Triple
T8007371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nancy Juvonen |
E186395
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nancy Juvonen |
E186395
|
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: Nancy Juvonen | Statement: [Nancy Juvonen, name, Nancy Juvonen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy Juvonen Context triple: [Nancy Juvonen, name, Nancy Juvonen]
-
A.
Nancy Juvonen
chosen
Nancy Juvonen is an American film producer and co-founder of Flower Films, known for her longtime collaboration with Drew Barrymore and marriage to television host Jimmy Fallon.
-
B.
Linda Rogoff
Linda Rogoff was an American musician and manager best known as the wife and longtime partner of actor George Segal.
-
C.
Lorrie Baranek
Lorrie Baranek is a television producer best known for her executive production work on the current affairs series "The Problem with Jon Stewart."
-
D.
Janet M. Lang
Janet M. Lang is a scholar and co-author known for her collaborative work with James G. Blight on Cold War history and crisis decision-making.
-
E.
Lisa Lassek
Lisa Lassek is an American film and television editor known for her frequent collaborations with Joss Whedon on projects such as major Marvel superhero films and cult TV series.
- 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_69ca82abaffc8190ab8af79cdbc31ab3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3cf8a6048190970685a83fd2f59d |
completed | March 31, 2026, 3:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe12c068c8190a6ea7e924a7748c6 |
completed | March 31, 2026, 2:58 p.m. |
Created at: March 30, 2026, 5:18 p.m.