Triple

T10128603
Position Surface form Disambiguated ID Type / Status
Subject Alfred Morton Githens E226277 entity
Predicate employer P7 FINISHED
Object Lord & Hewlett
Lord & Hewlett was an American architectural firm active in the early 20th century, known for designing significant public and institutional buildings.
 E841726 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: Lord & Hewlett | Statement: [Alfred Morton Githens, employer, Lord & Hewlett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lord & Hewlett
Context triple: [Alfred Morton Githens, employer, Lord & Hewlett]
  • A. Hewletts
    Hewletts was the original name of what is now known as Hewlett station, a railway stop likely serving the Hewlett area.
  • B. Harman
    Harman is a given name and surname used in various cultures, often considered a variant of the name Hermann.
  • C. Wyman-Gordon
    Wyman-Gordon is an industrial manufacturer known for producing high-strength forged components, particularly for the aerospace and energy industries.
  • D. Hart-Davis
    Hart-Davis is a British surname notably associated with figures such as science broadcaster and historian Adam Hart-Davis.
  • E. Swinton
    Swinton is a town in the City of Salford, Greater Manchester, England, known historically for its role in the coal mining and textile industries.
  • 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: Lord & Hewlett
Triple: [Alfred Morton Githens, employer, Lord & Hewlett]
Generated description
Lord & Hewlett was an American architectural firm active in the early 20th century, known for designing significant public and institutional buildings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lord & Hewlett
Target entity description: Lord & Hewlett was an American architectural firm active in the early 20th century, known for designing significant public and institutional buildings.
  • A. Hewletts
    Hewletts was the original name of what is now known as Hewlett station, a railway stop likely serving the Hewlett area.
  • B. Harman
    Harman is a given name and surname used in various cultures, often considered a variant of the name Hermann.
  • C. Wyman-Gordon
    Wyman-Gordon is an industrial manufacturer known for producing high-strength forged components, particularly for the aerospace and energy industries.
  • D. Hart-Davis
    Hart-Davis is a British surname notably associated with figures such as science broadcaster and historian Adam Hart-Davis.
  • E. Swinton
    Swinton is a small rural village in the historic county of Berwickshire in the Scottish Borders region of Scotland.
  • 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_69ca843057b48190a86730167f5d6b98 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cdd333186c819088bbf617967f24fa completed April 2, 2026, 2:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2cc72848481909dcbfc9fe3f6d379 completed April 5, 2026, 8:56 p.m.
NEDg Description generation batch_69d2cda6452c81908d67ea322da3cf70 completed April 5, 2026, 9:01 p.m.
NED2 Entity disambiguation (via description) batch_69d2ce71fa888190b8dd13df83a2cd78 completed April 5, 2026, 9:04 p.m.
Created at: March 30, 2026, 9:05 p.m.