Triple

T7022143
Position Surface form Disambiguated ID Type / Status
Subject Penghu Airport E162851 entity
Predicate cityServed P82 FINISHED
Object Magong E173986 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: Magong | Statement: [Penghu Airport, cityServed, Magong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Magong
Context triple: [Penghu Airport, cityServed, Magong]
  • A. Magong chosen
    Magong is the main urban center and largest city of Taiwan’s Penghu (Pescadores) archipelago, serving as its political, economic, and transportation hub.
  • B. Shekou
    Shekou is a coastal district in Shenzhen, China, known as a major transportation and commercial hub with significant port facilities and expatriate communities.
  • C. Beihai
    Beihai is a coastal city in China's Guangxi Zhuang Autonomous Region, known for its beaches, maritime trade, and the scenic Silver Beach tourist area.
  • D. Macau
    Macau is a Special Administrative Region of China known for its blend of Portuguese and Chinese cultures and its major casino and tourism industry.
  • E. Macau
    Macau is a coastal municipality in the Brazilian state of Rio Grande do Norte, known for its salt production and fishing activities.
  • 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_69c6885b26248190a857541e3d10e299 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6e1ec34a48190b64cafb94e2f8706 completed March 27, 2026, 8 p.m.
NED1 Entity disambiguation (via context triple) batch_69c79439f28481909c028f4f86062b0f completed March 28, 2026, 8:41 a.m.
Created at: March 27, 2026, 2:35 p.m.