Triple

T12749863
Position Surface form Disambiguated ID Type / Status
Subject Warnemünde Lighthouse E304700 entity
Predicate locatedNear P294 FINISHED
Object Warnemünde beach E330763 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: Warnemünde beach | Statement: [Warnemünde Lighthouse, locatedNear, Warnemünde beach]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Warnemünde beach
Context triple: [Warnemünde Lighthouse, locatedNear, Warnemünde beach]
  • A. Timmendorfer Strand
    Timmendorfer Strand is a popular seaside resort town on Germany’s Baltic Sea coast, known for its long sandy beaches and tourism.
  • B. Ventspils Beach
    Ventspils Beach is a popular sandy seaside destination on Latvia’s Baltic coast, known for its clean Blue Flag-certified shoreline and family-friendly recreational facilities.
  • C. Liepāja beach
    Liepāja beach is a long, sandy Baltic Sea shoreline in the Latvian city of Liepāja, known for its wide dunes, fine white sand, and popular seaside recreation.
  • D. Südstrand
    Südstrand is a popular German seaside beach area known for its sandy shoreline, coastal promenades, and recreational tourism.
  • E. Warnemünde chosen
    Warnemünde is a seaside district and popular Baltic Sea resort of the German city of Rostock, known for its wide sandy beaches and maritime atmosphere.
  • 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_69d7bdf1fcd081909ffb0e0d6fa3a07d completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96bd75f508190aaae0969f33d1523 completed April 10, 2026, 9:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c964c508190b4d6a094b388280b completed May 2, 2026, 10:37 p.m.
Created at: April 9, 2026, 5:27 p.m.