Triple

T2028993
Position Surface form Disambiguated ID Type / Status
Subject Gordon Brown E44472 entity
Predicate spouse P13 FINISHED
Object Sarah Brown
Sarah Brown is a British public relations executive and charity campaigner, best known as the wife of former UK Prime Minister Gordon Brown.
 E226854 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: Sarah Brown | Statement: [Gordon Brown, spouse, Sarah Brown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sarah Brown
Context triple: [Gordon Brown, spouse, Sarah Brown]
  • A. Sarah
    Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
  • B. Anne
    Anne is the birth name of Nancy Reagan, the former First Lady of the United States and wife of President Ronald Reagan.
  • C. Anne
    Anne is traditionally revered in Christian tradition as the mother of the Virgin Mary and the grandmother of Jesus.
  • D. Anne
    Anne is the given name of Anne Morrow Lindbergh, the American author and aviator who was married to famed aviator Charles Lindbergh.
  • E. Mary
    Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
  • 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: Sarah Brown
Triple: [Gordon Brown, spouse, Sarah Brown]
Generated description
Sarah Brown is a British public relations executive and charity campaigner, best known as the wife of former UK Prime Minister Gordon Brown.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sarah Brown
Target entity description: Sarah Brown is a British public relations executive and charity campaigner, best known as the wife of former UK Prime Minister Gordon Brown.
  • A. Sarah
    Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
  • B. Anne
    Anne is the birth name of Nancy Reagan, the former First Lady of the United States and wife of President Ronald Reagan.
  • C. Anne
    Anne is traditionally revered in Christian tradition as the mother of the Virgin Mary and the grandmother of Jesus.
  • D. Anne
    Anne is the given name of Anne Morrow Lindbergh, the American author and aviator who was married to famed aviator Charles Lindbergh.
  • E. Mary
    Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
  • 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_69a889144f2481909932f0746a93023d completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9136f888190b0fd03530e9eda1e completed March 7, 2026, 5:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0afea7848190b53b8813a567879d completed March 8, 2026, 11:49 p.m.
NEDg Description generation batch_69ae0b8cda248190b88b353c1768c3d7 completed March 8, 2026, 11:51 p.m.
NED2 Entity disambiguation (via description) batch_69ae0c31f00c8190bb29098f95ee4cb9 completed March 8, 2026, 11:54 p.m.
Created at: March 4, 2026, 7:38 p.m.