Triple
T9565166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Girl |
E230771
|
entity |
| Predicate | leadCharacter |
P1668
|
FINISHED |
| Object | Jessica Day |
E277735
|
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: Jessica Day | Statement: [New Girl, leadCharacter, Jessica Day]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Day Context triple: [New Girl, leadCharacter, Jessica Day]
-
A.
Jessica Day
chosen
Jessica Day is the quirky, optimistic schoolteacher protagonist of the sitcom "New Girl," portrayed by Zooey Deschanel.
-
B.
Libby Snyder
Libby Snyder is known as the spouse of American poet James Wright.
-
C.
Justine Waddell
Justine Waddell is a South African-born British actress known for her roles in period dramas and independent films, including the fantasy film "The Fall" (2006).
-
D.
Mallory Grennan
Mallory Grennan is the troubled former military prison guard at the center of the graphic novel "Thumbprint," whose past abuses in Iraq return to haunt her in civilian life.
-
E.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
- 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_69ca847e53a88190a60eed7e02257f10 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd996a01b081908e2782f41520f73d |
completed | April 1, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1af4770f88190b9952c4308c0c384 |
completed | April 5, 2026, 12:39 a.m. |
Created at: March 30, 2026, 8:04 p.m.