Triple

T13320540
Position Surface form Disambiguated ID Type / Status
Subject Tsinan E317302 entity
Predicate hasChineseName P4878 FINISHED
Object 济南 E80751 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: 济南 | Statement: [Tsinan, hasChineseName, 济南]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 济南
Context triple: [Tsinan, hasChineseName, 济南]
  • A. Jining
    Jining is a city in Shandong Province, China, historically significant as a transport and commercial hub along the Grand Canal.
  • B. Jinan chosen
    Jinan is the capital city of Shandong Province in eastern China, known for its numerous natural springs and rich historical and cultural heritage.
  • C. Weifang
    Weifang is a prefecture-level city in eastern China known for its kite-making tradition and annual international kite festival.
  • D. Zibo
    Zibo is an industrial and historical city in eastern China known for its ceramics, petrochemical industry, and role as a former capital of the ancient State of Qi.
  • E. Binzhou
    Binzhou is a prefecture-level city in northern Shandong Province, China, located near the lower reaches of the Yellow River and known for its developing industrial and agricultural economy.
  • 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_69d806b4d62c81908d4ced1665414be5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d990faa95481908a7fd297959c062e completed April 11, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69f716ee695c81909ffeeb0901ee66c1 completed May 3, 2026, 9:35 a.m.
Created at: April 9, 2026, 9:30 p.m.