Triple

T15350978
Position Surface form Disambiguated ID Type / Status
Subject Volkswagen plant Salzgitter E367049 entity
Predicate locatedIn P40 FINISHED
Object Salzgitter E75169 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: Salzgitter | Statement: [Volkswagen plant Salzgitter, locatedIn, Salzgitter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Salzgitter
Context triple: [Volkswagen plant Salzgitter, locatedIn, Salzgitter]
  • A. Salzgitter chosen
    Salzgitter is a major industrial city in central Germany known for its steel production and location within the federal state of Lower Saxony.
  • B. Recklinghausen
    Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
  • C. Eisenhüttenstadt
    Eisenhüttenstadt is an industrial planned city in eastern Germany, known for its large steelworks and postwar socialist urban design.
  • D. Clausthal
    Clausthal is a historic mining town in Lower Saxony, Germany, best known today for its technical university and association with figures like microbiologist Robert Koch.
  • E. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • 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_69d85a1355608190a6673ddb67231d54 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e290efc8190b22c95dcd3e5f57f completed April 16, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff01fd53688190939787a3d6ff3bb9 completed May 9, 2026, 9:44 a.m.
Created at: April 10, 2026, 3:17 a.m.