Triple

T14405346
Position Surface form Disambiguated ID Type / Status
Subject U-Bahnhof Seestraße E357181 entity
Predicate hasStationCode P1289 FINISHED
Object SZ
SZ is the official station code used to identify the Berlin U-Bahn station Seestraße.
 E1097553 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: [U-Bahnhof Seestraße, hasStationCode, SZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SZ
Context triple: [U-Bahnhof Seestraße, hasStationCode, SZ]
  • A. SZ
    SZ is the vehicle registration code for the German city of Salzgitter in Lower Saxony.
  • B. 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.
  • C. SZF
    SZF is the IATA airport code for Samsun-Çarşamba Airport, a regional airport serving the city of Samsun in northern Turkey.
  • D. SZB
    SZB is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • 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
  • 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: [U-Bahnhof Seestraße, hasStationCode, SZ]
Generated description
SZ is the official station code used to identify the Berlin U-Bahn station Seestraße.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SZ
Target entity description: SZ is the official station code used to identify the Berlin U-Bahn station Seestraße.
  • A. SZ
    SZ is the vehicle registration code for the German city of Salzgitter in Lower Saxony.
  • B. 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.
  • C. SZF
    SZF is the IATA airport code for Samsun-Çarşamba Airport, a regional airport serving the city of Samsun in northern Turkey.
  • D. SZB
    SZB is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • 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_69d82793421c8190861eb0e673b085de completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de908804048190a4fe58afc2e0a5b6 completed April 14, 2026, 7:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd5523267081908d972b60b6039528 completed May 8, 2026, 3:14 a.m.
NEDg Description generation batch_69fd5671e4688190ab1b7a7ed6c0cfb8 completed May 8, 2026, 3:20 a.m.
NED2 Entity disambiguation (via description) batch_69fd57710f648190a1344ac1363acce1 completed May 8, 2026, 3:24 a.m.
Created at: April 10, 2026, 1:17 a.m.