Triple

T11837121
Position Surface form Disambiguated ID Type / Status
Subject Nushki District E281545 entity
Predicate hasBorder P224 FINISHED
Object Kharan District E273614 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: Kharan District | Statement: [Nushki District, hasBorder, Kharan District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kharan District
Context triple: [Nushki District, hasBorder, Kharan District]
  • A. Kharan District chosen
    Kharan District is an administrative district in the Balochistan province of Pakistan, known for its arid desert landscape and sparse population.
  • B. Kharan
    Kharan is a town in Balochistan, Pakistan, serving as the administrative center of Kharan District.
  • C. Barkhan District
    Barkhan District is an administrative district in the northeastern part of Pakistan’s Balochistan province, known for its rugged terrain and predominantly tribal population.
  • D. Karak District
    Karak District is an administrative district in Pakistan’s Khyber Pakhtunkhwa province, known for its Pashtun population and significant oil and gas reserves.
  • E. Rat Burana District
    Rat Burana District is a riverside administrative district in Bangkok, Thailand, known for its industrial areas and key transport links across the Chao Phraya River.
  • 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_69d6ab276f8c8190b1966a0ef11349ac completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a62fec0881908c7b89c0b5bcc9a2 completed April 10, 2026, 7:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69f16765aac481908b4cb474b141d842 completed April 29, 2026, 2:05 a.m.
Created at: April 8, 2026, 9:43 p.m.