Triple

T1970112
Position Surface form Disambiguated ID Type / Status
Subject Suizhou E42779 entity
Predicate borderedBy P224 FINISHED
Object Henan Province E35116 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: Henan Province | Statement: [Suizhou, borderedBy, Henan Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Henan Province
Context triple: [Suizhou, borderedBy, Henan Province]
  • A. Henan Province chosen
    Henan Province is a populous landlocked province in central China, historically regarded as a cradle of Chinese civilization and home to several ancient capitals.
  • B. Hubei Province
    Hubei Province is a landlocked region in central China known for its capital city Wuhan, major role in industry and transportation, and significant historical and cultural heritage.
  • C. Shaanxi Province
    Shaanxi Province is a landlocked region in north-central China known for its historical capital Xi'an and the Terracotta Army of Emperor Qin Shi Huang.
  • D. Hebei
    Hebei is a northern Chinese province surrounding Beijing and Tianjin, historically significant as a major political, military, and industrial region.
  • E. Zhejiang Province
    Zhejiang Province is a coastal province in eastern China known for its dynamic private-sector economy, major port cities like Ningbo, and scenic areas such as Hangzhou and the West Lake.
  • 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_69a88711151c8190940b2572095059d7 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb3d17274819084cd352a3d2a8151 completed March 7, 2026, 5:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2702954c8190b95de378e263574a completed March 9, 2026, 1:48 a.m.
Created at: March 4, 2026, 7:36 p.m.