Triple

T19731363
Position Surface form Disambiguated ID Type / Status
Subject Faizabad division E473860 entity
Predicate hasSettlement P1068 FINISHED
Object Gauriganj 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: Gauriganj | Statement: [Faizabad division, hasSettlement, Gauriganj]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gauriganj
Context triple: [Faizabad division, hasSettlement, Gauriganj]
  • A. Gauriganj chosen
    Gauriganj is a town in the Indian state of Uttar Pradesh that serves as the administrative and political center of the Amethi region.
  • B. Bikramganj
    Bikramganj is a town in the Rohtas district of Bihar, India, known as a local commercial and educational center for surrounding rural areas.
  • C. Gairatganj
    Gairatganj is a town in the Raisen district of the central Indian state of Madhya Pradesh.
  • D. Sitarganj
    Sitarganj is a town in the Udham Singh Nagar district of Uttarakhand, India, known for its agricultural surroundings and growing industrial development.
  • E. Bhadgaon
    Bhadgaon is another name for Bhaktapur, a historic Newar city in the Kathmandu Valley of Nepal renowned for its well-preserved medieval architecture, art, and culture.
  • 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_69d8e517ebd48190979ee76723bcfadf completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e649fd18148190a6e85b2be0069dde completed April 20, 2026, 3:45 p.m.
Created at: April 10, 2026, 1:47 p.m.