Triple

T3021908
Position Surface form Disambiguated ID Type / Status
Subject Lichtenfels station E82479 entity
Predicate locatedInAdministrativeTerritory P40 FINISHED
Object Bavaria E7752 NE 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: Bavaria | Statement: [Lichtenfels station, locatedInAdministrativeTerritory, Bavaria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bavaria
Context triple: [Lichtenfels station, locatedInAdministrativeTerritory, 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.

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_69ad8b1fb34081908c1b873e2b7273e1 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9a963034819093d96566e9b0cea9 completed March 8, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2357438648190b2dc11ae9b568a15 completed March 12, 2026, 3:39 a.m.
Created at: March 8, 2026, 3 p.m.