Triple

T19056845
Position Surface form Disambiguated ID Type / Status
Subject Wollerau E466419 entity
Predicate hasCantonCode P28599 FINISHED
Object SZ
SZ is the official abbreviation for the Swiss canton of Schwyz, one of the founding cantons of Switzerland.
 E1355947 NE FINISHED

How this triple was built (4 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: SZ | Statement: [Wollerau, hasCantonCode, SZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SZ
Context triple: [Wollerau, hasCantonCode, SZ]
  • A. SZ
    SZ is the vehicle registration code for the German city of Salzgitter in Lower Saxony.
  • B. SZ
    SZ is the official station code used to identify the Berlin U-Bahn station Seestraße.
  • C. S/Z
    S/Z is Roland Barthes’s influential structuralist analysis of Balzac’s short story “Sarrasine,” renowned for its detailed demonstration of textual codes and readerly versus writerly texts.
  • D. SZF
    SZF is the IATA airport code for Samsun-Çarşamba Airport, a regional airport serving the city of Samsun in northern Turkey.
  • E. SZB
    SZB is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: SZ
Triple: [Wollerau, hasCantonCode, SZ]
Generated description
SZ is the official abbreviation for the Swiss canton of Schwyz, one of the founding cantons of Switzerland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SZ
Target entity description: SZ is the official abbreviation for the Swiss canton of Schwyz, one of the founding cantons of Switzerland.
  • A. SZ
    SZ is the vehicle registration code for the German city of Salzgitter in Lower Saxony.
  • B. SZ
    SZ is the official station code used to identify the Berlin U-Bahn station Seestraße.
  • C. S/Z
    S/Z is Roland Barthes’s influential structuralist analysis of Balzac’s short story “Sarrasine,” renowned for its detailed demonstration of textual codes and readerly versus writerly texts.
  • D. SZF
    SZF is the IATA airport code for Samsun-Çarşamba Airport, a regional airport serving the city of Samsun in northern Turkey.
  • E. SZB
    SZB is the IATA airport code for Sultan Abdul Aziz Shah Airport, a secondary airport serving the Kuala Lumpur area in Malaysia.
  • F. None of above. chosen

Provenance (5 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_69d8dd040fb881909af2a964f65ad208 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5dc067f788190b3b149dfee370435 completed April 20, 2026, 7:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a05c5596cac8190bd83e77f5997644f completed May 14, 2026, 12:51 p.m.
NEDg Description generation batch_6a05c98f2fd08190b5b64567c7425c17 completed May 14, 2026, 1:09 p.m.
NED2 Entity disambiguation (via description) batch_6a05c9ed55f48190ab6352a49cae48da completed May 14, 2026, 1:11 p.m.
Created at: April 10, 2026, 12:03 p.m.