Triple

T14714564
Position Surface form Disambiguated ID Type / Status
Subject Tania E345642 entity
Predicate relatedName P3889 FINISHED
Object Tatyana E68776 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: Tatyana | Statement: [Tania, relatedName, Tatyana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tatyana
Context triple: [Tania, relatedName, Tatyana]
  • A. Tatyana chosen
    Tatyana is a feminine given name of Slavic origin, particularly common in Russian-speaking countries.
  • B. Наташа
    Наташа — одна из главных героинь пьесы Максима Горького «На дне», олицетворяющая трагическую судьбу бедной и угнетённой женщины в мире социального дна.
  • C. Nadezhda Vasilyeva
    Nadezhda Vasilyeva is known primarily as a daughter of Vasily Stalin, the son of Soviet leader Joseph Stalin.
  • D. Nadezhda Vasilyeva
    Nadezhda Vasilyeva is a costume designer known for her work on the film "Two Women."
  • E. Tatyana Larina
    Tatyana Larina is the introspective and emotionally sincere heroine of Alexander Pushkin’s novel in verse "Eugene Onegin," often regarded as one of Russian literature’s most iconic female characters.
  • 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb98513b081908b230f6ac79c72ad completed April 14, 2026, 10:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5dee8988190b80cb487c12bfc2d completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 1:29 a.m.