Triple

T4173242
Position Surface form Disambiguated ID Type / Status
Subject Elena E86412 entity
Predicate isVariantOf P455 FINISHED
Object Helena E37304 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: Helena | Statement: [Elena, isVariantOf, Helena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helena
Context triple: [Elena, isVariantOf, Helena]
  • A. Helena
    Helena is the capital city of the U.S. state of Montana, known for its historic gold rush origins and scenic location in the northern Rocky Mountains.
  • B. Helena chosen
    Helena, also known as Saint Helena, was the mother of Roman Emperor Constantine the Great and is traditionally credited with finding the True Cross and promoting Christianity within the Roman Empire.
  • C. Helena
    Helena is a fan-favorite, feral yet vulnerable clone and assassin from the TV series "Orphan Black," portrayed by Tatiana Maslany.
  • D. Larissa
    Larissa is a major city in central Greece known as an important agricultural, commercial, and transportation hub of the Thessaly region.
  • E. Larissa
    Larissa is one of Neptune’s small, irregularly shaped inner moons, discovered in 1981 and composed primarily of dark, icy material.
  • 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_69aed93de98c8190ad838ce507b77c8a completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af02e65b548190be095df62091b960 completed March 9, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b57f51cac88190b6e849181fb78938 completed March 14, 2026, 3:31 p.m.
Created at: March 9, 2026, 3:45 p.m.