Triple
T9565475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer's Body |
E230778
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Diablo Cody |
E241147
|
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: Diablo Cody | Statement: [Jennifer's Body, writer, Diablo Cody]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diablo Cody Context triple: [Jennifer's Body, writer, Diablo Cody]
-
A.
Diablo Cody
chosen
Diablo Cody is an Academy Award–winning American screenwriter and author best known for her sharp, character-driven scripts in films such as "Juno" and "Young Adult."
-
B.
Anna Kaufman
Anna Kaufman is the daughter of acclaimed American screenwriter and director Charlie Kaufman.
-
C.
Leslye Headland
Leslye Headland is an American playwright, screenwriter, and director best known for co-creating the Netflix series "Russian Doll" and for her sharp, darkly comedic storytelling.
-
D.
Lake Bell
Lake Bell is an American actress, writer, and director known for her work in film and television comedies and dramas, including roles in projects like "In a World..." and "Boston Legal."
-
E.
Zoe Lister-Jones
Zoe Lister-Jones is an American actress, writer, and filmmaker known for her work in independent film and television, as well as for directing and writing genre-bending features.
- 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_69ca847f22188190a56e4a97625bef22 |
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_69d152abb0788190ab2e204d9a082ccf |
completed | April 4, 2026, 6:04 p.m. |
Created at: March 30, 2026, 8:04 p.m.