Triple

T21986630
Position Surface form Disambiguated ID Type / Status
Subject Sihltal Zürich Uetliberg Bahn E542976 entity
Predicate shortName P43 FINISHED
Object SZU
SZU is a Swiss railway company operating regional train services in the Sihltal valley and to the Uetliberg mountain near Zurich.
 E1512834 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: SZU | Statement: [Sihltal Zürich Uetliberg Bahn, shortName, SZU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SZU
Context triple: [Sihltal Zürich Uetliberg Bahn, shortName, SZU]
  • A. SZU
    SZU is the commonly used abbreviation for Shenzhen University, a comprehensive public university located in Shenzhen, China.
  • B. SZF
    SZF is the IATA airport code for Samsun-Çarşamba Airport, a regional airport serving the city of Samsun in northern Turkey.
  • C. SZF
    SZF is the commonly used abbreviation for the Faculty of Social Sciences at the University of Latvia, encompassing disciplines such as sociology, communication, and political science.
  • D. ZZU
    ZZU is the commonly used abbreviation for Zhengzhou University, a major comprehensive public university located in Zhengzhou, Henan Province, China.
  • E. SZ
    SZ is the vehicle registration code used for the Austrian district of Schwaz in the state of Tyrol.
  • 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: SZU
Triple: [Sihltal Zürich Uetliberg Bahn, shortName, SZU]
Generated description
SZU is a Swiss railway company operating regional train services in the Sihltal valley and to the Uetliberg mountain near Zurich.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SZU
Target entity description: SZU is a Swiss railway company operating regional train services in the Sihltal valley and to the Uetliberg mountain near Zurich.
  • A. SZU
    SZU is the commonly used abbreviation for Shenzhen University, a comprehensive public university located in Shenzhen, China.
  • B. SZF
    SZF is the IATA airport code for Samsun-Çarşamba Airport, a regional airport serving the city of Samsun in northern Turkey.
  • C. SZF
    SZF is the commonly used abbreviation for the Faculty of Social Sciences at the University of Latvia, encompassing disciplines such as sociology, communication, and political science.
  • D. ZZU
    ZZU is the commonly used abbreviation for Zhengzhou University, a major comprehensive public university located in Zhengzhou, Henan Province, China.
  • E. SZ
    SZ is the official station code used to identify the Berlin U-Bahn station Seestraße.
  • 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_69e0c48136b081908831fa907cc02e18 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f12709cb288190a2620e337fea364c completed April 28, 2026, 9:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a6d7fa4e8819099c77eba507dedb0 completed May 18, 2026, 1:38 a.m.
NEDg Description generation batch_6a0a6efbe8e48190bc3cae3d531f5229 completed May 18, 2026, 1:44 a.m.
NED2 Entity disambiguation (via description) batch_6a0a6fd5f38c8190a420d6700b5f7383 completed May 18, 2026, 1:48 a.m.
Created at: April 16, 2026, 8:04 p.m.