Triple

T11489782
Position Surface form Disambiguated ID Type / Status
Subject Xiangxiang City E272374 entity
Predicate subdivisionName2 P766 FINISHED
Object Xiangtan E52036 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: Xiangtan | Statement: [Xiangxiang City, subdivisionName2, Xiangtan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xiangtan
Context triple: [Xiangxiang City, subdivisionName2, Xiangtan]
  • A. Xiangtan chosen
    Xiangtan is a prefecture-level city in central Hunan Province, China, known as an important industrial and commercial hub and for encompassing Shaoshan, the birthplace of Mao Zedong.
  • B. Changde
    Changde is a city in northwestern Hunan Province, China, historically significant as a major battleground during the Second Sino-Japanese War.
  • C. Hengyang
    Hengyang is a major industrial and transportation hub city in southern China, located along the Xiang River in the south of Hunan Province.
  • D. Yiyang
    Yiyang is a prefecture-level city in south-central China known for its location along the Zi River and its role as an important regional center in Hunan Province.
  • E. Chenzhou
    Chenzhou is a prefecture-level city in southern Hunan Province, China, known as a regional transport hub and for its rich mineral resources and scenic mountainous landscapes.
  • 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_69d6aae1b09881909ce2ded3fa0c14fa completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d85a20df608190992543b4d7006f8a completed April 10, 2026, 2:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69e6e7a6cd3081909c8a86aa0870523c completed April 21, 2026, 2:57 a.m.
Created at: April 8, 2026, 9:36 p.m.