Triple

T11844218
Position Surface form Disambiguated ID Type / Status
Subject Maria Karoline Flachsland E281729 entity
Predicate familyName P18 FINISHED
Object Flachsland E281729 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: Flachsland | Statement: [Maria Karoline Flachsland, familyName, Flachsland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flachsland
Context triple: [Maria Karoline Flachsland, familyName, Flachsland]
  • A. Flachsland chosen
    Flachsland is a German-language surname associated with individuals such as Maria Karoline Flachsland.
  • B. Rübeland
    Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
  • C. Maasland
    Maasland is a historical region in the Low Countries centered along the river Meuse, known for its medieval political and cultural significance.
  • D. Schwanfeld
    Schwanfeld is a small municipality in the Lower Franconia region of Bavaria, Germany, known for its rural character and historical roots.
  • E. Löwenberger Land
    Löwenberger Land is a rural municipality in the Oberhavel district of Brandenburg, Germany, known for its agricultural landscape and small villages north of Berlin.
  • 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_69d6ab287ba48190a5178779fd19b9b7 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a65a597c8190b09f57463b279afc completed April 10, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69f1679729c08190a9f6750586f90d8d completed April 29, 2026, 2:06 a.m.
Created at: April 8, 2026, 9:43 p.m.