Triple
T12090377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Before I Go to Sleep |
E287924
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Liza Chasin |
E597710
|
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: Liza Chasin | Statement: [Before I Go to Sleep, producer, Liza Chasin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liza Chasin Context triple: [Before I Go to Sleep, producer, Liza Chasin]
-
A.
Liza Chasin
chosen
Liza Chasin is a film and television producer known for her work on independent and studio projects, including "The Ballad of Jack and Rose."
-
B.
Liza Snyder
Liza Snyder is an American television actress best known for her comedic roles on sitcoms such as "Yes, Dear" and "Man with a Plan."
-
C.
Ilene Chaiken
Ilene Chaiken is an American television writer and producer best known as the creator of "The L Word" and a key creative force behind several high-profile drama series.
-
D.
Liza Weil
Liza Weil is an American actress best known for her roles as Paris Geller on "Gilmore Girls" and Bonnie Winterbottom on "How to Get Away with Murder."
-
E.
Liz Gorinsky
Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915161f848190a6355c1e372eadaa |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6684b79c48190a663e9f5504ba20c |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:48 p.m.