Triple

T13861794
Position Surface form Disambiguated ID Type / Status
Subject Loisach E333213 entity
Predicate flowsInto P408 FINISHED
Object Isar at Wolfratshausen E66587 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: Isar at Wolfratshausen | Statement: [Loisach, flowsInto, Isar at Wolfratshausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isar at Wolfratshausen
Context triple: [Loisach, flowsInto, Isar at Wolfratshausen]
  • A. Bad Reichenhall
    Bad Reichenhall is a Bavarian spa town in southeastern Germany, renowned for its alpine setting and historic salt production.
  • B. Isar chosen
    The Isar is a major river in the Austrian and German Alps that flows through cities such as Munich before joining the Danube.
  • C. Isar
    Isar is a structured, human-readable proof language designed for writing formal proofs within the Isabelle interactive theorem prover.
  • D. Gundremmingen
    Gundremmingen is a small Bavarian municipality best known for hosting one of Germany’s major nuclear power plants.
  • E. Würzburg Riese
    Würzburg Riese was a larger, more powerful German World War II ground-based radar system used primarily for air defense and gun-laying.
  • 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_69d81c5ced9c8190b0e9bcc6effe5959 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de05c20db88190acb842748aa01039 completed April 14, 2026, 9:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0ff1f78819088ae58f703e2c9ff completed May 3, 2026, 9:41 p.m.
Created at: April 9, 2026, 10:14 p.m.