Triple

T11477600
Position Surface form Disambiguated ID Type / Status
Subject Bannu campus E272062 entity
Predicate regionServed P82 FINISHED
Object Bannu E54094 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: Bannu | Statement: [Bannu campus, regionServed, Bannu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bannu
Context triple: [Bannu campus, regionServed, Bannu]
  • A. Bannu chosen
    Bannu is a historic city in northwestern Pakistan known as a regional commercial and cultural center in the Khyber Pakhtunkhwa province.
  • B. Turbat
    Turbat is a major city in southern Balochistan, Pakistan, known as a commercial and cultural center of the Makran region.
  • C. Amarkot
    Amarkot is an alternative name for Umarkot, a historic town and district in the Sindh province of Pakistan known for its cultural and Mughal-era significance.
  • D. Attock
    Attock is a historic city in northern Pakistan strategically located along the Indus River, long serving as a key gateway between the Punjab region and Khyber Pakhtunkhwa.
  • E. Shujabad
    Shujabad is a city in southern Punjab, Pakistan, known for its agricultural economy and proximity to the regional center of Multan.
  • 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_69d6aae0c8d881908a5a360c0be3242e completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8294e0fe08190b018e840146e27ca completed April 9, 2026, 10:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69e60434966c81909a277b6a0fd9f358 completed April 20, 2026, 10:47 a.m.
Created at: April 8, 2026, 9:36 p.m.