Triple

T14305567
Position Surface form Disambiguated ID Type / Status
Subject Jinhua E354684 entity
Predicate hasFamousProduct P1448 FINISHED
Object Jinhua ham E1090878 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: Jinhua ham | Statement: [Jinhua, hasFamousProduct, Jinhua ham]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jinhua ham
Context triple: [Jinhua, hasFamousProduct, Jinhua ham]
  • A. Jinhua ham chosen
    Jinhua ham is a famous Chinese dry-cured ham from Zhejiang province, renowned for its rich umami flavor and use in traditional cuisine and stocks.
  • B. Hangzhouhua
    Hangzhouhua is a regional Chinese dialect spoken in and around the city of Hangzhou in Zhejiang province.
  • C. Ningbohua
    Ningbohua is a Chinese Wu dialect spoken primarily in and around the city of Ningbo in Zhejiang province.
  • D. Zhenjiang vinegar
    Zhenjiang vinegar is a famous Chinese black rice vinegar known for its rich, mellow flavor and long aging process, widely used in Jiangsu cuisine and across China.
  • E. Yanjiao
    Yanjiao is a rapidly growing commuter town in Hebei province, China, known for its proximity to Beijing and large population of daily commuters working in the capital.
  • 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_69d8278ed42c8190b9f882dcce611347 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de85afabe48190926d6098047f4bcf completed April 14, 2026, 6:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd4684e2648190b46328252ac9d51b completed May 8, 2026, 2:12 a.m.
Created at: April 10, 2026, 1:12 a.m.