Triple

T12508508
Position Surface form Disambiguated ID Type / Status
Subject Bannu Municipal Committee E299013 entity
Predicate locatedIn P40 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 Municipal Committee, locatedIn, Bannu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bannu
Context triple: [Bannu Municipal Committee, locatedIn, 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. Risalpur
    Risalpur is a town in Pakistan’s Khyber Pakhtunkhwa province known as a major military and air force training center.
  • C. Turbat
    Turbat is a major city in southern Balochistan, Pakistan, known as a commercial and cultural center of the Makran region.
  • D. 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.
  • E. 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.
  • 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_69d6ada4cd388190ae3bbf83ff87057a completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9541c1ca08190a4026c394ebcbeb5 completed April 10, 2026, 7:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6557243788190854208113600324e completed May 2, 2026, 7:50 p.m.
Created at: April 8, 2026, 9:57 p.m.