Triple

T22922689
Position Surface form Disambiguated ID Type / Status
Subject Via Rail Cornwall station E568906 entity
Predicate hasStationCode P1289 FINISHED
Object XCF
XCF is the station code used by Via Rail to identify Cornwall railway station in Ontario, Canada.
 E1562457 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: XCF | Statement: [Via Rail Cornwall station, hasStationCode, XCF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: XCF
Context triple: [Via Rail Cornwall station, hasStationCode, XCF]
  • A. XMP
    XMP (Extensible Metadata Platform) is Adobe’s XML-based framework for embedding and managing standardized metadata within digital files such as images, documents, and multimedia.
  • B. XIMAGE
    XIMAGE is an astronomical image display and analysis tool commonly used in high-energy astrophysics data processing.
  • C. XFS
    XFS is a high-performance 64-bit journaling file system originally developed by SGI, widely used on Linux for handling large files and parallel I/O workloads.
  • D. Fxfs
    Fxfs is a modern, native filesystem designed specifically for Google's Fuchsia operating system, emphasizing reliability, performance, and flexibility.
  • E. GIMPA
    GIMPA is a leading Ghanaian tertiary institution specializing in management, leadership, and public administration education and training.
  • 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: XCF
Triple: [Via Rail Cornwall station, hasStationCode, XCF]
Generated description
XCF is the station code used by Via Rail to identify Cornwall railway station in Ontario, Canada.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: XCF
Target entity description: XCF is the station code used by Via Rail to identify Cornwall railway station in Ontario, Canada.
  • A. XMP
    XMP (Extensible Metadata Platform) is Adobe’s XML-based framework for embedding and managing standardized metadata within digital files such as images, documents, and multimedia.
  • B. XIMAGE
    XIMAGE is an astronomical image display and analysis tool commonly used in high-energy astrophysics data processing.
  • C. XFS
    XFS is a high-performance 64-bit journaling file system originally developed by SGI, widely used on Linux for handling large files and parallel I/O workloads.
  • D. Fxfs
    Fxfs is a modern, native filesystem designed specifically for Google's Fuchsia operating system, emphasizing reliability, performance, and flexibility.
  • E. GIMPA
    GIMPA is a leading Ghanaian tertiary institution specializing in management, leadership, and public administration education and training.
  • 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_69e2458d90c88190a58cead4e781ca6a completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f180d6841c81908df6d4e501860a15 completed April 29, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bc245a0548190a786f56114ea88ac completed May 19, 2026, 1:52 a.m.
NEDg Description generation batch_6a0bc35c3ea08190a3b9111297774e6c completed May 19, 2026, 1:56 a.m.
NED2 Entity disambiguation (via description) batch_6a0bc4415f288190a4dab3885ec931ca completed May 19, 2026, 2 a.m.
Created at: April 17, 2026, 3:43 p.m.