Triple

T9223414
Position Surface form Disambiguated ID Type / Status
Subject Laichingen E221618 entity
Predicate locatedNear P294 FINISHED
Object Münsingen E758514 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: Münsingen | Statement: [Laichingen, locatedNear, Münsingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Münsingen
Context triple: [Laichingen, locatedNear, Münsingen]
  • A. Münsingen
    Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
  • B. Menzingen
    Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
  • C. Memmingen
    Memmingen is a historic town in the Bavarian region of Germany, known for its well-preserved medieval old town and role as a regional transport hub.
  • D. Miesbach
    Miesbach is a historic town in southern Germany known for its traditional Bavarian culture and picturesque Alpine foothill setting.
  • E. Mössingen chosen
    Mössingen is a town in the German state of Baden-Württemberg, located at the foot of the Swabian Jura and known for its scenic surroundings and regional industry.
  • 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_69ca83ec8db08190a9110df8232885d2 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccda7903208190b4e29a1591aab78a completed April 1, 2026, 8:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0e33fa3d48190bc5f4ba72b422b85 completed April 4, 2026, 10:09 a.m.
Created at: March 30, 2026, 7:28 p.m.