Triple

T15428475
Position Surface form Disambiguated ID Type / Status
Subject Madhesh Province E369573 entity
Predicate containsCity P294 FINISHED
Object Gaur E167994 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: Gaur | Statement: [Madhesh Province, containsCity, Gaur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gaur
Context triple: [Madhesh Province, containsCity, Gaur]
  • A. Gaur chosen
    Gaur is a ruined medieval city on the India–Bangladesh border, historically a major capital of Bengal known for its Islamic architecture and archaeological remains.
  • B. Gaur
    Gaur is the bull-like animal mascot representing the Indian football club FC Goa.
  • C. Nandi bull
    Nandi bull is the sacred bull and mount of the Hindu god Shiva, widely revered and depicted in temples and religious art across South Asia.
  • D. Ghaur
    Ghaur is a powerful Deviant priest and recurring Marvel Comics supervillain known for masterminding cosmic-scale schemes against heroes such as the Eternals and the Avengers.
  • E. barasingha (swamp deer)
    The barasingha, or swamp deer, is a large Indian deer species distinguished by its many-tined antlers and preference for marshy grassland habitats.
  • 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_69d85a1849f48190bf898068b2806fae completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03ec31f4881908b26ff7c381d7bc9 completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a8099448190be71f7b649c7e545 completed May 9, 2026, 11:29 a.m.
Created at: April 10, 2026, 3:20 a.m.