality.
-
D.
Gloria Nathanson
Gloria Nathanson was the wife of American novelist and poet Robert Nathan.
-
E.
Nina Loeb
Nina Loeb was an American socialite and member of the prominent Loeb banking family who married financier and Federal Reserve pioneer Paul Warburg.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15755b5548190acfa29eecb11e675 |
completed | April 16, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf1abad48190a42510605c30d0b3 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:54 a.m.