Triple
T22980057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonathan Tunick |
E571432
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jonathan Tunick |
—
|
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: Jonathan Tunick | Statement: [Jonathan Tunick, name, Jonathan Tunick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Tunick Context triple: [Jonathan Tunick, name, Jonathan Tunick]
-
A.
Jonathan Tunick
chosen
Jonathan Tunick is a renowned American orchestrator, musical director, and composer best known for his long-standing collaboration with Stephen Sondheim on numerous Broadway productions.
-
B.
Alan Sytner
Alan Sytner was a British nightclub owner and impresario best known for creating Liverpool’s iconic Cavern Club, which became a pivotal venue in the rise of The Beatles and the Merseybeat scene.
-
C.
Michele Moerth
Michele Moerth is best known as the wife of the late British actor Ben Cross, recognized for his role in the film "Chariots of Fire."
-
D.
James Lindenbaum
James Lindenbaum is a technology entrepreneur best known as a co-founder of the cloud platform-as-a-service company Heroku.
-
E.
Andrew Shulkind
Andrew Shulkind is a cinematographer known for his atmospheric and visually immersive work in genre films and television.
- 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18294c4c8819083ef85d9cb736613 |
completed | April 29, 2026, 4:01 a.m. |
Created at: April 17, 2026, 3:49 p.m.