Triple

T15344372
Position Surface form Disambiguated ID Type / Status
Subject Wolfsburg Castle E366876 entity
Predicate namedAfter P63 FINISHED
Object Wolfsburg (city) E74139 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: Wolfsburg (city) | Statement: [Wolfsburg Castle, namedAfter, Wolfsburg (city)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wolfsburg (city)
Context triple: [Wolfsburg Castle, namedAfter, Wolfsburg (city)]
  • A. Wolfsburg chosen
    Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
  • B. Wolfsburg region
    The Wolfsburg region is an area in Lower Saxony, Germany, centered around the city of Wolfsburg and known for its industrial significance, particularly as the headquarters of Volkswagen.
  • C. Warendorf
    Warendorf is a historic town in western Germany’s North Rhine-Westphalia, known for its well-preserved medieval old town and strong equestrian traditions.
  • D. Delmenhorst
    Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
  • E. Herford
    Herford is a historic town in northwestern Germany known for its medieval architecture and location in the region of North Rhine-Westphalia.
  • 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_69e03e163a3c8190ab933411372c1573 completed April 16, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff9085d388819092be2fd8a0268e23 completed May 9, 2026, 7:52 p.m.
Created at: April 10, 2026, 3:17 a.m.