Triple

T3408101
Position Surface form Disambiguated ID Type / Status
Subject Liaoning E71823 entity
Predicate majorCity P316 FINISHED
Object Liaoyang
Liaoyang is an ancient industrial city in northeastern China known for its historical significance and role in the region’s heavy industry.
 E374598 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: Liaoyang | Statement: [Liaoning, majorCity, Liaoyang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Liaoyang
Context triple: [Liaoning, majorCity, Liaoyang]
  • A. Benxi
    Benxi is an industrial and mining city in eastern Liaoning Province, China, known for its steel production and nearby scenic karst landscapes.
  • B. Shenyang
    Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
  • C. Anshan
    Anshan was an ancient city and region in southwestern Iran that served as an early center of Elamite and later Achaemenid Persian power.
  • D. Anshan
    Anshan is a major industrial city in northeastern China, historically known as one of the country’s leading steel-producing centers.
  • E. Fushun
    Fushun is an industrial city in northeastern China known historically for its coal mining and heavy industry.
  • 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: Liaoyang
Triple: [Liaoning, majorCity, Liaoyang]
Generated description
Liaoyang is an ancient industrial city in northeastern China known for its historical significance and role in the region’s heavy industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Liaoyang
Target entity description: Liaoyang is an ancient industrial city in northeastern China known for its historical significance and role in the region’s heavy industry.
  • A. Benxi
    Benxi is an industrial and mining city in eastern Liaoning Province, China, known for its steel production and nearby scenic karst landscapes.
  • B. Shenyang
    Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
  • C. Anshan
    Anshan was an ancient city and region in southwestern Iran that served as an early center of Elamite and later Achaemenid Persian power.
  • D. Anshan
    Anshan is a major industrial city in northeastern China, historically known as one of the country’s leading steel-producing centers.
  • E. Fushun
    Fushun is an industrial city in northeastern China known historically for its coal mining and heavy industry.
  • 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_69ad85ac312481909e7027ced1456a9f completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb9056acc8190a9c50ec374851ac8 completed March 8, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69b44ee17d9881908af3d79b7fb0de9d completed March 13, 2026, 5:52 p.m.
NEDg Description generation batch_69b44f69b03881908d8352de5e66638b completed March 13, 2026, 5:54 p.m.
NED2 Entity disambiguation (via description) batch_69b44ffac9748190bb58f95f859580c5 completed March 13, 2026, 5:57 p.m.
Created at: March 8, 2026, 3:15 p.m.