Triple

T6908608
Position Surface form Disambiguated ID Type / Status
Subject Borkum E159874 entity
Predicate district P2709 FINISHED
Object Leer E176336 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: Leer | Statement: [Borkum, district, Leer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leer
Context triple: [Borkum, district, Leer]
  • A. Leer chosen
    Leer is a historic town in northwestern Germany known for its maritime heritage and traditional East Frisian culture.
  • B. Lees
    Lees is a village in the Metropolitan Borough of Oldham, Greater Manchester, England, historically part of Lancashire.
  • C. Lezgin
    Lezgin is a Northeast Caucasian language spoken primarily by the Lezgin people in southern Dagestan (Russia) and northern Azerbaijan.
  • D. Lectoure
    Lectoure is a historic town in southwestern France, in the Gers department of the Occitanie region, known for its medieval architecture and hilltop setting.
  • E. Lleó
    Lleó is a Spanish-language surname of Catalan origin borne by various individuals, including Cuban jurist and former president Manuel Urrutia Lleó.
  • 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_69c68839ccb88190b4aa5cc1aca3448f completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6d9be98748190b5cb698e66e3aa42 completed March 27, 2026, 7:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69c74901aee08190a5e132200fd58c05 completed March 28, 2026, 3:20 a.m.
Created at: March 27, 2026, 2:25 p.m.