Triple
T18508919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lia McHugh |
E452278
|
entity |
| Predicate | portrays |
P264
|
FINISHED |
| Object | Jessica Nolan |
—
|
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: Jessica Nolan | Statement: [Lia McHugh, portrays, Jessica Nolan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Nolan Context triple: [Lia McHugh, portrays, Jessica Nolan]
-
A.
Jessica Nolan
chosen
Jessica Nolan is a fictional character portrayed as an American woman, likely featured in a narrative work such as a film, television series, or novel.
-
B.
Carol Nolan
Carol Nolan is a private individual known primarily as a relative of Sarah Nolan, with no widely documented public profile.
-
C.
Beth Nolan
Beth Nolan is an American lawyer and legal scholar who served as White House Counsel to President Bill Clinton.
-
D.
Sarah O’Meara
Sarah O’Meara is known as the spouse of Australian film director Paul Cox.
-
E.
Tia Nolan
Tia Nolan is a film editor known for her work on major studio comedies and feature films, including the romantic comedy "Friends with Benefits."
- 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53344c6b081908e780ed5c815a766 |
completed | April 19, 2026, 7:55 p.m. |
Created at: April 10, 2026, 11:36 a.m.