Triple
T2939509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 47 Ronin |
E79349
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Scott Stuber |
E214905
|
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: Scott Stuber | Statement: [47 Ronin, producer, Scott Stuber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Stuber Context triple: [47 Ronin, producer, Scott Stuber]
-
A.
Scott Stuber
chosen
Scott Stuber is an American film producer and entertainment executive known for overseeing original films at Netflix and producing major Hollywood movies.
-
B.
Chris Stolte
Chris Stolte is a computer scientist and entrepreneur best known as a co-founder and former chief development officer of the data visualization company Tableau Software.
-
C.
Mike Sievert
Mike Sievert is an American business executive best known for leading T-Mobile US through its high-growth, "Un-carrier" strategy and major merger with Sprint.
-
D.
Bob Suter
Bob Suter was an American defenseman best known as a member of the "Miracle on Ice" 1980 U.S. Olympic hockey team and later a prominent youth hockey coach and scout.
-
E.
Mike Schuster
Mike Schuster is a computer scientist known for his contributions to deep learning and sequence modeling, including work on neural machine translation at Google.
- 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_69ad8b0fbab081908f6a61567c045d8d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad986d9a248190927efc1a0c7d247f |
completed | March 8, 2026, 3:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c16c05481908f451b492ca9f47f |
completed | March 14, 2026, 11:52 a.m. |
Created at: March 8, 2026, 2:56 p.m.