Triple

T7653050
Position Surface form Disambiguated ID Type / Status
Subject Wolfenbüttel E173302 entity
Predicate nearbyCity P350 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: [Wolfenbüttel, nearbyCity, Salzgitter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Salzgitter
Context triple: [Wolfenbüttel, nearbyCity, 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_69c6995473348190a4f41d110d619a18 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7018c34a88190be6089a9105bd4b0 completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c89af47b9c819087d42f1b01c413fe completed March 29, 2026, 3:22 a.m.
Created at: March 27, 2026, 3:59 p.m.