Triple

T19166228
Position Surface form Disambiguated ID Type / Status
Subject Bahawalnagar E469188 entity
Predicate hasNearbySettlement P4647 FINISHED
Object Haroonabad NE NERFINISHED

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: Haroonabad | Statement: [Bahawalnagar, hasNearbySettlement, Haroonabad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haroonabad
Context triple: [Bahawalnagar, hasNearbySettlement, Haroonabad]
  • A. Haroonabad chosen
    Haroonabad is a town in Pakistan known for its agricultural surroundings and role as a local commercial center.
  • B. Jauharabad
    Jauharabad is a planned town in Pakistan’s Punjab province, known for its proximity to key industrial and strategic facilities.
  • C. Hafizabad
    Hafizabad is a prominent city in Pakistan’s Punjab province, known for its rice production and role as an agricultural and commercial center.
  • D. Wazirabad
    Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd09d5a081909ae43c286651ae5a completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5f15ee064819087f9fd822236298f completed April 20, 2026, 9:26 a.m.
Created at: April 10, 2026, 12:06 p.m.