Triple

T9800987
Position Surface form Disambiguated ID Type / Status
Subject A4 E237835 entity
Predicate hasEuropeanRouteDesignation P65888 FINISHED
Object E54
E54 is a European route that forms part of the international E-road network, connecting cities across multiple countries in Europe.
E822156 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: E54 | Statement: [A4, hasEuropeanRouteDesignation, E54]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: E54
Context triple: [A4, hasEuropeanRouteDesignation, E54]
  • A. E60
    E60 is a major trans-European route that stretches from Brest in France to Irkeshtam on the Kyrgyz–Chinese border, crossing numerous countries across the continent.
  • B. E42
    E42 is a major European route that runs across parts of Western Europe, connecting key cities and motorways in countries such as France, Belgium, Luxembourg, and Germany.
  • C. E53
    E53 is the internal BMW designation for the first-generation BMW X5 mid-size luxury SUV produced from 1999 to 2006.
  • D. E65
    E65 is a major European route that runs north–south through several countries in Central and Southeastern Europe, connecting important cities and transport hubs.
  • E. E45
    E45 is a major north–south European route that runs from northern Scandinavia through central Europe down to Italy, connecting multiple countries along its corridor.
  • 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: E54
Triple: [A4, hasEuropeanRouteDesignation, E54]
Generated description
E54 is a European route that forms part of the international E-road network, connecting cities across multiple countries in Europe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: E54
Target entity description: E54 is a European route that forms part of the international E-road network, connecting cities across multiple countries in Europe.
  • A. E60
    E60 is a major trans-European route that stretches from Brest in France to Irkeshtam on the Kyrgyz–Chinese border, crossing numerous countries across the continent.
  • B. E42
    E42 is a major European route that runs across parts of Western Europe, connecting key cities and motorways in countries such as France, Belgium, Luxembourg, and Germany.
  • C. E53
    E53 is the internal BMW designation for the first-generation BMW X5 mid-size luxury SUV produced from 1999 to 2006.
  • D. E65
    E65 is a major European route that runs north–south through several countries in Central and Southeastern Europe, connecting important cities and transport hubs.
  • E. E45
    E45 is a major north–south European route that runs from northern Scandinavia through central Europe down to Italy, connecting multiple countries along its corridor.
  • 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_69ca84dd4608819097ff4ed00feca280 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda62a11a88190880e0cce24923b14 completed April 1, 2026, 11:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c44a652c81908f644e1a5efe3eb1 completed April 5, 2026, 2:09 a.m.
NEDg Description generation batch_69d1c4fc4cc88190b020f672b9f9ba27 completed April 5, 2026, 2:12 a.m.
NED2 Entity disambiguation (via description) batch_69d1c5a1cfb08190b6c16e5309dbf2b8 completed April 5, 2026, 2:14 a.m.
Created at: March 30, 2026, 8:29 p.m.