Triple

T817139
Position Surface form Disambiguated ID Type / Status
Subject Prince of Mindelheim E17672 entity
Predicate sovereignOver P8163 FINISHED
Object Mindelheim 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: Mindelheim | Statement: [Prince of Mindelheim, sovereignOver, Mindelheim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mindelheim
Context triple: [Prince of Mindelheim, sovereignOver, Mindelheim]
  • A. 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.
  • B. Borsigwalde
    Borsigwalde is a residential locality in the Berlin borough of Reinickendorf, known for its industrial heritage linked to the Borsig engineering works.
  • C. 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.
  • D. Friedenau
    Friedenau is a residential district in southwestern Berlin known for its historic architecture, leafy streets, and literary heritage.
  • E. Starnberg
    Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
  • 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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab621d2c819083f10bff4f66c482 completed March 1, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5e95e25881909c3e167417c0ae47 completed March 7, 2026, 5:21 p.m.
Created at: March 1, 2026, 7:38 p.m.