Triple

T8962315
Position Surface form Disambiguated ID Type / Status
Subject Landkreis Sigmaringen E214035 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object SIG E707126 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: SIG | Statement: [Landkreis Sigmaringen, hasVehicleRegistrationCode, SIG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SIG
Context triple: [Landkreis Sigmaringen, hasVehicleRegistrationCode, SIG]
  • A. SIG chosen
    SIG is the vehicle registration code for the district of Sigmaringen in the German state of Baden-Württemberg.
  • B. SIG
    SIG is an acronym commonly used by the Association for Computing Machinery to denote its specialized Special Interest Groups that focus on particular areas of computing research and practice.
  • C. SIG
    SIG is the public utility company of Geneva, Switzerland, responsible for providing services such as electricity, gas, water, and energy solutions to the region.
  • D. SIG
    SIG is the IATA airport code for Fernando Luis Ribas Dominicci Airport, a regional airport serving San Juan, Puerto Rico.
  • E. Sig
    Sig is a common shortened form of the given name Sigmund, often used as an informal or familiar nickname.
  • 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_69ca839cd6008190a1546a701a56710c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6749e5008190a01f42a2e772dd54 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc94e088881909506b229d1fff44a completed April 3, 2026, 2:06 p.m.
Created at: March 30, 2026, 7:01 p.m.