Triple

T724028
Position Surface form Disambiguated ID Type / Status
Subject E14681 entity
Predicate vehicleRegistrationCodeFor P1173 FINISHED
Object Fürth E44190 NE FINISHED

How this triple was built (3 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: Fürth | Statement: [FÜ, vehicleRegistrationCodeFor, Fürth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fürth
Context triple: [FÜ, vehicleRegistrationCodeFor, Fürth]
  • A. Fürth chosen
    Fürth is a historic city in northern Bavaria, Germany, known for its well-preserved old town and proximity to Nuremberg within the Franconian metropolitan region.
  • B. Starnberg
    Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
  • C. Sieber
    Sieber is a small river in the German state of Lower Saxony that flows through the Harz Mountains and into the Oder.
  • D. Schöngarth
    Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
  • E. Nischel
    Nischel is the local colloquial nickname for the large Karl Marx Monument in Chemnitz, Germany.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: vehicleRegistrationCodeFor
Context triple: [FÜ, vehicleRegistrationCodeFor, Fürth]
  • A. vehicleRegistrationCode chosen
    Indicates the official registration identifier assigned to a vehicle, typically used for legal identification and record-keeping.
  • B. vehicleUsed
    Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
  • C. countryOfRegistry
    Indicates the country in which an entity (such as a ship, aircraft, or company) is officially registered.
  • D. aircraftRegistration
    Indicates that an aircraft is assigned a specific official registration identifier or code.
  • E. vehicleType
    Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
  • F. None of above.

Provenance (4 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_69a4934c753c81909b309027e48b9b3a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5a6ab508190b70a05a9d77829a5 completed March 1, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a65e3c22f08190b71734d9605a92f6 completed March 3, 2026, 4:06 a.m.
PD Predicate disambiguation batch_69a4a4f700cc81908c6de3eedf68433c completed March 1, 2026, 8:43 p.m.
Created at: March 1, 2026, 7:37 p.m.