Triple
T14541378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonas L.A. |
E341176
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Roger S. H. Schulman |
—
|
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: Roger S. H. Schulman | Statement: [Jonas L.A., executiveProducer, Roger S. H. Schulman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roger S. H. Schulman Context triple: [Jonas L.A., executiveProducer, Roger S. H. Schulman]
-
A.
Roger S. H. Schulman
chosen
Roger S. H. Schulman is an American screenwriter and producer known for his work on animated and family films and television series.
-
B.
Douglas Shulman
Douglas Shulman is an American public official who served as the head of the U.S. Internal Revenue Service (IRS) during the late 2000s and early 2010s.
-
C.
Carl Shulman
Carl Shulman is a researcher and thinker known for his work on existential risk, AI alignment, and long-term future strategy, particularly through his role at Oxford’s Future of Humanity Institute.
-
D.
Neil B. Shulman
Neil B. Shulman is an American physician and author best known for writing the novel that inspired the film "Doc Hollywood."
-
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_69d822db9c8481908213ceb39585f792 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb1bd0dd4819094c8b2f2aa6b1c5e |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 1:22 a.m.