Triple

T4762842
Position Surface form Disambiguated ID Type / Status
Subject Bhojpur district E105736 entity
Predicate borders P224 FINISHED
Object Saran district E429903 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: Saran district | Statement: [Bhojpur district, borders, Saran district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saran district
Context triple: [Bhojpur district, borders, Saran district]
  • A. Saran district chosen
    Saran district is an administrative district in the Indian state of Bihar, known for its historical significance and location along the Ganges and Ghaghara rivers.
  • B. Sarine district
    Sarine district is an administrative district in the canton of Fribourg in western Switzerland, centered around the city of Fribourg and encompassing surrounding municipalities.
  • C. Badrashin district
    Badrashin district is an administrative region in Giza Governorate, Egypt, known for encompassing several important archaeological and historical sites near ancient Memphis.
  • D. Pishin District
    Pishin District is an administrative district in the Balochistan province of Pakistan, known for its agricultural economy and predominantly Pashtun population.
  • E. Siha District
    Siha District is an administrative district in northern Tanzania, located within the Kilimanjaro Region near the slopes of Mount Kilimanjaro.
  • 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_69bd43f14cac819081c7c69803648211 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd651091cc81909b835439c85e842f completed March 20, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69be3a81c9dc8190b9e7f399ac1de268 completed March 21, 2026, 6:28 a.m.
Created at: March 20, 2026, 1:20 p.m.