Triple

T5690484
Position Surface form Disambiguated ID Type / Status
Subject Taishanese E125416 entity
Predicate spokenIn P2266 FINISHED
Object Taishan E468895 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: Taishan | Statement: [Taishanese, spokenIn, Taishan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taishan
Context triple: [Taishanese, spokenIn, Taishan]
  • A. Taishan chosen
    Taishan is a county-level city in Guangdong Province, China, known as the ancestral homeland of many overseas Chinese and for its coastal scenery and historic villages.
  • B. Mount Tai
    Mount Tai is one of China’s most famous and historically significant sacred mountains, revered in Chinese religion and culture for millennia.
  • C. Tanluan
    Tanluan was a pioneering Chinese Buddhist monk and philosopher whose teachings helped systematize and popularize Pure Land Buddhism in East Asia.
  • D. Tianzhu Peak
    Tianzhu Peak is the tallest summit of China’s Wudang Mountains, a range famed for its Taoist temples and martial arts heritage.
  • E. Mount Zao
    Mount Zao is a prominent volcanic mountain range in northern Japan known for its ski resorts, hot springs, and the emerald-colored Okama crater 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023e340a08190b6175fad3e9a32b6 completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07dd76f008190970c3b17ec8cbfd8 completed March 22, 2026, 11:40 p.m.
Created at: March 22, 2026, 3:44 p.m.