Triple

T19062286
Position Surface form Disambiguated ID Type / Status
Subject Shenzhen urban core E466562 entity
Predicate containsDistrict P22582 FINISHED
Object Nanshan District 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: Nanshan District | Statement: [Shenzhen urban core, containsDistrict, Nanshan District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nanshan District
Context triple: [Shenzhen urban core, containsDistrict, Nanshan District]
  • A. Nanshan District chosen
    Nanshan District is a major urban district of Shenzhen, China, known as a key technology and innovation hub that hosts many leading tech companies and research institutions.
  • B. Yuanbao District
    Yuanbao District is an urban administrative district within the city of Dandong in Liaoning Province, northeastern China.
  • C. Tianxin District
    Tianxin District is a central urban district of Changsha, the capital city of Hunan Province in China, known for its historical sites and commercial areas.
  • D. Pengjiang District
    Pengjiang District is the central urban district and administrative, economic, and cultural core of Jiangmen City in Guangdong Province, China.
  • E. Shuangxi District
    Shuangxi District is a rural, mountainous district in eastern New Taipei City, Taiwan, known for its rivers, old streets, and natural scenery.
  • 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_69d8dd040fb881909af2a964f65ad208 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e195ac048190a57b57e585d022d4 completed April 20, 2026, 8:19 a.m.
Created at: April 10, 2026, 12:03 p.m.