Triple

T12027517
Position Surface form Disambiguated ID Type / Status
Subject Inbursa Aquarium E286315 entity
Predicate namedAfter P63 FINISHED
Object Inbursa E959854 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: Inbursa | Statement: [Inbursa Aquarium, namedAfter, Inbursa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Inbursa
Context triple: [Inbursa Aquarium, namedAfter, Inbursa]
  • A. Inbursa chosen
    Inbursa is a Mexican financial services company best known for its banking, insurance, and investment operations, founded and controlled by billionaire Carlos Slim.
  • B. Kuala Lumpur
    Kuala Lumpur is the capital and largest city of Malaysia, known for its modern skyline dominated by the Petronas Twin Towers and its role as the country’s cultural, financial, and economic center.
  • C. Kota
    Kota is a major industrial and educational city in southeastern Rajasthan, India, known for its coaching institutes and power plants along the Chambal River.
  • D. Singa city
    Singa city is the capital of Sudan’s Sennar State, serving as an important administrative and commercial center along the Blue Nile.
  • E. Putrajaya
    Putrajaya is Malaysia’s planned federal administrative capital, known for its modern architecture, landscaped boulevards, and numerous government complexes.
  • 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_69d6ab4669e48190b59246358b0383ab completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903f13ae8819097a5740f7c51df82 completed April 10, 2026, 2:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49d4f4c80819082ffc0c5aa3505a0 completed May 1, 2026, 12:32 p.m.
Created at: April 8, 2026, 9:47 p.m.