Triple

T15023849
Position Surface form Disambiguated ID Type / Status
Subject Shah Abdul Latif University (campus), Sukkur E378156 entity
Predicate locatedIn P40 FINISHED
Object Sukkur E77817 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: Sukkur | Statement: [Shah Abdul Latif University (campus), Sukkur, locatedIn, Sukkur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sukkur
Context triple: [Shah Abdul Latif University (campus), Sukkur, locatedIn, Sukkur]
  • A. Sukkur chosen
    Sukkur is a major city in Pakistan known for its strategic location on the Indus River and its role as an important commercial and cultural center in northern Sindh.
  • B. 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.
  • C. 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.
  • D. Bahawalnagar
    Bahawalnagar is a prominent city in Pakistan’s Punjab province, known as an agricultural and commercial hub near the border with India.
  • 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_69d85cd46b2c819090d054c27787f677 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded7de117c8190a1b9fa8d1602057e completed April 15, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a5f38488190b441dd0b385024b1 completed May 9, 2026, 11:28 a.m.
Created at: April 10, 2026, 2:56 a.m.