Triple

T1647454
Position Surface form Disambiguated ID Type / Status
Subject Warring States period E35614 entity
Predicate hasPart P35 FINISHED
Object state of Qi E142968 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: state of Qi | Statement: [Warring States period, hasPart, state of Qi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: state of Qi
Context triple: [Warring States period, hasPart, state of Qi]
  • A. State of Qi chosen
    The State of Qi was a powerful ancient Chinese kingdom during the Zhou dynasty, noted for its economic strength, political reforms, and cultural influence among the Warring States.
  • B. state of Qin
    The state of Qin was an ancient Chinese kingdom in the western region of China that rose to power during the Warring States period and ultimately unified China, laying the foundation for the Qin dynasty.
  • C. Zhenjin
    Zhenjin was the designated heir and favored son of Kublai Khan, known for his Confucian education and role in the early Yuan dynasty’s administration before his premature death.
  • D. Shëngjin
    Shëngjin is a coastal town and port in northwestern Albania on the Adriatic Sea, historically significant for its strategic maritime position.
  • E. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a640ea88190822906da575d5165 completed March 5, 2026, 4:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad60a4bd5481908b46f44364c15592 completed March 8, 2026, 11:42 a.m.
Created at: March 4, 2026, 7:28 p.m.