Triple

T14978305
Position Surface form Disambiguated ID Type / Status
Subject Wuling E373509 entity
Predicate predecessorOf P97 FINISHED
Object Changde region E76664 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: Changde region | Statement: [Wuling, predecessorOf, Changde region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Changde region
Context triple: [Wuling, predecessorOf, Changde region]
  • A. Changde chosen
    Changde is a city in northwestern Hunan Province, China, historically significant as a major battleground during the Second Sino-Japanese War.
  • B. Yongzhou
    Yongzhou is a prefecture-level city in southern Hunan Province, China, known for its long history and location at the confluence of the Xiang and Xiao rivers.
  • C. Huaihua
    Huaihua is a prefecture-level city in southwestern Hunan Province, China, known as a regional transportation hub and home to several ethnic minority communities.
  • D. Hengyang
    Hengyang is a major industrial and transportation hub city in southern China, located along the Xiang River in the south of 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_69d85ccbbcd48190acb56e7cf104d8ad completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6fbd138819092254ea37388026c completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69feae0652dc8190a90a1c3b07a6ed94 completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 2:51 a.m.