Triple
T13508276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Den of Thieves |
E321069
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Nick O'Brien
Nick O'Brien is a hard-edged, morally ambiguous Los Angeles County Sheriff's detective who leads an elite unit in the crime thriller film "Den of Thieves."
|
E1122801
|
NE FINISHED |
How this triple was built (4 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: Nick O'Brien | Statement: [Den of Thieves, mainCharacter, Nick O'Brien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nick O'Brien Context triple: [Den of Thieves, mainCharacter, Nick O'Brien]
-
A.
Ken O'Brien
Ken O'Brien is a former American football quarterback best known for his Pro Bowl career with the New York Jets in the 1980s.
-
B.
Graham O'Brien
Graham O'Brien is a companion of the Thirteenth Doctor in the long-running British science fiction television series Doctor Who.
-
C.
Nick O'Hagan
Nick O'Hagan is a film producer best known for his work on the British World War II-era thriller "Glorious 39."
-
D.
Greg O’Connor
Greg O’Connor is a film producer known for his work on crime and drama features, including the 2008 police drama "Pride and Glory."
-
E.
Dan O'Brien
Dan O'Brien is a former American decathlete and Olympic gold medalist widely regarded as one of the greatest decathletes in history.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nick O'Brien Triple: [Den of Thieves, mainCharacter, Nick O'Brien]
Generated description
Nick O'Brien is a hard-edged, morally ambiguous Los Angeles County Sheriff's detective who leads an elite unit in the crime thriller film "Den of Thieves."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nick O'Brien Target entity description: Nick O'Brien is a hard-edged, morally ambiguous Los Angeles County Sheriff's detective who leads an elite unit in the crime thriller film "Den of Thieves."
-
A.
Ken O'Brien
Ken O'Brien is a former American football quarterback best known for his Pro Bowl career with the New York Jets in the 1980s.
-
B.
Graham O'Brien
Graham O'Brien is a companion of the Thirteenth Doctor in the long-running British science fiction television series Doctor Who.
-
C.
Nick O'Hagan
Nick O'Hagan is a film producer best known for his work on the British World War II-era thriller "Glorious 39."
-
D.
Greg O’Connor
Greg O’Connor is a film producer known for his work on crime and drama features, including the 2008 police drama "Pride and Glory."
-
E.
Dan O'Brien
Dan O'Brien is a former American decathlete and Olympic gold medalist widely regarded as one of the greatest decathletes in history.
- F. None of above. chosen
Provenance (5 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf85a74081909eb08751fc55ce8f |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe387c02108190badf9b5051cd9c7a |
completed | May 8, 2026, 7:24 p.m. |
| NEDg | Description generation | batch_69fe3df36364819081a7275b2ac604a6 |
completed | May 8, 2026, 7:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe3e4c9cd08190b83fd437fa96297d |
completed | May 8, 2026, 7:49 p.m. |
Created at: April 9, 2026, 9:43 p.m.