Triple

T4549758
Position Surface form Disambiguated ID Type / Status
Subject Mindelheim E110132 entity
Predicate hasCastle P22469 FINISHED
Object Mindelburg E110132 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: Mindelburg | Statement: [Mindelheim, hasCastle, Mindelburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mindelburg
Context triple: [Mindelheim, hasCastle, Mindelburg]
  • A. Gavere
    Gavere is a municipality in the Belgian province of East Flanders, known for its rural character and several constituent villages.
  • B. Warffum
    Warffum is a historic village in the Dutch province of Groningen, known for its traditional architecture and open-air museum showcasing rural life.
  • C. Vredenburg
    Vredenburg is a town on South Africa’s West Coast that serves as a regional commercial and service hub near Saldanha Bay.
  • D. Mindelheim chosen
    Mindelheim is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former status as a princely seat.
  • E. Terneuzen
    Terneuzen is a port city and municipality in the southwestern Netherlands, known for its location on the Western Scheldt and its role in shipping access to the port of Ghent.
  • 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_69bd4412524c8190be5bcc9ddee91848 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57f3f8348190868e274ac4df87ce completed March 20, 2026, 2:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdb94cab408190956ef333aa810a3b completed March 20, 2026, 9:17 p.m.
Created at: March 20, 2026, 1:05 p.m.