Triple
T22474304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DC League of Super-Pets |
E555585
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Jared Stern |
—
|
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: Jared Stern | Statement: [DC League of Super-Pets, screenwriter, Jared Stern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jared Stern Context triple: [DC League of Super-Pets, screenwriter, Jared Stern]
-
A.
Jared Stern
chosen
Jared Stern is an American screenwriter, producer, and director known for his work on animated films such as DC League of Super-Pets and contributions to various family-oriented comedies.
-
B.
Joshua Michael Stern
Joshua Michael Stern is an American film director and screenwriter known for helming biographical and dramatic feature films.
-
C.
Jared Levine
Jared Levine is a film producer known for his work on the movie "Narc."
-
D.
Jared Kaplan
Jared Kaplan is a theoretical physicist and AI researcher known for his work on deep learning scaling laws and contributions to large language model development.
-
E.
Jared Kleinman
Jared Kleinman is a sarcastic, tech-savvy high school student who serves as comic relief and a reluctant accomplice in the musical "Dear Evan Hansen."
- 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_69e11e52c2048190952dc5df209b9bed |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15be2d5388190a59d11b3403d998b |
completed | April 29, 2026, 1:16 a.m. |
Created at: April 16, 2026, 8:49 p.m.