Triple

T16584009
Position Surface form Disambiguated ID Type / Status
Subject Hongcun E402906 entity
Predicate locatedIn P40 FINISHED
Object Yi County E643972 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: Yi County | Statement: [Hongcun, locatedIn, Yi County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yi County
Context triple: [Hongcun, locatedIn, Yi County]
  • A. Yi County chosen
    Yi County is an administrative county under the jurisdiction of Huangshan City in Anhui Province, eastern China, known for its traditional Huizhou culture and historic villages.
  • B. Uiju County
    Uiju County is a county in North Pyongan Province, North Korea, located near the border with China along the Yalu River.
  • C. Hoechang County
    Hoechang County is a county in South Pyongan Province, North Korea, historically noted as the burial site of Mao Anying, son of Chinese leader Mao Zedong.
  • D. Ji County
    Ji County is a historical county in China known as the birthplace of the renowned Three Kingdoms-era general Deng Ai.
  • E. Uiryeong County
    Uiryeong County is a rural county in South Gyeongsang Province, South Korea, known as the birthplace of Samsung founder Lee Byung-chul.
  • 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_69d88387363c8190a97a0c942130de97 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e35999f80c8190852fd4137bc45a80 completed April 18, 2026, 10:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a009d2bbe9c81909031d79f93faca6a completed May 10, 2026, 2:58 p.m.
Created at: April 10, 2026, 5:16 a.m.