Triple

T1409593
Position Surface form Disambiguated ID Type / Status
Subject South India E31773 entity
Predicate hasMajorCity P316 FINISHED
Object Hyderabad E13440 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: Hyderabad | Statement: [South India, hasMajorCity, Hyderabad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hyderabad
Context triple: [South India, hasMajorCity, Hyderabad]
  • A. Hyderabad chosen
    Hyderabad is a major city in southern India known for its historic Charminar monument, rich Hyderabadi cuisine, and growing technology industry.
  • B. Hyderabad
    Hyderabad is a major city in the Sindh province of Pakistan, known for its historical significance, vibrant culture, and role as an important commercial and industrial center.
  • C. Vijayawada
    Vijayawada is a major commercial and cultural city in the Indian state of Andhra Pradesh, known as a key transportation hub and an important center for trade, education, and politics in the region.
  • D. Bengaluru
    Bengaluru is a major Indian metropolis known as the country’s leading technology and innovation hub, often called the “Silicon Valley of India.”
  • E. Aurangabad
    Aurangabad is a historic city in the Indian state of Maharashtra, known for its rich cultural heritage and proximity to UNESCO World Heritage Sites like the Ajanta and Ellora Caves.
  • 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_69a49918e1f88190ba610f9dc8114578 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c3e0bfd08190a50820bc7585c28f completed March 1, 2026, 10:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad293cb8f0819085bea7914abf0683 completed March 8, 2026, 7:46 a.m.
Created at: March 1, 2026, 7:59 p.m.