Triple

T15844193
Position Surface form Disambiguated ID Type / Status
Subject Khan Abdul Qayyum Khan E384172 entity
Predicate residence P75 FINISHED
Object Rawalpindi E35027 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: Rawalpindi | Statement: [Khan Abdul Qayyum Khan, residence, Rawalpindi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rawalpindi
Context triple: [Khan Abdul Qayyum Khan, residence, Rawalpindi]
  • A. Rawalpindi chosen
    Rawalpindi is a major city in Pakistan’s Punjab province, historically significant as a former temporary national capital and now a key commercial and military center.
  • B. Lahore
    Lahore is a major cultural, historical, and economic center of Pakistan, known for its rich Mughal heritage, educational institutions, and role in the region’s political history.
  • C. Islamabad
    Islamabad is Pakistan’s planned, modern capital city known for its high standard of living, greenery, and role as the country’s political and administrative center.
  • D. Peshawar
    Peshawar is one of Pakistan’s oldest and largest cities, a historic cultural and economic hub located near the Khyber Pass in the country’s northwest.
  • E. Faisalabad
    Faisalabad is a major industrial city in Pakistan’s Punjab province, known especially for its large textile industry and role as a commercial hub.
  • 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_69d86da422088190aac39e32e6c68429 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e142eb20088190bb45e37ce3291ef2 completed April 16, 2026, 8:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa9321f308190b8101162ed671e5e completed May 9, 2026, 9:37 p.m.
Created at: April 10, 2026, 4:50 a.m.