Triple

T157982
Position Surface form Disambiguated ID Type / Status
Subject Writings E3219 entity
Predicate containsBook P5478 FINISHED
Object Ruth
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.
 E19795 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: Ruth | Statement: [Writings, containsBook, Ruth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ruth
Context triple: [Writings, containsBook, 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. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • C. Miryam
    Miryam is the Hebrew form of the name of the Virgin Mary, the mother of Jesus in Christian tradition.
  • D. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • E. Margaret
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • 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: Ruth
Triple: [Writings, containsBook, Ruth]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ruth
Target entity description: 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.
  • 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. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • C. Miryam
    Miryam is the Hebrew form of the name of the Virgin Mary, the mother of Jesus in Christian tradition.
  • D. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • E. Margaret
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • 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_69a2527757ec819090b8becb2cf1a862 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a25bac998c819099f2bed899220a78 completed Feb. 28, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2d4cbaedc81908aa7cf4df2661ccc completed Feb. 28, 2026, 11:43 a.m.
NEDg Description generation batch_69a2d5c4532081909855cb9fd5395624 completed Feb. 28, 2026, 11:47 a.m.
NED2 Entity disambiguation (via description) batch_69a2d63a6bc481908d6d3bd0f7e05ada completed Feb. 28, 2026, 11:49 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.