Triple

T14124375
Position Surface form Disambiguated ID Type / Status
Subject Lüliang E339988 entity
Predicate hasAdministrativeDivision P747 FINISHED
Object Lishi District E1157588 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: Lishi District | Statement: [Lüliang, hasAdministrativeDivision, Lishi District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lishi District
Context triple: [Lüliang, hasAdministrativeDivision, Lishi District]
  • A. Lishi District chosen
    Lishi District is an urban administrative district in Shanxi Province, China, serving as the political and economic center of Lüliang City.
  • B. Guiren District
    Guiren District is an administrative district in the southern Taiwanese city of Tainan, known for its mix of suburban communities, agriculture, and educational institutions.
  • C. Zhifu District
    Zhifu District is the central urban district and administrative, commercial, and cultural core of Yantai in Shandong Province, China.
  • D. Nianzishan District
    Nianzishan District is an urban district under the jurisdiction of Qiqihar City in Heilongjiang Province, northeastern China.
  • E. Kuiwen District
    Kuiwen District is an urban administrative district and commercial center of Weifang City in Shandong Province, China.
  • 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_69d81c6a95b481909e39111e0c1f31ee completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de6096976481909dc79066c5165a50 completed April 14, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff2ce12e78819080b3fe19c57ef3ef completed May 9, 2026, 12:47 p.m.
Created at: April 9, 2026, 10:22 p.m.