Triple

T4050799
Position Surface form Disambiguated ID Type / Status
Subject Operation Go E84179 entity
Predicate capturedCity P8411 FINISHED
Object Guilin E235421 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: Guilin | Statement: [Operation Go, capturedCity, Guilin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guilin
Context triple: [Operation Go, capturedCity, Guilin]
  • A. Guilin chosen
    Guilin is a scenic city in southern China’s Guangxi region, famed for its dramatic karst mountains and picturesque Li River landscapes.
  • B. Zhangjiajie
    Zhangjiajie is a scenic city in northwestern Hunan Province, China, famed for its towering sandstone pillars and dramatic national forest park that inspired the floating mountains in the film "Avatar."
  • C. Luyang
    Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
  • D. Nanning
    Nanning is the capital and largest city of China’s Guangxi Zhuang Autonomous Region, known as a key economic hub and “Green City” in the Lingnan cultural area.
  • E. Jixi
    Jixi is a historic county-level city in Anhui Province, China, known for its traditional Huizhou culture, architecture, and scenic mountainous landscapes.
  • 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_69aed930bd5c819083e7dcc14fc44f69 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb8413848190992e4b5f3b29b43c completed March 9, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b55659256081909154569cedd1694a completed March 14, 2026, 12:36 p.m.
Created at: March 9, 2026, 3:37 p.m.