Triple
T11049438
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Street |
E261207
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Matt Buckner
Matt Buckner is the American college student protagonist of the film "Green Street Hooligans," who becomes deeply involved with an English football firm after moving to London.
|
E904960
|
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: Matt Buckner | Statement: [Green Street, character, Matt Buckner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Buckner Context triple: [Green Street, character, Matt Buckner]
-
A.
John Buckner
John Buckner is a relatively obscure individual whose primary current distinction is simply being noted as a bearer of the Buckner surname.
-
B.
A. J. Buckner
A. J. Buckner is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Buckner.
-
C.
Randy Buckner
Randy Buckner is an American neuroscientist known for his influential research on the brain’s default mode network and memory using neuroimaging techniques.
-
D.
Don Burnett
Don Burnett was a British-born American actor best known for his film and television roles in the 1950s and 1960s.
-
E.
Ray Heindorf
Ray Heindorf was an American composer, arranger, and musical director best known for his work on numerous Hollywood film scores during the mid-20th century.
- 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: Matt Buckner Triple: [Green Street, character, Matt Buckner]
Generated description
Matt Buckner is the American college student protagonist of the film "Green Street Hooligans," who becomes deeply involved with an English football firm after moving to London.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matt Buckner Target entity description: Matt Buckner is the American college student protagonist of the film "Green Street Hooligans," who becomes deeply involved with an English football firm after moving to London.
-
A.
John Buckner
John Buckner is a relatively obscure individual whose primary current distinction is simply being noted as a bearer of the Buckner surname.
-
B.
A. J. Buckner
A. J. Buckner is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Buckner.
-
C.
Randy Buckner
Randy Buckner is an American neuroscientist known for his influential research on the brain’s default mode network and memory using neuroimaging techniques.
-
D.
Don Burnett
Don Burnett was a British-born American actor best known for his film and television roles in the 1950s and 1960s.
-
E.
Ray Heindorf
Ray Heindorf was an American composer, arranger, and musical director best known for his work on numerous Hollywood film scores during the mid-20th century.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79868c78881908c8e3672c05ae7ec |
completed | April 9, 2026, 12:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e74e1dc881908afc01b328cda843 |
completed | April 18, 2026, 8:19 p.m. |
| NEDg | Description generation | batch_69e3f01f9d048190b553184f0f6ce29c |
completed | April 18, 2026, 8:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3f3fef9308190b0354ed436c32e4c |
completed | April 18, 2026, 9:13 p.m. |
Created at: April 8, 2026, 9:26 p.m.