Triple

T13093765
Position Surface form Disambiguated ID Type / Status
Subject Kepulauan Sangihe Regency E310527 entity
Predicate hasMainTown P14082 FINISHED
Object Tahuna E1040026 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: Tahuna | Statement: [Kepulauan Sangihe Regency, hasMainTown, Tahuna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tahuna
Context triple: [Kepulauan Sangihe Regency, hasMainTown, Tahuna]
  • A. Tahuna chosen
    Tahuna is the main town and administrative center of the Sangihe Islands in North Sulawesi, Indonesia.
  • B. Balamban
    Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
  • C. Bucoda
    Bucoda is a small town in Thurston County, Washington, known for its historic coal-mining roots and its claim as the "World's Tiniest Town with the Biggest Halloween Spirit."
  • D. Tanauan
    Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
  • E. Bamban
    Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
  • 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_69d806a733548190989cfd4ce981ca33 completed April 9, 2026, 8:05 p.m.
NER Named-entity recognition batch_69d9813cd1b881909871a318fdd60672 completed April 10, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75469a3f08190a7e417872147b455 completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:03 p.m.