Triple

T3056152
Position Surface form Disambiguated ID Type / Status
Subject Bette E60484 entity
Predicate hasRelatedName P3889 FINISHED
Object Isabel E114378 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: Isabel | Statement: [Bette, hasRelatedName, Isabel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isabel
Context triple: [Bette, hasRelatedName, Isabel]
  • A. Isabel chosen
    Isabel is a feminine given name of Spanish origin, widely used in Spanish- and Portuguese-speaking countries and borne by numerous notable historical and contemporary figures.
  • B. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • C. Isabela
    Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
  • D. Eliza
    Eliza is a feminine given name of Hebrew origin, often considered a shortened form of Elizabeth and commonly used in English-speaking countries.
  • E. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • 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_69ad8578137c81908259dcb27c7d6d7c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ad9bf7ebd48190ad5748a18fa9a56a completed March 8, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f8724bd481909ea8c71e92801096 completed March 11, 2026, 11:19 p.m.
Created at: March 8, 2026, 3:02 p.m.