Triple

T979911
Position Surface form Disambiguated ID Type / Status
Subject Grand Est E21143 entity
Predicate containsCity P294 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: [Grand Est, containsCity, Metz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metz
Context triple: [Grand Est, containsCity, 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_69a493c2b62c8190b616351789ec47f8 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b47b58ec81908d95f151b9af3dae completed March 1, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36f094888190b3ccb2acb266941c completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 7:40 p.m.