Triple

T5938366
Position Surface form Disambiguated ID Type / Status
Subject Guru Ram Das E132101 entity
Predicate founded P104 FINISHED
Object City of Amritsar E23792 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: City of Amritsar | Statement: [Guru Ram Das, founded, City of Amritsar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Amritsar
Context triple: [Guru Ram Das, founded, City of Amritsar]
  • A. Amritsar chosen
    Amritsar is a historic city in the Indian state of Punjab, renowned as the spiritual center of Sikhism and home to the Golden Temple.
  • B. Jalandhar
    Jalandhar is a major city in the Indian state of Punjab, known as an important commercial and cultural center, particularly famous for its sports goods and manufacturing industries.
  • C. Phagwara
    Phagwara is a prominent industrial and commercial city in the Indian state of Punjab, known for its textile and agricultural machinery industries and its location on the major highway between Ludhiana and Jalandhar.
  • D. Nawanshahr
    Nawanshahr is a town and district headquarters in the Doaba region of Punjab, India, known for its agricultural base and significant Punjabi diaspora.
  • E. Ludhiana
    Ludhiana is a major industrial city in the Indian state of Punjab, known especially for its textile and hosiery manufacturing.
  • 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_69c0085c55dc8190aa90e242c956e2fa completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c038edd1b88190a608f9bf8a8090cf completed March 22, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11cc81b3081908a35c4230eba3f06 completed March 23, 2026, 10:58 a.m.
Created at: March 22, 2026, 4:01 p.m.