Triple

T2822285
Position Surface form Disambiguated ID Type / Status
Subject Charsadda E54837 entity
Predicate locatedNear P294 FINISHED
Object Peshawar E53256 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: Peshawar | Statement: [Charsadda, locatedNear, Peshawar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peshawar
Context triple: [Charsadda, locatedNear, Peshawar]
  • A. Peshawar chosen
    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.
  • B. Quetta
    Quetta is a major city in western Pakistan known as the provincial capital of Balochistan and a key commercial and military center near the Afghan border.
  • C. Rawalpindi
    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.
  • D. Multan
    Multan is a historic city in southern Punjab, Pakistan, renowned as a major cultural, commercial, and Sufi spiritual center with a legacy spanning over two millennia.
  • E. 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.
  • 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_69ab49e100c0819082a40cb797383243 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde704af88190a132626acc99745f completed March 7, 2026, 8:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69b66b1c9cb881908df6998f752f13d0 completed March 15, 2026, 8:17 a.m.
Created at: March 6, 2026, 9:59 p.m.