Triple
T8801065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-Yves Escoffier |
E209406
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object | Nurse Betty |
E253594
|
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: Nurse Betty | Statement: [Jean-Yves Escoffier, workedOn, Nurse Betty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nurse Betty Context triple: [Jean-Yves Escoffier, workedOn, Nurse Betty]
-
A.
Nurse Betty
chosen
Nurse Betty is a 2000 dark comedy film about a Kansas waitress who, after a traumatic event, becomes delusionally convinced she is living inside her favorite soap opera.
-
B.
Nanny
Nanny is the kind-hearted, loyal housekeeper who helps care for Pongo and Perdita’s puppies in Disney’s "One Hundred and One Dalmatians."
-
C.
Betty Blue
Betty Blue is a 1986 French romantic drama film, directed by Jean-Jacques Beineix, that became a cult classic for its intense portrayal of obsessive love and emotional collapse.
-
D.
Bettie
Bettie is a feminine given name, often used as a diminutive or variant of names like Bettina or Elizabeth.
-
E.
The Patsy
The Patsy is a 1964 comedy film directed by and starring Jerry Lewis, in which he plays a hapless bellboy groomed to become a superstar.
- 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fb8aab88190befed16301e08efc |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6f6fdd688190bf40bbde0be991e1 |
completed | April 3, 2026, 7:42 a.m. |
Created at: March 30, 2026, 6:44 p.m.