Triple

T364066
Position Surface form Disambiguated ID Type / Status
Subject Indian Space Research Organisation E7920 entity
Predicate headquartersLocation P62 FINISHED
Object Bengaluru E12663 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: Bengaluru | Statement: [Indian Space Research Organisation, headquartersLocation, Bengaluru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bengaluru
Context triple: [Indian Space Research Organisation, headquartersLocation, Bengaluru]
  • A. Bengaluru chosen
    Bengaluru is a major Indian metropolis known as the country’s leading technology and innovation hub, often called the “Silicon Valley of India.”
  • B. Hyderabad
    Hyderabad is a major city in southern India known for its historic Charminar monument, rich Hyderabadi cuisine, and growing technology industry.
  • C. Chennai
    Chennai is a major coastal metropolis in southern India, serving as the capital of Tamil Nadu and a key cultural, economic, and automotive hub.
  • D. Mumbai
    Mumbai is a densely populated coastal metropolis in western India that serves as the country’s financial hub and the center of its film industry, Bollywood.
  • E. Pune
    Pune is a major cultural, educational, and IT hub in the western Indian state of Maharashtra, known for its universities, historical significance, and rapidly growing urban economy.
  • 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_69a2e7e880008190a6ad7e06e5d03007 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebd1016481909b8ba3b047a47145 completed Feb. 28, 2026, 1:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a457fa35988190851216c84ad63232 completed March 1, 2026, 3:15 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.