Triple

T19632220
Position Surface form Disambiguated ID Type / Status
Subject Oldenburg, Germany E471297 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object OL NE NERFINISHED

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: OL | Statement: [Oldenburg, Germany, vehicleRegistrationCode, OL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: OL
Context triple: [Oldenburg, Germany, vehicleRegistrationCode, OL]
  • A. OL
    OL is a UK postcode area covering Oldham and surrounding parts of Greater Manchester and nearby regions in North West England.
  • B. OL
    OL is the commonly used abbreviation for Olympique Lyonnais, a major French football club best known internationally for its highly successful women's team.
  • C. OL chosen
    OL is the vehicle registration code for the city of Oldenburg in the German state of Lower Saxony.
  • D. OL
    OL is the post-nominal abbreviation used by recipients of Papua New Guinea’s Order of Logohu, a national honor recognizing distinguished service.
  • E. OL
    OL is the official abbreviation for the Order of Luthuli, a South African national honor awarded for exceptional contributions to democracy, human rights, and nation-building.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e511f28481909f4bc3ea9191e54a completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e641036ee881909fdd8170fe4cdac9 completed April 20, 2026, 3:06 p.m.
Created at: April 10, 2026, 1:44 p.m.