Triple

T16210759
Position Surface form Disambiguated ID Type / Status
Subject T11 E393456 entity
Predicate previousRoadNumber P22310 FINISHED
Object RN193
RN193 is a former French national road designation that was replaced or renumbered as part of the country’s road network reclassification.
 E1201034 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: RN193 | Statement: [T11, previousRoadNumber, RN193]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RN193
Context triple: [T11, previousRoadNumber, RN193]
  • A. N199
    N199 is a regional Dutch road that provides key access to and from the town of Bunschoten in the Netherlands.
  • B. RN12
    RN12 is a major national road in France that serves as an important route connecting several towns and regions in the western part of the country.
  • C. R19
    R19 is a regional commuter rail line in Catalonia that forms part of the Rodalies de Catalunya network.
  • D. RN2
    RN2 is a major national highway in its country, designated as Route Nationale No. 2 and serving as an important transportation corridor.
  • E. N93
    The N93 is a Nokia smartphone from the mid-2000s known for its swivel design and advanced video recording capabilities for its time.
  • 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: RN193
Triple: [T11, previousRoadNumber, RN193]
Generated description
RN193 is a former French national road designation that was replaced or renumbered as part of the country’s road network reclassification.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RN193
Target entity description: RN193 is a former French national road designation that was replaced or renumbered as part of the country’s road network reclassification.
  • A. N199
    N199 is a regional Dutch road that provides key access to and from the town of Bunschoten in the Netherlands.
  • B. RN12
    RN12 is a major national road in France that serves as an important route connecting several towns and regions in the western part of the country.
  • C. R19
    R19 is a regional commuter rail line in Catalonia that forms part of the Rodalies de Catalunya network.
  • D. RN2
    RN2 is a major national highway in its country, designated as Route Nationale No. 2 and serving as an important transportation corridor.
  • E. N93
    The N93 is a Nokia smartphone from the mid-2000s known for its swivel design and advanced video recording capabilities for its time.
  • 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_69d87f1f5bd08190bd01cac0d5b9d2ef completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e22713282481909c7c0d0782213461 completed April 17, 2026, 12:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0007932f088190b6c20913cfb932f4 completed May 10, 2026, 4:20 a.m.
NEDg Description generation batch_6a0009d6e7288190907075f5ee72f3ad completed May 10, 2026, 4:30 a.m.
NED2 Entity disambiguation (via description) batch_6a000ad7a72481909a74643d8c0c9b5a completed May 10, 2026, 4:34 a.m.
Created at: April 10, 2026, 5:03 a.m.