Triple

T20041703
Position Surface form Disambiguated ID Type / Status
Subject Philippsburg fortress E497435 entity
Predicate near P350 FINISHED
Object Speyer NE NERFINISHED

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: Speyer | Statement: [Philippsburg fortress, near, Speyer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Speyer
Context triple: [Philippsburg fortress, near, Speyer]
  • A. Speyer chosen
    Speyer is a historic city in southwestern Germany on the Rhine River, renowned for its Romanesque imperial cathedral, a UNESCO World Heritage Site.
  • B. Trier
    Trier is a historic city in western Germany, renowned as one of the country’s oldest cities with extensive Roman ruins and medieval landmarks.
  • C. Mainz
    Mainz is a historic German city on the Rhine River known as a major ecclesiastical and political center of the Holy Roman Empire and today as the capital of the state of Rhineland-Palatinate.
  • D. Lingolsheim
    Lingolsheim is a suburban commune in northeastern France, located just southwest of Strasbourg in the Grand Est region.
  • E. Saarbrücken
    Saarbrücken is a German city on the Saar River known as an industrial, cultural, and educational center near the French border.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da627278c88190babe4297a9df1236 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e662eb9a6081909d06dc1d457b4d5a completed April 20, 2026, 5:31 p.m.
Created at: April 11, 2026, 3:37 p.m.