Triple

T17169782
Position Surface form Disambiguated ID Type / Status
Subject Hohe Marter E416697 entity
Predicate hasStationCode P1289 FINISHED
Object U3-HM
U3-HM is the station code for the Hohe Marter stop on Nuremberg’s U3 underground metro line.
E91577 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: U3-HM | Statement: [Hohe Marter, hasStationCode, U3-HM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: U3-HM
Context triple: [Hohe Marter, hasStationCode, U3-HM]
  • A. U3
    U3 is one of the main rapid transit lines of the Frankfurt U-Bahn network, connecting central Frankfurt with its northern suburbs.
  • B. U3
    U3 is a rapid transit line of the Munich U-Bahn system in Germany, serving multiple key districts and connecting important transport hubs across the city.
  • C. U3
    U3 is one of the main lines of the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • D. U3
    U3 is a line of the Berlin U-Bahn rapid transit system serving several districts in the German capital.
  • E. U3
    U3 is a light rail line of the Stuttgart Stadtbahn network serving various districts within the Stuttgart metropolitan area.
  • 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: U3-HM
Triple: [Hohe Marter, hasStationCode, U3-HM]
Generated description
U3-HM is the station code for the Hohe Marter stop on Nuremberg’s U3 underground metro line.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: U3-HM
Target entity description: U3-HM is the station code for the Hohe Marter stop on Nuremberg’s U3 underground metro line.
  • A. U3
    U3 is one of the main rapid transit lines of the Frankfurt U-Bahn network, connecting central Frankfurt with its northern suburbs.
  • B. U3
    U3 is a rapid transit line of the Munich U-Bahn system in Germany, serving multiple key districts and connecting important transport hubs across the city.
  • C. U3 chosen
    U3 is one of the main lines of the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • D. U3
    U3 is a line of the Berlin U-Bahn rapid transit system serving several districts in the German capital.
  • E. U3
    U3 is a light rail line of the Stuttgart Stadtbahn network serving various districts within the Stuttgart metropolitan area.
  • F. None of above.

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_69d886d5f34c8190b24564dfaa63f3fb completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f91831d88190b262227fc41c9067 completed April 18, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01483f85648190acaeb197013e1f1b completed May 11, 2026, 3:08 a.m.
NEDg Description generation batch_6a014a1993a48190bf65e590ff57c9c2 completed May 11, 2026, 3:16 a.m.
NED2 Entity disambiguation (via description) batch_6a014a7fa5208190a0a60649fe6292d1 completed May 11, 2026, 3:18 a.m.
Created at: April 10, 2026, 5:37 a.m.