Triple

T13053709
Position Surface form Disambiguated ID Type / Status
Subject Central Uttar Pradesh E327511 entity
Predicate hasMajorCity P316 FINISHED
Object Hardoi E561327 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: Hardoi | Statement: [Central Uttar Pradesh, hasMajorCity, Hardoi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hardoi
Context triple: [Central Uttar Pradesh, hasMajorCity, Hardoi]
  • A. Hardoi district chosen
    Hardoi district is an administrative district in the Indian state of Uttar Pradesh, known for its predominantly agricultural landscape and location in the central part of the state.
  • B. Saharsa
    Saharsa is a city in the northeastern Indian state of Bihar, known as a major agricultural and commercial center in the Kosi river region.
  • C. Sohagpur
    Sohagpur is a town in the Narmadapuram district of Madhya Pradesh, India, known as a local commercial center and access point to nearby forested and wildlife areas.
  • D. Hazratganj
    Hazratganj is a historic and bustling commercial and cultural hub in Lucknow, known for its colonial-era architecture, shopping arcades, and vibrant street life.
  • E. Harda
    Harda is a town and administrative district headquarters in the central Indian state of Madhya Pradesh, known for its agricultural economy and railway connectivity.
  • 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_69d8076e64308190904fb5c93517c901 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d980bb52d88190b5be12000e27a2c9 completed April 10, 2026, 10:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbdcc3e881908d9a246558b1c20e completed May 3, 2026, 4:15 a.m.
Created at: April 9, 2026, 8:58 p.m.