Triple

T15864521
Position Surface form Disambiguated ID Type / Status
Subject Canton, South Dakota E384674 entity
Predicate namedAfter P63 FINISHED
Object Canton, China E242593 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: Canton, China | Statement: [Canton, South Dakota, namedAfter, Canton, China]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Canton, China
Context triple: [Canton, South Dakota, namedAfter, Canton, China]
  • A. Canton, China chosen
    Canton, China is the former English name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • B. Hsiangcheng, China
    Hsiangcheng, China is a town in Henan Province known as the birthplace of author and social critic Os Guinness.
  • C. City of Canton
    The City of Canton is a local municipal government that administers public services and infrastructure, including the Canton Municipal Airport, for its community.
  • D. Hangtou
    Hangtou is a town in Shanghai, China, known as the southern terminus of the Shanghai Metro’s Line 18.
  • E. Caizhou
    Caizhou was a historic Chinese city best known as the final capital of the Jurchen-led Jin dynasty before its fall to the Mongols.
  • 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_69d86da4e86481909f1325fdc971b5ec completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1555e4ee48190a3b27b4ab9bdb1c8 completed April 16, 2026, 9:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa945d9808190a65f5182db341393 completed May 9, 2026, 9:38 p.m.
Created at: April 10, 2026, 4:50 a.m.