Triple

T16583228
Position Surface form Disambiguated ID Type / Status
Subject New Fourth Army Incident E402888 entity
Predicate location P40 FINISHED
Object Maolin, Anhui E402888 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: Maolin, Anhui | Statement: [New Fourth Army Incident, location, Maolin, Anhui]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maolin, Anhui
Context triple: [New Fourth Army Incident, location, Maolin, Anhui]
  • A. Maolin, Anhui chosen
    Maolin, Anhui is a town in eastern China notable as the site of the New Fourth Army Incident during the Second Sino-Japanese War.
  • B. Xuancheng
    Xuancheng is a county-level city in southeastern Anhui Province, China, known for its historical heritage and traditional Chinese ink production.
  • C. Jing County, Anhui
    Jing County, located in Anhui Province, China, is a county-level administrative region known for its traditional Huizhou culture and historic architecture.
  • D. Chizhou
    Chizhou is a prefecture-level city in southeastern China known for its proximity to the scenic Mount Jiuhua, one of the four sacred mountains of Chinese Buddhism.
  • E. Suzhou (Anhui)
    Suzhou (Anhui) is a county-level city in northern Anhui Province, China, known as an important regional center for agriculture and light industry.
  • 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_69e35999088c8190900497f18728bd0b completed April 18, 2026, 10:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a007597905881909df7dc49961b6a02 completed May 10, 2026, 12:09 p.m.
Created at: April 10, 2026, 5:16 a.m.