Triple
T1408617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Lear |
E31752
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Charlotte Rosen |
E145631
|
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: Charlotte Rosen | Statement: [Norman Lear, spouse, Charlotte Rosen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charlotte Rosen Context triple: [Norman Lear, spouse, Charlotte Rosen]
-
A.
Charlotte Rosen
chosen
Charlotte Rosen was the first wife of influential American television writer and producer Norman Lear, known primarily in relation to his early life and career.
-
B.
Elizabeth Paepcke
Elizabeth Paepcke was an American philanthropist and cultural visionary who, with her husband Walter, played a central role in transforming Aspen, Colorado into an international center for arts, ideas, and intellectual dialogue.
-
C.
Marianne Ehrlich
Marianne Ehrlich was the daughter of Nobel Prize–winning German physician and immunologist Paul Ehrlich.
-
D.
Ruth Weinstein
Ruth Weinstein is one of the children of disgraced American film producer Harvey Weinstein.
-
E.
Helen Gardner
Helen Gardner is a noted literary scholar and critic, best known for her influential work on English poetry and Renaissance literature.
- 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3c10f44819085e1c4601423740d |
completed | March 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa035474881908e283cd1af65beea |
completed | March 10, 2026, 4:38 a.m. |
Created at: March 1, 2026, 7:59 p.m.