Triple

T15947293
Position Surface form Disambiguated ID Type / Status
Subject Olsztynek E386716 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object NOL E783892 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: NOL | Statement: [Olsztynek, vehicleRegistrationCode, NOL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: NOL
Context triple: [Olsztynek, vehicleRegistrationCode, NOL]
  • A. NOL
    NOL is the Amtrak station code for New Orleans’ main intercity rail hub, the New Orleans Union Passenger Terminal.
  • B. NOL chosen
    NOL is a Polish vehicle registration code assigned to cars registered in the Olsztyn County area, which includes the town of Jeziorany.
  • C. Nol
    Nol is the given name of Lon Nol, the Cambodian military leader and politician who served as Prime Minister and later led the Khmer Republic in the early 1970s.
  • D. NUOL
    NUOL is the principal public university in Laos, offering a wide range of higher education and research programs across multiple disciplines.
  • E. NOB
    NOB is the abbreviation for Dutch National Opera & Ballet, the leading institution for opera and ballet performances in the Netherlands.
  • 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_69d86da882448190a82ea962fe343b79 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156d2fda8819085279d2a0f8a02ab completed April 16, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb5c0c8a481908aa7a40bca15e38e completed May 9, 2026, 10:31 p.m.
Created at: April 10, 2026, 4:53 a.m.