Triple

T4288246
Position Surface form Disambiguated ID Type / Status
Subject G1 trains E97322 entity
Predicate networkLocation P33705 FINISHED
Object Bavaria E7752 NE FINISHED

How this triple was built (3 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: Bavaria | Statement: [G1 trains, networkLocation, Bavaria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bavaria
Context triple: [G1 trains, networkLocation, Bavaria]
  • A. Bavaria chosen
    Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
  • B. Swabia (Bavaria)
    Swabia (Bavaria) is an administrative region in southwestern Bavaria, Germany, known for its distinct Swabian cultural heritage and mix of industrial cities and rural landscapes.
  • C. Saxony
    Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
  • D. Pfalz
    Pfalz is a major wine-producing region in southwestern Germany known for its diverse vineyards and high-quality white wines.
  • E. Saarland
    Saarland is a small federal state in southwestern Germany known for its industrial history, Franco-German cultural influences, and location along the borders with France and Luxembourg.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: networkLocation
Context triple: [G1 trains, networkLocation, Bavaria]
  • A. locationRelativeToIP
    Indicates a spatial or geographic relationship between an entity and a given IP address’s inferred location.
  • B. locationInSystem chosen
    Indicates that one entity is situated within or belongs to the spatial or organizational bounds of a particular system.
  • C. platformLocation
    Indicates the spatial position or placement of a platform relative to a reference point or environment.
  • D. connectsLocation
    Indicates a relationship where one entity serves as a link or route that joins or provides access between two locations.
  • E. network
    Indicates that one entity is connected to or interacts with another through a system of relationships, communication, or information exchange.
  • F. None of above.

Provenance (4 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_69b3454595848190a0e6bbb6a2bea040 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35060b70081908479c95b2afe8ec5 completed March 12, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c6e3fbbc81908594bb7c9b02f873 completed March 14, 2026, 8:36 p.m.
PD Predicate disambiguation batch_69b347fc4c0c8190a7fcd814e27308a5 completed March 12, 2026, 11:10 p.m.
Created at: March 12, 2026, 11:08 p.m.