Triple

T14823058
Position Surface form Disambiguated ID Type / Status
Subject Gujrat E348502 entity
Predicate hasNearbyCity P350 FINISHED
Object Sialkot E101948 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: Sialkot | Statement: [Gujrat, hasNearbyCity, Sialkot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sialkot
Context triple: [Gujrat, hasNearbyCity, Sialkot]
  • A. Sialkot chosen
    Sialkot is a major industrial city in Pakistan’s Punjab province, renowned globally for its production of sports goods and surgical instruments.
  • B. Chakwal
    Chakwal is a city in Pakistan’s Punjab province, known as a regional administrative and commercial center in the Potohar Plateau area.
  • C. Khanewal
    Khanewal is a prominent city in Pakistan’s Punjab province, known as an important railway junction and agricultural trade center.
  • D. Bahawalpur
    Bahawalpur is a historic city in southern Punjab, Pakistan, known for its former princely state status, grand palaces, and proximity to the Cholistan Desert.
  • E. Bahawalnagar
    Bahawalnagar is a prominent city in Pakistan’s Punjab province, known as an agricultural and commercial hub near the border with India.
  • 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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decfe7a8dc8190bcb5ecfa1cbf1601 completed April 14, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe8bc7aba08190b2e125b174751a53 completed May 9, 2026, 1:20 a.m.
Created at: April 10, 2026, 1:51 a.m.