Triple

T552131
Position Surface form Disambiguated ID Type / Status
Subject North China Plain E11862 entity
Predicate contains P35 FINISHED
Object Shijiazhuang E79577 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: Shijiazhuang | Statement: [North China Plain, contains, Shijiazhuang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shijiazhuang
Context triple: [North China Plain, contains, Shijiazhuang]
  • A. Shijiazhuang chosen
    Shijiazhuang is the capital and largest city of Hebei Province in northern China, known as a major industrial and transportation hub.
  • B. Tianjin
    Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
  • C. Anyang
    Anyang is an ancient city in northern China renowned as one of the historical capitals of the Shang dynasty and a major archaeological site.
  • D. Taiyuan
    Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
  • E. Tangshan
    Tangshan is a major industrial city in northern China, historically known for its coal mining, steel production, and the devastating 1976 earthquake.
  • 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_69a4932941d08190815efd422f0b4ca7 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a499047bd4819089ca8345f1b6e46c completed March 1, 2026, 7:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5370a97881908916b387ff6b02af completed March 7, 2026, 4:33 p.m.
Created at: March 1, 2026, 7:32 p.m.