Triple

T8371204
Position Surface form Disambiguated ID Type / Status
Subject Erbach (Donau) E197457 entity
Predicate hasSubdivision P747 FINISHED
Object Ringingen E487696 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: Ringingen | Statement: [Erbach (Donau), hasSubdivision, Ringingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ringingen
Context triple: [Erbach (Donau), hasSubdivision, Ringingen]
  • A. Walkringen
    Walkringen is a rural municipality in the canton of Bern in Switzerland, known for its agricultural landscape and traditional Swiss village character.
  • B. Rönninge
    Rönninge is a locality in Stockholm County, Sweden, serving as the central town of Salem Municipality.
  • C. Oftringen
    Oftringen is a municipality in the canton of Aargau in northern Switzerland, known as a regional transport hub and commercial center.
  • D. Sipplingen chosen
    Sipplingen is a small lakeside municipality in southern Germany situated on the northern shore of Lake Constance in the state of Baden-Württemberg.
  • E. Rælingen
    Rælingen is a municipality in Viken county, Norway, known for its proximity to Oslo and its mix of residential areas, forests, and lakes.
  • 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_69ca82f56730819080cec5d991c76f4c completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb80a509dc81909e0ea4c66b21d84f completed March 31, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc79752b48190933ea644a59f2305 completed April 2, 2026, 1:34 a.m.
Created at: March 30, 2026, 6:01 p.m.