Triple

T10548500
Position Surface form Disambiguated ID Type / Status
Subject Scheemda railway station E248882 entity
Predicate locatedIn P40 FINISHED
Object Scheemda E248882 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: Scheemda | Statement: [Scheemda railway station, locatedIn, Scheemda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Scheemda
Context triple: [Scheemda railway station, locatedIn, Scheemda]
  • A. Scheemda chosen
    Scheemda is a village in the municipality of Oldambt in the province of Groningen in the northeastern Netherlands.
  • B. Wessum
    Wessum is a village and district within the town of Ahaus in the state of North Rhine-Westphalia, Germany.
  • C. Eemshaven
    Eemshaven is a major seaport and energy hub in the north of the Netherlands, known for its power plants, data centers, and offshore wind connections.
  • D. Veendam
    Veendam is a town and municipality in the province of Groningen in the northeastern Netherlands, historically known for peat extraction and later for its industrial development.
  • E. De Koog
    De Koog is a coastal village and popular seaside resort on the Dutch Wadden Island of Texel, known for its beaches, dunes, and tourism.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d526d305d081909b48d244e1cfa092 completed April 7, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a10f17081909fd9465cf35685a1 completed April 10, 2026, 10:30 p.m.
Created at: April 6, 2026, 12:33 p.m.