Triple

T5001610
Position Surface form Disambiguated ID Type / Status
Subject Hag Fold E112384 entity
Predicate hasStationCode P1289 FINISHED
Object HGF
HGF is the National Rail station code for Hag Fold railway station in Greater Manchester, England.
 E485944 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: HGF | Statement: [Hag Fold, hasStationCode, HGF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HGF
Context triple: [Hag Fold, hasStationCode, HGF]
  • A. HGF
    HGF is the abbreviation for the Helmholtz Association, Germany’s largest scientific research organization spanning multiple disciplines and large-scale facilities.
  • B. HG
    HG is the postcode area designation covering Harrogate and surrounding parts of North Yorkshire, England.
  • C. HVF
    HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
  • D. GDF
    GDF is the Global Drug Facility, an international mechanism that supplies quality-assured medicines and diagnostics to support tuberculosis control and treatment programs worldwide.
  • E. HGA
    HGA is a multidisciplinary architecture and engineering firm known for designing innovative, human-centered buildings and environments across sectors such as healthcare, education, and the arts.
  • 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: HGF
Triple: [Hag Fold, hasStationCode, HGF]
Generated description
HGF is the National Rail station code for Hag Fold railway station in Greater Manchester, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HGF
Target entity description: HGF is the National Rail station code for Hag Fold railway station in Greater Manchester, England.
  • A. HGF
    HGF is the abbreviation for the Helmholtz Association, Germany’s largest scientific research organization spanning multiple disciplines and large-scale facilities.
  • B. HG
    HG is the postcode area designation covering Harrogate and surrounding parts of North Yorkshire, England.
  • C. HVF
    HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
  • D. GDF
    GDF is the Global Drug Facility, an international mechanism that supplies quality-assured medicines and diagnostics to support tuberculosis control and treatment programs worldwide.
  • E. HGA
    HGA is a multidisciplinary architecture and engineering firm known for designing innovative, human-centered buildings and environments across sectors such as healthcare, education, and the arts.
  • 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_69bd4433d0b08190877e83959ef40d81 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd72bf0de08190a07419514afc3a06 completed March 20, 2026, 4:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69be92598ff88190b63a589524180272 completed March 21, 2026, 12:43 p.m.
NEDg Description generation batch_69be9323c5fc819084a4dbf59bab24cd completed March 21, 2026, 12:46 p.m.
NED2 Entity disambiguation (via description) batch_69be93bafdbc8190b7add3fbcc33a317 completed March 21, 2026, 12:48 p.m.
Created at: March 20, 2026, 1:34 p.m.