Triple

T15626647
Position Surface form Disambiguated ID Type / Status
Subject Pakistan Army Aviation Corps E375694 entity
Predicate trainingLocation P40 FINISHED
Object Gujranwala E68483 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: Gujranwala | Statement: [Pakistan Army Aviation Corps, trainingLocation, Gujranwala]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gujranwala
Context triple: [Pakistan Army Aviation Corps, trainingLocation, Gujranwala]
  • A. Gujranwala chosen
    Gujranwala is a major industrial city in Pakistan’s Punjab province, known for its manufacturing base and historical significance in the region.
  • B. Jhang
    Jhang is a historic city in the Punjab province of Pakistan, known for its cultural heritage and as the birthplace of several notable figures.
  • C. Mandi Bahauddin
    Mandi Bahauddin is a prominent city in Pakistan’s Punjab province, known as an agricultural and commercial hub in the region.
  • D. Khanewal
    Khanewal is a prominent city in Pakistan’s Punjab province, known as an important railway junction and agricultural trade center.
  • E. Rahim Yar Khan
    Rahim Yar Khan is a major city in southern Punjab, Pakistan, known as an important commercial and agricultural center in the Seraiki-speaking region.
  • 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_69d85cd035a48190b73d5579ab73969a completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e9e5e248190ae54cda1fde51efb completed April 16, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe6287f48190a54d0d029dc503f8 completed May 9, 2026, 11:08 p.m.
Created at: April 10, 2026, 4:14 a.m.