Triple

T8532595
Position Surface form Disambiguated ID Type / Status
Subject Guangzhou Metro Line 7 E201990 entity
Predicate hasStation P35 FINISHED
Object Shaxi
Shaxi is a metro station in Guangzhou, China, serving passengers on the city’s Line 7 rapid transit route.
 E743161 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: Shaxi | Statement: [Guangzhou Metro Line 7, hasStation, Shaxi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Shaxi
Context triple: [Guangzhou Metro Line 7, hasStation, Shaxi]
  • A. Teisheba
    Teisheba is the Urartian storm and war god, often associated with thunder, rain, and military power in the ancient Near Eastern pantheon.
  • B. Shuafat
    Shuafat is a Palestinian neighborhood and refugee camp in East Jerusalem known for its dense population, complex political status, and challenging living conditions.
  • C. Hezhe
    The Hezhe are a small Tungusic ethnic group in northeastern China, traditionally known for fishing and hunting along the Amur and Ussuri rivers and for their distinctive riverine culture.
  • D. Shina
    Shina is an Indo-Aryan language spoken primarily in the Gilgit-Baltistan region of Pakistan and surrounding Himalayan areas.
  • E. Shand
    Shand is the maiden surname of Camilla, Queen Consort of the United Kingdom.
  • 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: Shaxi
Triple: [Guangzhou Metro Line 7, hasStation, Shaxi]
Generated description
Shaxi is a metro station in Guangzhou, China, serving passengers on the city’s Line 7 rapid transit route.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Shaxi
Target entity description: Shaxi is a metro station in Guangzhou, China, serving passengers on the city’s Line 7 rapid transit route.
  • A. Teisheba
    Teisheba is the Urartian storm and war god, often associated with thunder, rain, and military power in the ancient Near Eastern pantheon.
  • B. Shuafat
    Shuafat is a Palestinian neighborhood and refugee camp in East Jerusalem known for its dense population, complex political status, and challenging living conditions.
  • C. Hezhe
    The Hezhe are a small Tungusic ethnic group in northeastern China, traditionally known for fishing and hunting along the Amur and Ussuri rivers and for their distinctive riverine culture.
  • D. Shina
    Shina is an Indo-Aryan language spoken primarily in the Gilgit-Baltistan region of Pakistan and surrounding Himalayan areas.
  • E. Shand
    Shand is the maiden surname of Camilla, Queen Consort of the United Kingdom.
  • 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_69ca832355b08190b8b6a4ab4a4a3554 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe678fe448190a50c6b0d149b081f completed March 31, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce890333d08190b510d970e6d6fee5 completed April 2, 2026, 3:19 p.m.
NEDg Description generation batch_69ce8a9ba0448190ae7637f24b8a8032 completed April 2, 2026, 3:26 p.m.
NED2 Entity disambiguation (via description) batch_69ce8bda33548190a8f6985a48d65a39 completed April 2, 2026, 3:31 p.m.
Created at: March 30, 2026, 6:17 p.m.