Triple

T7865747
Position Surface form Disambiguated ID Type / Status
Subject Aklanon language E182609 entity
Predicate spokenIn P2266 FINISHED
Object Aklan, Philippines E250504 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: Aklan, Philippines | Statement: [Aklanon language, spokenIn, Aklan, Philippines]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aklan, Philippines
Context triple: [Aklanon language, spokenIn, Aklan, Philippines]
  • A. Aklan chosen
    Aklan is a province in the Philippines known for the world-famous Boracay Island and its vibrant Ati-Atihan Festival.
  • B. Guimaras
    Guimaras is a small island province in the Philippines known for its mango production, coastal scenery, and predominantly Hiligaynon-speaking population.
  • C. Capiz
    Capiz is a province in the Western Visayas region of the Philippines, known for its coastal landscapes, seafood, and use of the Hiligaynon language.
  • D. Leyte
    Leyte is a large island province in the Eastern Visayas region of the Philippines, known for its rich cultural traditions and historical significance, including major World War II events.
  • E. Palawan
    Palawan is a large island province in the western Philippines known for its stunning limestone cliffs, clear turquoise waters, rich marine biodiversity, and popular ecotourism destinations like El Nido and Puerto Princesa.
  • 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_69ca82894d9081908a832bfce71a4714 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3844eacc81908f8e1e5fc4dafec8 completed March 31, 2026, 2:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbdf6fb33881908cf7bd68915aa6b4 completed March 31, 2026, 2:51 p.m.
Created at: March 30, 2026, 4:54 p.m.