Triple

T30924260
Position Surface form Disambiguated ID Type / Status
Subject Ha Mim E787813 entity
Predicate hasInterpretationStatus P13959 FINISHED
Object subject of exegetical debate LITERAL 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: subject of exegetical debate | Statement: [Ha Mim, hasInterpretationStatus, subject of exegetical debate]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasInterpretationStatus
Context triple: [Ha Mim, hasInterpretationStatus, subject of exegetical debate]
  • A. interpretiveStatus chosen
    Indicates the evaluative or explanatory stance assigned to something, such as how it is understood, classified, or interpreted within a given context.
  • B. containsInterpretationOf
    Indicates that one entity includes or embodies an interpretation or understanding of another entity.
  • C. hasInterpretationStyle
    Indicates a relationship where an entity is associated with a particular manner, method, or style in which it is interpreted or understood.
  • D. hasExplicitInterpretation
    Indicates that something is associated with a clearly defined and unambiguous meaning or interpretation.
  • E. isInterpretableIn
    Indicates that one formal system, language, or theory can be meaningfully represented, understood, or given a semantics within another system, language, or theory.
  • F. None of above.

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_69f224bfaca88190b9d0dfcc86297fe9 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69ff1ba8694481909ceb36f26ca85612 completed May 9, 2026, 11:34 a.m.
PD Predicate disambiguation batch_69ff1b27f0f08190a9e74308c5b3d1ba completed May 9, 2026, 11:31 a.m.
Created at: April 29, 2026, 8:51 p.m.