Triple

T12454396
Position Surface form Disambiguated ID Type / Status
Subject Rhine-Main S-Bahn E297618 entity
Predicate hasLine P35 FINISHED
Object S7
S7 is a line of the Rhine-Main S-Bahn rapid transit network serving the Frankfurt Rhine-Main metropolitan region in Germany.
 E983302 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: S7 | Statement: [Rhine-Main S-Bahn, hasLine, S7]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: S7
Context triple: [Rhine-Main S-Bahn, hasLine, S7]
  • A. S7
    S7 is a Berlin S-Bahn rapid transit line that runs across the city, connecting key districts between Potsdam and Ahrensfelde.
  • B. S7
    S7 is the IATA airline designator for S7 Airlines, a major Russian carrier based in Novosibirsk.
  • C. S75
    S75 is a line of the Berlin S-Bahn urban rail network serving routes within the Berlin metropolitan area.
  • D. S7 Technics
    S7 Technics is a Russian aircraft maintenance, repair, and overhaul (MRO) company that services both S7 Airlines and other carriers.
  • E. S7 Stock
    S7 Stock is a type of London Underground train used on the Circle and other sub-surface lines, featuring air-conditioning, walk-through carriages, and modern passenger information systems.
  • 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: S7
Triple: [Rhine-Main S-Bahn, hasLine, S7]
Generated description
S7 is a line of the Rhine-Main S-Bahn rapid transit network serving the Frankfurt Rhine-Main metropolitan region in Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: S7
Target entity description: S7 is a line of the Rhine-Main S-Bahn rapid transit network serving the Frankfurt Rhine-Main metropolitan region in Germany.
  • A. S7
    S7 is a Berlin S-Bahn rapid transit line that runs across the city, connecting key districts between Potsdam and Ahrensfelde.
  • B. S7
    S7 is the IATA airline designator for S7 Airlines, a major Russian carrier based in Novosibirsk.
  • C. S75
    S75 is a line of the Berlin S-Bahn urban rail network serving routes within the Berlin metropolitan area.
  • D. S7 Technics
    S7 Technics is a Russian aircraft maintenance, repair, and overhaul (MRO) company that services both S7 Airlines and other carriers.
  • E. S7 Stock
    S7 Stock is a type of London Underground train used on the Circle and other sub-surface lines, featuring air-conditioning, walk-through carriages, and modern passenger information systems.
  • 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_69d6ada166c48190b902972cd2408fa3 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94da0b5988190b9df26dd3bb87337 completed April 10, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63f190c788190adceaab8117d52a6 completed May 2, 2026, 6:14 p.m.
NEDg Description generation batch_69f6405f9f6481909bcc3b2e3deeae7e completed May 2, 2026, 6:20 p.m.
NED2 Entity disambiguation (via description) batch_69f6416ba1bc8190a772bffe4d83ec15 completed May 2, 2026, 6:24 p.m.
Created at: April 8, 2026, 9:56 p.m.