Triple

T7508020
Position Surface form Disambiguated ID Type / Status
Subject Aurangabad railway station E177441 entity
Predicate connectsTo P845 FINISHED
Object Nanded E55065 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: Nanded | Statement: [Aurangabad railway station, connectsTo, Nanded]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nanded
Context triple: [Aurangabad railway station, connectsTo, Nanded]
  • A. Nanded chosen
    Nanded is a historic city in the Indian state of Maharashtra, known as an important Sikh pilgrimage center and a major urban hub in the Marathwada region.
  • B. Latur
    Latur is a city in the Marathwada region of western India known for its agricultural economy and for being the epicenter of a devastating earthquake in 1993.
  • C. Gondia
    Gondia is a district in the Indian state of Maharashtra, known for its rice production and proximity to forests and wildlife reserves.
  • D. Sangli
    Sangli is a city in the Indian state of Maharashtra known for its fertile agricultural surroundings and prominence in sugar and turmeric production.
  • E. Chandrapur
    Chandrapur is a district in the eastern part of Maharashtra, India, known for its coal mining industry, forests, and wildlife, including the Tadoba-Andhari Tiger Reserve.
  • 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_69c69f276b108190af2cc790b6554544 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f5b8ab5c8190828ee8d144068828 completed March 27, 2026, 9:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8460be99881909cada82cf7563421 completed March 28, 2026, 9:20 p.m.
Created at: March 27, 2026, 3:45 p.m.