Triple

T3720290
Position Surface form Disambiguated ID Type / Status
Subject Historical Books (Christian canon) E81621 entity
Predicate includesBook P1393 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: [Historical Books (Christian canon), includesBook, Ruth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ruth
Context triple: [Historical Books (Christian canon), includesBook, 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 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.
  • C. 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.
  • 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_69ad8b1b7ef081908d2d381bbf54985a completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adca9b5ca8819094299fffc02606ce completed March 8, 2026, 7:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4ce184c9c8190813ce589c48007c1 completed March 14, 2026, 2:55 a.m.
Created at: March 8, 2026, 3:34 p.m.