Triple

T9813664
Position Surface form Disambiguated ID Type / Status
Subject Saar River E238341 entity
Predicate flowsThrough P225 FINISHED
Object Sarrebourg E294134 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: Sarrebourg | Statement: [Saar River, flowsThrough, Sarrebourg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sarrebourg
Context triple: [Saar River, flowsThrough, Sarrebourg]
  • A. Sarrebourg chosen
    Sarrebourg is a small historic town in northeastern France known for its cultural heritage and location in the Moselle department of the Grand Est region.
  • B. Sarreguemines
    Sarreguemines is a town in northeastern France near the German border, historically known for its ceramics and faience production.
  • C. Obernai
    Obernai is a historic Alsatian town in northeastern France known for its well-preserved medieval architecture, wine production, and picturesque setting along the Alsace Wine Route.
  • D. 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.
  • E. Haguenau
    Haguenau is a historic town in northeastern France’s Alsace region, known for its medieval heritage, cultural traditions, and role as a local economic center.
  • 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_69ca84defac48190abc1148804f184c1 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb22410208190b82b81a4df800f80 completed April 2, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1e41725948190a40ddcf9552e9bf1 completed April 5, 2026, 4:24 a.m.
Created at: March 30, 2026, 8:30 p.m.