Triple
T23062686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyad |
E574945
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Andrew Lazar |
—
|
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: Andrew Lazar | Statement: [Nyad, producer, Andrew Lazar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrew Lazar Context triple: [Nyad, producer, Andrew Lazar]
-
A.
Andrew Lazar
chosen
Andrew Lazar is an American film producer known for his work on movies such as "Get Smart" and other major studio comedies and dramas.
-
B.
Alan Lazar
Alan Lazar is a South African-born composer and musician known for scoring film and television projects, including the soundtrack for "Holiday in the Wild."
-
C.
Edward Zorinsky
Edward Zorinsky was a U.S. Senator from Nebraska and former mayor of Omaha known for his moderate Democratic politics and service in the late 20th century.
-
D.
George Kozmetsky
George Kozmetsky was an American technology entrepreneur, investor, and educator best known as a co-founder of Teledyne and a major figure in fostering innovation and high-tech industry growth.
-
E.
Mark Antokolsky
Mark Antokolsky was a renowned 19th-century Russian-Jewish sculptor celebrated for his realistic and emotionally expressive historical and religious works.
- 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a0c3c881909f137ad511c216ac |
completed | April 29, 2026, 4:31 a.m. |
Created at: April 17, 2026, 3:55 p.m.