Triple

T15172136
Position Surface form Disambiguated ID Type / Status
Subject Międzyrzecz E362510 entity
Predicate hasTwinTown P919 FINISHED
Object Seelow E1148380 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: Seelow | Statement: [Międzyrzecz, hasTwinTown, Seelow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seelow
Context triple: [Międzyrzecz, hasTwinTown, Seelow]
  • A. Seelow chosen
    Seelow is a small town in eastern Brandenburg, Germany, best known today as the administrative center of the Märkisch-Oderland district and for its proximity to the historic Seelow Heights battlefield of World War II.
  • B. Schkopau
    Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
  • C. Teesdorf
    Teesdorf is a municipality in Lower Austria known for its motorsport testing facilities and rural setting south of Vienna.
  • D. Degendorf
    Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
  • E. Beelitz
    Beelitz is a small German town in the state of Brandenburg, best known for its historic asparagus cultivation and the nearby Beelitz-Heilstätten sanatorium complex.
  • 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_69d85a087b7c81908baa94a53dac8d68 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e006501b488190a2ab09dbf1532571 completed April 15, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff755e0f2c819088293d8a55d7883a completed May 9, 2026, 5:56 p.m.
Created at: April 10, 2026, 3:09 a.m.