Triple

T1986087
Position Surface form Disambiguated ID Type / Status
Subject Göttingen railway station E43143 entity
Predicate hasDS100Code P1289 FINISHED
Object HG LITERAL FINISHED

How this triple was built (2 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: HG | Statement: [Göttingen railway station, hasDS100Code, HG]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasDS100Code
Context triple: [Göttingen railway station, hasDS100Code, HG]
  • A. deviceIndicates
    Indicates that a device provides a signal, status, or output that conveys information about a condition, event, or state.
  • B. hasStationCode chosen
    Indicates that an entity is associated with a specific station identification code.
  • C. hasMIC
    Indicates that an entity has a specified Minimum Inhibitory Concentration (MIC) value in relation to an antimicrobial agent.
  • D. hasHardwareCompatibilityWith
    Indicates that two hardware components or systems can operate together correctly and reliably without conflicts or incompatibilities.
  • E. hasDigitalAccess
    Indicates that an entity has the ability or permission to use or access digital resources, services, or information.
  • F. None of above.

Provenance (3 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb96f932881908bebfc4176fda7c0 completed March 7, 2026, 5:36 a.m.
PD Predicate disambiguation batch_69abb798d288819083132cf14605bd02 completed March 7, 2026, 5:28 a.m.
Created at: March 4, 2026, 7:37 p.m.