Triple

T8684586
Position Surface form Disambiguated ID Type / Status
Subject South Woodford E206124 entity
Predicate hasCommercialArea P459 FINISHED
Object George Lane
George Lane is a main shopping and commercial street in South Woodford, London, known for its mix of retail, dining, and local services.
 E752410 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: George Lane | Statement: [South Woodford, hasCommercialArea, George Lane]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: George Lane
Context triple: [South Woodford, hasCommercialArea, George Lane]
  • A. Richard Lane
    Richard Lane was a co-founder of the British publishing house Penguin Books, which became famous for pioneering affordable, high-quality paperback editions.
  • B. Robert Lane
    Robert Lane is a musician and composer known for creating the soundtrack to the documentary film "In Prison My Whole Life."
  • C. Martin Lane
    Martin Lane is a central father figure and newspaper editor on the 1960s American sitcom "The Patty Duke Show."
  • D. Samuel Lane
    Samuel Lane was a 19th-century British surgeon best known for founding St Mary’s Hospital in London.
  • E. George Haight
    George Haight was a film producer active in early 20th-century American cinema, known for his work on projects such as "The Story of Vernon and Irene Castle."
  • 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: George Lane
Triple: [South Woodford, hasCommercialArea, George Lane]
Generated description
George Lane is a main shopping and commercial street in South Woodford, London, known for its mix of retail, dining, and local services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: George Lane
Target entity description: George Lane is a main shopping and commercial street in South Woodford, London, known for its mix of retail, dining, and local services.
  • A. Richard Lane
    Richard Lane was a co-founder of the British publishing house Penguin Books, which became famous for pioneering affordable, high-quality paperback editions.
  • B. Robert Lane
    Robert Lane is a musician and composer known for creating the soundtrack to the documentary film "In Prison My Whole Life."
  • C. Martin Lane
    Martin Lane is a central father figure and newspaper editor on the 1960s American sitcom "The Patty Duke Show."
  • D. Samuel Lane
    Samuel Lane was a 19th-century British surgeon best known for founding St Mary’s Hospital in London.
  • E. George Haight
    George Haight was a film producer active in early 20th-century American cinema, known for his work on projects such as "The Story of Vernon and Irene Castle."
  • 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_69ca835379688190aa06b9d98e684d58 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc4aeae740819099093906ccc5f640 completed March 31, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf287f05748190b41c606eaae5d0b7 completed April 3, 2026, 2:39 a.m.
NEDg Description generation batch_69cf2bcff84881908a7985fdf8189583 completed April 3, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69cf2ca1ddac8190a36367e6bba8e3c8 completed April 3, 2026, 2:57 a.m.
Created at: March 30, 2026, 6:32 p.m.