Triple

T12806474
Position Surface form Disambiguated ID Type / Status
Subject Haller E306154 entity
Predicate hasNotableBearer P458 FINISHED
Object Margaret Haller
Margaret Haller is an American author best known for her books on etiquette and social behavior.
 E1215790 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: Margaret Haller | Statement: [Haller, hasNotableBearer, Margaret Haller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margaret Haller
Context triple: [Haller, hasNotableBearer, Margaret Haller]
  • A. Margaret Heidenry
    Margaret Heidenry is a screenwriter best known for her work on the animated Disney sequel "Cinderella III: A Twist in Time."
  • B. Margaret Rudkin
    Margaret Rudkin was an American businesswoman and food industry pioneer best known for building Pepperidge Farm from a home baking venture into a major commercial bakery brand.
  • C. Barbara Hall
    Barbara Hall is a Canadian politician who served as the 60th mayor of Toronto in the 1990s and later became Ontario's chief commissioner of human rights.
  • D. Barbara Hall
    Barbara Hall is an American television writer and producer best known for creating series such as "Madam Secretary" and "Joan of Arcadia."
  • E. Margaret Engemann
    Margaret Engemann was the wife of pioneering American mathematician and cybernetics founder Norbert Wiener.
  • 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: Margaret Haller
Triple: [Haller, hasNotableBearer, Margaret Haller]
Generated description
Margaret Haller is an American author best known for her books on etiquette and social behavior.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margaret Haller
Target entity description: Margaret Haller is an American author best known for her books on etiquette and social behavior.
  • A. Margaret Heidenry
    Margaret Heidenry is a screenwriter best known for her work on the animated Disney sequel "Cinderella III: A Twist in Time."
  • B. Margaret Rudkin
    Margaret Rudkin was an American businesswoman and food industry pioneer best known for building Pepperidge Farm from a home baking venture into a major commercial bakery brand.
  • C. Barbara Hall
    Barbara Hall is a Canadian politician who served as the 60th mayor of Toronto in the 1990s and later became Ontario's chief commissioner of human rights.
  • D. Barbara Hall
    Barbara Hall is an American television writer and producer best known for creating series such as "Madam Secretary" and "Joan of Arcadia."
  • E. Margaret Engemann
    Margaret Engemann was the wife of pioneering American mathematician and cybernetics founder Norbert Wiener.
  • 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_69d7bdf366888190a8cccb982606889c completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96e7f370c8190b3fc39c1b63394c6 completed April 10, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a004f3548b48190aec852723654bd35 completed May 10, 2026, 9:26 a.m.
NEDg Description generation batch_6a00509164cc8190a381ba0a1de95ed1 completed May 10, 2026, 9:32 a.m.
NED2 Entity disambiguation (via description) batch_6a005447f1948190a939c0051891e444 completed May 10, 2026, 9:47 a.m.
Created at: April 9, 2026, 5:31 p.m.