Triple

T917139
Position Surface form Disambiguated ID Type / Status
Subject Book of Ruth E19795 entity
Predicate hasMainCharacter P1183 FINISHED
Object Ruth E19795 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: Ruth | Statement: [Book of Ruth, hasMainCharacter, Ruth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ruth
Context triple: [Book of Ruth, hasMainCharacter, Ruth]
  • A. Ruth
    Ruth is the given name of Ruth Bader Ginsburg, the pioneering U.S. Supreme Court Justice and prominent advocate for gender equality and civil rights.
  • B. Ruth
    Ruth is the surname of Babe Ruth, the legendary American baseball player widely regarded as one of the greatest hitters in the sport's history.
  • C. Ruth chosen
    Ruth is a book of the Hebrew Bible/Old Testament that tells the story of a Moabite woman whose loyalty and faith lead to her becoming an ancestor of King David.
  • D. Ruth
    Ruth is a supporting character in the comedy Western film "A Million Ways to Die in the West," known for being a devout Christian prostitute engaged to the protagonist's best friend.
  • E. Ruth Rose
    Ruth Rose was an American screenwriter best known for co-writing the classic 1933 monster film "King Kong."
  • 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_69a4939f91a08190ba68c2c81eab90fe completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b2f8e26c81908b768d3e9e67689d completed March 1, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a826d8f9408190aa286bb809507797 completed March 4, 2026, 12:34 p.m.
Created at: March 1, 2026, 7:39 p.m.