Triple

T10366217
Position Surface form Disambiguated ID Type / Status
Subject In the Loop E244256 entity
Predicate character P662 FINISHED
Object Simon Foster
Simon Foster is a bumbling and indecisive British government minister whose gaffes help drive the political satire and chaos in the film "In the Loop."
 E857420 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: Simon Foster | Statement: [In the Loop, character, Simon Foster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Simon Foster
Context triple: [In the Loop, character, Simon Foster]
  • A. Simon Foster
    Simon Foster is a British politician who serves as the elected Police and Crime Commissioner for the West Midlands, overseeing policing strategy and accountability in the region.
  • B. Jeff Foster
    Jeff Foster is an entrepreneur best known for founding the global athletic footwear and apparel brand Reebok.
  • C. Jon Foster
    Jon Foster is an American actor and musician known for his film and television roles as well as for being part of the electronic-soul duo Kaneholler.
  • D. Dave Foster
    Dave Foster is an American musician best known as the original drummer for the punk rock band The Warriors.
  • E. Phil Foster
    Phil Foster was an American actor and comedian best known for playing Laverne’s father, Frank DeFazio, on the sitcom "Laverne & Shirley."
  • 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: Simon Foster
Triple: [In the Loop, character, Simon Foster]
Generated description
Simon Foster is a bumbling and indecisive British government minister whose gaffes help drive the political satire and chaos in the film "In the Loop."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Simon Foster
Target entity description: Simon Foster is a bumbling and indecisive British government minister whose gaffes help drive the political satire and chaos in the film "In the Loop."
  • A. Simon Foster
    Simon Foster is a British politician who serves as the elected Police and Crime Commissioner for the West Midlands, overseeing policing strategy and accountability in the region.
  • B. Jeff Foster
    Jeff Foster is an entrepreneur best known for founding the global athletic footwear and apparel brand Reebok.
  • C. Jon Foster
    Jon Foster is an American actor and musician known for his film and television roles as well as for being part of the electronic-soul duo Kaneholler.
  • D. Dave Foster
    Dave Foster is an American musician best known as the original drummer for the punk rock band The Warriors.
  • E. Phil Foster
    Phil Foster was an American actor and comedian best known for playing Laverne’s father, Frank DeFazio, on the sitcom "Laverne & Shirley."
  • 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e96f25f48190a41c8b0206b9238c completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d750c8c7588190a31bac5b774155fe completed April 9, 2026, 7:10 a.m.
NEDg Description generation batch_69d751ab890c8190b1549619049dab91 completed April 9, 2026, 7:13 a.m.
NED2 Entity disambiguation (via description) batch_69d7619e97588190a1443f9438c6efc0 completed April 9, 2026, 8:21 a.m.
Created at: April 6, 2026, noon