Triple
T15625244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lightyear |
E375660
|
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: [Lightyear, cinematographyBy, Jeremy Lasky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeremy Lasky Context triple: [Lightyear, 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.
Jesse Lafser
Jesse Lafser is an American singer-songwriter known for her folk- and Americana-influenced music and introspective songwriting.
-
C.
Gage Lansky
Gage Lansky is a screenwriter best known for his work on the film "Safe Haven."
-
D.
Ryan Roslansky
Ryan Roslansky is the CEO of LinkedIn, known for leading the professional networking platform’s product and business strategy.
-
E.
Jeremy Stoppelman
Jeremy Stoppelman is an American entrepreneur best known as the co-founder and longtime CEO of Yelp, a popular online review platform for local businesses.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9e5e248190ae54cda1fde51efb |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff875e49748190a2a4aceb649762b4 |
completed | May 9, 2026, 7:13 p.m. |
Created at: April 10, 2026, 4:14 a.m.