Triple
T13694159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbershop |
E328341
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Marshall Todd
Marshall Todd is a screenwriter best known for co-writing the hit comedy film "Barbershop."
|
E1058022
|
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: Marshall Todd | Statement: [Barbershop, screenwriter, Marshall Todd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marshall Todd Context triple: [Barbershop, screenwriter, Marshall Todd]
-
A.
Scott Marshall
Scott Marshall is an American film and television director known for his work on comedies and for being the son of filmmaker Garry Marshall.
-
B.
Marshall Lancaster
Marshall Lancaster is a British actor best known for his role as DC Chris Skelton in the television series "Life on Mars" and its sequel "Ashes to Ashes."
-
C.
Marshall Harvey
Marshall Harvey is a film editor best known for his work on movies such as the dark comedy "The 'Burbs."
-
D.
Marshall Pease
Marshall Pease is a computer scientist best known for co-authoring the seminal paper that introduced the Byzantine Generals Problem in distributed computing and fault tolerance.
-
E.
Bill Marshall
Bill Marshall was a Canadian film producer and cultural entrepreneur best known for co-founding and helping establish the Toronto International Film Festival as a major global cinema event.
- 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: Marshall Todd Triple: [Barbershop, screenwriter, Marshall Todd]
Generated description
Marshall Todd is a screenwriter best known for co-writing the hit comedy film "Barbershop."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marshall Todd Target entity description: Marshall Todd is a screenwriter best known for co-writing the hit comedy film "Barbershop."
-
A.
Scott Marshall
Scott Marshall is an American film and television director known for his work on comedies and for being the son of filmmaker Garry Marshall.
-
B.
Marshall Lancaster
Marshall Lancaster is a British actor best known for his role as DC Chris Skelton in the television series "Life on Mars" and its sequel "Ashes to Ashes."
-
C.
Marshall Harvey
Marshall Harvey is a film editor best known for his work on movies such as the dark comedy "The 'Burbs."
-
D.
Marshall Pease
Marshall Pease is a computer scientist best known for co-authoring the seminal paper that introduced the Byzantine Generals Problem in distributed computing and fault tolerance.
-
E.
Bill Marshall
Bill Marshall was a Canadian film producer and cultural entrepreneur best known for co-founding and helping establish the Toronto International Film Festival as a major global cinema event.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc8757b648190a26181efbad09a43 |
completed | April 12, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d4f35888190b2c3df62bde1ce6e |
completed | May 3, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69f7a15f3c908190be380355972def6e |
completed | May 3, 2026, 7:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a2234390819093814fd435f9c42c |
completed | May 3, 2026, 7:29 p.m. |
Created at: April 9, 2026, 9:54 p.m.