Triple

T15058960
Position Surface form Disambiguated ID Type / Status
Subject Huyton railway station E379571 entity
Predicate hasStationCode P1289 FINISHED
Object HUY
HUY is the National Rail station code for Huyton railway station in Merseyside, England.
 E1135875 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: HUY | Statement: [Huyton railway station, hasStationCode, HUY]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HUY
Context triple: [Huyton railway station, hasStationCode, HUY]
  • A. Huy
    Huy is a historic city in the Walloon region of Belgium, known for its medieval architecture and strategic location along the Meuse River.
  • B. Huy
    Huy is a hill range and forested area in Saxony-Anhalt, Germany, known for its natural landscapes and historical sites.
  • C. Hu
    Hu is a common Chinese surname borne by many notable figures, including former Chinese president Hu Jintao.
  • D. HuHi
    HuHi is a South Korean professional League of Legends player known primarily for his role as a mid laner in the North American competitive scene.
  • E. HUU
    HUU is the students' union representing and supporting students at the University of Hull through services, activities, and advocacy.
  • 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: HUY
Triple: [Huyton railway station, hasStationCode, HUY]
Generated description
HUY is the National Rail station code for Huyton railway station in Merseyside, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HUY
Target entity description: HUY is the National Rail station code for Huyton railway station in Merseyside, England.
  • A. Huy
    Huy is a historic city in the Walloon region of Belgium, known for its medieval architecture and strategic location along the Meuse River.
  • B. Huy
    Huy is a hill range and forested area in Saxony-Anhalt, Germany, known for its natural landscapes and historical sites.
  • C. Hu
    Hu is a common Chinese surname borne by many notable figures, including former Chinese president Hu Jintao.
  • D. HuHi
    HuHi is a South Korean professional League of Legends player known primarily for his role as a mid laner in the North American competitive scene.
  • E. HUU
    HUU is the students' union representing and supporting students at the University of Hull through services, activities, and advocacy.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dedee50afc8190bf7b0f4bbe8c60a3 completed April 15, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5c2fcec8190800d1bda82c7352e completed May 9, 2026, 3:10 a.m.
NEDg Description generation batch_69fea9c0ad748190989920efe418001c completed May 9, 2026, 3:28 a.m.
NED2 Entity disambiguation (via description) batch_69feaa2fbafc8190b592d89b07b49765 completed May 9, 2026, 3:29 a.m.
Created at: April 10, 2026, 3:01 a.m.