Triple
T2142799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toy Story 3 |
E46998
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Jeremy Lasky |
E276257
|
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: Jeremy Lasky | Statement: [Toy Story 3, cinematographyBy, Jeremy Lasky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeremy Lasky Context triple: [Toy Story 3, cinematographyBy, Jeremy Lasky]
-
A.
Jeremy Lasky
chosen
Jeremy Lasky is an American cinematographer best known for his work at Pixar Animation Studios on films such as Cars and other major animated features.
-
B.
Ryan Roslansky
Ryan Roslansky is the CEO of LinkedIn, known for leading the professional networking platform’s product and business strategy.
-
C.
Joshua Michael Stern
Joshua Michael Stern is an American film director and screenwriter known for helming biographical and dramatic feature films.
-
D.
Nick Wechsler
Nick Wechsler is an American actor best known for his television roles, including playing Jack Porter on the drama series "Revenge."
-
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 (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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe206db0819095772af5358dca55 |
completed | March 7, 2026, 5:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af989e2cf48190b9c5d053491e3a96 |
completed | March 10, 2026, 4:05 a.m. |
Created at: March 4, 2026, 7:44 p.m.