Triple

T12607749
Position Surface form Disambiguated ID Type / Status
Subject Abdul Qadeer Khan E301027 entity
Predicate workLocation P7 FINISHED
Object Kahuta E697236 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: Kahuta | Statement: [Abdul Qadeer Khan, workLocation, Kahuta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kahuta
Context triple: [Abdul Qadeer Khan, workLocation, Kahuta]
  • A. Kahuta chosen
    Kahuta is a town in Pakistan’s Punjab province known for hosting the country’s primary nuclear research and enrichment facilities.
  • B. Huta
    Huta is a village located in western Ukraine’s Ivano-Frankivsk Oblast, known for its proximity to the Carpathian Mountains and forested landscapes.
  • C. Tarusa
    Tarusa is a small historic town in western Russia known for its scenic location on the Oka River and its associations with Russian artists and writers.
  • D. Kanyākubja
    Kanyākubja is an ancient North Indian city of great historical and cultural significance, known today as Kannauj.
  • E. Hanuabada
    Hanuabada is a large traditional coastal village near Port Moresby in Papua New Guinea, known as a historic center of Motuan culture and seafaring.
  • 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_69d7bdea2ca881908f379526c13b1145 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d954e90efc81909951dbe698afa851 completed April 10, 2026, 7:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ecd1b748190bd961497b30e1ae5 completed May 2, 2026, 8:30 p.m.
Created at: April 9, 2026, 5:11 p.m.