Triple

T20810575
Position Surface form Disambiguated ID Type / Status
Subject Satna district E512285 entity
Predicate hasMajorTown P316 FINISHED
Object Satna NE NERFINISHED

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: Satna | Statement: [Satna district, hasMajorTown, Satna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Satna
Context triple: [Satna district, hasMajorTown, Satna]
  • A. Satna chosen
    Satna is a prominent industrial and commercial city in central India, known especially for its large cement factories and strategic location in Madhya Pradesh.
  • B. Sabni
    Sabni was an ancient Egyptian official from the Old Kingdom whose rock-cut tomb at Aswan’s Tombs of the Nobles is notable for its detailed inscriptions and scenes illustrating his life and status.
  • C. Yaganti
    Yaganti is a historic temple town in Andhra Pradesh, India, renowned for its ancient Sri Yagantiswamy (Shiva) temple and distinctive rock formations.
  • D. Savnur
    Savnur is a town in the Indian state of Karnataka, historically associated with the birth of the Maratha statesman Madhav Rao I.
  • E. Sawerigadi
    Sawerigadi is a town in Indonesia located in the province of Southeast Sulawesi.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4cd25088190b48ca9700cd24efc completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2d27a4881908b34679385d8b94b completed April 21, 2026, 12:20 a.m.
Created at: April 16, 2026, 12:40 p.m.