Triple

T10892299
Position Surface form Disambiguated ID Type / Status
Subject Haselünne E257208 entity
Predicate district P2709 FINISHED
Object Emsland E47544 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: Emsland | Statement: [Haselünne, district, Emsland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Emsland
Context triple: [Haselünne, district, Emsland]
  • A. Emsland chosen
    Emsland is a rural region in western Germany known for its agriculture, peatlands, and location along the River Ems near the Dutch border.
  • B. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • C. Havelland
    Havelland is a rural district in western Brandenburg, Germany, known for its river landscapes along the Havel, historic towns, and agricultural character.
  • D. Ammerland
    Ammerland is a locality within the Bavarian municipality of Münsing in Germany, situated near Lake Starnberg.
  • E. Westfalen
    Westfalen is a historical region in northwestern Germany, now largely part of the state of North Rhine-Westphalia, known for its distinct cultural identity and medieval heritage.
  • 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_69d6aa8550c8819095508a2ed9acf3db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d75206354881908b148f2df3938513 completed April 9, 2026, 7:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69e23b9e5ddc81908cbd27e8db49dbaf completed April 17, 2026, 1:54 p.m.
Created at: April 8, 2026, 9:21 p.m.