Triple

T15907621
Position Surface form Disambiguated ID Type / Status
Subject Mark Greene E385760 entity
Predicate notableRelationship P1481 FINISHED
Object Susan Lewis E517213 NE FINISHED

How this triple was built (2 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: Susan Lewis | Statement: [Mark Greene, notableRelationship, Susan Lewis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Susan Lewis
Context triple: [Mark Greene, notableRelationship, Susan Lewis]
  • A. Susan Lewis chosen
    Susan Lewis is a fictional emergency physician and central character on the television series "ER," known for her dedication, compassion, and complex personal storylines.
  • B. Susie Lewis
    Susie Lewis is an animator and producer best known for her work on the MTV animated series "Daria."
  • C. Karen Lewis
    Karen Lewis is a film and television producer known for her work on the project "Exile."
  • D. Karen Lewis
    Karen Lewis is a British television producer best known for her work on acclaimed drama series such as "Last Tango in Halifax."
  • E. Karen Lewis
    Karen Lewis is a television producer known for her work on the British drama series "Years and Years."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d86da686e4819097cbf3b1fc2d881d completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1565c11bc819091b1fd85901a832d completed April 16, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb055307081908a13c98a0e16780c completed May 9, 2026, 10:08 p.m.
Created at: April 10, 2026, 4:52 a.m.