Triple

T6158296
Position Surface form Disambiguated ID Type / Status
Subject Mvita E137377 entity
Predicate alternativeName P39 FINISHED
Object Kimvita E22244 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: Kimvita | Statement: [Mvita, alternativeName, Kimvita]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kimvita
Context triple: [Mvita, alternativeName, Kimvita]
  • A. Kimvita chosen
    Kimvita is a major coastal dialect of Swahili spoken primarily in and around Mombasa, Kenya.
  • B. Damkina
    Damkina is a Mesopotamian earth and mother goddess, best known as the consort of the god Enki (Ea) and mother of the Babylonian chief god Marduk.
  • C. Wonokitri
    Wonokitri is a village in East Java, Indonesia, known as a gateway settlement for visitors heading to the Mount Bromo area.
  • D. Shivta
    Shivta is an ancient Nabatean and Byzantine desert city in Israel, known for its remarkably well-preserved ruins and role along historic trade routes.
  • E. Kohunlich
    Kohunlich is a large Maya archaeological site in southern Quintana Roo, Mexico, noted for its monumental architecture and well-preserved stucco masks.
  • 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_69c008a54fc88190b6ce4416490ca79d completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05d32ef548190bc215d052d3497fe completed March 22, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1418ba6488190aaf8d3070555d60a completed March 23, 2026, 1:35 p.m.
Created at: March 22, 2026, 4:17 p.m.