Triple

T9243451
Position Surface form Disambiguated ID Type / Status
Subject Ziradei E222123 entity
Predicate partOf P40 FINISHED
Object Siwan district E429904 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: Siwan district | Statement: [Ziradei, partOf, Siwan district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siwan district
Context triple: [Ziradei, partOf, Siwan district]
  • A. Siwan district chosen
    Siwan district is an administrative district in the Indian state of Bihar, known for its agrarian economy and historical association with prominent political leaders.
  • B. Sheohar district
    Sheohar district is a small administrative district in the Indian state of Bihar, known for its predominantly rural character and agrarian economy.
  • C. Nagar District
    Nagar District is an administrative region in Gilgit-Baltistan, northern Pakistan, known for its mountainous terrain, glaciers, and strategic location along the Karakoram Highway.
  • D. Saha District
    Saha District is an administrative district (gu) in the southwestern part of Busan, South Korea, known for its coastal areas and residential neighborhoods.
  • E. Hapur district
    Hapur district is an administrative district in the Indian state of Uttar Pradesh, located in the National Capital Region and known for its proximity to Delhi and growing industrial and transport connectivity.
  • 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_69ca83ee26cc81909ac624e190597d6d completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cd03edd37481908ea2f6dac354f04f completed April 1, 2026, 11:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0c72be25c8190931204b21966502e completed April 4, 2026, 8:09 a.m.
Created at: March 30, 2026, 7:30 p.m.