Triple

T3644443
Position Surface form Disambiguated ID Type / Status
Subject China National Highway 106 E77263 entity
Predicate passesThroughCity P416 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: [China National Highway 106, passesThroughCity, Shijiazhuang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shijiazhuang
Context triple: [China National Highway 106, passesThroughCity, 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. Cangzhou
    Cangzhou is a prefecture-level city in eastern Hebei Province, China, known for its location near the Bohai Sea and its traditional martial arts heritage.
  • C. Baoding
    Baoding is a historic prefecture-level city in central Hebei Province, China, known as a regional transportation hub and former military and administrative center.
  • D. Langfang
    Langfang is a prefecture-level city in northern China situated between Beijing and Tianjin, known for its strategic location and growing industrial and service sectors.
  • E. Hengshui
    Hengshui is a prefecture-level city in southeastern Hebei Province, China, known for its traditional culture, agriculture, and growing industrial base.
  • 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_69ad85de1b988190a45f8dbfebc806fc completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc35c28908190b253f4835918a2b4 completed March 8, 2026, 6:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69b44f3207688190b8d93e2e833b15a0 completed March 13, 2026, 5:53 p.m.
Created at: March 8, 2026, 3:24 p.m.