Triple

T8095189
Position Surface form Disambiguated ID Type / Status
Subject Stuttgart region E188965 entity
Predicate containsCity P294 FINISHED
Object Göppingen E751532 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: Göppingen | Statement: [Stuttgart region, containsCity, Göppingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Göppingen
Context triple: [Stuttgart region, containsCity, Göppingen]
  • A. Göppingen chosen
    Göppingen is a town in the German state of Baden-Württemberg known as an industrial and administrative center in the Filstal valley near Stuttgart.
  • B. Pforzheim
    Pforzheim is a city in southwestern Germany, historically known for its jewelry and watchmaking industry and its heavy destruction during World War II.
  • C. Schwäbisch Gmünd
    Schwäbisch Gmünd is a historic town in the German state of Baden-Württemberg, known for its medieval architecture and long tradition of metalworking and jewelry craftsmanship.
  • D. Reutlingen
    Reutlingen is a city in southwestern Germany known for its location at the foot of the Swabian Jura and its well-preserved medieval old town.
  • E. Heilbronn
    Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
  • 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_69ca82b7b3e88190b9041ab0ef28b3cb completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4291f1d4819098985ac2b20b6c75 completed March 31, 2026, 3:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69d02f04ab008190a4c98a8f00ec4c20 completed April 3, 2026, 9:20 p.m.
Created at: March 30, 2026, 5:30 p.m.