Triple
T13610364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Smile Back |
E325171
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Paige Dylan |
E490063
|
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: Paige Dylan | Statement: [I Smile Back, screenwriter, Paige Dylan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paige Dylan Context triple: [I Smile Back, screenwriter, Paige Dylan]
-
A.
Paige Dylan
chosen
Paige Dylan is an American screenwriter and actress best known for her work in film and television and for being married to musician Jakob Dylan.
-
B.
Maria Dylan
Maria Dylan is one of Bob Dylan and Sara Dylan’s daughters, known primarily as a member of the Dylan family.
-
C.
Paige Brown
Paige Brown is a film producer best known for her work on the mystery adventure movie "Enola Holmes."
-
D.
Jesse Dylan
Jesse Dylan is an American film director and producer known for directing feature films, music videos, and commercials, and as the son of musician Bob Dylan and artist Sara Dylan.
-
E.
Emily Sweeney
Emily Sweeney is a dermatologist who appears as Rajesh Koothrappali’s love interest on the television sitcom "The Big Bang Theory."
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0aa9a1481908c6f92495aff86c6 |
completed | April 12, 2026, 2:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f9a9f9c81909b0a8f4f51c461ae |
completed | May 3, 2026, 5:02 p.m. |
Created at: April 9, 2026, 9:50 p.m.