Triple

T14225020
Position Surface form Disambiguated ID Type / Status
Subject Navi Mumbai E352594 entity
Predicate hasResidentialZone P9064 FINISHED
Object Turbhe E919778 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: Turbhe | Statement: [Navi Mumbai, hasResidentialZone, Turbhe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Turbhe
Context triple: [Navi Mumbai, hasResidentialZone, Turbhe]
  • A. Turbhe chosen
    Turbhe is a suburban locality in Navi Mumbai, India, known for its railway station that serves as an important stop on the Mumbai suburban network.
  • B. Shakardara
    Shakardara is a town and administrative settlement in Pakistan’s Khyber Pakhtunkhwa province, known for its role within the Kohat region.
  • C. Shahdara
    Shahdara is a densely populated residential and commercial locality in East Delhi, India, known as one of the city’s oldest suburbs and a key transport hub.
  • D. Shahdara
    Shahdara is a locality on the northern outskirts of Lahore, Pakistan, serving as a major transport hub and gateway into the city.
  • E. Shingora
    Shingora is a film featuring Indian actress and model Persis Khambatta, known for her distinctive screen presence and international appeal.
  • 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_69d8278a06e481908b5d6af0a8afe737 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6228e53c8190abbe4e2d88a7362a completed April 14, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd281611b48190b787e38ba9c733a4 completed May 8, 2026, 12:02 a.m.
Created at: April 10, 2026, 1:06 a.m.