Triple

T19043682
Position Surface form Disambiguated ID Type / Status
Subject Bhangi Misl E466073 entity
Predicate controlledCity P7580 FINISHED
Object Sialkot NE NERFINISHED

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: [Bhangi Misl, controlledCity, Sialkot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sialkot
Context triple: [Bhangi Misl, controlledCity, 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 (2 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_69d8dd0359648190bc2a9202c5cf29d2 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d802a75c8190a4ce45e5fbffc1b7 completed April 20, 2026, 7:38 a.m.
Created at: April 10, 2026, 12:03 p.m.