Triple

T2503050
Position Surface form Disambiguated ID Type / Status
Subject YREB E52510 entity
Predicate keyCity P235 FINISHED
Object Changsha E29743 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: Changsha | Statement: [YREB, keyCity, Changsha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Changsha
Context triple: [YREB, keyCity, Changsha]
  • A. Changsha chosen
    Changsha is the capital city of Hunan Province in south-central China, known as a historic cultural center and major regional economic hub.
  • B. Zhuzhou
    Zhuzhou is a major industrial and transportation hub city in south-central China, known especially for its rail transit and manufacturing industries.
  • C. Changde
    Changde is a city in northwestern Hunan Province, China, historically significant as a major battleground during the Second Sino-Japanese War.
  • D. Xiangtan
    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.
  • E. Hengyang
    Hengyang is a major industrial and transportation hub city in southern China, located along the Xiang River in the south of Hunan Province.
  • 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_69ab4957b3a88190adf968ae0c1b931c completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd1cb4f6481908d4e0a1dc0d84d3c completed March 7, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98a8887c8190bd00eaf48bc77781 completed March 10, 2026, 4:06 a.m.
Created at: March 6, 2026, 9:46 p.m.