Triple

T15017639
Position Surface form Disambiguated ID Type / Status
Subject Mrs Moore E377996 entity
Predicate portrayedBy P1507 FINISHED
Object Helen Griffin
Helen Griffin was a Welsh actress and playwright known for her work in film, television, and theatre, including roles in productions such as "Doctor Who" and "Human Traffic."
 E1138594 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: Helen Griffin | Statement: [Mrs Moore, portrayedBy, Helen Griffin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Helen Griffin
Context triple: [Mrs Moore, portrayedBy, Helen Griffin]
  • A. Helen Harrington
    Helen Harrington is known as the wife of American minimalist painter Brice Marden.
  • B. Eileen Harris Norton
    Eileen Harris Norton is a philanthropist and arts patron known for supporting contemporary artists and cultural institutions.
  • C. Patricia Haines
    Patricia Haines was a British actress known for her television and film roles in the 1950s and 1960s.
  • D. Patricia Murray
    Patricia Murray is known as the spouse of American glass artist and sculptor Dan Dailey.
  • E. Janet McKenzie Hill
    Janet McKenzie Hill was an influential American cookbook author and early 20th-century culinary educator known for popularizing scientific home cooking and baking.
  • 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: Helen Griffin
Triple: [Mrs Moore, portrayedBy, Helen Griffin]
Generated description
Helen Griffin was a Welsh actress and playwright known for her work in film, television, and theatre, including roles in productions such as "Doctor Who" and "Human Traffic."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Helen Griffin
Target entity description: Helen Griffin was a Welsh actress and playwright known for her work in film, television, and theatre, including roles in productions such as "Doctor Who" and "Human Traffic."
  • A. Helen Harrington
    Helen Harrington is known as the wife of American minimalist painter Brice Marden.
  • B. Eileen Harris Norton
    Eileen Harris Norton is a philanthropist and arts patron known for supporting contemporary artists and cultural institutions.
  • C. Patricia Haines
    Patricia Haines was a British actress known for her television and film roles in the 1950s and 1960s.
  • D. Patricia Murray
    Patricia Murray is known as the spouse of American glass artist and sculptor Dan Dailey.
  • E. Janet McKenzie Hill
    Janet McKenzie Hill was an influential American cookbook author and early 20th-century culinary educator known for popularizing scientific home cooking and baking.
  • 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_69d85cd3a3c881908c71fc424d459c17 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded7633fcc8190b2231f43252bc46f completed April 15, 2026, 12:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69feb7d956808190a3f17ef14c21d3af completed May 9, 2026, 4:28 a.m.
NEDg Description generation batch_69feb8e3c33081908a45f027d529b8fe completed May 9, 2026, 4:32 a.m.
NED2 Entity disambiguation (via description) batch_69febac2d3548190811323a40b09bc7e completed May 9, 2026, 4:40 a.m.
Created at: April 10, 2026, 2:55 a.m.