Triple

T7787217
Position Surface form Disambiguated ID Type / Status
Subject Kazi Nazrul University, Asansol E187276 entity
Predicate locatedIn P40 FINISHED
Object Asansol E167887 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: Asansol | Statement: [Kazi Nazrul University, Asansol, locatedIn, Asansol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Asansol
Context triple: [Kazi Nazrul University, Asansol, locatedIn, Asansol]
  • A. Asansol chosen
    Asansol is a major industrial and coal-mining city in eastern India, known as one of the largest urban centers in the state of West Bengal.
  • B. Durgapur
    Durgapur is a major industrial city in eastern India known for its steel plants and planned urban infrastructure.
  • C. Raniganj
    Raniganj is a coal-mining town in West Bengal, India, historically significant as one of the earliest centers of the Indian coal industry and now a key urban hub in the Durgapur–Asansol region.
  • D. Barasat
    Barasat is a suburban city in the North 24 Parganas district of West Bengal, India, forming part of the Kolkata metropolitan area and serving as an important residential and commercial hub.
  • E. Baranagar
    Baranagar is a densely populated suburban city in the northern part of Kolkata, India, known for its industrial areas, educational institutions, and cultural heritage.
  • 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_69ca82af2d2c8190963861f5e0b8bf21 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cadf2462248190863f838f0e077923 completed March 30, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb59f230d48190ad4cb08e9e73f19e completed March 31, 2026, 5:21 a.m.
Created at: March 30, 2026, 4:24 p.m.