Triple

T13661790
Position Surface form Disambiguated ID Type / Status
Subject Ambedkar Nagar district E327014 entity
Predicate hasUrbanCenters P11388 FINISHED
Object Tanda E841488 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: Tanda | Statement: [Ambedkar Nagar district, hasUrbanCenters, Tanda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tanda
Context triple: [Ambedkar Nagar district, hasUrbanCenters, Tanda]
  • A. Tanda
    Tanda was a historic city in Bengal that served as an important political and administrative center under the Karrani dynasty in the 16th century.
  • B. Tanda chosen
    Tanda is a town located in the Rampur district of the Indian state of Uttar Pradesh.
  • C. Tiba
    Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
  • D. Tulunan
    Tulunan is a rural municipality in the province of North Cotabato on the island of Mindanao in the Philippines, known primarily for its agricultural economy.
  • E. Yanda
    Yanda is a lesser-known Dogon language variety spoken by the Dogon people of Mali in West Africa.
  • 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_69d8076d8270819092afc2f0e9c359a8 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc620df208190afaccf3ddd10aa60 completed April 12, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78b08d27c8190badc612c26423c0e completed May 3, 2026, 5:51 p.m.
Created at: April 9, 2026, 9:52 p.m.