Triple
T3043465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Den of Thieves |
E83185
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Mark Canton |
E237854
|
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: Mark Canton | Statement: [Den of Thieves, producer, Mark Canton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Canton Context triple: [Den of Thieves, producer, Mark Canton]
-
A.
Mark Canton
chosen
Mark Canton is an American film producer and former studio executive known for overseeing and producing numerous Hollywood films across genres.
-
B.
Marc Randolph
Marc Randolph is an American entrepreneur and co-founder of Netflix who played a key role in pioneering the subscription-based streaming and DVD-by-mail business model.
-
C.
Mike Nolan
Mike Nolan is a name shared by several notable individuals, including a British singer from the pop group Bucks Fizz and various sports coaches and players.
-
D.
Ben Boulware
Ben Boulware is an American former linebacker best known for starring on Clemson University's national championship-winning football team and earning All-American honors.
-
E.
Max Cullen
Max Cullen is an Australian character actor known for his extensive work in film, television, and theatre over several decades.
- 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_69ad8b2298908190a7cb4e9bdbf064d0 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b5ec5988190b8b6c95c743c6d1e |
completed | March 8, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ded35e008190be7dd72aa7537a3b |
completed | March 11, 2026, 9:29 p.m. |
Created at: March 8, 2026, 3:01 p.m.