Triple
T2129523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Bloody Valentine 3D |
E46504
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Todd Farmer
Todd Farmer is an American screenwriter best known for his work on horror films such as "My Bloody Valentine 3D" and "Jason X."
|
E237855
|
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: Todd Farmer | Statement: [My Bloody Valentine 3D, screenwriter, Todd Farmer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Todd Farmer Context triple: [My Bloody Valentine 3D, screenwriter, Todd Farmer]
-
A.
Mark Farmer
Mark Farmer is a British actor best known for his roles in the television series "Grange Hill," "Minder," and "Johnny Jarvis."
-
B.
Neil Farmer
Neil Farmer is a British author known for his works on organizational psychology and performance improvement.
-
C.
Brian Farmer
Brian Farmer is a notable individual recognized for achievements significant enough to be distinguished among others sharing the surname Farmer.
-
D.
Frank Farmer
Frank Farmer is the stoic former Secret Service agent turned professional bodyguard who is hired to protect a famous singer in the film "The Bodyguard."
-
E.
Hugh Farmer
Hugh Farmer was an 18th-century English dissenting minister and theologian known for his influential writings on miracles and biblical interpretation.
- 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: Todd Farmer Triple: [My Bloody Valentine 3D, screenwriter, Todd Farmer]
Generated description
Todd Farmer is an American screenwriter best known for his work on horror films such as "My Bloody Valentine 3D" and "Jason X."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Todd Farmer Target entity description: Todd Farmer is an American screenwriter best known for his work on horror films such as "My Bloody Valentine 3D" and "Jason X."
-
A.
Mark Farmer
Mark Farmer is a British actor best known for his roles in the television series "Grange Hill," "Minder," and "Johnny Jarvis."
-
B.
Neil Farmer
Neil Farmer is a British author known for his works on organizational psychology and performance improvement.
-
C.
Brian Farmer
Brian Farmer is a notable individual recognized for achievements significant enough to be distinguished among others sharing the surname Farmer.
-
D.
Frank Farmer
Frank Farmer is the stoic former Secret Service agent turned professional bodyguard who is hired to protect a famous singer in the film "The Bodyguard."
-
E.
Hugh Farmer
Hugh Farmer was an 18th-century English dissenting minister and theologian known for his influential writings on miracles and biblical interpretation.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb77ccc4819087bee5dbb91b5ae8 |
completed | March 7, 2026, 5:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae51a36398819081df18cc18bc3456 |
completed | March 9, 2026, 4:50 a.m. |
| NEDg | Description generation | batch_69ae528634608190bf10e3abf5a2c2d9 |
completed | March 9, 2026, 4:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae536431bc8190b9f293d74046cb27 |
completed | March 9, 2026, 4:58 a.m. |
Created at: March 4, 2026, 7:44 p.m.