Triple

T6184600
Position Surface form Disambiguated ID Type / Status
Subject Government of Taiwan E138025 entity
Predicate hasSeatOfGovernment P761 FINISHED
Object Taipei E14412 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: Taipei | Statement: [Government of Taiwan, hasSeatOfGovernment, Taipei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taipei
Context triple: [Government of Taiwan, hasSeatOfGovernment, Taipei]
  • A. Taipei, Taiwan chosen
    Taipei, Taiwan is the capital and largest city of Taiwan, known as a major political, economic, and cultural center in East Asia.
  • B. Tainan
    Tainan is a historic city in southern Taiwan known for its well-preserved temples, traditional culture, and status as the island’s former capital.
  • C. Taichung
    Taichung is a major city in central Taiwan known for its cultural attractions, mild climate, and role as an important economic and transportation hub.
  • D. Kaohsiung
    Kaohsiung is a major port city in southern Taiwan known for its heavy industry, modern harborfront, and growing cultural and arts scene.
  • E. Keelung
    Keelung is a major port city in northeastern Taiwan known for its busy harbor, seafood markets, and coastal scenery.
  • 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_69c008a8fd408190b7ec6e42934974a6 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c061020d148190ae2edf2b363f1e24 completed March 22, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c65378e14c8190901ac43d6d8b26b0 completed March 27, 2026, 9:52 a.m.
Created at: March 22, 2026, 4:19 p.m.