Triple

T1240781
Position Surface form Disambiguated ID Type / Status
Subject Imperial Diet E26652 entity
Predicate location P40 FINISHED
Object Metz E90772 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: Metz | Statement: [Imperial Diet, location, Metz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metz
Context triple: [Imperial Diet, location, Metz]
  • A. Metz chosen
    Metz is a historic city in northeastern France known for its Gothic Saint-Stephen Cathedral, Roman and medieval heritage, and role as the capital of the Moselle department in the Grand Est region.
  • B. Thionville
    Thionville is a town in northeastern France near the Luxembourg border, known historically as a strategic industrial and military center in the Moselle region.
  • C. Colmar
    Colmar is a picturesque historic town in northeastern France’s Alsace region, renowned for its well-preserved medieval and early Renaissance architecture and canals.
  • D. Strasbourg
    Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
  • E. 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.
  • 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_69a4948689d08190b3a4a3f388c02148 completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4bf4343e48190a232abd8475880a0 completed March 1, 2026, 10:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae02f319788190ac9a634803154832 completed March 8, 2026, 11:14 p.m.
Created at: March 1, 2026, 7:47 p.m.