Triple
T18248191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heartburn |
E437008
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Rachel Samstat |
—
|
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: Rachel Samstat | Statement: [Heartburn, mainCharacter, Rachel Samstat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Samstat Context triple: [Heartburn, mainCharacter, Rachel Samstat]
-
A.
Rachel Samstat
chosen
Rachel Samstat is the witty, food-obsessed narrator of Nora Ephron’s novel "Heartburn," whose crumbling marriage and sharp humor drive the story’s blend of comedy and heartbreak.
-
B.
Barbara Goldsmith
Barbara Goldsmith was an American author, journalist, and philanthropist known for her influential works of narrative history and her advocacy for human rights and freedom of expression.
-
C.
Jacqueline Saltzman
Jacqueline Saltzman was the wife of film producer Harry Saltzman, who co-produced the early James Bond movies.
-
D.
Gail Katz
Gail Katz is an American film and television producer known for working on major Hollywood projects including the disaster drama "The Perfect Storm."
-
E.
Sari Gilman
Sari Gilman is a film editor best known for her work on the Academy Award–winning documentary "Taxi to the Dark Side."
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e89b288190a286797ec2cd60a8 |
completed | April 19, 2026, 3:42 p.m. |
Created at: April 10, 2026, 10:33 a.m.