Triple

T15378533
Position Surface form Disambiguated ID Type / Status
Subject Lishui E367736 entity
Predicate hasAdministrativeDivision P747 FINISHED
Object Liandu District
Liandu District is the central urban district and administrative seat of Lishui City in Zhejiang Province, China.
E1247300 NE FINISHED

How this triple was built (4 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: Liandu District | Statement: [Lishui, hasAdministrativeDivision, Liandu District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Liandu District
Context triple: [Lishui, hasAdministrativeDivision, Liandu District]
  • A. Linwei District
    Linwei District is an urban administrative district in Weinan, Shaanxi Province, China, serving as the city's central political and economic area.
  • B. Neihu District
    Neihu District is a suburban and technology-focused district in northeastern Taipei, Taiwan, known for its science parks, residential communities, and natural scenery.
  • C. Dawan District
    Dawan District is an administrative district in Klungkung Regency on the island of Bali, Indonesia.
  • D. Linzi District
    Linzi District is an urban district of Zibo in Shandong Province, China, historically known as the ancient capital of the State of Qi.
  • E. Zhanqian District
    Zhanqian District is an urban administrative district under the jurisdiction of Yingkou City in Liaoning Province, China.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Liandu District
Triple: [Lishui, hasAdministrativeDivision, Liandu District]
Generated description
Liandu District is the central urban district and administrative seat of Lishui City in Zhejiang Province, China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Liandu District
Target entity description: Liandu District is the central urban district and administrative seat of Lishui City in Zhejiang Province, China.
  • A. Linwei District
    Linwei District is an urban administrative district in Weinan, Shaanxi Province, China, serving as the city's central political and economic area.
  • B. Neihu District
    Neihu District is a suburban and technology-focused district in northeastern Taipei, Taiwan, known for its science parks, residential communities, and natural scenery.
  • C. Dawan District
    Dawan District is an administrative district in Klungkung Regency on the island of Bali, Indonesia.
  • D. Linzi District
    Linzi District is an urban district of Zibo in Shandong Province, China, historically known as the ancient capital of the State of Qi.
  • E. Zhanqian District
    Zhanqian District is an urban administrative district under the jurisdiction of Yingkou City in Liaoning Province, China.
  • F. None of above. chosen

Provenance (5 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_69d85a1551a08190ba2caea7cd51c639 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e6044488190b0499db109f7f821 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01232149f88190b385fca6a7588d7b completed May 11, 2026, 12:30 a.m.
NEDg Description generation batch_6a0123ddb9148190af5037d834e1fe54 completed May 11, 2026, 12:33 a.m.
NED2 Entity disambiguation (via description) batch_6a01248b53d88190a4ec4fa6cee89bb1 completed May 11, 2026, 12:36 a.m.
Created at: April 10, 2026, 3:19 a.m.