Triple

T15645503
Position Surface form Disambiguated ID Type / Status
Subject Zhonghe–Xinlu line E376165 entity
Predicate hasStation P35 FINISHED
Object Xianse Temple station NE NERFINISHED

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: Xianse Temple station | Statement: [Zhonghe–Xinlu line, hasStation, Xianse Temple station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xianse Temple station
Context triple: [Zhonghe–Xinlu line, hasStation, Xianse Temple station]
  • A. Xianse Temple Station chosen
    Xianse Temple Station is a metro station in New Taipei, Taiwan, that serves the Sanchong District on the Taipei Metro network.
  • B. Longshan Temple station
    Longshan Temple station is a Taipei Metro rapid transit station serving the historic Wanhua district and the nearby Longshan Temple.
  • C. Shandao Temple station
    Shandao Temple station is a metro station on the Taipei Metro system in Taiwan, serving the area around the historic Shandao Temple.
  • D. Ximenkou station
    Ximenkou station is an underground metro station on the Guangzhou Metro system, located near the historic Ximenkou area in central Guangzhou, China.
  • E. Changshou Lu Station
    Changshou Lu Station is an underground metro station on the Guangzhou Metro system serving the bustling Changshou Road commercial area in Guangzhou, China.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d85cd1564c8190991adda63bfab4b0 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04ed5b8b081908d7127964eed3b09 completed April 16, 2026, 2:52 a.m.
Created at: April 10, 2026, 4:15 a.m.