Triple

T5147427
Position Surface form Disambiguated ID Type / Status
Subject Siege of Philippsburg (1734) E116106 entity
Predicate location P40 FINISHED
Object Philippsburg E230104 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: Philippsburg | Statement: [Siege of Philippsburg (1734), location, Philippsburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Philippsburg
Context triple: [Siege of Philippsburg (1734), location, Philippsburg]
  • A. Philippsburg chosen
    Philippsburg is a historic town in southwestern Germany, known for its former fortress on the Rhine and its strategic military significance in early modern European wars.
  • B. Homburg
    Homburg is a town in southwestern Germany known as an administrative and economic center within the state of Saarland.
  • C. Speyer
    Speyer is a historic city in southwestern Germany on the Rhine River, renowned for its Romanesque imperial cathedral, a UNESCO World Heritage Site.
  • D. Colmar-Berg
    Colmar-Berg is a small town in central Luxembourg known for being the residence of the Grand Ducal family and the site of a major Goodyear tire factory.
  • E. Wissembourg
    Wissembourg is a historic town in northeastern France’s Alsace region, known for its well-preserved medieval architecture and proximity to the German border.
  • 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_69bd4446c0e08190a7c29dc74976bf03 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd78afc32081909fd4de3dbf31ea3a completed March 20, 2026, 4:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf7fcb783881909cc693e4832a19e3 completed March 22, 2026, 5:36 a.m.
Created at: March 20, 2026, 1:43 p.m.