Triple

T10865100
Position Surface form Disambiguated ID Type / Status
Subject Goslar station E256506 entity
Predicate hasDS100Code P1289 FINISHED
Object HGOS
HGOS is the DS100 railway station code used to identify Goslar station in Germany’s rail network.
 E889921 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: HGOS | Statement: [Goslar station, hasDS100Code, HGOS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HGOS
Context triple: [Goslar station, hasDS100Code, HGOS]
  • A. HGS
    HGS is the National Rail station code assigned to Hastings railway station in East Sussex, England.
  • B. HG
    HG is the postcode area designation covering Harrogate and surrounding parts of North Yorkshire, England.
  • C. HG
    HG is the vehicle registration code used on license plates for the German town of Homburg vor der Höhe and its surrounding district.
  • D. GOS
    GOS is the acronym for the Global Observing System, an international network of instruments and facilities that continuously monitor the Earth's atmosphere, oceans, and land for weather and climate services.
  • E. HGD
    HGD is the National Rail station code for Hungerford railway station in Berkshire, England.
  • 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: HGOS
Triple: [Goslar station, hasDS100Code, HGOS]
Generated description
HGOS is the DS100 railway station code used to identify Goslar station in Germany’s rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HGOS
Target entity description: HGOS is the DS100 railway station code used to identify Goslar station in Germany’s rail network.
  • A. HGS
    HGS is the National Rail station code assigned to Hastings railway station in East Sussex, England.
  • B. HG
    HG is the postcode area designation covering Harrogate and surrounding parts of North Yorkshire, England.
  • C. HG
    HG is the vehicle registration code used on license plates for the German town of Homburg vor der Höhe and its surrounding district.
  • D. GOS
    GOS is the acronym for the Global Observing System, an international network of instruments and facilities that continuously monitor the Earth's atmosphere, oceans, and land for weather and climate services.
  • E. HGD
    HGD is the National Rail station code for Hungerford railway station in Berkshire, England.
  • 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_69d6aa83d1448190a66d93c32394d21f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7516b2f148190adbacd35fc8c2056 completed April 9, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69dff7d5359c8190b46a6b817938eb67 completed April 15, 2026, 8:40 p.m.
NEDg Description generation batch_69e0026fda3c8190b60174b252d57e12 completed April 15, 2026, 9:26 p.m.
NED2 Entity disambiguation (via description) batch_69e00581fde08190b28b8dde4a21d59e completed April 15, 2026, 9:39 p.m.
Created at: April 8, 2026, 9:20 p.m.