Triple

T16381930
Position Surface form Disambiguated ID Type / Status
Subject Mumbai–Pune Expressway E397826 entity
Predicate startPointCity P22794 FINISHED
Object Navi Mumbai E352594 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: Navi Mumbai | Statement: [Mumbai–Pune Expressway, startPointCity, Navi Mumbai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Navi Mumbai
Context triple: [Mumbai–Pune Expressway, startPointCity, Navi Mumbai]
  • A. Navi Mumbai chosen
    Navi Mumbai is a planned satellite city across the harbor from Mumbai, developed to decongest the main metropolis and featuring organized residential, commercial, and industrial zones.
  • B. Thane
    Thane is a major city in western India known for its numerous lakes and its proximity to Mumbai.
  • C. Panvel
    Panvel is a major railway and commercial hub in Navi Mumbai, Maharashtra, serving as an important junction and gateway between Mumbai and the wider Konkan region.
  • D. Kalyan-Dombivli
    Kalyan-Dombivli is a major twin-city and residential-industrial hub in the Thane district of Maharashtra, forming an important suburban cluster near Mumbai.
  • E. Andheri
    Andheri is a major residential, commercial, and transport hub in Mumbai, India, known for its busy railway station, metro connectivity, and proximity to the city’s airports and film industry areas.
  • 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_69d87f2880b48190ae1a9673a3bbef80 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e319dd0e0c8190812bde6a2f7d9644 completed April 18, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a004f41703c81908fb040a9107045ae completed May 10, 2026, 9:26 a.m.
Created at: April 10, 2026, 5:08 a.m.