Triple

T6583217
Position Surface form Disambiguated ID Type / Status
Subject Saterland E157352 entity
Predicate locatedNear P294 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: [Saterland, locatedNear, Leer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leer
Context triple: [Saterland, locatedNear, 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. La Lecture
    La Lecture is an early 20th-century painting by Pablo Picasso that depicts a contemplative female figure and reflects his evolving style during his transition from Cubism toward a more classical, figurative approach.
  • 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_69c6882b3a108190b3a9eb343ae4162c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae938184819088234aad9cc997e1 completed March 27, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d572c4708190844f4b1abee8ca86 completed March 27, 2026, 7:07 p.m.
Created at: March 27, 2026, 1:54 p.m.