Triple

T20133768
Position Surface form Disambiguated ID Type / Status
Subject Artemis E490965 entity
Predicate epithet P743 FINISHED
Object Potnia Theron NE NERFINISHED

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: Potnia Theron | Statement: [Artemis, epithet, Potnia Theron]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Potnia Theron
Context triple: [Artemis, epithet, Potnia Theron]
  • A. Potnia Theron chosen
    Potnia Theron is an ancient Greek title meaning "Mistress of Animals," associated especially with Artemis as a goddess who rules over and protects wild creatures.
  • B. Megan Boone
    Megan Boone is an American actress best known for her leading role as FBI profiler Elizabeth Keen on the television series "The Blacklist."
  • C. Charlize Theron
    Charlize Theron is an Academy Award–winning South African–American actress and producer known for her versatile performances in films such as "Monster," "Mad Max: Fury Road," and "Atomic Blonde."
  • D. Lucy Lawless
    Lucy Lawless is a New Zealand actress and singer best known for her iconic role as the warrior princess Xena in the television series "Xena: Warrior Princess."
  • E. Kate Beckinsale
    Kate Beckinsale is an English actress known for her versatile film career, including prominent roles in action, drama, and comedy films such as the Underworld series and various Hollywood productions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da62651a0c8190a3e05e95e056a66b completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6676556c481909ffc80cd2009b65a completed April 20, 2026, 5:50 p.m.
Created at: April 11, 2026, 11:32 p.m.