Triple
T17008981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howard Marks |
E412646
|
entity |
| Predicate | subjectOf |
P38
|
FINISHED |
| Object |
film Mr Nice
Mr Nice is a 2010 British biographical crime film about the life of Welsh drug smuggler and author Howard Marks.
|
E1244583
|
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: film Mr Nice | Statement: [Howard Marks, subjectOf, film Mr Nice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: film Mr Nice Context triple: [Howard Marks, subjectOf, film Mr Nice]
-
A.
The Nice Guys
The Nice Guys is a 2016 neo-noir action-comedy film directed by Shane Black, starring Russell Crowe and Ryan Gosling as mismatched private investigators in 1970s Los Angeles.
-
B.
Mr. Director
Mr. Director is the formal style of address used for the Cabinet-level head of the United States Office of Management and Budget.
-
C.
The Nance
The Nance is a Broadway play by Douglas Carter Beane that explores the life and struggles of a gay burlesque performer in 1930s New York.
-
D.
Fukrey
Fukrey is a popular 2013 Indian Hindi-language comedy film about a group of slackers in Delhi whose get-rich-quick schemes lead to chaotic and humorous consequences.
-
E.
The Flick
The Flick is a Pulitzer Prize–winning play by Annie Baker that portrays the lives of three underpaid employees working in a run-down Massachusetts movie theater.
- 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: film Mr Nice Triple: [Howard Marks, subjectOf, film Mr Nice]
Generated description
Mr Nice is a 2010 British biographical crime film about the life of Welsh drug smuggler and author Howard Marks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: film Mr Nice Target entity description: Mr Nice is a 2010 British biographical crime film about the life of Welsh drug smuggler and author Howard Marks.
-
A.
The Nice Guys
The Nice Guys is a 2016 neo-noir action-comedy film directed by Shane Black, starring Russell Crowe and Ryan Gosling as mismatched private investigators in 1970s Los Angeles.
-
B.
Mr. Director
Mr. Director is the formal style of address used for the Cabinet-level head of the United States Office of Management and Budget.
-
C.
The Nance
The Nance is a Broadway play by Douglas Carter Beane that explores the life and struggles of a gay burlesque performer in 1930s New York.
-
D.
Fukrey
Fukrey is a popular 2013 Indian Hindi-language comedy film about a group of slackers in Delhi whose get-rich-quick schemes lead to chaotic and humorous consequences.
-
E.
The Flick
The Flick is a Pulitzer Prize–winning play by Annie Baker that portrays the lives of three underpaid employees working in a run-down Massachusetts movie theater.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d3853f548190910240a2145cc890 |
completed | April 18, 2026, 6:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dc222d108190934ef2b3aa46aa22 |
completed | May 10, 2026, 7:27 p.m. |
| NEDg | Description generation | batch_6a0114d7d03c8190943777f4eac956fd |
completed | May 10, 2026, 11:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01159a08b081908fc82adc7cca532a |
completed | May 10, 2026, 11:32 p.m. |
Created at: April 10, 2026, 5:33 a.m.